Ethnic Segregation Between Schools: Is It Increasing or Decreasing in England? 9781529204797

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“Uses school enrolment data to show that English state schools are becoming more, not less, diverse in terms of ethnic segregation.” Tim Butler, King’s College London

Gemma Catney, Queen’s University Belfast

There is an enduring belief amongst some that segregation is worsening and undermining social cohesion, and that this is especially visible in the growing divides between the schools in which our children are educated.

Professor Ron Johnston OBE is a Fellow of the British Academy and the Academy of Social Sciences.

This book uses up-to-date evidence to interrogate some of the controversial claims made by the 2016 Casey Review, providing an analysis of contemporary patterns of ethnic, residential and social segregation, and looking at the ways that these changing geographies interact with each other.

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9 781529 204780

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ISBN 978-1-5292-0478-0

RICHARD HARRIS AND RON JOHNSTON

Richard Harris is Professor of Quantitative Social Geography at the School of Geographical Sciences, University of Bristol, and a Fellow of the Academy of Social Sciences.

ETHNIC SEGREGATION BETWEEN SCHOOLS

“Challenges myths and interrogates assumptions, with a refreshing integration of analyses of school, neighbourhood and socio-economic segregation. Essential reading for those seeking to answer one of the most pertinent questions facing society today.”

E T HN I C SEGREG AT I O N BETWEEN SC HO O LS I S I T I N C RE AS I N G OR D EC RE AS I N G I N E N G L AN D ? RI C H A RD H A RRI S A N D RO N J O HN STO N

ETHNIC SEGREGATION BETWEEN SCHOOLS Is It Increasing or Decreasing in England? Richard Harris and Ron Johnston

First published in Great Britain in 2020 by Bristol University Press    1-​9 Old Park Hill       Bristol           BS2 8BB          UK            t: +44 (0)117 954 5940    www.bristoluniversitypress.co.uk               

North America office: c/​o The University of Chicago Press 1427 East 60th  Street Chicago, IL 60637,  USA t: +1 773 702  7700 f: +1 773-​702-​9756 [email protected] www.press.uchicago.edu

© Bristol University Press 2020 British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-​in-​Publication Data A catalog record for this book has been requested ISBN 978-​1-​5292-​0478-​0 hardcover ISBN 978-​1-​5292-​0480-​3  ePub ISBN 978-​1-​5292-​0479-​7  ePdf The right of Richard Harris and Ron Johnston to be identified as authors of this work has been asserted by them in accordance with the Copyright, Designs and Patents Act 1988. All rights reserved: no part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise without the prior permission of Bristol University Press. Every reasonable effort has been made to obtain permission to reproduce copyrighted material. If, however, anyone knows of an oversight, please contact the publisher. The statements and opinions contained within this publication are solely those of the authors and not of The University of Bristol or Bristol University Press. The University of Bristol and Bristol University Press disclaim responsibility for any injury to persons or property resulting from any material published in this publication. Bristol University Press works to counter discrimination on grounds of gender, race, disability, age and sexuality. Cover design by Blu Inc. Front cover image: iStock Printed and bound in in Great Britain by CPI Group (UK) Ltd, Croydon, CR0 4YY Bristol University Press uses environmentally responsible print partners

RH: To Hannah and Abigail RJ: To my great grandchildren, Jack and Lottie

Contents List of Figures List of Tables Preface Executive Summary 1 2 3 4 5 6 7

vi xi xiii xvii

Ethnic Segregation in England: Discourse and Debate The Changing Ethnic Composition of the School-​Age Population Measures of Segregation and Diversity Across Local Authorities How Concentrated Are Ethnic Groups in Schools? Does School Choice Add to Residential Ethnic Segregation? Do Socio-​Economic Separations Add to Ethnic Segregation? Conclusion: Ethnic Segregation Is Not Increasing

References Summary of Key Findings Technical Appendix: Measures of Segregation Index

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1 25 65 89 113

151 177 181 187 191 197

List of Figures 1.1

1.2

2.1 2.2

2.3 2.4 2.5

2.6

2.7

The relationship between whether the gain or loss of the White British population per local authority to 2011 exceeds what would be expected given its 2001 age profile, and the percentage of its population that were not White British in 2001 The relationship of Figure 1.1 refitted to include both the age profile and the number of each local authority’s population that was of a mixed and partly white ethnicity in 2001 Map of the local authorities The number of pupils in each of nine ethnic groups and English state primary schools over the period from 2010 to 2017 The number of live births in England and Wales, 1940 to 2016 The number of each ethnic group as a percentage of the total number in state primary schools The relative (percentage point change in the percentage of all pupils White British) and absolute (number of pupils as a percentage of their total in 2010) changes in the prevalence of White British pupils in primary schools in the 150 local authorities The percentage increase (or decrease) in the White British primary school population from 2010 to 2017 vs the percentage increase in all other groups, for local authorities with one third or more of their pupils not White British in 2010 The percentage increase (or decrease) in the White British primary school population from 2010 to 2017 vs the percentage increase in the Asian groups, for local authorities with one third or more of their pupils not White British in 2010

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15

16

28 29

31 32 33

38

40

List of Figures

2.8 (a-i) Maps showing the percentage of each local authority’s state primary pupils belonging to the named ethnic group in 2017 2.9 The number of pupils in each of nine ethnic groups and state-​funded English secondary schools over the period from 2010 to 2017 2.10 The number of each ethnic group as a percentage of the total number in state secondary schools 2.11 The relative (percentage point change in the percentage of all pupils White British) and absolute (number of pupils as a percentage of their total in 2010) changes in the prevalence of White British pupils in secondary schools in the 150 local authorities 2.12 The percentage increase/​decrease in the White British secondary school population from 2010 to 2017 vs the percentage increase in all other groups, for local authorities with one third or more of their pupils not White British in 2010 2.13 (a-i) Maps showing the percentage of each local authority’s state secondary pupils belonging to the named ethnic group in 2017 2.14 The geographical distribution of the three types of local authority based on the ethnic mix of their school pupils 3.1 Index of dissimilarity (ID) scores for primary and secondary state school pupils in English local authorities, 2010–​17 3.2 Local authorities ranked from most (rank 1) to least contribution to the ID scores in 2017 3.3 Index of exposure (IE) scores estimated at the LEA scale showing either the named group’s potential ‘exposure’ to the White British or, for the bottom-​ right chart, the White British exposure to other ethnic groups 3.4 The total number of White British pupils in the ten local authorities with the highest percentages of the named ethnic group among their pupils in 2017 3.5 Showing the potential for equal, cross-​exposure (PECE) at the local authority level between those pairs of groups for which the potential is greatest, and also the level of exposure (averaged IE score) for those same groups, in primary schools in 2017

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41

47

48 49

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54

60 69

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Ethnic Segregation Between Schools

3.6

3.7

3.8 3.9

4.1

4.2

4.3

4.4

4.5

4.6 4.7

4.8

5.1

Showing the PECE at the local authority level between those pairs of groups for which the potential is greatest, and also the level of exposure (averaged IE score) for those same groups, in secondary schools in 2017 Map of the local authorities ranked by how closely the ethnic composition of the pupils in their schools matches that for England in 2017 The authorities ranked from most (rank 1) to least ethnically diverse Showing the diversity (entropy) scores for the ethnic composition of the LEAs’ schools population in 2010 and 2017 The percentage of each of nine ethnic groups that are in primary schools where the White British form a majority, are the largest group or form a very low percentage of the school’s pupils The percentage of each of nine ethnic groups that are in secondary schools where the White British form a majority, are the largest group or form a very low percentage of the school’s pupils The percentages of each ethnic group in primary schools where their own ethnic group is predominant, in a majority and/​or the largest group The percentages of each ethnic group in secondary schools where their own ethnic group is predominant, in a majority and/​or the largest group Showing the average diversity of schools in each of the local education authorities in 2017; LEAs with an increase in the diversity index of greater than 0.10 since 2011 are also indicated The average ethnic diversity of primary and secondary schools in locations across England in 2011 and 2017 Showing the LEAs with the most diverse schools on average in 2017, those that have diversified most since 2011, and those with both least diversity and least diversification The percentage of each group in schools with a diversity greater than or equal to the value shown on the horizontal axes Comparing the segregation of the White British from other ethnic groups in schools and neighbourhoods across England, by year

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80

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83 84

97

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104 105

106

120

List of Figures

5.2

5.3

5.4 5.5

5.6

5.7

5.8

5.9

5.10

The segregation of the White British from other groups by school, neighbourhood and the cluster type of their LEA The expected and observed percentages of Asian (Bangladeshi, Indian and Pakistani) pupils in English schools The expected and observed percentages of black (Black African and Black Caribbean) pupils in English schools Showing the over-​or under-​recruitment of (upper plots) the White British and (bottom plots) the Pakistani pupils in Blackburn’s schools in 2017 relative to a neighbourhood allocation of pupils to schools, and how that recruitment relates to the expected percentage of Pakistani pupils in each school Showing the over-​or under-​recruitment of (upper plots) the White British and (bottom plots) the Indian pupils in Blackburn’s schools in 2017 relative to a neighbourhood allocation of pupils to schools, and how that recruitment relates to the expected percentage of Indian pupils in each school The estimated probability of members of the three main ethnic groups in Blackburn attending a primary school with a greater percentage of their group than that expected for their nearest school Showing the over-​or under-​recruitment of (upper plots) the White British and (bottom plots) the Pakistani pupils in Oldham’s schools in 2017 relative to a neighbourhood allocation of pupils to schools, and how that recruitment relates to the expected percentage of Pakistani pupils in each school Showing the over-​or under-​recruitment of (upper plots) the White British and (bottom plots) the Bangladeshi pupils in Oldham’s schools in 2017 relative to a neighbourhood allocation of pupils to schools, and how that recruitment relates to the expected percentage of Pakistani pupils in each school The estimated probability of members of the three main ethnic groups in Oldham attending a primary school with a greater percentage of their group than that expected for their nearest school

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130 139

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Ethnic Segregation Between Schools

6.1 6.2

6.3

6.4

6.5

6.6

6.7

7.1

The percentage of each ethnic group that was FSM eligible in the years from 2011 to 2017 Comparing the ID scores for FSM eligible and ineligible pupils in terms of their segregation from pupils of other ethnic groups in the primary schools of selected local authorities in England in 2017 Looking at how socio-​economic segregation (as measured by FSM eligibility) adds to ethnic segregation for the primary aged pupils in 2017 Looking at how socio-​economic segregation (as measured by FSM eligibility) adds to ethnic segregation for the secondary aged pupils in 2017 The percentages of FSM eligible and other pupils in below good and outstanding primary schools by ethnic group in 2017 The percentages of FSM eligible and other pupils in below good and outstanding secondary schools by ethnic group in 2017 The ‘school gap’ for White British FSM eligible pupils by local authority; London’s local authorities are shaded black The percentage change in the number of White British primary school pupils for English wards

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155 159

165

166

172

173

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180

List of Tables 2.1

2.2

2.3 2.4

2.5 3.1

4.1

4.2

4.3

Percentage change in the number of each ethnic 31 group in English state primary schools relative to their number in 2010 Showing which ethnic groups increased (+) or 35 decreased in number (-​) in primary schools in selected local authorities from 2010 to 2017 Percentage change in the number of each ethnic group 47 relative to their number in 2010 Showing which ethnic groups increased (+) or 50 decreased in number (-​) in secondary schools in selected local authorities Summary of the average characteristics of each cluster 61 of local authorities Showing, for each ethnic group, the average 86 percentage of the school-​age population that is of the same ethnicity as themselves in the local authorities in which they go to school Percentage of primary schools in each year where 94 the White British were predominant, in the majority, the largest group, account for a low percentage and account for a very low percentage of pupils, respectively Percentage of secondary schools in each year where 94 the White British were predominant, in the majority, the largest group, account for a low percentage and account for a very low percentage of pupils, respectively Showing the percentages of primary school pupils who 95 are not White British who are in schools where the White British predominate, are in the majority, are the largest group, are relatively few in number or are very few

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4.4

4.5 5.1

5.2

6.1

6.2

Showing the percentages of primary school pupils who 96 are not White British who are in schools where the White British predominate, are in the majority, are the largest group, are relatively few in number or are very few Estimated number of schools in which each ethnic 109 group is in the majority in 2011 and 2017 Showing the differences in the segregation scores for 123 schools when compared to neighbourhoods for pairs of ethnic groups in Cluster 1 for 2017 Showing the differences in the segregation scores for 124 schools when compared to neighbourhoods for pairs of ethnic groups in Cluster 2 for 2017 Showing, for the average pupil in each ethnic group 156 and in English state schools, (a) the percentage of their group that was FSM eligible in their school, (b) the percentage of pupils in their school who were FSM eligible, and (c) the percentage of all FSM eligible pupils in the school who were from that group Showing the percentage of each ethnic group in below 171 good and outstanding schools; (a) top rows: nationally in 2017; (b) middle rows: nationally in 2011; (c) bottom rows: London in 2017

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Preface The issue of segregation –​across residential areas, across and within schools, within workplaces and in terms of access to the most economically advantageous positions in society, for ­example –​has long attracted public and political attention. It has stimulated much academic research, describing situations both national and local, exploring their impacts on economic, social, cultural and political life, and seeking to influence public policy. There is a long tradition of such work in the US, reflecting not only a population history over the last three centuries or more built on immigration from a wide range of cultural origins but also the particular situation of discriminated groups there  –​notably the descendants of black slaves. Although geographical patterns of immigrant settlement have characterized the UK over a longer period –​Jewish, Huguenot, Roma and Irish immigrants have settled in particular areas, for example, almost all of them urban –​segregation has only become a consistent feature of national debate since the Second World War. This was generated by large-​scale immigration of groups, mainly from the former British Empire, with skin colour and cultural identities (language, religion, dress and so on) different from those of the majority (White British) population that have stimulated tensions –​feelings of ‘them and us’ differences –​and led to discrimination in labour and housing markets. Such tensions have occasionally generated inter-​g roup conflicts –​‘race riots’ –​which have led to government responses seeking inter-​g roup harmony, not least through legislation that seeks equality across ethnic groups and encourages their integration into the dominant society, even if some members of minority groups wish to retain elements of their cultural/​ancestral identity. Over the last 50 years or so the UK has attracted many immigrants from a variety of backgrounds. Despite many aspects of public policy designed both to manage those migrant streams and to promote social harmony, inter-​g roup tensions remain in place, if less intense than in

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the earlier decades of that period. One aspect of the multi-​ethnic/​ multi-​cultural society that is frequently raised as a problem constraining the development of that society being ‘at ease with itself ’ is segregation. Because members of the different cultural groups now present in British towns and cities in considerable numbers tend to congregate in separate residential areas –​in part because of choice, in part because of constraints, financial and otherwise –​inter-​group contact, and especially contact between those groups and the majority population, is restricted. That restricted contact is often perceived to have negative rather than positive consequences; the different groups are said to be relatively ignorant of each other, consequently viewing their counterparts in a negative light. Contact would (or should) reduce such negative impressions. Segregation –​spatial distancing –​is thus portrayed as a ‘bad thing’ that should be reduced; if it is, society will be more harmonious (or so it is argued). One particular arena where segregation is considered especially potentially harmful is in the country’s schools. These are where young people learn about and can study alongside others who are different from themselves and their immediate families and neighbours. But if schools are segregated, such learning and accommodation will not happen; just as residential separation can stimulate negative attitudes towards ‘others’ who are not encountered locally, so too can schools. And as schools are prime locales within which young people in their most formative years encounter differences, if those schools reflect residential patterns of segregation then learning potentials will be constrained. Desegregation is a desirable outcome, therefore. So are British residential neighbourhoods and the schools that they populate segregated? Neighbourhoods and their schools have long been the outcome of processes of separation of socio-​economic classes. Is it the same with ethnic groups, especially those relatively recent arrivals in the country and their descendants, that differ from the long-​established society on one or more cultural characteristics, as well as (often) their socio-​economic status? Mapping patterns of residential segregation in Britain’s cities and towns has been possible for the last few decades as an increasing volume and quality of statistical data have been made available from national censuses. Using standard measures developed in the United States –​in some cases modified –​patterns have been displayed and set in an international context. Ethnic minority groups are generally less segregated in British than US cities but more so than in some other countries that have experienced substantial multi-​cultural immigration, such as

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Preface

Australia and New Zealand. Much less is known about the country’s schools because comparable data on their ethnic composition has not been available until the last two decades and then only for England (the focus of this study too). Debates about the intensity, or otherwise, of school segregation have thus been based on very partial data only, often anecdotal. That paucity of data, certainly data that are widely available for analysis and interpretation, means that much debate about the extent of segregation in schools, as a basis for public policy, has been based on assertion as much as evidence. And that assertion frequently makes claims that the ‘problem’ –​that is the extent of segregation –​is serious and increasingly so: that segregation is intense and increasing. But is that the case? Answering that question is the purpose of this book, making full use of the rich data that are available for schools and the pupils they contain. Mapping, measuring, analysing and interpreting similar data have been a focus of both of our academic careers –​one much longer than the other! –​and the subject of research that we have undertaken jointly as colleagues at the University of Bristol over the last 15 or so years. This book stems from that research but neither repeats nor summarizes it: all of the analyses included in the book are new, designed to address specific questions regarding the extent and recent change in ethnic segregation in English schools –​unfortunately no comparable data allow us to include Northern Ireland, Scotland and Wales too –​over a recent period of seven years, presenting a picture that no other data sources, such as a national census (last conducted in 2011) can provide. Our key findings are summarized at the back of the book, which we hope will inform not only academic inquiries but also public debate and policy. A technical appendix containing formal definitions of the various measures used in this book has also been included. In producing this book we are deeply indebted to the School of Geographical Sciences at the University of Bristol, which has provided us with a collegial and supportive environment where such collaborations can flourish and where we have gained greatly from interactions with our colleagues both there (notably Kelvyn Jones, David Manley and Dewi Owen) and in other departments (Simon Burgess and Deborah Wilson especially). In 2020, the School of Geographical Sciences is celebrating its centenary year and we are proud to be members of the Quantitative Spatial Science Research Group, which continues to build on the deep foundations provided by Peter Haggett and Les Hepple, among others.

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Finally, our sincere thanks to the team at Bristol University Press who have provided much encouragement and support as we worked on the book. We are very grateful for their confidence –​and patience. Richard Harris Ron Johnston October 2019

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Executive Summary This book provides a new study of ethnic segregation across English state schools in the period from 2011 to 2017. It examines whether patterns of school-​level segregation decreased or increased over the period, how those patterns compare with patterns of residential segregation, whether particular types of schools are associated with greater ethnic separations, and whether socio-​economic differences add to the geographies of ethnic segregation. We find that high levels of ethnic segregation do exist between the majority White British and some other ethnic groups such as the Bangladeshi and Pakistani, more so at the primary than secondary level of schooling, and increased also for the more affluent of the White British. However, there is no compelling evidence that ethnic segregation is increasing –​instead, the general trend is towards desegregation and greater ethnic diversity within local authority areas and their schools. Nor is there persuasive evidence that ethnic segregation is exacerbated greatly (at least, not directly) by the present system of school choice because school intakes appear comparable to the characteristics of their surrounding neighbourhoods in their ethnic composition. Chapter 1 examines the recurring discourse over the last 20 years about segregation in the UK (more especially England), showing how it frames policy debates and media representations about segregation, social integration and community cohesion. Some of it promotes the idea that segregation is worsening, when it is not, and implies that segregation is ‘voluntary’, side-​lining social and economic causes. A more recent perspective is one of ‘white avoidance’ whereby the White British are said to avoid the places, including schools, containing greater percentages of non-​White British groups. Such language suggests reactionary behaviour –​the White British are said to avoid particular places –​when, instead, they could be attracted to particular places, having the greater financial means to reside in them. Importantly, and as Chapter 2 explores, there are demographic factors to consider: the White British of school age have declined in number

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over recent years, a cyclical trend that can be linked back to the post-​ Second World War ‘baby boom’. Meanwhile, other ethnic groups –​ generally younger –​have grown in number. These differential growth rates affect measures of segregation: when one group is growing but another is in (temporary) decline, that will affect the ethnic composition of schools and neighbourhoods. A number of previous studies show that where the White British remain most prevalent those places are still becoming more ethnically diverse. Chapter 3 shows that the school-​age population is becoming more diverse in almost every one of the local education authorities in England and that is driven by a decline –​relative and in some places absolute –​in the White British, and an increase in other groups, notably the Mixed, White Other, Pakistani and Asian Other. The net result is that the White British have greater potential to be ‘exposed’ to other groups than they did in the past (to reside alongside and be schooled with them) whereas the potential exposure of minority groups to the White British has declined. However, diversification at the local education authority scale is not a guarantee against segregation. Quite the opposite: if there was no ethnic diversity there could be no ethnic segregation either. A sometimes quoted statistic is that the majority of ethnic minority students attend schools where ‘minority’ groups are in the majority. That statistic is correct but, as Chapter 4 explores, too easily misinterpreted. Only White British students typically are in a school where their own ethnic group forms a majority; for most ethnic minority pupils the largest group they will encounter at school is still the White British. The exceptions to this are the Bangladeshi and Pakistani groups, and more so for primary than secondary schools. Schools with very high percentages of any one minority group do exist but are exceedingly rare, becoming rarer. The overwhelming trend is that schools are becoming more ethnically diverse with increased potential for pupils to be educated alongside pupils of other ethnic groups. Chapter 5 asks whether school levels of ethnic segregation reflect neighbourhood ones and, if not, where not, and for which types of school are the differences greatest? It is motivated by the English school system providing an element of choice, which means that the choices parents make and/​or the allocation criteria used to assign pupils to schools have the potential to increase, above that which is due to residential differences, the ethnic segregation between groups, across schools. Our general finding is that intakes into schools reflect the neighbourhoods that surround them, being not dissimilar to what is expected under a more strictly neighbourhood-​based system of pupil

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Executive Summary

allocation. In fact, only the average Bangladeshi and Pakistani pupil, and also the average Indian and Black African primary school pupil, were in a school, in 2017, with an intake less diverse than neighbourhoods. Even for those the difference is small. All others of the Indian, Asian Other, Black African, Black Caribbean, Mixed, White Other and White British pupils are, on average, in a school of an ethnic diversity comparable to that of neighbourhoods. There are some place-​based exceptions, but they are not the norm. Nevertheless, some schools are more able to eschew geographically-​ based processes of admission and recruit over wider areas, becoming more ethnically (and socially) selective as a result. These include academically selective grammar schools and some –​but by no means all –​ faith schools. Chapter 6 reflects on how ethnic and social segregation are linked. It provides evidence that those of the White British who are not eligible for free school meals are generally the most segregated from /​the least exposed to other ethnic groups, with academically and some religiously selective schools adding most to the social separations from the various school types. It also identifies an under-​representation of the free school meal eligible of the White British in the schools rated outstanding and an over-​representation of the same in the lower rated schools. In summary, and in answer to the book’s title, ethnic segregation is decreasing between ethnic groups across most state schools in England.

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Ethnic Segregation in England: Discourse and Debate Summary The idea that ethnic segregation is growing in England is sometimes implied in Government-​backed policy documents and reinforced by the media, notwithstanding empirical evidence to the contrary. This chapter introduces how debates about segregation have been framed, reproduced and applied to what is happening within schools. It notes a tendency to present segregation as something due to minority groups despite those groups becoming more spread out and living in more mixed neighbourhoods.

Introduction This book provides a new study of segregation between ethnic groups across English state schools in the period from 2011 to 2017. It examines whether patterns of school-​level segregation decreased or increased over the period, how those compare with patterns of residential segregation, their association with particular types of schools, and how geographies of ethnic segregation reflect geographies of social segregation. The study is important given the limited information about what is happening in terms of the patterns of ethnic segregation since 2011 (the data of the last national census) and –​more especially –​the enduring belief among some that segregation is both worsening and undermining social cohesion in the country. Concerns about segregation rarely disappear, but on occasion they gain more prominence. Of recent note is the government-​sponsored

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Ethnic Segregation Between Schools

Casey Review: A Review into Opportunity and Integration. It reignited debate about whether Britain is becoming more socially and ethnically divided in ways that might undermine social integration and community cohesion (Casey, 2016). The review pays attention to schools, arguing that the school-​age population is more ethnically segregated than its residential patterns of living. Its findings have been influential. Drawing upon them, the Government’s Integrated Communities Strategy Green Paper states that ‘in some areas, there is a relatively high degree of separation of pupils of different ethnicities across schools’ (HM Government, 2018: 11). Taking the suggestion that school segregation should be understood as the extent to which schools are representative of local populations, the social integration charity, The Challenge, undertook its own study of school segregation in England for the period from 2011 to 2016, reaching the conclusion that a quarter of primary and four in ten secondary schools were ‘ethnically segregated or potentially contributing to segregation’ (The Challenge, 2017: 13; but see Burgess and Harris, 2017 for a response to this claim). An impression can be given that the problems of segregation are large and getting worse: that as the nation becomes more ethnically diverse it also becomes more ethnically divided. Consider this paragraph from The Challenge’s report: ‘[T]‌he results suggest some even more worrying trends. The more deeply segregated areas in 2011 have made little progress, some have become even more segregated, others have stayed the same and other have improved slightly’ (The Challenge, 2017: 13). Or, where The Casey Review states that ‘South Asian communities […] live in higher concentrations at ward level than any other ethnic group. The concentrations […] are growing in many areas’ (Casey, 2016: 10). Whereas the review acknowledges that many members of ‘minority’ groups have become more dispersed among the rest of the population (so less geographically concentrated), there is no such nuance in the headline ‘British towns and cities are becoming more segregated, study finds’.1 While it would be tempting to dismiss this as the product of the headline writer, in fact the study it reports on states clearly that ‘Segregation is increasing in a number of very particular respects in the UK, especially the growing isolation of the White majority from minorities in urban zones.’2 What is notable about these reports is that they appear to conflict with a wide body of academic literature that shows a decrease in ethnic residential segregation in the decade to 2011, and decreasing ethnic segregation between schools too (Burgess, 2016).

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Ethnic Segregation in England

Defining segregation Segregation is an emotive word. It conjures up images of sharp and potentially enforced territorial divisions between people of different races and/​or cultures; of intolerance, of acts of injustice, of inequality of opportunity and of outright racism. All of these can and have been associated with segregation. In academic usage, however –​and in the way we employ the term –​it is more neutral: it means only that when we map where the members of different ethnic groups are living or attending school, we will find a geographical patterning that varies by ethnic group –​some groups are disproportionately prevalent in places where other groups are not. That means there is an ‘unevenness’ in their geographical distribution: location X may contain a much larger share of ethnic group A than it does of ethnic group B; location Y may be the opposite. And that may mean that either or both of those ethnic groups are geographically clustered, leaving them ‘concentrated’ within areas where their own ethnic group predominates the rest of the population. But not necessarily so, since it is possible for most of a minority group to be found in just a limited number of neighbourhoods yet for those neighbourhoods still to be ethnically diverse. We introduce measures to capture these various components of segregation –​unevenness, concentration and (a lack of) diversity –​later in the book. These both literally and emotively measured definitions of segregation do not mean that acts of stigmitization, inequalities of opportunity, white and class privileges or other causes and consequences of segregation do not exist nor that we are dismissive of them. However, they come without the alarmism and, worse, the vilification of minority groups that is found in some media headlines. They also come without the presumption that segregation is invariably a bad thing. Geographical clustering and the concentration of particular groups into particular places can have both positive and negative consequences. Among the positives are the possibilities for members to promote their own agency among otherwise oppressive structures, emotional support and the economic value of concentration and political empowerment (Kaplan, 2018). It is possible that ‘voluntary’ segregation fosters community membership and citizenship (Merry, 2013). We are not saying that processes of segregation do not matter; they do. But if, for example, particular people come together to offer mutual support to each other within a wider environment that is otherwise hostile to them, then to speak of that segregation being harmful to, say, social cohesion may easily slip into a rhetoric

3

Ethnic Segregation Between Schools

that effectively ‘blames the victims’ for circumstances they did not create –​including the negative possibilities of segregation, such as its association with physical deprivation, violence towards or mistrust of others, economic and educational disadvantage, and the psychological consequences of stigmatization (Kaplan, 2018). It is presently unrealistic for all parts of society to exhibit zero segregation, if it is meant that every residential neighbourhood (or school) should reflect the national (here, English) case. In 2011, that would have meant that 79.8 per cent of each neighbourhood’s population was White British, 5.7 per cent was from Other White groups (and predominantly neither Irish nor Gypsy/​Irish Travellers), 2.3 per cent from mixed/​multiple ethnicity groups (the largest being White and Black Caribbean), 7.8 per cent from Asian groups (the largest, Indian), 3.5 per cent from Black groups (the largest, Black African), and the remainder (1.0 per cent) from other groups. In 2011, 80.4 per cent of the White British population was living in statistical wards (similar to electoral wards) that were more than the national (mean) average percentage White British. At the same time, 66.1 per cent of the Other White population was in wards with more than the average Other White population, 65.0 per cent of the mixed population was in wards more than average mixed, 77.8 per cent of the Asian population, 82.4 per cent of the Black population and 79.0 of those from other groups. If the groups were randomly spread out across the country then each of those percentages would be about 50. They are higher in all cases because there is a geography to where some groups are more likely to be living and others not. Even if a state of no segregation was achievable, it would not necessarily be desirable nor equitable in so far as it would disproportionately affect the smaller groups who would be left living and learning as a minority within the schools and neighbourhoods. The question of how much segregation is too much is a matter of debate (as are the questions, too much for whom, who judges and on what criteria?) (Merry, 2013). More easily it can be agreed that in a socially inclusive and culturally tolerant society in which there is greater ethnic pluralism, the hope is for ethnic segregation to decrease over time. Whether that is happening in England is something this book will explore.

The ongoing discourse of segregation From time to time a person’s comments on segregation hit the headlines in ways others do not, acting as a catalyst for further debate. Among

4

Ethnic Segregation in England

them are parts of a speech given to an audience in Manchester on 22 September 2005, by the then chair of the UK Commission for Racial Equality, Trevor Phillips.3 It included the words: The fact is that we are a society which, almost without noticing it, is becoming more divided by race and religion. We are becoming more unequal by ethnicity. Our schools –​ and I mean the ordinary schools, not Faith schools –​are becoming more exclusive […] Residentially, some districts are on their way to becoming fully fledged ghettoes –​black holes into which no-​one goes without fear and trepidation, and from which no-​one ever escapes undamaged. The speech was widely reported in the media. Its impact can be linked both to language and timing, coming less than three months after the 7 July terrorist attacks in London and about three years after the publication of The Cantle Report, commissioned by the Home Secretary to consider issues of community cohesion following the ethnic tensions and riots in three north English towns in 2001 (Oldham, Burnley and Bradford) (Cantle, 2001). The Cantle Report wrote of ‘the depth of polarisation of our towns and cities’ in areas of education, employment, worship and social activities, meaning that ‘many communities operate on the basis of a series of parallel lives’ (Cantle, 2001:  9). It made a link between segregation and polarization, stating that ‘problems of polarisation seem more likely to arise where there is a concentration of one particular ethnic group [because] segregation reduces opportunities for understanding between faiths and cultures and for the development of tolerance’ (Cantle, 2001: 58). That segregation, Phillips implied, was increasing. When Philips spoke of “sleepwalking our way to segregation”, made reference to New Orleans, to the “manifest neglect of a poor, largely African American district” (Hurricane Katrina had occurred about three weeks before), and suggested that “the cameras returned to the slow massacre of young men and women that is taking place on our streets in London, Manchester, Birmingham and Nottingham” (a reference to knife crime and gang rivalries), he was drawing comparisons between the ghettoes of American cities, the patterns of segregation in the UK, and the lives of those living either side of the Atlantic blighted by them. It is now over a decade after Phillips’ speech and almost two decades after The Cantle Report but the language of polarization and segregation

5

Ethnic Segregation Between Schools

continues to permeate the media, prompted by Government concerns about social and community cohesion. In 2016, for example, the national Metro newspaper had the headline: ‘Diverse yet divided: UK is growing apart’,4 whereas The Sun raised the hyperbole with, ‘GHETTO BLASTER Mass immigration to Britain has changed it beyond recognition and turned communities into ghettos, reveals damning report’.5 Both were published in repose to the findings of The Casey Review. The same year, the Daily Mail published an article with the headline, ‘Ghetto Britain: Entire districts segregated, warns report’.6 The report it cites was co-​authored by Ted Cantle (the same Cantle who chaired the report into community cohesion in 2001) and does invoke the language of ghetto: it writes of some places ‘tending more towards ghettoisation in some cases’, and states that ‘in terms of the position of the majority group in relation to minorities as a whole […] it is possible to assert that segregation remains, or is increasing’ (Cantle and Kaufmann, 2016) –​a proposition that Cantle’s co-​author, Eric Kaufmann, has explored further in his book Whiteshift in terms of its impact on politics and society (Kaufmann, 2018). Nevertheless, to suggest that Britain has ghettoes and that residential segregation is increasing was controversial when Phillips gave his speech in 2005, and it remains so when it is repeated today, sometimes focusing on a specific ethnoreligious group –​Muslims. Earlier in 2016, the Daily Mail had published the headline ‘Warning on “UK Muslim ghettoes” ’ to accompany an interview with Phillips ahead of a national television documentary entitled What British Muslims Really Think. The headline was a reflection of his comment that British Muslims are becoming “a nation within a nation”. In 2018, the cultural political magazine Standpoint wrote about ‘a country increasingly segregated by faith and culture’ in an article entitled ‘The battle for British Muslims’ integration’ (Ware, 2018).7 However, the idea that Muslims live (increasingly) in residential ‘ghettoes’ appears to have little factual basis. A 2016 study (using 2011 data) observes that ‘there are substantial concentrations of Muslims in parts of some British towns and cities, but the great majority of the country’s Muslims live in neighbourhoods and blocks [and even more so intra-​city districts] where they form a minority of the population only’ (Johnston et al, 2016a). Those authors argue that there is no evidence that Muslims are becoming more geographically concentrated. In a separate and earlier study of Birmingham, a movement away from concentrated inner areas –​a process of desegregation –​was identified (Gale, 2013).

6

Ethnic Segregation in England

Sleepwalking towards segregation? It is instructive to consider how the talk of ghettoes and of increasing segregation arose in Phillips’ 2005 speech. According to one commentator, Phillips was influenced by a presentation at an annual geography conference that drew on work to which one of us contributed (Peach, 2009). It is true, for example, that a 2001 publication uses the word ghetto to describe a census neighbourhood where at least 60 per cent of its residents (in 1991) were of a single ethnic group other than White and where at least 30 per cent of the total number of that ethnic group across a wider city space were living in that neighbourhood (Johnston et al, 2001).8 As Peach observes, such a definition does not mean than the neighbourhood necessarily is as mono-​ethnic as those found in some American cities because, under the definition, up to 40 per cent of the neighbourhood population can be from other ethnic groups. It only reveals a concentration of one group in one location that is unusual for the UK (though not for the white population) (Peach, 2009). The use of the term ghetto was dropped in later papers. Its initial usage was not to endorse but to challenge the way it was informing discussion of social problems in British cities: hence the paper states ‘that there is little evidence of much ethnic group concentration into polarised enclaves and ghettos in English cities, save for a small number of groups in a few places’ (Johnston et al, 2001: 601). It goes on to say: There are strong elements of pluralism, with members of certain ethnic groups –​notably those from South Asia –​ clustering in certain parts of most cities […] but rarely has this resulted in exclusive enclaves or ghettos. Where they do cluster, however, members of the ethnic groups tend to live in relatively mixed areas, with large proportions of them in areas where the host society [i.e. the White British] form a majority; their societies are not spatially ‘closed’ –​ although a similar conclusion cannot be drawn about their host society, most of whose members, even in the cities with the largest ethnic populations, live in predominantly white residential areas. (Johnston et al, 2001: 609) It is a misreading to suggest this or the subsequent conference paper support the view that the UK has a growing problem of ghettos; in fact, Phillips apologized for presenting the work in this way.9 But the same quote does point towards the asymmetry that is found in debates about segregation and about who is described as segregated or segregating: it

7

Ethnic Segregation Between Schools

is usually minority ethnic groups and rarely the more numerous White British. A stark, visual example of this can be found on the BBC News website, under the headline ‘Segregation at “worrying levels” in parts of Britain’.10 It has a photograph of three, presumably Muslim, women, dressed in black and with their heads and most of their faces covered. While the image does reflect the concerns of The Casey Review, what it fails to convey is that the ethnic group most likely to be living in neighbourhoods where their own ethnic group predominates is not, say, Bangladeshis nor Pakistanis, it is the White British.

Decreasing segregation Given that studies of 1991 census data found little evidence to justify the term ghetto, did that remain true after subsequent censuses (in 2001 and 2011)? It did. A recent (2014) paper follows others in arguing that ethnic residential segregation was decreasing between 1991 and 2001, that the degree of segregation was relatively low when compared with most urban areas in the US, and that ‘at the neighbourhood level there is little evidence to regard ethnic residential segregation as a problem’ (Farley and Blackman, 2014: 50). That final claim may be overstated as there are some places where the concentration of particular groups into particular places has increased –​for example, Pakistanis in Bradford. However, within Bradford there is evidence of what has been described as depolarized segregation, meaning that not only was there an increase in the percentage of Pakistanis living in Pakistani-​dominated areas, there also was an increase in the percentage of the white population living in those areas, as well as an increase in the percentage of Pakistanis in mixed, white-​dominated areas (Poulsen and Johnston, 2006). Processes of depolarized segregation are evident in other places also. It has been shown that as segregation within local authority districts declined between 1991 and 2001, segregation between districts and between regions increased but only as a share of a total that declined as ethnic diversity rose (McCulloch, 2007). What is happening is that as groups spread out across a wider range of places the scale of their residential patterning shifts from a neighbourhood to a larger geographical scale but the overall strength of the segregation pattern decreases because they are moving into more ethnically mixed neighbourhoods. Similar results are observed in the changes from 2001 to 2011, which are consistent with a dual process whereby ethnic minority groups have spread out from their former enclaves but there also has been spatial contraction of the White British away from cities such as London

8

Ethnic Segregation in England

(Harris and Owen, 2018). Pooling together the various studies of the period from 2001 to 2011, the evidence is of: • a pattern of spatial retrenchment and contraction of the White British out of London and other former industrial/​manufacturing cities; • a process of dispersion and spatial diffusion of minority groups across cities as their numbers grow and they move out from their previous enclaves, creating more diverse neighbourhoods; • the places that those groups move to tend to have declining numbers of White British residents; but, • the places that the White British are moving to are gradually also becoming more ethnically diverse (Catney, 2016a, 2016b; Johnston et al, 2013, 2014). If school intakes reflect the residential neighbourhoods within which they are situated, then the overall trend of reduced and/​or depolarized segregation ought to be reflected by schools too. Generally it is; we consider this further in later chapters.11

Causes of segregation An explanation that sometimes is given for how different ethno-​cultural groups come to form distinct geographical clusters and enclaves is homophily –​a preference by individuals to associate and reside with people of a shared or similar background to themselves. For immigrant populations, for example, the cultural distance that might otherwise be encountered in moving to a new country is reduced by residing in a place alongside one’s ethno-​cultural peers, where there is potential for greater support and for the provision of goods and services tailored to that population; indeed many immigrants move to the UK through a process known as ‘chain migration’, being sponsored by kin or members of other social networks and helped to find both homes and employment in relatively close proximity to their sponsors’ own homes. Famously, the Schelling models –​early computer-​based models of population dynamics showing the spatial outcomes that could occur under various conditions of interaction among the members of the population –​showed that such a preference need not be strong for it to generate sharply segregated communities over time (Schelling, 1971).12 However, the risk of such thinking is that it makes minority groups  –​and, in the current social and political climate, especially Muslims  –​responsible for their own segregation because of their

9

Ethnic Segregation Between Schools

presumed desire to live among their co-​ethnics, whereas various authors have challenged and provided evidence against the discourse of British Muslim self-​segregation (Phillips, 2006; Finney and Simpson, 2009). It has been argued that measurement of and discussion about segregation in the UK risks promoting the idea that what is measured is voluntary segregation, arising from the outcome of residential choices and a preference to live with one’s ethno-​cultural peers when, in fact, ethnic and social segregation overlap and are easily confounded (Kapoor, 2013). There are underlying social and economic causes of social segregation and therefore of ethnic segregation as well, but these causes can be obscured by a rhetoric of choice and of voluntarily choosing to be where one lives. The key question is, is it voluntary? In principle, any household can exercise choice in where they live whether they be from a minority group or otherwise. However, those choices are considerably constrained by the operation of the housing market. Some groups –​for cultural or other reasons –​may prefer certain housing market sectors than others (ownership versus renting, for example); some may be precluded from entering some sectors (because they cannot afford to buy a property even with a mortgage, for example, or do not qualify for social housing), or from some segments of a sector (they can only afford the cheapest owner-​occupied housing, which is commonly geographically separated from the more expensive). Among the main ethnic groups, the 2011 census for England showed that whereas around two-​thirds of all Indian and Pakistani households were, like White British households, living in owner-​occupied properties, only about two fifths of Bangladeshis and Black Caribbeans were in that tenure, and only one quarter of Black Africans. Only 7 per cent of Indian households were in social housing as were 13 per cent of Pakistanis and 17 per cent of White British, but the percentages rose to over 40 for Bangladeshis, Black Africans and Black Caribbeans. Such differences reflect various combinations of choice and constraint, with clear implications for the geographies of where group members live: residential segregation reflects housing market operations, and not only tenure type but also price variations within each tenure. Moreover, in some cases ‘the choice’ will be barely existent (if at all) due to the shortage of social housing, reduction in how much will be paid to private rentiers to accommodate those whose housing costs are paid for by the state (in effect, removing low income households from expensive parts of cities), and the ‘bedroom tax’ (a punitive measure imposed on housing benefit recipients who are in accommodation deemed to have more bedrooms that necessary).

10

Ethnic Segregation in England

Put simply, housing ‘choice’ is linked to what you can afford. A  2018 House of Commons briefing paper shows that income inequality in the UK is higher than average for the European Union, and that the UK is among the bottom fifth of OECD countries for income equality (House of Commons, 2018). Income inequality rose most sharply between the late 1970s and early 1990s, after which there has been a slight increase overall, with housing costs an important contributory factor (because they are proportionately greater for those on lower incomes). The inequalities intersect with ethnicity. The (UK) Equality and Human Rights Commission (a successor to the Commission for Racial Equality) gives evidence of an ethnicity pay gap, defined as the difference between the average hourly pay of ethnic minorities and White British people. As its report notes, this is a longstanding phenomenon ‘often associated with social disadvantage and is arguably also caused by discrimination’ (Longhi and Bryninm, 2017: 7). For males, the White British outperform ethnic minorities in terms of pay, with the exceptions of Indian and Chinese men (whether foreign-​born or British), and also British-​born Black African men who, on average, had similar earnings to White British men. It notes that ‘Pakistani and Bangladeshi males had particularly severe pay gaps, especially those born outside the UK’ (Longhi and Bryninm, 2017: 8). For females, and excepting Pakistani and Bangladeshi immigrant women, the pay gap does not fall along ethnic lines  –​on average, ethnic minority females were found to earn similar amounts or more than White British women. However, these are averages. In terms of higher earning households, with a gross weekly income of £1,000 or more, those include 24 per cent of all households in the UK but only 21 per cent of Bangladeshi, 17 per cent of Pakistani and 16 per cent of Black households.13 Economic disadvantages intersect with other disparities. The Race Disparity Audit (Cabinet Office, 2017) summarizes a range of data available on the Ethnicity Facts and Figures website.14 It shows disparities between ethnic groups in all areas of life, including poverty levels and living standards, education, employment, housing, policing, and criminal justice and health. Authors including Reni Eddo-​Lodge and Kalwant Bhopal have written passionately about issues of white privilege in British society, how entrenched they are and how difficult to challenge (Eddo-​Lodge, 2017; Bhopal, 2018). Patterns of ethnic segregation intersect strongly with neighbourhoods of socio-​economic disadvantage, with decreased exposure to the White British an increased characteristic of the most disadvantaged neighbourhoods where

11

Ethnic Segregation Between Schools

minority groups are disproportionately concentrated, and increasingly so over the period 1991 to 2011 (Harris et al, 2017). In 2011, London housed 50.9 per cent of the entire Bangladeshi population of England, and 20.1 of the Pakistani population (by comparison, it housed 15.4 per cent of the total residential population of England). If these groups have the greatest pay gaps then how can they afford to live in the capital? The answer is by increasing densification –​the sub-​division of properties into smaller living spaces or by increased sharing across extended families (Johnston et al, 2016c). Where there is an increased dependency on rental accommodation there is less opportunity for the gain in and inter-​generational transfer of wealth through asset appreciation. This links to the writings of the economist, Thomas Piketty: privately held wealth and its transfer between generations are key drivers in social and economic inequalities in contemporary capitalist economies –​it strongly influences the type of housing you can afford and where it is located (Piketty, 2014).

