On Fragile Grounds

A replication of Are Muslim immigrants

different in terms of cultural

integration

Technical documentation

Mahmood Arailowast Jonas Karlssondagger and Michael LundholmDagger

December 19 2008

Abstract

This is a technical documentation of Arai et al (2008) which repli-cates ldquoAre Muslim Immigrants Different in terms of Cultural Integra-tionrdquo by Alberto Bisin Eleonora Patacchini Thierry Verdier andYves Zenou published in Journal of European Economic Association6 445-456 2008

Bisin et al (2008) report that they have 5963 observations in theirstudy Using their empirical setup we can only identify 1901 relevantobservations in the original data After removing missing values we areleft with 818 observations We cannot replicate any of their resultsand our estimations yield no support for their claims

lowastCorresponding author Department of Economics and SULCIS Stockholm UniversitySE 106 91 Stockholm Sweden mahmoodarainesusedaggerThe Institute for Social Research and SULCIS Stockholm University

jonaskarlssonsofisuseDaggerDepartment of Economics Stockholm University michaellundholmnesuse

1

Contents

1 Introduction 3

2 Data and variable description 421 Data 422 Reading data and selecting variables 523 Defining the relevant (ethnic minority) sample 524 Recoding of missing values 625 Variable definitions 6

251 Religious affiliation 7252 Importance of religion 7253 Attitude towards inter-marriage 7254 Importance of racial composition in schools 8255 Born in the UK 8256 Age at and years since arrival 8257 Female 9258 Arranged marriage 9259 Discrimination 92510 Children 102511 No British education 112512 British basic education 112513 British higher education 122514 Foreign education 122515 Labour market status 122516 No parents 152517 Contacts with parents 152518 English language 152519 Household income 162520 Ward variables 17

26 Discrimination own ethnicity 1827 Defining the subset 1828 Sample statistics 18

3 Regression Results 20

4 Concluding remarks 21

5 Production notes 21

2

1 Introduction

This is a replication of the empirical results reported in Bisin et al (2008)They use British data and analyse how Muslims and non-Muslims differin cultural integration measured as (i) Importance of Religion (ii) Atti-tude Towards Inter Marriage and (iii) Importance of Racial Composition inSchools1

In the abstract of their paper they write

ldquo Muslims integrate less and more slowly than non-Muslims We also find no evidence that segregated neighbourhoods breedintense religious and cultural identities for ethnic minorities es-pecially for Muslimsrdquo (Bisin et al 2008 p 245)

We wanted to check the robustness of their results when considering theethnic and religious heterogeneity within both groups Muslims and non-Muslims Among other things we were concerned about the measures ofcultural values used in the paper These measures capture ethnic and re-ligious attributes in different degrees for different groups For example thevariable Attitudes towards Inter-Marriage with the majority UK populationcaptures only inter-ethnic marriage for the Christian ethnic minorities butboth inter-ethnic and inter-religious marriage for Muslims

However already an initial inspection of data disclosed that the numberof observations in Bisin et al (2008) exceeded the total number of observa-tions in the ethnic minority sample We communicated this to the authorsand they answered that there were some coding errors We have receivedrevised codes and a revised version of their specifications and tables Theirrevised codes yield fewer observations than the sample in the published ver-sion but still more than we can identify in the relevant sample of the originaldata As far as we can see a source of the large number of observations intheir revised codes is that dummy variable definitions include observationswith missing values in the reference categories (defined as zeros) The un-derlying codes to the published paper were however not made available andthe exact nature of the original errors are therefore unknown to us

Bisin et al (2008) report that they have 5963 observations in their studywhereas the ethnic minority sample in Berthoud et al (1997) consists of5226 observations Implementing their empirical setup we can only identify1901 relevant observations in the original data After removing missingvalues we are left with 818 observations Using the remaining sample andrunning their specifications we find no results that support their claims Ourreplication therefore stopped here and we did not perform any sensitivityanalysis The great loss of observations implies that the remaining sample

1To facilitate comparability we use the same labels on the variables as Bisin et al(2008)

3

is most likely not representative Therefore we hesitate to draw inferencefrom the regressions results

In this paper we only document the replication and report and commentresults using the variable definitions the variable names and the specifica-tions used in Bisin et al (2008) We choose a procedure that makes it easyto reproduce our results Influenced by Koenker and Zeileis (2007) we usean integrated approach where data management estimations and the textthat rely on these computations are all integrated in one single file Thisstrategy has the advantage that it makes is easy to adjust the codes andautomatically generate a revised version of the paper

All data analysis is made in R (R Development Core Team 2008) andall code files related to this project can be found on httppeoplesuse~lundhfragile_grounds

In this technical documentation we present our results in greater detailbut also all our working procedures variables definitions etc In additionthe central part of our codes are included with typeset comments This isdone as an attempt to implement Literate Statistical Programming

The remainder of the paper is organised as follows The data and variabledefinitions are described in Section 2 Regression results are presented inSection 3 The paper is concluded in Section 4 Finally the productionprocedure is described in Section 5

2 Data and variable description

21 Data

The data set is the Fourth National Survey of Ethnic Minorities 1993-1994(FNSEM) see (Berthoud et al 1997)2 Itrsquos main objective were

ldquoto describe the social and economic conditions of Britainrsquosmain ethnic minority groups including their health and tocompare these with the social and economic conditions ofthe white majority

to assess changes over time through comparisons with otherwork

to show how the position of ethnic minority groups is relatedto the social and ethnic compositions of the areas in whichthey live

to explore diversity among different ethnic minority groups2The data can be accessed from the UK Data Archive (UKDA) via Athens UK

Data Archive is found at httpwwwdata-archiveacuk and Athens at httpwww

athensacuk

4

1 Introduction

This is a replication of the empirical results reported in Bisin et al (2008)They use British data and analyse how Muslims and non-Muslims differin cultural integration measured as (i) Importance of Religion (ii) Atti-tude Towards Inter Marriage and (iii) Importance of Racial Composition inSchools1

In the abstract of their paper they write

ldquo Muslims integrate less and more slowly than non-Muslims We also find no evidence that segregated neighbourhoods breedintense religious and cultural identities for ethnic minorities es-pecially for Muslimsrdquo (Bisin et al 2008 p 245)

We wanted to check the robustness of their results when considering theethnic and religious heterogeneity within both groups Muslims and non-Muslims Among other things we were concerned about the measures ofcultural values used in the paper These measures capture ethnic and re-ligious attributes in different degrees for different groups For example thevariable Attitudes towards Inter-Marriage with the majority UK populationcaptures only inter-ethnic marriage for the Christian ethnic minorities butboth inter-ethnic and inter-religious marriage for Muslims

However already an initial inspection of data disclosed that the numberof observations in Bisin et al (2008) exceeded the total number of observa-tions in the ethnic minority sample We communicated this to the authorsand they answered that there were some coding errors We have receivedrevised codes and a revised version of their specifications and tables Theirrevised codes yield fewer observations than the sample in the published ver-sion but still more than we can identify in the relevant sample of the originaldata As far as we can see a source of the large number of observations intheir revised codes is that dummy variable definitions include observationswith missing values in the reference categories (defined as zeros) The un-derlying codes to the published paper were however not made available andthe exact nature of the original errors are therefore unknown to us

Bisin et al (2008) report that they have 5963 observations in their studywhereas the ethnic minority sample in Berthoud et al (1997) consists of5226 observations Implementing their empirical setup we can only identify1901 relevant observations in the original data After removing missingvalues we are left with 818 observations Using the remaining sample andrunning their specifications we find no results that support their claims Ourreplication therefore stopped here and we did not perform any sensitivityanalysis The great loss of observations implies that the remaining sample

1To facilitate comparability we use the same labels on the variables as Bisin et al(2008)

3

is most likely not representative Therefore we hesitate to draw inferencefrom the regressions results

In this paper we only document the replication and report and commentresults using the variable definitions the variable names and the specifica-tions used in Bisin et al (2008) We choose a procedure that makes it easyto reproduce our results Influenced by Koenker and Zeileis (2007) we usean integrated approach where data management estimations and the textthat rely on these computations are all integrated in one single file Thisstrategy has the advantage that it makes is easy to adjust the codes andautomatically generate a revised version of the paper

All data analysis is made in R (R Development Core Team 2008) andall code files related to this project can be found on httppeoplesuse~lundhfragile_grounds

In this technical documentation we present our results in greater detailbut also all our working procedures variables definitions etc In additionthe central part of our codes are included with typeset comments This isdone as an attempt to implement Literate Statistical Programming

The remainder of the paper is organised as follows The data and variabledefinitions are described in Section 2 Regression results are presented inSection 3 The paper is concluded in Section 4 Finally the productionprocedure is described in Section 5

2 Data and variable description

21 Data

The data set is the Fourth National Survey of Ethnic Minorities 1993-1994(FNSEM) see (Berthoud et al 1997)2 Itrsquos main objective were

ldquoto describe the social and economic conditions of Britainrsquosmain ethnic minority groups including their health and tocompare these with the social and economic conditions ofthe white majority

to assess changes over time through comparisons with otherwork

to show how the position of ethnic minority groups is relatedto the social and ethnic compositions of the areas in whichthey live

to explore diversity among different ethnic minority groups2The data can be accessed from the UK Data Archive (UKDA) via Athens UK

Data Archive is found at httpwwwdata-archiveacuk and Athens at httpwww

athensacuk

4

is most likely not representative Therefore we hesitate to draw inferencefrom the regressions results

In this paper we only document the replication and report and commentresults using the variable definitions the variable names and the specifica-tions used in Bisin et al (2008) We choose a procedure that makes it easyto reproduce our results Influenced by Koenker and Zeileis (2007) we usean integrated approach where data management estimations and the textthat rely on these computations are all integrated in one single file Thisstrategy has the advantage that it makes is easy to adjust the codes andautomatically generate a revised version of the paper

All data analysis is made in R (R Development Core Team 2008) andall code files related to this project can be found on httppeoplesuse~lundhfragile_grounds

In this technical documentation we present our results in greater detailbut also all our working procedures variables definitions etc In additionthe central part of our codes are included with typeset comments This isdone as an attempt to implement Literate Statistical Programming

The remainder of the paper is organised as follows The data and variabledefinitions are described in Section 2 Regression results are presented inSection 3 The paper is concluded in Section 4 Finally the productionprocedure is described in Section 5

2 Data and variable description

21 Data

The data set is the Fourth National Survey of Ethnic Minorities 1993-1994(FNSEM) see (Berthoud et al 1997)2 Itrsquos main objective were

ldquoto describe the social and economic conditions of Britainrsquosmain ethnic minority groups including their health and tocompare these with the social and economic conditions ofthe white majority

to assess changes over time through comparisons with otherwork

to show how the position of ethnic minority groups is relatedto the social and ethnic compositions of the areas in whichthey live

to explore diversity among different ethnic minority groups2The data can be accessed from the UK Data Archive (UKDA) via Athens UK

Data Archive is found at httpwwwdata-archiveacuk and Athens at httpwww

athensacuk

4

to describe perceptions and experience of racial discrimina-tion and social harassmentrdquo

Berthoud et al (1997)

For our coding we have used FNSEM (1993a) which contains the projectinstructions and FNSEM (1993b) which is the data description file includedin the files obtained when the entire data set is downloaded from the UKDA

In the following we present how the original data are used to define thedata set used in the estimations We present extracts of our R code (RDevelopment Core Team 2008) with extensive comments and discussionsFor details about our working procedures and how we document the researchsee section 5 on page 21 In the code chunks ldquogtrdquo denotes the R prompt andldquo+rdquo continuation of the previous line

22 Reading data and selecting variables

We load package foreign to read STATA data format Data is read from theunpacked Stata-version of the data and ldquo_rdquo in variable names are convertedto ldquordquo

gt library(foreign)

gt FNSEM lt- dataframe(readdta(3685dta

+ convertunderscore=TRUE))

After reading the data we select a subset of variables to be used Thiscode is in araietal_sourceRnw but not shown here It is also available inaraietal_sourceR

23 Defining the relevant (ethnic minority) sample

The data consist of two samples Ethnic Minorities and Whites We areonly interested in the former and remove all Whites The variable ethnicindicates ethnic group of the individual according to the British standardand is used for this purpose

One of the three measures of cultural integration in Bisin et al (2008) isImportance of Religion Whether a respondent has a religion or belongs toa church is registered in question s6 Those who do not have a religion ordo not belong to a church are coded 2 we remove these observations fromthe sample since they cannot be classified in a religious group

A variable used in Bisin et al (2008) concerns the role of the respondentand his or her parents about choosing the respondentrsquos husbandwife Sincethis information is only available for married and previously married personsthe unmarried persons are removed from the sample

Furthermore respondents were faced with one out of three question-naires (green (catageory 1) yellow (category 2) and pink (category 3)) The

5

questions involved in the study are only answered by individuals who werefaced with the green questionnaire Therefore we keep only these in thesample

gt FNSEM lt- FNSEM[FNSEM$ethnic=white amp

+ isna(FNSEM$ethnic)]

gt FNSEM lt- FNSEM[FNSEM$s6=2]

gt FNSEM lt- FNSEM[FNSEM$a1e=3]

gt U lt- FNSEM lt- FNSEM[FNSEM$question==1]

An issue where Bisin et al (2008) is imprecise is whether the questionsthey address regard Muslimsnon-Muslims or Muslimnon-Muslim immi-grants Different sample selections are possible here The model specifi-cations in Bisin et al (2008) implies that White Muslims are excluded andnative ethnic minority Muslims are included This sample definition does notmatch Bisin et al (2008) writing using the terms Muslim and non-Muslimimmigrants as the sample includes natives

24 Recoding of missing values

The data set contains several codes for missing values These missing valuescan be of different characters eg nonndashavailables lsquocanrsquot sayrsquo or because therespondent was filtered in a previous filter question We employ the strategyto code all these as nonndashavailables in R ie code NA In the data set genuinenon-availables are generally coded as minus1 We set all minus1 to NA in the entiredata set

gt isna(U) lt- U==-1

In addition to minus1 several other codes (na 7 8 9 98 99 997 and 999)are occasionally used in the data set to indicate various unknown categoriesSome of minus1 and other unknown categories are nonndashavailables and have tobe deleted in estimations This is done after we have coded all our variablesin section 27 In questions following a filter question NA may have to beset in a category Some of these other codes used to denote unknowns aregenuine NArsquos and has to be removed Others will be included in a categoryThis is done variable by variable below

25 Variable definitions

Using the same variable names as in Bisin et al (2008) we define the vari-ables at the precision described by the authors We here give our interpre-tation of the variable definitions in Bisin et al (2008)

6

251 Religious affiliation

Question s6 asks whether the respondent belongs to a church or has a re-ligion For those who answer yes question s7 asks which that church orreligion is We define two religious affiliations muslim (which are all whoanswered category 3 (muslim) on question s7) and non-muslim (all who didnot answer category 3 (muslim) on question s7) All nonndashreligious that isthose who answered category 2 on question s6 are already removed from thesample Observations containing na are recoded to NA (FNSEM 1993a p112f)

gt isna(U$s7) lt- U$s7==na

gt U$Religion lt- ifelse(U$s7==muslimmuslimnon-muslim)

252 Importance of religion

Question s9 is about the importance of religion To grade the Importanceof Religion respondents have to choose between the following categories 1not at all important 2 not very important 3 fairly important or 4 veryimportant Following standard coding practice of such questions 1 and 2should be one category and 3 and 4 another but Bisin et al (2008) chooseto put 1 2 and 3 in the same category

Among those who have answered the question very few have chosenthe alternative 1 or 2 in their answer implying very skewed distributionFollowing Bisin et al (2008) code 4 (lsquoVery importantrsquo) as answer on questions9 is coded TRUE else FALSE Codes 8 (lsquoCanrsquot sayrsquo) and 9 are coded asNA (FNSEM 1993a p 112f)

gt isna(U$s9) lt- U$s9==8 | U$s9==9

gt U$ImportanceofReligion lt- U$s9 == 4

253 Attitude towards inter-marriage

Question s34a is ldquoWould you personally mind if a close relative were tomarry a white personrdquo It serves as a filter question to s34b (ldquoWould youmind very much or just a littlerdquo) which is asked to those who answered yes(cod 1) on s34a We code those who answer yes on both questions (Mind ampMind very much) as TRUE The category FALSE refers then to those who(Do not mind) or (Mind amp Mind Little) Code 8 (Canrsquot say) on s34a andcode 9 on s34a and on s34b are assigned as NA (FNSEM 1993a p 125)

gt isna(U$s34a) lt- U$s34a==8 | U$s34a==9

gt isna(U$s34b) lt- U$s34b==8

gt U$AttitudeTowardsInterMarriage lt-

+ U$s34a==1 amp U$s34b==1

7

254 Importance of racial composition in schools

Two questions are asked s23 is

ldquoIf you were choosing a school for an eleven-year old child ofyours would your choice be influenced by how many (RESPON-DENTrsquoS ETHNIC ORIGIN) children there were in the schoolrdquo(FNSEM 1993a p 120)

and s24a asks that if the available school were similar in other ways wouldyou prefer to send this child to school with fewer than half of the pupils(code 1) about half of the students (code 2) more than half (code 3) wereof your own ethnic origin s23 is not a filter question Importance of RacialComposition in Schools is set to TRUE if s24a is equal to 3 and FALSEotherwise Code 7 (No preference) is coded as FALSE Codes 8 (Canrsquot say)and 9 are assigned as NA (FNSEM 1993a p 120)

gt isna(U$s24a) lt- U$s24a==8 | U$s24a==9

gt U$ImportanceofRacialCompositioninSchools lt- U$s24a==3

255 Born in the UK

Defines who is born in the United Kingdom (question a3) Category 16 isNorthern Ireland category 17 England and Wales and category 18 ScotlandCode 99 is assigned as NA (FNSEM 1993a p 107)

gt isna(U$a3)lt- U$a3==99

gt U$BornintheUK lt- U$a3==16 | U$a3==17 | U$a3==18

256 Age at and years since arrival

This part defines the variables Age at arrival and Years since arrival by usinginformation about year of migration (question a4n) age (question a1an) andthe year (variable year when the interview is made) The interview is madein 93 or 94) The result is that some individuals get the age at arrival minus1which presumably is due to rounding of years since arrival and the age

All born in the UK are coded as 0 for AgeatArrival and YearsSinceArrival Since these two variables are related to the age of the immigrantsone could also add an interaction variable between BornintheUK and Ageto account for effect of age for the natives The effect of age for natives is notrepresented in the model as specified by Bisin et al (2008) We experimentedwith this and results were basically unchanged The interaction variableis insignificant in all specifications Code 99 is assigned as NA for year(FNSEM 1993a pp 105 107)

8

gt isna(U$a4n) lt- U$a4n==98 | U$a4n==99

gt isna(U$year)lt- U$year==99

gt U$Age lt- U$a1an

gt U$YearsSinceArrival lt- ifelse(U$BornintheUK==TRUE

+ 0 U$year-U$a4n)

gt U$AgeatArrival lt- U$Age - U$YearsSinceArrival

gt U$AgeatArrival lt- replace(U$AgeatArrival

+ U$BornintheUK==TRUE 0)

257 Female

Definition of females via question hh2as Code na is coded as NA (FNSEM1993a pp 318)

gt isna(U$hh2as) lt- U$hh2as ==na

gt U$Female lt- U$hh2a==female

258 Arranged marriage

In question s39 Indian Pakistani Bangladeshi and Chinese respondents whohas ever been married were asked a question about the decision regardingtheir marriage The question ask about the role of the respondent and hisor her parents about choosing the respondentrsquos husbandwife In categories1 and 2 of s39 the respondentrsquos parents made the final decision and thesecategories define the dummy where the respondent is or has been living inan arranged marriage (code 1 all other are coded 0) Notice that singlesand Caribbeans have not received this question Singles are already removedfrom the sample (see section 23 Caribbeans are coded 0 This means thatthe Caribbeans do not marry according to the decision of their parentsCategory 8 (Canrsquot say) and category 9 are assigned NA (FNSEM 1993app 127)

gt isna(U$s39) lt- U$s39==8 | U$s39==9

gt U$ArrangedMarriage lt- ifelse(

+ U$s39==3 | U$s39==4 | U$s39==5 |

+ U$ethnic==caribbean FALSE TRUE)

259 Discrimination

The discrimination variable is based on a series of questions related to dis-crimination v1a-v1d about physical attacks v9a about insults j55a andj63a discrimination at work Basically anyone answering that they havebeen discriminated for any of these reasons are coded 1 else code 0

Questions v1a-v1d is a series of filter questions Question v1a asks if therespondent have been attacked (yes or no) question v1b how many attacks

9

the respondent has been enduring and question v1c asks those who havebeen attacked once if they believe the attack had to do with reasons todo with race or colour and v1d asks the same question and regards thosewho have been attacked more than once Generally code 8 and code 9are assigned NA except for question v1b where also code 7 is assigned NA(FNSEM 1993a pp 154ff 163 195 and 199)

gt vjlist lt- c(paste(v1letters[14]sep=)

+ v9aj55aj63a)

gt isna(U[vjlist]) lt- U[vjlist]==8 |

+ U[vjlist]==9

gt isna(U$v1b) lt- U$v1b==7 | U$v1b==8 |

+ U$v1b==9

gt U$Discrimination lt-

+ (U$v1a==1 amp U$v1b==1 amp U$v1c==1) |

+ (U$v1a==1 amp

+ (U$v1b gt= 2 amp U$v1b lt= 6) amp

+ U$v1d==1) | U$v9a==1 | U$j55a==1 |

+ U$j63a==1

2510 Children

No question about the number of children is asked Instead the number ofchildren has to be calculated indirectly via the number of children not livingat home (questions f16a and f16b1n-f16b3n) and the relation between therespondent and other persons living in the household (questions hh2cb-hh2cm)

The number of children out of home is calculated in the following wayIf children out of home is TRUE (f16a=1) then the number of childrenequals the sum of f16b1n f16b2n and f16b3n (the number of children notliving at home below 5 years between 5 and 15 years and above 15 yearsof age) Else if there are no children out of home (ie if f16a=2) thenthe number of children out of home is set to 0 Missing values are coded asbelow (FNSEM 1993a p 57)

gt isna(U$f16a) lt- U$f16a==8 | U$f16a==9

gt isna(U$f16b1n) lt- U$f16b1n==99

gt isna(U$f16b2n) lt- U$f16b2n==99

gt isna(U$f16b3n) lt- U$f16b3n==98 |

+ U$f16b3n==99

gt U$ChildnotatHome lt- ifelse(U$f16a==1

+ U$f16b1n+U$f16b2n+U$f16b3n0)

Questions hh2cb-hh2cm are about the relationship between the re-spondent and other individuals in the household (person b c d etc to person

10

m) category 5 being child of the respondent First we check if the personis a child to the respondent and then all children are summed over the re-spondents household adding the variable measuring number of children notat home Generally codes 98 and 99 are assigned NA (FNSEM 1993a p318)

gt hhlist lt- c(paste(hh2c letters[213] sep=))

gt isna(U[hhlist]) lt- U[hhlist]==98 | U[hhlist]==99

gt U$Children lt-

+ U$ChildnotatHome + apply(apply(subset(U

+ select=c(hh2cbhh2cm)) 2

+ function(x) x==5)1 function(x) sum(x

+ narm=TRUE))

2511 No British education

Question q1 asks whether the respondent has any British education Code2 is no Code 8 (Canacutet say) is kept in the alternative category since theseindividuals will answer the question q3 about foreign education Code 9 inq1 is assigned NA (FNSEM 1993a p 96)

gt isna(U$q1) lt- U$q1==9

gt U$NoBritishEducation lt- U$q1==2

2512 British basic education

We could not exactly see how this variable was defined in Bisin et al (2008)They define the British high education as Andashlevel and above One interpre-tation is then that O-level are educations included in the basic level Thisinterpretation is implemented here NA is assigned to all observations forwhich the filter question No British Education was NA (FNSEM 1993a pp96ff)

gt q2alist lt- c(paste(q2a c(181218) sep=))

gt U$BritishBasicEducation lt-

+ apply(apply(U[q2alist] 2function(x)

+ x==1)1 function(x) sum(x narm=TRUE))=0

gt U$BritishBasicEducation lt- ifelse(

+ isna(U$NoBritishEducation)

+ NAU$BritishBasicEducation)

11

2513 British higher education

Bisin et al (2008) explicitly defined British higher education as A-levelGiven the definition of British Basic education the reference group willinclude trade apprenticeships as well as university educations NA is assignedto all observations for which the filter question No British Education wasNA (FNSEM 1993a pp 96ff)

gt U$BritishHigherEducation lt- apply(apply(subset(

+ Uselect=c(q2a9q2a11q2a19q2a20))

+ 2function(x) x==1)1

+ function(x) sum(x narm=TRUE))=0

gt U$BritishHigherEducation lt- ifelse(

+ isna(U$NoBritishEducation)

+ NAU$BritishHigherEducation)

2514 Foreign education

Foreign educations is question q3 asked to all who answered lsquonorsquo or lsquoCanrsquotrsquosayrsquo on question q1 The answer yes is coded as (code 1) and no is codedas(code 2) Contrary to the above educational variables NA is not assignedto all NA on No British Education since some of them (code 8) actually wasasked the question q3 Instead NA is assigned to all observations for whichthe filter question q1 was 1 or 9 and to all code 8 (Canrsquot say) and code 9 onquestion q3 (FNSEM 1993a p 99)

gt isna(U$q3) lt- U$q3==8 | U$q3==9

gt U$ForeignEducation lt- U$q3==1

2515 Labour market status

We code the labour market status using j1b (in paid work last week or not)and j3occ (classification of activity either last weekrsquos activity or potentialactivity during the last ten years) The variable j1b takes the value 1 forpaid work last week and 2 otherwise The variable j3occ is coded as follows(FNSEM 1993ab the former pp 81f)

1 Self-employed (25+ employees)2 Self-employed (1-24 employees)3 Self-employed (no employees)4 Self-employed (employees not known)5 Manager (establishment of 25+ employees)6 Manager (establishment of 1-24 employees)7 Manager (employees not known)8 Foremansupervisor9 Other employee

12

10 Employee status unknown11 Not knownnot answered

Employee In order to be classified as an employee the individual has tohave answered yes (value 1) in j1b and be classified as employee in j3occ(value 9) and have the value NotAssignedNA is assigned to categories 10and 11 in j3occ

gt isna(U$j3occ) lt- U$j3occ==10 | U$j3occ==11

gt U$LabourMarketStatus lt- ifelse(U$j1b==1 amp

+ U$j3occ==9 amp isna(U$j1b==1 amp

+ U$j3occ==9) EmployeeNotAssigned)

Self Employed Selfndashemployed are also coded using j1b and j3occ aboveCategories 1minus 4 in j3occ are defined as self-employed We also require thatj1b is equal 1 (SelfndashEmployed)

gt U$LabourMarketStatus lt- replace(

+ U$LabourMarketStatus

+ (U$j3occ==1 | U$j3occ==2 |

+ U$j3occ==3 | U$j3occ==4) amp

+ U$j1b==1SelfEmployed)

Manager Managers are also coded using j1b and j3occ see above Cat-egories 5minus 8 in j3occ are defined as managers (including supervisors)

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ U$j1b==1 amp U$j3occgt4 amp U$j3occlt9

+ Manager)

Unemployed The question hh5ds describes the respondentrsquos labour mar-ket status Unemployment is defined via this variable hh5ds is coded inthe following way (FNSEM 1993b)

1 Full-time education2 Govt training programme3 Full-time paid work4 Part-time paid work5 Waiting to take up paid work6 Registered unemployed7 Unemployed not registered8 Permanently sick or disabled9 Wholly retired from work

13

10 Looking after the home11 Doing something else12 NA

We define unemployed as category 6 and 7 (Bisin et al 2008 p 324)There are few cases where the individual is classified as Employee accordingto our definition above and is reported to be unemployed in hh5ds Thiscan for example be part-time unemployment We classify these individualsas having Unclear labour market status These will be checked later andbe removed if they are few in the final sample

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==6 | U$hh5ds == 7) amp

+ U$LabourMarketStatus=NotAssigned

+ Unclear)

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==6 | U$hh5ds == 7) amp

+ U$LabourMarketStatus==NotAssigned

+ Unemployed)

Out of labour force The category out of labour force is defined as thosehaving values (125891011) in hh5ds or value 2 in j1b or j2

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==1 | U$hh5ds == 2 |

+ U$hh5ds==5 | U$hh5ds == 8 |

+ U$hh5ds==9 | U$hh5ds == 10|

+ U$hh5ds==11 | U$j1b == 2 |

+ U$j2 == 2 ) amp

+ U$LabourMarketStatus==NotAssigned

+ OutOfLabourForce)

Remaining observations with the value NotAssigned in Labour Mar-ketStatus will be assigned NA At this point there are 4 observations codedas Unclear These are now recoded as NA

gt isna(U$LabourMarketStatus) lt-

+ U$LabourMarketStatus == NotAssigned

gt isna(U$LabourMarketStatus) lt-

+ U$LabourMarketStatus == Unclear

We will create dummy variables using this variable before we run ourmodels

14

2516 No parents

Variable No parents means that the respondent is not living with his orher parents This variable is coded with question f14a (which takes code 1for both alive code 2 father alive code 3 for mother alive and 8 for bothdead) and f14b (which takes code 2 if both living parents do not live withthe respondent and code 6 if the only living parent does not live with therespondent else it takes one of the values 1 or 3 minus 5) No parents shouldbe coded TRUE if either both parents are dead or the respondent does notlive with any living parents However since we follow Bisin et al (2008) wecode this variable including only those who have both their parents dead orboth paprents live away from the respondent This definition implies thatthose who have a parent living away and one parent dead are assigned thevalue FALSE Code 8 and 9 are assigned NA (FNSEM 1993a p 56)

gt isna(U$f14a) lt- U$f14a==9

gt isna(U$f14b) lt- U$f14b==9

gt U$NoParents lt- U$f14a==8 | U$f14b==2

2517 Contacts with parents

The three variables measuring contacts with parents are defined via threequestions asking about the number of physical contacts (question f15bn)the number of contacts via telephone (question f15cn) and the number ofcontacts via letters (question f15dn) that the respondent has had with hisor her parents during the last four weeks conditional on not both parentsbeing dead All three takes the value of the underlaying variable if at leastone parent is alive and the value 0 if both parents are dead Code 999 onall three variables and code 997 on f15bn are assigned NA (FNSEM 1993ap 56)

gt isna(U$f15bn) lt- U$f15bn==997 | U$f15bn==999

gt isna(U$f15cn) lt- U$f15cn==999

gt isna(U$f15dn) lt- U$f15dn==999

gt U$ParentsPhysicalContacts lt- ifelse(

+ U$f14a=8 U$f15bn0)

gt U$ParentsTelephoneCalls lt- ifelse(

+ U$f14a=8 U$f15cn0)

gt U$ParentsLetters lt- ifelse(

+ U$f14a=8 U$f15dn0)