White avoidance Rather than focusing on where minority groups live and for what reasons, some other writing has approached looking at the patterns of segregation in the UK through the lens of the actions of the majority group, the White British. For example, the ‘loss’ in the number of White British living in London from the 2001 to the 2011 census did not go unnoticed. The political and current affairs magazine, Prospect, published a commentary under the headline ‘White flight’, with a byline stating ‘Britain’s new problem –​segregation’ (Goodhart, 2013). Not only did the White British fall as a percentage of London’s total population, becoming a minority (of the total) for the first time –​but still by far the largest group (44.9 per cent) –​ the actual number of White British residents decreased too (from 4.29 to 3.67 million).15 This despite an overall increase in London’s population (from 7.17 to 8.17 million) and a slight increase in the number of White British residents in England, excluding London (from 38.46 to 38.61 million). When measured as a whole, the White British in London, metropolitan areas and other large cities are the only group for which segregation from other ethnic groups increased between 2001 and 2011 in England and Wales (Catney, 2013). Cantle and Kaufmann view this as part of ‘a growing isolation of the White majority from minorities in urban zones’, and say that ‘for most of those towns and cities which had a disproportionately low number of White British in 2001 […] this had

12

Ethnic Segregation in England

become even more disproportionate by 2011’ (Cantle and Kaufmann, 2016). To substantiate this they cite, among other examples, the London borough of Tower Hamlets, which was 43.1 per cent White British in 2001 but 31.2 per cent in 2011, and also Newham, which changed from 33.6 to 16.7 per cent White British. Implicit to Cantle’s and Kaufmann’s report is the idea of ‘White avoidance’. Elsewhere it is explicit: Kaufmann states that White British avoidance is the principal driver of segregation between majority and minorities (taken as a whole), arguing that the segregation ‘remains stuck at a high level’.16 Although it is not directly stated what or who the White British are avoiding, the phrase means the avoidance of places containing greater percentages of non-​White British groups (whether it be the groups themselves or something else about the places that is avoided). Complicating Cantle’s and Kaufmann’s interpretation of the data (as they acknowledge) is the need to consider demographic effects and the age structures of the different ethnic groups in different places. If one ethnic group is younger than another and has a greater number of children then that group will form a greater percentage of the local population not because of any process of segregation and not because of any avoidance but simply because children usually live with one or more parents, increasing the concentration of the ethnic group in that locality. Demographic factors are relevant because the White British are, on average, older:  a median age of 40–​44  years in England in 2011, compared with 20–​24 years for the Bangladeshi population and 25–​29 years for the Pakistani population (Harris, 2016). Family structure and size matter too. In 2011, ‘Asians were the least likely of any of the broad ethnic groups to live in a single person households’, with ‘Pakistanis and Bangladeshis […] most likely to be in households with dependent children’.17 To give an idea of the effect age differences are having, it is possible to predict the total number of White British living in each local authority in England in 2011 from their number in 2001, by age group and by gender. This (a regression model) gives predictions that are correct to within, on average, 3.7 per cent of their actual value in 2011.18 Our interest is less in the model itself than in the places where the number (the ‘loss’) of White British is greater than the model predicts: these do include Newham (where the actual number is down 26.6 per cent from what otherwise might be expected); they also include Enfield, Lewisham and Harrow, among others, each of which is in London. However, they do not include Tower Hamlets (where there are 18.2 per cent more White British residents

13

Ethnic Segregation Between Schools

than predicted); nor do they include Hammersmith, Hackney and Haringey, also in London. Figure 1.1 shows that there is no straightforward relationship between whether, by 2011, a local authority contained more or less than its demographically expected number of White British residents and the percentage of its population that was White British in 2001. Instead, there is some evidence of: • greater than expected numbers of White British in areas of lower but not zero percentages of populations from other ethnic groups; • fewer than expected numbers of White British in areas with a sizeable percentage but not a majority of the population from groups other than the White British; • greater than expected numbers of White British in areas with quite large percentages of their populations from other groups; and • a clearly fewer than expected number of White British in one local authority (Newham) but also a similar ‘loss’ in another local authority (Oadby and Wigston, just south of and essentially a suburb of Leicester), which had a much lower percentage of its residents being not White British in 2001. Our impression of the relationship changes further if the number of each local authority’s population of a mixed and partly white ethnicity in 2001 is included in the model (see Figure 1.2). It now appears that higher percentages of the population not being White British in 2001 can be associated with higher than expected numbers of White British in 2011, the reverse of the avoidance argument claimed. This might be because areas of mixed ethnicity are the ones that at least some of the White British avoid (though not, presumably, the ones that are parents of mixed ethnicity children) so including mixed ethnicity in the model controls for (removes) the very thing that is of interest. However, there is another possible explanation: how people describe their ethnicity in the census is a matter of constrained choice and self-​ identity –​they can select from a list of categories available (or, more correctly, it is selected by whoever is completing the census form). It is known that some people switch their identity from one census to the next (Simpson et al, 2016); also that the percentage of the population identifying with a Mixed and White ethnicity has risen from 1.0 to 1.7 per cent over the period 2001 to 2011 in England, and from 2.3 to 3.5 per cent over the same period in London. It is therefore possible that some of the loss of the White British from urban areas is offset by the growth in the number of the Mixed ethnicity group as some people

14

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Ethnic Segregation in England

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% Gain (or loss) of White British vs expectation

Figure 1.1: The relationship between whether the gain or loss of the White British population per local authority to 2011 exceeds what would be expected given its 2001 age profile, and the percentage of its population that were not White British in 2001

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Ethnic Segregation Between Schools

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Figure 1.2: The relationship of Figure 1.1 refitted to include both the age profile and the number of each local authority’s population that was of a mixed and partly white ethnicity in 2001

Ethnic Segregation in England

have either changed their identity or had children with partners from other ethnic groups. In any case, the evidence for white avoidance is ambiguous – a point we return to in the book’s conclusion.

The Casey Review Despite its negative connotations and uncertain validity, the phrase ‘White avoidance’ does shift attention to the residential choices and behaviours of the White British and away from any alleged propensity to self-​segregate by minority groups.19 As such, it offers a rejoinder to The Casey Review (the review into integration and opportunity in isolated and deprived communities) (Casey, 2016). Reflecting on patterns of settlement and segregation, the review’s executive summary identifies processes of desegregation but also of polarization: ‘as the diversity of the nation has increased […] people from minority groups have become more dispersed and in some cases more concentrated and segregated’ (original emphasis). It states that ‘South Asian communities (people of Pakistani, Indian and Bangladeshi ethnicity) live in higher concentrations at ward level than any other ethnic minority group’ and claims that ‘these concentrations at ward level are growing in many areas’. To help substantiate this, it notes that there were 24 wards (in 12 local authorities) where more than 40 per cent of the population identified as Pakistani in the 2011 census, up from the 12 wards (in seven local authorities) in 2001. Those figures are correct; less so is their interpretation. In 2001, the 12 wards were, on average, 49.4 per cent Pakistani. In 2011, the 24 were 51.6 per cent Pakistani. This does mean that more wards have a higher concentration of Pakistanis than before; but what is not mentioned is the cause. By omission, it implies that it is something due to the ethnic minority groups themselves. But that is not actually true. Looking at the numbers, the total number of Pakistanis in the 12 wards was 102,848 in 2001, and 207,138 in the 24 wards in 2011 –​approximately twice the number of Pakistanis reflecting twice as many wards. However, those 12 and 24 wards contained 55,934 and 78,363 of the White British, respectively, which is not even close to a pro rata doubling and, as such, suggests that by 2011 the wards contained fewer White British than expected. Consequently, if it can be said that the Pakistanis have become more concentrated then primarily it is due to the reduced number of White British rather than the increased number of Pakistanis. However, it may not be accurate to say it at all: discounting the White British population, then an average of 69.4 per cent of the remaining population was Pakistani in the 12

17

Ethnic Segregation Between Schools

wards in 2001; 66.3 per cent in the 24 in 2011, suggesting a decreased concentration. White British households were leaving those areas and not being replaced to the same extent by other White British, but this did not involve a wholesale replacement of White British by Pakistanis; instead other groups were also moving into the areas, making them more diverse in their ethnic composition (see also Kauffmann, 2018). The same argument can be made of the 20 wards (in eight local authorities) that are identified in the review as being more than 40 per cent Indian, up from 16 (in six local authorities) in 2001 –​the primary reason for the changes is the reduced number of White British who are living in them not the increased number of Indians. The average number of Indians in each of the 20 wards is 6,493, which is barely changed from the average of 6,324 in each of the 16 ten years earlier. But for the White British, the average has decreased from 3,026 to 1,911. When the White British are discounted, an average of 67.1 per cent of the remaining population was Indian in the 16 wards in 2001; 59.7 per cent in the 20 in 2011. Yet, the claim that residential and community segregation is driven by some (minority) ethnic groups is repeated by the recent Government Integrated Communities Strategy Green Paper (HM Government, 2018). It states that ‘there are town and city neighbourhoods where ethnic minority communities are increasing in concentration with growing isolation from White British communities. In 2001, 119 wards were majority non-​White. In 2011, this had risen to 429 wards’ (Nandi, 2018). Again, this viewpoint is myopic: it makes no mention of the White British. Why might the increased concentrations matter? The report draws on contact theory and the idea that ‘residential segregation impacts on opportunities for social mixing and may lead to higher levels of mistrust between people of different backgrounds’. This may well be true: in the context of schooling, research has shown that pupils from one ethnic group feel more positive towards another group if they encounter more pupils from that group in their school (Burgess and Platt, 2018). However, the green paper ignores the possibility that the number of majority non-​White wards has increased because non-​ White groups have spread out (therefore raising their percentages in a greater number of wards) and have done so into neighbourhoods that are more ethnically diverse –​less white, perhaps, but still with a greater mix of ethnic groups overall. If intergroup contact helps in social integration, and if desegregation helps intergroup contact, then this spreading out ought to be socially positive because ‘it is difficult to imagine successful reduction of prejudice or intergroup conflict

18

Ethnic Segregation in England

without sustained, positive contact between members of the two previously antipathetic groups. Contact is not the solution, but it must be part of any solution to the challenge posed by the enduring power of prejudice and its pernicious consequences’ (Hewstone, 2009: 289). If ethnic segregation is reducing in schools –​and that is the focus of this study –​then there should be greater potential for contact.

The role of schools and school choice Another feature shared by The Casey Review and the Government green paper is a concern about the segregation of pupils by school. The green paper identifies that ‘segregated schools are a product of where people live, admissions policies and parental choice’, meaning that school patterns of segregation reflect residential ones but do not map exactly to one another because pupils do not have to and, in any case, cannot always attend their nearest school (Casey, 2016: 26). Previous research has shown greater segregation across schools than across neighbourhoods in 2001 –​considerably so for primary schools (generally smaller than secondary schools, drawing on smaller areas for their intakes), and more so for South Asian populations than for Black groups (Johnston et al, 2006). However, a more recent study indicated that for the great majority of schools the proportion of their pupils from South Asian (or Black) minorities is commensurate with what appear to be their core catchment areas, as observed from the data (Johnston et al, 2017). There are exceptions, as Chapter 5 will reveal, but broadly schools do seem to meet the second (of 13) principles outlined by the Social Integration Commission for a healthy and well-​integrated society: for schools’ intakes to reflect the economic and ethnic diversity of their communities (Social Integration Commission, 2015).20 Even if they did not, differences between school and neighbourhood levels of segregation do not mean that the segregation is increasing. There is agreement with The Casey Review in the literature in so far as school children do appear to be more residentially segregated than the population at large, albeit that this difference occurs within a trend of decreasing segregation for both schools and neighbourhoods (Harris, 2017). However, comparing the school-​aged population with the entire population of neighbourhoods is not comparing like with like, partly because of the different age profiles of the various ethnic groups (what percentage of them is of school age) and also because segregation is related to life stage: it has been shown that for the White British, levels of segregation (from other groups) were highest for the school-​age population in both the 2001 and 2011 censuses, although

19

Ethnic Segregation Between Schools

they have increased most for those approaching retirement age (Sabater and Catney, 2019). Here again we may suspect a demographic component: the effects of an aging White British population. Whether school choice acts to exacerbate social or ethnic segregation has been widely studied in the UK as well as elsewhere (Gorard et al, 2003; Bakker et al, 2011; Fox and Buchanan, 2017). Opinion varies with the context but, generally, there is little evidence that school choice increases the social and ethnic diversity of schools over that of surrounding neighbourhoods and many claims that it decreases it to a greater or lesser extent. This decrease can happen in two ways: first, if there is a tendency for pupils (or their parents) to prefer a school with more of the pupils from an ethnic and cultural background similar to their own; second, if different groups of pupils have different choice sets, perhaps due to ease of access to a school, a preference for a particular type of school or because of the admissions policies that schools operate, which may differentially act to attract or to exclude various pupils in ways that create ethnic separations. Although England is said to operate a system of school choice, more correctly it has a system that allows for the expression of preference. That preference will be met assuming there are spaces available at the school (and assuming there is no process of academic selection –​about 5 per cent of English state secondary schools have an entrance exam). In practice, demand for places can exceed supply, in which case admissions criteria will apply. These usually prioritize children with educational needs or in public care and then siblings of current pupils, after which commonly they adopt geographical criteria, giving increased chance of entry to those living closest to the school (and within its priority area, if it has one). ‘Faith schools’ –​those linked to religious groups and denominations –​can select at least some of their pupils by religious criteria (no more than half in the case of new schools opened since 2010), and some secondary schools select up to 10 per cent of their pupils by aptitude for sport, music, the performing arts or similar. Less commonly, forms of randomized selection are used. It is because of the geographical criteria that school patterns of segregation will tend to reflect neighbourhood ones (Johnston et al, 2017). Critics of school choice argue that it most benefits those with the financial means to live closest to what are valued as the best schools. The same would be true under a neighbourhood-​based system where pupils are required to attend their nearest, local school because the perceived value of that school would be capitalized in the house prices (perhaps more so if where you live directly determines where you are educated with no possibility of exception). However, there may

20

Ethnic Segregation in England

be something about the processes of marketization, choice, inducing competition between schools for pupils and therefore funding, and also the publication of ‘league tables’ of school performance that act to raise the stakes, sharpening the differences between ‘winners’ and ‘losers’ under a system of (constrained) school choice. A 2017 study by the Department for Education found house prices to be 8 per cent higher near the best-​performing primary schools than in the surrounding area, and 6.8 per cent higher near the best secondary schools.21 An earlier commentary describes the link between schools and house prices as ‘an established fact’.22 Common belief assumes that the financially disadvantaged are most likely to lose out in terms of exercising choice to access the most desirable schools but recent research paints a slightly more complicated picture in terms of the secondary school choices made in England in the academic year 2014/​15 (Burgess et al, 2017). It shows that about 55 per cent of pupils listed their closest school as one of their preferences (typically up to three schools can be chosen, though in some places it is more; the system of allocation does not penalize pupils for placing a school lower down in their rankings but does assign pupils to their most preferred school at which there are available places). If the more disadvantaged families are most constrained to attend their nearest school then the percentage of free school meal (FSM) eligible pupils who choose their nearest school would be expected to be higher than the percentage of ineligible pupils who do so.23 In fact, the reverse is true although there is little substantial difference: 52.4 per cent of parents of FSM pupils choose their nearest secondary school and 55.5 per cent of non-​FSM pupils. The percentage of pupils with an offer to attend their first choice school is 85.4 per cent nationally, varying little between FSM pupils (84.1 per cent) and non-​FSM (85.6 per cent).24 FSM pupils make slightly fewer choices but, again, the difference is slight. However, once measures of ethnicity, neighbourhood deprivation and the local density of schools are considered, important differences emerge: FSM eligible pupils and/​or those in more disadvantaged neighbourhoods are more likely to make one choice, to select a first choice school of somewhat lower quality (in terms of educational attainment) and, whereas the majority of pupils who do not get their first choice still receive an offer from a school of similar academic quality, those in the most disadvantaged neighbourhoods have the greater likelihood to receive an offer from a lower quality school. The Government green paper observes (with concern) that ‘as of January 2017, 60% of minority pupils were in schools where minority

21

Ethnic Segregation Between Schools

ethnic pupils are in the majority’ (HM Government, 2018: 11). This does imply that ethnic minority groups are quite strongly separated from the White British educationally (but not necessarily in mono-​ ethnic schools since ‘minority groups’ is a catchall term for a range of groups) but, again, the argument is lop-​sided:  the greater percentage of White British pupils in majority White British schools is not commented on; neither is there any reflection on whether this might be a perfectly reasonable outcome from a system that actively encourages choice (and that can be underpinned in terms of a liberal theory of social justice) (Brighouse, 2000).25

Conclusion The problem with most studies of segregation in the UK (as in many other countries) is that they rely on census data that are collected only once every ten years. Although such data are unparalleled in their coverage of (almost) the entire residential population and their ability to offer analysis at small area, neighbourhood scales, they also are dated. It is now heading towards a decade since the last UK census but the results of the next census are unlikely to be known until about 2023. In the interim much of the data that inform the policy debates, including much of that quoted in The Casey Review and other recent publications, are not necessarily representative of current geographical patterns and trends, a problem that the review acknowledges. In practice, little is known about what has happened since 2011. An exception is a 2018 study using consumer data to classify peoples’ names by ethnicity and to measure residential ethnic segregation to the period 2016 (Lan et al, 2018). Its conclusions serve as a useful summary of the wider literature discussed in this chapter: The results suggest that Britain is becoming more ethnically diverse over time with shrinking White British majorities. A decrease in the overall residential segregation in Britain can be identified from the changing of [segregation] indices for most ethnic groups except for a small increase for the White British group […] It is believed that these changes are a consequence of a natural demographic process of fertility, mortality, migration and immigration of the population. (Lan et al, 2018: 82) With that in mind, it seems unlikely that previous trends towards decreasing and depolarized segregation have reversed and that schools,

22

Ethnic Segregation in England

whose intakes reflect the composition of surrounding neighbourhoods even within a system of school choice, should have become more ethnically differentiated from one another. However, it remains a possibility and one that this book examines in detail. Notes 1

2

3 4

5

6

7 8 9 10 11

12

13

14 15

The headline is from The Independent, 3 November 2016, www.independent.co.uk/​ news/​segregated-​ethnic-​minorities-​uk-​towns-​cities-​society-​white-​majority-​ british-​a7395491.html The study is presented on the openDemocracy website, at www.opendemocracy. net/​wfd/​ted-​cantle-​and-​eric-​kaufmann/​is-​segregation-​on-​increase-​in-​uk The text of the speech is available at https://​bit.ly/​2jNkwnW The Metro, 5 December 2016, http://m ​ etro.co.uk/2​ 016/​12/​05/​diverse-​yet-​divided-​ uk-​is-​growing-​apart-​casey-​report-​finds-​6303352/​ The Sun, 5 December 2016, www.thesun.co.uk/​news/​2327147/​british-​towns-​ have-c​ hanged-​beyond-​recognition-​as-​mass-​immigration-t​ urns-c​ ommunities-i​ nto-​ ghettos-​as-​report-​raps-​governments-​for-​failing-​to-​deal-​with-​crisis/​ Daily Mail Online, 3 November 2016, www.dailymail.co.uk/n ​ ews/a​ rticle-3​ 899606/​ Ghetto-​Britain-​Entire-​districts-​segregated-​warns-​report-​urges-​school-​intakes-​ mixed.html The Channel 4 documentary was first broadcast on 13 April 2016. In later work the threshold was raised to 70 per cent. BBC News, 30 August 2006, http://​news.bbc.co.uk/​1/​hi/​uk/​5297760.stm BBC News, 5 December 2016, www.bbc.co.uk/​news/​uk-​38200989 Nevertheless, misimpressions linger: in teaching, one of us uses a newspaper article from a few years ago (the exact date has been forgotten), with the headline ‘Ethnic segregation rising in schools, research finds’. Immediately below is a quote from the person who did the research (Professor Simon Burgess at the University of Bristol), who says, ‘ethnic segregation in schools is generally declining, although the student population was becoming more diverse’. In other words, the headline contradicts the actual findings and states that ethnic segregation is rising, when it is not. That might be testimony to how entrenched is the idea of a worsening situation –​so much so that it can be asserted even when the empirical evidence shows the opposite! His simulations showed that a segregation pattern resulting from individuals’ choices of neighbours was much greater than the desired level: in one, with a population comprising two groups –​B and W –​each forming half of the total, if the B residents of an area preferred that half of their nearest neighbours were also B the the resultant sorting of B and W households within the neighbourhood would result in two thirds of both B and W households having no neighbour of the same colour as themselves. See www.ethnicity-​facts-​figures.service.gov.uk/​work-​pay-​and-​benefits/​pay-​and-​ income/​household-​income/​latest. See www.ethnicity-​facts-​figures.service.gov.uk Only a minority of London’s population was born outside the UK:  36.7 per cent, of which 23.7 per cent was from EU countries (8.7 per cent of London’s population).

23

Ethnic Segregation Between Schools 16

17

18

19

20

21 22

23

24

25

See https://​policyexchange.org.uk/​eric-​kaufmann-​majority-​avoidance-​one-​of-​ the-​few-​holes-​in-​caseys-​strong-​report/​. This slightly contradics an earlier article suggesting ‘white attraction rather than repulsion seems to be the story’: http://​ blogs.lse.ac.uk/​politicsandpolicy/​white-​flight-​in-​england/​ See www.ethnicity- ​ f acts- ​ f igures.service.gov.uk/​ e thnicity-​ i n-​ t he-​ u k/​ ethnicity-​and-​type-​of-​family-​or-​household. The model simply uses the number of White British by age category and by gender in each local authority in 2001 to predict, with a polynomial regression model, the total number of White British in 2011. Although there may still be a temptation to ascribe the reason why the White British ‘avoid’ certain places to the behaviours or cultures of the people who live in those places. The sentiment is clear but to put it into practice would require a more precise definition. The question is over what geographical scale the intakes into school should reflect local ‘communities’ –​should they reflect their immediate neighbourhoods or a larger geographical area? The question matters because the answer affects the admissions criteria to be applied. BBC News, 20 March 2017, www.bbc.co.uk/​news/​uk-​england-​39327149 See also Orford (2018) and https://​blogs.lse.ac.uk/​politicsandpolicy/​school-​house-​ prices-​gibbons/​. FSM eligibility is an accepted, albeit imperfect, measure of potential economic disadvantage that is linked to eligibility for various welfare payments/​tax credits: https://​www.gov.uk/​apply-​free-​school-​meals. Although, interestingly, only 80.6 per cent of FSM pupils attend their first choice school as opposed to 86.0 per cent of non-​FSM pupils, which might imply a greater amount of residential movement in the year prior to starting secondary school among FSM pupils. But for a critique see Foster (2002).

24

2

The Changing Ethnic Composition of the School-​Age Population Summary Patterns of ethnic segregation are affected by demographic processes changing the number of each ethnic group living and going to school in a particular area. Notably, the White British formed a smaller proportion of the secondary school aged population in England in 2017 than they did in 2010 because of a decline in the number of White British pupils against a rise in the number of other ethnic groups, except Black Caribbeans. However, nationally the number of White British (but not Black Caribbeans) in primary schools has increased. There are geographical variations in the extent of these changes, with places like Harrow, Redbridge, Newham and Luton seeing greatest percentage declines in their number of White British primary pupils. Nevertheless, many local authorities appear either to be ethnically diverse or are becoming more so.

Introduction Chapter 1 mentioned the effect of demographic changes on measures of segregation over time. Because children usually live with one or more parent and because ‘minority’ groups are, on average, younger than the White British, what may appear to be increasing segregation could more simply be a reflection of (ethnic minority) families raising children, which creates an increase in the number of that group within particular schools and neighbourhoods (prior to those children leaving home and dispersing geographically). The slight increase in

25

Ethnic Segregation Between Schools

residential ethnic segregation between the 1991 and the 2001 UK censuses, for example, which itself is disputed (Farley and Blackman, 2014), can be attributed to demographic change and to the different age structures of the various groups, subsequently declining to 2011 and, we anticipate, beyond. This chapter looks at how and where changes to the ethnic composition of the school-​age population have occurred over the period from 2010 to 2017 in English state schools. It provides the demographic context in which interpretation of any changing patterns of segregation need to be situated.

About the data The data we are using in this chapter and the next are publicly available from www.gov.uk/​government/​collections/​statistics-​school-​ and-​pupil-​numbers. Of specific interest are the local authority tables, which provide counts of pupils and their characteristics. We obtain from these the numbers of various ethnic groups in state schools in those authorities. The authorities, historically known as local education authorities (LEAs), are a legacy of the ways local government has been structured in England, an amalgam of boroughs, counties and local authority districts. Many schools (in most cases academies and free schools, educating almost half of all pupils in English state schools in 2018) are now funded directly by the Department for Education with independence from the authorities within which they are located (see Chapter 5) (Ball, 2017).1 These can determine their own admissions policies, within national guidelines. Nevertheless, each authority retains some coordinating, monitoring or functional role in the local provision of educational services, serving also as a statistical unit for the dissemination of data. Schools controlled and funded by a local authority must apply its admissions policy, with slight variations allowed in some cases (faith schools, for example, which are discussed in Chapter 5). For this chapter, the authorities are convenient (and, for these data, the smallest) geographical areas for analysing national and sub-​national trends. They can be used to examine change because they remain the same over the period of the study. However, we acknowledge their major limitation, which is that they vary greatly in size and internal consistency. The largest is Kent, which, in 2017, had 203,442 pupils. The smallest is Rutland, which had 5,071.2 Whereas most cities form a single authority  –​ for example Birmingham with its 169,614

26

THE ETHNIC COMPOSITION OF THE SCHOOL-AGED

pupils –​each borough of London is its own authority. In some cases a local authority –​such as Calderdale and Kirklees in West Yorkshire –​ contains a number of separate towns with different ethnic population compositions that will be reflected in their schools. In others, the boundary of the local authority does not reflect any obvious settlement boundary: the house where one of us lives is in South Gloucestershire but there is continuous housing from it into the City of Bristol, the centre of which is less than six miles away. Outside London and the six former metropolitan counties (Greater Manchester, Merseyside, South Yorkshire, Tyne and Wear, West Midlands, and West Yorkshire), the ‘shire counties’ are predominantly composed of relatively small towns and rural areas, with most of their largest settlements being separate educational authorities (such as Swindon within Wiltshire). That is not the case in all counties, however; some cities –​such as Exeter, Ipswich and Norwich –​have not been granted separate local government status (Exeter is within Devon’s local education authority, Ipswich within Suffolk’s and Norwich within Norfolk’s). All in all, they are not geographical standardized units for analysis, so while an authority can be compared with itself, over time, comparing it with other authorities raises a question of comparability. A further limitation is that the ethnicity data count pupils of minimum compulsory school age (five years) and above but exclude pupils in fee-​charging schools – an omission that occurs throughout the book.3 In England, in January 2017, 6.7 per cent of all pupils were in fee-​charging schools although the figure varies regionally: 2.6 per cent in the North East; 4.1 per cent in the North West; 3.6 per cent in Yorkshire and The Humber; 4.2 per cent in the East Midlands; 4.5 per cent in the West Midlands; 6.9 per cent in the East of England; 15.2 per cent in Inner London; 7.7 per cent in Outer London; 10.7 per cent in the South East; and 7.0 per cent in the South West.4 The data therefore have an under-​count of the total number in each ethnic group of school age especially within London. However, for the numbers in state education then, barring any errors in the data, they are correct.5 A map of the local authorities is provided in Figure 2.1.6

The situation for primary schools The national picture Figure  2.2 shows the number of pupils of selected ethnic groups in English state primary schools in the years from 2010 to 2017. To reduce the size of the chart and to focus the discussion, we have omitted some

27

Ethnic Segregation Between Schools

Figure 2.1: Map of the local authorities

60 58

64

54

71

NORTH EAST

59 55 62 63 56

53

57

61

YORKSHIRE & THE HUMBER

146

74 66 86 65

NORTH WEST

137

78 138

150

140

143

141

149

142 67 68 77 80 136 79 148 76 83 75 73 81 87 72 85 84 82 130

144

8

5

7

1

129

WEST MIDLANDS

131 127

122 124 126 128

97

37

119

100

103 99 106

107

19

30 15 14 51 34 32 46 13 90 35 24 22

109 114

10 12

6

123

11

96

113 118

16

17

133

135

SOUTH WEST

9

3 4

132

134

125

26 39 29 40

88 105 38 33

121

92

102

101

112

48 50

28 25

49 104

98

116 108

45 21 36

18 20 42 31 95 52 43 23

27

47 41 44 117

111

EAST MIDLANDS

147

2

69

70

145

139

89

94

EAST OF ENGLAND LONDON SOUTH EAST

91

93 110

115

120

Note: The map has been distorted slightly to give more visibility to the smaller local authorities.22 The shading is to differentiate regions. Key to local authorities: EAST MIDLANDS 1: Derby, 2: Derbyshire, 3: Leicester, 4: Leicestershire, 5: Lincolnshire, 6: Northamptonshire, 7: Nottingham, 8: Nottinghamshire, 9: Rutland EAST OF ENGLAND 10: Bedford, 11: Cambridgeshire, 12: Central Bedfordshire, 13: Essex, 14: Hertfordshire, 15: Luton, 16: Norfolk, 17: Peterborough, 18: Southend-​on-​Sea, 19: Suffolk, 20: Thurrock LONDON 21: Barking and Dagenham, 22: Barnet, 23: Bexley, 24: Brent, 25: Bromley, 26: Camden, 27: City of London, Westminster, 28: Croydon, 29: Ealing, 30: Enfield, 31: Greenwich, 32: Hackney, 33: Hammersmith and Fulham, 34: Haringey, 35: Harrow, 36: Havering, 37: Hillingdon, 38: Hounslow, 39: Islington, 40: Kensington and Chelsea, 41: Kingston upon Thames, 42: Lambeth, 43: Lewisham, 44: Merton, 45: Newham, 46: Redbridge, 47: Richmond upon Thames, 48: Southwark, 49: Sutton, 50: Tower Hamlets, 51: Waltham Forest, 52: Wandsworth NORTH EAST 53: Darlington, 54: Durham, 55: Gateshead, 56: Hartlepool, 57: Middlesbrough, 58: Newcastle upon Tyne, 59: North Tyneside, 60: Northumberland, 61: Redcar and Cleveland, 62: South Tyneside, 63: Stockton-​ on-​Tees, 64: Sunderland NORTH WEST 65: Blackburn with Darwen, 66: Blackpool, 67: Bolton, 68: Bury, 69: Cheshire East, 70: Cheshire West and Chester, 71: Cumbria, 72: Halton, 73: Knowsley, 74: Lancashire, 75: Liverpool, 76: Manchester, 77: Oldham, 78: Rochdale, 79: Salford, 80: Sefton, 81: St Helens, 82: Stockport, 83: Tameside, 84: Trafford, 85: Warrington, 86: Wigan, 87: Wirral SOUTH EAST 88: Bracknell Forest, 89: Brighton and Hove, 90: Buckinghamshire, 91: East Sussex, 92: Hampshire, 93: Isle of Wight, 94: Kent, 95: Medway, 96: Milton Keynes, 97: Oxfordshire, 98: Portsmouth, 99: Reading, 100: Slough, 101: Southampton, 102: Surrey, 103: West Berkshire, 104: West Sussex, 105: Windsor and Maidenhead, 106: Wokingham SOUTH WEST 107: Bath and North East Somerset,

28

THE ETHNIC COMPOSITION OF THE SCHOOL-AGED

Figure 2.1: (continued) Note (continued) 108: Bournemouth, 109: Bristol, City of, 110: Cornwall, Isles of Scilly, 111: Devon, 112: Dorset, 113: Gloucestershire, 114: North Somerset, 115: Plymouth, 116: Poole, 117: Somerset, 118: South Gloucestershire, 119: Swindon, 120: Torbay, 121: Wiltshire WEST MIDLANDS 122: Birmingham, 123: Coventry, 124: Dudley, 125: Herefordshire, 126: Sandwell, 127: Shropshire, 128: Solihull, 129: Staffordshire, 130: Stoke-​on-​Trent, 131: Telford and Wrekin, 132: Walsall, 133: Warwickshire, 134: Wolverhampton, 135: Worcestershire YORKSHIRE AND THE HUMBER 136: Barnsley, 137: Bradford, 138: Calderdale, 139: Doncaster, 140: East Riding of Yorkshire, 141: Kingston upon Hull, City of, 142: Kirklees, 143: Leeds, 144: North East Lincolnshire, 145: North Lincolnshire, 146: North Yorkshire, 147: Rotherham, 148: Sheffield, 149: Wakefield, 150: York Source: Authors’ own calculations.

Figure 2.2: The number of pupils in each of nine ethnic groups and English state primary schools over the period from 2010 to 2017 ABAN

AIND

150

150

150

100

100

100

50

50

50

APKN

Count (000s)

AOTH

BAFR

BCRB

150

150

150

100

100

100

50

50

50

MIXD

WOTW

WBRI 2600

250

250

200

200

150

150

100 2010 2012 2014 2016

100 2010 2012 2014 2016

2500 2400 2300 2200 2100 2010 2012 2014 2016

Year Note: ABAN = Asian Bangladeshi; AIND = Asian Indian; AOTH = Asian Other; APKN = Asian Pakistani; BAFR = Black African; BCRB = Black Caribbean; MIXD = Mixed (joint) ethnicity; WOTW = White Other; WBRI = White British. Also, the vertical scale varies by chart Source: Authors’ own calculations.

of the least common ethnic groups such as a Traveller of Irish heritage, the White Irish and the Chinese, and will do the same throughout the book. Those that are left are those of Asian Bangladeshi heritage (ABAN), Asian Indian (AIND), Asian Pakistani (APKN), Asian Other (AOTH), Black African (BAFR), Black Caribbean (BCRB), those of a Mixed (joint) ethnicity (MIXD), White Other (WOTW, includes, among others, white immigrants from the EU, as well as from Commonwealth

29

Ethnic Segregation Between Schools

countries but excludes the Irish, Travellers and members of Gypsy/​Roma communities) and the White British (WBRI).7 Note that the vertical scale is not fixed but varies according to the initial size of each group. Eight of the nine groups have increased in number over the period. The recent increase for the White British is a reversal of previous decline (although that decline and the subsequent growth have both been slight in relative terms); only the number of Black Caribbeans has decreased year on year, due to an aging population that is no longer substantially supplemented by immigration. Of the groups, the White British and the Black Caribbeans were the oldest, on average, in the 2011 census population. In 2016, the five most common non-​UK countries of birth for British residents were Poland, India, Pakistan, Ireland and Romania.8 Unfortunately the school data do not allow separate identification of various groups within the White Other total, which precludes any discussion of the impact of recent East European migration on the composition of schools in some places where it has been substantial, such as Peterborough.9 The growth of most groups contributes to an increase in the total number of primary pupils of compulsory school age of 15.6 per cent from 2010 to 2017. However, not every group increased at that national rate. Table 2.1 shows the percentage changes, each with the same 2010 baseline: from 2010 to 2011, from 2010 to 2012, 2010 to 2013, and so forth to 2017. Relative to their starting size, the groups that have grown above the national rate are the Pakistanis, Indians, Black Africans, and, more especially, the Asian Other, Mixed, and White Other groups (with growth in the last of these likely dominated by the post-​2004 inflow of migrants from the East European countries that joined the EU then and subsequently). Groups that have grown slower than the national rate are the Bangladeshis, White British and, of course, the Black Caribbeans. Focusing on the White British, their growth is about one third of the rate for all primary pupils. Their number declined to 2011 but rose thereafter. This is evidence of a demographic cycle that is also evident in Figure 2.3, which shows the number of live births (of any ethnicity) in England and Wales over the period from 1940 to 2016. Approximately every 20–​25 years the number of births peaks as the (predominantly White British) children of the post-​war baby boom themselves had children, then those children had children, and so the cycle repeats through the generations.10 The most recent rise is supplemented by the children of immigrant populations, but still the longer-​term cycle is evident. Recognizing this trend is important because when some commentators observe the decline in the number of White British in some schools or neighbourhoods, they do not always appreciate that this can be due to a wider demographic change

30

THE ETHNIC COMPOSITION OF THE SCHOOL-AGED

Table 2.1: Percentage change in the number of each ethnic group in English state primary schools relative to their number in 2010. The rows are ordered from greatest to least growth in the period from 2010 to 2017 Group

2010–1 ​ 1 2010–1 ​ 2 2010–1 ​ 3 2010–1 ​ 4 2010–​15 2010–​16 2010–​17

WOTW

5.2

12.7

24.5

40.9

58.3

77.7

94.1

MIXD

6.3

13.6

22.0

31.7

40.2

49.0

57.8

AOTH

7.4

21.8

31.7

37.9

43.5

48.3

52.2

BAFR

6.3

13.2

21.1

29.2

35.2

40.9

43.7

AIND

2.7

5.9

10.1

15.8

21.3

27.2

34.1

APKN

4.1

8.3

11.5

15.1

17.9

20.6

22.4

ABAN

2.9

5.9

8.6

10.7

12.0

12.7

15.0

WBRI

−0.6

−0.5

0.4

2.2

3.1

4.1

5.2

BCRB

−0.8

−1.4

−2.2

−2.5

−6.1

−9.4

−12.1

0.8

2.4

4.7

8.1

10.6

13.2

15.6

(All)*

Note: * The national growth rate. Source: Authors’ own calculations.

Figure 2.3: The number of live births in England and Wales, 1940 to 2016

Number of live births (000s)

900

800

700

600

500 1940

1960

1980

2000

Year Source: Office for National Statistics.

that periodically reverses. Numbers in schools and neighbourhoods reflect changes to the birth rate over time and those changes are not synchronized across ethnic groups. The White British now form a reduced percentage of all pupils in primary schools, an inevitable consequence of their growth rate being lower than for all

31

Ethnic Segregation Between Schools

Figure 2.4: The number of each ethnic group as a percentage of the total number in state primary schools ABAN

AIND 10

10

8

8

8

6

6

6

4

4

4

2

2

2

0

0 2010

2012

2014

2016

0 2010

2012

APKN

%

AOTH

10

2014

2016

2010

BAFR 10

10

8

8

8

6

6

6

4

4

4

2

2

2

0

0 2012

2014

2016

MIXD

2012

2014

2016

2010

8

8

74

6

6

72

4

4

70

2

2

68

0

0 2016

2014

2016

WBRI 76

2014

2012

WOTW 10

2012

2016

0 2010

10

2010

2014 BCRB

10

2010

2012

66 2010

2012

2014

2016

2010

2012

2014

2016

Year Note: The vertical scale is different for the much larger White British group. Source: Authors’ own calculations.

but one other group (Black Caribbeans): they have declined from 73.8 to 67.2 per cent of the total over the period 2010 to 2017. However, the White British are not the only group who now form a lower fraction of the total population. It has occurred for the Bangladeshi group also (because their numeric growth is less than for other groups), as well as, again, the Black Caribbeans. The changes are shown in Figure 2.4. The White British remain a much larger group than any other, being almost ten times larger as a percentage of all primary school pupils than what has become the second largest, the White Other group. Whether it remains second after Brexit remains to be seen.

Geographical differences Although the national figures provide context, they conceal geographical variation in the data. Average trends need not reflect local circumstances. Figure 2.5 shows (along the horizontal axis) the percentage point change in, for each authority, the percentage of its primary school pupils who

32

newgenrtpdf

Figure 2.5: The relative (percentage point change in the percentage of all pupils White British) and absolute (number of pupils as a percentage of their total in 2010) changes in the prevalence of White British pupils in primary schools in the 150 local authorities ●

33

% change in number of WBRI pupils

50

●

Suffolk (82%)

Hackney (16%)

●

25

●

Central Bedfordshire (80%)

Bedford (49%)

Lambeth (16%)

● ● ●

●

Thurrock (60%)

● ● ● ●●

●

Sutton (52%)

0

●

●

Havering (64%) ● ●

●

●

●

●

Bexley (58%)

● ●● ● ●● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● Tower ● ● ● Kensington and Chelsea (19%) ●● ● Hounslow (20%) ● ●

●

● ●

●

●● ●

●

Hillingdon (31%)

● ●Camden ●

Hamlets (10%) ●

Enfield (19%)

Croydon (27%) ●

●

Barking and Dagenham (23%)

Luton (21%)

Redbridge (14%)

−25

●

●

Newham (5%)

Harrow (11%) ●

−15

−10

−5

Percentage point change in % pupils WBRI Note: For the highlighted authorities, the percentage of all pupils who were White British in 2017 is labelled. Source: Authors’ own calculations.

●

(23%)

City of London, Westminster (13%)

Brent (9%)

●

Halton (94%)

●

●

●

●

● ●

● ● ●● ● ● ● ● ●●● ● ● ●

●

● ●

●

●

●

●●

●

●

●

Haringey (20%)

●

0

THE ETHNIC COMPOSITION OF THE SCHOOL-AGED

Isle of Wight (92%)

Ethnic Segregation Between Schools

were White British from 2010 to 2017. For example, in Barking and Dagenham the percentage of its pupils who were White British in 2017 was 22.9 (shown rounded to 23 per cent in the chart), a percentage point decrease of 16.8 since 2010. A contributor to that decline was there being fewer White British primary pupils in Barking and Dagenham in 2017 than in 2010 –​there were approximately 4,800 in state schools, a decline in number of 18.2 per cent. That reduction in number was not a mathematical inevitability since it is possible for the White British to form a reduced percentage of all pupils in an authority yet still increase in number overall. That would place the authority to the left of the zero value on the horizontal axis of Figure 2.5 and above the zero value on the vertical axis. Not only is it possible, it is the most common occurrence: of the 144 (of 150) authorities with a percentage point decline in the White British as a percentage of all pupils, 108 nevertheless had more White British pupils in their primary schools in 2017 than they did in 2010. That leaves 36 where the number of White British pupils declined, of which 20 are in London –​where the greatest declines are in Harrow (decline of 40.4 per cent), Redbridge (of 24.8 per cent) and Newham (23.8) –​and the other 16 are Luton (decline of 18.2 per cent), Blackburn (4.3), Slough (4.2), Coventry (3.0), Sandwell (2.4), Birmingham (2.0), Poole (2.0), and Blackpool, Wolverhampton, Dudley, Wokingham, Liverpool, Newcastle, Buckinghamshire, Walsall and Oldham (each with a decline of less than 2 per cent). To supplement Figure 2.5, Table 2.2 provides a simple visualization of the changes to the ethnic composition of the 10 per cent of local authorities with the lowest percentages of White British primary school pupils in 2017, and also of the 10 per cent of authorities with the highest. It shows whether the number of each ethnic group has increased (+) or decreased (-​) in total in each of the local authority’s primary schools over the 2010–​17 period, giving an indication of the magnitude of the change: ++ means that the number has increased by greater than 50 per cent; +++ by 100 per cent or more; -​-​is a decrease of greater than 50 per cent (it never reaches 100 per cent). For the authorities with the lowest percentages of White British, in most the number of that group has declined but not all –​Hackney, Lambeth and Haringey have had an increase. Haringey is notable for its decline of all the minority groups in its primary schools except the Bangladeshi, Mixed and White Other groups; Lambeth and, to a lesser extent, Hackney show declines in a number of the Asian groups. Newham, Tower Hamlets, Ealing, Slough, Waltham Forest, Hounslow and Luton have seen an increase in all or all but one of the other groups as the White British have declined. The growth of the

34

newgenrtpdf

Table 2.2: Showing which ethnic groups increased (+) or decreased in number (-​) in primary schools in selected local authorities from 2010 to 2017 (see text for details) LA

Brent

WBRI

ABAN

AIND

APKN

AOTH

BAFR

BCRB

MIXD

WOTW

(All)

5.3

-​

+

+

+

+

+

-​

+

+++

+

35

8.8

-​

-​

+

+

+

+

-​

+

+++

+

Tower Hamlets

10.1

-​

+

++

++

++

+

-​

++

++

+

Harrow

11.4

-​

-​

+

+

+

-​

-​

+

+++

+

City of London & Westminster

13.0

-​

-​

+

+

+

+

-​

+

+

+

Redbridge

13.5

-​

++

+

+

+

-​

-​

+

++

+

Ealing

14.5

-​

+++

+

+

+

+

-​

+

++

+

Hackney*

15.7

+

+

-​

-​

+

+

-​

+

+

+

Slough

16.1

-​

++

++

++

++

+

+

++

++

+

Lambeth*

16.4

+

-​

-​

-​

+

+

-​

+

+

+

Waltham Forest

17.5

-​

+

+

+

+

+

-​

+

++

+

Enfield

19.4

-​

+

+

+

+

+

-​

+

+

+

Kensington and Chelsea

19.5

-​

-​

+++

+++

+

+

-​

+

++

+

Hounslow

19.6

-​

++

+

+

++

+

+

+

+++

+

Haringey*

20.0

+

+

-​

-​

-​

-​

-​

+

+

+

Luton

20.8

-​

+

+

+

++

+

-​

+

+++

+

(continued)

THE ETHNIC COMPOSITION OF THE SCHOOL-AGED

Newham

%WBRI (2017)

newgenrtpdf

Table 2.2: (continued) %WBRI (2017)

WBRI

ABAN

AIND

APKN

AOTH

BAFR

BCRB

MIXD

WOTW

(All)

Dorset

91.3

+

-​

+

+

++

++

NA

++

+

+

Wirral

91.3

+

++

++

++

++

++

-​

++

++

+

Devon

91.4

+

+

++

++

++

++

-​

++

++

+

Sunderland

91.4

+

+

+

+

++

+

NA

+++

++

+

Isle of Wight*

91.6

++

+

+

+

+++

NA

NA

+++

+

++

Derbyshire

92.0

+

++

+

+

+

++

-​

++

+++

+

Cornwall, Isles of Scilly

92.2

+

+

+

+

++

++

-​

++

+

+

East Riding of Yorkshire

92.3

+

NA

+

+

++

++

NA

++

-​

+

Knowsley

92.4

+

NA

++

++

+++

-​

NA

+++

+++

+

Cumbria

93.2

+

+

+

+

+

+

NA

++

+++

+

Halton

93.5

+

NA

-​

-​

NA

NA

NA

+++

-​-​

+

Hartlepool

93.5

+

+

-​

-​

+++

++

-​

++

++

+

St. Helens

94.1

+

-​

-​

-​

+++

NA

-​-​

++

++

+

Durham

94.4

+

-​

+

+

+

++

NA

++

+++

+

Northumberland

94.6

+

+

+

+

++

-​

NA

++

++

+

Redcar and Cleveland

95.9

+

++

-​

-​

-​-​

-​-​

NA

+++

-​

+

ENGLAND

67.2

+

+

+

+

++

+

-​

++

++

+

Note: * LA where the White British have increased as a share of the total population. ++/​-​-​is a reduction/​increase of greater than 50 per cent; +++ is an increase of greater than 100 per cent. The rows (excluding England) are ordered in ascending order of the percentage of the authority’s primary pupils who are White British.

Ethnic Segregation Between Schools

36

LA

THE ETHNIC COMPOSITION OF THE SCHOOL-AGED

White Other group is evident, as is the decline of the Black Caribbean group everywhere other than Slough and Hounslow. In the authorities with the highest percentages of White British the numbers of White British primary school pupils have grown. So, in most cases, have the number of the other groups, although not the Indian and Pakistani groups in the northern authorities of Halton, Hartlepool, St Helens, and Redcar and Cleveland. Overall the results suggest what previous studies have also: that areas where the White British are declining in number are diversifying ethnically, as too are the places where the White British are increasing. Clearly there are places where the White British are in decline and other groups are increasing in number. Does that matter? Arguments that it might, which usually rest on a form of contact theory, are discussed in the previous chapter:  if contact fosters tolerance and understanding (perhaps especially among school children) then any educational divides, which are likely to be a consequence of residential divides, are problematic for creating a more integrated society. Assume that the White British have declined most where other groups have increased most. Even if that was the case –​and we look for greater evidence of it in a moment –​it would remain important to avoid presumption about the direction of the change. In particular, it would be wrong to give the impression that the White British are leaving after other ethnic groups become more prevalent if, actually, it was, for whatever reasons, the reduction in the number of White British and their non-​replacement by others from the same group that made the housing stock available for other groups to move into. Reasons include the White British being older on average and, for some, more likely to have the economic means and the type of job that permit them to avoid the increasingly high densification of housing (the high occupancy rates) in London especially (Johnston et al, 2016c). It is possible that some people are moving in reaction to the ethno-​cultural changes occurring in ‘their’ neighbourhoods, but we must not jump to that conclusion when there are competing explanations for the declining numbers. In any case, if we filter out places where fewer of the non-​White British groups live and focus on the local authorities where more do –​ specifically, where fewer than two thirds of the primary pupils were White British in 2010 –​then we find no particular relationship between the percentage increase in groups other than the White British per local authority and the decrease in the White British (against the 2010 baseline).11 It is shown in Figure 2.6 and essentially is flat. Although some authorities, such as Harrow, have had a decrease in the number of White British as minority groups have grown, others, including Hackney, have seen both increase.12

37

newgenrtpdf

Figure 2.6: The percentage increase (or decrease) in the White British primary school population from 2010 to 2017 vs the percentage increase in all other groups, for local authorities with one third or more of their pupils not White British in 2010

20

Hackney

Ethnic Segregation Between Schools

38

% increase in WBRI, 2010−17

●

Lambeth ● ●

●

Camden

●

Islington

●

●

● City of London, Westminster

Reading

Barnet

●● ●

●

●

●

●

●

Hounslow

●

Enfield

Kensington and Chelsea

●

●

● Brent

●

Milton Keynes

●

●

●

●

●●

●

Richmond upon Thames

Rochdale

Southwark ●

Tower Hamlets ●

●

● ●

●

Bedford

●

Lewisham

Kirklees

●

0

●

Wandsworth

Haringey

● ●

●

●

Croydon

Hillingdon

●

−20

●

Luton

Newham ●

Barking and Dagenham

●

Redbridge

Harrow

−40

●

0

25

50

75

% increase in groups other than WBRI, 2010−17 Source: Authors’ own calculations.

100

THE ETHNIC COMPOSITION OF THE SCHOOL-AGED

That remains true if we look more specifically at Asian groups and the White British. The relationship is a little more negative in a statistical sense (as one variable increases the other decreases, on average, although the correlation is weak) but it still cautions against generalization because there are many places where both groups have grown in number: Bedford, for example, and Reading –​see Figure 2.7. This does not preclude increased segregation of the White British and minority groups: currently we are looking at local authority not school level data; it is possible that a response to increased ethnic diversity within an authority is for ethnic groups to separate more between schools within it. Nevertheless, at the local authority scale it is false to suggest that the White British are invariably leaving or avoiding (the schools of) places where the numbers in other ethnic groups are growing. Unsurprisingly, there is a geography to how prevalent each ethnic group is in the various local authorities’ schools.13 The maps of Figures 2.8a-​i summarize the geographical variation for 2017. Each is shaded according to the percentage of all primary pupils the groups contribute per authority. The shading emphasizes places where the percentages are atypically high and also those where it is atypically low.14 In addition, the maps have been annotated so that any authority marked with a + is one where the count of the ethnic group has increased by more than 2 per cent and by more than 50 people over the period from 2010 to 2017; those further overlaid with an × (giving an asterisk shape overall) are ones where the White British have decreased by more than 2 per cent and by more than 50 people. Places where the White British have decreased while another group has increased are identified. Looking at these maps and taking the other analyses into consideration, the following conclusions are reached. First, the number of White British pupils in state primary schools has increased in most authorities over the period, with the notable exceptions of parts of London and the West Midlands (for example, Birmingham, Coventry and Wolverhampton). Second, the White Other group has increased in nearly all local authorities, and especially in the East of England, South West and West Midlands. Third, the Bangladeshi group, unlike the other Asian groups, has grown in very few local authorities (of which a number are in London), and the increase for all Asian groups is quite limited in the North East, occurring largely in Newcastle. There has been growth in the Black African group in all local authorities in the East of England but, as with the Asian groups, less so in both the North East and the South West (where the exceptions are Bristol, Gloucestershire, South Gloucestershire, Swindon and Wiltshire). Increases in the Black Caribbean group are confined to a few parts

39

newgenrtpdf

Figure 2.7: The percentage increase (or decrease) in the White British primary school population from 2010 to 2017 vs the percentage increase in the Asian groups, for local authorities with one third or more of their pupils not White British in 2010 Hackney

20

Lambeth ● ●

Camden

Rochdale

Lewisham

●

● ● Barnet

●

Richmond upon Thames

●

●

●

Islington

●

● ●

● ●

● Tower Hamlets ● ●

● ●

●

Brent

●

Southwark

● ●

●

Milton Keynes

● ●

●

●

Hounslow

●

Kensington and Chelsea

Reading

●

Kirklees ●●

City of London, Westminster

Bedford

●

●

●

0

●

Wandsworth

Haringey

● ●

●

Enfield

Croydon

●

Hillingdon

●

−20

●

Luton

Newham ●

Barking and Dagenham

●

Redbridge

Harrow

−40

●

0

25

50

75

% increase in Asian population, 2010−17 Source: Authors’ own calculations.

100

Ethnic Segregation Between Schools

40

% increase in WBRI, 2010−17

●

THE ETHNIC COMPOSITION OF THE SCHOOL-AGED

Figures 2.8 a-​i: Maps showing the percentage of each local authority’s state primary pupils belonging to the named ethnic group in 2017 Figure 2.8a (White British) % WBRI under 35.9 35.9 to < 74.2 74.2 to < 88 = or > 88

Decrease in WBRI PLACES WHERE WBRI HAVE DECREASED: Barking and Dagenham, Bexley, Blackburn with Darwen, Brent, City of London, Westminster, Coventry, Croydon, Enfield, Greenwich, Harrow, Havering, Hillingdon, Hounslow, Kensington and Chelsea, Luton, Newham, Redbridge, Sandwell, Slough, Tower Hamlets, Waltham Forest

Figure 2.8b (Asian Bangladeshi) % ABAN under 0.2 0.2 to < 0.5 0.5 to < 1.6 = or > 1.6 Increase in ABAN Decrease in WBRI

PLACES WHERE ABAN HAVE INCREASED AND WBRI HAVE DECREASED: Barking and Dagenham, Enfield, Havering, Hillingdon, Hounslow, Luton, Newham, Redbridge, Sandwell, Tower Hamlets

41

Ethnic Segregation Between Schools

Figures 2.8 a-​i: (Continued) Figure 2.8c (Asian Indian) % AIND under 0.5 0.5 to < 1.2 1.2 to < 4.2 = or > 4.2

Increase in AIND Decrease in WBRI

PLACES WHERE AIND HAVE INCREASED AND WBRI HAVE DECREASED: Barking and Dagenham, Bexley, Brent, Coventry, Croydon, Greenwich, Harrow, Havering, Hillingdon, Hounslow, Luton, Newham, Redbridge, Sandwell, Slough, Tower Hamlets

Figure 2.8d (Asian Other) % AOTH under 0.6 0.6 to < 1.1 1.1 to < 2.9 = or > 2.9

Increase in AOTH Decrease in WBRI PLACES WHERE AOTH HAVE INCREASED AND WBRI HAVE DECREASED: Barking and Dagenham, Bexley, Blackburn with Darwen, Brent, Coventry, Croydon, Enfield, Greenwich, Harrow, Havering, Hillingdon, Hounslow, Luton, Newham, Sandwell, Slough, Tower Hamlets, Waltham Forest

42

THE ETHNIC COMPOSITION OF THE SCHOOL-AGED

Figure 2.8e (Asian Pakistani) % APKN under 0.2 0.2 to < 1.1 1.1 to < 6.7 = or > 6.7

Increase in APKN Decrease in WBRI PLACES WHERE APKN HAVE INCREASED AND WBRI HAVE DECREASED: Barking and Dagenham, Blackburn with Darwen, Brent, Coventry, Croydon, Enfield, Harrow, Havering, Hillingdon, Hounslow, Luton, Newham, Redbridge, Sandwell, Slough, Waltham Forest

Figure 2.8f (Black African) % BAFR under 0.3 0.3 to < 1.6 1.6 to < 7.9 = or > 7.9

Increase in BAFR Decrease in WBRI

PLACES WHERE BAFR HAVE INCREASED AND WBRI HAVE DECREASED: Barking and Dagenham, Bexley, Blackburn with Darwen, Coventry, Croydon, Enfield, Greenwich, Havering, Hillingdon, Hounslow, Kensington and Chelsea, Luton, Sandwell, Slough, Tower Hamlets, Waltham Forest

43

Ethnic Segregation Between Schools

Figures 2.8 a-​i: (Continued) Figure 2.8g (Black Caribbean) % BCRB under 0 0 to < 0.2 0.2 to < 1.7 = or > 1.7

Increase in BCRB Decrease in WBRI

PLACES WHERE BCRB HAVE INCREASED AND WBRI HAVE DECREASED: Havering, Hillingdon, Sandwell

Figure 2.8h (Mixed ethnicity) % MIXD under 2.9 2.9 to < 5.4 5.4 to < 8.9 = or > 8.9

Increase in MIXD Decrease in WBRI PLACES WHERE MIXD HAVE INCREASED AND WBRI HAVE DECREASED: Barking and Dagenham, Bexley, Blackburn with Darwen, Brent, City of London, Westminster, Coventry, Croydon, Enfield, Greenwich, Harrow, Havering, Hillingdon, Hounslow, Kensington and Chelsea, Luton, Newham, Redbridge, Sandwell, Slough, Tower Hamlets, Waltham Forest

44

THE ETHNIC COMPOSITION OF THE SCHOOL-AGED

Figure 2.8i (White Other) % WOTW under 3.2 3.2 to < 5.5 5.5 to < 10.9 = or > 10.9

Increase in WOTW Decrease in WBRI PLACES WHERE WOTW HAVE INCREASED AND WBRI HAVE DECREASED: Barking and Dagenham, Bexley, Blackburn with Darwen, Brent, Coventry, Croydon, Enfield, Greenwich, Harrow, Havering, Hillingdon, Hounslow, Kensington and Chelsea, Luton, Newham, Redbridge, Sandwell, Slough, Tower Hamlets, Waltham Forest

Note: An authority marked with a + had an increase in the number of the ethnic group’s pupils from 2010. Where that is overlaid with an x (to form an asterisk), there was a decrease in the number of White British pupils. Source: Authors’ own calculations.

of the East of England (Central Bedfordshire, Essex and Thurrock), London (Bromley, Havering and Hillingdon), to the Medway in the South East –​suggesting migration to London’s outer boroughs and beyond, from the inner London areas where the original migrants settled –​and to Sandwell and Walsall in the West Midlands. In contrast, the Mixed ethnicity group has grown in almost every local authority. The three exceptions are Rutland in the East Midlands, and Darlington and Hartlepool, both in the North East. Keep in mind that these figures are of the ethnicity of pupils who attend state primary schools in each local authority –​it is where they go to school not necessarily where they live. Nevertheless, and notwithstanding some flows across local authority boundaries, we expect them to reflect the residential locations of pupils, especially for primary schools where pupils tend to travel to a nearby school, partly out of convenience and partly because most schools employ geographically

45

Ethnic Segregation Between Schools

based admissions criteria favouring, in most cases, those who live nearby. In the sense that there are geographical variations in the maps so also there are spatial separations. Whether these are evidence of segregation is something to be explored in Chapter 3. For now, while the decline in the White British is notable in some parts of conurbations, the maps show that there are many places where not only are the White British of primary school age increasing in number, so too are other ethnic groups.

The situation for secondary schools Secondary school pupils are, of course, older than their primary school counterparts (on average by about six years). Although we might expect similar ethnic geographies and evidence of population growth, demographic cycles as well as processes of residential mobility (including those motivated by school choice) mean they need not be identical. Further, whereas some students leave school at 16 and others stay at the same school for their post-​16 education, others move to non-​school institutions (such as further education colleges) and are not counted in the school population. This pattern may vary both between places –​some local education authorities have few, if any, sixth forms for post-​16 education in their secondary schools –​and between ethnic groups, making comparisons between the school population and the local age structure difficult. Figure 2.9 shows the number in English state secondary schools of each of the same nine ethnic groups looked at previously, again over the period from 2010 to 2017. The starkest difference is the decline of the White British for the duration of the period. Unlike for primary pupils, there has not yet been a reversal in this decline; it will occur in the future when the rising number of primary pupils enter into secondary schools. The percentage increases/​decreases are shown in Table 2.3 with 2010 as the baseline. The decline in the number of White British is 11.2 per cent. Consequently, the percentage of all pupils who are White British in state secondary schools has decreased from 77.3 to 69.5 (see Figure 2.10). All other groups except the Black Caribbeans have seen their percentage increase. Even so, the percentage of pupils who are White British in secondary state schools remains slightly greater (in 2017) than the percentage in primary schools, which, to recall, was 67.2 per cent.

Geographical differences As for primary pupils, we are interested in the geography of changes to the ethnic composition of the secondary school-​age population. Figure 2.11 is comparable to Figure 2.5 but this time looking at the

46

THE ETHNIC COMPOSITION OF THE SCHOOL-AGED

Figure 2.9: The number of pupils in each of nine ethnic groups and state-​ funded English secondary schools over the period from 2010 to 2017 ABAN

AIND

150

150

150

100

100

100

50

50

50

APKN

Count (000s)

AOTH

BAFR

BCRB

150

150

150

100

100

100

50

50

50

MIXD

WOTW

WBRI 2600

250

250

200

200

150

150

100 2010 2012 2014 2016

100 2010 2012 2014 2016

2500 2400 2300 2200 2100 2010 2012 2014 2016

Year Note: The vertical scale varies by chart. Source: Authors’ own calculations.

Table 2.3: Percentage change in the number of each ethnic group relative to their number in 2010 group

2010–1 ​ 1 2010–1 ​ 2 2010–1 ​ 3 2010–1 ​ 4 2010–​15 2010–​16 2010–​17

WOTW

0.3

3.5

9.4

16.7

26.5

37.4

47.0

MIXD

6.1

10.6

14.3

18.2

24.5

30.7

40.5

AOTH

6.9

17.4

20.2

23.2

27.8

33.4

40.5

APKN

5.3

10.2

15.5

21.7

28.9

34.2

40.4

BAFR

6.9

11.9

16.0

20.0

26.2

31.8

40.1

ABAN

5.9

10.4

16.6

22.0

27.8

34.4

39.5

AIND

1.3

1.8

3.3

4.8

8.0

12.2

16.8

BCRB

0.0

−1.7

−2.3

−4.2

−3.6

−4.5

−5.1

WBRI

−0.6

−2.7

−4.7

−7.2

−8.8

−10.2

−11.2

0.3

−0.6

−1.4

−2.3

−2.3

−2.1

−1.2

(All)

Note: The rows are ordered from greatest to least growth in the period from 2010 to 2017.

47

Ethnic Segregation Between Schools

Figure 2.10: The number of each ethnic group as a percentage of the total number in state secondary schools ABAN

AIND 10

10

8

8

8

6

6

6

4

4

4

2

2

2

0

0 2010

2012

2014

2016

0 2010

2012

APKN

%

AOTH

10

2014

2016

2010

BAFR 10

10

8

8

8

6

6

6

4

4

4

2

2

2

0

0 2012

2014

2016

MIXD

2012

2014

2016

8

8

6

6

4

4

2

2

0 2012

2014

2012

2016

2014

2016

WBRI 76 74 72 70 68 66

0 2010

2010

WOTW 10

2016

0 2010

10

2014 BCRB

10

2010

2012

2010

Note: The vertical scale varies by chart.

2012

2014

2016

2010

2012

2014

2016

Year

Source: Authors’ own calculations.

relative and absolute changes in the prevalence of White British pupils among secondary school pupils in each of the 150 local authorities. Nearly all LEAs had a reduced number of White British secondary pupils by 2017 compared to 2010 (true of 135 of the 150 authorities). Losses have been greatest in Barking and Dagenham, Luton and Redbridge. A few authorities have an increased number of White British pupils, notably Hackney –​which also had the greatest percentage increase in the number of White British primary pupils –​and also Richmond. Table 2.4 summarizes the changes taking places in the authorities with the lowest and highest percentages of White British secondary school pupils in 2017. As for primary pupils, the numbers of White British secondary pupils have declined in most of the authorities with lowest percentages of the White British, with the exceptions of Lambeth, the City of London with Westminster, Hackney, Haringey and Southwark, where they have increased. The Bangladeshi group has increased in number in all of the authorities with lowest percentages of White British, as have the Mixed and also the White Other (except in

48

newgenrtpdf

Figure 2.11: The relative (percentage point change in the percentage of all pupils White British) and absolute (number of pupils as a percentage of their total in 2010) changes in the prevalence of White British pupils in secondary schools in the 150 local authorities Hackney (17%) ●

Richmond upon Thames (62%)

25

●

Hammersmith and Fulham (27%) ●

●

City of London, Westminster (14%) ●●

Bristol, City of (65%)

Rutland (93%) ●

●

●

● ● Lambeth (12%) ●

0

●

●

●

●

● Haringey (19%)

●

● ●● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ●●●● ●● ● ● ● ● ● ● ● ●● ● ●● ●● ●● ●● ● ● ● ● ● ●●●● ● ●● North●East ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● (20%) Hounslow

East Riding of Yorkshire (94%)

● ● ●

●

●

● ●

Sutton (47%)

−25

●

● ●

● ●

●

●

●

● ●

Slough (16%)

●

●

Harrow (12%) ●

● ●

Luton (22%)

−20

Lincolnshire (93%)

●

Knowsley (95%)

Newham (6%)

●

Barking and Dagenham (27%)

● ●

●

●

● Waltham Forest (17%)

Enfield (19%)

Isle of Wight (93%)

●

●

Tower Hamlets (8%)

Redbridge (14%)

−10

0

Percentage point change in % pupils WBRI Note: For the highlighted authorities, the percentage of all pupils who were White British in 2017 is labelled. Source: Authors’ own calculations.

10

THE ETHNIC COMPOSITION OF THE SCHOOL-AGED

49

% change in number of WBRI pupils

50

newgenrtpdf

Table 2.4: Showing which ethnic groups increased (+) or decreased in number (-​) in secondary schools in selected local authorities (see text for details) Local Authority

%WBRI (2017)

WBRI

ABAN

AIND

APKN

AOTH

BAFR

BCRB

MIXD

WOTW

(All)

4.8

-​

+

+

+

+

+

-​

+

++

+

Newham

5.9

-​

++

+

+

+

+

-​

++

++

+

Tower Hamlets

7.6

-​

+

+

+

+

+

-​

+

-​

+

Harrow

11.5

-​

+

+

+

++

+

+

++

+++

+

Lambeth

11.8

+

+

+

+

++

++

+

++

++

+

Redbridge

13.7

-​

+++

-​

-​

+

+

-​

+

++

+

City of London & Westminster

14.3

+

+

++

++

+

+

-​

+

+

+

Slough

15.7

-​

++

+

+

+

+

-​

+

+

+

Ealing

16.2

-​

+++

-​

-​

+

+

-​

+

++

+

Hackney

16.6

+

+

++

++

+

++

+

++

++

++

Waltham Forest

16.9

-​

+

-​

-​

+

+

-​

+

++

+

Haringey*

19.1

+

+

-​

-​

-​

-​

-​

+

+

+

Enfield

19.3

-​

+

-​

-​

+

+

-​

+

+

+

Lewisham

19.8

-​

+

-​

-​

+

+

-​

+

+

+

Southwark

20.0

+

+

+

+

+

+

-​

++

+

+

Hounslow

20.2

-​

++

-​

-​

+

+

-​

+

+

+

Ethnic Segregation Between Schools

50

Brent

newgenrtpdf

Table 2.4: (continued) %WBRI (2017)

WBRI

ABAN

AIND

APKN

AOTH

BAFR

BCRB

MIXD

WOTW

(All)

Wigan

92.3

-​

-​

+

+

+

++

NA

+

+++

-​

Cheshire West and Chester

92.3

-​

+

+

+

+

-​

NA

+

++

-​

North East Lincolnshire*

92.6

-​

+

-​

-​

-​

-​

NA

-​

-​

-​

Barnsley

92.8

-​

NA

-​

-​

+

++

NA

++

+++

-​

Sefton

92.9

-​

+

++

++

+++

+

-​-​

+

+

-​

Cornwall, Isles of Scilly

92.9

-​

-​

+

+

+

NA

NA

++

+

-​

Isle of Wight*

93.0

-​

-​-​

-​

-​

-​

-​

NA

-​

-​

-​

Rutland

93.5

+

NA

-​

-​

NA

++

NA

++

++

+

East Riding of Yorkshire*

93.7

-​

NA

-​

-​

-​-​

-​

NA

++

-​-​

-​

Derbyshire

93.8

-​

-​

+

+

+

++

-​

+

++

-​

Hartlepool

94.0

-​

+

-​

-​

+++

+++

NA

+

+++

-​

Halton

94.1

+

NA

NA

NA

-​

-​

NA

+

-​

+

St. Helens

94.7

-​

-​

-​

-​

++

+

NA

+

++

-​

Northumberland

95.0

-​

+

+

+

+

++

NA

++

-​

-​

Knowsley*

95.1

-​

-​

NA

NA

NA

-​

NA

+

+++

-​

Durham

95.5

-​

-​

+

+

++

+++

NA

++

+

-​

ENGLAND

69.5

-​

+

+

+

+

+

-​

+

+

-​

Note: * LA where the White British have increased as a share of the total population. ++ is an increase of greater than 50 per cent; +++ is an increase of greater than 100 per cent. The rows (excluding England) are ordered in ascending order of the percentage of the authority’s primary pupils who are White British.

THE ETHNIC COMPOSITION OF THE SCHOOL-AGED

51

Local Authority

Ethnic Segregation Between Schools

Tower Hamlets) and Black African (except Haringey) groups. Declines in the number of Indian and Pakistani pupils are evident in Redbridge, Ealing, Waltham Forest, Haringey, Enfield, Lewisham and Hounslow, and the Black Caribbeans have decreased in all the authorities except Harrow, Lambeth and Hackney. Turning to the authorities with the highest percentages of White British secondary pupils, nearly all have seen a decline in the total number of all pupils, this being largely due to the decline of the White British. Often that decline is exacerbated by declines in other groups, especially in North East Lincolnshire and the Isle of Wight. The groups that have grown in the greatest number of these authorities are the Mixed, White Other, Asian Other and Black Africans. In nearly all the number of White British secondary pupils has fallen, excepting Rutland and Halton. A sizeable decline occurred in the Isle of Wight where there is an aging population; in 2011, the most common age category for its residents was 60–​64 years (it was 50–​54 in 2001).15 Once again, it would be wrong to assume that percentage declines in the number of White British are greatest in the local authorities where other groups are increasing fastest. That would be evidenced by a downward sloping line in Figure  2.12 whereas, if anything, the reverse is true  –​it is marginally upwards sloping (declines are least or the White British are increasing where other groups are increasing at the fastest rates).16 Maps (Figures 2.13a-​i) showing where each ethnic group is most and least prevalent in secondary schools have a similar design to that used for primary schools. However, rather than highlight the few places where the White British and another ethnic group have increased in number from 2010 to 2017 (remember there are only 15 authorities where the number of White British secondary school pupils increased), the maps instead are annotated so that a + indicates an increase in the (absolute) number of the named ethnic group in secondary schools over the period, an × indicates an increase in the number in primary schools, and one overlaid upon the other (to form an asterisk) indicates an increase in both. They show the few local authorities where the White British increased in number in secondary schools, the greater number where they increased in primary schools, and the very few (mainly in London) with an increase for both. This contrasts with the White Other and Mixed groups that have grown in almost every local authority and at both stages of education. The Bangladeshi group is unusual in that any increases in number are more for secondary schools than for primary (this is evident in the north east of London) while the Black Caribbean group is the one that is least marked by growth. For most, although their growth is geographically clustered, it is common to see an increase in

52

newgenrtpdf

Figure 2.12: The percentage increase/​decrease in the White British secondary school population from 2010 to 2017 vs the percentage increase in all other groups, for local authorities with one third or more of their pupils not White British in 2010 ●

25

Hackney

●

Richmond upon Thames Hammersmith and Fulham

City of London, Westminster

●

●

Southwark

Islington ●

●

●

●

Lambeth

Barnet

0

●

● ● ●

● ●

●

●

● ●

● ● Hounslow ●

−25

●

Waltham Forest

0

●

●

●

●

Harrow

Newham

●

●

●

Enfield

●

●

●

Slough

●

●

●

●

Tower Hamlets

20

Luton

●

Redbridge

●

40

●

Barking and Dagenham

60

% increase in groups other than WBRI, 2010−17 Source: Authors’ own calculations.

THE ETHNIC COMPOSITION OF THE SCHOOL-AGED

53

% increase in WBRI, 2010−17

50

Ethnic Segregation Between Schools

Figures 2.13 a-​i: Maps showing the percentage of each local authority’s state secondary pupils belonging to the named ethnic group in 2017 Figure 2.13a (White British) % WBRI under 37.6 37.6 to < 76.5 76.5 to < 89.8 over 89.8 Increase in secondary pupils Increase in primary pupils

Number of authorities with an increase at both primary and secondary levels: 13

Figure 2.13b (Asian Bangladeshi) % ABAN under 0.2 0.2 to < 0.6 0.6 to < 1.7 over 1.7 Increase in secondary pupils Increase in primary pupils

Number of authorities with an increase at both primary and secondary levels: 20

54

THE ETHNIC COMPOSITION OF THE SCHOOL-AGED

Figure 2.13c (Asian Indian) % AIND under 0.4 0.4 to < 1 1 to < 4 over 4

Increase in secondary pupils Increase in primary pupils

Number of authorities with an increase at both primary and secondary levels: 54

Figure 2.13d (Asian Other) % AOTH under 0.5 0.5 to < 1.1 1.1 to < 2.6 over 2.6

Increase in secondary pupils Increase in primary pupils

Number of authorities with an increase at both primary and secondary levels: 63

55

Ethnic Segregation Between Schools

Figures 2.13 a-​i: (Continued) Figure 2.13e (Asian Pakistani) % APKN under 0.2 0.2 to < 1 1 to < 6.9 over 6.9

Increase in secondary pupils Increase in primary pupils

Number of authorities with an increase at both primary and secondary levels: 63

Figure 2.13f (Black African) % BAFR under 0.3 0.3 to < 1.4 1.4 to < 8 over 8

Increase in secondary pupils Increase in primary pupils

Number of authorities with an increase at both primary and secondary levels: 77

56

THE ETHNIC COMPOSITION OF THE SCHOOL-AGED

Figure 2.13g (Black Caribbean) % BCRB under 0 0 to < 0.3 0.3 to < 2.3 over 2.3

Increase in secondary pupils Increase in primary pupils

Number of authorities with an increase at both primary and secondary levels: 5

Figure 2.13h (Mixed ethnicity) % MIXD under 2.3 2.3 to < 4.4 4.4 to < 8.2 over 8.2

Increase in secondary pupils Increase in primary pupils

Number of authorities with an increase at both primary and secondary levels: 137

57

Ethnic Segregation Between Schools

Figures 2.13 a-​i: (Continued) Figure 2.13i (White Other) % WOTW under 2.4 2.4 to < 4 4 to < 8.1 over 8.1

Increase in secondary pupils Increase in primary pupils

Number of authorities with an increase at both primary and secondary levels: 127

Note: An authority marked with a + had an increase in the number of the ethnic group’s secondary pupils, an authority with an x had an increase in the number of the ethnic group’s primary pupils, and where one is overlaid upon the other (forming an asterisk) both have increased. Source: Authors’ own calculations.

the number of primary school pupils to be located in authorities at the edge of clusters of other local authorities where both the primary and secondary numbers have grown. Although not irrefutable evidence, such a patterning is characteristic of a process whereby families from these various minority groups are spreading outwards.

Consolidation To help round off this chapter, a summary is offered of the patterns and trends that are found in the data. Each local authority is different but there also are overlapping characteristics: some have a larger rate of fall in the number of White British pupils; some less so. Some have a large percentage of pupils from minority groups; others do not. We can use the data to group (‘cluster’) the authorities on a broadly like-​with-​like

58

THE ETHNIC COMPOSITION OF THE SCHOOL-AGED

basis and study the groups that emerge. The process is known as cluster analysis and it is the basis by which some classifications of neighbourhood types are created.17 Any geographical pattern that emerges from the groupings is because some authorities that are near to each other have school populations with similar ethnic profiles. A problem with cluster analysis is that it is dependent upon the number of groups created and the type of grouping method employed. It is also dependent upon the data, which should reflect the interest and purpose of the study –​here 54 variables measuring the ethnic compositions of local authorities’ school-​age populations and changes to them.18 The 150 local authorities were clustered into three groups using a method that is robust against outliers (unusual cases) with the choice of three groups made using an optimization procedure.19 Looking at the map of these three cluster-​groups (Figure 2.14), there are some clear spatial patterns. Cluster 2 is found only in much of London, but not near some parts of its border. Places most characteristic of this cluster are Islington, the City of London with Westminister, and Southwark. Cluster 1 is found around the edges of London, especially to the west and then extending northwards into central England, the Midlands and parts of the North West as well as West Yorkshire. Places characteristic of this cluster include Derby, Leeds, Northamptonshire, Kirklees and Manchester. Cluster 3 is the largest; it includes but is not limited to rural England, and is characterized by West Sussex, Cambridgeshire, Warrington, Oxfordshire and Nottinghamshire. Cluster 1 contains 44 local authorities, Cluster 2 has 16, and Cluster 3, 90. Four of the five councils required by the Government (in 2018) to adopt new integration plans to deal with problems of segregation are in Cluster 1. Those are Bradford, Blackburn, Peterborough and Walsall. The fifth, Waltham Forest, is in Cluster 2.20 Each is identified in Figure 2.14. Figure 2.14 clearly distinguishes from the remainder of the country major segments of metropolitan England and some of the largest free-​ standing cities. Virtually all of London’s boroughs are in Clusters 1 and 2, and there are no LEAs outside London in Cluster 2. Cluster 1 includes a band of local authorities in the home counties to the north west of the capital that have substantial non-​white school populations; many of them contain large towns or cities such as Reading, Slough, Luton, Milton Keynes and Peterborough. The other Cluster 1 authorities include several boroughs in three of the six former metropolitan counties  –​Greater Manchester, the West Midlands and West Yorkshire –​but none in the other three –​ Merseyside, South Yorkshire and Tyne and Wear: the issue of large

59

Ethnic Segregation Between Schools

Figure 2.14: The geographical distribution of the three types of local authority based on the ethnic mix of their school pupils

Cluster 1 2 3

Bradford

Blackburn Peterborough

Walsall

Waltham Forest

Note: The locations of the five councils required, in 2018, to adopt integration plans are shown. Source: Authors’ own calculations.

and growing non-​white populations outside London, with the potential for ethnic segregation in their schools, does not extend equally across all of the country’s major conurbations. Multi-​ethnic and multi-​cultural populations are much less a feature of cities like Liverpool, Newcastle upon Tyne and Sheffield, plus smaller ones such as Barnsley, St Helens and Sunderland, than they are of Birmingham, Leeds and Manchester, Bradford, Coventry and Oldham. Some cities outside those conurbations are also in Cluster 1  –​ notably Blackburn, Derby, Leicester, Nottingham and Southampton –​but other large cities, notably Bristol, are not. This is not to suggest that issues of ethnic segregation in schools are entirely confined to the places in Clusters 1 and 2 –​places like Bristol and Liverpool clearly

60

THE ETHNIC COMPOSITION OF THE SCHOOL-AGED

Table 2.5: Summary of the average characteristics of each cluster of local authorities Cluster 1: Ethnic diversity with higher declines of White British pupils Higher percentages of Indian and Pakistani pupils compared to other clusters Higher rates of increase of Bangladeshi primary pupils, and of Asian Other and White Other secondary pupils Higher rate of decrease of White British pupils Lower rate of decrease of Black Caribbean pupils Includes Derby, Leeds, Northamptonshire, Kirklees, Manchester Cluster 2: Ethnic diversity where the growth of minority groups is slowing and the White British are becoming more prevalent in some cases Higher percentages of Black Caribbean and White Other pupils Higher percentage of Black African primary pupils Lower percentage of White British pupils Decreased percentages of Black African, Indian and Pakistani pupils over time period Lower rates of increase of Asian Other, Black African, Indian, Mixed ethnicity, Pakistani, White Other pupils, and of Bangladeshi secondary pupils Higher rate of decrease of Black Caribbean pupils Includes Islington, City of London with Westminster, Southwark Cluster 3: Characterized by the White British but becoming more ethnically diverse Lower percentages of Asian Other, Bangladeshi, Black African, Black Caribbean, Indian, Mixed, Pakistani and White Other pupils Higher percentage of White British pupils Higher rates of increase of Asian Other, Indian, Black African, Black Caribbean, Mixed, Pakistani and White Other pupils Includes West Sussex, Cambridgeshire, Warrington, Oxfordshire, Nottinghamshire

do experience them. But the continued concentration of England’s multi-​ ethnic school populations in a relatively small number of local education authorities is a clear feature of the country’s geography that these analyses have portrayed (although there is also evidence that minority groups are spreading out). A summary of the characteristics of each cluster group is in Table 2.5.21

Conclusion In summary, and in so far as the clusters portray the ethnic mix in the local authorities and the changes to it, a picture of increasing diversification is suggested across large parts of the country. There may be exceptions to this, especially in terms of the White British and some of the places grouped into Cluster 1 (broadly, those of existing ethnic diversity with greater declines of White British pupils). Nevertheless, throughout this chapter much of the analysis has pointed in the same

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direction –​to increasingly diverse school-​age populations within local authorities. Whether this translates into diverse schools or serves, instead, as the pre-​condition for segregation has yet to be examined. Notes 1

2 3 4 5

6

7

8

9

10

11

12

13

14

15

The money used to fund academies and free schools is calculated using the same local funding formulas as for other schools in the local authority, with sizeable regional variations: see https://​fullfact.org/​education/​spending-​schools-​england/​ for a summary of the current system and planned changes to it. The median average is 35,148 pupils. Independent schools, also known as private and, confusingly, public schools. This information is available in the same source as for the ethnicity counts. An error appears in the count of secondary school pupils in Knowsley in the North West of England. Here the number of the ‘White Other’ group implausibly decreases from 2,038 in 2010 to 22 the year after (a more realistic number based on subsequent years). That the number of White British pupils rose from 4,311 to 6,528 over the same 12 months suggests an initial coding error. We have made a correction to the data. The offshore Isles of Scilly is one such authority, as is the City of London, an historic financial district within the capital. In practice, those two authorities contain very few pupils and data about them are not always available. As a consequence, they have been merged with nearby Cornwall and Westminster, respectively, leaving a total of 150 authorities in the analysis. The Mixed group is largely included to remind readers of the growth in this group, reflecting ethnic integration at the most intimate level. Word limits prevent us from discussing this group in greater detail and from looking at its subgroups (for example, Mixed White and Asian or Mixed White and Black). www.ons.gov.uk/​peoplepopulationandcommunity/​populationandmigration/​ internationalmigration/​bulletins/​ukpopulationbycountryofbirthandnationality/​ 2016#what-​is-​the-​nationality-​of-​non-​uk-​born-​residents. See www.peterboroughtoday.co.uk/o ​ ur-r​ egion/c​ ambridgeshire/​half-​of-​pupils-​at-​ peterborough-​s-​schools-​expected-​to-​be-​ethnic-​minorities-​by-​2020-​1-​7618343 and www.bbc.co.uk/​news/​election-​2015-​england-​32204991. Albeit that the initial wave dampens over time and the total number of children never rises back to the post-​war and 1960s highs. The fewer than two thirds thresholds is simply to leave focus on the 46 authorities with what was the lowest percentages of White British primary school pupil in 2010. In each of the 46 authorities the total number of primary school pupils (summed across all ethnic groups) has increased. Unsurprising because there is a geographical patterning to the residential geography of each ethnic group (if there was not there would be no concerns about ethnic segregation!) The darkest shading is always for those with percentages in the top 20 per cent for the group and the lightest shading is for those in the bottom 20 per cent; the middle break-​point is the median. See www.iwight.com/​azservices/​documents/​2552-​Census-​Atlas-​2011-​Section-​ 2-​Population-​religion-​and-​ethnicity.pdf

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THE ETHNIC COMPOSITION OF THE SCHOOL-AGED 16

17 18

19

20 21

22

There also is no negative relationship between the rate of decrease in the White British population in secondary schools and the rate of increase in the Asian one. See, for example, www.opengeodemographics.com The variables are the percentages of, separately, primary and secondary pupils in each of the nine ethnic groups per local authority in 2017, the increase or decrease in those percentages since 2010 (a percentage point difference measuring the relative change), and the percentage change in the number of each group to 2017 using 2010 as a baseline (a percentage difference reflecting the absolute change in number). Specifically, a k-​medoids algorithm procedure was used. Each of the 54 variables was mean-​centred and scaled by its standard deviation; the rate of change was capped at 100. BBC News, 14 March 2018, www.bbc.co.uk/​news/​uk-​43395310 A further problem with cluster analysis is that it encourages over-​generalization. It is usual to describe the clusters in terms of the average characteristics of their members but it is important to remember that the average need not apply fully to any member of the group. An average is just that, not a specific case. Even so, it can be revealing to look at those averages and identify what makes each cluster distinctive. These are summarized in Table 2.13. Using the procedure developed by Harris et al (2018).

63

3

Measures of Segregation and Diversity Across Local Authorities Summary Data about the school-​age population in LEAs provide contextual information about what is happening in terms of patterns of ethnic segregation at a broad geographic scale. In regard to the number of each ethnic group per local authority, the White British now have greater potential to be ‘exposed’ to other groups than they did in the past (to reside and be schooled alongside them) because the numbers of those other groups have grown. The reverse is not true, however, because the potential exposure of ‘minority’ groups to the White British has declined with their reduced number or lower growth rate compared to most other groups. All but ten authorities have a more diverse school-​age population overall.

Introduction Chapter 2 looked at the changing numbers of various ethnic groups in the state schools of English LEAs over the period from 2010 to 2017. It found a decreasing percentage of the pupils to be White British, with that decline due to a smaller number of that group in secondary schools (but not primary schools, where their number is rising), and also due to the rising number of pupils from other ethnic groups (with the exception of Black Caribbeans, whose numbers have declined in both primary and secondary schools). The picture sketched was one of increasing diversity across large parts of the country at the LEA scale, with the school-​age population less

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Ethnic Segregation Between Schools

dominated by the White British, though it remains by far the largest group. Although a decline in the White British is notable in some conurbations, there are many other places where not only are the White British increasing in number, so too are other ethnic groups. In this chapter, we turn to the sorts of formal measures of segregation widely used in academic writing and in policy research. Doing so allows us to both to colour the picture more fully and to introduce the measures ahead of more geographically nuanced analysis from Chapter 4 onwards.

Measuring segregation Segregation means that when we map where members of different ethnic groups are living or schooled, those maps are not all the same and each shows geographical variation. Put simply, ‘different’ people live in different places. Approached from a mathematical perspective, if a person is selected at random from each of two (ethnic) groups, the probability that the person from ethnic group X is living in, say, St Pauls, Bristol, is not the same as the probability that the person from ethnic group Y is living there. The probabilities differ because there are social, economic, demographic, historic and cultural factors that mean some groups are more likely to be found in particular locations than others. Segregation is therefore a measured outcome of underlying social structures, the constraints those impose on people’s opportunities and choices, the decisions people take, and the spatial differences that are generated as a consequence. How to measure segregation has been widely debated in the literature (for example, Massey and Denton, 1988; Reardon and Firebaugh, 2002; Allen and Vignoles, 2007; Yao et al, 2019). There is not and cannot be one definitive measure of segregation because it depends upon how segregation is conceived. Is it the geographical distributions of two groups across a study region and how poorly those distributions match one other? Is it the amount of geographical clustering that a group exhibits and/​or how concentrated it is into particular locations? Is it how (ethnically) mixed and/​or diverse the locations are or how likely it is that a member of one ethnic group will encounter a member of another in their school or neighbourhood? All these concepts are linked:  if the distributions do not match then one of the groups is more prevalent and more clustered into locations where the other group is not; that implies those locations are not as diverse as they could be and so there is a reduction in the probability that a member of one group will encounter a member of

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MEASURES OF SEGREGATION AND DIVERSITY

another. However, they are not exactly equal nor fully interchangeable understandings of what is meant by segregation and of how to measure it. In this chapter we use a number of measures to build up the layers of the picture we paint. Those measures are described in broad terms. Their formal specifications can be found in the technical appendix at the end of the book.