2518 English language

There are several language variables measuring whether the respondent isspeaking English with different individuals at home with older at home

15

with younger at work and with friends We construct theses variables usingquestion s12a which asks whether the respondent regularly speak to anyonein Britain in any other language than English and s12f s12g s12hand s12i which asks which language is spoken to the above mentionedcategories of individuals Each of s12f s12g s12h and s12i comesin 18 versions (eg s12f1 s12f18) where each question 1ndash15 iscoded yes if the respondent speaks the language Question 16 is ldquoNeverspeaks to these peopleNot ap[plicable]rdquo question 17 NA and question 18ldquo None of the above answered positiverdquo Question 1 is always regardingEnglish

The respondent is coded as English speaker if either s12a is answerednegatively or s12aX1 where X=fghi is answered positively Onlyrespondents for which either s12a is NA or all of s12X1 s12X16 areanswered negatively are coded as NA Below is the code for English SpokenAt Home With Older

gt isna(U$s12a) lt- U$s12a== 8 | U$s12a == 9

gt s12flist lt- c(paste(s12f 216 sep=))

gt U$oOLD lt- apply(U[s12flist]==yes 1 sum)

gt U$EnglishSpokenatHomewithOlder lt-

+ ((U$s12a==1 | isna(U$s12a)) amp

+ U$s12f1==yes) | U$s12a==2

gt isna(U$EnglishSpokenatHomewithOlder) lt-

+ U$oOLD==0 amp

+ U$EnglishSpokenatHomewithOlder==FALSE

gt U$EnglishSpokenatHomewithOlder lt-

+ replace(U$EnglishSpokenatHomewithOlder

+ U$oOLDgt0 amp

+ isna(U$EnglishSpokenatHomewithOlder)FALSE)

gt U$DONOTSPEAKWITHOLDER lt- ifelse(U$s12f16==no01)

The codes for English Spoken At Home With Younger English SpokenAt Work and English Spoken With Friends are equivalent These codesare in araietal_sourceRnw but not shown here It is also available inaraietal_sourceR

2519 Household income

The question hh40 provides information in which interval the householdincome of the respondentrsquos household is We assign the midpoints in theseintervals as the household income For the lowest bracket this income is themidpoint of [0 77] For the highest bracket we assign the income which is thelowest income in the bracket plus the income interval down to the midpintof the second highest bracket ie 789 + 788minus731

2 = 8175 This method

16

underestimates the range of the highest bracket but to a lesser extent thanthe lower limit 789

gt isna(U$hh40) lt- U$hh40==refused |

+ U$hh40==cant say | U$hh40==na

gt U$HouseholdIncome lt- c(3859651350

+ 173523052600318036604140

+ 471552955870649570207595

+ 8175)[U$hh40]

2520 Ward variables

Ward density of own ethnic group is measured by the variable wown in theoriginal data This variable is coded 1 minus 7 depending on the density of therespondentrsquos own ethnic group is in the ward of the respondent (FNSEM1993b)

1 Up to 1992 2-4993 5-9994 10-14995 15-24996 25-32997 33 or more

We recode the variable to take the midpoints of the density intervals in thesame fashion as the household income variable was recoded This meansthat if the respondent is the lowest interval the density is set to be 1 etc Inthe highest interval we set the density to be the lowest density in the intervalplus the density distance down to the midpoint of the second highest densityinterval ie 33 + 33minus25

2 = 37The variable wunemp is coded 1minus 6 depending on unemployment rate in

the ward of the respondent (FNSEM 1993b)

1 Up to 1992 2-4993 5-9994 10-14995 15-206 20 or more

We recode this variable to instead take the midpoints of the intervals aswe did for the household income This means that if the respondent is thelowest interval the the rate is set to be 1 etc In the highest interval we setthe rate to be the lowest rate in the interval plus the distance down to themidpoint of the second highest rate interval ie 20 + 20minus15

2 = 225

17

gt U$WardDensityOwnEthnicity lt- (c(1035

+ 75125200290370)[U$wown])100

gt U$WardUnemploymentRate lt- c(103575125

+ 175225)[U$wunemp]

26 Discrimination own ethnicity

Finally we define a variable describing the discrimination against the ownethnic group It is defined as the average of the variable Discrimination overethnic groups after the removal of nonndashavailables

gt GroupDiscrimination lt-

+ tapply(U$DiscriminationU$ethnic

+ function(x) mean(x narm=TRUE))

gt U$DiscriminationOwnEthnicity lt-

+ GroupDiscrimination[U$ethnic]

27 Defining the subset

We define dummy variables for labour market status to have the same vari-able labels as in Bisin et al (2008)

gt U$Employee lt- asnumeric(U$LabourMarketStatus==Employee)

gt U$Manager lt- asnumeric(U$LabourMarketStatus==Manager)

gt U$SelfEmployed lt-

+ asnumeric(U$LabourMarketStatus==SelfEmployed)

gt U$OUTOFLABOURFORCE lt-

+ asnumeric(U$LabourMarketStatus==OutOfLabourForce)

gt U$Unemployed lt-

+ asnumeric(U$LabourMarketStatus==Unemployed)

We save a data set keeping all observations containing nonndashavailables

gt UOriginal lt- U

We then choose the variables to keep in U This code is in araietal_sourceRnwbut not shown here It is also available in araietal_sourceR

Finally we remove all observations containing nonndashavailables from U

gt U lt- naomit(U)

28 Sample statistics

Table 1 compares the number of observations in this sample before (seesection 27 ie data frame UOriginal) and after (see section 27 iedata frame U) nonndashavailables are removed with the numbers of observations

18

reported by the Bisin et al (2008) study The number of observations forvarious groups in the nonndashMuslim category are not reported in the Bisinet al (2008) paper These numbers are therefore missing in the table Thecategory definitions are from the original dataset and involves no recodingon our part

After removing observations with missing values on all variables of inter-est (ldquoAfterrdquo in Table 1) we are left with 330 Muslims and 488 non-MuslimsThe sample selections induced by the choice of variables and the missingvalues in these variables lead to a loss of 57 percent of the relevant sampleof the original data3

The sample means reported in Bisin et al (2008) seem to be unweightedSince data instructions says that the data should always be weighted tables2 and 3 report weighted and unweighted sample means before and afterremoval of non-availables and the Bisin et al (2008) data4 Comparingmeans the Bisin et al (2008) data seem to be different from the originalsample The variables Attitude Towards Inter Marriage and Importance ofRacial Composition in Schools in Bisin et al (2008) data deviate largelyfrom corresponding averages in the original data The deviation is extremein case of Importance of Racial Composition in Schools The original samplehas a mean for this variable that is 2 percent for non-Muslims (compare with33 percent in Bisin et al (2008)) and 6 percent for Muslims (compare with65 percent in Bisin et al (2008)) Due to this extremely skewed distributionit is hardly meaningfull to run a regression on this variable

Notice that also the distribution of the variable Importance of Religionwould be extremely skewed using standard coding of this type of variablesSuch a coding would imply that religion is important when the respondentanswer ldquoVery Importantrdquo and ldquoFairly Importantrdquo to the question ldquoHow im-portant is religion to the way you live your liferdquo

The sample means in our data after removing accumulated missing val-ues due to all variables in the estimations deviate marginally in general fromthe original data The similarities here are partly due to the fact that thestatistics are based exactly on the same variable definition in our implemen-tation In some respects the deviations are larger For further comparisonswe refer to Tables 2 and 3

Due to the fact that the large majority of observations from the originaldata are lost the remaining sample is likely to be contaminated with sampleselection bias To compare the characteristics of the remaining sample withthe original sample says something about systematic attrition with respectto observables The sample selection bias with respect to unobservablescannot however be resolved

3The variables written in capital letters are created to ensure wellndashdefined referencecategories They are included in our regressions but we cannot say whether they areincluded in the regressions of Bisin et al (2008)

4See FNSEM (1993b)

19

3 Regression Results

We use linear probability models (LPM)5 Our results are presented in Tables4 and 5 Bisin et al (2008) write that

1 ldquoMuslims integrate less and more slowly than non-Muslimsrdquo (abstractp 445) and

2 ldquo there is no evidence that segregated neighbourhoods breed intensereligious and cultural identities On the contrary intense identitiesin our data are more prominent in relatively mixed neighbourhoodsrdquo(p 446)

The first claim is based on their reported results concerning the variableYears Since Arrival In this way Bisin et al (2008) compare cohorts ofMuslims and non Muslims and attempt to say something about the evolutionof values over time They do not follow individuals over time but nonethelesscall these cohort differences ldquoIntegration over timerdquo They report negativecoefficients for Years Since Arrival but the estimates are smaller in absolutevalue for Muslims than for non-Muslims In our case the coefficients forYears Since Arrival reported in Tables 4 and 5 are insignificant in all casesexcept in the regression for Importance of Religion for Muslims where it isnegative This is opposite to what Bisin et al (2008) claim

The second claim is based on their reported results concerning the vari-able Ward Density Own Ethnicity Bisin et al (2008) report negative andsignificant estimates for Ward Density Own Ethnicity in all six specifica-tions Their negative coefficient for this variable would imply that ethnicminorities put more weight on religion mind more about inter-ethnic mar-riage and have stronger taste for ethnically profiled schools as we move fromneighbourhoods (Wards) with high density of their own ethnicity to neigh-bourhoods where people from their own ethnicity are scarce This is not atall what we find in our replication

5Bisin et al (2008) use probit estimations Our attempts to use probit run into con-vergence problems The convergence problems are severe for the model using Importanceof Racial Composition in Schools as dependent variable Hence our choice of LPM

Another issue is that Bisin et al (2008) should have included dummy variables indicatingreligious affiliation Christians Sikhs and others in the non-Muslim category to checksimilarities and differences among non-Muslims as well In this respect we follow theirmodel specification

Moreover Bisin et al (2008) should have adjusted for within ward correlations Thismight matter for their standard errors which might be underestimated In our case withalmost no significant results this would not matter much The variable is not available inthe data set and we did not make much effort to obtain it

All estimated models include 7 UK-region dummies and the variables DONOT

SPEAKWITHOLDER DONOTSPEAKATWORK and DONOTSPEAKWITHFRIENDS It turnedout that the variable DONOTSPEAKWITHYOUNGER is TRUE for few observations and can-not be included in the model

20

In our estimations the estimated coefficients for this variable are allpositive but far from significant The P-values are 069 097 and 059 forMuslims and 015 014 and 03 for non-Muslims contradicting the Bisin et al(2008) results

Inspecting the results presented in Tables 4 and 5 there are many sim-ilarities and few differences in the estimated coefficients for Muslims andnon-Muslims Our results are generally very different from results reportedby Bisin et al (2008) We are however doubtful whether it is possibleto draw any reliable inference from these results due to great loss of ob-servations and possible sample selection bias together with the problem ofendogeneity (also mentioned by Bisin et al (2008))

4 Concluding remarks

The Bisin et al (2008) paper rests on fragile grounds Our examinationof the data using their variable definitions and the same set-up indicatesthat their claims about differences between Muslims and non-Muslims andtheir conclusion that strong ReligiousEthnic identities are found in mixedneighbourhoods does not hold There is no systematic relation betweenethnic minoritiesrsquo views on religion inter-ethnic marriage or ethnic profile ofschools and the density of their own ethnic minority in their neighbourhoodHowever we hesitate to draw inference from these results since the great lossof observations (57 percent) implies that the remaining sample is most likelynot representative

5 Production notes

To facilitate reproducibility and save others timely interpretations of whatis done in this paper we attempt to follow Literate Statistical Programmingprocedures For documenting our results we have used Sweave by Leisch(2002) in combination with the LATEX family of programs using the packagesinputenc fontenc natbib Sweave fancyvrb color url hyperref andmultirow

All code (R code LATEX code and BibTEX data base code) used to do theeconometric estimations to produce this technical documentation includingall tables and to produce the companion paper Arai et al (2008) is containedin the file araietal_sourceRnw

The estimations and the documentation can be reproduced in by follow-ing the instructions at the top of the file httppeoplesuse~lundhfragile_groundsaraietal_sourceRnw

Our results were obtained on a i486-pc-linux-gnu platform using R ver-sion 281 beta (2008-12-12 r47173) (R Development Core Team 2008) with

21

questions involved in the study are only answered by individuals who werefaced with the green questionnaire Therefore we keep only these in thesample

gt FNSEM lt- FNSEM[FNSEM$ethnic=white amp

+ isna(FNSEM$ethnic)]

gt FNSEM lt- FNSEM[FNSEM$s6=2]

gt FNSEM lt- FNSEM[FNSEM$a1e=3]

gt U lt- FNSEM lt- FNSEM[FNSEM$question==1]

An issue where Bisin et al (2008) is imprecise is whether the questionsthey address regard Muslimsnon-Muslims or Muslimnon-Muslim immi-grants Different sample selections are possible here The model specifi-cations in Bisin et al (2008) implies that White Muslims are excluded andnative ethnic minority Muslims are included This sample definition does notmatch Bisin et al (2008) writing using the terms Muslim and non-Muslimimmigrants as the sample includes natives

24 Recoding of missing values

The data set contains several codes for missing values These missing valuescan be of different characters eg nonndashavailables lsquocanrsquot sayrsquo or because therespondent was filtered in a previous filter question We employ the strategyto code all these as nonndashavailables in R ie code NA In the data set genuinenon-availables are generally coded as minus1 We set all minus1 to NA in the entiredata set

gt isna(U) lt- U==-1

In addition to minus1 several other codes (na 7 8 9 98 99 997 and 999)are occasionally used in the data set to indicate various unknown categoriesSome of minus1 and other unknown categories are nonndashavailables and have tobe deleted in estimations This is done after we have coded all our variablesin section 27 In questions following a filter question NA may have to beset in a category Some of these other codes used to denote unknowns aregenuine NArsquos and has to be removed Others will be included in a categoryThis is done variable by variable below

25 Variable definitions

Using the same variable names as in Bisin et al (2008) we define the vari-ables at the precision described by the authors We here give our interpre-tation of the variable definitions in Bisin et al (2008)

6

251 Religious affiliation

Question s6 asks whether the respondent belongs to a church or has a re-ligion For those who answer yes question s7 asks which that church orreligion is We define two religious affiliations muslim (which are all whoanswered category 3 (muslim) on question s7) and non-muslim (all who didnot answer category 3 (muslim) on question s7) All nonndashreligious that isthose who answered category 2 on question s6 are already removed from thesample Observations containing na are recoded to NA (FNSEM 1993a p112f)

gt isna(U$s7) lt- U$s7==na

gt U$Religion lt- ifelse(U$s7==muslimmuslimnon-muslim)

252 Importance of religion

Question s9 is about the importance of religion To grade the Importanceof Religion respondents have to choose between the following categories 1not at all important 2 not very important 3 fairly important or 4 veryimportant Following standard coding practice of such questions 1 and 2should be one category and 3 and 4 another but Bisin et al (2008) chooseto put 1 2 and 3 in the same category

Among those who have answered the question very few have chosenthe alternative 1 or 2 in their answer implying very skewed distributionFollowing Bisin et al (2008) code 4 (lsquoVery importantrsquo) as answer on questions9 is coded TRUE else FALSE Codes 8 (lsquoCanrsquot sayrsquo) and 9 are coded asNA (FNSEM 1993a p 112f)

gt isna(U$s9) lt- U$s9==8 | U$s9==9

gt U$ImportanceofReligion lt- U$s9 == 4

253 Attitude towards inter-marriage

Question s34a is ldquoWould you personally mind if a close relative were tomarry a white personrdquo It serves as a filter question to s34b (ldquoWould youmind very much or just a littlerdquo) which is asked to those who answered yes(cod 1) on s34a We code those who answer yes on both questions (Mind ampMind very much) as TRUE The category FALSE refers then to those who(Do not mind) or (Mind amp Mind Little) Code 8 (Canrsquot say) on s34a andcode 9 on s34a and on s34b are assigned as NA (FNSEM 1993a p 125)

gt isna(U$s34a) lt- U$s34a==8 | U$s34a==9

gt isna(U$s34b) lt- U$s34b==8

gt U$AttitudeTowardsInterMarriage lt-

+ U$s34a==1 amp U$s34b==1

7

254 Importance of racial composition in schools

Two questions are asked s23 is

ldquoIf you were choosing a school for an eleven-year old child ofyours would your choice be influenced by how many (RESPON-DENTrsquoS ETHNIC ORIGIN) children there were in the schoolrdquo(FNSEM 1993a p 120)

and s24a asks that if the available school were similar in other ways wouldyou prefer to send this child to school with fewer than half of the pupils(code 1) about half of the students (code 2) more than half (code 3) wereof your own ethnic origin s23 is not a filter question Importance of RacialComposition in Schools is set to TRUE if s24a is equal to 3 and FALSEotherwise Code 7 (No preference) is coded as FALSE Codes 8 (Canrsquot say)and 9 are assigned as NA (FNSEM 1993a p 120)

gt isna(U$s24a) lt- U$s24a==8 | U$s24a==9

gt U$ImportanceofRacialCompositioninSchools lt- U$s24a==3

255 Born in the UK

Defines who is born in the United Kingdom (question a3) Category 16 isNorthern Ireland category 17 England and Wales and category 18 ScotlandCode 99 is assigned as NA (FNSEM 1993a p 107)

gt isna(U$a3)lt- U$a3==99

gt U$BornintheUK lt- U$a3==16 | U$a3==17 | U$a3==18

256 Age at and years since arrival

This part defines the variables Age at arrival and Years since arrival by usinginformation about year of migration (question a4n) age (question a1an) andthe year (variable year when the interview is made) The interview is madein 93 or 94) The result is that some individuals get the age at arrival minus1which presumably is due to rounding of years since arrival and the age

All born in the UK are coded as 0 for AgeatArrival and YearsSinceArrival Since these two variables are related to the age of the immigrantsone could also add an interaction variable between BornintheUK and Ageto account for effect of age for the natives The effect of age for natives is notrepresented in the model as specified by Bisin et al (2008) We experimentedwith this and results were basically unchanged The interaction variableis insignificant in all specifications Code 99 is assigned as NA for year(FNSEM 1993a pp 105 107)

8

gt isna(U$a4n) lt- U$a4n==98 | U$a4n==99

gt isna(U$year)lt- U$year==99

gt U$Age lt- U$a1an

gt U$YearsSinceArrival lt- ifelse(U$BornintheUK==TRUE

+ 0 U$year-U$a4n)

gt U$AgeatArrival lt- U$Age - U$YearsSinceArrival

gt U$AgeatArrival lt- replace(U$AgeatArrival

+ U$BornintheUK==TRUE 0)

257 Female

Definition of females via question hh2as Code na is coded as NA (FNSEM1993a pp 318)

gt isna(U$hh2as) lt- U$hh2as ==na

gt U$Female lt- U$hh2a==female

258 Arranged marriage

In question s39 Indian Pakistani Bangladeshi and Chinese respondents whohas ever been married were asked a question about the decision regardingtheir marriage The question ask about the role of the respondent and hisor her parents about choosing the respondentrsquos husbandwife In categories1 and 2 of s39 the respondentrsquos parents made the final decision and thesecategories define the dummy where the respondent is or has been living inan arranged marriage (code 1 all other are coded 0) Notice that singlesand Caribbeans have not received this question Singles are already removedfrom the sample (see section 23 Caribbeans are coded 0 This means thatthe Caribbeans do not marry according to the decision of their parentsCategory 8 (Canrsquot say) and category 9 are assigned NA (FNSEM 1993app 127)

gt isna(U$s39) lt- U$s39==8 | U$s39==9

gt U$ArrangedMarriage lt- ifelse(

+ U$s39==3 | U$s39==4 | U$s39==5 |

+ U$ethnic==caribbean FALSE TRUE)

259 Discrimination

The discrimination variable is based on a series of questions related to dis-crimination v1a-v1d about physical attacks v9a about insults j55a andj63a discrimination at work Basically anyone answering that they havebeen discriminated for any of these reasons are coded 1 else code 0

Questions v1a-v1d is a series of filter questions Question v1a asks if therespondent have been attacked (yes or no) question v1b how many attacks

9

the respondent has been enduring and question v1c asks those who havebeen attacked once if they believe the attack had to do with reasons todo with race or colour and v1d asks the same question and regards thosewho have been attacked more than once Generally code 8 and code 9are assigned NA except for question v1b where also code 7 is assigned NA(FNSEM 1993a pp 154ff 163 195 and 199)

gt vjlist lt- c(paste(v1letters[14]sep=)

+ v9aj55aj63a)

gt isna(U[vjlist]) lt- U[vjlist]==8 |

+ U[vjlist]==9

gt isna(U$v1b) lt- U$v1b==7 | U$v1b==8 |

+ U$v1b==9

gt U$Discrimination lt-

+ (U$v1a==1 amp U$v1b==1 amp U$v1c==1) |

+ (U$v1a==1 amp

+ (U$v1b gt= 2 amp U$v1b lt= 6) amp

+ U$v1d==1) | U$v9a==1 | U$j55a==1 |

+ U$j63a==1

2510 Children

No question about the number of children is asked Instead the number ofchildren has to be calculated indirectly via the number of children not livingat home (questions f16a and f16b1n-f16b3n) and the relation between therespondent and other persons living in the household (questions hh2cb-hh2cm)

The number of children out of home is calculated in the following wayIf children out of home is TRUE (f16a=1) then the number of childrenequals the sum of f16b1n f16b2n and f16b3n (the number of children notliving at home below 5 years between 5 and 15 years and above 15 yearsof age) Else if there are no children out of home (ie if f16a=2) thenthe number of children out of home is set to 0 Missing values are coded asbelow (FNSEM 1993a p 57)

gt isna(U$f16a) lt- U$f16a==8 | U$f16a==9

gt isna(U$f16b1n) lt- U$f16b1n==99

gt isna(U$f16b2n) lt- U$f16b2n==99

gt isna(U$f16b3n) lt- U$f16b3n==98 |

+ U$f16b3n==99

gt U$ChildnotatHome lt- ifelse(U$f16a==1

+ U$f16b1n+U$f16b2n+U$f16b3n0)

Questions hh2cb-hh2cm are about the relationship between the re-spondent and other individuals in the household (person b c d etc to person

10

m) category 5 being child of the respondent First we check if the personis a child to the respondent and then all children are summed over the re-spondents household adding the variable measuring number of children notat home Generally codes 98 and 99 are assigned NA (FNSEM 1993a p318)

gt hhlist lt- c(paste(hh2c letters[213] sep=))

gt isna(U[hhlist]) lt- U[hhlist]==98 | U[hhlist]==99

gt U$Children lt-

+ U$ChildnotatHome + apply(apply(subset(U

+ select=c(hh2cbhh2cm)) 2

+ function(x) x==5)1 function(x) sum(x

+ narm=TRUE))

2511 No British education

Question q1 asks whether the respondent has any British education Code2 is no Code 8 (Canacutet say) is kept in the alternative category since theseindividuals will answer the question q3 about foreign education Code 9 inq1 is assigned NA (FNSEM 1993a p 96)

gt isna(U$q1) lt- U$q1==9

gt U$NoBritishEducation lt- U$q1==2

2512 British basic education

We could not exactly see how this variable was defined in Bisin et al (2008)They define the British high education as Andashlevel and above One interpre-tation is then that O-level are educations included in the basic level Thisinterpretation is implemented here NA is assigned to all observations forwhich the filter question No British Education was NA (FNSEM 1993a pp96ff)

gt q2alist lt- c(paste(q2a c(181218) sep=))

gt U$BritishBasicEducation lt-

+ apply(apply(U[q2alist] 2function(x)

+ x==1)1 function(x) sum(x narm=TRUE))=0

gt U$BritishBasicEducation lt- ifelse(

+ isna(U$NoBritishEducation)

+ NAU$BritishBasicEducation)

11

2513 British higher education

Bisin et al (2008) explicitly defined British higher education as A-levelGiven the definition of British Basic education the reference group willinclude trade apprenticeships as well as university educations NA is assignedto all observations for which the filter question No British Education wasNA (FNSEM 1993a pp 96ff)

gt U$BritishHigherEducation lt- apply(apply(subset(

+ Uselect=c(q2a9q2a11q2a19q2a20))

+ 2function(x) x==1)1

+ function(x) sum(x narm=TRUE))=0

gt U$BritishHigherEducation lt- ifelse(

+ isna(U$NoBritishEducation)

+ NAU$BritishHigherEducation)

2514 Foreign education

Foreign educations is question q3 asked to all who answered lsquonorsquo or lsquoCanrsquotrsquosayrsquo on question q1 The answer yes is coded as (code 1) and no is codedas(code 2) Contrary to the above educational variables NA is not assignedto all NA on No British Education since some of them (code 8) actually wasasked the question q3 Instead NA is assigned to all observations for whichthe filter question q1 was 1 or 9 and to all code 8 (Canrsquot say) and code 9 onquestion q3 (FNSEM 1993a p 99)

gt isna(U$q3) lt- U$q3==8 | U$q3==9

gt U$ForeignEducation lt- U$q3==1

2515 Labour market status

We code the labour market status using j1b (in paid work last week or not)and j3occ (classification of activity either last weekrsquos activity or potentialactivity during the last ten years) The variable j1b takes the value 1 forpaid work last week and 2 otherwise The variable j3occ is coded as follows(FNSEM 1993ab the former pp 81f)

1 Self-employed (25+ employees)2 Self-employed (1-24 employees)3 Self-employed (no employees)4 Self-employed (employees not known)5 Manager (establishment of 25+ employees)6 Manager (establishment of 1-24 employees)7 Manager (employees not known)8 Foremansupervisor9 Other employee

12

10 Employee status unknown11 Not knownnot answered

Employee In order to be classified as an employee the individual has tohave answered yes (value 1) in j1b and be classified as employee in j3occ(value 9) and have the value NotAssignedNA is assigned to categories 10and 11 in j3occ

gt isna(U$j3occ) lt- U$j3occ==10 | U$j3occ==11

gt U$LabourMarketStatus lt- ifelse(U$j1b==1 amp

+ U$j3occ==9 amp isna(U$j1b==1 amp

+ U$j3occ==9) EmployeeNotAssigned)

Self Employed Selfndashemployed are also coded using j1b and j3occ aboveCategories 1minus 4 in j3occ are defined as self-employed We also require thatj1b is equal 1 (SelfndashEmployed)

gt U$LabourMarketStatus lt- replace(

+ U$LabourMarketStatus

+ (U$j3occ==1 | U$j3occ==2 |

+ U$j3occ==3 | U$j3occ==4) amp

+ U$j1b==1SelfEmployed)

Manager Managers are also coded using j1b and j3occ see above Cat-egories 5minus 8 in j3occ are defined as managers (including supervisors)

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ U$j1b==1 amp U$j3occgt4 amp U$j3occlt9

+ Manager)

Unemployed The question hh5ds describes the respondentrsquos labour mar-ket status Unemployment is defined via this variable hh5ds is coded inthe following way (FNSEM 1993b)

1 Full-time education2 Govt training programme3 Full-time paid work4 Part-time paid work5 Waiting to take up paid work6 Registered unemployed7 Unemployed not registered8 Permanently sick or disabled9 Wholly retired from work

13

10 Looking after the home11 Doing something else12 NA

We define unemployed as category 6 and 7 (Bisin et al 2008 p 324)There are few cases where the individual is classified as Employee accordingto our definition above and is reported to be unemployed in hh5ds Thiscan for example be part-time unemployment We classify these individualsas having Unclear labour market status These will be checked later andbe removed if they are few in the final sample

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==6 | U$hh5ds == 7) amp

+ U$LabourMarketStatus=NotAssigned

+ Unclear)

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==6 | U$hh5ds == 7) amp

+ U$LabourMarketStatus==NotAssigned

+ Unemployed)

Out of labour force The category out of labour force is defined as thosehaving values (125891011) in hh5ds or value 2 in j1b or j2

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==1 | U$hh5ds == 2 |

+ U$hh5ds==5 | U$hh5ds == 8 |

+ U$hh5ds==9 | U$hh5ds == 10|

+ U$hh5ds==11 | U$j1b == 2 |

+ U$j2 == 2 ) amp

+ U$LabourMarketStatus==NotAssigned

+ OutOfLabourForce)

Remaining observations with the value NotAssigned in Labour Mar-ketStatus will be assigned NA At this point there are 4 observations codedas Unclear These are now recoded as NA

gt isna(U$LabourMarketStatus) lt-

+ U$LabourMarketStatus == NotAssigned

gt isna(U$LabourMarketStatus) lt-

+ U$LabourMarketStatus == Unclear

We will create dummy variables using this variable before we run ourmodels

14

2516 No parents

Variable No parents means that the respondent is not living with his orher parents This variable is coded with question f14a (which takes code 1for both alive code 2 father alive code 3 for mother alive and 8 for bothdead) and f14b (which takes code 2 if both living parents do not live withthe respondent and code 6 if the only living parent does not live with therespondent else it takes one of the values 1 or 3 minus 5) No parents shouldbe coded TRUE if either both parents are dead or the respondent does notlive with any living parents However since we follow Bisin et al (2008) wecode this variable including only those who have both their parents dead orboth paprents live away from the respondent This definition implies thatthose who have a parent living away and one parent dead are assigned thevalue FALSE Code 8 and 9 are assigned NA (FNSEM 1993a p 56)

gt isna(U$f14a) lt- U$f14a==9

gt isna(U$f14b) lt- U$f14b==9

gt U$NoParents lt- U$f14a==8 | U$f14b==2

2517 Contacts with parents

The three variables measuring contacts with parents are defined via threequestions asking about the number of physical contacts (question f15bn)the number of contacts via telephone (question f15cn) and the number ofcontacts via letters (question f15dn) that the respondent has had with hisor her parents during the last four weeks conditional on not both parentsbeing dead All three takes the value of the underlaying variable if at leastone parent is alive and the value 0 if both parents are dead Code 999 onall three variables and code 997 on f15bn are assigned NA (FNSEM 1993ap 56)

gt isna(U$f15bn) lt- U$f15bn==997 | U$f15bn==999

gt isna(U$f15cn) lt- U$f15cn==999

gt isna(U$f15dn) lt- U$f15dn==999

gt U$ParentsPhysicalContacts lt- ifelse(

+ U$f14a=8 U$f15bn0)

gt U$ParentsTelephoneCalls lt- ifelse(

+ U$f14a=8 U$f15cn0)

gt U$ParentsLetters lt- ifelse(

+ U$f14a=8 U$f15dn0)

2518 English language

There are several language variables measuring whether the respondent isspeaking English with different individuals at home with older at home