The index of dissimilarity The index of dissimilarity (ID) is the most used of all the various measures of segregation available. The logic of it is simple. Assume there are no socio-​economic constraints nor any personal choices affecting where people live. If that is so then if an area contains, say, 1 per cent of everyone who belongs to ethnic group X then it ought to contain about 1 per cent of everyone belonging to ethnic group Y too (because there is no reason for the percentages to differ). But if they do differ, both for it and other locations within the study region, then the greater those differences are the more they show that where group X is more likely to live are the places where group Y is less likely to do so (and vice versa). In other words, the greater the index value, the more unevenly the geographical distribution of group X matches to the geographical distribution of group Y . The index ranges from a theoretical minimum of zero (‘no segregation’, if the percentage of X equals the percentage of Y in each area) to one (‘complete segregation’, if where X is, Y is not).1 Figure 3.1 arises from applying the ID to the same tables of school pupil data used in Chapter 2. These provide the numbers of various ethnic groups in state schools in each of 150 English local (education) authorities. The units of analysis are therefore the local authorities and what is measured is how unevenly one ethnic group is distributed in comparison to a second across those local authorities –​for example, the Black Caribbeans and the White British (top-​left of Figure 3.1). If all pupils attended a school in the local authority within which they reside (and if all pupils attended a state school) then what we would be looking at is the pattern of ethnic residential segregation at the local authority scale (the differences between but not within those authorities). Not all pupils do but, even so, a reasonably close association is expected between the number of each group in each local authority’s schools and the number of the group that live there. Note that the vertical axis varies by chart.2 To aid interpretability, the plots are ordered by decreasing levels of segregation (specifically, by the value of the ID score for primary school pupils in 2017): the

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Ethnic Segregation Between Schools

segregation of the White British from the Black Caribbeans (top-​ left of Figure  3.1) is much greater than between the Mixed and White Other groups (bottom-​r ight). In addition, to not exaggerate very small changes, the height of each graph is always equal to a difference of 0.10 units between the top and bottom of the vertical axis, which means the amount by which the line rises or falls from one year to the next is always proportional, across all graphs, to the change in the ID score. Hence, the decrease in segregation between the Black Africans and White British (top-​r ight of the figure) looks and is greater than the decrease between the Black Caribbeans and White British. Finally, note that the index is symmetrical: the ID value for Bangladeshis and Indians (ABAN ~ AIND), for example, is the same as if their order was reversed (AIND ~ ABAN). The duplication is omitted from Figure 3.1; each pairwise comparison is included only once. What do we learn from the charts? First, the amount of segregation can be large: the index value contrasting the spatial distributions of Black Caribbean and White British pupils at the local authority scales is 0.74 for primary school pupils, and 0.73 for secondary ones (in 2017). Both are much closer to the maximum segregation value of one than they are to the theoretical minimum of zero.3 A common interpretation of such indices is that if the two distributions were to be the same, then at the primary school level and holding the numbers of one group (for example, the White British) constant across the 150 LEAs then 0.74 (74 per cent) of the Black Caribbean group would have to be relocated to reduce the ID value to 0.0. Second, almost all the index values are characterized by decline over the study period, especially for the primary school-​age population. For the values shown in Figure 3.1, the maximum ID score in 2010 was 0.76 with a mean of 0.49 and a median of 0.51. By 2017, the maximum was 0.74, with a mean of 0.47 and a median of 0.50. The changes are not always dramatic nor substantial, but it would be surprising if they were. Between any two consecutive years most of the pupils in schools and therefore local authorities are the same (because only one year group is fully replaced). It would require a lot of movement of pupils between local authorities and/​or a sudden demographic change in the numbers of each ethnic group for the values to shift markedly over the period. What is important is the direction of change and that, to re-​iterate, is generally one of decreasing segregation at the local authority scale. Third, there are some exceptions, many including the Bangladeshi group; an increase in the ID scores for Bangladeshi and White British pupils, for instance. This should not be exaggerated, however, because the change

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MEASURES OF SEGREGATION AND DIVERSITY

Figure 3.1: Index of dissimilarity (ID) scores for primary and secondary state school pupils in English local authorities, 2010–​17 Phase BCRB ~ WBRI 0.76 0.74 0.72 0.70 0.68 0.66 2010 2012 2014 2016

ABAN ~ WBRI 0.68 0.65 0.63 0.60 2010 2012 2014 2016

APKN ~ WOTW 0.62 0.60 0.58 0.55 2010 2012 2014 2016

0.54 0.52 0.50 0.48 0.46 0.44 2010 2012 2014 2016

ID

APKN ~ MIXD 0.51 0.48 0.45 2010 2012 2014 2016

AOTH ~ BCRB

2010 2012 2014 2016

2010 2012 2014 2016

AIND ~ WOTW

2010 2012 2014 2016

WBRI ~ WOTW

0.40 0.38 0.35 0.33 0.30 2010 2012 2014 2016

BAFR ~ BCRB 0.30 0.28 0.25 0.22 2010 2012 2014 2016

2010 2012 2014 2016

0.55 0.52 0.50 0.48 0.45 2010 2012 2014 2016

AIND ~ APKN

0.50 0.48 0.45 0.42 0.40 2010 2012 2014 2016

ABAN ~ BAFR 0.52 0.50 0.48 0.45 2010 2012 2014 2016

AOTH ~ WBRI

0.52 0.50 0.48 0.46 0.44 0.42 2010 2012 2014 2016

AIND ~ BAFR 0.47 0.45 0.42 0.40 2010 2012 2014 2016

AOTH ~ BAFR 0.40 0.38 0.35 0.33 0.30 2010 2012 2014 2016

BAFR ~ WOTW 0.39 0.36 0.33 2010 2012 2014 2016

MIXD ~ WBRI

AIND ~ AOTH 0.35 0.32 0.30 0.28

0.35 0.32 0.30 0.28 2010 2012 2014 2016

AOTH ~ WOTW

0.35 0.32 0.30 0.28 0.25 2010 2012 2014 2016

2010 2012 2014 2016

BCRB ~ MIXD 0.47 0.45 0.42 0.40 0.38 2010 2012 2014 2016

BAFR ~ MIXD 0.40 0.38 0.35 0.33 0.30 2010 2012 2014 2016

APKN ~ BAFR

ABAN ~ APKN

AIND ~ MIXD 0.42 0.40 0.38 0.35

0.50 0.48 0.45 0.42

APKN ~ BCRB

0.58 0.55 0.52 0.50 0.48 2010 2012 2014 2016

0.60 0.57 0.55 0.52

2010 2012 2014 2016

BCRB ~ WOTW 0.47 0.45 0.42 0.40

0.48 0.45 0.42 0.40

BAFR ~ WBRI

0.70 0.68 0.65 0.63 0.60 2010 2012 2014 2016

AIND ~ WBRI 0.60 0.57 0.55 0.52

AIND ~ BCRB

AOTH ~ APKN

0.55 0.52 0.50 0.48 0.45 2010 2012 2014 2016

ABAN ~ WOTW 0.58 0.55 0.52 0.50 0.48 2010 2012 2014 2016

2010 2012 2014 2016

ABAN ~ MIXD 0.54 0.52 0.50 0.48 0.46 0.44 2010 2012 2014 2016

APKN ~ WBRI

ABAN ~ AOTH 0.54 0.51 0.48

2010 2012 2014 2016

Secondary

0.66 0.64 0.62 0.60 0.58 0.56 2010 2012 2014 2016

ABAN ~ AIND 0.58 0.55 0.52 0.50 0.48 2010 2012 2014 2016

ABAN ~ BCRB 0.55 0.52 0.50 0.48

Primary

AOTH ~ MIXD 0.28 0.25 0.22 0.20 2010 2012 2014 2016

2010 2012 2014 2016

MIXD ~ WOTW

0.22 0.20 0.17 0.15 0.12 2010 2012 2014 2016

Year Note: The plots broadly are ordered by decreasing values of the index. The vertical scale varies by chart. Source: Authors’ own calculations.

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Ethnic Segregation Between Schools

is tiny for both the primary and secondary school populations:  an increase of only 0.01 over the period. It is effectively zero, meaning no change. Fourth, the segregation that is measured for the primary school pupils is usually greater than that for the secondary pupils, with the reverse being consistently true in only five cases, four of which involve the Indian group (the fifth is between the Black African and White Other groups). This may imply that there is some movement between local authorities that acts to reduce the spatial clustering of most minority groups during the period from starting primary to ending secondary school but less so for Indians. A summary measure can be obtained indicating how much each authority is contributing to the segregation indices overall –​see the appendix for details. Those measures are then ranked from 1 (most overall impact on the ID scores) to a maximum of 150 (least) (or a value less than 150 in the case of tied ranks). These ranks come with a warning to treat them as indicative not definitive. The reasons for the caveat are two-​fold. First, because the differences between close ranks will be trivial it is better to treat them as a rough not exact position. Second, because the authorities vary in shape, size and internal consistency this questions whether they should really be so directly compared (see the comments about this in Chapter 2). Still, Figure 3.2 maps the ranks for 2017 –​treat it as a map suggesting which places have unusually high (or low) shares of one or more of the ethnic groups. The authority with most impact upon the differences between local authorities is Tower Hamlets. It is followed by Birmingham, Bradford, Leicester and Newham. It is important to understand that the map says nothing about the distributions of pupils within local authorities nor whether they are attending the same schools as one another because neither can be observed from the current data. All the measure shows is that some authorities are different from others in having larger shares of minority population groups and lower shares of the White British. For example, Tower Hamlets has a disproportionately large share of Bangladeshi pupils and Birmingham of Pakistani pupils (and of other groups too). Those larger shares, together with the lower shares of the White British pupils, contribute to the geographical unevenness of the populations across local authorities, which is what the ID is measuring here. In other words, it is because these authorities have an ethnic profile that is atypical for England that they contribute most greatly to the ID score. Whether this results in diverse or segregated schools is something we will come to in later chapters.

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MEASURES OF SEGREGATION AND DIVERSITY

Figure 3.2: Local authorities ranked from most (rank 1) to least contribution to the ID scores in 2017

Rank under 38 38 to < 76 76 to < 110 = and > 110

60 98

127

105 118 84

33

41

109 142 146

HIGHEST RANKED (Top 10%) Tower Hamlets, Birmingham, Bradford, Leicester, Newham, Lambeth, Croydon, Lewisham, Harrow, Hampshire, Redbridge, Essex, Kent, Southwark, Enfield

143

114

32 3

17 139

72 112

76

92

111

87

107

117 101 135

30

16

66

67

99 128

83

89

108

28

63

75

29

18

137

88 120 97 70

116 94

68 77

71 44

110

103

23

82

19 141 78

81

37

22

80

62

2 104 38

150

4 40

133

69

64

50

93

113

57

126 96

36

102

79

100

43

130

35

125 121 148

106 115

39 138

95

136

53

20

45

10

147

14 1 6

119 24 61

8 7

90

134 13

59 56

47 144

140 132

12

5 27 131

58

123 86 74 46

26 11

52 73

149 145 42 91

54

48 31

65 9 25 49 51

15

55

21

122

129 34

85

124

Note: The rankings are indicative only (see text for why they should be treated with caution). Source: Authors’ own calculations.

Index of exposure The ID measures how (un)evenly two groups are distributed across a study region relative to one another. What it does not consider, at least not directly, is how likely it is that a member of one group will encounter another person from the same group within a typical local authority. In 2017, 68.2 per cent of all pupils were White British whereas 1.7 per cent were Bangladeshi. If the two groups were evenly distributed, then in each local authority a Bangladeshi pupil’s potential

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Ethnic Segregation Between Schools

for contact with a White British pupil would be high (because there are so many White British pupils) but each White British pupil’s potential for contact with a Bangladeshi pupil would be low (because there are so few Bangladeshis). The relevant question for this study is whether such potentials are increasing or decreasing. To measure this, we use the index of exposure (IE), which ranges from 0 , if no members of one group are educated in the same local authorities as the members of another group, to a value approaching 1 , for when the members of one group are educated in authorities where almost all the pupils are from another group. The index is not symmetrical because, as the example describes, one group’s exposure to a second can be high at the same time that the second’s to the first is low. Because debates about integration, community cohesion and segregation tend to be framed around the White British in distinction to other groups, it makes sense to measure either the (average) ‘exposure’ of the White British to a member of any other ethnic group (combined) or the exposure of any one minority group to the White British. Here exposure is meant in a very limited sense. The fact that two pupils from two different ethnic groups are attending schools in the same local authority does not mean they are attending the same schools nor that they are interacting in any deep and meaningful sense. Nevertheless, the possibility that they are is presumably greater than for two pupils that are not even in the same local authority. Here the index can be regarded as estimating the potential for contact between groups based on which authority’s school the pupils attend. The actual contact could be higher or lower. The IE scores for each of the eight minority groups are shown in Figure 3.3. Again, to aid interpretation, they are ordered by decreasing value of the index for primary school pupils in 2017. On this basis, the group with most (potential) exposure to the White British is that of Mixed ethnicity, followed by the White Other and Asian Other groups. These are the most ethnically heterogeneous groups. In the case of the Mixed ethnicity group, which is dominated by those that are part White British, it is suggestive of a higher level of assimilation than for other groups, which is probably not surprising given the parentage. For many in the White Other group there is a shared European, Christian and/​or English language background that supports assimilation, and some of the Asian Other group have, perhaps, less pronounced ethno-​cultural and religious differences than some of the other groups. The group with least (potential) exposure to the White British are the Black Caribbeans, despite the colonial links.

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MEASURES OF SEGREGATION AND DIVERSITY

Notably, the potential exposure of each of the eight minority groups to the White British has decreased over the period for both the primary and secondary school populations. The decline is greater for the secondary school pupils, reflecting the declining number of White British in secondary schools. In primary schools the number of White British has been increasing since about 2012 (see Chapter 2) but that increase is not reflected in any increases in the index other than a marginal increase for the Black Africans. This is because other groups are growing at a faster rate. Put simply, the ratio of the number of White British to the number from other ethnic groups continues to decline and so, therefore, does the index. The IE measuring exposure of the White British to any other ethnic group is shown to the bottom-​r ight of Figure 3.3. For this, the previous situation is reversed: whereas the potential exposure of the eight minority groups to the White British has declined, that of the White British to other Figure 3.3: Index of exposure (IE) scores estimated at the LEA scale showing either the named group’s potential ‘exposure’ to the White British or, for the bottom-​right chart, the White British exposure to other ethnic groups Phase MIXD

Primary

Secondary

WOTW

0.650 0.625 0.600 0.575

AOTH

0.650

0.575

0.625

0.550

0.600

0.525

0.575

0.500

0.550 2010

2012

2014

2016

2010

2012

APKN

2014

2016

IE

0.525

0.425

0.480

0.400

0.475 2014

2016

2010

ABAN

0.450

2012

2014

2016

2010

0.250

0.400

0.225

0.400

0.375

0.200

0.375

0.350

0.175

0.350

0.325 2014

2016

2014

2016

WBRI

0.425

2012

2012

BCRB

0.425

2010

2016

0.450

0.510

0.500

2014

0.475

0.540

0.550

2012

2012

BAFR

0.575

2010

2010

AIND

0.150 2010

2012

2014

2016

2010

2012

2014

2016

Year Note: The vertical scale varies by chart and the plots broadly are ordered by decreasing values of the index. Source: Authors’ own calculations.

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Ethnic Segregation Between Schools

ethnic groups has increased by about one third. On face value, the results seem paradoxical: the minority groups’ exposure to the White British has decreased yet the White British’s exposure to the minority groups has increased over the period. There is no contradiction, however, because –​recall –​the index is not symmetrical. But why is it happening and what does it suggest about any changing patterns of segregation? Chapter 2 gave evidence of a process of spatial diffusion whereby minority groups are becoming more prevalent in areas that remain predominantly White British. Other studies have shown the same –​ members of the various minority ethnic groups were more widely spread across England’s LEAs in 2017 than they were in 2011. Consequently, the White British exposure to other ethnic groups is increasing. Chapter 2 also observed a decrease of the White British in some local authorities. The total number of White British primary school pupils has been increasing in English state schools since 2012 but 12 local authorities have had a year-​on-​year decline for at least four of the five years to 2017. Ten of those are in London; the other two are Luton and Coventry, of which Luton had a year-​on-​year decline for all of the five years, as did the London authorities of Barking and Dagenham, Croydon, Harrow and Redbridge. It is of interest that Luton (ranked third youngest among ‘cities’ for the average age of its inhabitants) and Coventry (ranked fifth) are, with London (sixth), among the ‘cities’ with the youngest average age populations in the UK, with that partly driven by their younger migrant populations (the youngest are Slough and Oxford, with Oxford being a well-​known university city, with its centre dominated by university colleges).4 If other groups either grow in number in the places where the White British are declining or increase in the places where the White British are increasing but do so at a faster rate, then the (average) exposure of those other groups to the White British decreases. An important question is whether the results from the IE change the interpretation of what was learned from the ID? The ID generally is falling (indicating a more even distribution of the ethnic groups across local authorities) but we cannot overlook the fact that the IE is indicating a decrease in the potential for the non-​White British groups to encounter the White British (in the school-​age population)? Should we expect that segregation is, in fact, growing? Not necessarily. As a hypothetical example, consider an ethnic group, X , and its exposure to the White British. Assume there are just ten local authorities, each containing 10 per cent of the total number of X , and that is true in both 2010 and 2017. Assume also that there is no decline in the number of White British in each local authority; in

74

MEASURES OF SEGREGATION AND DIVERSITY

fact, the number may have increased. However, because each authority has an increased ethnic diversity, the percentage of its total population who are White British has decreased. Under this scenario, one of increasing ethnic diversity and no reduction in the number of White British, the measured exposure of ethnic group X to the White British will nevertheless decline. This is the problem with the IE; it is governed by the size of the exposure group as a fraction of the total population –​a fraction that will change over time due to changes not only in its number but the number of other groups too. The real-​world relevance of the example depends upon whether numbers of the White British actually do avoid decline and also whether local authorities are becoming more diverse. The second of these considerations we turn to presently. For now, we take each of the eight minority groups in turn and identify the ten authorities where the group was most numerous in 2017. We then count up the number of White British pupils in those ten authorities for each of the years from 2010 to 2017 and look at whether that number is changing. We do this separately for primary and secondary school pupils. The results are shown in Figure 3.4. The declines in the numbers of White British secondary school pupils are obvious but we know this to be part of a broader demographic change. In primary schools, from about 2012 onwards, the numbers of White British generally are increasing but with two exceptions: the numbers of White British living in local authorities with the highest percentages of Bangladeshi pupils (although that decline may have stabilized) and those in local authorities with the highest percentages of Indian pupils (where it is less clear that the decline has ceased although the rate of decline appears to have diminished). It is instructive to identify the authorities where either of the Bangladeshi and Indian groups has increased in number by more than 10 per cent in primary schools over the period from 2010 to 2017 but where also the White British decreased to fewer than 90 per cent of their original total. These are Barking and Dagenham, Croydon, Enfield, Hillingdon, Luton, Newham, Redbridge and Harrow. If the threshold for the White British decline is relaxed to 95 per cent then we add in Bexley, Hounslow, Brent, City of London, Westminster, Kensington and Chelsea and Tower Hamlets.5 If it is further changed to allow for minor decreases in the White British (with the threshold at 99 per cent) then Birmingham, Coventry, Dudley, Ealing, Havering, Sandwell, Slough, Sutton, Waltham Forest, Wolverhampton, Blackpool, Bromley, Greenwich and Poole are included. In principle, it is possible to examine each authority’s individual impact upon the IE in a way similar to what was done for the ID.

75

Ethnic Segregation Between Schools

Figure 3.4: The total number of White British pupils in the ten local authorities with the highest percentages of the named ethnic group among their pupils in 2017 Phase ABAN

Number of White British pupils

125000

Primary

Secondary

AIND

125000

100000

100000

100000

75000

75000

75000

50000

50000

50000

25000

25000 2010

2012

2014

2016

APKN

125000

25000 2010

2012

2014

2016

BAFR

125000

2010

100000

100000

75000

75000

75000

50000

50000

50000

25000

25000 2012

2014

2016

MIXD

125000

100000 75000

50000

50000

25000

2016

2012

2014

2016

2010

2012

2014

2016

WOTW

125000

75000

2014

25000 2010

100000

2012

BCRB

125000

100000

2010

AOTH

125000

25000 2010

2012

2014

2016

2010

2012

2014

2016

Year Source: Authors’ own calculations.

The problem with doing so is that some local authorities are larger than others and will have more impact on the index simply because their populations are larger overall, which typically increases their share of the total White British population and therefore their share of the total index.6 An alternative approach permits us to sidestep the problem, at the same time extending the analysis from looking solely at the White British vis-​à-​vis the minority groups to considering other pairs of groups as well. This alternative measures what could be described as the potential for equal (or balanced), cross-​exposure between two groups (PECE). The best way to understand this is to consider the circumstance when any pair of ethnic groups has the highest potential exposure to each other: it is when the exposure of group X to Y is high but so also is the exposure of group Y to X, meaning they are in balance and the population is not dominated by only one of the two groups. That potential reaches its maximum when each of X and Y form half of the total number of pupils in the local authority concerned. Hence, the PECE is maximized at a value of one when (1) the numbers of group X and

76

MEASURES OF SEGREGATION AND DIVERSITY

Y are in a 1:1 ratio, and (2) when, together, X and Y form 100 per cent of the total (school) population (which means, (3) that there are no other ethnic groups in the local authority). The index will reduce to zero the greater the imbalance of X and Y and/​or the smaller the size of those groups as a percentage of the total population. Again, the detail is in the appendix. For each authority, we are looking at nine different ethnic groups that can be paired together in 36 possible ways (from left to right and down in Figure 3.3, MIXD ~ WOTW, MIXD ~ AOTH, MIXD ~ APKN, …, MIXD ~ WBRI, WOTW ~ AOTH, WOTW ~ APKN, …, BCRB ~ WBRI; allowing that this index is symmetrical and so WOTW ~ MIXD is equivalent to MIXD ~ WOTW). Given that there are 150 local authorities, 150 × 36 index values are generated. These are highly skewed, with very many low values and a much smaller proportion that are larger: for the data on primary school pupils in the local authorities in 2017, the values range from 0.000 to 0.684, with a median of 0.012 and an interquartile range from 0.003 to 0.042. Overall, the groups with greatest potential to be exposed to each other in most equal measure are the White Other and White British (most especially in Barnet), those of Mixed ethnicity and the White Other group (most especially in Kensington and Chelsea), and those of Mixed ethnicity and the White British (also in Kensington and Chelsea). The highest value of the index is recorded for the Pakistani and White British groups in Bradford (see Figure 3.5). For the secondary data for pupils in the same year, the values range from 0.000 to 0.748, with a median of 0.013 and an interquartile range from 0.004 to 0.040. The index value is, again, highest on average for the White Other and the White British (though now most particularly in Enfield), then the Mixed ethnicity and the White Other, and the Mixed ethnicity and the White British groups (still most especially in Kensington and Chelsea). The highest value of the index is, as before, for the Pakistani and White British groups in Bradford. Of course, any PECE need not be realized. A less positive way of describing the same index is as measuring the potential for there to be marked separation between two dominant population groups within a local authority. Consider Bradford, for example, where the White British comprised 42.8 per cent of its state school pupils in 2017, and the Pakistanis 36.5 per cent (the largest percentage in any local authority). It is because those percentages are not far from being equal and because, together, those groups form 79.3 per cent of all Bradford’s pupils that the index value is so high. The potential for interaction is

77

newgenrtpdf

Figure 3.5: Showing the potential for equal, cross-​exposure (PECE) at the local authority level between those pairs of groups for which the potential is greatest, and also the level of exposure (averaged IE score) for those same groups, in primary schools in 2017

78

Level of exposure within schools (from schools data)

0.3

Peterborough WOTW ~ WBRI ●

●

Merton WOTW ~ WBRI Barking and Dagenham BAFR ~ WBRI ●

●

0.2

●

●

●

● ●

● ● ● ● ●● ● ● ● ●

0.1

●

●

●

●● ● ●● ●

●

●

● ● ●

● ● ●

●

●

●

●

Greenwich BAFR ~ WBRI ●

●

● ● Oldham APKN ~ WBRI ●

●

Luton APKN ~ WBRI

Blackburn with Darwen APKN ~ WBRI

●

●

●

●

Bradford APKN ~ WBRI

Leicester AIND ~ WBRI

●

Birmingham APKN ~ WBRI

●

Haringey BAFR ~ WBRI

0.3

0.4

0.5

0.6

Potential for equal, cross-exposure (from LEA data) Source: Authors’ own calculations.

0.7

Ethnic Segregation Between Schools

●

Blackburn with Darwen AIND ~ APKN

MEASURES OF SEGREGATION AND DIVERSITY

there but so too is the potential for segregation since they need not be in the same schools as one another within the local authority. To measure whether they are or not requires data about pupils and their schools, discussed in the following chapter. For now, it is enough to know that the data can be used with the IE to calculate the exposure of (1) the Pakistanis to the White British and, (2) of the White British to the Pakistanis within Bradford’s schools. As we know, the IE is not symmetric so (1) and (2) will not be equal. For the discussion that follows, it will suffice to collapse these two values into one average and treat that as a summary measure of the exposure of the Pakistanis to the White British, and of the White British to the Pakistanis, within Bradford’s schools. Making the same calculation for all groups with the highest potential for equal, cross-​exposure within local authorities we look at whether that potential is realized. Figure 3.5 is for the primary school aged in 2017. Positioned furthest along the horizontal axis, the Pakistani and White British pupils in Bradford have the greatest potential for cross-​interaction. However, their actual exposure to each other is low within schools –​not as low as, say, the Pakistani and White British in Blackburn, but those never had the same potential for cross-​interaction that the corresponding groups in Bradford did. The implication is that the White British and the Pakistani are highly separated across Bradford’s schools. A similar conclusion can be drawn for the Indian and White British groups in Leicester. Figure  3.6 is for the secondary school aged. The exposure of the Pakistani to the White British, and vice versa, is higher within Bradford’s secondary schools than it was for its primary schools. However, it is no greater (on average) than between Indians and the White British in Leicester despite the Bradford case having the greater potential for interaction. The White British and Pakistani in Bradford provide an example of two groups that are separated across schools (and most likely also residential neighbourhoods) there. Not all groups separate like this. Indians and Pakistanis in Blackburn’s schools, for example, are quite highly exposed to each other –​ more so than expected.7

Segregation or diversity? There are links between segregation and diversity in the sense that the former is not possible without the latter. If everyone is the same (for example, if everyone is White British) then it is not possible for the population to separate or be separated along ethnic lines. But, given

79

newgenrtpdf

Blackburn with Darwen AIND ~ APKN

0.3

●

Bexley BAFR ~ WBRI ●

Wolverhampton AIND ~ WBRI ●

Rochdale APKN ~ WBRI ●

●

●

0.2

Lambeth BAFR ● ~ BCRB ●

●

●

●

●

Greenwich BAFR ~ WBRI

● ● ●

● ● ●

● ● ●

●●

● ●●

● ● ● ●

●●

●

● ●

●

Leicester AIND ~ WBRI ●

Bradford APKN ~ WBRI

●

●

●

●

● ● ● ● ●

●

0.1

Haringey BAFR ~ WBRI ●

● ●

Birmingham APKN ~ WBRI ●

●

Blackburn with Darwen AIND ~ WBRI ●

Redbridge AIND ~ WBRI

0.3

0.4

0.5

0.6

Potential for equal, cross-exposure (from LEA data) Source: Authors’ own calculations.

0.7

Ethnic Segregation Between Schools

80

Level of exposure within schools (from schools data)

Figure 3.6: Showing the PECE at the local authority level between those pairs of groups for which the potential is greatest, and also the level of exposure (averaged IE score) for those same groups, in secondary schools in 2017

MEASURES OF SEGREGATION AND DIVERSITY

Figure 3.7: Map of the local authorities ranked by how closely the ethnic composition of the pupils in their schools matches that for England in 2017 (rank 1, shaded darkest, is most closely)

Rank under 26 26 to < 51 51 to < 76 76 to < 100 100 to < 130 over 130

121 97

9 86

111 78

122

120

74 103 77

36

124

100 58 85

61

107 125

79 118

1

59 76

54 44

102

99

38

94

104

70

67

116

87

101

16

25

123 82 17

PLACES THE LEAST LIKE ENGLAND: Newham, Brent, Islington, City of London, Westminster, Lewisham, Camden, Hackney, Haringey, Redbridge, Southwark

47

57

109

53

98

26 115 119

72

56

34

10

88

75

11 83

30 110

40

45

22

32

3

113

91 6 52 80

89

66

130

2

139 138

27

105 112

21

69

63

137

133

4 18 132 28

146 114

8 24

37

60 65

33 15

46

48

150 127 5

141 92 147

50 96 126 93

144 142

145 148

20 39 128 135

81

134 143

23 140 149 131 12

49

42 136

19

90

51

41

14 55

13

73

7

68

31

43

129 35 71

108

64 62

29

106 117

84

95

Source: Authors’ own calculations.

that there is ethnic diversity within England then what would a ‘non-​ segregated’ society look like? It might be one where the percentage of each ethnic group found in each local authority is the same as for the nation overall –​so, 68.2 per cent of all school pupils would be White British in each local authority in 2017, 6.0 per cent would be White Other, 1.7 per cent Bangladeshi, 2.9 per cent Indian, and so forth. In Figure 3.7, the local authorities that deviate least (rank 1) from those national percentages are mapped, as are the authorities that deviate most.8 As before, the rankings should be treated as approximate rather than exact. Even so, what stands out is how different London is from the rest.

81

Ethnic Segregation Between Schools

Assume that the objective of government policy was to obtain a close similarity between the ethnic composition of each local authority and that of England as a whole. Such a policy could be presented as one that promotes integration and tackles segregation but, in practice, it has three major shortcomings. The first is that it is not clear how it could be achieved in a society that, on the one hand, affords people choice in where they live and are educated yet also, through more structural inequalities, constrains that choice, including by what people can afford. The second is that even if it were achievable, doing so would disproportionately affect smaller ethnic groups since it is they that would be left spread out in relatively small numbers across the local authorities. Meanwhile, the White British would be in the majority, everywhere. Aside from arguments about whether such a policy is discriminatory in its outcomes it would create negative social or educational outcomes if there are benefits in being around one’s own ethno-​cultural peers. The third, which is linked to the second, is that a local authority that reflects the nation as a whole is not especially diverse, not currently. It remains dominantly White British. Consider the nine ethnic groups focused on so far and a tenth that will include everyone else. As a thought exercise, imagine a scenario where each group is represented equally in each local authority; that is each forms 10 per cent of the school-​age population. Authorities can be identified that accord most fully or least to this hypothetical situation by calculating what is called the entropy index, h . Here it is scaled to lie in the range 0 (no diversity, only one ethnic group is present) to 1 (most diversity; each ethnic group forms an equal percentage of the area’s population). Again, the details are in the appendix. In 2017, the entropy value for England as a whole was 0.55, having risen from 0.46 in 2010. The ranks of these values are shown in Figure 3.8. They provide an alternative perspective to what has been presented before, highlighting places of greatest and least diversity among the pupils (combining primary and secondary school pupils together in their calculation). Again, it would be idealistic to suppose that greater diversity necessarily means less segregated schools, greater integration and better community cohesion. Indeed, among the top ten most diverse locations is Waltham Forest, one of five councils receiving government funding from 2018 to improve community relations, and also Newham, whose mayor, in 2013, introduced a controversial integration plan to prevent what reportedly he described as apartheid.9 However, the results do highlight the importance of one’s point of view. The ethnic make-​ up of Newham, for example, is quite unusual when compared with

82

MEASURES OF SEGREGATION AND DIVERSITY

Figure 3.8: The authorities ranked from most (rank 1) to least ethnically diverse

Rank under 26 26 to < 50 50 to < 75 75 to < 100 100 to < 120 over 120

146 125

62 116

128 127

147

145

110 139 114

70

149

126 87 118

45

115 142

56

60 82

130 41

93 52

132

46

69

124

134

92

101

140

119

129

55

91

148 111 61

THE MOST DIVERSE PLACES: Redbridge, Newham, Ealing, Brent, Waltham Forest, Hounslow, Harrow, Wandsworth, Luton, Hackney

105

99

135

81

29

79 141 144

104

53

72

65

112

33

42

109 85 136

43

36

68

76 31

131

27 89

37

39

4

16 47 107

83 57

113

6

3 28

21

8

20

51

133 138

75

108

103

12 54 67 59

30 44

18 11

49

71 86

35 106

88 78

80

94

1 2

26 50 17

48 34 14 123

10

15 23

13

97 32 63

5 25

24

74 66

96

120

64

9 7

100 22

121 58

84

38 95

73

102

98

107

40

90

19

77

137 143

117

122

Source: Authors’ own calculations.

England as a whole but the reason it is unusual is because it is diverse. Where there is diversity there also is the possibility of ethnic and cultural tension. Yet without it there can be no exposure to ethno-​cultural differences at all. On average, the entropy measure increased from 2010 to 2017, rising in 140 of the 150 local authorities, which is a pattern of increasing ethnic diversity. The increases are shown in Figure 3.9 and were greatest in places that include Sutton (where the percentage of White British pupils fell from 65.7 to 49.4, and the percentages of the Asian, Black, White

83

newgenrtpdf

Figure 3.9: Showing the diversity (entropy) scores for the ethnic composition of the LEAs’ schools population in 2010 and 2017 1.00

Newham ● ●

● ●

●

City of London, Westminster ●

●

●

●

Sandwell

84

Diversity score (2017)

0.75

●

●

Havering

● ● ●

● ●

Newcastle upon Tyne

0.50

●

● ● ●

0.25

●● ● ●● ●● ●● ● ●● ● ● ● ●●●● ●

●

● ●● ● ●● ● ●

● ● ● ● ●

● ● ● ●

●● ●● ●

●

●

●

● ●

Bexley

●

● ●

Thurrock

●

● ●●

● ● ● ●

●●

● ●

● ● ● ● ●

●

●

●

●

●

Haringey

●

●

● ●

Stoke-on-Trent ●

●

●

●

●

Coventry

Bolton ●

● ● ●

● ●

●

●

Brent

● ●

Ethnic Segregation Between Schools

●

Sutton

●● ● ●

●

●

● ●

●

●

●

●

● ●

●

● ● ●

●

Salford

●

● ● ●

● ●

● ● ●

●

●

●

●

East Riding of Yorkshire ●

● ●

Halton

0.00 0.00

0.25

0.50

0.75

1.00

Diversity score (2010) Note: The dotted lines are the scores for England in each of the two years and that places above the solid line have become more ethnically diverse over the period. Source: Authors’ own calculations.

MEASURES OF SEGREGATION AND DIVERSITY

Other and Mixed groups all increased), Havering (where the percentage White British fell from 77.6 to 65.8, whereas the percentages of the other aforementioned groups again increased), and Newcastle upon Tyne (where the explanation is also that the minority groups increased in percentage whereas the percentage White British decreased, from 80.3 to 69.3). In 34 of the 140 authorities where the ethnic diversity of the school pupils increased, each of the Bangladeshi, Indian, Pakistani, Asian Other, Black African, Black Caribbean, Mixed and White Other groups increased as a percentage of the total whereas the percentage White British fell. In 90, at least seven of the eight minority groups increased as a percentage while the White British decreased (of the eight, the group that increased least frequently was the Black Caribbeans), and in 117, at least six groups (with the Black Caribbeans and then Bangladeshis least likely to be among them). Overall, the White British decreased as a percentage of all pupils in 144 of the 150 local authorities (the six exceptions are the East Riding of Yorkshire, Hackney, Halton, Haringey, Isle of Wight and Knowsley), whereas the Bangladeshis increased as a percentage in 109, the Indians increased in 116, the Pakistanis in 137, Asian Other in 137, Black Africans in 129, Black Caribbeans in 60, Mixed ethnicity in all 150, and the White Other in 142 (the eight exceptions are the City of London with Westminster, Dudley, East Riding, Gloucestershire, Halton, Redcar and Cleveland, Trafford and Walsall). Evidently, the school-​age population is becoming more diverse in almost every local authority in England and that is driven both by a decline in the White British (at secondary level) and an increase in other groups, notably the Mixed, White Other, Pakistani and Asian Other ones.

Consolidation In closing the chapter, we undertake a final set of analyses. They ask, for Bangladeshi pupils, what on average is the percentage of the pupils that also is Bangladeshi in the local authority in which they go to school. For the Indian pupils, they ask what is the average percentage that also is Indian, and so forth for the other ethnic groups. What is measured is described in the academic literature as an index of isolation –​the greater the percentage, the more (potentially) isolated a member of the ethnic group is with other members of the same group. The results are shown in Table 3.1. They show, for example, that in 2017, the average White British pupil attended a state primary school in a local authority where 76 per cent of the primary school pupils also were White British. That was a decrease from 81.3 in 2010. That change

85

Ethnic Segregation Between Schools

Table 3.1: Showing, for each ethnic group, the average percentage of the school-​age population that is of the same ethnicity as themselves in the local authorities in which they go to school Primary 2017

Primary 2010

Change

Secondary 2017

Secondary 2010

ABAN

16.9

18.6

−1.7

18.8

16.6

2.2

AIND

9.4

9.3

0.1

9.9

10.1

−0.2

AOTH

4.3

4.3

0.0

4.4

3.7

0.8

APKN

15.0

15.4

−0.4

15.7

12.2

3.5

BAFR

11.3

11.7

−0.4

11.4

10.7

0.7

BCRB

5.3

7.7

−2.4

6.3

7.4

−1.1

MIXD

Change

7.5

6.1

1.4

6.5

5.1

1.4

WOTW

10.2

7.5

2.8

8.4

6.3

2.1

WBRI

76.0

81.3

−5.4

78.3

83.5

−5.2

Source: Authors’ own calculations.

and a very similar one for the White British of secondary school age encapsulates what has been emphasized throughout the chapter: the local authorities are becoming ethnically more diverse and they are less dominated by White British state school pupils. For secondary school pupils and for some ethnic groups it can be argued that they are becoming more isolated within the local authorities: the Bangladeshi, Pakistani, Mixed ethnicity and White Other groups have all had an increase of at least one percentage point. However, this is most likely attributable to demographic cycles, at least in part; specifically, the fall in numbers of the White British. For primary school pupils, only the Other White and Mixed ethnicity groups are educated in local authorities where, on average, the percentages of pupils of the same ethnic group as themselves have risen by more than 1 per cent. These are two of only three groups for which their number, as a percentage of the total state primary school-​age population, has increased throughout the period from 2010 to 2017 and for which the rate of increase has shown no evidence of decline (see Figure 2.4; the third group is Indians). The White Other group is heterogeneous and includes but is not limited to immigration from the EU (as well as from North America, Australasia, and so forth). The growth in the Mixed group reflects greater ethnic mixing from joint race parentage. It also is possible that as the country has become more ethnically diverse, more people are comfortable expressing their ethnic identity as something other than White British: ethnic identity is not a fixed category but a choice (usually for pupils or their parents)

86

MEASURES OF SEGREGATION AND DIVERSITY

from a set of categories; as such, it is a reflection of a person’s own, sometimes changing, self-​identity. In any case, in so far as the numbers of pupils in the local authorities reflect residential patterns of living, several observations may be drawn from Table 3.1. First, of all the ethnic groups the White British are the most likely to be residentially segregated across England’s 150 LEAs and consequently the most educationally segregated too –​with higher levels of segregation anticipated between the White British and the Black Caribbean, Bangladeshi, Pakistani and Black African groups (Figure 3.1). Nonetheless, those levels of segregation are expected to be in decline. Second, patterns of segregation are anticipated to be greater when looking at secondary schools than primary schools, a consequence of demographic trends. Finally, of all the groups aside from the White British, there is greatest prospect of Bangladeshi and Pakistani secondary school pupils attending a school where their own group forms a large percentage of the intake. However, none of these are necessary outcomes. Whether the predictions hold true is something to be considered in the following chapter when we look specifically at schools.

Conclusion The final picture for this chapter is similar to the initial sketches of Chapter 2: it suggests decreasing segregation and increasing diversity overall. That portrait remains broad-​brushed: we have no evidence, at this stage, that the schools within local authorities are also exhibiting a diminishing pattern of segregation. We have noted that ethnic separations are only possible in the presence of diversity and so it a possibility that as local authorities have become more diverse so their schools have become more differentiated from one another in regard to the ethnic compositions of their pupils. We address this in the following chapter by considering the tendency for pupils to be in schools with their own ethnic peers and whether that tendency is rising. Notes 1

2

If percentages are used instead of proportions then the index will range from 0 to 100. Preferably, the vertical scale would be fixed enabling direct comparison of all the ID values, allowing the highest and lowest to be discerned more easily. Unfortunately, doing this renders illegible the changes over time and between primary and secondary schools because the differences between each pair of ethnicities (in regard to their index values) is much greater than the differences within pairs (from one year to the next, and between primary and secondary pupils) –​the former masks the latter.

87

Ethnic Segregation Between Schools 3

4

5

6

7

8

9

Or to a value of 0.02, which is what is expected if either those primary or secondary pupils were to be randomly distributed across the local authorities. The expected value is greater than zero because one group, the Black Caribbeans, is so much smaller than the other, the White British; it would be hard, if not impossible, for their distributions to be the same across all the local authorities (and therefore for the ID to be zero). It is, however, close to zero because of the relatively large sizes of local authorities. See Winship (1977). Here ‘cities’ means a conurbation and not necessarily a formally designated city; see BBC News, 19 March 2018, www.bbc.co.uk/​news/​uk-​43316697 Of these, all but Luton are Outer London boroughs. Luton is just beyond the Greater London boundary, indicative of the spread of minority ethnic problems away from the central London locations where they initially clustered. This is not a problem that affects the calculation of each authority’s contribution to the ID. Most of the Indians living in Blackburn are Muslims, whereas in England as a whole the majority are Hindu. The great majority of Pakistanis are also Muslims so that Indians and Pakistanis in Blackburn share the same religion, which is not the case elsewhere. Specifically, the authorities are ranked by the sum of the log of the absolute differences in their percentages of each the nine ethnic groups and the percentages for England as a whole. The results will tend to reflect what percentage of an authority’s pupils are White British but not solely so. ‘Apartheid was wrong in South Africa, it would be wrong here’:  see Evening Standard, 19 September 2013, www.standard.co.uk/​news/​politics/​newham-​ mayor-a​ ccused-​of-​attack-​on-​immigrants-​after-​launching-​ethnic-​integration-​plan-​ 8825853.html

88

4

How Concentrated Are Ethnic Groups in Schools? Summary The Casey Review cites a study by the think-​tank Demos that shows the majority of ethnic minority students attend schools where ‘minority’ groups are in the majority. That statistic is correct but too easily misinterpreted. Only White British students typically are in a school where their own ethnic group forms a majority; for most ethnic minority pupils the largest group they will encounter at school is also the White British. The exceptions to this are the Bangladeshi and Pakistani groups, and more so in primary than in secondary schools. Nevertheless, the overwhelming trend is that schools are becoming more ethnically diverse with an increased potential for pupils to be educated alongside pupils of other ethnic groups.

Introduction Nearly every LEA has become more ethnically diverse in terms of the overall characteristics of the pupils who attend its schools. However, until recently there has been a national decline in the number of White British of school age in the population and this has occurred during a period when the number of most other ethnic groups has increased. The result of these demographic changes is that whereas the White British now have more potential to be schooled with pupils from other ethnic groups (because the groups have spread out across England), those other ethnic groups are, relatively speaking, less exposed to the White British than before (because the White British have either not grown at the same rate or have had a numeric decline in some places).

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Ethnic Segregation Between Schools

On face value those findings support what is written in The Casey Review and its claim that ‘as the diversity of the nation has increased […] people from minority groups have become both more dispersed and in some cases more concentrated and segregated’ (Casey, 2016: Executive Summary, para 29). In that claim, the association of segregation is with concentration –​the idea the segregation is evidenced when people live in areas or are educated in schools where their own ethnic group forms a sizable percentage of the total number of residents or pupils. But where the quote says ‘some’, our initial findings suggest it would better be written as few, if a decrease in segregation is associated with increasing diversification. Chapter 3 identified only ten authorities (of 150) where the ethnic diversity of their pupils had decreased; in each of those the decrease was slight (in most, little different from zero), and in only four had the number of White British pupils actually declined over the period from 2010 to 2017, those being the City of London with Westminster, Brent, Newham and Tower Hamlets. However, analysis at the authority scale tells little about what is happening across individual schools. It is possible that as the populations of places become more diverse, so that diversity sows the seeds of separation. Within authorities the differences between the schools could be growing; as the population diversifies overall, members of each individual ethnic group could become more concentrated in schools where other members of the same ethnic group are prevalent. This chapter examines that possibility.

About the data In this and subsequent chapters, we change the scale of the analysis and look at pupils and their schools. We do this by using data taken from the National Pupil Database (NPD) under licence from the Department for Education. These data tell us the ethnicity of cohorts of individual pupils who were in state schools in each of the years from 2011 to 2017 (among other characteristics). For each year we look at two groups of pupils: those in years 3 or 4 of state primary schools (who are aged 7–​9 years at the time of the data collection), and those in years 9 or 10 of state secondary schools (aged 13–​15). The ethnic groups that we focus on are as in previous chapters: ABAN (Asian Bangladeshi), AIND (Asian Indian), APKN (Asian Pakistani); AOTH (Asian Other); BAFR (Black African); BCRB (Black Caribbean); MIXD (Mixed (joint) ethnicity); WOTW (White Other) and WBRI (White British). There are two reasons for focusing on these specific cohorts rather than everyone in the schools. The first is that these are the ‘middle’

90

How Concentrated Are Ethnic Groups in Schools?

years of each phase of schooling, in a period that has given time for those who were not happy with their initial school allocation to change school and before when those who will move before the next stage of education generally do so (in particular, those who move towards the end of primary schooling to the catchment of a preferred secondary school) (Sweet et al, 2018). These years therefore cover a relatively settled period in the patterns of school choice and assignment. Second, looking at just those two years of primary and secondary education means that any changes that might be taking place are not smoothed out over periods of five years or more, which is what happens if all pupils in each school are looked at en masse (because only one year group changes each year). With a focus on just two years, any changes appear more immediately. There still is a smoothing over two years but we choose this as a price worth paying for the increased sample size. It means, for example, that the pupils in year 3 in 2011 (actually the fourth year of primary school for most pupils) will be in year 4 in 2012 and be part of the data and their analysis on both occasions. After two years, however, they will drop out of consideration for primary schools.1,2 Prior to the analysis, some cleaning of the data has been made, omitting pupils in less typical schools, specifically special schools (for pupils with special educational needs), pupil referral units (for pupils outside mainstream education), and very small schools (where the number of pupils in the two years is fewer than ten). As in earlier chapters, the analysis is of English state schools only as the same data are not collected from fee-​charging schools. This is a regrettable omission and one that ought to be addressed so that patterns of segregation, inequality of opportunity and social mobility within and generated by the education system can be understood more fully. However, non-​state schools currently are not required to provide such information despite their charitable status. Finally, the data are geocoded at the scale of schools –​we know which school each individual pupil attended –​and also lower level super output areas (LSOAs): we know which LSOA each pupil resides in. LSOAs are the second tier of the English (and Welsh) electoral geography; they are aggregations of the smaller output areas (OAs). We use these rather than OAs (or postcodes) for reasons of personal data protection. In 2017, the data contained an average of 71 pupils per LSOA, up from 68 in 2011 (which suggests there would be too few observations to support analysis at a sub-​LSOA scale anyway –​OAs are roughly one tenth the size of LSOAs). Knowing the LSOA of schools and the pupils’ residences allows the local authority within which they are located to be

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Ethnic Segregation Between Schools

identified. These local authorities are different from (but sub-​divisions of) the LEAs used in early chapters. There are 326 of these authorities in England.3 Although geographically smaller than the education authorities –​though some of these authorities are their own education authority (such as the London borough councils) –​they still vary in shape and size, with some containing one or more urban settlements within their boundaries. For example, Calderdale contains the town of Halifax plus several smaller towns such as Brighouse and Elland, and adjoining Kirklees contains both Huddersfield and Dewsbury as well as a number of other former ‘heavy woollen district’ towns with large ethnic minority populations such as Batley and Heckmondwike. Occasionally, we refer to postal towns as well as the local authorities to enrich the geography detail, although we avoid doing so where there is a risk of disclosing the details of a specific school.4 Generally the local authority geography is preferred.