15

with younger at work and with friends We construct theses variables usingquestion s12a which asks whether the respondent regularly speak to anyonein Britain in any other language than English and s12f s12g s12hand s12i which asks which language is spoken to the above mentionedcategories of individuals Each of s12f s12g s12h and s12i comesin 18 versions (eg s12f1 s12f18) where each question 1ndash15 iscoded yes if the respondent speaks the language Question 16 is ldquoNeverspeaks to these peopleNot ap[plicable]rdquo question 17 NA and question 18ldquo None of the above answered positiverdquo Question 1 is always regardingEnglish

The respondent is coded as English speaker if either s12a is answerednegatively or s12aX1 where X=fghi is answered positively Onlyrespondents for which either s12a is NA or all of s12X1 s12X16 areanswered negatively are coded as NA Below is the code for English SpokenAt Home With Older

gt isna(U$s12a) lt- U$s12a== 8 | U$s12a == 9

gt s12flist lt- c(paste(s12f 216 sep=))

gt U$oOLD lt- apply(U[s12flist]==yes 1 sum)

gt U$EnglishSpokenatHomewithOlder lt-

+ ((U$s12a==1 | isna(U$s12a)) amp

+ U$s12f1==yes) | U$s12a==2

gt isna(U$EnglishSpokenatHomewithOlder) lt-

+ U$oOLD==0 amp

+ U$EnglishSpokenatHomewithOlder==FALSE

gt U$EnglishSpokenatHomewithOlder lt-

+ replace(U$EnglishSpokenatHomewithOlder

+ U$oOLDgt0 amp

+ isna(U$EnglishSpokenatHomewithOlder)FALSE)

gt U$DONOTSPEAKWITHOLDER lt- ifelse(U$s12f16==no01)

The codes for English Spoken At Home With Younger English SpokenAt Work and English Spoken With Friends are equivalent These codesare in araietal_sourceRnw but not shown here It is also available inaraietal_sourceR

2519 Household income

The question hh40 provides information in which interval the householdincome of the respondentrsquos household is We assign the midpoints in theseintervals as the household income For the lowest bracket this income is themidpoint of [0 77] For the highest bracket we assign the income which is thelowest income in the bracket plus the income interval down to the midpintof the second highest bracket ie 789 + 788minus731

2 = 8175 This method

16

underestimates the range of the highest bracket but to a lesser extent thanthe lower limit 789

gt isna(U$hh40) lt- U$hh40==refused |

+ U$hh40==cant say | U$hh40==na

gt U$HouseholdIncome lt- c(3859651350

+ 173523052600318036604140

+ 471552955870649570207595

+ 8175)[U$hh40]

2520 Ward variables

Ward density of own ethnic group is measured by the variable wown in theoriginal data This variable is coded 1 minus 7 depending on the density of therespondentrsquos own ethnic group is in the ward of the respondent (FNSEM1993b)

1 Up to 1992 2-4993 5-9994 10-14995 15-24996 25-32997 33 or more

We recode the variable to take the midpoints of the density intervals in thesame fashion as the household income variable was recoded This meansthat if the respondent is the lowest interval the density is set to be 1 etc Inthe highest interval we set the density to be the lowest density in the intervalplus the density distance down to the midpoint of the second highest densityinterval ie 33 + 33minus25

2 = 37The variable wunemp is coded 1minus 6 depending on unemployment rate in

the ward of the respondent (FNSEM 1993b)

1 Up to 1992 2-4993 5-9994 10-14995 15-206 20 or more

We recode this variable to instead take the midpoints of the intervals aswe did for the household income This means that if the respondent is thelowest interval the the rate is set to be 1 etc In the highest interval we setthe rate to be the lowest rate in the interval plus the distance down to themidpoint of the second highest rate interval ie 20 + 20minus15

2 = 225

17

gt U$WardDensityOwnEthnicity lt- (c(1035

+ 75125200290370)[U$wown])100

gt U$WardUnemploymentRate lt- c(103575125

+ 175225)[U$wunemp]

26 Discrimination own ethnicity

Finally we define a variable describing the discrimination against the ownethnic group It is defined as the average of the variable Discrimination overethnic groups after the removal of nonndashavailables

gt GroupDiscrimination lt-

+ tapply(U$DiscriminationU$ethnic

+ function(x) mean(x narm=TRUE))

gt U$DiscriminationOwnEthnicity lt-

+ GroupDiscrimination[U$ethnic]

27 Defining the subset

We define dummy variables for labour market status to have the same vari-able labels as in Bisin et al (2008)

gt U$Employee lt- asnumeric(U$LabourMarketStatus==Employee)

gt U$Manager lt- asnumeric(U$LabourMarketStatus==Manager)

gt U$SelfEmployed lt-

+ asnumeric(U$LabourMarketStatus==SelfEmployed)

gt U$OUTOFLABOURFORCE lt-

+ asnumeric(U$LabourMarketStatus==OutOfLabourForce)

gt U$Unemployed lt-

+ asnumeric(U$LabourMarketStatus==Unemployed)

We save a data set keeping all observations containing nonndashavailables

gt UOriginal lt- U

We then choose the variables to keep in U This code is in araietal_sourceRnwbut not shown here It is also available in araietal_sourceR

Finally we remove all observations containing nonndashavailables from U

gt U lt- naomit(U)

28 Sample statistics

Table 1 compares the number of observations in this sample before (seesection 27 ie data frame UOriginal) and after (see section 27 iedata frame U) nonndashavailables are removed with the numbers of observations

18

reported by the Bisin et al (2008) study The number of observations forvarious groups in the nonndashMuslim category are not reported in the Bisinet al (2008) paper These numbers are therefore missing in the table Thecategory definitions are from the original dataset and involves no recodingon our part

After removing observations with missing values on all variables of inter-est (ldquoAfterrdquo in Table 1) we are left with 330 Muslims and 488 non-MuslimsThe sample selections induced by the choice of variables and the missingvalues in these variables lead to a loss of 57 percent of the relevant sampleof the original data3

The sample means reported in Bisin et al (2008) seem to be unweightedSince data instructions says that the data should always be weighted tables2 and 3 report weighted and unweighted sample means before and afterremoval of non-availables and the Bisin et al (2008) data4 Comparingmeans the Bisin et al (2008) data seem to be different from the originalsample The variables Attitude Towards Inter Marriage and Importance ofRacial Composition in Schools in Bisin et al (2008) data deviate largelyfrom corresponding averages in the original data The deviation is extremein case of Importance of Racial Composition in Schools The original samplehas a mean for this variable that is 2 percent for non-Muslims (compare with33 percent in Bisin et al (2008)) and 6 percent for Muslims (compare with65 percent in Bisin et al (2008)) Due to this extremely skewed distributionit is hardly meaningfull to run a regression on this variable

Notice that also the distribution of the variable Importance of Religionwould be extremely skewed using standard coding of this type of variablesSuch a coding would imply that religion is important when the respondentanswer ldquoVery Importantrdquo and ldquoFairly Importantrdquo to the question ldquoHow im-portant is religion to the way you live your liferdquo

The sample means in our data after removing accumulated missing val-ues due to all variables in the estimations deviate marginally in general fromthe original data The similarities here are partly due to the fact that thestatistics are based exactly on the same variable definition in our implemen-tation In some respects the deviations are larger For further comparisonswe refer to Tables 2 and 3

Due to the fact that the large majority of observations from the originaldata are lost the remaining sample is likely to be contaminated with sampleselection bias To compare the characteristics of the remaining sample withthe original sample says something about systematic attrition with respectto observables The sample selection bias with respect to unobservablescannot however be resolved

3The variables written in capital letters are created to ensure wellndashdefined referencecategories They are included in our regressions but we cannot say whether they areincluded in the regressions of Bisin et al (2008)

4See FNSEM (1993b)

19

3 Regression Results

We use linear probability models (LPM)5 Our results are presented in Tables4 and 5 Bisin et al (2008) write that

1 ldquoMuslims integrate less and more slowly than non-Muslimsrdquo (abstractp 445) and

2 ldquo there is no evidence that segregated neighbourhoods breed intensereligious and cultural identities On the contrary intense identitiesin our data are more prominent in relatively mixed neighbourhoodsrdquo(p 446)

The first claim is based on their reported results concerning the variableYears Since Arrival In this way Bisin et al (2008) compare cohorts ofMuslims and non Muslims and attempt to say something about the evolutionof values over time They do not follow individuals over time but nonethelesscall these cohort differences ldquoIntegration over timerdquo They report negativecoefficients for Years Since Arrival but the estimates are smaller in absolutevalue for Muslims than for non-Muslims In our case the coefficients forYears Since Arrival reported in Tables 4 and 5 are insignificant in all casesexcept in the regression for Importance of Religion for Muslims where it isnegative This is opposite to what Bisin et al (2008) claim

The second claim is based on their reported results concerning the vari-able Ward Density Own Ethnicity Bisin et al (2008) report negative andsignificant estimates for Ward Density Own Ethnicity in all six specifica-tions Their negative coefficient for this variable would imply that ethnicminorities put more weight on religion mind more about inter-ethnic mar-riage and have stronger taste for ethnically profiled schools as we move fromneighbourhoods (Wards) with high density of their own ethnicity to neigh-bourhoods where people from their own ethnicity are scarce This is not atall what we find in our replication

5Bisin et al (2008) use probit estimations Our attempts to use probit run into con-vergence problems The convergence problems are severe for the model using Importanceof Racial Composition in Schools as dependent variable Hence our choice of LPM

Another issue is that Bisin et al (2008) should have included dummy variables indicatingreligious affiliation Christians Sikhs and others in the non-Muslim category to checksimilarities and differences among non-Muslims as well In this respect we follow theirmodel specification

Moreover Bisin et al (2008) should have adjusted for within ward correlations Thismight matter for their standard errors which might be underestimated In our case withalmost no significant results this would not matter much The variable is not available inthe data set and we did not make much effort to obtain it

All estimated models include 7 UK-region dummies and the variables DONOT

SPEAKWITHOLDER DONOTSPEAKATWORK and DONOTSPEAKWITHFRIENDS It turnedout that the variable DONOTSPEAKWITHYOUNGER is TRUE for few observations and can-not be included in the model

20

In our estimations the estimated coefficients for this variable are allpositive but far from significant The P-values are 069 097 and 059 forMuslims and 015 014 and 03 for non-Muslims contradicting the Bisin et al(2008) results

Inspecting the results presented in Tables 4 and 5 there are many sim-ilarities and few differences in the estimated coefficients for Muslims andnon-Muslims Our results are generally very different from results reportedby Bisin et al (2008) We are however doubtful whether it is possibleto draw any reliable inference from these results due to great loss of ob-servations and possible sample selection bias together with the problem ofendogeneity (also mentioned by Bisin et al (2008))

4 Concluding remarks

The Bisin et al (2008) paper rests on fragile grounds Our examinationof the data using their variable definitions and the same set-up indicatesthat their claims about differences between Muslims and non-Muslims andtheir conclusion that strong ReligiousEthnic identities are found in mixedneighbourhoods does not hold There is no systematic relation betweenethnic minoritiesrsquo views on religion inter-ethnic marriage or ethnic profile ofschools and the density of their own ethnic minority in their neighbourhoodHowever we hesitate to draw inference from these results since the great lossof observations (57 percent) implies that the remaining sample is most likelynot representative

5 Production notes

To facilitate reproducibility and save others timely interpretations of whatis done in this paper we attempt to follow Literate Statistical Programmingprocedures For documenting our results we have used Sweave by Leisch(2002) in combination with the LATEX family of programs using the packagesinputenc fontenc natbib Sweave fancyvrb color url hyperref andmultirow

All code (R code LATEX code and BibTEX data base code) used to do theeconometric estimations to produce this technical documentation includingall tables and to produce the companion paper Arai et al (2008) is containedin the file araietal_sourceRnw

The estimations and the documentation can be reproduced in by follow-ing the instructions at the top of the file httppeoplesuse~lundhfragile_groundsaraietal_sourceRnw

Our results were obtained on a i486-pc-linux-gnu platform using R ver-sion 281 beta (2008-12-12 r47173) (R Development Core Team 2008) with

21

251 Religious affiliation

Question s6 asks whether the respondent belongs to a church or has a re-ligion For those who answer yes question s7 asks which that church orreligion is We define two religious affiliations muslim (which are all whoanswered category 3 (muslim) on question s7) and non-muslim (all who didnot answer category 3 (muslim) on question s7) All nonndashreligious that isthose who answered category 2 on question s6 are already removed from thesample Observations containing na are recoded to NA (FNSEM 1993a p112f)

gt isna(U$s7) lt- U$s7==na

gt U$Religion lt- ifelse(U$s7==muslimmuslimnon-muslim)

252 Importance of religion

Question s9 is about the importance of religion To grade the Importanceof Religion respondents have to choose between the following categories 1not at all important 2 not very important 3 fairly important or 4 veryimportant Following standard coding practice of such questions 1 and 2should be one category and 3 and 4 another but Bisin et al (2008) chooseto put 1 2 and 3 in the same category

Among those who have answered the question very few have chosenthe alternative 1 or 2 in their answer implying very skewed distributionFollowing Bisin et al (2008) code 4 (lsquoVery importantrsquo) as answer on questions9 is coded TRUE else FALSE Codes 8 (lsquoCanrsquot sayrsquo) and 9 are coded asNA (FNSEM 1993a p 112f)

gt isna(U$s9) lt- U$s9==8 | U$s9==9

gt U$ImportanceofReligion lt- U$s9 == 4

253 Attitude towards inter-marriage

Question s34a is ldquoWould you personally mind if a close relative were tomarry a white personrdquo It serves as a filter question to s34b (ldquoWould youmind very much or just a littlerdquo) which is asked to those who answered yes(cod 1) on s34a We code those who answer yes on both questions (Mind ampMind very much) as TRUE The category FALSE refers then to those who(Do not mind) or (Mind amp Mind Little) Code 8 (Canrsquot say) on s34a andcode 9 on s34a and on s34b are assigned as NA (FNSEM 1993a p 125)

gt isna(U$s34a) lt- U$s34a==8 | U$s34a==9

gt isna(U$s34b) lt- U$s34b==8

gt U$AttitudeTowardsInterMarriage lt-

+ U$s34a==1 amp U$s34b==1

7

254 Importance of racial composition in schools

Two questions are asked s23 is

ldquoIf you were choosing a school for an eleven-year old child ofyours would your choice be influenced by how many (RESPON-DENTrsquoS ETHNIC ORIGIN) children there were in the schoolrdquo(FNSEM 1993a p 120)

and s24a asks that if the available school were similar in other ways wouldyou prefer to send this child to school with fewer than half of the pupils(code 1) about half of the students (code 2) more than half (code 3) wereof your own ethnic origin s23 is not a filter question Importance of RacialComposition in Schools is set to TRUE if s24a is equal to 3 and FALSEotherwise Code 7 (No preference) is coded as FALSE Codes 8 (Canrsquot say)and 9 are assigned as NA (FNSEM 1993a p 120)

gt isna(U$s24a) lt- U$s24a==8 | U$s24a==9

gt U$ImportanceofRacialCompositioninSchools lt- U$s24a==3

255 Born in the UK

Defines who is born in the United Kingdom (question a3) Category 16 isNorthern Ireland category 17 England and Wales and category 18 ScotlandCode 99 is assigned as NA (FNSEM 1993a p 107)

gt isna(U$a3)lt- U$a3==99

gt U$BornintheUK lt- U$a3==16 | U$a3==17 | U$a3==18

256 Age at and years since arrival

This part defines the variables Age at arrival and Years since arrival by usinginformation about year of migration (question a4n) age (question a1an) andthe year (variable year when the interview is made) The interview is madein 93 or 94) The result is that some individuals get the age at arrival minus1which presumably is due to rounding of years since arrival and the age

All born in the UK are coded as 0 for AgeatArrival and YearsSinceArrival Since these two variables are related to the age of the immigrantsone could also add an interaction variable between BornintheUK and Ageto account for effect of age for the natives The effect of age for natives is notrepresented in the model as specified by Bisin et al (2008) We experimentedwith this and results were basically unchanged The interaction variableis insignificant in all specifications Code 99 is assigned as NA for year(FNSEM 1993a pp 105 107)

8

gt isna(U$a4n) lt- U$a4n==98 | U$a4n==99

gt isna(U$year)lt- U$year==99

gt U$Age lt- U$a1an

gt U$YearsSinceArrival lt- ifelse(U$BornintheUK==TRUE

+ 0 U$year-U$a4n)

gt U$AgeatArrival lt- U$Age - U$YearsSinceArrival

gt U$AgeatArrival lt- replace(U$AgeatArrival

+ U$BornintheUK==TRUE 0)

257 Female

Definition of females via question hh2as Code na is coded as NA (FNSEM1993a pp 318)

gt isna(U$hh2as) lt- U$hh2as ==na

gt U$Female lt- U$hh2a==female

258 Arranged marriage

In question s39 Indian Pakistani Bangladeshi and Chinese respondents whohas ever been married were asked a question about the decision regardingtheir marriage The question ask about the role of the respondent and hisor her parents about choosing the respondentrsquos husbandwife In categories1 and 2 of s39 the respondentrsquos parents made the final decision and thesecategories define the dummy where the respondent is or has been living inan arranged marriage (code 1 all other are coded 0) Notice that singlesand Caribbeans have not received this question Singles are already removedfrom the sample (see section 23 Caribbeans are coded 0 This means thatthe Caribbeans do not marry according to the decision of their parentsCategory 8 (Canrsquot say) and category 9 are assigned NA (FNSEM 1993app 127)

gt isna(U$s39) lt- U$s39==8 | U$s39==9

gt U$ArrangedMarriage lt- ifelse(

+ U$s39==3 | U$s39==4 | U$s39==5 |

+ U$ethnic==caribbean FALSE TRUE)

259 Discrimination

The discrimination variable is based on a series of questions related to dis-crimination v1a-v1d about physical attacks v9a about insults j55a andj63a discrimination at work Basically anyone answering that they havebeen discriminated for any of these reasons are coded 1 else code 0

Questions v1a-v1d is a series of filter questions Question v1a asks if therespondent have been attacked (yes or no) question v1b how many attacks

9

the respondent has been enduring and question v1c asks those who havebeen attacked once if they believe the attack had to do with reasons todo with race or colour and v1d asks the same question and regards thosewho have been attacked more than once Generally code 8 and code 9are assigned NA except for question v1b where also code 7 is assigned NA(FNSEM 1993a pp 154ff 163 195 and 199)

gt vjlist lt- c(paste(v1letters[14]sep=)

+ v9aj55aj63a)

gt isna(U[vjlist]) lt- U[vjlist]==8 |

+ U[vjlist]==9

gt isna(U$v1b) lt- U$v1b==7 | U$v1b==8 |

+ U$v1b==9

gt U$Discrimination lt-

+ (U$v1a==1 amp U$v1b==1 amp U$v1c==1) |

+ (U$v1a==1 amp

+ (U$v1b gt= 2 amp U$v1b lt= 6) amp

+ U$v1d==1) | U$v9a==1 | U$j55a==1 |

+ U$j63a==1

2510 Children

No question about the number of children is asked Instead the number ofchildren has to be calculated indirectly via the number of children not livingat home (questions f16a and f16b1n-f16b3n) and the relation between therespondent and other persons living in the household (questions hh2cb-hh2cm)

The number of children out of home is calculated in the following wayIf children out of home is TRUE (f16a=1) then the number of childrenequals the sum of f16b1n f16b2n and f16b3n (the number of children notliving at home below 5 years between 5 and 15 years and above 15 yearsof age) Else if there are no children out of home (ie if f16a=2) thenthe number of children out of home is set to 0 Missing values are coded asbelow (FNSEM 1993a p 57)

gt isna(U$f16a) lt- U$f16a==8 | U$f16a==9

gt isna(U$f16b1n) lt- U$f16b1n==99

gt isna(U$f16b2n) lt- U$f16b2n==99

gt isna(U$f16b3n) lt- U$f16b3n==98 |

+ U$f16b3n==99

gt U$ChildnotatHome lt- ifelse(U$f16a==1

+ U$f16b1n+U$f16b2n+U$f16b3n0)

Questions hh2cb-hh2cm are about the relationship between the re-spondent and other individuals in the household (person b c d etc to person

10

m) category 5 being child of the respondent First we check if the personis a child to the respondent and then all children are summed over the re-spondents household adding the variable measuring number of children notat home Generally codes 98 and 99 are assigned NA (FNSEM 1993a p318)

gt hhlist lt- c(paste(hh2c letters[213] sep=))

gt isna(U[hhlist]) lt- U[hhlist]==98 | U[hhlist]==99

gt U$Children lt-

+ U$ChildnotatHome + apply(apply(subset(U

+ select=c(hh2cbhh2cm)) 2

+ function(x) x==5)1 function(x) sum(x

+ narm=TRUE))

2511 No British education

Question q1 asks whether the respondent has any British education Code2 is no Code 8 (Canacutet say) is kept in the alternative category since theseindividuals will answer the question q3 about foreign education Code 9 inq1 is assigned NA (FNSEM 1993a p 96)

gt isna(U$q1) lt- U$q1==9

gt U$NoBritishEducation lt- U$q1==2

2512 British basic education

We could not exactly see how this variable was defined in Bisin et al (2008)They define the British high education as Andashlevel and above One interpre-tation is then that O-level are educations included in the basic level Thisinterpretation is implemented here NA is assigned to all observations forwhich the filter question No British Education was NA (FNSEM 1993a pp96ff)

gt q2alist lt- c(paste(q2a c(181218) sep=))

gt U$BritishBasicEducation lt-

+ apply(apply(U[q2alist] 2function(x)

+ x==1)1 function(x) sum(x narm=TRUE))=0

gt U$BritishBasicEducation lt- ifelse(

+ isna(U$NoBritishEducation)

+ NAU$BritishBasicEducation)

11

2513 British higher education

Bisin et al (2008) explicitly defined British higher education as A-levelGiven the definition of British Basic education the reference group willinclude trade apprenticeships as well as university educations NA is assignedto all observations for which the filter question No British Education wasNA (FNSEM 1993a pp 96ff)

gt U$BritishHigherEducation lt- apply(apply(subset(

+ Uselect=c(q2a9q2a11q2a19q2a20))

+ 2function(x) x==1)1

+ function(x) sum(x narm=TRUE))=0

gt U$BritishHigherEducation lt- ifelse(

+ isna(U$NoBritishEducation)

+ NAU$BritishHigherEducation)

2514 Foreign education

Foreign educations is question q3 asked to all who answered lsquonorsquo or lsquoCanrsquotrsquosayrsquo on question q1 The answer yes is coded as (code 1) and no is codedas(code 2) Contrary to the above educational variables NA is not assignedto all NA on No British Education since some of them (code 8) actually wasasked the question q3 Instead NA is assigned to all observations for whichthe filter question q1 was 1 or 9 and to all code 8 (Canrsquot say) and code 9 onquestion q3 (FNSEM 1993a p 99)

gt isna(U$q3) lt- U$q3==8 | U$q3==9

gt U$ForeignEducation lt- U$q3==1

2515 Labour market status

We code the labour market status using j1b (in paid work last week or not)and j3occ (classification of activity either last weekrsquos activity or potentialactivity during the last ten years) The variable j1b takes the value 1 forpaid work last week and 2 otherwise The variable j3occ is coded as follows(FNSEM 1993ab the former pp 81f)

1 Self-employed (25+ employees)2 Self-employed (1-24 employees)3 Self-employed (no employees)4 Self-employed (employees not known)5 Manager (establishment of 25+ employees)6 Manager (establishment of 1-24 employees)7 Manager (employees not known)8 Foremansupervisor9 Other employee

12

10 Employee status unknown11 Not knownnot answered

Employee In order to be classified as an employee the individual has tohave answered yes (value 1) in j1b and be classified as employee in j3occ(value 9) and have the value NotAssignedNA is assigned to categories 10and 11 in j3occ

gt isna(U$j3occ) lt- U$j3occ==10 | U$j3occ==11

gt U$LabourMarketStatus lt- ifelse(U$j1b==1 amp

+ U$j3occ==9 amp isna(U$j1b==1 amp

+ U$j3occ==9) EmployeeNotAssigned)

Self Employed Selfndashemployed are also coded using j1b and j3occ aboveCategories 1minus 4 in j3occ are defined as self-employed We also require thatj1b is equal 1 (SelfndashEmployed)

gt U$LabourMarketStatus lt- replace(

+ U$LabourMarketStatus

+ (U$j3occ==1 | U$j3occ==2 |

+ U$j3occ==3 | U$j3occ==4) amp

+ U$j1b==1SelfEmployed)

Manager Managers are also coded using j1b and j3occ see above Cat-egories 5minus 8 in j3occ are defined as managers (including supervisors)

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ U$j1b==1 amp U$j3occgt4 amp U$j3occlt9

+ Manager)

Unemployed The question hh5ds describes the respondentrsquos labour mar-ket status Unemployment is defined via this variable hh5ds is coded inthe following way (FNSEM 1993b)

1 Full-time education2 Govt training programme3 Full-time paid work4 Part-time paid work5 Waiting to take up paid work6 Registered unemployed7 Unemployed not registered8 Permanently sick or disabled9 Wholly retired from work

13

10 Looking after the home11 Doing something else12 NA

We define unemployed as category 6 and 7 (Bisin et al 2008 p 324)There are few cases where the individual is classified as Employee accordingto our definition above and is reported to be unemployed in hh5ds Thiscan for example be part-time unemployment We classify these individualsas having Unclear labour market status These will be checked later andbe removed if they are few in the final sample

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==6 | U$hh5ds == 7) amp

+ U$LabourMarketStatus=NotAssigned

+ Unclear)

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==6 | U$hh5ds == 7) amp

+ U$LabourMarketStatus==NotAssigned

+ Unemployed)

Out of labour force The category out of labour force is defined as thosehaving values (125891011) in hh5ds or value 2 in j1b or j2

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==1 | U$hh5ds == 2 |

+ U$hh5ds==5 | U$hh5ds == 8 |

+ U$hh5ds==9 | U$hh5ds == 10|

+ U$hh5ds==11 | U$j1b == 2 |

+ U$j2 == 2 ) amp

+ U$LabourMarketStatus==NotAssigned

+ OutOfLabourForce)

Remaining observations with the value NotAssigned in Labour Mar-ketStatus will be assigned NA At this point there are 4 observations codedas Unclear These are now recoded as NA

gt isna(U$LabourMarketStatus) lt-

+ U$LabourMarketStatus == NotAssigned

gt isna(U$LabourMarketStatus) lt-

+ U$LabourMarketStatus == Unclear

We will create dummy variables using this variable before we run ourmodels

14

2516 No parents

Variable No parents means that the respondent is not living with his orher parents This variable is coded with question f14a (which takes code 1for both alive code 2 father alive code 3 for mother alive and 8 for bothdead) and f14b (which takes code 2 if both living parents do not live withthe respondent and code 6 if the only living parent does not live with therespondent else it takes one of the values 1 or 3 minus 5) No parents shouldbe coded TRUE if either both parents are dead or the respondent does notlive with any living parents However since we follow Bisin et al (2008) wecode this variable including only those who have both their parents dead orboth paprents live away from the respondent This definition implies thatthose who have a parent living away and one parent dead are assigned thevalue FALSE Code 8 and 9 are assigned NA (FNSEM 1993a p 56)

gt isna(U$f14a) lt- U$f14a==9

gt isna(U$f14b) lt- U$f14b==9

gt U$NoParents lt- U$f14a==8 | U$f14b==2

2517 Contacts with parents

The three variables measuring contacts with parents are defined via threequestions asking about the number of physical contacts (question f15bn)the number of contacts via telephone (question f15cn) and the number ofcontacts via letters (question f15dn) that the respondent has had with hisor her parents during the last four weeks conditional on not both parentsbeing dead All three takes the value of the underlaying variable if at leastone parent is alive and the value 0 if both parents are dead Code 999 onall three variables and code 997 on f15bn are assigned NA (FNSEM 1993ap 56)

gt isna(U$f15bn) lt- U$f15bn==997 | U$f15bn==999

gt isna(U$f15cn) lt- U$f15cn==999

gt isna(U$f15dn) lt- U$f15dn==999

gt U$ParentsPhysicalContacts lt- ifelse(

+ U$f14a=8 U$f15bn0)

gt U$ParentsTelephoneCalls lt- ifelse(

+ U$f14a=8 U$f15cn0)

gt U$ParentsLetters lt- ifelse(

+ U$f14a=8 U$f15dn0)

2518 English language

There are several language variables measuring whether the respondent isspeaking English with different individuals at home with older at home

15

with younger at work and with friends We construct theses variables usingquestion s12a which asks whether the respondent regularly speak to anyonein Britain in any other language than English and s12f s12g s12hand s12i which asks which language is spoken to the above mentionedcategories of individuals Each of s12f s12g s12h and s12i comesin 18 versions (eg s12f1 s12f18) where each question 1ndash15 iscoded yes if the respondent speaks the language Question 16 is ldquoNeverspeaks to these peopleNot ap[plicable]rdquo question 17 NA and question 18ldquo None of the above answered positiverdquo Question 1 is always regardingEnglish

The respondent is coded as English speaker if either s12a is answerednegatively or s12aX1 where X=fghi is answered positively Onlyrespondents for which either s12a is NA or all of s12X1 s12X16 areanswered negatively are coded as NA Below is the code for English SpokenAt Home With Older

gt isna(U$s12a) lt- U$s12a== 8 | U$s12a == 9

gt s12flist lt- c(paste(s12f 216 sep=))

gt U$oOLD lt- apply(U[s12flist]==yes 1 sum)

gt U$EnglishSpokenatHomewithOlder lt-

+ ((U$s12a==1 | isna(U$s12a)) amp

+ U$s12f1==yes) | U$s12a==2

gt isna(U$EnglishSpokenatHomewithOlder) lt-

+ U$oOLD==0 amp

+ U$EnglishSpokenatHomewithOlder==FALSE

gt U$EnglishSpokenatHomewithOlder lt-

+ replace(U$EnglishSpokenatHomewithOlder

+ U$oOLDgt0 amp

+ isna(U$EnglishSpokenatHomewithOlder)FALSE)

gt U$DONOTSPEAKWITHOLDER lt- ifelse(U$s12f16==no01)

The codes for English Spoken At Home With Younger English SpokenAt Work and English Spoken With Friends are equivalent These codesare in araietal_sourceRnw but not shown here It is also available inaraietal_sourceR

2519 Household income

The question hh40 provides information in which interval the householdincome of the respondentrsquos household is We assign the midpoints in theseintervals as the household income For the lowest bracket this income is themidpoint of [0 77] For the highest bracket we assign the income which is thelowest income in the bracket plus the income interval down to the midpintof the second highest bracket ie 789 + 788minus731

2 = 8175 This method

16

underestimates the range of the highest bracket but to a lesser extent thanthe lower limit 789

gt isna(U$hh40) lt- U$hh40==refused |

+ U$hh40==cant say | U$hh40==na

gt U$HouseholdIncome lt- c(3859651350

+ 173523052600318036604140

+ 471552955870649570207595

+ 8175)[U$hh40]

2520 Ward variables

Ward density of own ethnic group is measured by the variable wown in theoriginal data This variable is coded 1 minus 7 depending on the density of therespondentrsquos own ethnic group is in the ward of the respondent (FNSEM1993b)

1 Up to 1992 2-4993 5-9994 10-14995 15-24996 25-32997 33 or more

We recode the variable to take the midpoints of the density intervals in thesame fashion as the household income variable was recoded This meansthat if the respondent is the lowest interval the density is set to be 1 etc Inthe highest interval we set the density to be the lowest density in the intervalplus the density distance down to the midpoint of the second highest densityinterval ie 33 + 33minus25

2 = 37The variable wunemp is coded 1minus 6 depending on unemployment rate in

the ward of the respondent (FNSEM 1993b)

1 Up to 1992 2-4993 5-9994 10-14995 15-206 20 or more

We recode this variable to instead take the midpoints of the intervals aswe did for the household income This means that if the respondent is thelowest interval the the rate is set to be 1 etc In the highest interval we setthe rate to be the lowest rate in the interval plus the distance down to themidpoint of the second highest rate interval ie 20 + 20minus15

2 = 225

17

gt U$WardDensityOwnEthnicity lt- (c(1035

+ 75125200290370)[U$wown])100

gt U$WardUnemploymentRate lt- c(103575125

+ 175225)[U$wunemp]

26 Discrimination own ethnicity

Finally we define a variable describing the discrimination against the ownethnic group It is defined as the average of the variable Discrimination overethnic groups after the removal of nonndashavailables

gt GroupDiscrimination lt-

+ tapply(U$DiscriminationU$ethnic

+ function(x) mean(x narm=TRUE))

gt U$DiscriminationOwnEthnicity lt-

+ GroupDiscrimination[U$ethnic]

27 Defining the subset

We define dummy variables for labour market status to have the same vari-able labels as in Bisin et al (2008)

gt U$Employee lt- asnumeric(U$LabourMarketStatus==Employee)

gt U$Manager lt- asnumeric(U$LabourMarketStatus==Manager)

gt U$SelfEmployed lt-

+ asnumeric(U$LabourMarketStatus==SelfEmployed)

gt U$OUTOFLABOURFORCE lt-

+ asnumeric(U$LabourMarketStatus==OutOfLabourForce)

gt U$Unemployed lt-

+ asnumeric(U$LabourMarketStatus==Unemployed)

We save a data set keeping all observations containing nonndashavailables

gt UOriginal lt- U

We then choose the variables to keep in U This code is in araietal_sourceRnwbut not shown here It is also available in araietal_sourceR

Finally we remove all observations containing nonndashavailables from U

gt U lt- naomit(U)

28 Sample statistics

Table 1 compares the number of observations in this sample before (seesection 27 ie data frame UOriginal) and after (see section 27 iedata frame U) nonndashavailables are removed with the numbers of observations

18

reported by the Bisin et al (2008) study The number of observations forvarious groups in the nonndashMuslim category are not reported in the Bisinet al (2008) paper These numbers are therefore missing in the table Thecategory definitions are from the original dataset and involves no recodingon our part

After removing observations with missing values on all variables of inter-est (ldquoAfterrdquo in Table 1) we are left with 330 Muslims and 488 non-MuslimsThe sample selections induced by the choice of variables and the missingvalues in these variables lead to a loss of 57 percent of the relevant sampleof the original data3

The sample means reported in Bisin et al (2008) seem to be unweightedSince data instructions says that the data should always be weighted tables2 and 3 report weighted and unweighted sample means before and afterremoval of non-availables and the Bisin et al (2008) data4 Comparingmeans the Bisin et al (2008) data seem to be different from the originalsample The variables Attitude Towards Inter Marriage and Importance ofRacial Composition in Schools in Bisin et al (2008) data deviate largelyfrom corresponding averages in the original data The deviation is extremein case of Importance of Racial Composition in Schools The original samplehas a mean for this variable that is 2 percent for non-Muslims (compare with33 percent in Bisin et al (2008)) and 6 percent for Muslims (compare with65 percent in Bisin et al (2008)) Due to this extremely skewed distributionit is hardly meaningfull to run a regression on this variable

Notice that also the distribution of the variable Importance of Religionwould be extremely skewed using standard coding of this type of variablesSuch a coding would imply that religion is important when the respondentanswer ldquoVery Importantrdquo and ldquoFairly Importantrdquo to the question ldquoHow im-portant is religion to the way you live your liferdquo

The sample means in our data after removing accumulated missing val-ues due to all variables in the estimations deviate marginally in general fromthe original data The similarities here are partly due to the fact that thestatistics are based exactly on the same variable definition in our implemen-tation In some respects the deviations are larger For further comparisonswe refer to Tables 2 and 3

Due to the fact that the large majority of observations from the originaldata are lost the remaining sample is likely to be contaminated with sampleselection bias To compare the characteristics of the remaining sample withthe original sample says something about systematic attrition with respectto observables The sample selection bias with respect to unobservablescannot however be resolved

3The variables written in capital letters are created to ensure wellndashdefined referencecategories They are included in our regressions but we cannot say whether they areincluded in the regressions of Bisin et al (2008)

4See FNSEM (1993b)

19

3 Regression Results

We use linear probability models (LPM)5 Our results are presented in Tables4 and 5 Bisin et al (2008) write that

1 ldquoMuslims integrate less and more slowly than non-Muslimsrdquo (abstractp 445) and

2 ldquo there is no evidence that segregated neighbourhoods breed intensereligious and cultural identities On the contrary intense identitiesin our data are more prominent in relatively mixed neighbourhoodsrdquo(p 446)

The first claim is based on their reported results concerning the variableYears Since Arrival In this way Bisin et al (2008) compare cohorts ofMuslims and non Muslims and attempt to say something about the evolutionof values over time They do not follow individuals over time but nonethelesscall these cohort differences ldquoIntegration over timerdquo They report negativecoefficients for Years Since Arrival but the estimates are smaller in absolutevalue for Muslims than for non-Muslims In our case the coefficients forYears Since Arrival reported in Tables 4 and 5 are insignificant in all casesexcept in the regression for Importance of Religion for Muslims where it isnegative This is opposite to what Bisin et al (2008) claim

The second claim is based on their reported results concerning the vari-able Ward Density Own Ethnicity Bisin et al (2008) report negative andsignificant estimates for Ward Density Own Ethnicity in all six specifica-tions Their negative coefficient for this variable would imply that ethnicminorities put more weight on religion mind more about inter-ethnic mar-riage and have stronger taste for ethnically profiled schools as we move fromneighbourhoods (Wards) with high density of their own ethnicity to neigh-bourhoods where people from their own ethnicity are scarce This is not atall what we find in our replication

5Bisin et al (2008) use probit estimations Our attempts to use probit run into con-vergence problems The convergence problems are severe for the model using Importanceof Racial Composition in Schools as dependent variable Hence our choice of LPM

Another issue is that Bisin et al (2008) should have included dummy variables indicatingreligious affiliation Christians Sikhs and others in the non-Muslim category to checksimilarities and differences among non-Muslims as well In this respect we follow theirmodel specification

Moreover Bisin et al (2008) should have adjusted for within ward correlations Thismight matter for their standard errors which might be underestimated In our case withalmost no significant results this would not matter much The variable is not available inthe data set and we did not make much effort to obtain it

All estimated models include 7 UK-region dummies and the variables DONOT

SPEAKWITHOLDER DONOTSPEAKATWORK and DONOTSPEAKWITHFRIENDS It turnedout that the variable DONOTSPEAKWITHYOUNGER is TRUE for few observations and can-not be included in the model

20

In our estimations the estimated coefficients for this variable are allpositive but far from significant The P-values are 069 097 and 059 forMuslims and 015 014 and 03 for non-Muslims contradicting the Bisin et al(2008) results

Inspecting the results presented in Tables 4 and 5 there are many sim-ilarities and few differences in the estimated coefficients for Muslims andnon-Muslims Our results are generally very different from results reportedby Bisin et al (2008) We are however doubtful whether it is possibleto draw any reliable inference from these results due to great loss of ob-servations and possible sample selection bias together with the problem ofendogeneity (also mentioned by Bisin et al (2008))

4 Concluding remarks

The Bisin et al (2008) paper rests on fragile grounds Our examinationof the data using their variable definitions and the same set-up indicatesthat their claims about differences between Muslims and non-Muslims andtheir conclusion that strong ReligiousEthnic identities are found in mixedneighbourhoods does not hold There is no systematic relation betweenethnic minoritiesrsquo views on religion inter-ethnic marriage or ethnic profile ofschools and the density of their own ethnic minority in their neighbourhoodHowever we hesitate to draw inference from these results since the great lossof observations (57 percent) implies that the remaining sample is most likelynot representative

5 Production notes

To facilitate reproducibility and save others timely interpretations of whatis done in this paper we attempt to follow Literate Statistical Programmingprocedures For documenting our results we have used Sweave by Leisch(2002) in combination with the LATEX family of programs using the packagesinputenc fontenc natbib Sweave fancyvrb color url hyperref andmultirow

All code (R code LATEX code and BibTEX data base code) used to do theeconometric estimations to produce this technical documentation includingall tables and to produce the companion paper Arai et al (2008) is containedin the file araietal_sourceRnw

The estimations and the documentation can be reproduced in by follow-ing the instructions at the top of the file httppeoplesuse~lundhfragile_groundsaraietal_sourceRnw

Our results were obtained on a i486-pc-linux-gnu platform using R ver-sion 281 beta (2008-12-12 r47173) (R Development Core Team 2008) with

21

254 Importance of racial composition in schools

Two questions are asked s23 is

ldquoIf you were choosing a school for an eleven-year old child ofyours would your choice be influenced by how many (RESPON-DENTrsquoS ETHNIC ORIGIN) children there were in the schoolrdquo(FNSEM 1993a p 120)

and s24a asks that if the available school were similar in other ways wouldyou prefer to send this child to school with fewer than half of the pupils(code 1) about half of the students (code 2) more than half (code 3) wereof your own ethnic origin s23 is not a filter question Importance of RacialComposition in Schools is set to TRUE if s24a is equal to 3 and FALSEotherwise Code 7 (No preference) is coded as FALSE Codes 8 (Canrsquot say)and 9 are assigned as NA (FNSEM 1993a p 120)

gt isna(U$s24a) lt- U$s24a==8 | U$s24a==9

gt U$ImportanceofRacialCompositioninSchools lt- U$s24a==3

255 Born in the UK

Defines who is born in the United Kingdom (question a3) Category 16 isNorthern Ireland category 17 England and Wales and category 18 ScotlandCode 99 is assigned as NA (FNSEM 1993a p 107)

gt isna(U$a3)lt- U$a3==99

gt U$BornintheUK lt- U$a3==16 | U$a3==17 | U$a3==18

256 Age at and years since arrival

This part defines the variables Age at arrival and Years since arrival by usinginformation about year of migration (question a4n) age (question a1an) andthe year (variable year when the interview is made) The interview is madein 93 or 94) The result is that some individuals get the age at arrival minus1which presumably is due to rounding of years since arrival and the age

All born in the UK are coded as 0 for AgeatArrival and YearsSinceArrival Since these two variables are related to the age of the immigrantsone could also add an interaction variable between BornintheUK and Ageto account for effect of age for the natives The effect of age for natives is notrepresented in the model as specified by Bisin et al (2008) We experimentedwith this and results were basically unchanged The interaction variableis insignificant in all specifications Code 99 is assigned as NA for year(FNSEM 1993a pp 105 107)

8

gt isna(U$a4n) lt- U$a4n==98 | U$a4n==99

gt isna(U$year)lt- U$year==99

gt U$Age lt- U$a1an

gt U$YearsSinceArrival lt- ifelse(U$BornintheUK==TRUE

+ 0 U$year-U$a4n)

gt U$AgeatArrival lt- U$Age - U$YearsSinceArrival

gt U$AgeatArrival lt- replace(U$AgeatArrival

+ U$BornintheUK==TRUE 0)

257 Female

Definition of females via question hh2as Code na is coded as NA (FNSEM1993a pp 318)

gt isna(U$hh2as) lt- U$hh2as ==na

gt U$Female lt- U$hh2a==female

258 Arranged marriage

In question s39 Indian Pakistani Bangladeshi and Chinese respondents whohas ever been married were asked a question about the decision regardingtheir marriage The question ask about the role of the respondent and hisor her parents about choosing the respondentrsquos husbandwife In categories1 and 2 of s39 the respondentrsquos parents made the final decision and thesecategories define the dummy where the respondent is or has been living inan arranged marriage (code 1 all other are coded 0) Notice that singlesand Caribbeans have not received this question Singles are already removedfrom the sample (see section 23 Caribbeans are coded 0 This means thatthe Caribbeans do not marry according to the decision of their parentsCategory 8 (Canrsquot say) and category 9 are assigned NA (FNSEM 1993app 127)

gt isna(U$s39) lt- U$s39==8 | U$s39==9

gt U$ArrangedMarriage lt- ifelse(

+ U$s39==3 | U$s39==4 | U$s39==5 |

+ U$ethnic==caribbean FALSE TRUE)

259 Discrimination

The discrimination variable is based on a series of questions related to dis-crimination v1a-v1d about physical attacks v9a about insults j55a andj63a discrimination at work Basically anyone answering that they havebeen discriminated for any of these reasons are coded 1 else code 0

Questions v1a-v1d is a series of filter questions Question v1a asks if therespondent have been attacked (yes or no) question v1b how many attacks

9

the respondent has been enduring and question v1c asks those who havebeen attacked once if they believe the attack had to do with reasons todo with race or colour and v1d asks the same question and regards thosewho have been attacked more than once Generally code 8 and code 9are assigned NA except for question v1b where also code 7 is assigned NA(FNSEM 1993a pp 154ff 163 195 and 199)

gt vjlist lt- c(paste(v1letters[14]sep=)

+ v9aj55aj63a)

gt isna(U[vjlist]) lt- U[vjlist]==8 |

+ U[vjlist]==9

gt isna(U$v1b) lt- U$v1b==7 | U$v1b==8 |

+ U$v1b==9

gt U$Discrimination lt-

+ (U$v1a==1 amp U$v1b==1 amp U$v1c==1) |

+ (U$v1a==1 amp

+ (U$v1b gt= 2 amp U$v1b lt= 6) amp

+ U$v1d==1) | U$v9a==1 | U$j55a==1 |

+ U$j63a==1

2510 Children

No question about the number of children is asked Instead the number ofchildren has to be calculated indirectly via the number of children not livingat home (questions f16a and f16b1n-f16b3n) and the relation between therespondent and other persons living in the household (questions hh2cb-hh2cm)

The number of children out of home is calculated in the following wayIf children out of home is TRUE (f16a=1) then the number of childrenequals the sum of f16b1n f16b2n and f16b3n (the number of children notliving at home below 5 years between 5 and 15 years and above 15 yearsof age) Else if there are no children out of home (ie if f16a=2) thenthe number of children out of home is set to 0 Missing values are coded asbelow (FNSEM 1993a p 57)

gt isna(U$f16a) lt- U$f16a==8 | U$f16a==9

gt isna(U$f16b1n) lt- U$f16b1n==99

gt isna(U$f16b2n) lt- U$f16b2n==99

gt isna(U$f16b3n) lt- U$f16b3n==98 |

+ U$f16b3n==99

gt U$ChildnotatHome lt- ifelse(U$f16a==1

+ U$f16b1n+U$f16b2n+U$f16b3n0)

Questions hh2cb-hh2cm are about the relationship between the re-spondent and other individuals in the household (person b c d etc to person

10

m) category 5 being child of the respondent First we check if the personis a child to the respondent and then all children are summed over the re-spondents household adding the variable measuring number of children notat home Generally codes 98 and 99 are assigned NA (FNSEM 1993a p318)

gt hhlist lt- c(paste(hh2c letters[213] sep=))

gt isna(U[hhlist]) lt- U[hhlist]==98 | U[hhlist]==99

gt U$Children lt-

+ U$ChildnotatHome + apply(apply(subset(U

+ select=c(hh2cbhh2cm)) 2

+ function(x) x==5)1 function(x) sum(x

+ narm=TRUE))

2511 No British education

Question q1 asks whether the respondent has any British education Code2 is no Code 8 (Canacutet say) is kept in the alternative category since theseindividuals will answer the question q3 about foreign education Code 9 inq1 is assigned NA (FNSEM 1993a p 96)

gt isna(U$q1) lt- U$q1==9

gt U$NoBritishEducation lt- U$q1==2

2512 British basic education

We could not exactly see how this variable was defined in Bisin et al (2008)They define the British high education as Andashlevel and above One interpre-tation is then that O-level are educations included in the basic level Thisinterpretation is implemented here NA is assigned to all observations forwhich the filter question No British Education was NA (FNSEM 1993a pp96ff)

gt q2alist lt- c(paste(q2a c(181218) sep=))

gt U$BritishBasicEducation lt-

+ apply(apply(U[q2alist] 2function(x)

+ x==1)1 function(x) sum(x narm=TRUE))=0

gt U$BritishBasicEducation lt- ifelse(

+ isna(U$NoBritishEducation)

+ NAU$BritishBasicEducation)

11

2513 British higher education

Bisin et al (2008) explicitly defined British higher education as A-levelGiven the definition of British Basic education the reference group willinclude trade apprenticeships as well as university educations NA is assignedto all observations for which the filter question No British Education wasNA (FNSEM 1993a pp 96ff)

gt U$BritishHigherEducation lt- apply(apply(subset(

+ Uselect=c(q2a9q2a11q2a19q2a20))

+ 2function(x) x==1)1

+ function(x) sum(x narm=TRUE))=0

gt U$BritishHigherEducation lt- ifelse(

+ isna(U$NoBritishEducation)

+ NAU$BritishHigherEducation)

2514 Foreign education

Foreign educations is question q3 asked to all who answered lsquonorsquo or lsquoCanrsquotrsquosayrsquo on question q1 The answer yes is coded as (code 1) and no is codedas(code 2) Contrary to the above educational variables NA is not assignedto all NA on No British Education since some of them (code 8) actually wasasked the question q3 Instead NA is assigned to all observations for whichthe filter question q1 was 1 or 9 and to all code 8 (Canrsquot say) and code 9 onquestion q3 (FNSEM 1993a p 99)

gt isna(U$q3) lt- U$q3==8 | U$q3==9

gt U$ForeignEducation lt- U$q3==1

2515 Labour market status

We code the labour market status using j1b (in paid work last week or not)and j3occ (classification of activity either last weekrsquos activity or potentialactivity during the last ten years) The variable j1b takes the value 1 forpaid work last week and 2 otherwise The variable j3occ is coded as follows(FNSEM 1993ab the former pp 81f)

1 Self-employed (25+ employees)2 Self-employed (1-24 employees)3 Self-employed (no employees)4 Self-employed (employees not known)5 Manager (establishment of 25+ employees)6 Manager (establishment of 1-24 employees)7 Manager (employees not known)8 Foremansupervisor9 Other employee

12

10 Employee status unknown11 Not knownnot answered

Employee In order to be classified as an employee the individual has tohave answered yes (value 1) in j1b and be classified as employee in j3occ(value 9) and have the value NotAssignedNA is assigned to categories 10and 11 in j3occ

gt isna(U$j3occ) lt- U$j3occ==10 | U$j3occ==11

gt U$LabourMarketStatus lt- ifelse(U$j1b==1 amp

+ U$j3occ==9 amp isna(U$j1b==1 amp

+ U$j3occ==9) EmployeeNotAssigned)

Self Employed Selfndashemployed are also coded using j1b and j3occ aboveCategories 1minus 4 in j3occ are defined as self-employed We also require thatj1b is equal 1 (SelfndashEmployed)

gt U$LabourMarketStatus lt- replace(

+ U$LabourMarketStatus

+ (U$j3occ==1 | U$j3occ==2 |

+ U$j3occ==3 | U$j3occ==4) amp

+ U$j1b==1SelfEmployed)

Manager Managers are also coded using j1b and j3occ see above Cat-egories 5minus 8 in j3occ are defined as managers (including supervisors)

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ U$j1b==1 amp U$j3occgt4 amp U$j3occlt9

+ Manager)

Unemployed The question hh5ds describes the respondentrsquos labour mar-ket status Unemployment is defined via this variable hh5ds is coded inthe following way (FNSEM 1993b)

1 Full-time education2 Govt training programme3 Full-time paid work4 Part-time paid work5 Waiting to take up paid work6 Registered unemployed7 Unemployed not registered8 Permanently sick or disabled9 Wholly retired from work

13

10 Looking after the home11 Doing something else12 NA

We define unemployed as category 6 and 7 (Bisin et al 2008 p 324)There are few cases where the individual is classified as Employee accordingto our definition above and is reported to be unemployed in hh5ds Thiscan for example be part-time unemployment We classify these individualsas having Unclear labour market status These will be checked later andbe removed if they are few in the final sample

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==6 | U$hh5ds == 7) amp

+ U$LabourMarketStatus=NotAssigned

+ Unclear)

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==6 | U$hh5ds == 7) amp

+ U$LabourMarketStatus==NotAssigned

+ Unemployed)

Out of labour force The category out of labour force is defined as thosehaving values (125891011) in hh5ds or value 2 in j1b or j2

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==1 | U$hh5ds == 2 |

+ U$hh5ds==5 | U$hh5ds == 8 |

+ U$hh5ds==9 | U$hh5ds == 10|

+ U$hh5ds==11 | U$j1b == 2 |

+ U$j2 == 2 ) amp

+ U$LabourMarketStatus==NotAssigned

+ OutOfLabourForce)

Remaining observations with the value NotAssigned in Labour Mar-ketStatus will be assigned NA At this point there are 4 observations codedas Unclear These are now recoded as NA

gt isna(U$LabourMarketStatus) lt-

+ U$LabourMarketStatus == NotAssigned

gt isna(U$LabourMarketStatus) lt-

+ U$LabourMarketStatus == Unclear

We will create dummy variables using this variable before we run ourmodels

14

2516 No parents

Variable No parents means that the respondent is not living with his orher parents This variable is coded with question f14a (which takes code 1for both alive code 2 father alive code 3 for mother alive and 8 for bothdead) and f14b (which takes code 2 if both living parents do not live withthe respondent and code 6 if the only living parent does not live with therespondent else it takes one of the values 1 or 3 minus 5) No parents shouldbe coded TRUE if either both parents are dead or the respondent does notlive with any living parents However since we follow Bisin et al (2008) wecode this variable including only those who have both their parents dead orboth paprents live away from the respondent This definition implies thatthose who have a parent living away and one parent dead are assigned thevalue FALSE Code 8 and 9 are assigned NA (FNSEM 1993a p 56)

gt isna(U$f14a) lt- U$f14a==9

gt isna(U$f14b) lt- U$f14b==9

gt U$NoParents lt- U$f14a==8 | U$f14b==2

2517 Contacts with parents

The three variables measuring contacts with parents are defined via threequestions asking about the number of physical contacts (question f15bn)the number of contacts via telephone (question f15cn) and the number ofcontacts via letters (question f15dn) that the respondent has had with hisor her parents during the last four weeks conditional on not both parentsbeing dead All three takes the value of the underlaying variable if at leastone parent is alive and the value 0 if both parents are dead Code 999 onall three variables and code 997 on f15bn are assigned NA (FNSEM 1993ap 56)

gt isna(U$f15bn) lt- U$f15bn==997 | U$f15bn==999

gt isna(U$f15cn) lt- U$f15cn==999

gt isna(U$f15dn) lt- U$f15dn==999

gt U$ParentsPhysicalContacts lt- ifelse(

+ U$f14a=8 U$f15bn0)

gt U$ParentsTelephoneCalls lt- ifelse(

+ U$f14a=8 U$f15cn0)

gt U$ParentsLetters lt- ifelse(

+ U$f14a=8 U$f15dn0)

2518 English language

There are several language variables measuring whether the respondent isspeaking English with different individuals at home with older at home

15

with younger at work and with friends We construct theses variables usingquestion s12a which asks whether the respondent regularly speak to anyonein Britain in any other language than English and s12f s12g s12hand s12i which asks which language is spoken to the above mentionedcategories of individuals Each of s12f s12g s12h and s12i comesin 18 versions (eg s12f1 s12f18) where each question 1ndash15 iscoded yes if the respondent speaks the language Question 16 is ldquoNeverspeaks to these peopleNot ap[plicable]rdquo question 17 NA and question 18ldquo None of the above answered positiverdquo Question 1 is always regardingEnglish

The respondent is coded as English speaker if either s12a is answerednegatively or s12aX1 where X=fghi is answered positively Onlyrespondents for which either s12a is NA or all of s12X1 s12X16 areanswered negatively are coded as NA Below is the code for English SpokenAt Home With Older

gt isna(U$s12a) lt- U$s12a== 8 | U$s12a == 9

gt s12flist lt- c(paste(s12f 216 sep=))

gt U$oOLD lt- apply(U[s12flist]==yes 1 sum)

gt U$EnglishSpokenatHomewithOlder lt-

+ ((U$s12a==1 | isna(U$s12a)) amp

+ U$s12f1==yes) | U$s12a==2

gt isna(U$EnglishSpokenatHomewithOlder) lt-

+ U$oOLD==0 amp

+ U$EnglishSpokenatHomewithOlder==FALSE

gt U$EnglishSpokenatHomewithOlder lt-

+ replace(U$EnglishSpokenatHomewithOlder

+ U$oOLDgt0 amp

+ isna(U$EnglishSpokenatHomewithOlder)FALSE)

gt U$DONOTSPEAKWITHOLDER lt- ifelse(U$s12f16==no01)

The codes for English Spoken At Home With Younger English SpokenAt Work and English Spoken With Friends are equivalent These codesare in araietal_sourceRnw but not shown here It is also available inaraietal_sourceR

2519 Household income

The question hh40 provides information in which interval the householdincome of the respondentrsquos household is We assign the midpoints in theseintervals as the household income For the lowest bracket this income is themidpoint of [0 77] For the highest bracket we assign the income which is thelowest income in the bracket plus the income interval down to the midpintof the second highest bracket ie 789 + 788minus731

2 = 8175 This method

16

underestimates the range of the highest bracket but to a lesser extent thanthe lower limit 789

gt isna(U$hh40) lt- U$hh40==refused |

+ U$hh40==cant say | U$hh40==na

gt U$HouseholdIncome lt- c(3859651350

+ 173523052600318036604140

+ 471552955870649570207595

+ 8175)[U$hh40]

2520 Ward variables

Ward density of own ethnic group is measured by the variable wown in theoriginal data This variable is coded 1 minus 7 depending on the density of therespondentrsquos own ethnic group is in the ward of the respondent (FNSEM1993b)

1 Up to 1992 2-4993 5-9994 10-14995 15-24996 25-32997 33 or more

We recode the variable to take the midpoints of the density intervals in thesame fashion as the household income variable was recoded This meansthat if the respondent is the lowest interval the density is set to be 1 etc Inthe highest interval we set the density to be the lowest density in the intervalplus the density distance down to the midpoint of the second highest densityinterval ie 33 + 33minus25

2 = 37The variable wunemp is coded 1minus 6 depending on unemployment rate in

the ward of the respondent (FNSEM 1993b)

1 Up to 1992 2-4993 5-9994 10-14995 15-206 20 or more

We recode this variable to instead take the midpoints of the intervals aswe did for the household income This means that if the respondent is thelowest interval the the rate is set to be 1 etc In the highest interval we setthe rate to be the lowest rate in the interval plus the distance down to themidpoint of the second highest rate interval ie 20 + 20minus15

2 = 225

17

gt U$WardDensityOwnEthnicity lt- (c(1035

+ 75125200290370)[U$wown])100

gt U$WardUnemploymentRate lt- c(103575125

+ 175225)[U$wunemp]

26 Discrimination own ethnicity

Finally we define a variable describing the discrimination against the ownethnic group It is defined as the average of the variable Discrimination overethnic groups after the removal of nonndashavailables

gt GroupDiscrimination lt-

+ tapply(U$DiscriminationU$ethnic

+ function(x) mean(x narm=TRUE))

gt U$DiscriminationOwnEthnicity lt-

+ GroupDiscrimination[U$ethnic]

27 Defining the subset

We define dummy variables for labour market status to have the same vari-able labels as in Bisin et al (2008)

gt U$Employee lt- asnumeric(U$LabourMarketStatus==Employee)

gt U$Manager lt- asnumeric(U$LabourMarketStatus==Manager)

gt U$SelfEmployed lt-

+ asnumeric(U$LabourMarketStatus==SelfEmployed)

gt U$OUTOFLABOURFORCE lt-

+ asnumeric(U$LabourMarketStatus==OutOfLabourForce)

gt U$Unemployed lt-

+ asnumeric(U$LabourMarketStatus==Unemployed)

We save a data set keeping all observations containing nonndashavailables

gt UOriginal lt- U

We then choose the variables to keep in U This code is in araietal_sourceRnwbut not shown here It is also available in araietal_sourceR

Finally we remove all observations containing nonndashavailables from U

gt U lt- naomit(U)

28 Sample statistics

Table 1 compares the number of observations in this sample before (seesection 27 ie data frame UOriginal) and after (see section 27 iedata frame U) nonndashavailables are removed with the numbers of observations

18

reported by the Bisin et al (2008) study The number of observations forvarious groups in the nonndashMuslim category are not reported in the Bisinet al (2008) paper These numbers are therefore missing in the table Thecategory definitions are from the original dataset and involves no recodingon our part

After removing observations with missing values on all variables of inter-est (ldquoAfterrdquo in Table 1) we are left with 330 Muslims and 488 non-MuslimsThe sample selections induced by the choice of variables and the missingvalues in these variables lead to a loss of 57 percent of the relevant sampleof the original data3

The sample means reported in Bisin et al (2008) seem to be unweightedSince data instructions says that the data should always be weighted tables2 and 3 report weighted and unweighted sample means before and afterremoval of non-availables and the Bisin et al (2008) data4 Comparingmeans the Bisin et al (2008) data seem to be different from the originalsample The variables Attitude Towards Inter Marriage and Importance ofRacial Composition in Schools in Bisin et al (2008) data deviate largelyfrom corresponding averages in the original data The deviation is extremein case of Importance of Racial Composition in Schools The original samplehas a mean for this variable that is 2 percent for non-Muslims (compare with33 percent in Bisin et al (2008)) and 6 percent for Muslims (compare with65 percent in Bisin et al (2008)) Due to this extremely skewed distributionit is hardly meaningfull to run a regression on this variable