A classification of schools When discussions appear in the media or in policy documents about ethnic segregation in the English school system, they tend to pivot around the concern that pupils from minority groups are educated in schools where theirs and/​or other ethnic minorities are said to be greatly prevalent. An example of this appeared in The Guardian in 2011, with the headline ‘Headteacher expresses alarm over racial segregation in London schools. “It can’t be a good thing for London to be sleepwalking towards Johannesburg”, conference warned’ –​a mimicking of Trevor Phillips’ phrase (see Chapter 1) that was, presumably, deliberate. The headteacher (a vice-​chair of an association of fee-​charging schools) was reported to have said that there is a (state) school, in Stepney Green, east London, where 97 per cent of pupils are of Bangladeshi heritage, whereas other schools, in south London, took an ‘overwhelmingly’ high proportion of pupils of West African descent.5 Similar, if more measured comments appear in The Casey Review, which cites a study by the think-​tank Demos showing that in 2013 ‘more than 50% of ethnic minority students were in schools where ethnic minorities were the majority’ (Casey, 2018: Executive Summary, para 32). Another way of writing that is to say the majority of ethnic minority students were in schools where the White British formed less than half the intake. The expressions are equivalent although the second may better express what the reports are worried about –​the possibility that members of minority ethnic groups are schooled with

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How Concentrated Are Ethnic Groups in Schools?

high numbers of those ethnic groups and few White British. Such a circumstance is considered, by some, to be symptomatic of and/​or contributing to a segregated and non-​integrated society. To look at these claims about ‘majority-​minority’ schools and who attends them, we turn to a classification of schools. Our method is inspired by, but differs from, the typological approach used by Johnston et al to analyse census data and neighbourhoods.6 Here, the classification is of schools and, for now, simply focuses on what percentage of the cohorts in each school is White British. Schools are identified wherein: • A: the White British are predominant, accounting for more than 90 per cent of the pupils; • B: the White British are in the majority, accounting for more than 50 per cent of the pupils; • C:  the White British are the largest of the ethnic groups in the school and account for more than one third of the pupils; • D: the White British are relatively few in number, accounting for a low percentage of pupils –​less than 25 per cent; and, • E: the White British are very few in number, accounting for a very low percentage of pupils –​less than 10 per cent. Some of these categories overlap: a school in which the White British predominate is a school where they are in the majority and therefore also the largest group. Therefore, all schools in group A are also in B and C. However, some are mutually exclusive: for example, a school in any of A, B or C can be in neither D nor E. It is possible that some schools will not be in any group at all.7 Such an eventuality is, however, rare (it applies to about a tenth of 1 per cent of schools in any year). Using the typology, Tables 4.1 and 4.2 show the percentages of primary and secondary schools that fall within each category for each of the years from 2011 to 2017. The most notable and also largest change is the decrease in the percentage of schools in which the White British predominate (category A), declining from 50.4 in 2011 to 37.6 in 2017 for primary schools, and from 49.3 to 35.1 for secondary schools. This shows that it is not the norm for the White British to predominate in schools and suggests that even the schools with least ethnic diversity are diversifying, albeit that the White British remain the majority or the largest group (but less so than in the past). That the decrease is happening not just in secondary but also in primary schools –​where, to recall, the total number of White British is increasing –​supports the view that minority ethnic groups are not, as a whole, rooted to particular schools and places but are spreading out across the country. Places where the White British remain strongly

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Ethnic Segregation Between Schools

Table 4.1: Percentage of primary schools in each year where the White British were predominant, in the majority, the largest group, account for a low percentage and account for a very low percentage of pupils, respectively (see text for the definitions of these categories) 2011

2012

2013

2014

2015

2016

2017

A: WBRI, predominant

50.4

48.3

46.8

44.7

42.5

40.0

37.6

B: WBRI, majority

85.2

84.8

84.1

83.5

82.7

82.0

81.3

C: WBRI, largest

90.4

90.2

89.8

89.7

89.4

89.0

88.5

8.9

9.2

9.5

9.7

10.0

10.4

10.8

5.4

5.6

5.8

5.9

6.1

D: WBRI, low E: WBRI, very low (n)

5.0

5.3

15100

15134

15166 15187 15229 15277 15347

Source: Authors’ own calculations.

Table 4.2: Percentage of secondary schools in each year where the White British were predominant, in the majority, the largest group, account for a low percentage and account for a very low percentage of pupils, respectively 2011

2012

2013

2014

2015

2016

2017

A: WBRI, predominant

49.3

47.5

45.8

43.7

41.0

37.7

35.1

B: WBRI, majority

84.3

83.6

83.0

82.1

80.9

79.8

78.5

C: WBRI, largest

91.0

90.6

89.8

89.2

88.4

87.8

86.5

D: WBRI, low

8.9

9.3

9.8

10.4

11.3

12.2

13.3

E: WBRI, very low

4.2

4.5

4.9

5.5

5.9

6.5

7.0

3078

3076

3127

3111

3145

3175

3203

(n)

Source: Authors’ own calculations.

predominant (in more than 90 per cent of schools, in 2017) are especially found in parts of the North West of England –​Copeland (notably Whitehaven), Barrow-​in-​Furness, Allerdale (Wigton and Workington) and St Helens  –​but also North Norfolk (notably Norwich), West Somerset, Torridge (Bideford), Northumberland (Blyth, Cramlington and Hexham), North Kesteven (Lincoln) and Ryedale (Malton). In the bottom rows of each table are the percentages of schools with relatively low or very low numbers of White British (categories D and E). These have increased; more so for secondary than for primary schools but with an increase for both, albeit that they never represent more than 13.3 per cent of schools (which is much less than the percentage of schools where the White British dominate). The increase will be aided by the higher growth in number of most ethnic groups relative to the White British, which leaves the White British as a smaller percentage of the total intakes. Places where the White British

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How Concentrated Are Ethnic Groups in Schools?

are least prevalent (accounting for fewer than 10 per cent of pupils in more than half of the schools) are all in London –​Newham, Brent, Tower Hamlets, Harrow, Haringey, Lambeth, Redbridge and Ealing –​though such schools are also reasonably common in Slough, Birmingham, Leicester, Luton, Bradford, Blackburn, Manchester and Oldham (as well as other parts of London).

Are minority groups concentrated in schools where the White British are not? Having created the typology, we can address the question: were the majority of pupils who are not White British educated in schools where minority groups formed a collective majority? Yes. In 2011, 61.2 per cent of pupils in our data who are not White British were in primary schools where the White British did not form a majority (among the cohorts), rising to 62.3 per cent in 2017 (see Table 4.3). In secondary schools the percentages rise from 52.8 to 58.3 (see Table 4.4). However, it is too easy to overstate these findings, giving them undue weight in social and political discourse:  first, because they treat all groups that are not White British as one; second, because if we look at the percentage of pupils who are not White British but are in schools where the White British are the largest group (and account for at least one third of pupils, which is category C) a rather different impression arises –​it is the majority, in both primary and secondary schools (at 55.5 and 59.9 per cent, respectively, in 2017). There has even been a slight increase in that figure for primary schools (it was 54.4 in 2011). Consequently, although it may be true to say that members of minority groups are, on average, in schools where minority groups form a majority (schools that Table 4.3: Showing the percentages of primary school pupils who are not White British who are in schools where the White British predominate, are in the majority, are the largest group, are relatively few in number or are very few

A: WBRI, predominant B: WBRI, majority (minority groups in a collective majority)

2011

2012

2013

2014

2015

2016

2017

7.3

7.0

6.7

6.2

5.7

5.2

4.7

38.8

39.1

38.6

38.1

37.8

37.8

37.7

(61.2) (60.9) (61.4) (61.9) (62.2) (62.2) (62.3)

C: WBRI, largest

54.4

54.6

54.3

54.7

55.5

55.6

55.5

D: WBRI, low

44.3

44.0

44.2

44.0

43.6

43.3

43.3

E: WBRI, very low

28.4

28.6

28.3

28.8

28.4

27.5

27.6

Source: Authors’ own calculations.

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Ethnic Segregation Between Schools

Table 4.4: Showing the percentages of primary school pupils who are not White British who are in schools where the White British predominate, are in the majority, are the largest group, are relatively few in number or are very few 2011

2012

2013

2014

2015

2016

2017

A: WBRI, predominant

11.3

11.0

10.5

9.7

8.8

7.7

7.1

B: WBRI, majority

47.2

46.6

46.2

45.0

43.5

42.6

41.7

(minority groups in a collective majority)

(52.8) (53.4) (53.8) (55.0) (56.5) (57.4) (58.3)

C: WBRI, largest

65.9

65.6

63.8

63.4

61.8

61.3

59.9

D: WBRI, low

34.3

34.3

35.7

36.6

38.2

39.0

40.3

E: WBRI, very low

17.7

18.0

19.4

20.5

21.3

22.6

22.9

Source: Authors’ own calculations.

are majority-​minority), the White British remains as the group that members of minority groups are most likely to encounter in their schools. Still, it is also the case that by 2017 over 40 per cent of pupils whose ethnicity is not White British were in schools that were less than 25 per cent White British (category D). The percentage of ethnic minority pupils in schools with very low numbers of White British (less than 10 per cent, category E) has not increased for primary schools (the percentage was 28.4 in 2011 and declined slightly to 27.6 in 2017) but, for secondary schools, it has (from 17.7 to 22.9). Again, the national decline in the number of White British secondary school pupils is likely to be a contributing factor.

Looking at each ethnic group separately It is not satisfactory to treat all minority groups as one. Figures 4.1 and 4.2 extend the analysis and help show what the picture looks like if we separately consider each of the same nine of the larger ethnic groups that have been examined in previous chapters. For the purpose of legibility, they focus on the percentage of each group that was in schools where the White British were in the majority (category B), the largest group (C) or of a very low percentage (E) in each of the years 2011 to 2017. With regard to primary schools (Figure 4.1), two groups are more likely to be in schools where the White British form a very low percentage than in schools where the White British are the largest group. Those are the Bangladeshi and Pakistani. More than half of Bangladeshi pupils are in primary schools where the White British form a very low percentage and

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How Concentrated Are Ethnic Groups in Schools?

Figure 4.1: The percentage of each of nine ethnic groups that are in primary schools where the White British form a majority, are the largest group or form a very low percentage of the school’s pupils type

●

B: WBRI_majority

C: WBRI_largest

ABAN

E: WBRI_vlow

AIND

APKN

100 75 50 ●

25

●

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0 AOTH

BAFR

BCRB

100

%

75 50

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25

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0 MIXD

WOTW

WBRI

100

●

75 ●

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50

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25 0 2012

2014

2016

2012

2014

2016

2012

2014

2016

Year Source: Authors’ own calculations.

that proportion of Bangladeshi pupils is increasing. About 25 to 30 per cent of both groups are in schools where the White British are the largest group. For Black African and Black Caribbean pupils, a greater percentage, but still fewer than half, are in schools where the White British are the largest group. For Indians and those from the Other Asian group, more than half are, and it is about three quarters for those of Mixed ethnicity or of the Other White group. The White British are overwhelmingly in primary schools where theirs is the largest group and typically also the majority. It is very rare for a White British pupil to be in a school where the percentage of White British pupils is very low. Turning to secondary schools (Figure 4.2), the percentage in a secondary school where the White British are the largest group is, for all groups, greater than the corresponding percentage for primary schools. Secondary schools are larger than primary schools, typically five or six times so, recruiting pupils over greater distances. This appears to provide for greater mixing of the White British and other ethnic groups. Around half or more of the pupils in most ethnic groups are in secondary schools where the White British are the largest group. The Bangladeshi and

97

Ethnic Segregation Between Schools

Figure 4.2: The percentage of each of nine ethnic groups that are in secondary schools where the White British form a majority, are the largest group or form a very low percentage of the school’s pupils type

●

B: WBRI_majority

C: WBRI_largest

ABAN

E: WBRI_vlow

AIND

APKN

100 75 50 25

● ●

●

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BAFR

BCRB

100

%

75 50

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●

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● ●

25

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0 MIXD

WOTW

WBRI

100 75

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50

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25 0 2012

2014

2016

2012

2014

2016

2012

2014

2016

Year Source: Authors’ own calculations.

Pakistani groups are an exception to this (and, very marginally so, the Black Caribbeans). Schools (primary or secondary) where Bangladeshis are the largest group and the White British are few are overwhelmingly found in Tower Hamlets. Schools where Pakistanis are the largest group and the White British are few are less concentrated in any one place but can be found in places including Bradford, Dewsbury (Kirklees), Blackburn, Luton and Slough. For both primary and secondary schools, the percentage of White British pupils that are in schools where the White British predominate has declined. This suggests more ethnically diverse intakes are attending the schools that have been most mono-​ethnically White British in the past. However, as these and the percentages in schools where the White British are the largest group have declined, it is evident that the percentages where the White British are very few in number has increased. Is this evidence of increasing segregation? It could be but the increase is really a characteristic of secondary schools, not primary schools, which suggests that, as the nationally increasing numbers of White British pupils reach secondary from primary schools, the rise

98

How Concentrated Are Ethnic Groups in Schools?

in schools with a very low percentage of the White British will be slowed, cease or reverse.

Are minority groups concentrated in schools with pupils of the same ethnicity as themselves? Although it has been shown that a majority of all non-​White British pupils are in schools where ethnic minority groups form a collective majority, it does not mean that they are in a school where their own ethnic group is in a majority. Even where minority groups are in schools that are less than 10 per cent White British, it does not follow that those schools lack diversity. Potentially, it is the opposite. A school in which one ninth of the pupils is from each of the nine groups has relatively few White British members but has great ethnic diversity. If it so happened that every Bangladeshi pupil went to a school with that level of diversity then 100 per cent of Bangladeshi pupils would be in schools with a low prevalence of the White British but every Bangladeshi pupil would be part of a very diverse intake. What really matters in terms of the various dimensions of segregation and how it can be measured is how concentrated members of a group are with other members of the same group –​that is how common it is for pupils to be in a school where their own ethnic group forms a high percentage of the intake. For instance, what percentage of Bangladeshi pupils are in schools where Bangladeshi pupils are predominant (over 90 per cent of all pupils), form a majority (over 50 per cent), or are the largest group? What percentage of Indian pupils are in schools where Indians are predominant, the majority, or the largest group? What percentage of Pakistani pupils? … and so forth. The answers are shown in Figure  4.3, for primary schools, and Figure  4.4, for secondary schools. For no group other than the White British is it typical for a pupil to be in a school where their ethnic group forms a majority although it is more common for Pakistanis in primary schools than for any of the remaining seven groups. Nor is it typical for a pupil to be in a school where their ethnic co-​peers are the largest group, except again for the White British, and for Pakistanis in primary schools. In 2017, the only places that have schools in which Bangladeshis predominate the cohorts are in Oldham and Tower Hamlets. The only places for which it is true of Indians are in Coventry, Harrow, Leicester and Slough; for Pakistanis, Bradford, Halifax, Oldham and parts of Lancashire. There are no places with schools where Black African, Black Caribbean, Mixed or White Other groups predominate. The place where Bangladeshis are most likely to be the largest

99

Ethnic Segregation Between Schools

Figure 4.3: The percentages of each ethnic group in primary schools where their own ethnic group is predominant, in a majority and/​or the largest group type

●

A: predominant

ABAN

B: majority

C: largest

AIND

APKN

100 75 50 25 0

●

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●

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AOTH

●

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BAFR

●

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BCRB

100

%

75 50 25 0

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MIXD

●

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WOTW

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100 75 ●

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2012

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2012

2014

2016

Year Source: Authors’ own calculations.

group in the schools is Tower Hamlets (79 per cent of schools). For Indians it is Leicester (40 per cent); for Pakistanis, Slough (40 per cent) and Bradford (39 per cent); for the Asian Other group, Harrow (32 per cent); for Black Africans, Southwark (66 per cent); for Black Caribbeans, Lewisham (11 per cent) and Lambeth (10 per cent); for the Mixed ethnicity group, Kensington and Chelsea (16 per cent), and for the White Others, Enfield and Haringey (both 60 per cent).8 Overall, there were only 33 primary schools and nine secondary schools in 2017 where a group other than the White British predominated. That is 0.22 and 0.28 per cent of the total, respectively –​tiny fractions. There is little evidence that any group is becoming more concentrated with its ethnic co-​peers (with the possible exception of Black Africans and Pakistanis in secondary schools) and rather more evidence of either no change or declining concentrations, for primary school pupils especially. This is not to say that there are no schools in which a single ethnic group forms a high percentage of the intake –​clearly there are. However, it is to stress that it is unusual for groups other than the White British (for which it is becoming less common).

100

How Concentrated Are Ethnic Groups in Schools?

Figure 4.4: The percentages of each ethnic group in secondary schools where their own ethnic group is predominant, in a majority and/​or the largest group Type

●

A: predominant

ABAN

B: majority

C: largest

AIND

APKN

100 75 50 25 0

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AOTH

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BAFR

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MIXD

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WOTW

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2012

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2012

2014

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Year Source: Authors’ own calculations.

What also is suggested by Figures 4.3 and 4.4 is the possibility for relatively low levels of mixing between some of the Bangladeshi, Indian and Pakistani groups. Looking at this, in primary (and secondary) schools where the Bangladeshi are the largest group, an average of 3.0 per cent (1.8 per cent at secondary) of the pupils are Indian, and 7.5 per cent (7.4 per cent), Pakistani. In primary (secondary) schools where the Indians are the largest group, 2.6 per cent (also 2.6 per cent at secondary) are Bangladeshi, and 10.5 per cent (10.3 per cent) Pakistani. In primary (secondary) schools where the Pakistanis are the largest group, 6.3 per cent (7.6 per cent) are Bangladeshi, and 4.6 per cent (5.3 per cent) Indian. Of these three groups, the least exposure appears to be between the Bangladeshis (almost all of whom are Muslim) and Indians (most of whom are non-​Muslim).

The ethnic diversity of schools If pupils are becoming less concentrated in schools with others of the same ethnicity, then this suggests schools are becoming more ethnically

101

Ethnic Segregation Between Schools

Figure 4.5: Showing the average diversity of schools in each of the local education authorities in 2017; LEAs with an increase in the diversity index of greater than 0.10 since 2011 are also indicated

Mean diversity under 0.2 0.2 to < 0.36 0.36 to < 0.57 0.57 to < 0.76 = and > 0.76

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diverse. One way to confirm this is to use the entropy measure of diversity that was introduced in Chapter 3 and calculate it for each school. This will range from 0 for no diversity (if the cohorts in the school are all from a single ethnic group) to 1 for ‘complete diversity’ (if all of the nine ethnic groups plus a tenth representing all other pupils each form 10 per cent of the intake into the school). By way of geographical context, Figure 4.5 shows the school average entropy scores for LEAs in 2017.9 The most diverse schools are, on average, found in London, and especially Newham, Redbridge and Waltham Forest, although levels of diversity can also be high in some of the authorities around London (notably Slough, Luton and Reading), in the East and West

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How Concentrated Are Ethnic Groups in Schools?

Midlands (including Leicester, Sandwell and Wolverhampton) and in Manchester. There are 14 LEAs where the increase in diversity since 2011 is greater than 0.10, which are Havering, Stoke-​on-​Trent, Sutton, Sandwell, Oldham, Middlesbrough, Walsall, Salford, Wolverhampton, Thurrock, Nottingham, Newcastle upon Tyne, Portsmouth and Southend-​on-​Sea. In 2017, the average diversity score for primary schools was 0.32, an increase from 0.26 in 2011. For secondary schools it increased from 0.30 to 0.37. On average, secondary schools are more diverse than primary schools but both are diversifying. That trend towards increased average diversity is also evident when we go within LEAs to calculate the averages for local authorities /​postal towns, as Figure 4.6 confirms (see the introduction to this chapter for an explanation of the hybrid geography that we are using, and why). Note that nearly every location is above the solid line on the charts –​the line indicating no change –​which means that the average diversity of the locations’ schools has increased between 2011 and 2017. Indeed, there are only two locations where the average diversity of the schools has decreased between 2011 and 2017 by more than 0.02 (a very marginal change) for both primary and secondary schools.10 Those are Harrow, with a decrease in diversity of 0.05 for primary schools and 0.07 for secondary schools; and Barnet, with 0.04 and 0.02, respectively. In Harrow the change appears to be driven by the growth of the Indian group (with a 40 per cent increase in number) and, more especially, White Other groups (146 per cent increase) but also by the decline of the Black African (14 per cent decline) and Black Caribbean groups (25 per cent decline). In Barnet, it appears to be due to the growth of the White Other group (a 60 per cent increase).11 The values shown in Figure 4.6 are averages and do not preclude there being specific schools that have had greater decreases in their diversity. However, we estimate that only about 5 per cent of primary schools and 2 per cent of secondary schools had what may loosely be described as a non-​trivial decrease in the ethnic diversity of their cohorts from 2011 to 2017 –​where non-​trivial is defined as a change in their diversity score of greater than 0.10. Those primary schools are found disproportionately in Greenwich (an estimated 17 per cent of its schools), Haringey and Wirral (both 14 per cent), Richmond (13 per cent), Medway and Bexley (both 12 per cent), Barnet, Enfield and Ealing (each 11 per cent), and Bury (10 per cent). Almost half are faith schools (see Chapter  5). Only Tower Hamlets (an estimated four schools), Birmingham (also four) and Enfield (two) have more than one secondary school showing a non-​trivial decline in diversity. About one

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How Concentrated Are Ethnic Groups in Schools?

Figure 4.7: Showing the LEAs with the most diverse schools on average in 2017, those that have diversified most since 2011, and those with both least diversity and least diversification

Diverse (most) Diversifying (most) Least Other

Source: Authors’ own calculations.

quarter of the 2 per cent are faith schools; only one has an entrance examination. Figure 4.7 captures the geography of which LEAs are the most ethnically diverse (those that have an average school entropy score in the upper quartile for 2017), which are diversifying most (have a percentage of their schools with a non-​trivial increase that is in the upper quartile), which are the least diverse and have experienced least diversification (in the lowest 40 per cent for both), and the rest.12 The most diverse are in London but the category also includes Birmingham, Coventry, Leicester, Luton, Manchester, Reading, Sandwell and Slough. The

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Ethnic Segregation Between Schools

Figure 4.8: The percentage of each group in schools with a diversity greater than or equal to the value shown on the horizontal axes BCRB

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most diversifying include Havering in London, Newcastle upon Tyne, West Berkshire, Torbay, Trafford, Salford, Bracknell Forest, Stockport, Peterborough and Nottingham (which are the LEAs with the greatest percentages of their schools exhibiting a non-​trivial increase in diversity, each at more than 50 per cent). Those with least diversity and diversification include, especially, Cumbria, Halton, Hartlepool, North Tyneside, Northumberland, Redcar and Cleveland and Wirral. Overall, the trend is of increasing ethnic diversification. How does this affect the potential for pupils to be in contact with those of different ethnicities? In short, it increases it. Figure 4.8 has all the schools ranked from most to least diverse along the horizontal axes and shows what percentage of each ethnic group can be found in a school of equal or greater diversity than some chosen threshold. For example, a very small fraction (if any) of each group is in a school with ‘complete diversity’ (an entropy score of 1, at the left-​hand edge of the graph) but, of course, 100 per cent of each group is in a school with zero diversity or above. It is the values in-​between that are of interest, which show that half of the Black

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How Concentrated Are Ethnic Groups in Schools?

Caribbean pupils in our data are in diverse schools (with an entropy score of 0.75 or greater, which is in the highest 10 per cent of scores for English schools in 2017)13 and almost half of Black African pupils are too. Half of almost every ethnic group’s pupils are in schools with entropy scores above 0.50 (which places them just below or within the highest 25 per cent of scores) –​with one exception, the White British, who are concentrated in the least diverse schools (hence the curves in that graph are convex, whereas all of the others are concave). Of particular interest is that the position of the curves has shifted slightly to the left over the period from 2011 to 2017, meaning that a greater share of each group, including the White British, are in schools with greater ethnic diversity. Even so, there are differences between the minority groups. The graphs are ordered from left to right and downwards in terms of the decreasing ethnic diversity of the school that the median average pupil from the ethnic group attends. Black pupils tend to attend more diverse schools than Asian pupils and Asian pupils more diverse schools than the White and Mixed groups.

Consolidation This chapter finds little evidence that there are many schools in which the concentration of minority groups is increasing, and especially not of any one single ethnic group. Although schools with very high percentages of any one minority group do exist, they are exceedingly rare and do not warrant the disproportionate amount of attention they can receive in the media or in some political discussion, which in turn fuels some of the darker aspects of social media. In 2017, there were just nine schools (out of 18,550) that were over 90 per cent Bangladeshi, of which seven are primary schools. The schools are located in Tower Hamlets and Oldham. There were only six that were over 90 per cent Indian. All are primary schools; they are in Slough, Leicester, Harrow and Coventry. There were 16 that were over 90 per cent Pakistani, of which 15 are primary schools; these are in Bradford, Halifax, Pendle, Accrington and Oldham. In contrast, there were 6,889 which were over 90 per cent White British, of which 5,766 are primary schools. Such schools are most typical of Halton (notably, Runcorn and Widnes), St Helens, Northumberland (for example, Bedlington), Redcar and Cleveland, Cumbria (for example, Brampton and Wigton), and Durham but are present over much of the country. None of the remaining ethnic groups reaches the 90 per cent threshold in any school. Clearly it is rare for any ethnic group other than the White British to be educated in schools where that group is predominant and the exceptions are

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becoming rarer. In 2011, there were 13 schools (of 18,178) that were over 90 per cent Bangladeshi, of which 11 were primary schools. There were two that were over 90 per cent Indian, both of which were primary schools. There were 24 that were over 90 per cent Pakistani, of which 24 were primary schools. Finally, there were 9,132 schools that were over 90 per cent White British, of which 7,615 were primary schools. Therefore, the number of schools with an almost mono-​ethnic character fell between 2011 and 2017, except for the Indian group. If we lower the threshold and consider the number of schools in which each group forms a majority (but is not necessarily dominant) the results are shown in Table 4.5. Such cases remain rare except for the White British but there has been some increase in the number of schools that meet this criterion for the Indian, Black African and White Other groups (and, marginally, for the Pakistani group too). Yet, as a fraction of the total number of schools, the number in which any ethnic minority forms a majority is tiny (less than 3 per cent) and is effectively unchanged from 2001 to 2011. There is only one place where, in 2017, an ethnic group other than the White British formed a majority of pupils, in the majority of schools: Tower Hamlets, with the group being Bangladeshi in all but one case (where the group was those of an Other White ethnicity). The Other White group form a majority in a number of schools across London and also in a couple of schools in Boston (Lincolnshire), which has a large population of migrants from East European countries that joined the EU in 2004, among a few other places. There were 41 schools (of which 39 were primary schools) that had no White British pupils among the selected mid-​year cohorts in all of the seven years from 2011 to 2017; they were in Birmingham (11), Leicester (five), Oldham (five), Bradford (four), Tower Hamlets (four), Manchester (two), Rochdale (two), Blackburn, Bolton, Brent, Ealing, Lambeth, Newham, Peterborough and Southwark (one in each). Observations such as these are of the type that generate media headlines, but 41 is a very small fraction of all schools (it is about one quarter of 1 per cent). The 11 schools in Birmingham, the five in Leicester and the five in Oldham are about 3, 5 and 5 per cent of the total number of schools in those areas. Moreover, it is not necessarily a circumstance created by the school themselves, neither in who they admit nor who they attract; nor by the pupils or their parents. Recall that geography is an important component of admissions criteria for most English schools. In broad terms, the closer a pupil lives to a school the more likely she is to be admitted to it if demand for places exceeds

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109

ABAN

AIND

APKN

AOTH

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BCRB

MIXD

WOTH

WBRI

WOTH

(N)

2011

83

59

283

2

43

4

0

15

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(18,178)

2017

76

67

284

2

62

2

0

24

14,989

24

(18,550)

Source: Authors’ own calculations.

How Concentrated Are Ethnic Groups in Schools?

Table 4.5: Estimated number of schools in which each ethnic group is in the majority in 2011 and 2017

Ethnic Segregation Between Schools

the number available. That means pupils are likely to apply to a nearby school if they want to increase their chance of being admitted to it and, if so, the intakes of schools will reflect the ethnic composition of surrounding residential areas. This can be put to the test in a simple way. First, we focus on primary schools because they are smaller and more likely to recruit over shorter distances than secondary schools. Second, we will assume that the census neighbourhood in which each school is located –​specifically, the census LSOA –​is within the main catchment area of the school.14 Next, we identify the 39 primary schools that had no White British pupils among the cohorts of pupils over the period 2011 to 2017 and look at the ethnic compositions of the neighbourhoods within which they are located as measured in the 2011 census. We find that none of the schools is in a neighbourhood that had no children aged between 0 and 9 and White British in it. In that sense, it ought to have been possible for these schools to have recruited some of the White British among their intakes. However, these neighbourhoods were also characterized by low numbers of White British: 27 neighbourhoods had fewer than 25 White British residents aged 0–​9 years in the census data for 2011, 18 neighbourhoods had fewer than 10, and nine had fewer than five. The results suggest that the schools’ intakes are largely determined by the ethnic compositions of their local vicinities –​an idea that will be explored further in the following chapter. However, there are four schools that seem not to have recruited a White British pupil yet are located in neighbourhoods where the White British are the largest ethnic groups. All four are faith schools, three with a Muslim affiliation, the fourth Anglican.

Conclusion The results of this chapter strongly suggest that ethnic segregation is decreasing in English state schools with pupils generally less concentrated in schools with other pupils of the same ethnicity, and schools having become more ethnically diverse. There are exceptions, but they are exactly that –​exceptions (and rare ones, too) –​which questions the wisdom of drawing conclusions or making policy based upon them. Furthermore, there is some suggestion that school-​level patterns of ethnic segregation are a function of residential patterns, although processes of school allocation and selection may have still the potential to increase the former over the latter. Whether that is the case, and whether it is associated with particular types of school or locations, is explored in the following chapter.

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How Concentrated Are Ethnic Groups in Schools?

Notes 1

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Focusing on just these ‘middle years’ was not an option when we looked at the local education authority data in earlier chapters. The primary–​secondary school split has a few exceptions in England, in the form of ‘middle schools’, which span the two. Wikipedia lists 107 middle schools remaining in England in 2019. Pupils in these schools are still classified as of primary or of secondary school equivalent, dependent upon their age. Mainly comprised of a mixture of unitary authorities, non-​metropolitan district councils, the metropolitan borough councils and London’s borough councils, all of which are a product of successive political waves of local governmental organization and re-​organization. Postal towns are only identified if they contain at least seven primary schools and at least three secondary schools in the data for any one year. Guardian, 4 October 2011, www.theguardian.com/​education/​2011/​oct/​04/​ alarm-​over-​racial-​segregation-​london-​schools See Chapter 1 and also Johnston et al, 2005, 2009; Kaplan, 2018. The White British can be the largest group but still account for less than one third of pupils; or not be the largest group but still account for more than 25 per cent of pupils. In 2015, when Nigel Farage, then as leader of UKIP, characterized Peterborough as a city where East European immigrants had failed to integrate, 63 per cent of White Other primary and also secondary pupils were in a school where the White British were the largest group. There were no schools where the White Other group formed a majority. This suggests that Farage’s comments were, at a minimum, overstated, as was reported in the media at the time: BBC News, 11 April 2015, www.bbc.co.uk/​news/​election-​2015-​england-​32204991 Actually, the average of the averages for primary and secondary schools separately calculated. This is not entirely true. The greatest change was detected in a third location, the Isles of Scilly. However, this appears to be due to almost half the pupils being classified as White Other (perhaps Scillonian?) in 2011 and almost all as White British in 2017. In addition, in Harrow, there has been a decline in the Bangladeshi and White British groups, and a rise in Pakistani, Asian Other and Mixed ethnicity groups. In Barnet, a rise in the Asian Other, White British and Mixed groups. There are four LEAs that are in the top quartile for both diversity and diversification, which are Coventry, Manchester, Sandwell and Sutton. For the purpose of Figure 4.7 they are placed in the most diverse category although both Sandwell and Sutton are also among the LEAs that have more than 50 per cent of their schools exhibiting a non-​trivial increase in diversity. The values of the school-​level entropy scores in 2017 were 0.25 at the 50th percentile (median), 0.51 at the 75th percentile (third quartile), 0.74 at the 90th, 0.82 at the 95th and 0.96 at the 100th (maximum value, which was reached by five schools, all in London; a primary in each of Croydon, Hillingdon and Newham, and a secondary in both of Camden and Redbridge). This is a sensible assumption (because otherwise the school is outside its own main recruitment areas) but not necessarily always true: there is a ‘free school’ in Bristol, for example (meaning free from local authority control) where the centre point from where it admits 80 per cent of pupils is located a couple of miles from the

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school and, unlike the school itself, is centred within a very affluent part of the city: www.bristolfreeschool.org.uk/​admissionscriteria.php. Even in this case, 20 per cent of the intake is still prioritized from neighbourhoods that do surround the school (although there is also an overall admissions priority area that seems to exclude the poorer neighbourhoods).

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5

Does School Choice Add to Residential Ethnic Segregation? Summary The English systems of school choice and allocation may result in greater ethnic segregation between schools than between neighbourhoods. This chapter looks at that proposition and asks to what extent school levels of ethnic segregation reflect neighbourhood ethnic composition, where do they not, and for which types of school are the differences greatest? It makes a simple comparison of the segregation between schools and between neighbourhoods then, acknowledging the limitations of that comparison, employs a more sophisticated analysis to compare the diversity of schools’ intakes with what they would look like under a hypothetical system without choice. In the majority of cases, intakes into schools reflect the neighbourhoods that surround them and are not dissimilar to what would be expected under a neighbourhood-​based system of pupil allocation. There is little evidence that the current system of school choice raises ethnic segregation substantially.

Introduction The preceding chapter ended by suggesting patterns of ethnic segregation for schools are linked to those for neighbourhoods. If true, it is not surprising: although the English state school system is described as offering choice, it operates with constraints on that choice –​many of those constraints acting geographically. In general, the closest schools are the most accessible and the ones that a pupil has greatest chances of

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being admitted to. Consequently, school intakes are not independent of the neighbourhoods that surround each school. Even so, it is not a strictly neighbourhood-​based system –​pupils (or their parents) have a choice set that, albeit geographically constrained, need not be the same for even those who live in close proximity to one another. That means that it is possible for pupils to separate along ethnic lines. Whether they do or not is the subject of this chapter.

School choice and its potential effects on neighbourhood segregation In principle, any pupil is free to apply to any school and that school is obliged to admit the pupil if there are sufficient places available (assuming the school does not operate other admissions criteria that would limit entry, such as being academically selective or a single-​sex school). In practice, any school has a capacity limit and that requires ways to determine who gains a place.1 Admissions criteria commonly employ geographically bounded priority areas (essentially, catchment areas) as a first way to select from those who want to go to the school, and then use distance from the school as a further arbitrator. Even schools that may recruit over a wider area through some sort of randomization or by downplaying geography (by giving more weight to other selection criteria, such as religious affiliation or aptitude in a specialist subject) will do so within geographical limits. Still, the system is not a strictly neighbourhood-​based one because pupils are not automatically allocated to schools solely based on the neighbourhood in which they live. Imagine a system where there are as many schools as there are neighbourhoods and admissions operate on a one-​to-​one basis (one school for every neighbourhood and one neighbourhood for every school). Under these conditions, any segregation between schools is exactly equal to the segregation between neighbourhoods because the segregation patterns cannot differ. This does not mean that a no-​choice system must necessarily be better for reducing segregation overall because if people cannot choose schools they can still choose houses and that may cause people to divide along residential, neighbourhood lines (to the extent that they can afford to buy or rent housing within a neighbourhood that feeds into their preferred school). It is only to say that under a very strict neighbourhood system it is possible for school choice and admissions neither to add to nor to diminish the neighbourhood patterns of ethnic segregation. Now consider a many-​to-​one relationship (one school for every neighbourhood but multiple neighbourhoods feeding into every

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school). This is still a neighbourhood system because each pupil’s school is determined by her place of residence and it still offers no choice. However, the school and neighbourhood patterns of segregation can now differ. It is possible for the segregation between schools to be less than that between neighbourhoods if the schools’ intakes bring together a more diverse group of pupils than is found in the individual neighbourhoods. Taking this further, what if the neighbourhood allocation is loosened to introduce some element of school choice, allowing for multiple schools to serve each neighbourhood, and for each neighbourhood to supply pupils to multiple schools? The consequence must be to weaken the geographical ties between schools and neighbourhoods when compared to a strict neighbourhood allocation. Under a choice system, the divergence between neighbourhood and school patterns of segregation is determined by how open the choice is and, therefore, by how strongly geographical constraints still operate upon people’s decision-​making and their chances of admission into a preferred school. In England those chances are, on average, good: between 2008 and 2017 the percentage of applicants who received an offer from their first choice secondary school fluctuated between 82.0 and 86.7 per cent, with between 94.0 per cent and 96.5 per cent receiving an offer from one of their top three secondary schools.2 The peak was in 2013, the year of fewest applicants; the trough was in 2008, the year of most. Clearly demographic factors impact; currently the percentages are in decline again as the numbers of applicants rise.3 Even so, the percentages of first preference allocations are high, which might indicate that the choice system works well. However, it could also mean the opposite. Because it a system of partial choice, the correct interpretation rests on how much admissions remain geographically determined by the criteria that still operate; that is, by the extent to which the ‘choice’ is forced. In many cases, the nearest school will offer the greatest chance of admission but that does not mean that there is no point in applying to other schools. Chapter 1 cited research showing that in the academic year 2014/​15, only about 55 per cent of secondary school applicants listed their closest school as one of their preferences (which means 45 per cent did not, a surprisingly high percentage if there was no prospect of admission) (Burgess et al, 2017). Work on visualizing the recruitment spaces of secondary schools in London shows they overlap considerably, again indicating that people are applying to and being accepted into schools that are not their closest (Harris et al, 2015). Even if London is unusual in this respect because of the density of population and schools (and we are

115

Ethnic Segregation Between Schools

not suggesting it is –​certainly not when compared to the other cities and towns with large multi-​ethnic populations) –​it would still mean that the system of choice does not replicate a simple neighbourhood to school relationship in the places where a large number of ethnic minorities groups are living. But what if the factors influencing school choices are different for different ethnic (or social) groups? A choice system allows patterns of school and neighbourhood segregation to differ and the difference can go in either direction –​schools can be less or more segregated than neighbourhoods. It will be less if pupils choose ethnically mixed schools (or if the size of, especially, secondary schools forces together groups from neighbourhoods with different ethnic compositions). However, it will be more if, for example, different ethnic groups have different levels of preference for or constraints upon them to apply and be admitted to the various schools. If one group is better financially placed to travel further afield or if there is a tendency for a group to prefer a school with higher numbers of either their ethnic peers or those with a similar faith then it is possible for segregation between schools to be greater than that between neighbourhoods; pupils can separate by ethnicity or on an ethno-​religious basis. Previous research comparing school and neighbourhood ethnic segregation offers insight into whether school choice amplifies or attenuates neighbourhood levels of segregation. Of particular relevance are two previous studies of England that reach complementary conclusions. First, that there is greater segregation between English state schools than between neighbourhoods for black pupils but more especially for South Asian pupils (those of Indian, Pakistani or Bangladeshi heritage). Second, that the differences are greater for primary than for secondary schools, and characteristic of places with large non-​white populations that include Birmingham, Blackburn, Bradford, Oldham and Tower Hamlets. Third, that whereas the segregation for black pupils tends to be lower where those pupils are relatively numerous, for the South Asian groups, segregation is higher where they live in greater numbers (Burgess et al, 2005; Johnston et al, 2006). These findings fed into The Casey Review, which states that ‘the school age population is even more segregated than when compared to residential patterns of living’ (Casey, 2016: Executive Summary, para. 32, p. 11). Here Casey is drawing on a report by the think-​tank Demos that, in turn, draws upon the aforementioned studies (Demos, n.d.). Unfortunately, that means the claim is based on what was happening in 2001, which is the year those studies looked at. That was a time when residential ethnic segregation had increased from ten years

116

SCHOOL CHOICE AND ETHNIC SEGREGATION

earlier due to population growth among minority groups and prior to a subsequent reduction in segregation as the groups spread out (see Chapter 1). It is anachronistic to assume that the findings apply to the situation almost two decades later (the lack of contemporary data is acknowledged by the review). A more recent study, of primary schools only, looked at the situation in 2011, comparing the intakes into schools with the ethnic compositions of what appear to be their core recruitment areas as observed by plotting the residential locations of their pupils. That study provides the springboard for much of this chapter and will be returned to later (Johnston et al, 2017). For now it is sufficient to note that for the great majority of schools the proportion of their pupils from South Asian or black minorities is commensurate with the locales from which they recruit (and not simply because pupils are attending their nearest school), with the main exceptions being a small number of faith-​based schools that can add religious affiliation to their admissions criteria and recruit over wider areas. The study concludes that: Most of England’s schools […] mimic and reflect the ethnic composition of the areas they serve: a few –​many of them faith-​based institutions associated with the Roman Catholic church –​differ from the local population’s ethnic composition, reflecting their preference for pupils associated with that faith (relatively few of whom are either South Asian or Black), and some parents’ preference for such schools even if they are outside their home neighbourhoods. They are minor variants from a general pattern and not in any way evidence that England’s primary schools are in general more segregated than England’s urban residential mosaics. (Johnston et al, 2017: para. 17, emphasis added) The critical question is whether that assertion still holds true, for primary as well as for secondary schools.

Comparing the index of dissimilarity for schools and neighbourhoods As a simple way into the analysis, it is possible to compare the Index of Dissimilarity (ID) for schools with the ID for neighbourhoods and see which is greater. The ID was introduced in Chapter 3 as a measure of how much one ethnic group is segregated from another. To recall, it ranges from 0 (meaning ‘no segregation’) to 1 (‘complete segregation’;

117

Ethnic Segregation Between Schools

wherever the one group is, the other is not). Previously the index was used to look at how unevenly pairs of ethnic groups are distributed across local education authorities (LEAs); now the study is of schools and of neighbourhoods. Calculating the ID for schools is straightforward, requiring only the numbers of each ethnic group in each school. Those counts are provided by the data that were used in the previous chapter, taken from the National Pupil Database (NPD). Calculating the segregation for neighbourhoods should equally be straightforward as it requires only the counts per neighbourhood. The conceptual difficulty is what is meant by neighbourhood? A school is obvious: it is a discrete, self-​contained object (a collection of buildings) with a clear boundary. A neighbourhood is something more subjective and less clearly demarcated such as a person’s locale, local community or place of residence. Nevertheless, formalized definitions of neighbourhood exist, which gain de facto status as political or administrative units for the dissemination of census and other neighbourhood statistics. In the present case, the starting point for what is meant by a neighbourhood is decided for us because although the data are about individual pupils –​where they live and where they go to school –​we do not know any pupil’s precise residential address, only the lower level super output area (LSOA) in which each pupil lives (see Introduction to Chapter 4). Unfortunately, there is an immediate problem in trying to compare the ID for schools with that for LSOA neighbourhoods with the aim of exploring whether one is greater (more segregated) than the other. The problem is the smaller number of schools and the larger number of neighbourhoods. In 2017, the data cover 32,844 LSOAs, 15,347 primary schools and 3,203 secondary schools, meaning that there are twice as many LSOAs as primary schools, and ten times as many LSOAs as secondary schools. On average, the neighbourhoods will contain fewer pupils than schools. Why this matters is appreciated by observing that there are almost five times as many primary schools as secondary, which means secondary schools are bigger and less likely to replicate localized patterns of residential segregation. Simply by being bigger, secondary schools ought to recruit more diverse intakes and be less segregated than primary schools –​assuming that not all the neighbourhoods closest to a school have the same ethnic composition. By the same reasoning but, in this case, being smaller, the LSOAs are likely to appear more segregated than either primary or segregated schools. Comparing LSOAs with primary or secondary schools is not really comparing like with like.

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This might matter less if it were not for the fact that LSOAs are really just zones drawn on a map that could have been drawn in other locations and in other shapes and sizes too, changing their number and also the value of the ID.4 This raises what geographers refer to as the modifiable areal unit problem (MAUP) –​that the analytical results are affected by the geographical design (including shape, size and number) of what are being analysed (Openshaw, 1983). The numbers of schools are not arbitrary; they are the actual numbers of schools in the study. Nor are their sizes –​secondary schools are bigger and, as such, are more likely to force greater population mixing than primary schools. But, the expectation is that neighbourhoods will appear the most segregated of all simply because there is a larger but ultimately arbitrary number of them. If geography is a microscope then we see more (of the segregation) at the neighbourhood level simply because the neighbourhood data allow us to look in more detail. To help address this problem, two scales of neighbourhood are adopted in the index calculations that follow. When comparing primary schools to neighbourhoods, LSOAs are retained as a proxy for neighbourhood. However, for the comparison with secondary schools, neighbourhoods are defined by middle level super output areas (MSOAs). These are bigger than LSOAs, as the name suggests. There are 6,791 MSOAs in England, so the analysis will compare 32,844 LSOAs with approximately 15,000 primary schools, and 6,791 LSOAs with about 3,200 secondary schools. It is not a perfect comparison but the numbers are of the same order of magnitude. In both cases, the pupil data are used: hence, the ID scores for primary pupils compare the distribution of 7–​9-​year-​olds across primary schools with their distribution across LSOAs, and the scores for secondary schools compare the distribution of 13–​15-​year-​olds across secondary schools with their distribution across MSOAs. In 2017, the data have an average of 82 pupils per primary school and 71 pupils per LSOA; 329 pupils per secondary school and 344 pupils per MSOA. Focusing initially, as previously, on the subject that appears to worry policy makers most –​the amount of segregation of the White British from other ethnic groups –​Figure 5.1 shows the ID scores for the White British (WBRI) against each of the Bangladeshi (ABAN), Indian (AIND), Asian Other (AOTH), Pakistani (APKN), Black African (BAFR), Black Caribbean (BCRB), Mixed (joint) ethnicity (MIXD) and White Other (WOTW) groups. The highest levels of separation are between the White British and the Bangladeshi, Pakistani and Black Caribbean groups, which is true regardless of whether school or neighbourhood-​level segregation is looked at. But by these calculations,

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Ethnic Segregation Between Schools

neighbourhood segregation exceeds that of schools. That would seem to contradict earlier studies, except that this is for the whole of England and will include many areas of relatively sparse population (and little school choice) where the schools will almost inevitably bring in pupils over a wider area, quite possibly increasing their diversity but not qualifying those schools as especially diverse (they still will be predominantly White British). Remember also that the index is biased towards reporting greater segregation in neighbourhoods because of their greater number.5 The results also suggest that primary schools have greater amounts of segregation than secondary schools, which is as expected: the fewer secondary schools should cause more mixing. Leaving to one side, for the moment, the difficulty of comparing schools with neighbourhods, we can still look at what is happening for either schools or neighbourhoods and discern whether the measured segregation is increasing: that is, instead of looking at the differences between the lines in Figure 5.1, we can ask whether the gradient of each individual line is increasing. There is scant evidence that it is, either for schools or neighbourhoods. Rather, the index values appear flat or in decline. Nevertheless, such analysis can conceal geographical variation. It is important to focus on places where the minority groups

Figure 5.1: Comparing the segregation of the White British from other ethnic groups in schools and neighbourhoods across England, by year Phase

Primary

ABAN

Level

Secondary AIND

Neighbourhood AOTH

APKN

0.90

0.90

0.90

0.90

0.80

0.80

0.80

0.80

0.70

0.70

0.70

0.70

0.60

0.60

0.60

0.60

0.50

0.50

0.50

ID

12

14

16

12

BAFR

14

16

0.50 12

BCRB

14

16

12

MIXD

0.90

0.70

0.90

0.80

0.80

0.60

0.80

0.70

0.70

0.50

0.70

0.60

0.60

0.40

0.60

0.50

0.50

0.30

14

16

12

14

16

Source: Authors’ own calculations.