Notice that also the distribution of the variable Importance of Religionwould be extremely skewed using standard coding of this type of variablesSuch a coding would imply that religion is important when the respondentanswer ldquoVery Importantrdquo and ldquoFairly Importantrdquo to the question ldquoHow im-portant is religion to the way you live your liferdquo

The sample means in our data after removing accumulated missing val-ues due to all variables in the estimations deviate marginally in general fromthe original data The similarities here are partly due to the fact that thestatistics are based exactly on the same variable definition in our implemen-tation In some respects the deviations are larger For further comparisonswe refer to Tables 2 and 3

Due to the fact that the large majority of observations from the originaldata are lost the remaining sample is likely to be contaminated with sampleselection bias To compare the characteristics of the remaining sample withthe original sample says something about systematic attrition with respectto observables The sample selection bias with respect to unobservablescannot however be resolved

3The variables written in capital letters are created to ensure wellndashdefined referencecategories They are included in our regressions but we cannot say whether they areincluded in the regressions of Bisin et al (2008)

4See FNSEM (1993b)

19

3 Regression Results

We use linear probability models (LPM)5 Our results are presented in Tables4 and 5 Bisin et al (2008) write that

1 ldquoMuslims integrate less and more slowly than non-Muslimsrdquo (abstractp 445) and

2 ldquo there is no evidence that segregated neighbourhoods breed intensereligious and cultural identities On the contrary intense identitiesin our data are more prominent in relatively mixed neighbourhoodsrdquo(p 446)

The first claim is based on their reported results concerning the variableYears Since Arrival In this way Bisin et al (2008) compare cohorts ofMuslims and non Muslims and attempt to say something about the evolutionof values over time They do not follow individuals over time but nonethelesscall these cohort differences ldquoIntegration over timerdquo They report negativecoefficients for Years Since Arrival but the estimates are smaller in absolutevalue for Muslims than for non-Muslims In our case the coefficients forYears Since Arrival reported in Tables 4 and 5 are insignificant in all casesexcept in the regression for Importance of Religion for Muslims where it isnegative This is opposite to what Bisin et al (2008) claim

The second claim is based on their reported results concerning the vari-able Ward Density Own Ethnicity Bisin et al (2008) report negative andsignificant estimates for Ward Density Own Ethnicity in all six specifica-tions Their negative coefficient for this variable would imply that ethnicminorities put more weight on religion mind more about inter-ethnic mar-riage and have stronger taste for ethnically profiled schools as we move fromneighbourhoods (Wards) with high density of their own ethnicity to neigh-bourhoods where people from their own ethnicity are scarce This is not atall what we find in our replication

5Bisin et al (2008) use probit estimations Our attempts to use probit run into con-vergence problems The convergence problems are severe for the model using Importanceof Racial Composition in Schools as dependent variable Hence our choice of LPM

Another issue is that Bisin et al (2008) should have included dummy variables indicatingreligious affiliation Christians Sikhs and others in the non-Muslim category to checksimilarities and differences among non-Muslims as well In this respect we follow theirmodel specification

Moreover Bisin et al (2008) should have adjusted for within ward correlations Thismight matter for their standard errors which might be underestimated In our case withalmost no significant results this would not matter much The variable is not available inthe data set and we did not make much effort to obtain it

All estimated models include 7 UK-region dummies and the variables DONOT

SPEAKWITHOLDER DONOTSPEAKATWORK and DONOTSPEAKWITHFRIENDS It turnedout that the variable DONOTSPEAKWITHYOUNGER is TRUE for few observations and can-not be included in the model

20

In our estimations the estimated coefficients for this variable are allpositive but far from significant The P-values are 069 097 and 059 forMuslims and 015 014 and 03 for non-Muslims contradicting the Bisin et al(2008) results

Inspecting the results presented in Tables 4 and 5 there are many sim-ilarities and few differences in the estimated coefficients for Muslims andnon-Muslims Our results are generally very different from results reportedby Bisin et al (2008) We are however doubtful whether it is possibleto draw any reliable inference from these results due to great loss of ob-servations and possible sample selection bias together with the problem ofendogeneity (also mentioned by Bisin et al (2008))

4 Concluding remarks

The Bisin et al (2008) paper rests on fragile grounds Our examinationof the data using their variable definitions and the same set-up indicatesthat their claims about differences between Muslims and non-Muslims andtheir conclusion that strong ReligiousEthnic identities are found in mixedneighbourhoods does not hold There is no systematic relation betweenethnic minoritiesrsquo views on religion inter-ethnic marriage or ethnic profile ofschools and the density of their own ethnic minority in their neighbourhoodHowever we hesitate to draw inference from these results since the great lossof observations (57 percent) implies that the remaining sample is most likelynot representative

5 Production notes

To facilitate reproducibility and save others timely interpretations of whatis done in this paper we attempt to follow Literate Statistical Programmingprocedures For documenting our results we have used Sweave by Leisch(2002) in combination with the LATEX family of programs using the packagesinputenc fontenc natbib Sweave fancyvrb color url hyperref andmultirow

All code (R code LATEX code and BibTEX data base code) used to do theeconometric estimations to produce this technical documentation includingall tables and to produce the companion paper Arai et al (2008) is containedin the file araietal_sourceRnw

The estimations and the documentation can be reproduced in by follow-ing the instructions at the top of the file httppeoplesuse~lundhfragile_groundsaraietal_sourceRnw

Our results were obtained on a i486-pc-linux-gnu platform using R ver-sion 281 beta (2008-12-12 r47173) (R Development Core Team 2008) with

21

gt isna(U$a4n) lt- U$a4n==98 | U$a4n==99

gt isna(U$year)lt- U$year==99

gt U$Age lt- U$a1an

gt U$YearsSinceArrival lt- ifelse(U$BornintheUK==TRUE

+ 0 U$year-U$a4n)

gt U$AgeatArrival lt- U$Age - U$YearsSinceArrival

gt U$AgeatArrival lt- replace(U$AgeatArrival

+ U$BornintheUK==TRUE 0)

257 Female

Definition of females via question hh2as Code na is coded as NA (FNSEM1993a pp 318)

gt isna(U$hh2as) lt- U$hh2as ==na

gt U$Female lt- U$hh2a==female

258 Arranged marriage

In question s39 Indian Pakistani Bangladeshi and Chinese respondents whohas ever been married were asked a question about the decision regardingtheir marriage The question ask about the role of the respondent and hisor her parents about choosing the respondentrsquos husbandwife In categories1 and 2 of s39 the respondentrsquos parents made the final decision and thesecategories define the dummy where the respondent is or has been living inan arranged marriage (code 1 all other are coded 0) Notice that singlesand Caribbeans have not received this question Singles are already removedfrom the sample (see section 23 Caribbeans are coded 0 This means thatthe Caribbeans do not marry according to the decision of their parentsCategory 8 (Canrsquot say) and category 9 are assigned NA (FNSEM 1993app 127)

gt isna(U$s39) lt- U$s39==8 | U$s39==9

gt U$ArrangedMarriage lt- ifelse(

+ U$s39==3 | U$s39==4 | U$s39==5 |

+ U$ethnic==caribbean FALSE TRUE)

259 Discrimination

The discrimination variable is based on a series of questions related to dis-crimination v1a-v1d about physical attacks v9a about insults j55a andj63a discrimination at work Basically anyone answering that they havebeen discriminated for any of these reasons are coded 1 else code 0

Questions v1a-v1d is a series of filter questions Question v1a asks if therespondent have been attacked (yes or no) question v1b how many attacks

9

the respondent has been enduring and question v1c asks those who havebeen attacked once if they believe the attack had to do with reasons todo with race or colour and v1d asks the same question and regards thosewho have been attacked more than once Generally code 8 and code 9are assigned NA except for question v1b where also code 7 is assigned NA(FNSEM 1993a pp 154ff 163 195 and 199)

gt vjlist lt- c(paste(v1letters[14]sep=)

+ v9aj55aj63a)

gt isna(U[vjlist]) lt- U[vjlist]==8 |

+ U[vjlist]==9

gt isna(U$v1b) lt- U$v1b==7 | U$v1b==8 |

+ U$v1b==9

gt U$Discrimination lt-

+ (U$v1a==1 amp U$v1b==1 amp U$v1c==1) |

+ (U$v1a==1 amp

+ (U$v1b gt= 2 amp U$v1b lt= 6) amp

+ U$v1d==1) | U$v9a==1 | U$j55a==1 |

+ U$j63a==1

2510 Children

No question about the number of children is asked Instead the number ofchildren has to be calculated indirectly via the number of children not livingat home (questions f16a and f16b1n-f16b3n) and the relation between therespondent and other persons living in the household (questions hh2cb-hh2cm)

The number of children out of home is calculated in the following wayIf children out of home is TRUE (f16a=1) then the number of childrenequals the sum of f16b1n f16b2n and f16b3n (the number of children notliving at home below 5 years between 5 and 15 years and above 15 yearsof age) Else if there are no children out of home (ie if f16a=2) thenthe number of children out of home is set to 0 Missing values are coded asbelow (FNSEM 1993a p 57)

gt isna(U$f16a) lt- U$f16a==8 | U$f16a==9

gt isna(U$f16b1n) lt- U$f16b1n==99

gt isna(U$f16b2n) lt- U$f16b2n==99

gt isna(U$f16b3n) lt- U$f16b3n==98 |

+ U$f16b3n==99

gt U$ChildnotatHome lt- ifelse(U$f16a==1

+ U$f16b1n+U$f16b2n+U$f16b3n0)

Questions hh2cb-hh2cm are about the relationship between the re-spondent and other individuals in the household (person b c d etc to person

10

m) category 5 being child of the respondent First we check if the personis a child to the respondent and then all children are summed over the re-spondents household adding the variable measuring number of children notat home Generally codes 98 and 99 are assigned NA (FNSEM 1993a p318)

gt hhlist lt- c(paste(hh2c letters[213] sep=))

gt isna(U[hhlist]) lt- U[hhlist]==98 | U[hhlist]==99

gt U$Children lt-

+ U$ChildnotatHome + apply(apply(subset(U

+ select=c(hh2cbhh2cm)) 2

+ function(x) x==5)1 function(x) sum(x

+ narm=TRUE))

2511 No British education

Question q1 asks whether the respondent has any British education Code2 is no Code 8 (Canacutet say) is kept in the alternative category since theseindividuals will answer the question q3 about foreign education Code 9 inq1 is assigned NA (FNSEM 1993a p 96)

gt isna(U$q1) lt- U$q1==9

gt U$NoBritishEducation lt- U$q1==2

2512 British basic education

We could not exactly see how this variable was defined in Bisin et al (2008)They define the British high education as Andashlevel and above One interpre-tation is then that O-level are educations included in the basic level Thisinterpretation is implemented here NA is assigned to all observations forwhich the filter question No British Education was NA (FNSEM 1993a pp96ff)

gt q2alist lt- c(paste(q2a c(181218) sep=))

gt U$BritishBasicEducation lt-

+ apply(apply(U[q2alist] 2function(x)

+ x==1)1 function(x) sum(x narm=TRUE))=0

gt U$BritishBasicEducation lt- ifelse(

+ isna(U$NoBritishEducation)

+ NAU$BritishBasicEducation)

11

2513 British higher education

Bisin et al (2008) explicitly defined British higher education as A-levelGiven the definition of British Basic education the reference group willinclude trade apprenticeships as well as university educations NA is assignedto all observations for which the filter question No British Education wasNA (FNSEM 1993a pp 96ff)

gt U$BritishHigherEducation lt- apply(apply(subset(

+ Uselect=c(q2a9q2a11q2a19q2a20))

+ 2function(x) x==1)1

+ function(x) sum(x narm=TRUE))=0

gt U$BritishHigherEducation lt- ifelse(

+ isna(U$NoBritishEducation)

+ NAU$BritishHigherEducation)

2514 Foreign education

Foreign educations is question q3 asked to all who answered lsquonorsquo or lsquoCanrsquotrsquosayrsquo on question q1 The answer yes is coded as (code 1) and no is codedas(code 2) Contrary to the above educational variables NA is not assignedto all NA on No British Education since some of them (code 8) actually wasasked the question q3 Instead NA is assigned to all observations for whichthe filter question q1 was 1 or 9 and to all code 8 (Canrsquot say) and code 9 onquestion q3 (FNSEM 1993a p 99)

gt isna(U$q3) lt- U$q3==8 | U$q3==9

gt U$ForeignEducation lt- U$q3==1

2515 Labour market status

We code the labour market status using j1b (in paid work last week or not)and j3occ (classification of activity either last weekrsquos activity or potentialactivity during the last ten years) The variable j1b takes the value 1 forpaid work last week and 2 otherwise The variable j3occ is coded as follows(FNSEM 1993ab the former pp 81f)

1 Self-employed (25+ employees)2 Self-employed (1-24 employees)3 Self-employed (no employees)4 Self-employed (employees not known)5 Manager (establishment of 25+ employees)6 Manager (establishment of 1-24 employees)7 Manager (employees not known)8 Foremansupervisor9 Other employee

12

10 Employee status unknown11 Not knownnot answered

Employee In order to be classified as an employee the individual has tohave answered yes (value 1) in j1b and be classified as employee in j3occ(value 9) and have the value NotAssignedNA is assigned to categories 10and 11 in j3occ

gt isna(U$j3occ) lt- U$j3occ==10 | U$j3occ==11

gt U$LabourMarketStatus lt- ifelse(U$j1b==1 amp

+ U$j3occ==9 amp isna(U$j1b==1 amp

+ U$j3occ==9) EmployeeNotAssigned)

Self Employed Selfndashemployed are also coded using j1b and j3occ aboveCategories 1minus 4 in j3occ are defined as self-employed We also require thatj1b is equal 1 (SelfndashEmployed)

gt U$LabourMarketStatus lt- replace(

+ U$LabourMarketStatus

+ (U$j3occ==1 | U$j3occ==2 |

+ U$j3occ==3 | U$j3occ==4) amp

+ U$j1b==1SelfEmployed)

Manager Managers are also coded using j1b and j3occ see above Cat-egories 5minus 8 in j3occ are defined as managers (including supervisors)

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ U$j1b==1 amp U$j3occgt4 amp U$j3occlt9

+ Manager)

Unemployed The question hh5ds describes the respondentrsquos labour mar-ket status Unemployment is defined via this variable hh5ds is coded inthe following way (FNSEM 1993b)

1 Full-time education2 Govt training programme3 Full-time paid work4 Part-time paid work5 Waiting to take up paid work6 Registered unemployed7 Unemployed not registered8 Permanently sick or disabled9 Wholly retired from work

13

10 Looking after the home11 Doing something else12 NA

We define unemployed as category 6 and 7 (Bisin et al 2008 p 324)There are few cases where the individual is classified as Employee accordingto our definition above and is reported to be unemployed in hh5ds Thiscan for example be part-time unemployment We classify these individualsas having Unclear labour market status These will be checked later andbe removed if they are few in the final sample

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==6 | U$hh5ds == 7) amp

+ U$LabourMarketStatus=NotAssigned

+ Unclear)

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==6 | U$hh5ds == 7) amp

+ U$LabourMarketStatus==NotAssigned

+ Unemployed)

Out of labour force The category out of labour force is defined as thosehaving values (125891011) in hh5ds or value 2 in j1b or j2

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==1 | U$hh5ds == 2 |

+ U$hh5ds==5 | U$hh5ds == 8 |

+ U$hh5ds==9 | U$hh5ds == 10|

+ U$hh5ds==11 | U$j1b == 2 |

+ U$j2 == 2 ) amp

+ U$LabourMarketStatus==NotAssigned

+ OutOfLabourForce)

Remaining observations with the value NotAssigned in Labour Mar-ketStatus will be assigned NA At this point there are 4 observations codedas Unclear These are now recoded as NA

gt isna(U$LabourMarketStatus) lt-

+ U$LabourMarketStatus == NotAssigned

gt isna(U$LabourMarketStatus) lt-

+ U$LabourMarketStatus == Unclear

We will create dummy variables using this variable before we run ourmodels

14

2516 No parents

Variable No parents means that the respondent is not living with his orher parents This variable is coded with question f14a (which takes code 1for both alive code 2 father alive code 3 for mother alive and 8 for bothdead) and f14b (which takes code 2 if both living parents do not live withthe respondent and code 6 if the only living parent does not live with therespondent else it takes one of the values 1 or 3 minus 5) No parents shouldbe coded TRUE if either both parents are dead or the respondent does notlive with any living parents However since we follow Bisin et al (2008) wecode this variable including only those who have both their parents dead orboth paprents live away from the respondent This definition implies thatthose who have a parent living away and one parent dead are assigned thevalue FALSE Code 8 and 9 are assigned NA (FNSEM 1993a p 56)

gt isna(U$f14a) lt- U$f14a==9

gt isna(U$f14b) lt- U$f14b==9

gt U$NoParents lt- U$f14a==8 | U$f14b==2

2517 Contacts with parents

The three variables measuring contacts with parents are defined via threequestions asking about the number of physical contacts (question f15bn)the number of contacts via telephone (question f15cn) and the number ofcontacts via letters (question f15dn) that the respondent has had with hisor her parents during the last four weeks conditional on not both parentsbeing dead All three takes the value of the underlaying variable if at leastone parent is alive and the value 0 if both parents are dead Code 999 onall three variables and code 997 on f15bn are assigned NA (FNSEM 1993ap 56)

gt isna(U$f15bn) lt- U$f15bn==997 | U$f15bn==999

gt isna(U$f15cn) lt- U$f15cn==999

gt isna(U$f15dn) lt- U$f15dn==999

gt U$ParentsPhysicalContacts lt- ifelse(

+ U$f14a=8 U$f15bn0)

gt U$ParentsTelephoneCalls lt- ifelse(

+ U$f14a=8 U$f15cn0)

gt U$ParentsLetters lt- ifelse(

+ U$f14a=8 U$f15dn0)

2518 English language

There are several language variables measuring whether the respondent isspeaking English with different individuals at home with older at home

15

with younger at work and with friends We construct theses variables usingquestion s12a which asks whether the respondent regularly speak to anyonein Britain in any other language than English and s12f s12g s12hand s12i which asks which language is spoken to the above mentionedcategories of individuals Each of s12f s12g s12h and s12i comesin 18 versions (eg s12f1 s12f18) where each question 1ndash15 iscoded yes if the respondent speaks the language Question 16 is ldquoNeverspeaks to these peopleNot ap[plicable]rdquo question 17 NA and question 18ldquo None of the above answered positiverdquo Question 1 is always regardingEnglish

The respondent is coded as English speaker if either s12a is answerednegatively or s12aX1 where X=fghi is answered positively Onlyrespondents for which either s12a is NA or all of s12X1 s12X16 areanswered negatively are coded as NA Below is the code for English SpokenAt Home With Older

gt isna(U$s12a) lt- U$s12a== 8 | U$s12a == 9

gt s12flist lt- c(paste(s12f 216 sep=))

gt U$oOLD lt- apply(U[s12flist]==yes 1 sum)

gt U$EnglishSpokenatHomewithOlder lt-

+ ((U$s12a==1 | isna(U$s12a)) amp

+ U$s12f1==yes) | U$s12a==2

gt isna(U$EnglishSpokenatHomewithOlder) lt-

+ U$oOLD==0 amp

+ U$EnglishSpokenatHomewithOlder==FALSE

gt U$EnglishSpokenatHomewithOlder lt-

+ replace(U$EnglishSpokenatHomewithOlder

+ U$oOLDgt0 amp

+ isna(U$EnglishSpokenatHomewithOlder)FALSE)

gt U$DONOTSPEAKWITHOLDER lt- ifelse(U$s12f16==no01)

The codes for English Spoken At Home With Younger English SpokenAt Work and English Spoken With Friends are equivalent These codesare in araietal_sourceRnw but not shown here It is also available inaraietal_sourceR

2519 Household income

The question hh40 provides information in which interval the householdincome of the respondentrsquos household is We assign the midpoints in theseintervals as the household income For the lowest bracket this income is themidpoint of [0 77] For the highest bracket we assign the income which is thelowest income in the bracket plus the income interval down to the midpintof the second highest bracket ie 789 + 788minus731

2 = 8175 This method

16

underestimates the range of the highest bracket but to a lesser extent thanthe lower limit 789

gt isna(U$hh40) lt- U$hh40==refused |

+ U$hh40==cant say | U$hh40==na

gt U$HouseholdIncome lt- c(3859651350

+ 173523052600318036604140

+ 471552955870649570207595

+ 8175)[U$hh40]

2520 Ward variables

Ward density of own ethnic group is measured by the variable wown in theoriginal data This variable is coded 1 minus 7 depending on the density of therespondentrsquos own ethnic group is in the ward of the respondent (FNSEM1993b)

1 Up to 1992 2-4993 5-9994 10-14995 15-24996 25-32997 33 or more

We recode the variable to take the midpoints of the density intervals in thesame fashion as the household income variable was recoded This meansthat if the respondent is the lowest interval the density is set to be 1 etc Inthe highest interval we set the density to be the lowest density in the intervalplus the density distance down to the midpoint of the second highest densityinterval ie 33 + 33minus25

2 = 37The variable wunemp is coded 1minus 6 depending on unemployment rate in

the ward of the respondent (FNSEM 1993b)

1 Up to 1992 2-4993 5-9994 10-14995 15-206 20 or more

We recode this variable to instead take the midpoints of the intervals aswe did for the household income This means that if the respondent is thelowest interval the the rate is set to be 1 etc In the highest interval we setthe rate to be the lowest rate in the interval plus the distance down to themidpoint of the second highest rate interval ie 20 + 20minus15

2 = 225

17

gt U$WardDensityOwnEthnicity lt- (c(1035

+ 75125200290370)[U$wown])100

gt U$WardUnemploymentRate lt- c(103575125

+ 175225)[U$wunemp]

26 Discrimination own ethnicity

Finally we define a variable describing the discrimination against the ownethnic group It is defined as the average of the variable Discrimination overethnic groups after the removal of nonndashavailables

gt GroupDiscrimination lt-

+ tapply(U$DiscriminationU$ethnic

+ function(x) mean(x narm=TRUE))

gt U$DiscriminationOwnEthnicity lt-

+ GroupDiscrimination[U$ethnic]

27 Defining the subset

We define dummy variables for labour market status to have the same vari-able labels as in Bisin et al (2008)

gt U$Employee lt- asnumeric(U$LabourMarketStatus==Employee)

gt U$Manager lt- asnumeric(U$LabourMarketStatus==Manager)

gt U$SelfEmployed lt-

+ asnumeric(U$LabourMarketStatus==SelfEmployed)

gt U$OUTOFLABOURFORCE lt-

+ asnumeric(U$LabourMarketStatus==OutOfLabourForce)

gt U$Unemployed lt-

+ asnumeric(U$LabourMarketStatus==Unemployed)

We save a data set keeping all observations containing nonndashavailables

gt UOriginal lt- U

We then choose the variables to keep in U This code is in araietal_sourceRnwbut not shown here It is also available in araietal_sourceR

Finally we remove all observations containing nonndashavailables from U

gt U lt- naomit(U)

28 Sample statistics

Table 1 compares the number of observations in this sample before (seesection 27 ie data frame UOriginal) and after (see section 27 iedata frame U) nonndashavailables are removed with the numbers of observations

18

reported by the Bisin et al (2008) study The number of observations forvarious groups in the nonndashMuslim category are not reported in the Bisinet al (2008) paper These numbers are therefore missing in the table Thecategory definitions are from the original dataset and involves no recodingon our part

After removing observations with missing values on all variables of inter-est (ldquoAfterrdquo in Table 1) we are left with 330 Muslims and 488 non-MuslimsThe sample selections induced by the choice of variables and the missingvalues in these variables lead to a loss of 57 percent of the relevant sampleof the original data3

The sample means reported in Bisin et al (2008) seem to be unweightedSince data instructions says that the data should always be weighted tables2 and 3 report weighted and unweighted sample means before and afterremoval of non-availables and the Bisin et al (2008) data4 Comparingmeans the Bisin et al (2008) data seem to be different from the originalsample The variables Attitude Towards Inter Marriage and Importance ofRacial Composition in Schools in Bisin et al (2008) data deviate largelyfrom corresponding averages in the original data The deviation is extremein case of Importance of Racial Composition in Schools The original samplehas a mean for this variable that is 2 percent for non-Muslims (compare with33 percent in Bisin et al (2008)) and 6 percent for Muslims (compare with65 percent in Bisin et al (2008)) Due to this extremely skewed distributionit is hardly meaningfull to run a regression on this variable

Notice that also the distribution of the variable Importance of Religionwould be extremely skewed using standard coding of this type of variablesSuch a coding would imply that religion is important when the respondentanswer ldquoVery Importantrdquo and ldquoFairly Importantrdquo to the question ldquoHow im-portant is religion to the way you live your liferdquo

The sample means in our data after removing accumulated missing val-ues due to all variables in the estimations deviate marginally in general fromthe original data The similarities here are partly due to the fact that thestatistics are based exactly on the same variable definition in our implemen-tation In some respects the deviations are larger For further comparisonswe refer to Tables 2 and 3

Due to the fact that the large majority of observations from the originaldata are lost the remaining sample is likely to be contaminated with sampleselection bias To compare the characteristics of the remaining sample withthe original sample says something about systematic attrition with respectto observables The sample selection bias with respect to unobservablescannot however be resolved

3The variables written in capital letters are created to ensure wellndashdefined referencecategories They are included in our regressions but we cannot say whether they areincluded in the regressions of Bisin et al (2008)

4See FNSEM (1993b)

19

3 Regression Results

We use linear probability models (LPM)5 Our results are presented in Tables4 and 5 Bisin et al (2008) write that

1 ldquoMuslims integrate less and more slowly than non-Muslimsrdquo (abstractp 445) and

2 ldquo there is no evidence that segregated neighbourhoods breed intensereligious and cultural identities On the contrary intense identitiesin our data are more prominent in relatively mixed neighbourhoodsrdquo(p 446)

The first claim is based on their reported results concerning the variableYears Since Arrival In this way Bisin et al (2008) compare cohorts ofMuslims and non Muslims and attempt to say something about the evolutionof values over time They do not follow individuals over time but nonethelesscall these cohort differences ldquoIntegration over timerdquo They report negativecoefficients for Years Since Arrival but the estimates are smaller in absolutevalue for Muslims than for non-Muslims In our case the coefficients forYears Since Arrival reported in Tables 4 and 5 are insignificant in all casesexcept in the regression for Importance of Religion for Muslims where it isnegative This is opposite to what Bisin et al (2008) claim

The second claim is based on their reported results concerning the vari-able Ward Density Own Ethnicity Bisin et al (2008) report negative andsignificant estimates for Ward Density Own Ethnicity in all six specifica-tions Their negative coefficient for this variable would imply that ethnicminorities put more weight on religion mind more about inter-ethnic mar-riage and have stronger taste for ethnically profiled schools as we move fromneighbourhoods (Wards) with high density of their own ethnicity to neigh-bourhoods where people from their own ethnicity are scarce This is not atall what we find in our replication

5Bisin et al (2008) use probit estimations Our attempts to use probit run into con-vergence problems The convergence problems are severe for the model using Importanceof Racial Composition in Schools as dependent variable Hence our choice of LPM

Another issue is that Bisin et al (2008) should have included dummy variables indicatingreligious affiliation Christians Sikhs and others in the non-Muslim category to checksimilarities and differences among non-Muslims as well In this respect we follow theirmodel specification

Moreover Bisin et al (2008) should have adjusted for within ward correlations Thismight matter for their standard errors which might be underestimated In our case withalmost no significant results this would not matter much The variable is not available inthe data set and we did not make much effort to obtain it

All estimated models include 7 UK-region dummies and the variables DONOT

SPEAKWITHOLDER DONOTSPEAKATWORK and DONOTSPEAKWITHFRIENDS It turnedout that the variable DONOTSPEAKWITHYOUNGER is TRUE for few observations and can-not be included in the model

20

In our estimations the estimated coefficients for this variable are allpositive but far from significant The P-values are 069 097 and 059 forMuslims and 015 014 and 03 for non-Muslims contradicting the Bisin et al(2008) results

Inspecting the results presented in Tables 4 and 5 there are many sim-ilarities and few differences in the estimated coefficients for Muslims andnon-Muslims Our results are generally very different from results reportedby Bisin et al (2008) We are however doubtful whether it is possibleto draw any reliable inference from these results due to great loss of ob-servations and possible sample selection bias together with the problem ofendogeneity (also mentioned by Bisin et al (2008))

4 Concluding remarks

The Bisin et al (2008) paper rests on fragile grounds Our examinationof the data using their variable definitions and the same set-up indicatesthat their claims about differences between Muslims and non-Muslims andtheir conclusion that strong ReligiousEthnic identities are found in mixedneighbourhoods does not hold There is no systematic relation betweenethnic minoritiesrsquo views on religion inter-ethnic marriage or ethnic profile ofschools and the density of their own ethnic minority in their neighbourhoodHowever we hesitate to draw inference from these results since the great lossof observations (57 percent) implies that the remaining sample is most likelynot representative

5 Production notes

To facilitate reproducibility and save others timely interpretations of whatis done in this paper we attempt to follow Literate Statistical Programmingprocedures For documenting our results we have used Sweave by Leisch(2002) in combination with the LATEX family of programs using the packagesinputenc fontenc natbib Sweave fancyvrb color url hyperref andmultirow

All code (R code LATEX code and BibTEX data base code) used to do theeconometric estimations to produce this technical documentation includingall tables and to produce the companion paper Arai et al (2008) is containedin the file araietal_sourceRnw

The estimations and the documentation can be reproduced in by follow-ing the instructions at the top of the file httppeoplesuse~lundhfragile_groundsaraietal_sourceRnw

Our results were obtained on a i486-pc-linux-gnu platform using R ver-sion 281 beta (2008-12-12 r47173) (R Development Core Team 2008) with

21

the respondent has been enduring and question v1c asks those who havebeen attacked once if they believe the attack had to do with reasons todo with race or colour and v1d asks the same question and regards thosewho have been attacked more than once Generally code 8 and code 9are assigned NA except for question v1b where also code 7 is assigned NA(FNSEM 1993a pp 154ff 163 195 and 199)

gt vjlist lt- c(paste(v1letters[14]sep=)

+ v9aj55aj63a)

gt isna(U[vjlist]) lt- U[vjlist]==8 |

+ U[vjlist]==9

gt isna(U$v1b) lt- U$v1b==7 | U$v1b==8 |

+ U$v1b==9

gt U$Discrimination lt-

+ (U$v1a==1 amp U$v1b==1 amp U$v1c==1) |

+ (U$v1a==1 amp

+ (U$v1b gt= 2 amp U$v1b lt= 6) amp

+ U$v1d==1) | U$v9a==1 | U$j55a==1 |

+ U$j63a==1

2510 Children

No question about the number of children is asked Instead the number ofchildren has to be calculated indirectly via the number of children not livingat home (questions f16a and f16b1n-f16b3n) and the relation between therespondent and other persons living in the household (questions hh2cb-hh2cm)