120

16

0.50 12

Year

14 WOTW

0.90

12

School

14

16

12

14

16

SCHOOL CHOICE AND ETHNIC SEGREGATION

are most prevalent and see if the same trend towards desegregation from the White British remains true. As a step towards this, Figure 5.2 has the pupil data linked to the classification of LEAs developed in Chapter 2 but including only Clusters 1 and 2, since the LEAs in Cluster 3 have a predominance of White British pupils and we are interested in what is happening in the areas where minority groups are more prevalent. Taking the two in reverse order, recall that Cluster 2 is broadly described as containing areas of ethnic diversity where the growth of minority groups is slowing and the White British, in some places, are becoming more prevalent after a period of decline. It is found only in London and for much of London, except near parts of its border. Cluster 1 is characterized by more White British than Cluster 2 but with higher rates of increase of Asian Other, Indian, Black African, Black Caribbean, Mixed, Pakistani and White Other pupils. It is found around London, in the Midlands and the North West (see Figure 2.12). As with the national findings, those for Cluster 1 do not reflect the results of previous research. It has a pattern of school-​level segregation being less than neighbourhood segregation in nearly every case (possibly not between the White British and Pakistani nor White Other groups). What does reflect that research is Cluster 2:  here, primary school levels of segregation between the White British and Bangladeshis, and also between the White British and Black Africans, exceed those of neighbourhoods. Moreover, levels of secondary school segregation always exceed those of neighbourhoods for all the ethnic groups and the White British. Table 5.1 expands the analysis to consider the differences between schools and neighbourhoods for more pairs of groups than just those with the White British. Any value greater than zero means that the ID value for schools in Cluster 1 is greater than that for neighbourhoods for the pair of ethnic groups named in the rows and columns. The bottom-​left triangle of the table shows the differences for primary schools and neighbourhoods; the upper right triangle is for secondary schools and neighbourhoods. All values are for 2017. For primary schools, the levels of segregation are generally less than for neighbourhoods, with a few very marginal exceptions, of which the greatest is between the White Other and Pakistani groups (for which the school measure of segregation is greater than the neighbourhood measure by 0.02). The greater differences have the school measures of segregation being less than for neighbourhoods, most especially between the Black Carribeans and Mixed ethnicity (-​0.09), White Other (-​0.06) and White British (-​0.06) groups. For secondary

121

Ethnic Segregation Between Schools

Figure 5.2: The segregation of the White British from other groups by school, neighbourhood and the cluster type of their LEA

Cluster 1 Phase

Primary

ABAN

Level

Secondary AIND

Neighbourhood AOTH

APKN

0.90

0.90

0.90

0.90

0.80

0.80

0.80

0.80

0.70

0.70

0.70

0.70

0.60

0.60

0.60

0.60

0.50

0.50

0.50

ID

12

14

16

12

BAFR

14

16

0.50 12

BCRB

14

16

12

MIXD

0.90

0.70

0.90

0.80

0.80

0.60

0.80

0.70

0.70

0.50

0.70

0.60

0.60

0.40

0.60

0.50 12

14

16

14

16

16

0.50

0.30 12

14 WOTW

0.90

0.50

School

12

14

16

12

14

16

Year

Cluster 2 Phase

Primary

ABAN

Level

Secondary AIND

Neighbourhood AOTH

APKN

0.90

0.90

0.90

0.90

0.80

0.80

0.80

0.80

0.70

0.70

0.70

0.70

0.60

0.60

0.50

0.50

ID

12

14

16

0.60

BAFR

0.60 0.40 14

14

16

0.50 12

BCRB

0.80

12

0.60

0.50 12

16

0.70

0.80

0.60

0.70

0.50

0.60

0.40

0.50

0.30 12

14

14

16

12

MIXD

0.90

0.40

School

16

14

16

WOTW

0.80 0.60 0.40 12

14

16

12

14

16

Year Source: Authors’ own calculations.

schools, there are more instances of the school-​level segregation value being greater than for neighbourhoods, including between Indians and Pakistanis (a difference of 0.04), Indians and Black Africans (0.03), Black Caribbeans and the Mixed ethnicity group (0.02), Black Africans

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SCHOOL CHOICE AND ETHNIC SEGREGATION

Table 5.1: Showing the differences in the segregation scores for schools when compared to neighbourhoods for pairs of ethnic groups in Cluster 1 for 2017 ABAN ABAN

AIND 0.00

AOTH APKN

BAFR

BCRB MIXD

WBRI WOTW

0.00

0.02

−0.01

0.00

−0.01

−0.02

−0.02

−0.04

0.04

0.03

0.01

−0.03

0.01

−0.01

0.00

−0.01

0.01

0.00

−0.02

−0.03

−0.02

0.02

0.01

−0.05

−0.03

0.06

−0.02

0.02

0.00

0.02

0.00

−0.02

0.01

0.04

AIND

−0.01

AOTH

−0.02

−0.03

APKN

−0.04

−0.02 −0.02

BAFR

0.00

−0.02 −0.03

−0.01

BCRB

0.00

−0.05 −0.05

−0.02

−0.05

MIXD

−0.01

−0.03 −0.05

−0.01

−0.03

−0.09

WBRI

0.00

−0.01 −0.03

0.01

−0.01

−0.06

−0.02

WOTW

0.00

−0.01 −0.03

0.02

−0.04

−0.06

−0.02

0.02 0.01

Note: Values in the lower (bottom-​left) triangle compare primary schools with neighbourhoods; values in the upper (top-​right) triangle compare secondary schools with neighbourhoods. Source: Authors’ own calculations.

and the White British (0.02), the Mixed and White Other groups (0.04), and most greatly for the Black Africans and Black Caribbeans (0.06). The pairs of ethnicities for which secondary school-​level segregation appears less than for neighbourhods are the Pakistanis and White British (-​0.05), and the Indian and Asian Others (-​0.04). But almost all of those differences are slight: only four of them are greater then +/​-​0.06. Turning to Cluster 2, the main differences in segregation where that measured for primary schools is greater than that for neighbourhoods (although almost all of those differences are again very slight) are, as Figure 5.2 showed, between the White British and the Bangladeshi (0.03) and Black African groups (0.02), and less so between the Bangladeshi and Mixed ethnicity groups (0.02). The greatest differences, where that for primary schools is less than for neighbourhoods, involve the Indian groups, with the Asian Others (-​0.09), Mixed (-​0.08) and Black Africans (-​0.07). For secondary schools, where segregation at the school level is greater than for neighbourhoods it is greater between the Pakistani and Black Caribbean groups (0.05), the Black African and White British (0.05), and the Black Caribbean and Mixed groups (0.05). There are few instances where secondary schools have a lower segregation score than neighbourhoods, but among them is that between the Asian Other and Black African groups (-​0.03) and between the Asian Other and Indian groups (-​0.03).

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Ethnic Segregation Between Schools

Table 5.2: Showing the differences in the segregation scores for schools when compared to neighbourhoods for pairs of ethnic groups in Cluster 2 for 2017 ABAN ABAN

AIND −0.01

AOTH APKN

BAFR

BCRB MIXD

WBRI WOTW

0.01

0.00

0.02

−0.02

0.02

0.00

0.05

−0.02

0.01

0.01

0.00

0.02

0.03

0.03

0.00

−0.03

0.00

0.03

0.02

0.02

0.00

0.05

0.02

0.03

0.02

0.04

0.04

0.05

0.02

0.05

0.01

0.01

0.02

0.03

AIND

−0.03

AOTH

−0.02

−0.09

APKN

−0.03

−0.07 −0.05

BAFR

−0.01

−0.07 −0.05

−0.02

BCRB

0.00

−0.06 −0.03

−0.02

−0.03

MIXD

0.02

−0.08 −0.06

−0.03

−0.01

WBRI

0.03

−0.04 −0.02

0.00

0.02

0.00

−0.03

WOTW

0.00

−0.07 −0.05

−0.02

−0.01

−0.01

−0.03

−0.04

0.04 0.01

Note: Values in the lower (bottom-​left) triangle compare primary schools with neighbourhoods; values in the upper (top-​right) triangle compare secondary schools with neighbourhoods. Source: Authors’ own calculations.

Looking further at Tables 5.1 and 5.2, there are only four occasions when both primary and secondary levels of segregation are greater than for neighbourhoods: between the White British and the White Other groups in Cluster 1; and between the Bangladeshi and Mixed, Black African and White British, and the White British and White Other groups in Cluster 2. There are more occasions when both primary and secondary levels of segregation are less than for neighbourhoods: ten are for Cluster 1, between the Black Caribbean and White Other groups, and between the Indian and Asian Other groups, for example; three are for Cluster 2, including, again, between the Indian and Asian Other groups (also between the Black African and Asian Other, and Bangladeshi and Indian groups). The implication is that some groups are more likely to separate from home to school (for instance, the White British and White Others) whereas others are more likely to come together (the Indian and Asian Other groups). But there is no persuasive evidence that school-​level segregation is consistently greater than neighbourhood segregation. However, the index values are essentially averages and in much the same way as analysis at the national scale obscures what is happening for different types (clusters) of LEAs, analysis at the cluster scale obscures what is happening for distinct schools and locales. We therefore proceed by re-​framing the question, which moves from asking whether segregation between schools is greater than that between neighbourhoods to asking where

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SCHOOL CHOICE AND ETHNIC SEGREGATION

specifically is it greater, for which types of school, and for which ethnic groups?

A better comparison of schools and their neighbourhoods Having re-​framed the research question, it is important to recognize that the ID is not a particularly effective tool to answer it, even though it has been used that way in the past. The issue is not just that the scale of analysis is too coarse because that could be resolved by calculating the index values for smaller sub-​regions, providing extra geographical detail. The more fundamental problem is that it does not measure what is really of interest, which is whether the intakes into schools reflect the characteristics of the population that is found around those schools (if they do not, then some sort of sifting and sorting of pupils is taking place though the choice or allocation processes). It is possible, for example, that where the ethnic differences are greatest between neighbourhoods, differences between schools are least, and where the differences are least between neighbourhoods they are greatest between schools. Those two opposing trends will cancel each other out under the ID and give rise to the impression that schools and neighbourhoods display similar levels of segregation when actually their patterns differ entirely. The point is that the ID does not compare a school with its surrounding neighbourhoods nor a school with its catchment; consequently, it is an indirect comparison of school and neighbourhood segregation. To address this shortcoming, more direct comparisons have been developed that compare what is measured at one location (here, a school) with other locations around it (the surrounding neighbourhoods) (Harris, 2011; Harris, 2012). Processes of segregation are evidenced when the intake into a school differs from the geographical context within which the school is situated. This approach has been used to look at how the intakes into locally competing schools differ, and also to look at the separation of lower and higher attaining primary school students in the transition to secondary school (Harris, 2011; Harris, 2012). A recent study, flagged earlier in this chapter, compared the intakes into primary schools in 2011 with the ethnic compositions of what the data suggest to be their core catchment areas (Johnston et al, 2017). The study identifies how some faith schools (defined presently) obtain higher than expected numbers of especially black pupils and also how some community schools (the type of school historically most rooted

125

Ethnic Segregation Between Schools

in its immediate locale) attract higher than expected numbers of South Asian pupils. A weakness of modelling school catchments from their intakes is that it entails an element of circularity that risks downplaying the processes of segregation that may be occurring (Watts, 2013; for a response see Harris et al, 2013). To understand why, imagine a school has an admission policy that intentionally results in a gerrymandered catchment area, specifically targetting the places where a preferred group of pupils live. Under this occurrence, and assuming those pupils can be targetted precisely, then the school’s intake will reflect the characteristics of its ‘catchment’ exactly despite the process adding to any broader residential patterns of segregation by being highly selective. An alternative approach, used here, is to compare the ethnic profile of each school’s intake with that of the neighbourhoods that surround it. This is different from comparing it with its actual ‘catchment area’ because that catchment could bypass some or all of the nearest neighbourhoods (perhaps due to a land or water barrier, or an unbridged railway line). The idea is to compare each school’s intake to what it would look like under two hypothetical admission scenarios, each a variation on a no-​choice, neighbourhood-​based assignment whereby pupils go to their nearest school (for a similar idea, see Allen, 2007). Scenario 1 simply assigns each pupil to their nearest school, placing no limit on the number of pupils a school can receive. Scenario 2 is similar but attempts to respect capacity constraints: each pupil is allocated to the first nearest school but if there are more pupils than places (within a nominal tolerance), those who live furthest from the school are reallocated to their second nearest school, or third, fourth, up to and inclusive of the fifth. Each scenario is a simplification of any true assignment process, even one that operates without choice. The results are estimates only. Because we do not know the precise address of each pupil, they are allocated to schools in groups, by LSOA, rather than individually. Scenario 1 reflects a ‘least-​cost’ allocation, where the cost is distance. Because it assumes no capacity constraints it can leave some schools with many pupils while others have few or none. It nevertheless approximates what would occur under a strict neighbourhood system under which there was no rationing of places. Scenario 2 is the more realistic estimation of what would happen if all pupils apply to their nearest school but their chance of admission is dependent upon distance. Its veracity depends on estimates of the schools’ capacity and ignores other admission constraints (as does the first scenario; for example, academically selective or single-​sex schools. These unmodelled constraints apply mainly to secondary schools). This omission is accepted because we are interested

126

SCHOOL CHOICE AND ETHNIC SEGREGATION

in how much school intakes deviate from the neighbourhoods around them. If the differences are due to particular types of schools and their admissions criteria, then that is something to explore. A further simplifying assumption is that nearest schools are defined as those where the postcode of the school and the geographical centre of the LSOA have the least straight-​line distance between them. Those nearest are not necessarily the fastest to reach nor the most accessible. However, applying straight-​line distances is not unrealistic since these often are used in school admissions criteria too.

Are schools more segregated than their surrounding neighbourhoods? Figure  5.3  –​explained more fully, shortly  –​shows the combined percentage of Bangladeshi, Indian and Pakistani pupils in most state schools in 2017, plotted against the expected percentage under the two hypothetical scenarios. Remember that the expected value reflects the neighbourhoods around the school. Consequently, the expected percentage for many schools is low, reflecting the fact that South Asian populations are clustered into particular parts of the country (see Chapter 2). Only schools with an expected percentage above ten are included in the charts and in subsequent discussion of them (so schools that are almost entirely White British will not be considered). Looking at Figure 5.3, the two scenarios do not produce greatly different expectations. Consequently, each graph is similar to its pair in each row. It is instructive to discuss the graphs in more detail. If the intake into each school was exactly equal to what the scenarios predict then each school (each dot) would lie somewhere upon the dashed line. Clearly, they do not but vary around the line with some schools having a higher than expected percentage of the Asian groups (above the line) whereas for others it is lower (that is, below the line). Over all schools these differences will cancel each other out –​all the pupils are attending a school somewhere so if one school is ‘over-​recruiting’ Asian pupils another must be ‘under-​recruiting’. That is why the thick solid (regression) line of best fit tends to locate close to the dashed line. Nevertheless, for secondary schools, it does rotate away from the dotted line –​the fulcrum of that rotation is towards the centre of the line; to the left, the dashed line is below the solid one and to the right it is above it. That rotation occurs because there are some schools with a much greater than expected percentage of the Asian groups that are generally in the top-​left of the chart, as there also are schools with a much lower percentage, generally found in the bottom-​r ight.

127

Ethnic Segregation Between Schools

Figure 5.3: The expected and observed percentages of Asian (Bangladeshi, Indian and Pakistani) pupils in English schools. Scenario 1: Primary 100

●

●

● ●

75

●●

●

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There is a minority of schools where the difference between the neighbourhood percentage of the Asian groups and the percentage in the schools is large. These are highlighted on the charts: dependent upon the scenario taken, there are up to 93 of 2,615 primary schools (3.6 per cent) and 60 of 711 secondary schools (8.4 per cent) where the percentage Asian exceeds expectation by more than 25 percentage points; and up to 149 primaries (5.7 per cent) and 48 secondaries (6.8 per cent) where the percentage Asian is more than 25 percentage points less than expected. For the remaining schools the average absolute difference is 7.8  percentage points for primary schools and 8.1 for secondary schools. The charts suggest that most schools are recruiting the Asian groups in broad proportion to the characteristics of the neighbourhoods that surround them but there are some exceptional cases where the schools and neighbourhoods markedly differ. The cause of those differences should not be assumed to be the school choices made by Asian groups. A higher percentage of Asian pupils can result from other groups avoiding the school; a low percentage could be a consequence of admission policies.

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In regard to the direction of the difference, it is more common for a school to have lower than expected percentages of the Asian pupils than it is higher ones (the median average difference for the primary schools is -​0.86; for secondary schools it is -​0.32). However, this circumstance varies with the expected value and therefore the neighbourhood setting. The contours on the charts indicate where most of the observations are clustered; they indicate the most typical values. The largest number of schools is found to the bottom-​left of each chart where the expected percentage of the Asian groups is lower. This is not surprising given that these are minority groups in the overall population and therefore in schools too. What is interesting is the position of the contours relative to the dotted line. Note that where the expected percentage of the Asian groups is lower, there the contours are centred near to the dotted line although slightly below it (more clearly so for secondary than primary schools). This means that where neighbourhoods have a lower percentage of the Asian groups, the actual percentage in schools is typically lower still but not greatly so. Observe also that as the expected value increases, the contours shift to above the line. That means that in areas where the percentage of Asian pupils is higher, commonly the percentage in schools is even more. Recall, however, that the numbers need to balance themselves out across the schools. If the localities with higher percentages of Asian pupils have more schools with higher than expected percentages of those pupils than they do schools with lower than expected percentages, then either there are Asian pupils travelling from further afield to attend those schools or there must be a small number of schools with much lower than expected percentages of Asian pupils in those localities. The latter circumstance is found in the bottom-​r ight of each chart. These are the schools that are not, for whatever reason, recruiting the Asian pupils in the percentages that would be expected given the ethnic composition of nearby neighbourhoods. Where these schools are found and what types of school they are, are questions we return to presently. First, we turn to Figure  5.4, which is constructed in the same way as Figure 5.3 but looks at the observed and expected combined percentages of Black African and Black Caribbean pupils in schools. Very few schools have a percentage of these black pupils that is more than 25 points less than expected: just 12 of 1,813 primaries (0.66 per cent) and six of 484 secondaries (1.2 per cent). Some –​77 of primary schools (4.3 per cent) and 13 of secondary schools (2.7 per cent) –​have a percentage intake of black pupils that exceeds expectation by more

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Figure 5.4: The expected and observed percentages of black (Black African and Black Caribbean) pupils in English schools Scenario 1: Primary 100

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than 25 percentage points. For the remaining schools, the average absolute difference is 6.5 percentage points for primary schools, and 6.9 for secondaries. As for the Asian groups, the charts suggest that most schools are recruiting black pupils in broad proportion to the characteristics of the neighbourhoods that surround them, but there is a small minority of cases where schools and neighbourhoods greatly differ.

Types of school in England The preceding section drew attention to a minority of schools where the percentage intake of either Asian or black pupils differed substantially from the neighbourhoods that surround them, by 25 percentage points or more. The magnitude of the difference suggests that even though our estimation strategy is not exact, still there is strong evidence that these schools have compositions that do not reflect that of their immediate locales. A natural question to ask is whether they share any characteristics in regard either to their location or to their school type? England has a patchwork of school types that reflects their historic origins in the charitable and/​or church-​based provision of education, the development of the state sector and mandatory primary and

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secondary education, and, more recently, policies that introduced market-​based changes into the education sector, allowing or requiring schools to ‘opt out’ of the control of LEAs so permitting them greater autonomy though often as part of (usually) not-​for-​profit federations (groups of schools) and/​or with the backing of various institutions and organizations. The schools that make up this patchwork can be categorized in various ways. First, there are ‘faith schools’, linked to religious groups and denominations. The majority of these are Church of England or Roman Catholic schools that have existed for many years and fall into one of two types, voluntary aided or voluntary controlled. The former has greater autonomy from the local authority with the religious organization paying a small percentage of its capital costs but otherwise funded by the state. Roman Catholic schools are voluntary aided; the majority of Church of England schools are voluntary controlled. A House of Common report records that: At the start of January 2017 there were 6,814 state funded Faith schools in England. The majority were primary schools; 6,177 or 37% of all state funded primaries. The 637 secondary Faith schools made up 19% of all state funded mainstream secondaries. The proportion of state funded Faith schools has increased gradually over time from 35% of primaries and 16% of secondaries in January 2000 […] Church of England schools were the most common type among primary schools (26% of all primaries); Roman Catholic schools the most numerous type of faith school at secondary level (9%). Non-​Christian schools were very much in the minority. (Long and Bolton, 2018) There also are academies and free schools. Academies are publicly funded independent (but not private) schools, funded directly by the government rather than through the LEAs: some are schools that have opted out of local government control by a decision of their governors, after consultation with parents; others were required to change their status because they performed badly in the national quality inspections. Some were built as academies. They may also be faith schools and have greater autonomy in teaching and in setting admissions, which, for faith schools, may include membership of the religious group as a criterion (capped at a maximum of 50 per cent of the intake for new schools). Religious groups and trusts can run academies and free schools, as can charities, businesses and universities. Free schools are

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new state schools, whereas many academies are converted from schools previously maintained by local authorities. Free schools are explicitly not for profit and need not include a sponsor (they can be set up by parents or teachers) (Roberts, 2017). A further categorization is between the newer types of schools (academies and free schools, introduced to avoid local council control and to foster market principles) and the longer-​standing ones, which are the voluntary aided and controlled schools, and also community and foundation schools. What these older types have in common is that they are controlled, to a greater or lesser degree, by the LEA, most especially community schools, which are not influenced by business or religious groups. The governing body of a foundation school, few of which are faith schools, has greater freedom but not the same autonomy that academies and free schools do. Some schools are selective. Faith schools are to some degree (where religious affiliation is an admissions criterion), as are academies and free schools if they include aptitude in a particular subject or talent among their admissions criteria. The geographical component of admissions introduces residential selection and a few schools are single-​sex. However, selective usually refers to academic selection –​secondary schools that have an entrance examination. These are grammar schools. Although most were converted to mixed ability, comprehensive schools during the late 1960s and 1970s, 163 remain (and have been given permission to expand even by operating satellite campuses).6 Under the grammar school system, children who did not pass the entrance exam would usually attend a secondary modern school instead, though the expansion of other school types now increases the choice (secondary moderns still exist in function but not in their school name). Finally, there are the fee-​charging, private schools. These school about 7 per cent of English pupils but, as previously noted, are not included in the NPD and, therefore, not in our analyses. Other ways of mapping the proliferation of school types in England can be undertaken (Courtney, 2015).

Where and what type are the schools that differ most from expectation? Among the 93 primary schools whose pupils, in 2017, were more than 25 percentage points more from the Asian groups than expected under a neighbourhood allocation, 14 are in London (of which five are in Tower Hamlets), six in Leeds, five in each of Birmingham, Bolton and Kirklees (of which two are in Batley), and four in each

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of Halifax, Leicester, Oldham and Greater Manchester. The majority (50 of the 93) are community schools, and a further 13 are academies without a religious connection, but 23 are faith schools, predominantly Anglican but with some being Hindu, Muslim, Sikh or a Jewish school. None is Catholic. Among the 149 with a substantial under-​representation of the Asian pupils (more than 25 percentage points below expectation), 38 are in London (of which 14 are in Tower Hamlets and six in Redbridge), 12 are in Birmingham, 11 are in Lancashire (of which six are in Burnley or the nearby town of Nelson), and eight in Bradford. Of the 149, the clear majority (127) are faith schools, of which 102 are Catholic. Reflecting this, 95 of the schools are voluntary aided; 37 are either academies or free schools. The net result is that about 30 per cent of primary schools in Tower Hamlets, and one quarter in Nelson and in Dewsbury (a town within the metropolitan borough of Kirklees with a large Asian population) have a percentage intake of the Asian pupils that is substantively different from what the neighbourhoods would suggest, with a difference of more than 25  percentage points (whether greater or less than the neighbourhoods). Turning to secondary schools, of the 60 with an intake of the Asian groups more than 25 percentage points greater than the neighbourhoods suggest, ten are in London (of which two are in each of Newham, Tower Hamlets and Waltham Forest), nine in Birmingham, seven in Bradford, five in Blackburn and four in Oldham. Fifty are not affiliated with any faith group (of which half are an academy); the remaining ten are of a Muslim, Sikh or Hindu denomination, of which seven are free schools. Of the 60, six are grammar schools and one is a secondary modern. Of the 48 with an intake of the Asian groups, more than 25 percentage points below expectation, 15 are in London (of which four are in Tower Hamlets and three in Newham), three are in each of Birmingham, Bradford and Oldham. Thirty-​two of the 48 are faith schools, of which 25 are Catholic (and 19 of those voluntary aided; the remaining six are academies); five are Anglican. Two of the 48 are selective schools and one is a secondary modern. The net result is that over half of secondary schools in both Blackburn and Oldham have a percentage intake of the Asian pupils that substantively differs from expectation by 25 percentage points or more. The same is true of about one third of secondary schools in Tower Hamlets, Bradford and Burnley. The same type of analysis can be made in relation to the black groups. Of the 77 primary schools with an intake from those groups

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more than 25  percentage points greater than expected, 63 are in London, of which seven are in each of Greenwich, Hackney and Southwark, six in Barking and Dagenham, and five in both Enfield and Lewisham. Of the 14 outside London, three are in Leeds and two in each of Birmingham, Bristol, Coventry and Manchester. All but eight of the 77 schools are faith schools, of which 38 are Catholic and 27 are Anglican. The majority of the faith schools (53) are voluntary aided but 16 are academies. Of the 13 secondary schools, all but one is in London, and spread across ten of its local authorities. All but one is a faith school, of which eight are Catholic and three are Anglican, and ten are voluntary aided. All of the 13 are comprehensive schools. Turning to the few schools with a percentage intake of black pupils more than 25 percentage points less than expected, of the 12 primary schools, eight are in London (spread across seven local authorities) and two are in Bristol. Half are faith schools; half are not. Of the six secondary schools, four are in London, two are in Birmingham. Half are non-​Christian faith schools. There are five local authorities where between 10 and 15 per cent of primary schools have an intake of black pupils that differs from expectation by more than 25 percentage points (whether greater or less than neighbourhoods), all of which are in London: Barking and Dagenham, Hackney, Greenwich, Southwark and Lewisham. The same is true of 20 per cent of secondary schools in Hackney, and between 10 and 15 per cent of those in Lewisham and Brent. In summary, the majority of schools have a percentage intake of the Asian or black groups that broadly corresponds with the ethnic composition of the neighbourhoods around the schools. They are not exactly the same but typically differ by less than 10 percentage points in the majority of cases. A small minority of schools diverge more substantially from expectation, with differences exceeding 25 percentage points. Where the larger differences exist between schools and neighbourhoods for Asian groups, they tend to be characteristic of parts of the north of England and Yorkshire with relatively large South Asian populations  –​Blackburn, Bradford, Burnley and Nelson, Dewsbury and Oldham  –​but also some parts of London, notably Tower Hamlets. For black groups, they are largely confined to London. Academically selective schools do not feature greatly among those with the greatest school and neighbourhood differences but faith schools do: some (Anglican, and various non-​Christian faiths) have a relative over-​representation of Asian pupils; others, especially Catholic schools, have a relative under-​representation. Faith schools, both Catholic and Anglican, also are characteristic of the small number of schools with a substantial over-​ representation of black pupils.

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And what of the White British? There are 126 primary schools and 53 secondary schools where the percentage of White British pupils exceeds expectation by more than 25 percentage points (and where the expected percentage of either the Asian or Black groups is greater than ten). Among the primary schools, 38 are in London, among which six are in Barnet, five in Brent and five in Tower Hamlets, nine are in Birmingham, nine are in Lancashire, of which five are in Burnley and Nelson, and six are in Hertfordshire. Of the 126, 97 are faith schools, of which 50 are Catholic, 29 are Anglican and 16 are Jewish. The majority of those faith schools (80) are voluntary aided, of which 45 are Catholic. Among the secondary schools, 11 are in London, including two in each of Hackney, Hammersmith and Fulham, and Hillingdon, three are in each of Blackburn, Bradford and Oldham. Of the 53, 24 are faith schools, of which 13 are Catholic, 6 Anglican and 4 Jewish, and 29 are not. Only one is a community school; six are grammar schools. There are 132 primary schools and 73 secondary schools where the percentage of White British pupils is more than 25 percentage points less than expected. Of these, six are in Lancashire, five are in Lewisham, and four are in each of Bolton, Bradford, Bury, Calderdale (Halifax), Coventry, Kirklees (two in Batley), Leeds, Milton Keynes, Oldham and Tameside. The majority of the schools (77) are not faith schools but 57 are, including 33 Catholic schools and 18 Anglican. Thirty-​ three are voluntary aided; 50 are community schools. A further 35 are academies; eight are free schools. Of the secondary schools, 13 are in Birmingham, five in Blackburn, four in Manchester, and three in each of Bolton, Bradford, Lancashire (Preston and Burnley), Oldham and Wolverhampton. Of the 73, 57 have no religious affiliation, of which 30 are academies, and 16 are community schools. Five are non-​ Christian but religious free schools (Hindu, Muslim or Sikh). Seven are selective grammar schools. The net result is that over 10 per cent of primary schools in Brent, Barnet and Blackburn, two thirds of secondary schools in Blackburn, over 40 per cent of secondary schools in Oldham and one quarter or more in Hammersmith and Fulham, and in Wolverhampton, have an intake of White British pupils that differs from expectation by more than 25  percentage points (whether greater or less than local neighbourhoods). What is not clear, however, is whether these examples of schools that substantially either over-​or under-​recruit pupils from particular ethnic groups do so because of the LEA’s allocation procedures or, more likely, the selection decisions of one or more of the ethnic groups (with, for example, one group not selecting a local school and other groups selecting a school even though it is not local).

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Case studies We end this chapter by looking in more detail at what is happening in three of the places highlighted in the preceding discussion. We begin with Blackburn.

Blackburn In 2017, the most numerous groups in Blackburn’s schools were the White British (47 per cent of the total cohort, down from 56 per cent in 2011), Pakistanis (24 per cent, up from 19 per cent) and Indians (18 per cent, no change). Using the same typology of schools that was developed in Chapter 4, it is found that 90 per cent of the White British primary pupils were in a school where theirs was the majority group, and 86 per cent of the secondary pupils were in the majority. For the primary school pupils, this is much as expected under a neighbourhood allocation, for which the value would be 88 per cent.7 For the secondary school pupils, the value of 86 is notably greater than the expected 65 per cent. However, if we look at the percentage of the White British pupils who are in secondary schools where the White British predominate (accounting for more than 90 per cent of the pupils), the figure is 41 per cent, less than the expected value 60 per cent. Turning to the Pakistani primary and secondary pupils, 60 per cent of the former are in primary schools where theirs is the majority group, and 29 per cent of the latter were in the majority in secondary schools. This is considerably more than the expected values of 28 and 0 per cent, respectively. In this case, it is not because fewer are in schools where their group predominates –​none are, and none are expected to be. Similarly, for the Indian pupils, they are more concentrated with their ethnic co-​ peers than would otherwise be expected: 32 per cent of primary pupils are in a school where theirs was in the majority, against an expected value of 9 per cent; 43 per cent of secondary pupils, against an expectation of zero. The ID between the White British and Pakistani was, in 2017, 0.83 across the primary schools, and 0.68 across the secondary schools. While high, it has been falling for primary schools since 2015 (in which year it was 0.86); 2017 was the lowest year for secondary schools over the period 2011 to 2017 (the highest was in 2012, at 0.71). The ID between the White British and the Indians was even higher in 2017: 0.88 for primary schools and 0.76 for secondary schools. These have not varied greatly over the period from 2011. More notable is that the secondary school value is between 0.10 and 0.20 (10–​20 percentage points) greater than expected under a neighbourhood allocation

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of pupils to schools (the more typical difference is about 0.05). This is not due to the actions of the White British alone: if we look at the ID between the Indian and Pakistanis, while much lower (0.33 for primary schools in 2017; 0.30 for secondaries), for secondary schools it has been growing (it was 0.20 in 2013) and now differs from what is expected under a neighbourhood allocation by 0.16. The situation appears to be one where the White British are strongly segregated from the Pakistanis and Indians, especially between primary schools, but there is no evidence that that segregation is increasing. Instead, there may be evidence that there are secondary schools more likely to be chosen by the Indian group than either the Pakistani or White British. Figure 5.5 explores some of this in more detail, looking at the educational separations of the White British from Pakistanis in Blackburn’s schools. Figure 5.6 does the same but for the White British and Indians. A  small number of schools is omitted from the charts. These arise from two circumstances. The first is when there is only one school of a particular type (for example, a primary school of a ‘Christian Other’ denomination), which is then masked in the data so as not to precisely identify specific schools.8 The second is when the relative difference between what is observed and what is expected moves in a different direction to the absolute difference: for example, when the percentage of the school that is White British is less than expected but the actual number of pupils who are White British is greater. By removing these (rare) cases, left on the charts are the schools that are unambiguously ‘over’-​or ‘under’-​recruiting the White British relative to what would be expected under a system of neighbourhood allocation. Some examples help to explain the charts. Looking at the top-​left panel of Figure 5.5 there is a primary school, numbered 10 in the charts, that is in a location where the percentage of the Pakistani group among its pupils is expected to be in excess of 50 per cent under a neighbourhood-​based allocation –​that is, the school is surrounded by neighbourhoods that are majority Pakistani among the pupils. There is no evidence that the White British are deterred from going to this school; quite the opposite –​the percentage of the cohort that is White British in school 10 is almost 30 percentage points greater than expected. This ‘over-​recruitment’ of the White British appears to come with the ‘under-​recruitment’ of Pakistani pupils because –​now looking at the bottom-​left panel –​the same school has almost 30 percentage points fewer Pakistani pupils than expected. However, the net result of this is to make the school much more ethnically diverse than would otherwise be expected (it has an entropy score more than 0.10 greater than expected, as indicated by the larger enclosing circle).9

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School 10 is a Roman Catholic primary school. The apparent over-​recruitment of the White British and under-​recruitment of the Pakistanis (relative to the surrounding population) is a characteristic of almost every Catholic school in Blackburn, at both primary and secondary levels. This is true of schools 20, 6 and 22, for example, of which six appears to be more than 0.10 less diverse than expected (as indicated by the enclosing square). School 41 is an interesting exception –​it appears to have a greater than expected percentage of the Pakistani group (bottom-​left panel), a lower than expected percentage of the White British and, as a consequence, be much less diverse than expected but as a consequence of its Pakistani intake. In contrast to most of the Catholic schools, the Anglican primary schools tend to have lower percentages of the White British and higher percentages of the Pakistani group than might otherwise be expected. A number (but not all) are much more ethnically diverse than would otherwise be expected. This is despite most of the Anglican schools being either voluntarily aided or academies, which means that they could, in principle, exercise greater control over their admissions, as can the Catholic schools, which are all voluntarily aided. Faith schools also feature among those with an under-​recruitment of the White British, with two Muslim secondary schools (46 and more especially 7) having fewer White British pupils than expected. Indeed, there are no White British pupils (in our data) in either school. Even so, the percentage of the pupils who are Pakistani in those schools is close to expectation (and, for school 46, slightly below it). Instead, it is the Indian group that is ‘over-​represented’ in the Muslim secondary schools,10 as the bottom-​right panel of Figure 5.5 shows, and under-​represented in almost every Anglican and, more especially, Catholic faith school, the two main exceptions being primary schools 43 and 29 again (the numbering is consistent across Figures 5.5 and 5.6). The implication of what Figures 5.5 and 5.6 are showing is some pupils are living in ethnically mixed neighbourhoods but attending different schools from one another. Certainly, it is possible to find examples of this in the data:  there is an LSOA neighbourhood in which 11 of the primary school aged pupils (in our data) are Indian, ten Pakistani and 13 White British. Of the 13 White British, eight attended a different school from their Indian and Pakistani neighbours; four were at an Anglican faith school and the other four in a Catholic one. Interestingly, seven of the 11 Indians and four of the ten Pakistanis also are in an Anglican school but that particular faith school is attended by only one of their White British neighbours. In this LSOA, only

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Figure 5.5: Showing the over-​or under-​recruitment of (upper plots) the White British and (bottom plots) the Pakistani pupils in Blackburn’s schools in 2017 relative to a neighbourhood allocation of pupils to schools, and how that recruitment relates to the expected percentage of Pakistani pupils in each school (Number of schools omitted) Primary, 2; Secondary, 1 Secondary: (y) WBRI ~ (x) APKN

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Figure 5.6: Showing the over-​or under-​recruitment of (upper plots) the White British and (bottom plots) the Indian pupils in Blackburn’s schools in 2017 relative to a neighbourhood allocation of pupils to schools, and how that recruitment relates to the expected percentage of Indian pupils in each school (Number of schools omitted) Primary, 4; Secondary, 1

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one of the 11 Indians is in a school without any of their White British neighbours but five of the ten Pakistanis are. There is another LSOA where seven of the secondary school aged population are Indian, six Pakistani and ten White British. Of the White British, five are in schools that none of their Indian nor Pakistani neighbours attend, of which three are in a Catholic school. However, only one of the seven Indians attends a school without any of their White British neighbours (it is a Muslim school) and two of the six Pakistanis (in a free school). It would be naïve to claim that faith schools do not add to residential patterns of segregation –​some do. But this is not to the exclusion of other types of school also doing so (consider secondary school 40, for example, in both Figures 5.5 and 5.6 with its over-​recruitment of the White British and under-​recruitment of both Pakistanis and Indians). Nor is it a defining feature of faith schools: we have provided some examples already but note also, to the left of Figures 5.5 and 5.6, the four primary schools that are in areas where they are expected to recruit few if any of the ethnic minorities yet do so (more so the Pakistanis than the Indians and with a consequent under-​recruitment of the White British). The net result is that three of those schools are much more ethnically diverse than expected; all four are faith schools –​two Anglican, two Catholic. At the secondary level, however, all the schools that are much less ethnically diverse than expected are faith schools –​ selected by parents for their children either because they belong to that faith or, if they do not, they feel positively towards some of the characteristics of those faith schools (their disciplinary attitudes, for example, or their emphasis on academic success). Ultimately, what adds to residential patterns of ethnic segregation is if pupils bypass their nearest school and, in so doing, choose (or are allocated to) a school where there is a greater percentage of their ethnic group than would be expected at their nearest school. Figure 5.7 suggests that this is indeed what happens for some but not all of the three main ethnic groups in Blackburn:  focusing only on primary schools (because there are insufficient secondary schools to model the probabilities) it shows, for both the White British and Pakistanis, the smaller their presence in the neighbourhoods around their nearest school, the higher the estimated probability they will not attend that nearest school but instead go to one where they form a higher percentage of the intake.11 This is a process of educational separation adding to the residential ones although not one that appears to affect the Indian group. For the Pakistanis and Indians, there is no difference between those who are free school meal (FSM) eligible (a broad measure of

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Probability of attending a school with greater % of the group than nearest school

Figure 5.7: The estimated probability of members of the three main ethnic groups in Blackburn attending a primary school with a greater percentage of their group than that expected for their nearest school 1.00

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socio-​economic disadvantage) and those who are not (which is why the lines for those who are FSM eligible and not over-​plot each other on the chart) and their probability of ‘skipping’ the nearest school for another with greater percentage of their ethnic group is always less than half. Among the groups, it is the White British who are exceptionally unlikely to attend their nearest school when they are not expected to form a large percentage of it, most especially when they also are not FSM eligible. This suggests a process not just of ethnic separation but socio-​economic separation as well (see Chapter 6).

Oldham The second study is of Oldham, where the most numerous groups among the pupils are the White British (55 per cent of the total in 2017, down from 65 per cent in 2011), Pakistanis (18 per cent, up from 15 per cent) and Bangladeshis (14 per cent, marginally increased). The ID score of segregation was 0.80 between the White British and Pakistanis in primary schools in 2017 –​high but almost exactly that expected under a neighbourhood allocation to schools (slightly higher, at 0.82) and down from 0.83 in 2011. For secondary school pupils it was 0.66, again slightly less than expected under a neighbourhood allocation (0.68) and reduced from 0.73 in 2011. For

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the separation of the White British from the Bangladeshi pupils, the ID score has increased slightly for primaries from 0.85 in 2011 to 0.87 in 2017 (more than the 0.81 expected under a neighbourhood allocation), and from 0.72 to 0.74 for secondaries (higher than the expected 0.65). The separation of the Bangladeshis from the Pakistanis has declined from 0.71 to 0.65 (less than the expected 0.68) for primaries, and from 0.53 to 0.51 (less than the expected 0.59) for secondaries. In 2017, 18 per cent of the Bangladeshi pupils, 4 per cent of the Pakistani pupils and 23 per cent of the White British were in a primary school where their own ethnic group was predominant, and 30 per cent of Bangladeshis, 25 per cent of Pakistanis and 83 per cent of the White British were in a secondary school where theirs formed a majority. As in Blackburn, faith primary schools in Oldham can sometimes increase educational patterns of segregation but not always and, where they do so, it is not always the case that they are ‘over-​recruiting’ only the White British and not other ethnic groups. Nor are they the only type of school to either add to or take away from the residential patterns –​ non-​denominational schools do so too. For examples of the different cases, consider in Figures 5.8 and 5.9: primary school 89 (an Anglican faith school over-​recruiting the White British and under-​recruiting the Bangladeshi and Pakistani, with the net result that it is less ethnically diverse than expected); school 63 (a Catholic school over-​recruiting the White British and Pakistani, and under-​recruiting the Bangladeshi, resulting in it being more diverse than expected); school 81 (not a faith school, under-​recruiting the White British, marginally under-​ recruiting the Pakistani but over-​recruiting the Bangladeshi, leading to less diversity); and school 85 (not a faith school, under-​recruiting the Bangladeshi, with greater than expected diversity). At the secondary level, however, the effect of an Anglican faith school (school 12) is more pronounced: it is substantially over-​recruiting the White British and under-​recruiting the Bangladeshi and Pakistani. Figure 5.10 shows that the White British pupils are the least likely to attend their nearest primary school, but for all groups there is an negative relationship between their prevalence locally and the probability of them by-​passing the nearest school in favour of one with a percentage of their ethnic group that is higher than expected for that nearest school (although, for the Bangladeshis, that correlation appears to become positive beyond a certain threshold). For the White British, those that are FSM eligible are generally less likely to bypass their nearest school than those that are not, whereas for the Bangladeshis the FSM eligible appear more likely to do so.

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Figure 5.8: Showing the over-​or under-​recruitment of (upper plots) the White British and (bottom plots) the Pakistani pupils in Oldham’s schools in 2017 relative to a neighbourhood allocation of pupils to schools, and how that recruitment relates to the expected percentage of Pakistani pupils in each school (Number of schools omitted) Primary, 2; Secondary, 2 Primary: (y) WBRI ~ (x) APKN

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Tower Hamlets Finally, we consider Tower Hamlets. Limitations of space prevent us from going into details but, in brief, the two largest groups in 2017 were the Bangladeshi (65 per cent of pupils, effectively unchanged since 2011) and, much smaller, the White British (9 per cent, decreased from 12 per cent). The ID score between the White British and the Bangladeshi for primary schools has remained at around 0.63 throughout the 2011–​17 period; that for secondary schools began and ended the period at about 0.59. In 2017, 93 per cent of the Bangladeshi primary pupils and 97 per cent of the secondary pupils were in a school where theirs was the majority ethnic group. The same was true of about 5 per cent of the White British primary school pupils and none of the secondary pupils. The effects of faith schools are particularly notable in Tower Hamlets. Of 18 Christian faith primary schools, 15  ‘over-​recruit’ the White British relative to a neighbourhood-​based allocation, and all but two (both Anglican) under-​recruit the Bangladeshi group. Of the 47 primary schools that are not faith schools, 35 under-​recruit the White British and only four under-​recruit Bangladeshis. Clearly there is a propensity for the White British to choose faith schools more so than Bangladeshis. However, it is still the case that 16 of the 18 Christian faith schools have more than 25 per cent of the pupils drawn from

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the Bangladeshi group and ten (including eight of the nine Anglican schools; two of the seven Catholic) have more than half. In fact, 13 of the 18 are more ethnically diverse than would be expected under a neighbourhood-​based allocation while the same is true of only ten of the 49 primaries that are not faith-​based. In short, the faith-​based primaries do disproportionately attract the White British but, in an area like Tower Hamlets that is so dominated by another ethnic group, that actually increases diversity within the school. At the secondary level, too, it is the Christian faith schools that tend to have greater ethnic diversity than expected under a neighbourhood-​based allocation.