The number of children out of home is calculated in the following wayIf children out of home is TRUE (f16a=1) then the number of childrenequals the sum of f16b1n f16b2n and f16b3n (the number of children notliving at home below 5 years between 5 and 15 years and above 15 yearsof age) Else if there are no children out of home (ie if f16a=2) thenthe number of children out of home is set to 0 Missing values are coded asbelow (FNSEM 1993a p 57)

gt isna(U$f16a) lt- U$f16a==8 | U$f16a==9

gt isna(U$f16b1n) lt- U$f16b1n==99

gt isna(U$f16b2n) lt- U$f16b2n==99

gt isna(U$f16b3n) lt- U$f16b3n==98 |

+ U$f16b3n==99

gt U$ChildnotatHome lt- ifelse(U$f16a==1

+ U$f16b1n+U$f16b2n+U$f16b3n0)

Questions hh2cb-hh2cm are about the relationship between the re-spondent and other individuals in the household (person b c d etc to person

10

m) category 5 being child of the respondent First we check if the personis a child to the respondent and then all children are summed over the re-spondents household adding the variable measuring number of children notat home Generally codes 98 and 99 are assigned NA (FNSEM 1993a p318)

gt hhlist lt- c(paste(hh2c letters[213] sep=))

gt isna(U[hhlist]) lt- U[hhlist]==98 | U[hhlist]==99

gt U$Children lt-

+ U$ChildnotatHome + apply(apply(subset(U

+ select=c(hh2cbhh2cm)) 2

+ function(x) x==5)1 function(x) sum(x

+ narm=TRUE))

2511 No British education

Question q1 asks whether the respondent has any British education Code2 is no Code 8 (Canacutet say) is kept in the alternative category since theseindividuals will answer the question q3 about foreign education Code 9 inq1 is assigned NA (FNSEM 1993a p 96)

gt isna(U$q1) lt- U$q1==9

gt U$NoBritishEducation lt- U$q1==2

2512 British basic education

We could not exactly see how this variable was defined in Bisin et al (2008)They define the British high education as Andashlevel and above One interpre-tation is then that O-level are educations included in the basic level Thisinterpretation is implemented here NA is assigned to all observations forwhich the filter question No British Education was NA (FNSEM 1993a pp96ff)

gt q2alist lt- c(paste(q2a c(181218) sep=))

gt U$BritishBasicEducation lt-

+ apply(apply(U[q2alist] 2function(x)

+ x==1)1 function(x) sum(x narm=TRUE))=0

gt U$BritishBasicEducation lt- ifelse(

+ isna(U$NoBritishEducation)

+ NAU$BritishBasicEducation)

11

2513 British higher education

Bisin et al (2008) explicitly defined British higher education as A-levelGiven the definition of British Basic education the reference group willinclude trade apprenticeships as well as university educations NA is assignedto all observations for which the filter question No British Education wasNA (FNSEM 1993a pp 96ff)

gt U$BritishHigherEducation lt- apply(apply(subset(

+ Uselect=c(q2a9q2a11q2a19q2a20))

+ 2function(x) x==1)1

+ function(x) sum(x narm=TRUE))=0

gt U$BritishHigherEducation lt- ifelse(

+ isna(U$NoBritishEducation)

+ NAU$BritishHigherEducation)

2514 Foreign education

Foreign educations is question q3 asked to all who answered lsquonorsquo or lsquoCanrsquotrsquosayrsquo on question q1 The answer yes is coded as (code 1) and no is codedas(code 2) Contrary to the above educational variables NA is not assignedto all NA on No British Education since some of them (code 8) actually wasasked the question q3 Instead NA is assigned to all observations for whichthe filter question q1 was 1 or 9 and to all code 8 (Canrsquot say) and code 9 onquestion q3 (FNSEM 1993a p 99)

gt isna(U$q3) lt- U$q3==8 | U$q3==9

gt U$ForeignEducation lt- U$q3==1

2515 Labour market status

We code the labour market status using j1b (in paid work last week or not)and j3occ (classification of activity either last weekrsquos activity or potentialactivity during the last ten years) The variable j1b takes the value 1 forpaid work last week and 2 otherwise The variable j3occ is coded as follows(FNSEM 1993ab the former pp 81f)

1 Self-employed (25+ employees)2 Self-employed (1-24 employees)3 Self-employed (no employees)4 Self-employed (employees not known)5 Manager (establishment of 25+ employees)6 Manager (establishment of 1-24 employees)7 Manager (employees not known)8 Foremansupervisor9 Other employee

12

10 Employee status unknown11 Not knownnot answered

Employee In order to be classified as an employee the individual has tohave answered yes (value 1) in j1b and be classified as employee in j3occ(value 9) and have the value NotAssignedNA is assigned to categories 10and 11 in j3occ

gt isna(U$j3occ) lt- U$j3occ==10 | U$j3occ==11

gt U$LabourMarketStatus lt- ifelse(U$j1b==1 amp

+ U$j3occ==9 amp isna(U$j1b==1 amp

+ U$j3occ==9) EmployeeNotAssigned)

Self Employed Selfndashemployed are also coded using j1b and j3occ aboveCategories 1minus 4 in j3occ are defined as self-employed We also require thatj1b is equal 1 (SelfndashEmployed)

gt U$LabourMarketStatus lt- replace(

+ U$LabourMarketStatus

+ (U$j3occ==1 | U$j3occ==2 |

+ U$j3occ==3 | U$j3occ==4) amp

+ U$j1b==1SelfEmployed)

Manager Managers are also coded using j1b and j3occ see above Cat-egories 5minus 8 in j3occ are defined as managers (including supervisors)

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ U$j1b==1 amp U$j3occgt4 amp U$j3occlt9

+ Manager)

Unemployed The question hh5ds describes the respondentrsquos labour mar-ket status Unemployment is defined via this variable hh5ds is coded inthe following way (FNSEM 1993b)

1 Full-time education2 Govt training programme3 Full-time paid work4 Part-time paid work5 Waiting to take up paid work6 Registered unemployed7 Unemployed not registered8 Permanently sick or disabled9 Wholly retired from work

13

10 Looking after the home11 Doing something else12 NA

We define unemployed as category 6 and 7 (Bisin et al 2008 p 324)There are few cases where the individual is classified as Employee accordingto our definition above and is reported to be unemployed in hh5ds Thiscan for example be part-time unemployment We classify these individualsas having Unclear labour market status These will be checked later andbe removed if they are few in the final sample

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==6 | U$hh5ds == 7) amp

+ U$LabourMarketStatus=NotAssigned

+ Unclear)

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==6 | U$hh5ds == 7) amp

+ U$LabourMarketStatus==NotAssigned

+ Unemployed)

Out of labour force The category out of labour force is defined as thosehaving values (125891011) in hh5ds or value 2 in j1b or j2

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==1 | U$hh5ds == 2 |

+ U$hh5ds==5 | U$hh5ds == 8 |

+ U$hh5ds==9 | U$hh5ds == 10|

+ U$hh5ds==11 | U$j1b == 2 |

+ U$j2 == 2 ) amp

+ U$LabourMarketStatus==NotAssigned

+ OutOfLabourForce)

Remaining observations with the value NotAssigned in Labour Mar-ketStatus will be assigned NA At this point there are 4 observations codedas Unclear These are now recoded as NA

gt isna(U$LabourMarketStatus) lt-

+ U$LabourMarketStatus == NotAssigned

gt isna(U$LabourMarketStatus) lt-

+ U$LabourMarketStatus == Unclear

We will create dummy variables using this variable before we run ourmodels

14

2516 No parents

Variable No parents means that the respondent is not living with his orher parents This variable is coded with question f14a (which takes code 1for both alive code 2 father alive code 3 for mother alive and 8 for bothdead) and f14b (which takes code 2 if both living parents do not live withthe respondent and code 6 if the only living parent does not live with therespondent else it takes one of the values 1 or 3 minus 5) No parents shouldbe coded TRUE if either both parents are dead or the respondent does notlive with any living parents However since we follow Bisin et al (2008) wecode this variable including only those who have both their parents dead orboth paprents live away from the respondent This definition implies thatthose who have a parent living away and one parent dead are assigned thevalue FALSE Code 8 and 9 are assigned NA (FNSEM 1993a p 56)

gt isna(U$f14a) lt- U$f14a==9

gt isna(U$f14b) lt- U$f14b==9

gt U$NoParents lt- U$f14a==8 | U$f14b==2

2517 Contacts with parents

The three variables measuring contacts with parents are defined via threequestions asking about the number of physical contacts (question f15bn)the number of contacts via telephone (question f15cn) and the number ofcontacts via letters (question f15dn) that the respondent has had with hisor her parents during the last four weeks conditional on not both parentsbeing dead All three takes the value of the underlaying variable if at leastone parent is alive and the value 0 if both parents are dead Code 999 onall three variables and code 997 on f15bn are assigned NA (FNSEM 1993ap 56)

gt isna(U$f15bn) lt- U$f15bn==997 | U$f15bn==999

gt isna(U$f15cn) lt- U$f15cn==999

gt isna(U$f15dn) lt- U$f15dn==999

gt U$ParentsPhysicalContacts lt- ifelse(

+ U$f14a=8 U$f15bn0)

gt U$ParentsTelephoneCalls lt- ifelse(

+ U$f14a=8 U$f15cn0)

gt U$ParentsLetters lt- ifelse(

+ U$f14a=8 U$f15dn0)

2518 English language

There are several language variables measuring whether the respondent isspeaking English with different individuals at home with older at home

15

with younger at work and with friends We construct theses variables usingquestion s12a which asks whether the respondent regularly speak to anyonein Britain in any other language than English and s12f s12g s12hand s12i which asks which language is spoken to the above mentionedcategories of individuals Each of s12f s12g s12h and s12i comesin 18 versions (eg s12f1 s12f18) where each question 1ndash15 iscoded yes if the respondent speaks the language Question 16 is ldquoNeverspeaks to these peopleNot ap[plicable]rdquo question 17 NA and question 18ldquo None of the above answered positiverdquo Question 1 is always regardingEnglish

The respondent is coded as English speaker if either s12a is answerednegatively or s12aX1 where X=fghi is answered positively Onlyrespondents for which either s12a is NA or all of s12X1 s12X16 areanswered negatively are coded as NA Below is the code for English SpokenAt Home With Older

gt isna(U$s12a) lt- U$s12a== 8 | U$s12a == 9

gt s12flist lt- c(paste(s12f 216 sep=))

gt U$oOLD lt- apply(U[s12flist]==yes 1 sum)

gt U$EnglishSpokenatHomewithOlder lt-

+ ((U$s12a==1 | isna(U$s12a)) amp

+ U$s12f1==yes) | U$s12a==2

gt isna(U$EnglishSpokenatHomewithOlder) lt-

+ U$oOLD==0 amp

+ U$EnglishSpokenatHomewithOlder==FALSE

gt U$EnglishSpokenatHomewithOlder lt-

+ replace(U$EnglishSpokenatHomewithOlder

+ U$oOLDgt0 amp

+ isna(U$EnglishSpokenatHomewithOlder)FALSE)

gt U$DONOTSPEAKWITHOLDER lt- ifelse(U$s12f16==no01)

The codes for English Spoken At Home With Younger English SpokenAt Work and English Spoken With Friends are equivalent These codesare in araietal_sourceRnw but not shown here It is also available inaraietal_sourceR

2519 Household income

The question hh40 provides information in which interval the householdincome of the respondentrsquos household is We assign the midpoints in theseintervals as the household income For the lowest bracket this income is themidpoint of [0 77] For the highest bracket we assign the income which is thelowest income in the bracket plus the income interval down to the midpintof the second highest bracket ie 789 + 788minus731

2 = 8175 This method

16

underestimates the range of the highest bracket but to a lesser extent thanthe lower limit 789

gt isna(U$hh40) lt- U$hh40==refused |

+ U$hh40==cant say | U$hh40==na

gt U$HouseholdIncome lt- c(3859651350

+ 173523052600318036604140

+ 471552955870649570207595

+ 8175)[U$hh40]

2520 Ward variables

Ward density of own ethnic group is measured by the variable wown in theoriginal data This variable is coded 1 minus 7 depending on the density of therespondentrsquos own ethnic group is in the ward of the respondent (FNSEM1993b)

1 Up to 1992 2-4993 5-9994 10-14995 15-24996 25-32997 33 or more

We recode the variable to take the midpoints of the density intervals in thesame fashion as the household income variable was recoded This meansthat if the respondent is the lowest interval the density is set to be 1 etc Inthe highest interval we set the density to be the lowest density in the intervalplus the density distance down to the midpoint of the second highest densityinterval ie 33 + 33minus25

2 = 37The variable wunemp is coded 1minus 6 depending on unemployment rate in

the ward of the respondent (FNSEM 1993b)

1 Up to 1992 2-4993 5-9994 10-14995 15-206 20 or more

We recode this variable to instead take the midpoints of the intervals aswe did for the household income This means that if the respondent is thelowest interval the the rate is set to be 1 etc In the highest interval we setthe rate to be the lowest rate in the interval plus the distance down to themidpoint of the second highest rate interval ie 20 + 20minus15

2 = 225

17

gt U$WardDensityOwnEthnicity lt- (c(1035

+ 75125200290370)[U$wown])100

gt U$WardUnemploymentRate lt- c(103575125

+ 175225)[U$wunemp]

26 Discrimination own ethnicity

Finally we define a variable describing the discrimination against the ownethnic group It is defined as the average of the variable Discrimination overethnic groups after the removal of nonndashavailables

gt GroupDiscrimination lt-

+ tapply(U$DiscriminationU$ethnic

+ function(x) mean(x narm=TRUE))

gt U$DiscriminationOwnEthnicity lt-

+ GroupDiscrimination[U$ethnic]

27 Defining the subset

We define dummy variables for labour market status to have the same vari-able labels as in Bisin et al (2008)

gt U$Employee lt- asnumeric(U$LabourMarketStatus==Employee)

gt U$Manager lt- asnumeric(U$LabourMarketStatus==Manager)

gt U$SelfEmployed lt-

+ asnumeric(U$LabourMarketStatus==SelfEmployed)

gt U$OUTOFLABOURFORCE lt-

+ asnumeric(U$LabourMarketStatus==OutOfLabourForce)

gt U$Unemployed lt-

+ asnumeric(U$LabourMarketStatus==Unemployed)

We save a data set keeping all observations containing nonndashavailables

gt UOriginal lt- U

We then choose the variables to keep in U This code is in araietal_sourceRnwbut not shown here It is also available in araietal_sourceR

Finally we remove all observations containing nonndashavailables from U

gt U lt- naomit(U)

28 Sample statistics

Table 1 compares the number of observations in this sample before (seesection 27 ie data frame UOriginal) and after (see section 27 iedata frame U) nonndashavailables are removed with the numbers of observations

18

reported by the Bisin et al (2008) study The number of observations forvarious groups in the nonndashMuslim category are not reported in the Bisinet al (2008) paper These numbers are therefore missing in the table Thecategory definitions are from the original dataset and involves no recodingon our part

After removing observations with missing values on all variables of inter-est (ldquoAfterrdquo in Table 1) we are left with 330 Muslims and 488 non-MuslimsThe sample selections induced by the choice of variables and the missingvalues in these variables lead to a loss of 57 percent of the relevant sampleof the original data3

The sample means reported in Bisin et al (2008) seem to be unweightedSince data instructions says that the data should always be weighted tables2 and 3 report weighted and unweighted sample means before and afterremoval of non-availables and the Bisin et al (2008) data4 Comparingmeans the Bisin et al (2008) data seem to be different from the originalsample The variables Attitude Towards Inter Marriage and Importance ofRacial Composition in Schools in Bisin et al (2008) data deviate largelyfrom corresponding averages in the original data The deviation is extremein case of Importance of Racial Composition in Schools The original samplehas a mean for this variable that is 2 percent for non-Muslims (compare with33 percent in Bisin et al (2008)) and 6 percent for Muslims (compare with65 percent in Bisin et al (2008)) Due to this extremely skewed distributionit is hardly meaningfull to run a regression on this variable

Notice that also the distribution of the variable Importance of Religionwould be extremely skewed using standard coding of this type of variablesSuch a coding would imply that religion is important when the respondentanswer ldquoVery Importantrdquo and ldquoFairly Importantrdquo to the question ldquoHow im-portant is religion to the way you live your liferdquo

The sample means in our data after removing accumulated missing val-ues due to all variables in the estimations deviate marginally in general fromthe original data The similarities here are partly due to the fact that thestatistics are based exactly on the same variable definition in our implemen-tation In some respects the deviations are larger For further comparisonswe refer to Tables 2 and 3

Due to the fact that the large majority of observations from the originaldata are lost the remaining sample is likely to be contaminated with sampleselection bias To compare the characteristics of the remaining sample withthe original sample says something about systematic attrition with respectto observables The sample selection bias with respect to unobservablescannot however be resolved

3The variables written in capital letters are created to ensure wellndashdefined referencecategories They are included in our regressions but we cannot say whether they areincluded in the regressions of Bisin et al (2008)

4See FNSEM (1993b)

19

3 Regression Results

We use linear probability models (LPM)5 Our results are presented in Tables4 and 5 Bisin et al (2008) write that

1 ldquoMuslims integrate less and more slowly than non-Muslimsrdquo (abstractp 445) and

2 ldquo there is no evidence that segregated neighbourhoods breed intensereligious and cultural identities On the contrary intense identitiesin our data are more prominent in relatively mixed neighbourhoodsrdquo(p 446)

The first claim is based on their reported results concerning the variableYears Since Arrival In this way Bisin et al (2008) compare cohorts ofMuslims and non Muslims and attempt to say something about the evolutionof values over time They do not follow individuals over time but nonethelesscall these cohort differences ldquoIntegration over timerdquo They report negativecoefficients for Years Since Arrival but the estimates are smaller in absolutevalue for Muslims than for non-Muslims In our case the coefficients forYears Since Arrival reported in Tables 4 and 5 are insignificant in all casesexcept in the regression for Importance of Religion for Muslims where it isnegative This is opposite to what Bisin et al (2008) claim

The second claim is based on their reported results concerning the vari-able Ward Density Own Ethnicity Bisin et al (2008) report negative andsignificant estimates for Ward Density Own Ethnicity in all six specifica-tions Their negative coefficient for this variable would imply that ethnicminorities put more weight on religion mind more about inter-ethnic mar-riage and have stronger taste for ethnically profiled schools as we move fromneighbourhoods (Wards) with high density of their own ethnicity to neigh-bourhoods where people from their own ethnicity are scarce This is not atall what we find in our replication

5Bisin et al (2008) use probit estimations Our attempts to use probit run into con-vergence problems The convergence problems are severe for the model using Importanceof Racial Composition in Schools as dependent variable Hence our choice of LPM

Another issue is that Bisin et al (2008) should have included dummy variables indicatingreligious affiliation Christians Sikhs and others in the non-Muslim category to checksimilarities and differences among non-Muslims as well In this respect we follow theirmodel specification

Moreover Bisin et al (2008) should have adjusted for within ward correlations Thismight matter for their standard errors which might be underestimated In our case withalmost no significant results this would not matter much The variable is not available inthe data set and we did not make much effort to obtain it

All estimated models include 7 UK-region dummies and the variables DONOT

SPEAKWITHOLDER DONOTSPEAKATWORK and DONOTSPEAKWITHFRIENDS It turnedout that the variable DONOTSPEAKWITHYOUNGER is TRUE for few observations and can-not be included in the model

20

In our estimations the estimated coefficients for this variable are allpositive but far from significant The P-values are 069 097 and 059 forMuslims and 015 014 and 03 for non-Muslims contradicting the Bisin et al(2008) results

Inspecting the results presented in Tables 4 and 5 there are many sim-ilarities and few differences in the estimated coefficients for Muslims andnon-Muslims Our results are generally very different from results reportedby Bisin et al (2008) We are however doubtful whether it is possibleto draw any reliable inference from these results due to great loss of ob-servations and possible sample selection bias together with the problem ofendogeneity (also mentioned by Bisin et al (2008))

4 Concluding remarks

The Bisin et al (2008) paper rests on fragile grounds Our examinationof the data using their variable definitions and the same set-up indicatesthat their claims about differences between Muslims and non-Muslims andtheir conclusion that strong ReligiousEthnic identities are found in mixedneighbourhoods does not hold There is no systematic relation betweenethnic minoritiesrsquo views on religion inter-ethnic marriage or ethnic profile ofschools and the density of their own ethnic minority in their neighbourhoodHowever we hesitate to draw inference from these results since the great lossof observations (57 percent) implies that the remaining sample is most likelynot representative

5 Production notes

To facilitate reproducibility and save others timely interpretations of whatis done in this paper we attempt to follow Literate Statistical Programmingprocedures For documenting our results we have used Sweave by Leisch(2002) in combination with the LATEX family of programs using the packagesinputenc fontenc natbib Sweave fancyvrb color url hyperref andmultirow

All code (R code LATEX code and BibTEX data base code) used to do theeconometric estimations to produce this technical documentation includingall tables and to produce the companion paper Arai et al (2008) is containedin the file araietal_sourceRnw

The estimations and the documentation can be reproduced in by follow-ing the instructions at the top of the file httppeoplesuse~lundhfragile_groundsaraietal_sourceRnw

Our results were obtained on a i486-pc-linux-gnu platform using R ver-sion 281 beta (2008-12-12 r47173) (R Development Core Team 2008) with

21

m) category 5 being child of the respondent First we check if the personis a child to the respondent and then all children are summed over the re-spondents household adding the variable measuring number of children notat home Generally codes 98 and 99 are assigned NA (FNSEM 1993a p318)

gt hhlist lt- c(paste(hh2c letters[213] sep=))

gt isna(U[hhlist]) lt- U[hhlist]==98 | U[hhlist]==99

gt U$Children lt-

+ U$ChildnotatHome + apply(apply(subset(U

+ select=c(hh2cbhh2cm)) 2

+ function(x) x==5)1 function(x) sum(x

+ narm=TRUE))

2511 No British education

Question q1 asks whether the respondent has any British education Code2 is no Code 8 (Canacutet say) is kept in the alternative category since theseindividuals will answer the question q3 about foreign education Code 9 inq1 is assigned NA (FNSEM 1993a p 96)

gt isna(U$q1) lt- U$q1==9

gt U$NoBritishEducation lt- U$q1==2

2512 British basic education

We could not exactly see how this variable was defined in Bisin et al (2008)They define the British high education as Andashlevel and above One interpre-tation is then that O-level are educations included in the basic level Thisinterpretation is implemented here NA is assigned to all observations forwhich the filter question No British Education was NA (FNSEM 1993a pp96ff)

gt q2alist lt- c(paste(q2a c(181218) sep=))

gt U$BritishBasicEducation lt-

+ apply(apply(U[q2alist] 2function(x)

+ x==1)1 function(x) sum(x narm=TRUE))=0

gt U$BritishBasicEducation lt- ifelse(

+ isna(U$NoBritishEducation)

+ NAU$BritishBasicEducation)

11

2513 British higher education

Bisin et al (2008) explicitly defined British higher education as A-levelGiven the definition of British Basic education the reference group willinclude trade apprenticeships as well as university educations NA is assignedto all observations for which the filter question No British Education wasNA (FNSEM 1993a pp 96ff)

gt U$BritishHigherEducation lt- apply(apply(subset(

+ Uselect=c(q2a9q2a11q2a19q2a20))

+ 2function(x) x==1)1

+ function(x) sum(x narm=TRUE))=0

gt U$BritishHigherEducation lt- ifelse(

+ isna(U$NoBritishEducation)

+ NAU$BritishHigherEducation)

2514 Foreign education

Foreign educations is question q3 asked to all who answered lsquonorsquo or lsquoCanrsquotrsquosayrsquo on question q1 The answer yes is coded as (code 1) and no is codedas(code 2) Contrary to the above educational variables NA is not assignedto all NA on No British Education since some of them (code 8) actually wasasked the question q3 Instead NA is assigned to all observations for whichthe filter question q1 was 1 or 9 and to all code 8 (Canrsquot say) and code 9 onquestion q3 (FNSEM 1993a p 99)

gt isna(U$q3) lt- U$q3==8 | U$q3==9

gt U$ForeignEducation lt- U$q3==1

2515 Labour market status

We code the labour market status using j1b (in paid work last week or not)and j3occ (classification of activity either last weekrsquos activity or potentialactivity during the last ten years) The variable j1b takes the value 1 forpaid work last week and 2 otherwise The variable j3occ is coded as follows(FNSEM 1993ab the former pp 81f)

1 Self-employed (25+ employees)2 Self-employed (1-24 employees)3 Self-employed (no employees)4 Self-employed (employees not known)5 Manager (establishment of 25+ employees)6 Manager (establishment of 1-24 employees)7 Manager (employees not known)8 Foremansupervisor9 Other employee

12

10 Employee status unknown11 Not knownnot answered

Employee In order to be classified as an employee the individual has tohave answered yes (value 1) in j1b and be classified as employee in j3occ(value 9) and have the value NotAssignedNA is assigned to categories 10and 11 in j3occ

gt isna(U$j3occ) lt- U$j3occ==10 | U$j3occ==11

gt U$LabourMarketStatus lt- ifelse(U$j1b==1 amp

+ U$j3occ==9 amp isna(U$j1b==1 amp

+ U$j3occ==9) EmployeeNotAssigned)

Self Employed Selfndashemployed are also coded using j1b and j3occ aboveCategories 1minus 4 in j3occ are defined as self-employed We also require thatj1b is equal 1 (SelfndashEmployed)

gt U$LabourMarketStatus lt- replace(

+ U$LabourMarketStatus

+ (U$j3occ==1 | U$j3occ==2 |

+ U$j3occ==3 | U$j3occ==4) amp

+ U$j1b==1SelfEmployed)

Manager Managers are also coded using j1b and j3occ see above Cat-egories 5minus 8 in j3occ are defined as managers (including supervisors)

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ U$j1b==1 amp U$j3occgt4 amp U$j3occlt9

+ Manager)

Unemployed The question hh5ds describes the respondentrsquos labour mar-ket status Unemployment is defined via this variable hh5ds is coded inthe following way (FNSEM 1993b)

1 Full-time education2 Govt training programme3 Full-time paid work4 Part-time paid work5 Waiting to take up paid work6 Registered unemployed7 Unemployed not registered8 Permanently sick or disabled9 Wholly retired from work

13

10 Looking after the home11 Doing something else12 NA

We define unemployed as category 6 and 7 (Bisin et al 2008 p 324)There are few cases where the individual is classified as Employee accordingto our definition above and is reported to be unemployed in hh5ds Thiscan for example be part-time unemployment We classify these individualsas having Unclear labour market status These will be checked later andbe removed if they are few in the final sample

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==6 | U$hh5ds == 7) amp

+ U$LabourMarketStatus=NotAssigned

+ Unclear)

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==6 | U$hh5ds == 7) amp

+ U$LabourMarketStatus==NotAssigned

+ Unemployed)

Out of labour force The category out of labour force is defined as thosehaving values (125891011) in hh5ds or value 2 in j1b or j2

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==1 | U$hh5ds == 2 |

+ U$hh5ds==5 | U$hh5ds == 8 |

+ U$hh5ds==9 | U$hh5ds == 10|

+ U$hh5ds==11 | U$j1b == 2 |

+ U$j2 == 2 ) amp

+ U$LabourMarketStatus==NotAssigned

+ OutOfLabourForce)

Remaining observations with the value NotAssigned in Labour Mar-ketStatus will be assigned NA At this point there are 4 observations codedas Unclear These are now recoded as NA

gt isna(U$LabourMarketStatus) lt-

+ U$LabourMarketStatus == NotAssigned

gt isna(U$LabourMarketStatus) lt-

+ U$LabourMarketStatus == Unclear

We will create dummy variables using this variable before we run ourmodels

14

2516 No parents

Variable No parents means that the respondent is not living with his orher parents This variable is coded with question f14a (which takes code 1for both alive code 2 father alive code 3 for mother alive and 8 for bothdead) and f14b (which takes code 2 if both living parents do not live withthe respondent and code 6 if the only living parent does not live with therespondent else it takes one of the values 1 or 3 minus 5) No parents shouldbe coded TRUE if either both parents are dead or the respondent does notlive with any living parents However since we follow Bisin et al (2008) wecode this variable including only those who have both their parents dead orboth paprents live away from the respondent This definition implies thatthose who have a parent living away and one parent dead are assigned thevalue FALSE Code 8 and 9 are assigned NA (FNSEM 1993a p 56)

gt isna(U$f14a) lt- U$f14a==9

gt isna(U$f14b) lt- U$f14b==9

gt U$NoParents lt- U$f14a==8 | U$f14b==2

2517 Contacts with parents

The three variables measuring contacts with parents are defined via threequestions asking about the number of physical contacts (question f15bn)the number of contacts via telephone (question f15cn) and the number ofcontacts via letters (question f15dn) that the respondent has had with hisor her parents during the last four weeks conditional on not both parentsbeing dead All three takes the value of the underlaying variable if at leastone parent is alive and the value 0 if both parents are dead Code 999 onall three variables and code 997 on f15bn are assigned NA (FNSEM 1993ap 56)

gt isna(U$f15bn) lt- U$f15bn==997 | U$f15bn==999

gt isna(U$f15cn) lt- U$f15cn==999

gt isna(U$f15dn) lt- U$f15dn==999

gt U$ParentsPhysicalContacts lt- ifelse(

+ U$f14a=8 U$f15bn0)

gt U$ParentsTelephoneCalls lt- ifelse(

+ U$f14a=8 U$f15cn0)

gt U$ParentsLetters lt- ifelse(

+ U$f14a=8 U$f15dn0)

2518 English language

There are several language variables measuring whether the respondent isspeaking English with different individuals at home with older at home

15

with younger at work and with friends We construct theses variables usingquestion s12a which asks whether the respondent regularly speak to anyonein Britain in any other language than English and s12f s12g s12hand s12i which asks which language is spoken to the above mentionedcategories of individuals Each of s12f s12g s12h and s12i comesin 18 versions (eg s12f1 s12f18) where each question 1ndash15 iscoded yes if the respondent speaks the language Question 16 is ldquoNeverspeaks to these peopleNot ap[plicable]rdquo question 17 NA and question 18ldquo None of the above answered positiverdquo Question 1 is always regardingEnglish

The respondent is coded as English speaker if either s12a is answerednegatively or s12aX1 where X=fghi is answered positively Onlyrespondents for which either s12a is NA or all of s12X1 s12X16 areanswered negatively are coded as NA Below is the code for English SpokenAt Home With Older

gt isna(U$s12a) lt- U$s12a== 8 | U$s12a == 9

gt s12flist lt- c(paste(s12f 216 sep=))

gt U$oOLD lt- apply(U[s12flist]==yes 1 sum)

gt U$EnglishSpokenatHomewithOlder lt-

+ ((U$s12a==1 | isna(U$s12a)) amp

+ U$s12f1==yes) | U$s12a==2

gt isna(U$EnglishSpokenatHomewithOlder) lt-

+ U$oOLD==0 amp

+ U$EnglishSpokenatHomewithOlder==FALSE

gt U$EnglishSpokenatHomewithOlder lt-

+ replace(U$EnglishSpokenatHomewithOlder

+ U$oOLDgt0 amp

+ isna(U$EnglishSpokenatHomewithOlder)FALSE)

gt U$DONOTSPEAKWITHOLDER lt- ifelse(U$s12f16==no01)

The codes for English Spoken At Home With Younger English SpokenAt Work and English Spoken With Friends are equivalent These codesare in araietal_sourceRnw but not shown here It is also available inaraietal_sourceR

2519 Household income

The question hh40 provides information in which interval the householdincome of the respondentrsquos household is We assign the midpoints in theseintervals as the household income For the lowest bracket this income is themidpoint of [0 77] For the highest bracket we assign the income which is thelowest income in the bracket plus the income interval down to the midpintof the second highest bracket ie 789 + 788minus731

2 = 8175 This method

16

underestimates the range of the highest bracket but to a lesser extent thanthe lower limit 789

gt isna(U$hh40) lt- U$hh40==refused |

+ U$hh40==cant say | U$hh40==na

gt U$HouseholdIncome lt- c(3859651350

+ 173523052600318036604140

+ 471552955870649570207595

+ 8175)[U$hh40]

2520 Ward variables

Ward density of own ethnic group is measured by the variable wown in theoriginal data This variable is coded 1 minus 7 depending on the density of therespondentrsquos own ethnic group is in the ward of the respondent (FNSEM1993b)

1 Up to 1992 2-4993 5-9994 10-14995 15-24996 25-32997 33 or more

We recode the variable to take the midpoints of the density intervals in thesame fashion as the household income variable was recoded This meansthat if the respondent is the lowest interval the density is set to be 1 etc Inthe highest interval we set the density to be the lowest density in the intervalplus the density distance down to the midpoint of the second highest densityinterval ie 33 + 33minus25

2 = 37The variable wunemp is coded 1minus 6 depending on unemployment rate in

the ward of the respondent (FNSEM 1993b)

1 Up to 1992 2-4993 5-9994 10-14995 15-206 20 or more

We recode this variable to instead take the midpoints of the intervals aswe did for the household income This means that if the respondent is thelowest interval the the rate is set to be 1 etc In the highest interval we setthe rate to be the lowest rate in the interval plus the distance down to themidpoint of the second highest rate interval ie 20 + 20minus15

2 = 225

17

gt U$WardDensityOwnEthnicity lt- (c(1035

+ 75125200290370)[U$wown])100

gt U$WardUnemploymentRate lt- c(103575125

+ 175225)[U$wunemp]

26 Discrimination own ethnicity

Finally we define a variable describing the discrimination against the ownethnic group It is defined as the average of the variable Discrimination overethnic groups after the removal of nonndashavailables

gt GroupDiscrimination lt-

+ tapply(U$DiscriminationU$ethnic

+ function(x) mean(x narm=TRUE))

gt U$DiscriminationOwnEthnicity lt-

+ GroupDiscrimination[U$ethnic]

27 Defining the subset

We define dummy variables for labour market status to have the same vari-able labels as in Bisin et al (2008)

gt U$Employee lt- asnumeric(U$LabourMarketStatus==Employee)

gt U$Manager lt- asnumeric(U$LabourMarketStatus==Manager)

gt U$SelfEmployed lt-

+ asnumeric(U$LabourMarketStatus==SelfEmployed)

gt U$OUTOFLABOURFORCE lt-

+ asnumeric(U$LabourMarketStatus==OutOfLabourForce)

gt U$Unemployed lt-

+ asnumeric(U$LabourMarketStatus==Unemployed)

We save a data set keeping all observations containing nonndashavailables

gt UOriginal lt- U

We then choose the variables to keep in U This code is in araietal_sourceRnwbut not shown here It is also available in araietal_sourceR

Finally we remove all observations containing nonndashavailables from U

gt U lt- naomit(U)

28 Sample statistics

Table 1 compares the number of observations in this sample before (seesection 27 ie data frame UOriginal) and after (see section 27 iedata frame U) nonndashavailables are removed with the numbers of observations

18

reported by the Bisin et al (2008) study The number of observations forvarious groups in the nonndashMuslim category are not reported in the Bisinet al (2008) paper These numbers are therefore missing in the table Thecategory definitions are from the original dataset and involves no recodingon our part

After removing observations with missing values on all variables of inter-est (ldquoAfterrdquo in Table 1) we are left with 330 Muslims and 488 non-MuslimsThe sample selections induced by the choice of variables and the missingvalues in these variables lead to a loss of 57 percent of the relevant sampleof the original data3

The sample means reported in Bisin et al (2008) seem to be unweightedSince data instructions says that the data should always be weighted tables2 and 3 report weighted and unweighted sample means before and afterremoval of non-availables and the Bisin et al (2008) data4 Comparingmeans the Bisin et al (2008) data seem to be different from the originalsample The variables Attitude Towards Inter Marriage and Importance ofRacial Composition in Schools in Bisin et al (2008) data deviate largelyfrom corresponding averages in the original data The deviation is extremein case of Importance of Racial Composition in Schools The original samplehas a mean for this variable that is 2 percent for non-Muslims (compare with33 percent in Bisin et al (2008)) and 6 percent for Muslims (compare with65 percent in Bisin et al (2008)) Due to this extremely skewed distributionit is hardly meaningfull to run a regression on this variable

Notice that also the distribution of the variable Importance of Religionwould be extremely skewed using standard coding of this type of variablesSuch a coding would imply that religion is important when the respondentanswer ldquoVery Importantrdquo and ldquoFairly Importantrdquo to the question ldquoHow im-portant is religion to the way you live your liferdquo

The sample means in our data after removing accumulated missing val-ues due to all variables in the estimations deviate marginally in general fromthe original data The similarities here are partly due to the fact that thestatistics are based exactly on the same variable definition in our implemen-tation In some respects the deviations are larger For further comparisonswe refer to Tables 2 and 3

Due to the fact that the large majority of observations from the originaldata are lost the remaining sample is likely to be contaminated with sampleselection bias To compare the characteristics of the remaining sample withthe original sample says something about systematic attrition with respectto observables The sample selection bias with respect to unobservablescannot however be resolved

3The variables written in capital letters are created to ensure wellndashdefined referencecategories They are included in our regressions but we cannot say whether they areincluded in the regressions of Bisin et al (2008)

4See FNSEM (1993b)

19

3 Regression Results

We use linear probability models (LPM)5 Our results are presented in Tables4 and 5 Bisin et al (2008) write that

1 ldquoMuslims integrate less and more slowly than non-Muslimsrdquo (abstractp 445) and

2 ldquo there is no evidence that segregated neighbourhoods breed intensereligious and cultural identities On the contrary intense identitiesin our data are more prominent in relatively mixed neighbourhoodsrdquo(p 446)

The first claim is based on their reported results concerning the variableYears Since Arrival In this way Bisin et al (2008) compare cohorts ofMuslims and non Muslims and attempt to say something about the evolutionof values over time They do not follow individuals over time but nonethelesscall these cohort differences ldquoIntegration over timerdquo They report negativecoefficients for Years Since Arrival but the estimates are smaller in absolutevalue for Muslims than for non-Muslims In our case the coefficients forYears Since Arrival reported in Tables 4 and 5 are insignificant in all casesexcept in the regression for Importance of Religion for Muslims where it isnegative This is opposite to what Bisin et al (2008) claim

The second claim is based on their reported results concerning the vari-able Ward Density Own Ethnicity Bisin et al (2008) report negative andsignificant estimates for Ward Density Own Ethnicity in all six specifica-tions Their negative coefficient for this variable would imply that ethnicminorities put more weight on religion mind more about inter-ethnic mar-riage and have stronger taste for ethnically profiled schools as we move fromneighbourhoods (Wards) with high density of their own ethnicity to neigh-bourhoods where people from their own ethnicity are scarce This is not atall what we find in our replication

5Bisin et al (2008) use probit estimations Our attempts to use probit run into con-vergence problems The convergence problems are severe for the model using Importanceof Racial Composition in Schools as dependent variable Hence our choice of LPM

Another issue is that Bisin et al (2008) should have included dummy variables indicatingreligious affiliation Christians Sikhs and others in the non-Muslim category to checksimilarities and differences among non-Muslims as well In this respect we follow theirmodel specification

Moreover Bisin et al (2008) should have adjusted for within ward correlations Thismight matter for their standard errors which might be underestimated In our case withalmost no significant results this would not matter much The variable is not available inthe data set and we did not make much effort to obtain it

All estimated models include 7 UK-region dummies and the variables DONOT

SPEAKWITHOLDER DONOTSPEAKATWORK and DONOTSPEAKWITHFRIENDS It turnedout that the variable DONOTSPEAKWITHYOUNGER is TRUE for few observations and can-not be included in the model

20

In our estimations the estimated coefficients for this variable are allpositive but far from significant The P-values are 069 097 and 059 forMuslims and 015 014 and 03 for non-Muslims contradicting the Bisin et al(2008) results

Inspecting the results presented in Tables 4 and 5 there are many sim-ilarities and few differences in the estimated coefficients for Muslims andnon-Muslims Our results are generally very different from results reportedby Bisin et al (2008) We are however doubtful whether it is possibleto draw any reliable inference from these results due to great loss of ob-servations and possible sample selection bias together with the problem ofendogeneity (also mentioned by Bisin et al (2008))

4 Concluding remarks

The Bisin et al (2008) paper rests on fragile grounds Our examinationof the data using their variable definitions and the same set-up indicatesthat their claims about differences between Muslims and non-Muslims andtheir conclusion that strong ReligiousEthnic identities are found in mixedneighbourhoods does not hold There is no systematic relation betweenethnic minoritiesrsquo views on religion inter-ethnic marriage or ethnic profile ofschools and the density of their own ethnic minority in their neighbourhoodHowever we hesitate to draw inference from these results since the great lossof observations (57 percent) implies that the remaining sample is most likelynot representative

5 Production notes

To facilitate reproducibility and save others timely interpretations of whatis done in this paper we attempt to follow Literate Statistical Programmingprocedures For documenting our results we have used Sweave by Leisch(2002) in combination with the LATEX family of programs using the packagesinputenc fontenc natbib Sweave fancyvrb color url hyperref andmultirow

All code (R code LATEX code and BibTEX data base code) used to do theeconometric estimations to produce this technical documentation includingall tables and to produce the companion paper Arai et al (2008) is containedin the file araietal_sourceRnw

The estimations and the documentation can be reproduced in by follow-ing the instructions at the top of the file httppeoplesuse~lundhfragile_groundsaraietal_sourceRnw

Our results were obtained on a i486-pc-linux-gnu platform using R ver-sion 281 beta (2008-12-12 r47173) (R Development Core Team 2008) with

21

2513 British higher education

Bisin et al (2008) explicitly defined British higher education as A-levelGiven the definition of British Basic education the reference group willinclude trade apprenticeships as well as university educations NA is assignedto all observations for which the filter question No British Education wasNA (FNSEM 1993a pp 96ff)

gt U$BritishHigherEducation lt- apply(apply(subset(

+ Uselect=c(q2a9q2a11q2a19q2a20))

+ 2function(x) x==1)1

+ function(x) sum(x narm=TRUE))=0

gt U$BritishHigherEducation lt- ifelse(

+ isna(U$NoBritishEducation)

+ NAU$BritishHigherEducation)

2514 Foreign education

Foreign educations is question q3 asked to all who answered lsquonorsquo or lsquoCanrsquotrsquosayrsquo on question q1 The answer yes is coded as (code 1) and no is codedas(code 2) Contrary to the above educational variables NA is not assignedto all NA on No British Education since some of them (code 8) actually wasasked the question q3 Instead NA is assigned to all observations for whichthe filter question q1 was 1 or 9 and to all code 8 (Canrsquot say) and code 9 onquestion q3 (FNSEM 1993a p 99)

gt isna(U$q3) lt- U$q3==8 | U$q3==9

gt U$ForeignEducation lt- U$q3==1

2515 Labour market status

We code the labour market status using j1b (in paid work last week or not)and j3occ (classification of activity either last weekrsquos activity or potentialactivity during the last ten years) The variable j1b takes the value 1 forpaid work last week and 2 otherwise The variable j3occ is coded as follows(FNSEM 1993ab the former pp 81f)

1 Self-employed (25+ employees)2 Self-employed (1-24 employees)3 Self-employed (no employees)4 Self-employed (employees not known)5 Manager (establishment of 25+ employees)6 Manager (establishment of 1-24 employees)7 Manager (employees not known)8 Foremansupervisor9 Other employee

12

10 Employee status unknown11 Not knownnot answered

Employee In order to be classified as an employee the individual has tohave answered yes (value 1) in j1b and be classified as employee in j3occ(value 9) and have the value NotAssignedNA is assigned to categories 10and 11 in j3occ

gt isna(U$j3occ) lt- U$j3occ==10 | U$j3occ==11

gt U$LabourMarketStatus lt- ifelse(U$j1b==1 amp

+ U$j3occ==9 amp isna(U$j1b==1 amp

+ U$j3occ==9) EmployeeNotAssigned)

Self Employed Selfndashemployed are also coded using j1b and j3occ aboveCategories 1minus 4 in j3occ are defined as self-employed We also require thatj1b is equal 1 (SelfndashEmployed)

gt U$LabourMarketStatus lt- replace(

+ U$LabourMarketStatus

+ (U$j3occ==1 | U$j3occ==2 |

+ U$j3occ==3 | U$j3occ==4) amp

+ U$j1b==1SelfEmployed)

Manager Managers are also coded using j1b and j3occ see above Cat-egories 5minus 8 in j3occ are defined as managers (including supervisors)

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ U$j1b==1 amp U$j3occgt4 amp U$j3occlt9

+ Manager)

Unemployed The question hh5ds describes the respondentrsquos labour mar-ket status Unemployment is defined via this variable hh5ds is coded inthe following way (FNSEM 1993b)

1 Full-time education2 Govt training programme3 Full-time paid work4 Part-time paid work5 Waiting to take up paid work6 Registered unemployed7 Unemployed not registered8 Permanently sick or disabled9 Wholly retired from work

13

10 Looking after the home11 Doing something else12 NA

We define unemployed as category 6 and 7 (Bisin et al 2008 p 324)There are few cases where the individual is classified as Employee accordingto our definition above and is reported to be unemployed in hh5ds Thiscan for example be part-time unemployment We classify these individualsas having Unclear labour market status These will be checked later andbe removed if they are few in the final sample

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==6 | U$hh5ds == 7) amp

+ U$LabourMarketStatus=NotAssigned

+ Unclear)

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==6 | U$hh5ds == 7) amp

+ U$LabourMarketStatus==NotAssigned

+ Unemployed)

Out of labour force The category out of labour force is defined as thosehaving values (125891011) in hh5ds or value 2 in j1b or j2

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==1 | U$hh5ds == 2 |

+ U$hh5ds==5 | U$hh5ds == 8 |

+ U$hh5ds==9 | U$hh5ds == 10|

+ U$hh5ds==11 | U$j1b == 2 |

+ U$j2 == 2 ) amp

+ U$LabourMarketStatus==NotAssigned

+ OutOfLabourForce)

Remaining observations with the value NotAssigned in Labour Mar-ketStatus will be assigned NA At this point there are 4 observations codedas Unclear These are now recoded as NA

gt isna(U$LabourMarketStatus) lt-

+ U$LabourMarketStatus == NotAssigned

gt isna(U$LabourMarketStatus) lt-

+ U$LabourMarketStatus == Unclear

We will create dummy variables using this variable before we run ourmodels

14

2516 No parents

Variable No parents means that the respondent is not living with his orher parents This variable is coded with question f14a (which takes code 1for both alive code 2 father alive code 3 for mother alive and 8 for bothdead) and f14b (which takes code 2 if both living parents do not live withthe respondent and code 6 if the only living parent does not live with therespondent else it takes one of the values 1 or 3 minus 5) No parents shouldbe coded TRUE if either both parents are dead or the respondent does notlive with any living parents However since we follow Bisin et al (2008) wecode this variable including only those who have both their parents dead orboth paprents live away from the respondent This definition implies thatthose who have a parent living away and one parent dead are assigned thevalue FALSE Code 8 and 9 are assigned NA (FNSEM 1993a p 56)

gt isna(U$f14a) lt- U$f14a==9

gt isna(U$f14b) lt- U$f14b==9

gt U$NoParents lt- U$f14a==8 | U$f14b==2

2517 Contacts with parents

The three variables measuring contacts with parents are defined via threequestions asking about the number of physical contacts (question f15bn)the number of contacts via telephone (question f15cn) and the number ofcontacts via letters (question f15dn) that the respondent has had with hisor her parents during the last four weeks conditional on not both parentsbeing dead All three takes the value of the underlaying variable if at leastone parent is alive and the value 0 if both parents are dead Code 999 onall three variables and code 997 on f15bn are assigned NA (FNSEM 1993ap 56)

gt isna(U$f15bn) lt- U$f15bn==997 | U$f15bn==999

gt isna(U$f15cn) lt- U$f15cn==999

gt isna(U$f15dn) lt- U$f15dn==999

gt U$ParentsPhysicalContacts lt- ifelse(

+ U$f14a=8 U$f15bn0)

gt U$ParentsTelephoneCalls lt- ifelse(

+ U$f14a=8 U$f15cn0)

gt U$ParentsLetters lt- ifelse(

+ U$f14a=8 U$f15dn0)

2518 English language

There are several language variables measuring whether the respondent isspeaking English with different individuals at home with older at home

15

with younger at work and with friends We construct theses variables usingquestion s12a which asks whether the respondent regularly speak to anyonein Britain in any other language than English and s12f s12g s12hand s12i which asks which language is spoken to the above mentionedcategories of individuals Each of s12f s12g s12h and s12i comesin 18 versions (eg s12f1 s12f18) where each question 1ndash15 iscoded yes if the respondent speaks the language Question 16 is ldquoNeverspeaks to these peopleNot ap[plicable]rdquo question 17 NA and question 18ldquo None of the above answered positiverdquo Question 1 is always regardingEnglish

The respondent is coded as English speaker if either s12a is answerednegatively or s12aX1 where X=fghi is answered positively Onlyrespondents for which either s12a is NA or all of s12X1 s12X16 areanswered negatively are coded as NA Below is the code for English SpokenAt Home With Older

gt isna(U$s12a) lt- U$s12a== 8 | U$s12a == 9

gt s12flist lt- c(paste(s12f 216 sep=))

gt U$oOLD lt- apply(U[s12flist]==yes 1 sum)

gt U$EnglishSpokenatHomewithOlder lt-

+ ((U$s12a==1 | isna(U$s12a)) amp

+ U$s12f1==yes) | U$s12a==2

gt isna(U$EnglishSpokenatHomewithOlder) lt-

+ U$oOLD==0 amp

+ U$EnglishSpokenatHomewithOlder==FALSE

gt U$EnglishSpokenatHomewithOlder lt-

+ replace(U$EnglishSpokenatHomewithOlder

+ U$oOLDgt0 amp

+ isna(U$EnglishSpokenatHomewithOlder)FALSE)

gt U$DONOTSPEAKWITHOLDER lt- ifelse(U$s12f16==no01)

The codes for English Spoken At Home With Younger English SpokenAt Work and English Spoken With Friends are equivalent These codesare in araietal_sourceRnw but not shown here It is also available inaraietal_sourceR

2519 Household income

The question hh40 provides information in which interval the householdincome of the respondentrsquos household is We assign the midpoints in theseintervals as the household income For the lowest bracket this income is themidpoint of [0 77] For the highest bracket we assign the income which is thelowest income in the bracket plus the income interval down to the midpintof the second highest bracket ie 789 + 788minus731

2 = 8175 This method

16

underestimates the range of the highest bracket but to a lesser extent thanthe lower limit 789

gt isna(U$hh40) lt- U$hh40==refused |

+ U$hh40==cant say | U$hh40==na

gt U$HouseholdIncome lt- c(3859651350

+ 173523052600318036604140

+ 471552955870649570207595

+ 8175)[U$hh40]

2520 Ward variables

Ward density of own ethnic group is measured by the variable wown in theoriginal data This variable is coded 1 minus 7 depending on the density of therespondentrsquos own ethnic group is in the ward of the respondent (FNSEM1993b)

1 Up to 1992 2-4993 5-9994 10-14995 15-24996 25-32997 33 or more

We recode the variable to take the midpoints of the density intervals in thesame fashion as the household income variable was recoded This meansthat if the respondent is the lowest interval the density is set to be 1 etc Inthe highest interval we set the density to be the lowest density in the intervalplus the density distance down to the midpoint of the second highest densityinterval ie 33 + 33minus25

2 = 37The variable wunemp is coded 1minus 6 depending on unemployment rate in

the ward of the respondent (FNSEM 1993b)

1 Up to 1992 2-4993 5-9994 10-14995 15-206 20 or more

We recode this variable to instead take the midpoints of the intervals aswe did for the household income This means that if the respondent is thelowest interval the the rate is set to be 1 etc In the highest interval we setthe rate to be the lowest rate in the interval plus the distance down to themidpoint of the second highest rate interval ie 20 + 20minus15

2 = 225

17

gt U$WardDensityOwnEthnicity lt- (c(1035

+ 75125200290370)[U$wown])100

gt U$WardUnemploymentRate lt- c(103575125

+ 175225)[U$wunemp]

26 Discrimination own ethnicity

Finally we define a variable describing the discrimination against the ownethnic group It is defined as the average of the variable Discrimination overethnic groups after the removal of nonndashavailables

gt GroupDiscrimination lt-

+ tapply(U$DiscriminationU$ethnic

+ function(x) mean(x narm=TRUE))

gt U$DiscriminationOwnEthnicity lt-

+ GroupDiscrimination[U$ethnic]

27 Defining the subset

We define dummy variables for labour market status to have the same vari-able labels as in Bisin et al (2008)

gt U$Employee lt- asnumeric(U$LabourMarketStatus==Employee)

gt U$Manager lt- asnumeric(U$LabourMarketStatus==Manager)

gt U$SelfEmployed lt-

+ asnumeric(U$LabourMarketStatus==SelfEmployed)

gt U$OUTOFLABOURFORCE lt-

+ asnumeric(U$LabourMarketStatus==OutOfLabourForce)

gt U$Unemployed lt-

+ asnumeric(U$LabourMarketStatus==Unemployed)

We save a data set keeping all observations containing nonndashavailables

gt UOriginal lt- U

We then choose the variables to keep in U This code is in araietal_sourceRnwbut not shown here It is also available in araietal_sourceR

Finally we remove all observations containing nonndashavailables from U

gt U lt- naomit(U)

28 Sample statistics

Table 1 compares the number of observations in this sample before (seesection 27 ie data frame UOriginal) and after (see section 27 iedata frame U) nonndashavailables are removed with the numbers of observations

18

reported by the Bisin et al (2008) study The number of observations forvarious groups in the nonndashMuslim category are not reported in the Bisinet al (2008) paper These numbers are therefore missing in the table Thecategory definitions are from the original dataset and involves no recodingon our part

After removing observations with missing values on all variables of inter-est (ldquoAfterrdquo in Table 1) we are left with 330 Muslims and 488 non-MuslimsThe sample selections induced by the choice of variables and the missingvalues in these variables lead to a loss of 57 percent of the relevant sampleof the original data3

The sample means reported in Bisin et al (2008) seem to be unweightedSince data instructions says that the data should always be weighted tables2 and 3 report weighted and unweighted sample means before and afterremoval of non-availables and the Bisin et al (2008) data4 Comparingmeans the Bisin et al (2008) data seem to be different from the originalsample The variables Attitude Towards Inter Marriage and Importance ofRacial Composition in Schools in Bisin et al (2008) data deviate largelyfrom corresponding averages in the original data The deviation is extremein case of Importance of Racial Composition in Schools The original samplehas a mean for this variable that is 2 percent for non-Muslims (compare with33 percent in Bisin et al (2008)) and 6 percent for Muslims (compare with65 percent in Bisin et al (2008)) Due to this extremely skewed distributionit is hardly meaningfull to run a regression on this variable

Notice that also the distribution of the variable Importance of Religionwould be extremely skewed using standard coding of this type of variablesSuch a coding would imply that religion is important when the respondentanswer ldquoVery Importantrdquo and ldquoFairly Importantrdquo to the question ldquoHow im-portant is religion to the way you live your liferdquo

The sample means in our data after removing accumulated missing val-ues due to all variables in the estimations deviate marginally in general fromthe original data The similarities here are partly due to the fact that thestatistics are based exactly on the same variable definition in our implemen-tation In some respects the deviations are larger For further comparisonswe refer to Tables 2 and 3

Due to the fact that the large majority of observations from the originaldata are lost the remaining sample is likely to be contaminated with sampleselection bias To compare the characteristics of the remaining sample withthe original sample says something about systematic attrition with respectto observables The sample selection bias with respect to unobservablescannot however be resolved

3The variables written in capital letters are created to ensure wellndashdefined referencecategories They are included in our regressions but we cannot say whether they areincluded in the regressions of Bisin et al (2008)

4See FNSEM (1993b)

19

3 Regression Results

We use linear probability models (LPM)5 Our results are presented in Tables4 and 5 Bisin et al (2008) write that

1 ldquoMuslims integrate less and more slowly than non-Muslimsrdquo (abstractp 445) and

2 ldquo there is no evidence that segregated neighbourhoods breed intensereligious and cultural identities On the contrary intense identitiesin our data are more prominent in relatively mixed neighbourhoodsrdquo(p 446)

The first claim is based on their reported results concerning the variableYears Since Arrival In this way Bisin et al (2008) compare cohorts ofMuslims and non Muslims and attempt to say something about the evolutionof values over time They do not follow individuals over time but nonethelesscall these cohort differences ldquoIntegration over timerdquo They report negativecoefficients for Years Since Arrival but the estimates are smaller in absolutevalue for Muslims than for non-Muslims In our case the coefficients forYears Since Arrival reported in Tables 4 and 5 are insignificant in all casesexcept in the regression for Importance of Religion for Muslims where it isnegative This is opposite to what Bisin et al (2008) claim

The second claim is based on their reported results concerning the vari-able Ward Density Own Ethnicity Bisin et al (2008) report negative andsignificant estimates for Ward Density Own Ethnicity in all six specifica-tions Their negative coefficient for this variable would imply that ethnicminorities put more weight on religion mind more about inter-ethnic mar-riage and have stronger taste for ethnically profiled schools as we move fromneighbourhoods (Wards) with high density of their own ethnicity to neigh-bourhoods where people from their own ethnicity are scarce This is not atall what we find in our replication

5Bisin et al (2008) use probit estimations Our attempts to use probit run into con-vergence problems The convergence problems are severe for the model using Importanceof Racial Composition in Schools as dependent variable Hence our choice of LPM

Another issue is that Bisin et al (2008) should have included dummy variables indicatingreligious affiliation Christians Sikhs and others in the non-Muslim category to checksimilarities and differences among non-Muslims as well In this respect we follow theirmodel specification

Moreover Bisin et al (2008) should have adjusted for within ward correlations Thismight matter for their standard errors which might be underestimated In our case withalmost no significant results this would not matter much The variable is not available inthe data set and we did not make much effort to obtain it

All estimated models include 7 UK-region dummies and the variables DONOT

SPEAKWITHOLDER DONOTSPEAKATWORK and DONOTSPEAKWITHFRIENDS It turnedout that the variable DONOTSPEAKWITHYOUNGER is TRUE for few observations and can-not be included in the model

20

In our estimations the estimated coefficients for this variable are allpositive but far from significant The P-values are 069 097 and 059 forMuslims and 015 014 and 03 for non-Muslims contradicting the Bisin et al(2008) results

Inspecting the results presented in Tables 4 and 5 there are many sim-ilarities and few differences in the estimated coefficients for Muslims andnon-Muslims Our results are generally very different from results reportedby Bisin et al (2008) We are however doubtful whether it is possibleto draw any reliable inference from these results due to great loss of ob-servations and possible sample selection bias together with the problem ofendogeneity (also mentioned by Bisin et al (2008))

4 Concluding remarks

The Bisin et al (2008) paper rests on fragile grounds Our examinationof the data using their variable definitions and the same set-up indicatesthat their claims about differences between Muslims and non-Muslims andtheir conclusion that strong ReligiousEthnic identities are found in mixedneighbourhoods does not hold There is no systematic relation betweenethnic minoritiesrsquo views on religion inter-ethnic marriage or ethnic profile ofschools and the density of their own ethnic minority in their neighbourhoodHowever we hesitate to draw inference from these results since the great lossof observations (57 percent) implies that the remaining sample is most likelynot representative

5 Production notes

To facilitate reproducibility and save others timely interpretations of whatis done in this paper we attempt to follow Literate Statistical Programmingprocedures For documenting our results we have used Sweave by Leisch(2002) in combination with the LATEX family of programs using the packagesinputenc fontenc natbib Sweave fancyvrb color url hyperref andmultirow

All code (R code LATEX code and BibTEX data base code) used to do theeconometric estimations to produce this technical documentation includingall tables and to produce the companion paper Arai et al (2008) is containedin the file araietal_sourceRnw

The estimations and the documentation can be reproduced in by follow-ing the instructions at the top of the file httppeoplesuse~lundhfragile_groundsaraietal_sourceRnw

Our results were obtained on a i486-pc-linux-gnu platform using R ver-sion 281 beta (2008-12-12 r47173) (R Development Core Team 2008) with

21

10 Employee status unknown11 Not knownnot answered

Employee In order to be classified as an employee the individual has tohave answered yes (value 1) in j1b and be classified as employee in j3occ(value 9) and have the value NotAssignedNA is assigned to categories 10and 11 in j3occ

gt isna(U$j3occ) lt- U$j3occ==10 | U$j3occ==11

gt U$LabourMarketStatus lt- ifelse(U$j1b==1 amp

+ U$j3occ==9 amp isna(U$j1b==1 amp

+ U$j3occ==9) EmployeeNotAssigned)

Self Employed Selfndashemployed are also coded using j1b and j3occ aboveCategories 1minus 4 in j3occ are defined as self-employed We also require thatj1b is equal 1 (SelfndashEmployed)

gt U$LabourMarketStatus lt- replace(

+ U$LabourMarketStatus

+ (U$j3occ==1 | U$j3occ==2 |

+ U$j3occ==3 | U$j3occ==4) amp

+ U$j1b==1SelfEmployed)