Conclusion In this chapter we have asked whether state schools in England are more segregated than neighbourhoods. The answer really depends upon what is being measured, how, where and for which ethnic groups. However, in general, intakes into schools reflect the neighbourhoods that surround them and are not dissimilar to what is expected under a neighbourhood-​based system of pupil allocation. In fact, only the average Bangladeshi and Pakistani pupil, and Indian and Black African primary school pupils were in a school, in 2017, with an intake less diverse than neighbourhoods, and even for these the difference is small. All others of the Indian, Asian Other, Black African, Black Caribbean, Mixed, White Other and White British pupils were, on average, in a school of an ethnic diversity comparable to that of neighbourhoods. These results are not surprising. Because admissions criteria usually have a geographical component, and because pupils themselves may be constrained by how far they can travel geographically, patterns of segregation at a neighbourhood level will likely re-​emerge as patterns of segregation between schools. Nor would it be surprising if ethnic segregation between schools consistently was greater than that between neighbourhoods. After all, it is a system that promotes choice and one where it would take only a slight preference by parents to have their children schooled with others of a similar ethno-​cultural/​religious background as themselves for there to be some degree of separation along those lines and hence an increase in the measured segregation when comparing schools to neighbourhoods. What, arguably, is more surprising is that the differences are not greater and more widespread. In other words, rather than being disproportionately concerned by the exceptional cases where schools are not like neighbourhoods, we might be better reassured that in most cases they are.

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Geographically-​based admissions criteria are, by definition, geographically selective (so the ethnic composition of most schools reflects that of their surrounding neighbourhoods), but where geography has less of an influence on the allocation process it can be replaced with other criteria that are more selective. The obvious example is the case of some faith schools. Where religious affiliation becomes either an explicit or implicit admissions criterion then it is possible to recruit over wider areas and, in principle, to be more mixed. Some faith schools are but others are among the ones that are least representative of the neighbourhoods that surround them. Debates about faith schools point to these processes of ethnic and social selection and their potential divisiveness (Allen and West, 2011). However, it would be wrong to tar all such schools with the same brush because they vary in respect to whether they regard themselves as providing an education for a faith group or by a faith group for a wider community. What our results do suggest is the benefits (in terms of not adding to patterns of segregation) of retaining limits on the proportion of a school’s intake that can be recruited on the basis of faith. Some have argued that policies of school choice add to patterns of social and ethnic segregation with particular social and ethnic groups less able to access the ‘best’ schools. The problem with this argument is that in the absence of choice, and unless politicians and parents are willing to accept random allocations to schools (potentially over large areas), pupils will have to attend their nearest school; then what becomes critical is the means to choose where you live –​a means that is not equally distributed across the population. Removing school choice could initially prevent schools from being more segregated than neighbourhoods in some (but not widespread) circumstances yet if the lack of school choice acts to detract people from living in a particular area then it risks increasing segregation overall. It seems likely that school choice is less a factor in patterns of ethnic segregation between schools than other economic and social processes of spatial differentiation. Notes 1

2

3

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For example, infant classes are not supposed to exceed 30 pupils, which means admission targets for primary schools tend to be multiples of 30. See www.gov.uk/​ g over nment/​ s tatistics/ ​ s econdar y- ​ a nd- ​ p r imar y​school-​applications-​and-​offers-​2017 For primary schools, 90 per cent of applicants in 2017 received their first choice, and 97.2 per cent one of their top three. LSOAs are groups of contiguous small areas (OAs) defined to be homogeneous on two characteristics identified in census data –​housing type and tenure. LSOAs are thus relatively homogeneous on those criteria as well.

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6 7 8 9 10

11

This has been clearly demonstrated by recent analyses of census data for London (Johnston et al, 2016b). See https://​en.wikipedia.org/​wiki/​List_​of_​g rammar_​schools_​in_​England Here using scenario 2 for the hypothetical allocations. It is for the same reason that we choose not to map the data. Recall that the minimum entropy score is 0 and the maximum is 1. Although the majority of those claiming Indian ethnicity across England according to the 2011 census affiliate with the Hindu religion, Nintey-​one per cent of Indians in Blackburn are Muslims. The probabilities are modelled using a logit regression model that allows for a non-​linear relationship between the predictor variables and the response.

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6

Do Socio-​Economic Separations Add to Ethnic Segregation? Summary Most of the focus of this book has been on ethnic segregation, reflecting the discourse found in the media and prominent in government policy documents. However, there is a strong intersectionality between social and ethnic dis-​/​advantage, which means processes of socio-​economic separation are linked to patterns of ethnic segregation in ways that are not easily disentangled. The purpose of this chapter is not to try and do so but, instead, to look for evidence that within ethnic groups, and within a system of constrained school choice, the more or less affluent have different amounts of segregation from other ethnic groups, with this being related to the different types of school they attend. That evidence is found with those of the White British who are not eligible for free school meals (FSMs) generally the most segregated from /​least exposed to other ethnic groups, with the effects of academically selective and some religiously selective schools contributing to the differences.

Introduction To this point, the book has focused on ethnic segregation, with only passing mention of socio-​economic segregation and the idea that people of different incomes, affluence or class can reside and be educated separately from one another. Socio-​economic and ethnic segregation are intertwined. The spatial inequalities that divide people by wealth and social background sustain and are sustained by inequalities between ethnic groups too. Hence, there is a strong intersectionality between social and ethnic dis-​/​advantage, as Reni Eddo-​Lodge (2017), among others,

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has eloquently articulated (see Chapter  1). But, despite the overlap, the concepts of ethnic and socio-​economic segregation are not exactly reducible to each another. To talk of ethnic segregation is to look more at where people of different ethno-​cultural backgrounds are living and attending schools; to talk of socio-​economic segregation is to consider the same but for people of different socio-​economic positions and/or class. The attention we have given to ethnic segregation is a reflection of how segregation is conveyed in recent government policy documents. However, it is also the same focus that has been criticized –​rightly –​ for ignoring or, at least, downplaying the socio-​economic factors that generate inequalities of opportunity for different ethnic groups and create geographically differentiated outcomes in regard to employment, health, housing, health, the criminal justice system, education and so forth, all of which affect where people live and go to school. Disregarding such factors is not only myopic, it invites the inference that, where they do so, the members of different ethnic groups choose to live apart. Hence, the discourse turns to ethnic segregation being voluntary. That, in turn, leads to ‘solutions’ for segregation framed in terms of promoting shared values and citizenship, encouraging community cohesion and acting to promote trust between different groups, in the hope that they will mix more. Such aspirations may be laudable but risk becoming a distraction from or even an excuse for not addressing underlying social inequalities and causes. At worse, they generate a rhetoric that strays too close to ‘blaming the victims’ for circumstances that are not or are only partially of their own making; for example, to assume residential patterns of ethnic segregation are self-​created assumes that the constraints on where people live are no greater, on average, for any one ethnic group than they are for any other –​an assumption that is blind to socio-​spatial inequalities. With that in mind, this chapter looks at whether there is evidence of socio-​economic segregation that adds to and therefore contributes to the patterns of ethnic segregation that earlier chapters have identified (and found to be falling, overall). The aim is not to prove that ethnic segregation is caused by socio-​economic segregation, or vice versa; to try and do so would be problematic for various reasons. First, because it is something of a chicken and egg argument, which, to disentangle, would require different data using more complex methods of analysis incorporating greater (longitudinal) understanding of the pupils’ personal, familial and contextual circumstances over time. Second, because the idea of trying fully to separate out two things that are fundamentally interlinked would be something of an analytical contrivance. Third (and related to the second), there is the problem of

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SOCIO-ECONOMIC AND ETHNIC SEGREGATION

exogeneity, here meaning that although we can and will measure how a degree of socio-​economic separation can add to the patterns of ethnic segregation seen between schools, as a total measure of how much socio-​economic separations create ethnic segregation it is incomplete and biased downwards. That is because it does not account for the (very likely) possibility that the patterns of ethnic segregation being added to were themselves generated by socio-​economic processes that helped to create those patterns in the first place. Our purpose is more modest: it is to look for evidence that within ethnic groups the more or less affluent have different amounts of segregation from other ethnic groups. If they do not –​if they broadly are the same –​then there is little evidence that social separations are adding to the patterns of ethnic segregation that already exist (although, to re-​iterate, that does not mean that they did not contribute to creating those patterns). However, if there are differences then they help to demonstrate that the geographies of education (who is educated and where) are not independent of social geographies nor their spatial determinants and outcomes, as they play out in a system of (constrained) school choice.

Measuring the rate of free school meal eligibility between ethnic groups It is not difficult to find evidence that different ethnic groups have different socio-​economic opportunities and outcomes. It can be found on the Ethnicity facts and figures website, for example, as discussed in Chapter 1.1 The data that we have about pupils and their schools (these data are described in Chapter 4) are not measuring any particular outcome, other than which pupil is in which school  –​but they do allow us to consider the connections between ethnicity and eligibility for a FSM. FSM eligibility is a proxy for economic disadvantage. It is far from perfect for reasons that include its binary nature: whether a pupil is eligible for a FSM or not is a crude indicator of the various dimensions of poverty and deprivation she and her family could be facing, and people on lower incomes that are nevertheless not eligible for a FSM could still be facing hardship. Even so, it is indicative of someone whose family is more likely than others to be facing economic disadvantage of some sort because eligibility is linked to parental receipt of welfare payments: prior to April 2018 (which is the period our data cover) it could be received by households with an annual gross income of no more than £16,190, which would place people in roughly the

153

Ethnic Segregation Between Schools

lowest earning 16 per cent of households in the UK in 2017.2 With the subsequent introduction of universal credit the household income must now be less than £7,400 a year (after tax and not including any benefits received).3 Figure 6.1 shows the percentage of each of the nine ethnic groups that we have focused on through this book that is FSM eligible. Two observations can be drawn. First, FSM eligibility is not a stable measure of some consistent level of economic disadvantage. Has the percentage of Black Africans facing such disadvantage really halved between 2011 and 2017 as is implied by the drop from about 40 to 20 per cent in the percentage who are FSM eligible? That is highly unlikely. Rather, what it suggests is that national changes to (that is, the tightening up of) access to welfare and FSM receipt have impacted most upon the Black African and also Bangladeshi groups (a change that is likely to add to the social and economic disadvantages they already face). Second, there are clear differences between the groups: even by 2017, the percentage of Black Caribbeans FSM eligible was approximately double the percentage of the White British and about triple that of the Indian group. The different rates of FSM eligibility across the ethnic groups are suggestive of how ethnic segregation will intersect with socio-​economic segregation. For example, where there are greater numbers of the Bangladeshi group there are likely to be greater numbers of the FSM eligible too (because of the higher rate of FSM eligibility for the Bangladeshi group). To some extent that is true:  as the top row of the second column of Table  6.1 shows, the average Bangladeshi pupil, in 2017, was in a school where 22.5 per cent of their own ethnic group was FSM eligible, a figure only matched by the 22.7 per cent for Black Africans and greatly exceeding the 6.0 and 8.0 per cent for the Indian and White Other groups, respectively (who, in Figure 6.1, have lower rates of FSM eligibility overall). In fact, more than half of the Bangladeshi pupils (53.1 per cent) are in schools where the rate of FSM eligibility is greater than 1.5 times the national average for the schools and pupils in the data (the average is 14.5 per cent). That Bangladeshi majority compares with 41.4 per cent of the Black Caribbean pupils, 40.1 per cent of the Black African ones, 39.7 per cent of Pakistanis, 26.6 per cent of the White Other group, 25.7 per cent of those of Mixed ethnicity, 21.8 per cent of the Asian Other group, 17.1 per cent of the White British and 15.8 per cent of Indians. The figure for the Bangladeshis could arise because the FSM eligible of the Bangladeshi are in schools with very many others that also are Bangladeshi and FSM eligible. However, it is not simply that; rather, it is because they

154

SOCIO-ECONOMIC AND ETHNIC SEGREGATION

Figure 6.1: The percentage of each ethnic group that was FSM eligible in the years from 2011 to 2017 Phase

●

Primary

ABAN

Secondary

AIND

APKN

40 30

●

●

● ● ●

20

●

● ●

10

●

●

●

% FSM eligible

AOTH

●

●

●

●

●

●

●

●

●

●

●

●

●

● ●

20 ●

●

●

●

10

●

●

●

●

●

●

●

●

BCRB

●

30

●

●

BAFR

40

●

●

●

MIXD

WOTW

WBRI

40 30

●

●

●

●

20

●

●

● ●

●

10 12

14

16

12

● ●

●

14

●

●

16

●

●

●

●

●

12

14

16

Year Source: Authors’ own calculations.

tend to be concentrated in schools where higher proportions of the other ethnic groups are FSM eligible too. The third and fourth columns of Table 6.1 show this: the average Bangladeshi pupil was in a school where 24.6 per cent of all pupils were FSM eligible in 2017 although Bangladeshi pupils comprised only 26.1 per cent of all the FSM eligible pupils in the school. That 26.1 per cent is still higher than for most other groups, meaning that the average Bangladeshi pupil was more concentrated with FSM eligible pupils from the same ethnic group as themselves than was the average FSM eligible pupil from most other groups, but not to the extent that the average Pakistani pupil was (the average Pakistani pupil was in a school where 32.6 per cent of all FSM eligible pupils were Pakistani); neither was anywhere close to the extent that the average White British pupil was (for whom the comparable figure is 81.4 per cent). For all but the White British, when a FSM eligible pupil from an ethnic group encounters another FSM eligible pupil, that second pupil is more likely to be from an ethnic group other than their own. Table 6.1 also includes the figures for 2011. As discussed, they are not directly comparable with those for 2017 because of the way FSM

155

Ethnic Segregation Between Schools

Table 6.1: Showing, for the average pupil in each ethnic group and in English state schools, (a) the percentage of their group that was FSM eligible in their school, (b) the percentage of pupils in their school who were FSM eligible, and (c) the percentage of all FSM eligible pupils in the school who were from that group 2017

2011

Group

(a)

(b)

(c)

(a)

(b)

(c)

ABAN

22.5

24.6

26.1

35.8

36.7

29.6

AIND

6.0

13.2

11.3

9.5

19.0

11.9

APKN

18.3

19.9

32.6

26.0

27.1

35.2

AOTH

12.2

15.2

6.4

15.4

21.2

5.5

BAFR

22.7

20.4

19.6

39.5

30.5

22.0

BCRB

26.6

21.0

11.5

31.3

29.6

12.3

MIXD

20.8

15.8

12.3

25.7

20.2

10.3

7.9

16.3

7.3

15.1

21.5

9.1

13.5

13.0

81.4

14.9

14.9

84.3

WOTW WBRI

Source: Authors’ own calculations.

eligibility has been reduced/​restricted over the period. However, if it is assumed that ‘austerity politics’ have led to a shortening of the welfare safety net around people, with the effect that FSM eligibility has become a blunter tool to identify those facing economic hardship, then the 2011 values broaden the perspective of how ethnic and socio-​ economic segregation interact. Note the greater average percentage (now 36.7 per cent) of FSM eligible pupils in schools attended by Bangladeshis, with an average of 35.8 per cent of that ethnic group also being FSM eligible, and with Bangladeshis comprising an average of 29.6 per cent of all FSM eligible pupils in the schools they attend. Also in 2011, Pakistanis comprised over one third (35.2 per cent), on average, of all the FSM eligible pupils in the schools they attended, with Black Caribbeans typically being in a school where 31.3 per cent of other Black Caribbeans were FSM eligible, and with the average Black Africans pupil in a school where 39.5 per cent of their own group was FSM eligible.

How separated are those not FSM eligible from other ethnic groups? The preceding section addressed the national trends in FSM eligibility, observing the higher rates for the Bangladeshi, Black African and Black Caribbean groups, especially, who are also in schools where the average

156

SOCIO-ECONOMIC AND ETHNIC SEGREGATION

rate of FSM eligibility is higher than the national rate. Of these, the Bangladeshi group (but also the Pakistani) is the most concentrated in schools with its ethnic co-​peers (but by no means overwhelmingly so –​ see Chapter 4). The suggestion is that for groups such as the Bangladeshi, what appear as patterns of ethnic segregation (including those of being concentrated in schools with other Bangladeshi pupils) have underlying socio-​economic causes, which is why they tend to be concentrated with other FSM eligible pupils from their ethnic group as well as others. If correct, then any government policy that wishes to tackle ethnic segregation must also address its socio-​economic origins. As discussed in the introduction, there will be circumstances when socio-​economic and ethnic segregation are so closely entwined that they cannot really be separated out. That does not prevent us from looking for where and for which ethnic groups socio-​economic separation appears to add to the ethnic segregation. What we can identify, for instance, are places where the FSM ineligible of an ethnic group are more segregated from other ethnic groups than the FSM eligible are. One way to identify any socio-​economic difference is to use the index of dissimilarity (ID) introduced in Chapter 3 and returned to on other occasions in the book. Recall that this measures the spatial separation of two groups from each other in terms of how much the geographical distribution of one group matches (or, rather, fails to match) the geographical distribution of another, with the index ranging from zero (their geographical distributions are the same, relative to the size of each group) to one (wherever one group is located the other is not). The index can be used to measure, for example, how segregated the Bangladeshi group in Oldham’s primary schools is from everyone else (from every other ethnic group except themselves, combined).4 To consider the possibility of socio-​economic differences, the index is calculated twice: first, for how segregated those who are Bangladeshi and not FSM eligible are from every other ethnic group in Oldham’s primary schools; second, for those who are Bangladeshi and FSM eligible. If there is no socio-​economic effect independent of the ethnic segregation then those two values will be more or less the same.5 If they differ as, in fact, they do (see Figure 6.2) then socio-​economic as well as ethnic separations are occurring. Of course, the pair of calculations need be limited neither to Oldham nor to the Bangladeshi group. They can be calculated for each ethnic group and for every local authority in England, although we shall ignore the cases where the ethnic group comprises less than 5 per cent of the pupils in the authority, where less than 5 per cent of the group’s members are FSM eligible in the authority, and where more than 80

157

Ethnic Segregation Between Schools

per cent of the authority’s pupils are White British. This means we are sifting out the least diverse authorities and also the groups that are scarce within an authority. The results, shown for primary schools in 2017, are in Figure 6.2. Note that in nearly every case the observations are above the solid line drawn diagonally across the plots. That diagonal line indicates where the ID score for FSM eligible pupils would be equal to the ID score for those not eligible. It means that for most of the ethnic groups, in most of the local authorities, FSM eligible pupils are more segregated from other ethnic groups than those not eligible in regard to the primary schools they attend. There are some place-​based exceptions. The FSM eligible among the Bangladeshi in Newham’s primary schools appear less segregated from other ethnic groups (under the ID score) than the Bangladeshi who are not FSM eligible. The same may be said of the Black Caribbeans in Lambeth, those of Mixed ethnicity in Tower Hamlets, and those of the White Other group in Kensington and Chelsea. However, the main exceptions are for the White British: of the 77 local authorities that appear under the WBRI panel of Figure 6.2, 49 (64 per cent) record greater segregation from other ethnic groups for those who are not FSM eligible than for those who are. Among those 49, those with the greatest difference are Haringey, Bury, Ealing, Lambeth, Coventry and Bexley. The remaining 28 (where the FSM eligible of the White British appear more segregated from other ethnic groups than do those not eligible) include Nottingham, Solihull, Southampton, Middlesbrough and Leicester. One way that Figure  6.2 could be interpreted is that, among the ethnic minority groups, those who are FSM eligible are more concentrated in primary schools with their ethnic co-​peers than are those not eligible, whereas for the White British it is the other way around (with the FSM ineligible being the more concentrated with others of the White British). That interpretation is, however, generally true only for the White British and Bangladeshi. The average White British primary pupil, not eligible for a FSM and in any one of the 77 aforementioned local authorities in 2017, is in a school that is 71 per cent White British against an average of 64 per cent for those who are FSM eligible. The greatest differences are in Brent (average 50 per cent White British for the FSM ineligible; 23 per cent for the FSM eligible), Barnet (53 per cent vs 30 per cent), Haringey (43 per cent vs 21 per cent), Enfield (37 vs 19) and Bedford (66 vs 51), with differences of greater than 10 percentage points also in Wandsworth, Ealing, Hackney, Bradford, Hammersmith and Fulham, Croydon, Redbridge, Lambeth, Southwark, Bolton, Kirklees, Bury and Lewisham. The only other group for which the concentration of the FSM ineligible with their

158

newgenrtpdf

Figure 6.2: Comparing the ID scores for FSM eligible and ineligible pupils in terms of their segregation from pupils of other ethnic groups in the primary schools of selected local authorities in England in 2017 (see text for selection criteria) ABAN ~ the rest

AIND ~ the rest

1.0

159

ID score for pupils who are FSM eligible

0.8

●

●

● Bolton

●

● ● ●

●

Camden ●

Luton

Oldham

●

●

●

●

●● ● ● ● ● ●

● ●

●

● ● ●

Kirklees ●

● ●

●

●

● ●

●●

●

● ●●● ●

●

●

●

●

● ●

●

●

● ●●

● ●

●

●

Bury

Coventry

●

Coventry

Birmingham

0.6

●

●

●

Kirklees

●

Calderdale

Reading

●

Oldham

SOCIO-ECONOMIC AND ETHNIC SEGREGATION

Swindon Trafford

0.4

APKN ~ the rest

●

●

Newham

AOTH ~ the rest

BAFR ~ the rest

BCRB ~ the rest

1.0 0.8

Medway ●

0.6

● Bristol City of ●

Hillingdon

●

Leicester

Bexley●

● ●

●

●

Wandsworth Croydon ●● ●

0.4

●

●

Coventry

●

Merton

Wandsworth

● ● ●● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ●● ●● ● ● ●● ●● ●●

●

● ●

Enfield

●

●

Brent ●

● ● ●●● ●

●

●

Waltham Forest Lambeth

●

0.4

0.6

0.8

1.0

0.4

0.6

0.8

1.0

ID score for pupils who are not FSM eligible

0.4

0.6

0.8

1.0

newgenrtpdf

Figure 6.2: (continued) WOTW ~ the rest

WBRI ~ the rest

1.0 Portsmouth

0.8

●

Thurrock● ●

0.6

●

●

Medway ● ●

0.4

●

Southend−on−Sea ● ●

● ●●

● ●●

Swindon

● ●● ● ● ●● ● ●● ● ● ●

● ●●●● ● ● Reading ● ● ● ● ●

● ●●

● ●● ●● ● ●● ●● ●● ●● ● ● ● ●● ●●●● ●

Bexley

160

0.4

●

Blackburn with Darwen

Bexley

●

●

Milton ●Keynes ● ●●

● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ●●●● ● ● ● ●

Nottingham

●●

Trafford ●

●

● ●

● ●

● ●●● ● ●● ● ●● ● ● ● ● ●● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ●● ●● ●● ●●●● ●

Ealing

Kensington and Chelsea

Nottingham Lambeth

● ● ●● ●

Tower Hamlets

0.6

●● ● ● ● ●● ● ● ●● ●●

Bournemouth

0.8

1.0

Coventry

0.4

0.6

0.8

1.0

ID score for pupils who are not FSM eligible Source: Authors’ own calculations.

0.4

● ● ●

●

Bolton Haringey

● ● ●●

Bury

0.6

0.8

1.0

Ethnic Segregation Between Schools

ID score for pupils who are FSM eligible

MIXD ~ the rest

SOCIO-ECONOMIC AND ETHNIC SEGREGATION

ethnic co-​peers exceeds that for the FSM eligible by greater than 10  percentage points is for White Other group in Kensington and Chelsea, Slough and Hammersmith and Fulham. Aside from the White British, for most groups, in most places, there is negligible difference between those who are FSM eligible and those who are not in terms of the percentage of their ethnic group present in their average primary school. The main exception is the Bangladeshi group, for which the average FSM eligible pupil is in a primary school where 39 per cent of the pupils are Bangladeshi, whereas the average Bangladeshi pupil who is not eligible for a FSM is in a school where 29 per cent of the pupils are Bangladeshi. Therefore, the average FSM eligible and Bangladeshi primary school pupil is more concentrated with members of their own ethnic group than is the average Bangladeshi pupil who is not eligible. The reason for this is that Tower Hamlets schools one third of all FSM eligible, Bangladeshi primary pupils but only 16 per cent of those not eligible –​still a very high share of the national total but half that of those eligible. Nearby Newham and Redbridge, by contrast, school higher shares of those not eligible (10 and 5.0 per cent, respectively) than they do those eligible (6 and 2 per cent). There are no local authorities where the concentration of the FSM eligible with others of the same ethnic group exceeds that of their non-​eligible counterparts by more than 10 percentage points. Lowering the threshold to a difference of 5 percentage points, the only occasions where the average FSM eligible pupil is more concentrated with their own ethnic group than their ineligible counterpart is for Pakistanis and Indians in Trafford, Black Africans in Bristol, Indians in Trafford and in Swindon, and Pakistanis in Coventry. Therefore, while it is true to say that those who are not FSM eligible among the White British (who are not attending primary schools in the most mono-​ethnically White British local authorities) are, on average, more concentrated with their ethnic co-​peers than those who are eligible, for the other ethnic groups, in most local authorities, there is little difference. Turning to secondary schools (in 2017, not illustrated but using the same criteria for selecting local authorities that were used for primary schools), there are ten places where the average FSM ineligible pupil is in a school that has more than 10 percentage points of their own ethnic group than does the average eligible pupil. These occur for the White British in Hackney, Brent, Barnet, Haringey, Hammersmith and Fulham, Oldham and Ealing, and the Indian in Reading and Barnet. For the White British in Hackney and Brent, the difference exceeds 20 percentage points, at 23.9 and 21.3, respectively.

161

Ethnic Segregation Between Schools

Looking at the occasions when the average FSM eligible pupil is in a secondary school with a higher percentage of her ethnic co-​ peers than her FSM ineligible counterpart, there are none where the difference is greater than 10 percentage points, and only two where it is greater than 5  percentage points (for the Pakistani in Walsall and the Indian in Leicester). For the average White British pupil who is not FSM eligible, she is in a secondary school that is 68 per cent White British, compared with 63 per cent for a White British pupil who is eligible –​a difference of 5 percentage points. For the other ethnic groups, the difference is negligible except, again, for the Bangladeshis, for whom the average FSM eligible pupil is in a secondary school that is 49 per cent Bangladeshi, against 38 per cent for the average non-​eligible Bangladeshi pupil. Again, the difference is primarily due to Tower Hamlets schooling almost one third of all Bangladeshi and FSM eligible secondary pupils but only half that share of those not eligible.

Where do socio-​economic separations decrease the exposure to other ethnic groups for the White British? Of all the groups, it is only the White British for whom those that are not FSM eligible appear to have consistently greater amounts of segregation from other groups than do those who are eligible (that is socio-​economic separations add to ethnic ones). Another way to evidence this is demonstrated by looking at Brent. Within its state primary schools, 10 per cent of pupils (in our data) were White British in 2017. All things being equal, the same percentage of White British pupils would be expected in the schools attended by the FSM eligible among the White British. However, the actual figure averages 23 per cent –​an increase of 13 percentage points above expectation. That means the FSM eligible among the White British tend to be concentrated in schools with higher percentages of the White British in their intakes, indicating a degree of ethnic separation between these White British and other groups in Brent. For the schools attended by those among the White British in Brent who are not FSM eligible, the average percentage rises by a further 26 percentage points (to 49 per cent), indicating an even greater concentration of this group of (more affluent) White British with their ethnic co-​peers (and therefore less exposure to other groups). The two values can be plotted on a scatter plot ( x = 13 , y = 26 ). Before doing so, let us take a second example, which is for primary schools in Tower Hamlets. There, 10 per cent of pupils were White

162

SOCIO-ECONOMIC AND ETHNIC SEGREGATION

British in 2017 but in the schools attended by the FSM eligible of the White British the figure rises by 13 percentage points (to 23 per cent), and by a further 2 percentage points (to 25 per cent) for those not eligible. This example can also be plotted (with x = 13 , y = 2 ) and it is useful to note the difference from the first: both Brent and Tower Hamlets have the White British more concentrated with others of their group then would otherwise by expected but only in Brent is there a substantial difference between those who are FSM eligible and those who are not. Two more examples are instructive. In Kingston upon Thames, the percentage of pupils who are White British is 67 per cent, increasing by 12 percentage points (to 79 per cent), on average, for the primary schools attended by the White British who are FSM eligible. However, for those not eligible it is 75 per cent, a decrease of 4 percentage points on the previous figure. In this, more unusual case (where x = 12 , y = −4 ) it is the poorer of the White British that are the more concentrated with other White British and so less likely to encounter pupils of other ethnicities within their schools. Finally, consider Aylesbury, where the percentage of primary school pupils who are White British is 54, yet the percentage for the average White British FSM eligible pupil is 48, and for the average pupil who is not eligible 60 per cent. In this example ( x = −6 , y = 12 ), the FSM eligible of the White British are less concentrated than expected with their own ethnic group in their school whereas for those not eligible it is the opposite. Comparable calculations can be made across a range of locations, here using the same extended geography described in the beginning of Chapter 4 –​a geography largely based on local unitary authorities but sometimes supplemented by postal towns when it helps to add geographical detail. Those calculations generate a set of x and y values where the xs measure the increase (or, occasionally, decrease) over expectation in the percentage of White British pupils in schools attended by the FSM eligible of the White British pupils, and the ys measure the further increase (or, rarely, decrease) in that percentage when comparing not eligible pupils to the eligible ones. In broad terms, the xs measure the separations by ethnicity, the ys the further separations by economic advantage (that is, by not being FSM eligible). All these values and locations could be plotted together on a single, two-​dimensional scatterplot, except to do so would ignore that their contexts differ markedly:  the percentage who are White British in Tower Hamlets or Brent (see previous discussion) is much lower than, say, Wycombe (81 per cent) or Selby (91 per cent). For that reason,

163

Ethnic Segregation Between Schools

the calculations are split into six different charts –​the six panels of Figure 6.3 –​by the percentage of primary pupils in each area that is White British. Those percentages decline from left to right and from top to bottom in Figure 6.3: the areas in the top-​left panel have far greater percentages of their pupils being White British (ranging from 83.1 to 97.1 per cent) than do those in the bottom-​r ight (from 5.5 to 27.5 per cent).6 Despite the different contexts, there are commonalities between the graphs. For example, in nearly every case the points (the geographical locations) fall above the dashed, horizontal line (drawn at y = 0 ). What that means is in most locations (about 85 per cent), the average White British pupil without FSM eligibility is less likely to encounter pupils of other ethnicities in their primary school than is their FSM eligible counterpart (the few exceptions include Kingston upon Thames, as previously noted). Relatively poor White British pupils are more likely to be in schools with relatively large non-​White populations; relatively affluent White British pupils are not. Many but not all locations are above and between both the two solid black lines. These are the ones where the socio-​economic separations appear to be of greater magnitude than the ethnic ones that they add to. In this regard, and to return to the first two examples, Tower Hamlets is different from Brent, with Brent having the much greater separation along socio-​economic lines. Looking across the panels it can be seen that the socio-​economic separations increase as fewer of the area’s pupils are White British. To a degree, this is inevitable: where few of the pupils are of an ethnicity other than White British, nearly all pupils must be in a school with a very high percentage of White British pupils, regardless of their FSM eligibility. That said, even in those instances (shown in the first two panels of Figure 6.3) there is no mathematical necessity that requires FSM eligible pupils should, on average, be less concentrated than expected with others of the White British whereas those not eligible are more so, nor that in areas with much lower percentages of the White British that the differences by FSM eligibility should be so large (for example, in the bottom-​r ight two panels). In the vast majority of places, socio-​economic separations within the White British group are decreasing the opportunity for the wealthier among them to come into contact with primary school pupils from other ethnic groups. Figure  6.4 shows the same type of calculations but this time for secondary schools. There is less tendency for the average White British pupil who is FSM ineligible to be more concentrated with other White British pupils in her secondary school than is the average eligible pupil: it is true for about 56 per cent of the areas, markedly

164

newgenrtpdf

Figure 6.3: Looking at how socio-​economic segregation (as measured by FSM eligibility) adds to ethnic segregation for the primary aged pupils in 2017 (see text for further details) (83.5,97.1]

(76.2,83.5]

(69.8,76.2]

30

165

(Y) Measure of additional socio-economic separation (WBRI, not FSM eligible)

Harpenden

10

Warrington

Selby ● ●

● ● ● ●

●● ● ● ● ● ● ● ● ● ● ● ● West Lancashire ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ●

● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●

● Burnley ● ● ● ● ● ● ●● ● ● ● ● Halesowen ● ● ●● ● Kirklees ●● ● ● ●● ● ●● ● ● ● ●● ● ● Solihull ●● ● ● ●

● ●

● ●● ● ●

●

●●●●● ● ●● ● ● ●● ● ● ●● ●● ●● ●● ● ●●● ● ●● ●● ●● ●

● ● ● ● ●●

0

Rugby

Colne

●● ● ●

●● ● ● ● Wycombe ● ●

● ● ● ● ● ● ● ●●●

Hyde

● ●

Daventry

−10

(58.3,69.8]

(27.3,58.3]

[5.2,27.3]

30

Brent

● ●

Barnet

Haringey

● ●

20

Bedford High Wycombe ● Wandsworth ● Huddersfield Wellingborough ●● ● ● ●

10

● ● ● ● ● ●

● ●

Bolton

● ● ● Rochdale ● ● ● ● ● ● ● ● ● ● ●●● ● Accrington Halifax ● ● ● ●● ●● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● Southampton ● ●

● King's Lynn ●

0

Aylesbury Coventry

● ●

Burton ●

Enfield

Pendle

● ●

● ●

Ealing

Bradford

Redbridge

● ● ● ●

● ●

Keighley

● ●

● ● ● ● ● ●● ● ● ● Hackney ● Croydon

● ● ●● ● ● ● ● ● ●● Oadby and Wigston Oldham ● ● ● ● ● ● ● ● ●● ● ● Walsall Manchester ●● ● ● ● ● ● ● ● ● ● ● Sandwell ● ● Leicester

● ● ●● ●

Hammersmith and Fulham

●●

●

Westminster ●

● ●

Tower Hamlets

Kingston

−10 −10

0

10

20

−10

0

10

20

−10

0

10

20

(X) Measure of ethnic separation (WBRI, FSM eligible from other groups) Note: The values in brackets indicate the percentage of primary school pupils in each area who are White British. A square bracket means from or up to and including, a circular bracket means from or up to but not including – so a value of 27.3% would be included in the bottom right chart, not the one to its left. Source: Authors’ own calculations.

SOCIO-ECONOMIC AND ETHNIC SEGREGATION

20

newgenrtpdf

Figure 6.4: Looking at how socio-​economic segregation (as measured by FSM eligibility) adds to ethnic segregation for the secondary aged pupils in 2017 (see text for further details) (86.4,97.9]

(79.5,86.4]

(73.2,79.5]

30

166

(Y) Measure of additional socio-economic separation (WBRI, not FSM eligible)

Spalding

10

● ●● ●

● ● ●● ● ● Basildon ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Carlisle●●●● ● ● ● ● ● ● ● ●● ●

0

● ●

Loughborough

Great Yarmouth

Altrincham

●● ● ●● ● ● ● ●● ●● ●●● ● ●● ●●● ● ● ● ●● ●● ● ● ●●

● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●●●● ● ●● ● ● ●

Rossendale

●● ●

Chatham

Bishop's Stortford

Fenland ● ● ● ●● ●● ●

●● ● ●

Brentwood

Tonbridge

−10 (62.6,73.2]

(23.8,62.6]

[6.3,23.8]

30 Hackney ● ●

20

● ● ● ●

● ●

Barnet

● ●

Hammersmith and● ●Fulham ● ●

● ●

●

Pudsey ●

●

Bolton ● ● ●●

0

● Peterborough ● ●

● ●

● ● ●● ● ●● ● ●●●● ● Derby ● ● ● ●● ● ● ● ● ●● ● Bristol ● ● ● ● ● ● ● ● Dartford ●

●

Burnley

● ● ●

● ●

−10

0

10

● ● ●

●

Walsall

● ●● ●

● Nottingham

● ●

Halifax

● ●

● ● ● ●

● ●

Bradford

● ●●

●

●

● ●

Redbridge

●

● ●● ●

● Westminster

● Wandsworth ● ● ●

Manchester

● ● ● ● Oadby● ● and

●

●●● ● ● ●Luton ● ●

Wigston

Tower Hamlets

● ● ● ● ● Leicester ●

Sutton

Kingston

−10

Dewsbury

● ●

Ealing

Enfield

Blackburn

● ●

● ● ●

Bedford ●

Brent

Haringey

● ●

●● Bingley ● ●

Accrington

10

Pendle

Oldham

Watford

20

−10

0

10

20

−10

0

10

20

(X) Measure of ethnic separation (WBRI, FSM eligible from other groups) Note: The values in brackets indicate the percentage of secondary pupils in each area who are White British. See note for Figure 6.3 for an explanation about the use of square and circular brackets. Source: Authors’ own calculations.

Ethnic Segregation Between Schools

20

SOCIO-ECONOMIC AND ETHNIC SEGREGATION

down from the 85 per cent when primary schools were considered. It remains true that the socio-​economic separations increase as fewer of the area’s pupils are White British, although there are now more exceptions (especially in the third and fourth panels), many of which contain selective, grammar schools –​among them Altrincham, Bishop’s Stortford, Dartford, Halifax, Kingston upon Thames, Rossendale, Sutton and Watford. This raises the possibility that a grammar school presence can bring together groups based on academic rather than geographic selection and, to some extent, overcome residential separations. We consider this possibility more fully in a moment and, for now, simply note that if it is the case that academic selection can bring ethnic groups together, it only brings some of those ethnic groups together –​specifically, those achieving highest in the entrance exams, which itself is socio-​economically dependent: in 2017, grammar schools were educating 5.4 per cent of all those in the data not eligible for a FSM but only 0.86 per cent of those who were. This means the probability a FSM ineligible pupil would be recruited to a grammar school was over six times greater than for an eligible pupil (a large inequality that strongly questions the somewhat hackneyed political argument that grammar schools help to promote social mobility –​for a few, maybe, but seemingly at the expense of maintaining a system that better benefits the more wealthy) (Harris and Rose, 2013).

Which types of schools ‘over-​recruit’ the poorer of the White British pupils into more diverse schools, and which do not? We can determine which primary schools, in 2017, were recruiting a disproportionate share of those of the White British who were FSM eligible and in areas of greater ethnic diversity. The way to do it is based on the Index of Dissimilarity (ID): the schools of interest are those that had a large share of all the FSM eligible, White British pupils nationally relative to their share of the White British pupils who were not eligible, and in which schools the White British did not form a majority. Using that logic then the primary schools can be identified that are in the top 10 per cent of those that were not majority White British but had a disproportionate share of the White British, FSM eligible pupils. Those schools included 12.7 per cent of all primary schools without a religious affiliation (a little more than the expected value, which is 10 per cent) but only 5.9 per cent of Anglican schools, and 2.8 per cent of Catholic schools. This under-​representation of faith schools among the non-​majority White British primaries with a disproportionate share of the FSM

167

Ethnic Segregation Between Schools

eligible among their intakes could be due to the faith schools recruiting balanced shares of the FSM eligible and otherwise from the White British. However, if we now identify the primary schools that are in the top 10 per cent of those that were not majority White British and with a disproportionate share of the White British who were not FSM eligible then they include 8.0 per cent of those without a religious affiliation (slightly less than the expected value), 10.1 per cent of Anglican schools (essentially equal to the expected 10 per cent) but 18.9 per cent of Catholic schools (almost double expectation). The point is not that faith schools cannot bring the White British into contact with other groups because in a number of such schools they will. However, especially for Catholic primary schools, it is less likely to be the poorer of the White British who have that contact. At the secondary school level, 8.2 per cent of Anglican schools and 4.3 per cent of Catholic schools were among those that were not majority White British but had the most disproportionate shares of the FSM eligible from that group, whereas 22.4 per cent of such Anglican and 16.3 per cent of such Catholic schools had the most disproportionate shares of those not FSM eligible from the White British. Therefore, for both Anglican and Catholic secondary schools, it is less likely to be the poorer of the White British who are the ones in the more diverse faith schools. Even so, the much clearer difference is for grammar schools: 59.5 per cent of such schools that were not majority White British were among those with the most disproportionate shares of those not FSM eligible from the White British (six times expectation) and none had a disproportionate share of those who were. The results suggest that it is grammar schools that most add to ethnic segregation through the socio-​economic background of their intakes, followed by (some) faith schools and more especially Catholic faith schools. However, local contexts and their educational provision also shape geographical differences. In Brent (see bottom-​right panel of Figure 6.3), the separation of White British FSM ineligible from eligible primary pupils is due to the intakes into Jewish schools (among which the FSM eligible are under-​represented); they are over-​represented in Catholic and non-​denominational primary schools. In Haringey (same panel of Figure 6.3), there are three faith primary schools and also three community schools (non-​denominational) that are majority White British yet contain very few of the FSM eligible from that group. There also is a community primary school where three quarters of the pupils are FSM eligible but very few of the pupils are White British. In Hackney (see bottom-​r ight panel of Figure 6.4) the FSM eligible are over-​represented in the Anglican and Catholic secondary faith

168

SOCIO-ECONOMIC AND ETHNIC SEGREGATION

schools, and also the academy schools, and under-​represented in other faith and types of secondary school.7 Brent’s White British and FSM eligible secondary pupils (see, again, Figure 6.4) are over-​represented in academy schools and also Catholic secondary schools. In Haringey, there are two schools where the FSM eligible of the White British are most under-​represented; neither of those is a faith school. Hence, the social selectivity of schools is a function of local circumstances –​ local supply and demand, and the local operation of the educational market –​as well as the impact of admissions criteria.

Are FSM eligible pupils in the lower rated schools? Critics of school choice argue that economically disadvantaged groups will generally be the ‘losers’ under a system that places constraints on the supply of places (through admissions criteria and the limits on the number of places available at each school) and introduces competition between schools (for pupils) and between applicants (for places). Such competition will be to the detriment of disadvantaged groups if they lack the financial and other socio-​cultural capital needed to play the system in their favour and will decrease their chances of gaining entry into the ‘best’ (most sought-​after) schools (for example, by purchasing a house close to one). Evidence to support this concern can be found by calculating the percentage of pupils in the data that are in the highest rated, outstanding schools, and also the percentage in schools that are rated below good, in 2017. These are based on the ratings made by Office for Standards in Education, Children’s Services and Skills (OFSTED) inspectors, reporting on the quality of educational provision at the time of their last inspection. For the period of the study, the inspectors could rate schools as one of outstanding, good, requires improvement, inadequate and serious weaknesses or the school could be placed into special measures. Here we combine all but the outstanding and good schools into one below good group. Nationally, in 2017, 21 per cent of all schools were rated outstanding, 68 per cent good (the largest category), and 11 per cent below good (as requires improvement or as inadequate).8 For our data, an estimated 18.9 per cent of FSM eligible pupils were in below good schools, compared to 13.6 per cent of pupils not eligible. At the same time, 15.1 per cent of FSM eligible pupils were in outstanding schools, and 22.1 per cent of other pupils.9 Because rates of FSM eligibility are higher for most minority groups than for the White British, and because FSM eligible pupils were more likely to be in the lower rated schools, it could be assumed that a greater

169

Ethnic Segregation Between Schools

percentage of those minority groups will be in below good schools and a lower percentage in outstanding schools when compared to the White British. That assumption is wrong. Nationally, a greater percentage of the White British were in below good schools in 2017 than was any of the other groups. A lower percentage were in outstanding schools. The same was true in 2011, as Table 6.2 shows. Partly this difference is attributable to more of the other groups living in the capital where there is what has been described as the London effect, it having attained greatly improved schools (an effect that has been attributed to the ethnic composition of those schools) (Burgess, 2014). Yet, even within London, the Asian groups are less likely to be in a below good school and more likely to be in an outstanding one when compared to the White British, although access to the outstanding schools is least for the black groups. In general, while it can be argued that FSM eligible pupils are losing out in terms of access to what are rated by school inspectors to be outstanding schools, the extent of that loss varies by ethnic group and nationally impacts most upon the FSM eligible among the White British. Figure 6.5 shows the percentages of each ethnic group’s FSM eligible and not eligible pupils that are in below good and also outstanding schools, for primary schools in 2017. Figure 6.6 shows the corresponding values for secondary schools. Following on from the discussion of Table 6.2, of particular interest is the differences in the height of the bars for the White British group. Of the FSM eligible pupils who also are White British in primary schools, 15.2 per cent are in a below good school, whereas for those not eligible the percentage is 10.4. At the same time, 10.9 per cent of eligible White British pupils attend outstanding primary schools, whereas for those not eligible the percentage is 18.1. The sum of these differences is indicative of the ‘school gap’ between FSM eligible and other White British pupils in regard to how likely they are to be in a below good school and not in an outstanding one; it is evident for secondary schools, too. The ‘school gap’ is evident and to the detriment of those who are FSM eligible among the White British in 316 of 326 unitary authorities (the City of London and the Isles of Scilly are omitted), as Figure 6.7 shows. For primary schools, there are a few places where, unusually, the gap is in the FSM eligible pupils’ favour and those include Brent, Southwark and Waltham Forest, London. However, in places like Wellingborough (Northamptonshire) or Merton, Wandsworth and Croydon (all London), the gap is against the FSM eligible of the White British in primary schools. For secondary schools, it is obvious that authorities with an academically selective system exacerbate the gap, most especially in South Buckinghamshire (as part of the Buckinghamshire

170

newgenrtpdf

(a)

171

(b)

(c)

Rating

ABAN

AIND

APKN

AOTH

BAFR

BCRB

MIXD

WOTW

WBRI

below good

13.1

11.1

17.3

11.5

12.6

13.4

13.2

14.7

15.7

outstanding

30.0

33.3

23.5

28.1

26.3

26.7

24.7

21.3

20.7

Rating

ABAN

AIND

APKN

AOTH

BAFR

BCRB

MIXD

WOTW

WBRI

below good

10.9

11.6

16.4

12.1

12.2

13.8

13.1

12.3

17.1

outstanding

24.6

23.6

16.5

21.8

23.1

22.1

19.9

20.2

15.4

Rating

ABAN

AIND

APKN

AOTH

BAFR

BCRB

MIXD

WOTW

WBRI

below good

6.7

6.6

6.4

6.7

9.3

10.8

8.1

8.3

8.9

outstanding

37.4

37.3

36.5

35.7

31.0

31.0

35.4

29.9

33.6

SOCIO-ECONOMIC AND ETHNIC SEGREGATION

Table 6.2: Showing the percentage of each ethnic group in below good and outstanding schools; (a) top rows: nationally in 2017; (b) middle rows: nationally in 2011; (c) bottom rows: London in 2017

Ethnic Segregation Between Schools

Figure 6.5: The percentages of FSM eligible and other pupils in below good and outstanding primary schools by ethnic group in 2017 FSM

Eligible

Not Eligible

ABAN

AIND

APKN

AOTH

BAFR

BCRB

MIXD

WOTW

WBRI

30 20 10

Percentage of group

0

30 20 10 0

30 20 10 0 below good

outstanding

below good

outstanding

below good

outstanding

School rating Note: The dotted line is drawn at the overall percentage of all primary pupils in below good schools. Source: Authors’ own calculations.

system, which also includes Aylesbury Vale), Preston, and West Lindsey (Lincolnshire) (but not in Poole, Wolverhampton and Tunbridge Wells). The gap also is evident in parts of London such as Southwark and Lewisham (neither with a selective system).

Conclusion The purpose of this chapter was to emphasize an important point –​that patterns of ethnic segregation between schools must also be thought about in terms of processes of socio-​economic segregation. To many, this will be an obvious point and it is recognized in The Casey Review, which includes a chapter on social and economic exclusion (Casey, 2016). Even so, it is notable that when the introduction to the UK Government’s Integrated Communities Strategy Green Paper highlights both school and residential segregation, it does so only in terms of ethnic segregation (and is also couched in terms of the minority groups’ alleged separation from the White British, as opposed to the other way around) (HM Government, 2018: 11–​12).

172

SOCIO-ECONOMIC AND ETHNIC SEGREGATION

Figure 6.6: The percentages of FSM eligible and other pupils in below good and outstanding secondary schools by ethnic group in 2017 FSM

Eligible

Not Eligible

ABAN

AIND

APKN

AOTH

BAFR

BCRB

MIXD

WOTW

WBRI

40 30 20 10

Percentage of group

0

40 30 20 10 0

40 30 20 10 0 below good

outstanding

below good

outstanding

below good

outstanding

School rating Note: The dotted line is drawn at the overall percentage of all secondary pupils in below good schools. Source: Authors’ own calculations.

It can be argued, and has been shown computationally, that a general preference to live among one’s ethno-​cultural peers will create patterns of segregation over time –​this is what the Schelling model demonstrated (Schelling, 1971). However, a similar logic can be applied in a different way: if there are general constraints (such as access to housing and job markets or historical and contemporary prejudice), which mean that it is more likely that someone will live with their ethno-​cultural peers, then those socio-​economic processes will also create (and sustain) patterns of segregation over time. Hence, it is difficult to separate out ethnic from social segregation because the two are interlinked. What we have done, instead, is to look at how socio-​ economic separations add to ethnic ones in terms of school ‘choices’ and allocations. What we find is a tendency for the more affluent of the White British (those not FSM eligible) to be less exposed to other ethnic groups in their schools than are those who are less affluent (FSM eligible). In some places, this socio-​economic separation appears to have greater impact on the ethnic separations than does the ethnic segregation alone, with these socio-​economic separations being particularly

173

Ethnic Segregation Between Schools

Figure 6.7: The ‘school gap’ for White British FSM eligible pupils by local authority (see text for how the school gap is defined); London’s local authorities are shaded black Selective

FALSE

TRUE

Primary

Secondary

School gap − WBRI: FSM eligible vs not eligible

● ●

●

Adur

0

−30

Southwark

Brent

●● ● ● ● ● ● ● ● ●●●● ●● ● ● ●●●●●● ● ● ● Waltham Forest ●●● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ●●● ● ●●●● ●● ● ●● ●● ●●● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ●● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ●●● ● ●● ●●● ●● ● ● ●● ●●●● ● ●●● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ●●●● ● ● ● ● ● ●●● ●●●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ●●●●● ● ● ● ● ● ● ● ●●● ●●● ● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● Westminster ● ● Bury ●Richmond upon ● Thames ●● ● ●● ● ● ● ●● ● ● ● ● ● ● ● Croydon ● Derbyshire Dales Chiltern ● ● Enfield ● ● ● ●● ● ● ●

Northumberland

Melton

●

Preston

●

North Hertfordshire

Wandsworth

Merton

●

Wellingborough

South Derbyshire Brent

● ● Carlisle ● Poole ● Wolverhampton South Somerset ● ● ●●● ●●● ● ●● ●● ●●● ● ● ●● ● ● ● ●●●● ● ● ● ● ●● ● Tunbridge ● ● ●● ● ● ● Wells ● ● ● ● ● ●● ● ●● ● ●●●● ● ● ● ● ● ● ● ● ● ●●●●● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ●● ● ●●● ● ● ●● ●●●● ● ●● ● ●● ● ● ● ● ●●● ● ●● ● ● ● ●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ●● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ●● ●● ● ● ● Tower Hamlets ●●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ● ● ●●● Newham ● ●●● Bexley Hillingdon ● ● ● ● ● ● ● ● ● Enfield ● Epsom and●Ewell Slough ● ● ● ● ● ● Hammersmith and Fulham ● ● ●● ● Lewisham ● ● Lancaster Hertsmere ● ● Southwark

Maidstone

−60

Aylesbury Vale

Gloucester Reading

Preston ●

West Lindsey

South Bucks

0

25

50

75

0

25

50

75

Average % of schools' pupils not White British Source: Authors’ own calculations.

generated by the presence of academically selective schools and by some but by no means all faith schools (some others of which have the opposite effect). Overall, and nationally in 2017, the greatest segregation (as measured by the ID) was between those of the White British not eligible for a FSM and those of both the Indian and Bangladeshi groups who were eligible, between whom the segregation across primary schools is almost total (with an ID score of 0.96 in both cases and effectively unchanged since 2011). For primary schools, the segregation of the White British from the minority groups follows a roughly equivalent order in every case: the most segregated are the FSM eligible of the minority group from those not eligible of the White British; the next most segregated are the FSM eligible of the minority group from the FSM eligible of the White British; then sometimes it is those not eligible of the minority group from those not eligible of the White British (for the Bangladeshi, Black Caribbean, Pakistani and Black African groups), and sometimes those not eligible from the minority group from those eligible from the White British (for the Indian, Asian Other, White Other and Mixed ethnicity groups).

174

SOCIO-ECONOMIC AND ETHNIC SEGREGATION

This means that for the Bangladeshi, Black Caribbean, Pakistani and Black African groups, there is least segregation from the White British for those not FSM eligible. The same is true for secondary schools. Nationally, there is only one circumstance where segregation from the White British has increased by more than a nominal 2 percentage points since 2011, and that is of those not FSM eligible of the Bangladeshi from those who are FSM eligible of the White British, in secondary schools (for whom the ID increased from 0.75 to 0.78, not a substantial rise). While we acknowledge the difficulty of making meaningful comparisons over time due to the changing definition of who qualifies for FSM eligibility, in most cases the segregation has either remained the same or fallen. As we have found throughout the book, there is no trend toward increasing ethnic segregation but this does not preclude the possibility of rising social segregation. Notes 1 2

3 4

5 6

7 8

9

See www.ethnicity-​facts-​figures.service.gov.uk See www.ons.gov.uk/​peoplepopulationandcommunity/​personalandhousehold finances/​incomeandwealth/​bulletins/​householddisposableincomeandinequality/​ financialyearending2017 See www.gov.uk/​apply-​free-​school-​meals When the ID is used in this way, comparing one group with everyone else, it is sometimes referred to as the index of segregation. In principle, exactly so but not in practice for numeric reasons. Specifically, the categories breaks are at the maximum, 50th, 35th, 25th, 15th and 5th percentile, and the minimum –​a choice that highlights the places with lowest percentages of White British pupils. Academy school funding was originally targetted at ‘failing schools’ in urban areas. See www.gov.uk/​ g overnment/​ p ublications/​ o fsted-​ a nnual-​ report-​ 2 01617-​ education-​ c hildrens-​ s ervices-​ a nd-​ s kills/​ o fsted- ​ a nnual- ​ r eport- ​ 2 01617-​ data-​summary These are estimates because we are missing the information on about 5 per cent of schools, rising to 20 per cent in Doncaster.

175

7

Conclusion: Ethnic Segregation Is Not Increasing This book has examined ethnic segregation between English state schools and whether it has increased or decreased over the years since the last major data collection –​the national census of 2011. It has found that high levels of ethnic segregation do exist across schools between the majority White British population and some other ethnic groups such as the Bangladeshi and Pakistani, more so at the primary than secondary level of schooling, and more for those of greater affluence among the White British. However, the general trend has been towards desegregation and greater ethnic diversity within local authority areas and their schools. Because school intakes are broadly comparable in their ethnic composition to the characteristics of their surrounding neighbourhoods, as neighbourhoods have become more diverse so too have schools. Please refer to the summary of key findings (which follows the References) for an overview of each chapter. We acknowledge the limitations of the study. Most importantly, the data are not fully complete about pupils and their schools. In part, that is because we omitted schools out of the usual mainstream system. Occasionally there are absences or missing fields in the data although those are unlikely to introduce any major systematic bias to the results. What will is the absence of a very particular type of school –​private schools that are fee-​charging. Data about their pupils and about the schools’ social and ethnic compositions are not available in the same way nor to the same level of detail that they are for state schools, an omission that ought to be addressed given the charitable status of many private schools and their need to show that they are beneficial to the public. It is hard to fully address debates on social selection and the role of education in driving social mobility, without having data from an important, distinctive and –​in so far as it charges fees –​socially

177

Ethnic Segregation Between Schools

selective part of the sector. And our analysis has necessarily been confined to England only. We are conscious, too, of the risk of over-​objectification of groups and of the critiques against ‘thinking with ethnicity’ (Carter and Fenton, 2010). The ethnic categories we have used are common to a number of data sets and have an established usage. Nevertheless, we recognize that they are narrow measures of the various ethno-​ cultural, socio-​economic and religious differences between people, potentially concealing within or across group levels of segregation or desegregation. We also acknowledge that they are not a fully stable measure as people can and do change their expressed identity (Simpson et al, 2016). A further limitation is that we know little about what is occurring within schools. Although decreased segregation between schools is associated with greater diversity within them, there is still the potential for internal separations –​through academic streaming, for example (placing higher and lower attaining students into different classes) should that in some way fall along socio-​economic or ethno-​cultural lines; or by disciplinary processes that lead to some students being permanently excluded from their school. In 2016/​17, for instance, Black Caribbeans were 2.8 times more likely to be permanently excluded than the White British, although the rates vary geographically (the permanent exclusion rate for blacks in Gloucestershire is the highest of any local authority and over twice the national average).1 There are no doubt other criticisms that could be made but a final one to address here is the possibility that our viewpoint is rose-​tinted –​that it puts an overly positive spin on what is occurring, understating the loss of the White British from places where the ethnic minority population is most dominant. Such a criticism could draw on ­chapter 9 of Eric Kaufmann’s book, Whiteshift, in which he writes that ‘in cities up and down England, minority growth at ward level in the 2000s was inversely correlated with white growth’ (Kaufmann, 2018). He continues, focusing on the capital, stating: ‘London neighbourhoods [wards] where East European or non-​European minorities grew quickest experienced the largest white British losses’ (394–​5). Kaufmann does not deny that neighbourhoods (and, by extension, schools) have become more ethnically diverse just that diversification need not include the White British, for whom ‘the overwhelming story, which the statistical models tell, is one in which whites are moving towards the most heavily white neighbourhoods’ (401). But do they? We noted, in Chapter 1, that different ethnic groups have different age structures and that what may appear as the White

178

Conclusion

British ‘fleeing’ or ‘retreating’ (to use some of the nomenclature that Kaufmann considers) from particular places could be due to them being older, on average (therefore with different priorities on where to live, as well as different economic means to do so). To some degree we can sidestep that problem by looking just at the primary school aged and whether the numbers of White British have declined most in wards that were dominated by other ethnic groups in 2011. That is what our final figure, Figure 7.1, explores.2 On face value, there is some evidence for Kaufmann’s contention of an inverse correlation: a greater percentage of pupils from minority groups in 2011 is associated with a greater percentage decline in the number of young White British to 2017 (the lines on the graph are downward sloping). However, it is weak evidence. First, because the relationship explains only about 4 per cent of the variation between wards. Second, because it is only when the percentages not White British become very high (above about 70 per cent) that there is an average decline in the number of White British pupils and even then there is considerable variation –​there are wards in places like Tower Hamlets, Newham and Waltham Forest that have gained White British primary pupils. Third, there are many wards with low percentages of other groups that have nevertheless had a decline in the number of White British primary pupils. Fourth, it is the wards that had lower percentages of free school meal (FSM) eligible pupils in 2011 that have lost more of the White British, suggesting there is an economic dimension to the changes (because the more affluent are more likely to be able to move away). In short, it is not at all clear that the White British are choosing to segregate themselves away from other groups by moving towards the most heavily white neighbourhoods. Even if they were, those places are becoming more ethnically diverse as other ethnic groups move into them. Therefore, our principal conclusion remains:  there is no compelling evidence of widespread increase in ethnic segregation; ethnic segregation is decreasing. Notes 1

2

See www.ethnicity-​f acts-​figures.service.gov.uk/​education-​skills-​and-​training/​ absence-​and-​exclusions/​pupil-​exclusions/​latest It uses the pupil data to identify change in the number of White British primary pupils in wards that had at least ten White British school pupils living in then in 2011 and, in which, at least 1 per cent of the pupils were not White British.

179

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Figure 7.1: The percentage change in the number of White British primary school pupils for English wards ● ●

Epping Forest

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● ● ● ● ● ●● ●● ● ● ● ●● ●● ● ●●●● ● ● ●● ● ●●● ● ● ●● ● ● ● ● ● ● ●●●● ● ●●● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ●●●● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ●● ● ●● ●● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ●●● ● ●● ●●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●●●●●●● ● ● ● ● ●●● ● ● ● ● ●● ●● ● ●● ● ● ● ● ●● ● ●● ● ●● ●● ● ● ● ● ● ● ● ●● ●● ●● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ●● ●● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ●●● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ●● ● ● ● ● ● ●●● ● ●●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ●● ● ● ●● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ●● ●●● ● ● ● ●● ● ● ●● ● ●● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ●● ● ● ● ● ● ●● ● ● ●● ● ●● ● ● ● ● ●● ●● ● ●●● ● ● ● ● ●● ● ● ●● ●● ● ●●● ●● ● ● ● ● ● ● ●● ●● ● ● ●● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ●● ● ● ● ●● ● ● ● ●●●●●● ● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ●● ●●● ● ●● ● ● ●● ● ●●● ● ●●● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●●● ●● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ●● ● ●● ●● ● ● ●● ● ●● ●● ●●●● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ●● ●● ● ● ●● ● ● ● ● ●●● ● ●●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ●● ● ●●● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

Waltham Forest

Tower Hamlets

●

Slough

Redbridge

● ●

● Waltham Forest Slough Tower Hamlets ● ● ● ●● Tower Hamlets ● ●● Slough Slough

Slough

●

Waltham Forest

0

Slough

Slough

Waltham Forest

Hambleton

Oxford

●

Newcastle upon Tyne

●

●●

Redbridge

Slough

●

●

0

25

50

●● ● ● ●

Redbridge ●

Kensington and Chelsea

Newham

Redbridge

75

Bradford

● ●

FSM

Slough

Waltham Forest

North Norfolk

High Mid Low

100

Percentage of primary school pupils not White British, 2011 Note: The lines show the average change for wards in the highest 25 per cent for FSM eligibility in 2011 (high), the middle 50 per cent (mid), and the lowest 25 per cent (low). Source: Authors’ own calculations.

Ethnic Segregation Between Schools

180

Percentage change in the number of White British primary pupils, 2011−17

Swindon

●

200

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181

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Harris, R., Johnston, R., Jones, K. and Owen, D. (2013) ‘Are indices still useful for measuring socioeconomic segregation in UK schools? A response to Watts’, Environment and Planning A, 45(10): 2281– 9. Hewstone, M. (2009) ‘Living apart, living together? The role of intergroup contact in social integration’, Proceedings of the British Academy, 162: 243–​300. HM Government (2018) Integrated Communities Strategy Green Paper. London: Ministry of Housing, Communities and Local Government. House of Commons Library (2018) Income Inequality in the UK, Briefing Paper 7484, 22 March, Available from: http://​researchbriefings.files. parliament.uk/​documents/​CBP-​7484/​CBP-​7484.pdf Johnston, R., Forrest, J. and Poulsen, M. (2001) ‘Are there ethnic enclaves/ ghettoes in English cities?’, Urban Studies, 39(4): 591– 618. Johnston, R., Wilson, D. and Burgess, S. (2005) ‘England’s multiethnic educational system? A classification of secondary schools’, Environment and Planning A, 37: 45– 62. Johnston, R., Poulsen, M. and Forrest, J. (2009) ‘Measuring ethnic residential segregation: putting some more geography in’, Urban Geography, 30(1): 91– 109. Johnston, R., Poulsen, M. and Forrest, J. (2013) ‘Multiethnic residential areas in a multiethnic country? A decade of major change in England and Wales’, Environment and Planning A, 45(8): 753– 9. Johnston, R., Poulsen, M. and Forrest, J. (2014) ‘Increasing diversity within increasing diversity: the changing ethnic composition of London’s neighbourhoods 2001– 2011’, Population Space and Place, 21(1): 38– 53. Johnston, R., Manley, D. and Jones, K. (2016a) ‘In search of Britain’s Muslims ghettoes’, Environment and Planning A, 48(9): 1684– 90. Johnston, R., Burgess, S., Wilson, D. and Harris, R. (2006) ‘School and residential ethnic segregation: an analysis of variations across England’s local education authorities’, Regional Studies, 40 (9): 973– 90. Johnston, R., Jones, K., Manley, D. and Owen, D. (2016b) ‘Macroscale stability with micro- scale diversity: modelling changing ethnic minority residential segregation: London 2001– 2011’, Transactions of the Institute of British Geographers, 41(4): 38– 402. Johnston, R., Owen, D., Manley, D. and Harris, R. (2016c) ‘House price increases and higher density housing occupation: the response of non- white households in London, 2001– 2011’, International Journal of Housing Policy, 16(3): 357– 75. Johnston, R., Harris, R., Jones, K. and Manley, D. (2017) ‘Segregation at school and at home: an English exploration’, Belgeo, 2– 3. DOI: 10.4000/ belgeo.18730.

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Reardon, S.F. and Firebaugh, G. (2002) ‘Measures of multigroup segregation’, Sociological Methodology, 32(1): 33–​67. Roberts, N. (2017) ‘FAQs: academies and free schools’, Briefing Paper Number 07059, House of Commons Library, Available from: www. gov.uk/​types-​of-​school Sabater, A. and Catney, G. (2019) ‘Unpacking summary measures of ethnic residential segregation using an age group and age cohort perspective’, European Journal of Population, 35(1): 161–​89. Schelling, T.C. (1971) ‘Dynamic models of segregation’, Journal of Mathematical Sociology, 1(2): 143–​86. Simpson, L., Jivraj, S. and Warren, J. (2016) ‘The stability of ethnic identity in England and Wales 2001–​2011’, Journal of the Royal Statistical Society, Series A, 179(4): 1025–​49. Social Integration Commission (2015) Kingdom United? Thirteen Steps to Tackle Social Segregation. London: Social Integration Commission. Sweet, A., Harris, R. and Manley, D. (2018) ‘Better to stay or go? A longitudinal study of mobility over the compulsory educational life course’, Applied Spatial Analysis and Policy, 12(3): 697–​717. Ware, J. (2018) ‘The battle for British Muslims’ integration’, Standpoint, May 2018, Available from:  https://​standpointmag.co.uk/​features-​ may-​2018-​john-​ware-​british-​muslims-​integration-​schools-​hijab/​ Watts, M. (2013) ‘Commentary. Socioeconomic segregation in UK (secondary) schools: are index measures still useful?’, Environment and Planning A, 45(7): 1528–​35. Winship, R. (1977) ‘A revaluation of indexes of residential segregation’, Social Forces, 55(4): 1058–​66. Yao, J., Wong, D.W.S., Bailey, N. and Minton, J. (2019) ‘Spatial segregation measures: a methodological review’, Tijdschrift voor economische en sociale geografie, 29(3): 235–250.

186

Summary of Key Findings Chapter 2  Eight of nine ethnic groups have increased in number in English primary schools over the period of the study (2010–​17). The recent increase for the White British is a reversal of previous decline; only the number of Black Caribbeans has decreased year on year.  The groups that have grown above the national rate are the Pakistanis, Indians, Black Africans and, more especially, the Asian Other, Mixed, and White Other groups.  The White British now form a reduced percentage of all pupils in primary schools. However, they still form a much larger group than any other.  Of the 144 (of 150) local education authorities (LEAs) with a percentage point decline in the White British as a percentage of all pupils, 108 nevertheless had more White British pupils in their primary schools in 2017 than they did in 2010.  There has been a decline in the number of White British in secondary schools for the duration of the period. This is part of a demographic cycle.  It is incorrect to assume that percentage declines in the number of White British are greatest in the local authorities where other groups are increasing fastest.  The continued concentration of England’s multi-​ethnic school populations in a relatively small number of LEAs is a clear feature of the country’s geography although there is also evidence that minority groups are spreading out across the country.

Chapter 3  The amount of measured segregation between the White British and other groups can be large but is characterized by decline over the study period, especially for the primary school age population.

187

Ethnic Segregation Between Schools

 Whereas the potential ‘exposure’ of the eight minority groups to the White British has declined pro rata (at a LEA scale), that of the White British to other ethnic groups has increased.  In primary schools, from about 2012 onwards, the numbers of White British generally are increasing, but with two exceptions: the numbers of White British living in local authorities with the highest percentages of Bangladeshi pupils (although that decline may have stabilized) and those in local authorities with the highest percentages of Indian pupils (where the rate of decline appears to have diminished).  The White British and Pakistani in Bradford provide an example of two groups that are separated along school (and most likely also residential) lines. Not all groups separate like this. Indians and Pakistanis in Blackburn’s schools, for example, are quite highly exposed to each other –​more so than expected.  On average, there is a pattern of increasing ethnic diversity in 140 of the 150 local authorities. This is driven both by a decline in the White British (at secondary level) and an increase in other groups, notably the Mixed, White Other, Pakistani and Asian Other ones.

Chapter 4  A notable change is the decrease in the percentage of schools in which the White British predominate, albeit that the White British remain the majority or the largest group (but less so than in the past).  Places where the White British are least prevalent (accounting for fewer than 10 per cent of pupils in more than half of the schools) are all in London  –​Newham, Brent, Tower Hamlets, Harrow, Haringey, Lambeth, Redbridge and Ealing –​though such schools are also reasonably common in Slough, Birmingham, Leicester, Luton, Bradford, Blackburn, Manchester and Oldham (as well as other parts of London).  Although members of minority groups are, on average, in schools where minority groups form a majority, the White British remains as the group that members of minority groups are most likely to encounter in their schools.  With regard to primary schools, the Bangladeshi and Pakistani are the two groups most likely to be in schools where the White British form a very low percentage of pupils.  The White British are overwhelmingly in primary schools where theirs is the largest group and typically also the majority. It is very rare for a White British pupil to be in a school where the percentage of White British pupils is very low. 188

Summary of Key Findings

 Around half or more of pupils in most ethnic groups are in secondary schools where the White British are the largest group. The Bangladeshi and Pakistani groups are an exception to this (and, very marginally so, Black Caribbeans).  It is not typical for a pupil to be in a school where their own ethnic group is the largest group, except for the White British, and for Pakistanis in primary schools.  Although schools with very high percentages of any one minority group do exist, they are extremely rare, becoming rarer.  On average, secondary schools are more diverse than primary schools but both are diversifying. We estimate that only about 5 per cent of primary schools and 2 per cent of secondary schools had what may loosely be described as a non-​trivial decrease in the ethnic diversity of their cohorts from 2011 to 2017.  The trend of increasing ethnic diversification increases the potential for pupils to be in contact with those of different ethnicities in school.

Chapter 5  Nationally, the highest levels of separation are between the White British and the Bangladeshi, Pakistani and Black Caribbean groups, which is true regardless of whether school or neighbourhood-​level segregation is looked at.  Some groups appear more likely to separate from home to school (for instance, the White British and White Others) whereas others are more likely to come together (the Indian and Asian Other groups). But, nationally, there is no persuasive evidence that school-​level segregation is consistently greater than neighbourhood segregation.  Many schools are recruiting the Asian groups in broad proportion to the characteristics of the neighbourhoods that surround them but there are some exceptional cases where the schools and neighbourhoods markedly differ. The same is true for black groups.  Where the larger differences exist between schools and neighbourhoods for Asian groups, they tend to be characteristic of parts of the North West of England and Yorkshire –​Blackburn, Bradford, Burnley and Nelson, Dewsbury and Oldham –​but also some parts of London, notably Tower Hamlets. For black groups, they are largely confined to London.  Academically selective schools do not feature greatly among those with the greatest school and neighbourhood differences but faith schools do: some (Anglican, and various non-​Christian faiths) have a relative over-​representation of Asian pupils; others, especially 189

Ethnic Segregation Between Schools

Catholic schools, have a relative under-​representation. Faith schools, both Catholic and Anglican, also are characteristic of the small number of schools with a substantial over-​representation of black pupils.

Chapter 6  The different rates of free school meal (FSM) eligibility across the ethnic groups are suggestive of how ethnic segregation will intersect with socio-​economic segregation.  For most ethnic groups, in most local authorities, FSM eligible pupils are more segregated from other ethnic groups than those not eligible in regard to the primary schools they attend.  The main exceptions are for the White British: in the vast majority of places, socio-​economic separations within the White British group are decreasing the opportunity for the wealthier among them to come into contact with primary school pupils from other ethnic groups.  Primary schools can be identified that are in the top 10 per cent of those that were not majority White British but had a disproportionate share of the White British, FSM eligible pupils. Those schools included 12.7 per cent of all primary schools without a religious affiliation but only 5.9 per cent of Anglican schools, and 2.8 per cent of Catholic schools.  It is not that faith schools cannot bring the White British into contact with other groups because in a number of such schools they will. However, for both Anglican and Catholic secondary schools, it is less likely to be the poorer of the White British who are the ones in the more diverse faith schools.  Even so, the much clearer difference is for grammar schools: 59.5 per cent of such schools that were not majority White British were among those with the most disproportionate shares of those not FSM eligible from the White British.  Nationally, a greater percentage of the White British were in below good schools in 2017 than was any of the other groups. A lower percentage were in outstanding schools.  In general, while it can be argued that FSM eligible pupils are losing out in terms of access to what are rated as outstanding schools, the extent of that loss varies by ethnic group and nationally impacts most upon the FSM eligible among the White British.

190

Technical Appendix: Measures of Segregation This appendix sets out the formal specifications of the various segregation measures used throughout the book.

Definition of terms and notation Let X and Y denote two ethnic groups (for example, White British and Bangladeshi), and j a location (for example, a school, neighbourhood or local authority). The number of X in j is nxj and the number of Y is n yj . The total number of X , summed for all locations in a study region (or part thereof), is nx + and the total number of Y is n y+ . The share of all X in location j is nxj nx + , and the share of all Y is n yj n y+ . The total number of all groups in location j (e.g. the total number of pupils) is n + j , which means the proportion that are of group X in location j is nxj n + j , and the proportion of Y  is  n yj n + j .

The index of dissimilarity The index of dissimilarity (ID) measures how unevenly two groups are spread out geographically, relative to their overall size. It can be defined in terms of probability. Imagine that a pupil is selected at random from all those that belong to ethnic group X. The probability that pupil goes to school in location, j, is: P( j | X ) =

nxj nx +

191

Ethnic Segregation Between Schools

Similarly, the probability that a randomly selected member of ethnic group Y is in location  j  is: n yj

P ( j |Y ) =

ny +

The ID defines no segregation as when those probabilities are equal for all locations in the study region: when P ( j | X ) = P ( j |Y ) and, equivalently, P ( j | X ) − P ( j |Y ) = 0 for all j . Hence ∑ j

nxj nx +

−

n yj ny +

= 0.

The ID simply adds a scaling constant to the left-​hand side of that expression, usually 0.5, to ensure that the index ranges from 0 (no segregation, when the share of X is everywhere equal to the share of Y ) to 1 (total separation, wherever X is, Y is not): ID = 0.5∑ j

nxj

−

nx +

n yj ny +

The ID can be disaggregated to identify the locations contributing most to it. Any individual location’s contribution to the overall index value can be determined from: id j ∝

nxj nx +

−

n yj ny +

If there are more than two ethnic groups then the location’s contribution to the total segregation across all the groups can be calculated from the sum of all the pairwise calculations: id + j ∝ ∑∑ x

y

nxj

−

nx +

n yj ny +

In Chapter 3, the above equation is weighted, giving: id + j ∝

nxj + n yj n+ j

nxi

∑∑ n x

y

x+

−

n yi ny + 192

technical appendix

This places most weight on the segregation of the groups that are most prevalent in each location.

The indices of exposure and of isolation The indices of exposure (IE) and of isolation (II) are both related to how prevalent an ethnic group is in the place attended by the average member of that group –​they measure how ‘exposed’ the group is to other groups or how concentrated it is with itself. The probability that a person selected at random from group, X , is in location  j  is: P( j | X ) =

nxj nx +

Having selected that person, the probability of selecting, from the same location, another person of the same ethnicity is: P( X | j ) =

nxj − 1 n+ j − 1



nxj n+ j

The probability of that second person being from a different ethnic group is: 1 − P( X | j )  1 −

nxj n+ j

Multiplying the first and third of these probabilities together produces an index measuring one ethnic group’s average exposure to other groups:  nxj  IE = ∑   j  nx + 

 nxj  1 −  n+ j  

Or, it can be modified to calculate the exposure to any other specific group:  nxj   n yj  IE = ∑     j  nx +   n + j 

193

Ethnic Segregation Between Schools

The II simply calculates the average proportion of pupils who are of group X in the locations where group X is found:  nxj   nxj  II = ∑     j  nx +   n + j  In probabilistic terms, it is ∑P( j | X ).P( X | j ) , where P( j | X ).P( X | j ) j

is the joint probability of randomly selecting, from group X, a pupil that lives in location j, and then a second pupil, from location j, who also is a member of group X. The IE ranges from zero asymptotically to one, and the II asymptotically from zero to one.

The potential for equal cross-​exposure Chapter  3 introduces a new index  –​the potential for equal cross-​ exposure (PECE) between two groups –​that is at its maximum for any location when (a)  X and Y are of equal number ( nxj : nxj = 1), and (b) X and Y comprise the entire population of j (so  nxj + n yj = n + j ). The formal specification of the index is: PECI j =

nxj + n yi  tan −1 r j  n + j  tan −1 1 

where, if nx > n y , then r j =

ny nx

 else  r j =

nx ny

The index ranges from zero (there is none of one or both groups so there is no cross-​exposure) to one (both groups form half the total population).

Entropy index The entropy index, h j , measures the (ethnic) diversity of a location, and is calculated as:

( )

h j = − ∑pkj log pkj k

194

technical appendix

where k is one of N k different ethnic groups and pkj =

nkj n+ j

( )

with log pk set to  0  if  pk = 0 The index ranges from 0 (when the location is fully populated by one only group) to log N k (when there is an equal proportion of each group). To standardize it into the range from 0 to 1 the following scaling is applied:

( )

( )

h j ← h j / log N k

Typological measures Chapter 4 looks at how concentrated pupils are in schools with their own or other ethnic groups. To achieve this a typological approach is used (rather than an index) that classifies schools according to the percentage of their pupils that are from one or more specific groups at particular thresholds of interest –​for example, the percentage of White British pupils that are in a school that is majority White British. If nx ( p

xj >0.5 )

is the number of X in schools where that group forms a

majority then, as a percentage, it is simply

nx ( p

x > 0.5 )

nx +

× 100 . The higher

that value, the more concentrated is, in the example, the White British in majority White British schools.

195

Index Note: Figures and Tables (unless included within a page range) are indicated by italic page numbers. References to primary and secondary schools imply English state schools.

A academically selective schools  132 creating socio-​economic separation  168, 173–​4 grammar schools  135, 167 academy schools/​academies  131–​2, 133, 134, 135, 169 admissions criteria/​policies  20, 26 effect on economically disadvantaged groups  169 geographically-​based  45–​6, 108–​109, 148 hypothetical scenarios  126–​7 religious affiliation, faith schools  117, 132 and school choice  114–​16 age profiles of different ethnic groups  see demographic factors Anglican schools  133, 134, 135 in Blackburn  138, 141 lower percentage of White British  138 in Oldham  143 socio-​economic status of pupils  167–​9 Tower Hamlets case study  146, 147 Asian Bangladeshi (ABAN)  see Bangladeshi pupils Asian Indian (AOTH)  see Indian pupils Asian Pakistani (APKN)  see Pakistani pupils

B Bangladeshi pupils  concentrated in schools of same ethnicity  99–​101 decrease in IE scores  73 and FSM eligibility  154–​5, 157–​8, 161, 162, 174–​5 increase in ID scores  68–​70

low levels of mixing with Indian and Pakistani  101 Oldham  142–​3 percentage in primary schools of same ethnicity  96–​7 in primary schools  29, 32, 41 in secondary schools  47 Tower Hamlets  99, 146–​7 below good schools  169–​70, 171, 172, 173 Bhopal, Kalwant., white privilege  11 births in England and Wales (1940–​2016)  30, 31, 32 Black African (BAFR) pupils  and FSM eligibility  154, 155 ID for schools and neighbourhoods  123, 124 IE scores  73 in primary schools  29, 43 in secondary schools  47 Black Caribbean (BCRB) pupils  decrease in numbers  30, 32 and FSM eligibility  154, 155 ID score  68, 69 more exclusions from school  178 in primary schools  29, 44 in secondary schools  47 Blackburn  case study  136–​42 required (in 2018) to adopt integration plans  59, 60 Bradford  Pakistanis separated from White British in  8, 77–​9 required (in 2018) to adopt integration plans  59, 60 decline or lack of White British pupils  90, 95, 108 socio-​economic segregation  162, 163, 164, 169, 170

197

ETHNIC SEGREGATION BETWEEN SCHOOLS

C Cantle Report (2001)  5–​6, 12–​13 Cantle, Ted  6, 12–​13 Casey Review (2016)  2, 17–​19, 22, 90, 92–​3, 116, 172 catchment areas and school intake  125–​6 census data  problem of reliance on  22 showing decreasing segregation  8 varying descriptions of ethnicity  14–​15 Challenge, The, social integration charity  2 changing ethnic composition of school-​age population  25–​6 consolidation of patterns and trends  58–​61 data used by the study  26–​7 primary school situation  27–​46 secondary school situation  46–​58 summary  61–​2 Church of England schools  131 classification of schools  92–​5 cluster analysis of local authorities  58–​61 community schools  125, 132, 133, 135 community/​social cohesion  1–​2, 3–​4, 5, 6, 72, 82, 152 competition between schools  21, 169 concentration of ethnic groups in schools  89–​90 classification of schools  92–​5 cohorts studied  90–​2 consolidation of findings  107–​10 decrease in ethnic segregation  110 ethnic diversity of schools  101–​107 minority groups vs white British concentration  95–​6 separate analysis of the nine ethnic groups  96–​9 where own group predominates  99–​101 contact theory  18–​19, 37

D demographic factors  13 age profiles of different ethnic groups  13, 19–​20, 178–​9 and change in ethnic composition of school pupils  25–​6 live births (1940–​2016)  30, 31 segregation and life stage  20 Demos think tank, reports by  89, 92, 116–​17 depolarized segregation  8–​9, 22–​3 desegregation  17, 18–​19 general trend towards  177 discourse and debate  1–​2 causes of segregation  9–​12

decreasing segregation  8–​9 defining segregation  3–​4 ghettoes debate  7–​8 ongoing segregation discourse  4–​6 role of schools and school choice  19–​22 white avoidance  12–​19 disparities between ethnic groups in all areas of life  11–​12 dissimilarity index  see index of dissimilarity (ID) diversity  see ethnic diversity

E earnings inequality  11 economic causes of segregation  10–​12 Eddo-​Lodge, Reni, white privilege in British society  11, 151–​2 enclaves  7, 8, 9 entropy index  82–​3 formula  194–​5 increase in  83–​5 Equality and Human Rights Commission, UK  11 ethnic diversity  of schools  101–​107 within England  81–​5   see also entropy index ethnic identity, expression of  86–​7 ethnic integration  see integration ethnic pay gap  11 ethnic segregation  decrease in  176–​9 difficult to separate from social segregation  174

F Faith schools  20, 131, 148 in Blackburn  138, 141 with more than 25% black pupils  134 in Oldham  143 and socio-​economic background of pupils  167–​9 in Tower Hamlet  146–​7 White British  135 fee-​charging/​private schools  132 foundation schools  132 free school meal (FSM) eligibility  decreasing White British exposure to other ethnic groups  162–​7 differences between ethnic groups  153–​6 relationship to lower rated schools  169–​72 schools that ‘over-​recruit’ poor White British pupils into more diverse schools  167–​9

198

INDEX

separation of those not FSM eligible from other ethnic groups  156–​62 summary  172–​5 free schools  131–​2, 133, 135 funding of schools  26, 131

G geographical clustering  cluster analysis of 150 local authorities  58–​61 comparing schools with neighbourhoods  121–​4 positive and negative aspects  3–​4 geographically-​based admissions criteria  45–​6, 148 ghettoes/​ethnic enclaves  6, 7, 8 government integration policy, shortcomings of  82 grammar schools  132, 135, 167–​8

H house prices, link to school performance  20–​1 housing  affordability  10–​11 high occupancy rates  12, 37

I ID  see index of dissimilarity IE (index of exposure)  71–​9 formula for  193 income inequality  11 and housing  12   see also socio-​economic segregation index of dissimilarity (ID)  67–​71, 191–​3 in Blackburn  136–​7 comparison of schools and neighbourhoods  117–​25 identifying socio-​economic difference  157–​8, 167–​9, 174 limitations of  125 in Oldham  142–​3 in Tower Hamlets  146 index of exposure (IE)  71–​9, 193 index of isolation (II)  85, 193–​4 Indian pupils  Blackburn case study  136–​7, 138–​41 ID scores  69 IE scores  73 low levels of mixing with Bangladeshi and Pakistani  101 in primary schools  29, 32, 42 in secondary schools  47 Integrated Communities Strategy Green Paper (HM Government, 2018)  2, 18, 172 integration  Casey Review  2, 17–​18, 92–​3

councils to adopt government plans  59–​60 increase in  82 Social Integration Commission  19

J joint race parentage  see Mixed ethnicity group

K Kaufmann, Eric  Cantle Report (2001)  6, 12–​13 Whiteshift  6, 178–​9

L live births in England and Wales (1940–​2016)  30, 31, 32 local education authorities (LEAs)  26–​7 map of  28–​9 measures of segregation and diversity across  67–​87 with the most diverse schools  105, 106 ranked from most to least contribution to the ID scores  71 London  alarmist headlines about racial segregation in schools  92 decline in growth of minority groups  121 differences in secondary school recruitment  115–​16 improvement in schools, ‘London effect’  170 most diverse schools found in  102, 105–​106 and the ‘school gap’  170–​2 schools that differ from expectations  132–​5 and white avoidance  12–​14 White British least prevalent in  94–​5 lower level super output areas (LSOAs)  91–​2, 118–​19, 138, 141

M ‘majority-​minority’ schools  92–​6 measurement of segregation  65–​7 consolidation of analyses  85–​7 index of dissimilarity (ID)  67–​71 index of exposure (IE)  71–​9 segregation and diversity  79–​85 middle level super output areas (MSOAs)  119 Mixed ethnicity group  growth of  30, 45, 52, 85 ID scores  69 IE scores  72, 73

199

ETHNIC SEGREGATION BETWEEN SCHOOLS

in primary schools  44 in secondary schools  47, 57 modifiable areal unit problem (MAUP)  119 Muslims  6, 10, 88, 133, 138

N nearest/​closest school  constraint on school choice  113–​14 definitions of  126–​7 differing from ‘catchment area’  126 hypothetical assignment scenarios  126–​7 probability of attending, Blackburn  141–​2 probability of attending, Oldham  143, 146 school choice and preference  20, 21, 115 neighbourhood-​based system of pupil allocation  20–​1, 113–​14, 147 neighbourhood segregation  see school choice and residential ethnic segregation Newham, diversity of  82–​3

O Ofsted ratings of schools  169–​72 Oldham case study  142–​6 output areas (OAs), small LSOAs  91 outstanding schools  169–​70, 171, 172, 173

P Pakistani pupils  Blackburn case study  136–​8, 139, 141–​2 in Bradford, separation from White British  8, 77–​9 concentrated in schools of same ethnicity  99–​101 ID scores  69 IE scores  73 low levels of mixing with Bangladeshi and Indian  101 Oldham case study  142–​5 in primary schools  29, 43 in secondary schools  56 Peach, C., ghettoes  7 PECE  see potential for equal cross-​exposure Peterborough  East European migration  30 required (in 2018) to adopt integration plans  59 Phillips, Trevor  5, 6, 7, 92

Piketty, Thomas, housing and wealth transfer  12 potential for equal cross-​exposure (PECE)  76–​7, 79 formula for  194 primary schools  78 secondary schools  80 primary schools, changing ethnic composition  27, 29–​32 geographical differences  32–​46

R Race Disparity Audit (Cabinet Office)  11 ratings of schools, Ofsted  169–​72 residential ethnic segregation  2, 6, 8, 10, 18–​19, 22   see also school choice and residential ethnic segregation Roman Catholic schools  131, 133, 134, 135 in Blackburn  138, 141 in Oldham  143 and socio-​economic background of intake  168–​9

S Schelling model of segregation  9, 173 school choice  19–​22 and competition due to poverty  169 potential effects of removing  148 school choice and residential ethnic segregation  113–​14 case studies  136–​47 comparison of ID for schools and neighbourhoods  117–​25 alternative comparison models  125–​7 location and type of schools differing from expectations  132–​5 potential effects of school choice on neighbourhood segregation  114–​17 segregation of schools compared to surrounding neighbourhoods  127–​30 school classification  92–​5 ‘school gap’ for White British FSM eligible pupils  170, 174 school ratings  169–​72, 173 school types  130–​2 secondary schools, changing ethnic composition  46 geographical differences  46–​58 segregation  causes of  9–​12 debate about  4–​8 decline in  8–​9, 177–​9 defining  3–​4 measuring  66–​79 selective schools  132

200

INDEX

self-​segregation, British Muslims  10 socio-​economic segregation  151–​3 affluent pupils, separation from other ethnic groups  156–​62 free school meal (FSM) eligibility, ethnic group differences  153–​6 poorer pupils, link to lower rating of school  169–​72 schools ‘over-​recruiting’ poorer White British pupils  167–​9

T Tower Hamlets case study  146–​7 typological measures  195

V voluntary-​aided schools  131, 132, 133, 134, 135, 138 voluntary controlled schools  26, 131, 132 voluntary segregation  3, 10

W Walsall  required (in 2018) to adopt integration plans  59, 60 Waltham Forest  59, 60, 82 What British Muslims Really Think (TV documentary)  6 white avoidance  12–​17 Casey Review  17–​19 White British (WBRI) pupils 

decline of showing increasing ethnic diversity  83–​5 and FSM (free school meal) eligibility  likelihood of being in lower rated schools  170 schools recruiting those who are  167–​9 segregation in those who are not  162–​4 socio-​economic separation  173–​5 index of exposure  71–​9 minority groups in schools where WBRI are not  95–​6 places where least prevalent  94–​5 in primary schools  29, 32, 96–​7 decrease in  41 increase in  40 residential segregation more likely  87 and school classification  92–​4 in secondary schools  47, 97–​9 decrease in  25, 96 segregation from other ethnic groups, neighbourhood vs school  119–​24 case studies  136–​47 in schools where percentage differs from expectations  135 White Other (WOTW) pupils  29–​30 ID scores  69 IE scores  73 in primary schools  32, 45 in secondary schools  58 Whiteshift (Kaufmann)  6, 178–​9 within schools segregation, streaming  178

201

“Uses school enrolment data to show that English state schools are becoming more, not less, diverse in terms of ethnic segregation.” Tim Butler, King’s College London

Gemma Catney, Queen’s University Belfast

There is an enduring belief amongst some that segregation is worsening and undermining social cohesion, and that this is especially visible in the growing divides between the schools in which our children are educated.

Professor Ron Johnston OBE is a Fellow of the British Academy and the Academy of Social Sciences.

This book uses up-to-date evidence to interrogate some of the controversial claims made by the 2016 Casey Review, providing an analysis of contemporary patterns of ethnic, residential and social segregation, and looking at the ways that these changing geographies interact with each other.

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B R I S TO L

ISBN 978-1-5292-0478-0

RICHARD HARRIS AND RON JOHNSTON

Richard Harris is Professor of Quantitative Social Geography at the School of Geographical Sciences, University of Bristol, and a Fellow of the Academy of Social Sciences.

ETHNIC SEGREGATION BETWEEN SCHOOLS

“Challenges myths and interrogates assumptions, with a refreshing integration of analyses of school, neighbourhood and socio-economic segregation. Essential reading for those seeking to answer one of the most pertinent questions facing society today.”

E T HN I C SEGREG AT I O N BETWEEN SC HO O LS I S I T I N C RE AS I N G OR D EC RE AS I N G I N E N G L AN D ? RI C H A RD H A RRI S A N D RO N J O HN STO N