Manager Managers are also coded using j1b and j3occ see above Cat-egories 5minus 8 in j3occ are defined as managers (including supervisors)

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ U$j1b==1 amp U$j3occgt4 amp U$j3occlt9

+ Manager)

Unemployed The question hh5ds describes the respondentrsquos labour mar-ket status Unemployment is defined via this variable hh5ds is coded inthe following way (FNSEM 1993b)

1 Full-time education2 Govt training programme3 Full-time paid work4 Part-time paid work5 Waiting to take up paid work6 Registered unemployed7 Unemployed not registered8 Permanently sick or disabled9 Wholly retired from work

13

10 Looking after the home11 Doing something else12 NA

We define unemployed as category 6 and 7 (Bisin et al 2008 p 324)There are few cases where the individual is classified as Employee accordingto our definition above and is reported to be unemployed in hh5ds Thiscan for example be part-time unemployment We classify these individualsas having Unclear labour market status These will be checked later andbe removed if they are few in the final sample

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==6 | U$hh5ds == 7) amp

+ U$LabourMarketStatus=NotAssigned

+ Unclear)

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==6 | U$hh5ds == 7) amp

+ U$LabourMarketStatus==NotAssigned

+ Unemployed)

Out of labour force The category out of labour force is defined as thosehaving values (125891011) in hh5ds or value 2 in j1b or j2

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==1 | U$hh5ds == 2 |

+ U$hh5ds==5 | U$hh5ds == 8 |

+ U$hh5ds==9 | U$hh5ds == 10|

+ U$hh5ds==11 | U$j1b == 2 |

+ U$j2 == 2 ) amp

+ U$LabourMarketStatus==NotAssigned

+ OutOfLabourForce)

Remaining observations with the value NotAssigned in Labour Mar-ketStatus will be assigned NA At this point there are 4 observations codedas Unclear These are now recoded as NA

gt isna(U$LabourMarketStatus) lt-

+ U$LabourMarketStatus == NotAssigned

gt isna(U$LabourMarketStatus) lt-

+ U$LabourMarketStatus == Unclear

We will create dummy variables using this variable before we run ourmodels

14

2516 No parents

Variable No parents means that the respondent is not living with his orher parents This variable is coded with question f14a (which takes code 1for both alive code 2 father alive code 3 for mother alive and 8 for bothdead) and f14b (which takes code 2 if both living parents do not live withthe respondent and code 6 if the only living parent does not live with therespondent else it takes one of the values 1 or 3 minus 5) No parents shouldbe coded TRUE if either both parents are dead or the respondent does notlive with any living parents However since we follow Bisin et al (2008) wecode this variable including only those who have both their parents dead orboth paprents live away from the respondent This definition implies thatthose who have a parent living away and one parent dead are assigned thevalue FALSE Code 8 and 9 are assigned NA (FNSEM 1993a p 56)

gt isna(U$f14a) lt- U$f14a==9

gt isna(U$f14b) lt- U$f14b==9

gt U$NoParents lt- U$f14a==8 | U$f14b==2

2517 Contacts with parents

The three variables measuring contacts with parents are defined via threequestions asking about the number of physical contacts (question f15bn)the number of contacts via telephone (question f15cn) and the number ofcontacts via letters (question f15dn) that the respondent has had with hisor her parents during the last four weeks conditional on not both parentsbeing dead All three takes the value of the underlaying variable if at leastone parent is alive and the value 0 if both parents are dead Code 999 onall three variables and code 997 on f15bn are assigned NA (FNSEM 1993ap 56)

gt isna(U$f15bn) lt- U$f15bn==997 | U$f15bn==999

gt isna(U$f15cn) lt- U$f15cn==999

gt isna(U$f15dn) lt- U$f15dn==999

gt U$ParentsPhysicalContacts lt- ifelse(

+ U$f14a=8 U$f15bn0)

gt U$ParentsTelephoneCalls lt- ifelse(

+ U$f14a=8 U$f15cn0)

gt U$ParentsLetters lt- ifelse(

+ U$f14a=8 U$f15dn0)

2518 English language

There are several language variables measuring whether the respondent isspeaking English with different individuals at home with older at home

15

with younger at work and with friends We construct theses variables usingquestion s12a which asks whether the respondent regularly speak to anyonein Britain in any other language than English and s12f s12g s12hand s12i which asks which language is spoken to the above mentionedcategories of individuals Each of s12f s12g s12h and s12i comesin 18 versions (eg s12f1 s12f18) where each question 1ndash15 iscoded yes if the respondent speaks the language Question 16 is ldquoNeverspeaks to these peopleNot ap[plicable]rdquo question 17 NA and question 18ldquo None of the above answered positiverdquo Question 1 is always regardingEnglish

The respondent is coded as English speaker if either s12a is answerednegatively or s12aX1 where X=fghi is answered positively Onlyrespondents for which either s12a is NA or all of s12X1 s12X16 areanswered negatively are coded as NA Below is the code for English SpokenAt Home With Older

gt isna(U$s12a) lt- U$s12a== 8 | U$s12a == 9

gt s12flist lt- c(paste(s12f 216 sep=))

gt U$oOLD lt- apply(U[s12flist]==yes 1 sum)

gt U$EnglishSpokenatHomewithOlder lt-

+ ((U$s12a==1 | isna(U$s12a)) amp

+ U$s12f1==yes) | U$s12a==2

gt isna(U$EnglishSpokenatHomewithOlder) lt-

+ U$oOLD==0 amp

+ U$EnglishSpokenatHomewithOlder==FALSE

gt U$EnglishSpokenatHomewithOlder lt-

+ replace(U$EnglishSpokenatHomewithOlder

+ U$oOLDgt0 amp

+ isna(U$EnglishSpokenatHomewithOlder)FALSE)

gt U$DONOTSPEAKWITHOLDER lt- ifelse(U$s12f16==no01)

The codes for English Spoken At Home With Younger English SpokenAt Work and English Spoken With Friends are equivalent These codesare in araietal_sourceRnw but not shown here It is also available inaraietal_sourceR

2519 Household income

The question hh40 provides information in which interval the householdincome of the respondentrsquos household is We assign the midpoints in theseintervals as the household income For the lowest bracket this income is themidpoint of [0 77] For the highest bracket we assign the income which is thelowest income in the bracket plus the income interval down to the midpintof the second highest bracket ie 789 + 788minus731

2 = 8175 This method

16

underestimates the range of the highest bracket but to a lesser extent thanthe lower limit 789

gt isna(U$hh40) lt- U$hh40==refused |

+ U$hh40==cant say | U$hh40==na

gt U$HouseholdIncome lt- c(3859651350

+ 173523052600318036604140

+ 471552955870649570207595

+ 8175)[U$hh40]

2520 Ward variables

Ward density of own ethnic group is measured by the variable wown in theoriginal data This variable is coded 1 minus 7 depending on the density of therespondentrsquos own ethnic group is in the ward of the respondent (FNSEM1993b)

1 Up to 1992 2-4993 5-9994 10-14995 15-24996 25-32997 33 or more

We recode the variable to take the midpoints of the density intervals in thesame fashion as the household income variable was recoded This meansthat if the respondent is the lowest interval the density is set to be 1 etc Inthe highest interval we set the density to be the lowest density in the intervalplus the density distance down to the midpoint of the second highest densityinterval ie 33 + 33minus25

2 = 37The variable wunemp is coded 1minus 6 depending on unemployment rate in

the ward of the respondent (FNSEM 1993b)

1 Up to 1992 2-4993 5-9994 10-14995 15-206 20 or more

We recode this variable to instead take the midpoints of the intervals aswe did for the household income This means that if the respondent is thelowest interval the the rate is set to be 1 etc In the highest interval we setthe rate to be the lowest rate in the interval plus the distance down to themidpoint of the second highest rate interval ie 20 + 20minus15

2 = 225

17

gt U$WardDensityOwnEthnicity lt- (c(1035

+ 75125200290370)[U$wown])100

gt U$WardUnemploymentRate lt- c(103575125

+ 175225)[U$wunemp]

26 Discrimination own ethnicity

Finally we define a variable describing the discrimination against the ownethnic group It is defined as the average of the variable Discrimination overethnic groups after the removal of nonndashavailables

gt GroupDiscrimination lt-

+ tapply(U$DiscriminationU$ethnic

+ function(x) mean(x narm=TRUE))

gt U$DiscriminationOwnEthnicity lt-

+ GroupDiscrimination[U$ethnic]

27 Defining the subset

We define dummy variables for labour market status to have the same vari-able labels as in Bisin et al (2008)

gt U$Employee lt- asnumeric(U$LabourMarketStatus==Employee)

gt U$Manager lt- asnumeric(U$LabourMarketStatus==Manager)

gt U$SelfEmployed lt-

+ asnumeric(U$LabourMarketStatus==SelfEmployed)

gt U$OUTOFLABOURFORCE lt-

+ asnumeric(U$LabourMarketStatus==OutOfLabourForce)

gt U$Unemployed lt-

+ asnumeric(U$LabourMarketStatus==Unemployed)

We save a data set keeping all observations containing nonndashavailables

gt UOriginal lt- U

We then choose the variables to keep in U This code is in araietal_sourceRnwbut not shown here It is also available in araietal_sourceR

Finally we remove all observations containing nonndashavailables from U

gt U lt- naomit(U)

28 Sample statistics

Table 1 compares the number of observations in this sample before (seesection 27 ie data frame UOriginal) and after (see section 27 iedata frame U) nonndashavailables are removed with the numbers of observations

18

reported by the Bisin et al (2008) study The number of observations forvarious groups in the nonndashMuslim category are not reported in the Bisinet al (2008) paper These numbers are therefore missing in the table Thecategory definitions are from the original dataset and involves no recodingon our part

After removing observations with missing values on all variables of inter-est (ldquoAfterrdquo in Table 1) we are left with 330 Muslims and 488 non-MuslimsThe sample selections induced by the choice of variables and the missingvalues in these variables lead to a loss of 57 percent of the relevant sampleof the original data3

The sample means reported in Bisin et al (2008) seem to be unweightedSince data instructions says that the data should always be weighted tables2 and 3 report weighted and unweighted sample means before and afterremoval of non-availables and the Bisin et al (2008) data4 Comparingmeans the Bisin et al (2008) data seem to be different from the originalsample The variables Attitude Towards Inter Marriage and Importance ofRacial Composition in Schools in Bisin et al (2008) data deviate largelyfrom corresponding averages in the original data The deviation is extremein case of Importance of Racial Composition in Schools The original samplehas a mean for this variable that is 2 percent for non-Muslims (compare with33 percent in Bisin et al (2008)) and 6 percent for Muslims (compare with65 percent in Bisin et al (2008)) Due to this extremely skewed distributionit is hardly meaningfull to run a regression on this variable

Notice that also the distribution of the variable Importance of Religionwould be extremely skewed using standard coding of this type of variablesSuch a coding would imply that religion is important when the respondentanswer ldquoVery Importantrdquo and ldquoFairly Importantrdquo to the question ldquoHow im-portant is religion to the way you live your liferdquo

The sample means in our data after removing accumulated missing val-ues due to all variables in the estimations deviate marginally in general fromthe original data The similarities here are partly due to the fact that thestatistics are based exactly on the same variable definition in our implemen-tation In some respects the deviations are larger For further comparisonswe refer to Tables 2 and 3

Due to the fact that the large majority of observations from the originaldata are lost the remaining sample is likely to be contaminated with sampleselection bias To compare the characteristics of the remaining sample withthe original sample says something about systematic attrition with respectto observables The sample selection bias with respect to unobservablescannot however be resolved

3The variables written in capital letters are created to ensure wellndashdefined referencecategories They are included in our regressions but we cannot say whether they areincluded in the regressions of Bisin et al (2008)

4See FNSEM (1993b)

19

3 Regression Results

We use linear probability models (LPM)5 Our results are presented in Tables4 and 5 Bisin et al (2008) write that

1 ldquoMuslims integrate less and more slowly than non-Muslimsrdquo (abstractp 445) and

2 ldquo there is no evidence that segregated neighbourhoods breed intensereligious and cultural identities On the contrary intense identitiesin our data are more prominent in relatively mixed neighbourhoodsrdquo(p 446)

The first claim is based on their reported results concerning the variableYears Since Arrival In this way Bisin et al (2008) compare cohorts ofMuslims and non Muslims and attempt to say something about the evolutionof values over time They do not follow individuals over time but nonethelesscall these cohort differences ldquoIntegration over timerdquo They report negativecoefficients for Years Since Arrival but the estimates are smaller in absolutevalue for Muslims than for non-Muslims In our case the coefficients forYears Since Arrival reported in Tables 4 and 5 are insignificant in all casesexcept in the regression for Importance of Religion for Muslims where it isnegative This is opposite to what Bisin et al (2008) claim

The second claim is based on their reported results concerning the vari-able Ward Density Own Ethnicity Bisin et al (2008) report negative andsignificant estimates for Ward Density Own Ethnicity in all six specifica-tions Their negative coefficient for this variable would imply that ethnicminorities put more weight on religion mind more about inter-ethnic mar-riage and have stronger taste for ethnically profiled schools as we move fromneighbourhoods (Wards) with high density of their own ethnicity to neigh-bourhoods where people from their own ethnicity are scarce This is not atall what we find in our replication

5Bisin et al (2008) use probit estimations Our attempts to use probit run into con-vergence problems The convergence problems are severe for the model using Importanceof Racial Composition in Schools as dependent variable Hence our choice of LPM

Another issue is that Bisin et al (2008) should have included dummy variables indicatingreligious affiliation Christians Sikhs and others in the non-Muslim category to checksimilarities and differences among non-Muslims as well In this respect we follow theirmodel specification

Moreover Bisin et al (2008) should have adjusted for within ward correlations Thismight matter for their standard errors which might be underestimated In our case withalmost no significant results this would not matter much The variable is not available inthe data set and we did not make much effort to obtain it

All estimated models include 7 UK-region dummies and the variables DONOT

SPEAKWITHOLDER DONOTSPEAKATWORK and DONOTSPEAKWITHFRIENDS It turnedout that the variable DONOTSPEAKWITHYOUNGER is TRUE for few observations and can-not be included in the model

20

In our estimations the estimated coefficients for this variable are allpositive but far from significant The P-values are 069 097 and 059 forMuslims and 015 014 and 03 for non-Muslims contradicting the Bisin et al(2008) results

Inspecting the results presented in Tables 4 and 5 there are many sim-ilarities and few differences in the estimated coefficients for Muslims andnon-Muslims Our results are generally very different from results reportedby Bisin et al (2008) We are however doubtful whether it is possibleto draw any reliable inference from these results due to great loss of ob-servations and possible sample selection bias together with the problem ofendogeneity (also mentioned by Bisin et al (2008))

4 Concluding remarks

The Bisin et al (2008) paper rests on fragile grounds Our examinationof the data using their variable definitions and the same set-up indicatesthat their claims about differences between Muslims and non-Muslims andtheir conclusion that strong ReligiousEthnic identities are found in mixedneighbourhoods does not hold There is no systematic relation betweenethnic minoritiesrsquo views on religion inter-ethnic marriage or ethnic profile ofschools and the density of their own ethnic minority in their neighbourhoodHowever we hesitate to draw inference from these results since the great lossof observations (57 percent) implies that the remaining sample is most likelynot representative

5 Production notes

To facilitate reproducibility and save others timely interpretations of whatis done in this paper we attempt to follow Literate Statistical Programmingprocedures For documenting our results we have used Sweave by Leisch(2002) in combination with the LATEX family of programs using the packagesinputenc fontenc natbib Sweave fancyvrb color url hyperref andmultirow

All code (R code LATEX code and BibTEX data base code) used to do theeconometric estimations to produce this technical documentation includingall tables and to produce the companion paper Arai et al (2008) is containedin the file araietal_sourceRnw

The estimations and the documentation can be reproduced in by follow-ing the instructions at the top of the file httppeoplesuse~lundhfragile_groundsaraietal_sourceRnw

Our results were obtained on a i486-pc-linux-gnu platform using R ver-sion 281 beta (2008-12-12 r47173) (R Development Core Team 2008) with

21

10 Looking after the home11 Doing something else12 NA

We define unemployed as category 6 and 7 (Bisin et al 2008 p 324)There are few cases where the individual is classified as Employee accordingto our definition above and is reported to be unemployed in hh5ds Thiscan for example be part-time unemployment We classify these individualsas having Unclear labour market status These will be checked later andbe removed if they are few in the final sample

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==6 | U$hh5ds == 7) amp

+ U$LabourMarketStatus=NotAssigned

+ Unclear)

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==6 | U$hh5ds == 7) amp

+ U$LabourMarketStatus==NotAssigned

+ Unemployed)

Out of labour force The category out of labour force is defined as thosehaving values (125891011) in hh5ds or value 2 in j1b or j2

gt U$LabourMarketStatus lt-

+ replace(U$LabourMarketStatus

+ (U$hh5ds==1 | U$hh5ds == 2 |

+ U$hh5ds==5 | U$hh5ds == 8 |

+ U$hh5ds==9 | U$hh5ds == 10|

+ U$hh5ds==11 | U$j1b == 2 |

+ U$j2 == 2 ) amp

+ U$LabourMarketStatus==NotAssigned

+ OutOfLabourForce)

Remaining observations with the value NotAssigned in Labour Mar-ketStatus will be assigned NA At this point there are 4 observations codedas Unclear These are now recoded as NA

gt isna(U$LabourMarketStatus) lt-

+ U$LabourMarketStatus == NotAssigned

gt isna(U$LabourMarketStatus) lt-

+ U$LabourMarketStatus == Unclear

We will create dummy variables using this variable before we run ourmodels

14

2516 No parents

Variable No parents means that the respondent is not living with his orher parents This variable is coded with question f14a (which takes code 1for both alive code 2 father alive code 3 for mother alive and 8 for bothdead) and f14b (which takes code 2 if both living parents do not live withthe respondent and code 6 if the only living parent does not live with therespondent else it takes one of the values 1 or 3 minus 5) No parents shouldbe coded TRUE if either both parents are dead or the respondent does notlive with any living parents However since we follow Bisin et al (2008) wecode this variable including only those who have both their parents dead orboth paprents live away from the respondent This definition implies thatthose who have a parent living away and one parent dead are assigned thevalue FALSE Code 8 and 9 are assigned NA (FNSEM 1993a p 56)

gt isna(U$f14a) lt- U$f14a==9

gt isna(U$f14b) lt- U$f14b==9

gt U$NoParents lt- U$f14a==8 | U$f14b==2

2517 Contacts with parents

The three variables measuring contacts with parents are defined via threequestions asking about the number of physical contacts (question f15bn)the number of contacts via telephone (question f15cn) and the number ofcontacts via letters (question f15dn) that the respondent has had with hisor her parents during the last four weeks conditional on not both parentsbeing dead All three takes the value of the underlaying variable if at leastone parent is alive and the value 0 if both parents are dead Code 999 onall three variables and code 997 on f15bn are assigned NA (FNSEM 1993ap 56)

gt isna(U$f15bn) lt- U$f15bn==997 | U$f15bn==999

gt isna(U$f15cn) lt- U$f15cn==999

gt isna(U$f15dn) lt- U$f15dn==999

gt U$ParentsPhysicalContacts lt- ifelse(

+ U$f14a=8 U$f15bn0)

gt U$ParentsTelephoneCalls lt- ifelse(

+ U$f14a=8 U$f15cn0)

gt U$ParentsLetters lt- ifelse(

+ U$f14a=8 U$f15dn0)

2518 English language

There are several language variables measuring whether the respondent isspeaking English with different individuals at home with older at home

15

with younger at work and with friends We construct theses variables usingquestion s12a which asks whether the respondent regularly speak to anyonein Britain in any other language than English and s12f s12g s12hand s12i which asks which language is spoken to the above mentionedcategories of individuals Each of s12f s12g s12h and s12i comesin 18 versions (eg s12f1 s12f18) where each question 1ndash15 iscoded yes if the respondent speaks the language Question 16 is ldquoNeverspeaks to these peopleNot ap[plicable]rdquo question 17 NA and question 18ldquo None of the above answered positiverdquo Question 1 is always regardingEnglish

The respondent is coded as English speaker if either s12a is answerednegatively or s12aX1 where X=fghi is answered positively Onlyrespondents for which either s12a is NA or all of s12X1 s12X16 areanswered negatively are coded as NA Below is the code for English SpokenAt Home With Older

gt isna(U$s12a) lt- U$s12a== 8 | U$s12a == 9

gt s12flist lt- c(paste(s12f 216 sep=))

gt U$oOLD lt- apply(U[s12flist]==yes 1 sum)

gt U$EnglishSpokenatHomewithOlder lt-

+ ((U$s12a==1 | isna(U$s12a)) amp

+ U$s12f1==yes) | U$s12a==2

gt isna(U$EnglishSpokenatHomewithOlder) lt-

+ U$oOLD==0 amp

+ U$EnglishSpokenatHomewithOlder==FALSE

gt U$EnglishSpokenatHomewithOlder lt-

+ replace(U$EnglishSpokenatHomewithOlder

+ U$oOLDgt0 amp

+ isna(U$EnglishSpokenatHomewithOlder)FALSE)

gt U$DONOTSPEAKWITHOLDER lt- ifelse(U$s12f16==no01)

The codes for English Spoken At Home With Younger English SpokenAt Work and English Spoken With Friends are equivalent These codesare in araietal_sourceRnw but not shown here It is also available inaraietal_sourceR

2519 Household income

The question hh40 provides information in which interval the householdincome of the respondentrsquos household is We assign the midpoints in theseintervals as the household income For the lowest bracket this income is themidpoint of [0 77] For the highest bracket we assign the income which is thelowest income in the bracket plus the income interval down to the midpintof the second highest bracket ie 789 + 788minus731

2 = 8175 This method

16

underestimates the range of the highest bracket but to a lesser extent thanthe lower limit 789

gt isna(U$hh40) lt- U$hh40==refused |

+ U$hh40==cant say | U$hh40==na

gt U$HouseholdIncome lt- c(3859651350

+ 173523052600318036604140

+ 471552955870649570207595

+ 8175)[U$hh40]

2520 Ward variables

Ward density of own ethnic group is measured by the variable wown in theoriginal data This variable is coded 1 minus 7 depending on the density of therespondentrsquos own ethnic group is in the ward of the respondent (FNSEM1993b)

1 Up to 1992 2-4993 5-9994 10-14995 15-24996 25-32997 33 or more

We recode the variable to take the midpoints of the density intervals in thesame fashion as the household income variable was recoded This meansthat if the respondent is the lowest interval the density is set to be 1 etc Inthe highest interval we set the density to be the lowest density in the intervalplus the density distance down to the midpoint of the second highest densityinterval ie 33 + 33minus25

2 = 37The variable wunemp is coded 1minus 6 depending on unemployment rate in

the ward of the respondent (FNSEM 1993b)

1 Up to 1992 2-4993 5-9994 10-14995 15-206 20 or more

We recode this variable to instead take the midpoints of the intervals aswe did for the household income This means that if the respondent is thelowest interval the the rate is set to be 1 etc In the highest interval we setthe rate to be the lowest rate in the interval plus the distance down to themidpoint of the second highest rate interval ie 20 + 20minus15

2 = 225

17

gt U$WardDensityOwnEthnicity lt- (c(1035

+ 75125200290370)[U$wown])100

gt U$WardUnemploymentRate lt- c(103575125

+ 175225)[U$wunemp]

26 Discrimination own ethnicity

Finally we define a variable describing the discrimination against the ownethnic group It is defined as the average of the variable Discrimination overethnic groups after the removal of nonndashavailables

gt GroupDiscrimination lt-

+ tapply(U$DiscriminationU$ethnic

+ function(x) mean(x narm=TRUE))

gt U$DiscriminationOwnEthnicity lt-

+ GroupDiscrimination[U$ethnic]

27 Defining the subset

We define dummy variables for labour market status to have the same vari-able labels as in Bisin et al (2008)

gt U$Employee lt- asnumeric(U$LabourMarketStatus==Employee)

gt U$Manager lt- asnumeric(U$LabourMarketStatus==Manager)

gt U$SelfEmployed lt-

+ asnumeric(U$LabourMarketStatus==SelfEmployed)

gt U$OUTOFLABOURFORCE lt-

+ asnumeric(U$LabourMarketStatus==OutOfLabourForce)

gt U$Unemployed lt-

+ asnumeric(U$LabourMarketStatus==Unemployed)

We save a data set keeping all observations containing nonndashavailables

gt UOriginal lt- U

We then choose the variables to keep in U This code is in araietal_sourceRnwbut not shown here It is also available in araietal_sourceR

Finally we remove all observations containing nonndashavailables from U

gt U lt- naomit(U)

28 Sample statistics

Table 1 compares the number of observations in this sample before (seesection 27 ie data frame UOriginal) and after (see section 27 iedata frame U) nonndashavailables are removed with the numbers of observations

18

reported by the Bisin et al (2008) study The number of observations forvarious groups in the nonndashMuslim category are not reported in the Bisinet al (2008) paper These numbers are therefore missing in the table Thecategory definitions are from the original dataset and involves no recodingon our part

After removing observations with missing values on all variables of inter-est (ldquoAfterrdquo in Table 1) we are left with 330 Muslims and 488 non-MuslimsThe sample selections induced by the choice of variables and the missingvalues in these variables lead to a loss of 57 percent of the relevant sampleof the original data3

The sample means reported in Bisin et al (2008) seem to be unweightedSince data instructions says that the data should always be weighted tables2 and 3 report weighted and unweighted sample means before and afterremoval of non-availables and the Bisin et al (2008) data4 Comparingmeans the Bisin et al (2008) data seem to be different from the originalsample The variables Attitude Towards Inter Marriage and Importance ofRacial Composition in Schools in Bisin et al (2008) data deviate largelyfrom corresponding averages in the original data The deviation is extremein case of Importance of Racial Composition in Schools The original samplehas a mean for this variable that is 2 percent for non-Muslims (compare with33 percent in Bisin et al (2008)) and 6 percent for Muslims (compare with65 percent in Bisin et al (2008)) Due to this extremely skewed distributionit is hardly meaningfull to run a regression on this variable

Notice that also the distribution of the variable Importance of Religionwould be extremely skewed using standard coding of this type of variablesSuch a coding would imply that religion is important when the respondentanswer ldquoVery Importantrdquo and ldquoFairly Importantrdquo to the question ldquoHow im-portant is religion to the way you live your liferdquo

The sample means in our data after removing accumulated missing val-ues due to all variables in the estimations deviate marginally in general fromthe original data The similarities here are partly due to the fact that thestatistics are based exactly on the same variable definition in our implemen-tation In some respects the deviations are larger For further comparisonswe refer to Tables 2 and 3

Due to the fact that the large majority of observations from the originaldata are lost the remaining sample is likely to be contaminated with sampleselection bias To compare the characteristics of the remaining sample withthe original sample says something about systematic attrition with respectto observables The sample selection bias with respect to unobservablescannot however be resolved

3The variables written in capital letters are created to ensure wellndashdefined referencecategories They are included in our regressions but we cannot say whether they areincluded in the regressions of Bisin et al (2008)

4See FNSEM (1993b)

19

3 Regression Results

We use linear probability models (LPM)5 Our results are presented in Tables4 and 5 Bisin et al (2008) write that

1 ldquoMuslims integrate less and more slowly than non-Muslimsrdquo (abstractp 445) and

2 ldquo there is no evidence that segregated neighbourhoods breed intensereligious and cultural identities On the contrary intense identitiesin our data are more prominent in relatively mixed neighbourhoodsrdquo(p 446)

The first claim is based on their reported results concerning the variableYears Since Arrival In this way Bisin et al (2008) compare cohorts ofMuslims and non Muslims and attempt to say something about the evolutionof values over time They do not follow individuals over time but nonethelesscall these cohort differences ldquoIntegration over timerdquo They report negativecoefficients for Years Since Arrival but the estimates are smaller in absolutevalue for Muslims than for non-Muslims In our case the coefficients forYears Since Arrival reported in Tables 4 and 5 are insignificant in all casesexcept in the regression for Importance of Religion for Muslims where it isnegative This is opposite to what Bisin et al (2008) claim

The second claim is based on their reported results concerning the vari-able Ward Density Own Ethnicity Bisin et al (2008) report negative andsignificant estimates for Ward Density Own Ethnicity in all six specifica-tions Their negative coefficient for this variable would imply that ethnicminorities put more weight on religion mind more about inter-ethnic mar-riage and have stronger taste for ethnically profiled schools as we move fromneighbourhoods (Wards) with high density of their own ethnicity to neigh-bourhoods where people from their own ethnicity are scarce This is not atall what we find in our replication

5Bisin et al (2008) use probit estimations Our attempts to use probit run into con-vergence problems The convergence problems are severe for the model using Importanceof Racial Composition in Schools as dependent variable Hence our choice of LPM

Another issue is that Bisin et al (2008) should have included dummy variables indicatingreligious affiliation Christians Sikhs and others in the non-Muslim category to checksimilarities and differences among non-Muslims as well In this respect we follow theirmodel specification

Moreover Bisin et al (2008) should have adjusted for within ward correlations Thismight matter for their standard errors which might be underestimated In our case withalmost no significant results this would not matter much The variable is not available inthe data set and we did not make much effort to obtain it

All estimated models include 7 UK-region dummies and the variables DONOT

SPEAKWITHOLDER DONOTSPEAKATWORK and DONOTSPEAKWITHFRIENDS It turnedout that the variable DONOTSPEAKWITHYOUNGER is TRUE for few observations and can-not be included in the model

20

In our estimations the estimated coefficients for this variable are allpositive but far from significant The P-values are 069 097 and 059 forMuslims and 015 014 and 03 for non-Muslims contradicting the Bisin et al(2008) results

Inspecting the results presented in Tables 4 and 5 there are many sim-ilarities and few differences in the estimated coefficients for Muslims andnon-Muslims Our results are generally very different from results reportedby Bisin et al (2008) We are however doubtful whether it is possibleto draw any reliable inference from these results due to great loss of ob-servations and possible sample selection bias together with the problem ofendogeneity (also mentioned by Bisin et al (2008))

4 Concluding remarks

The Bisin et al (2008) paper rests on fragile grounds Our examinationof the data using their variable definitions and the same set-up indicatesthat their claims about differences between Muslims and non-Muslims andtheir conclusion that strong ReligiousEthnic identities are found in mixedneighbourhoods does not hold There is no systematic relation betweenethnic minoritiesrsquo views on religion inter-ethnic marriage or ethnic profile ofschools and the density of their own ethnic minority in their neighbourhoodHowever we hesitate to draw inference from these results since the great lossof observations (57 percent) implies that the remaining sample is most likelynot representative

5 Production notes

To facilitate reproducibility and save others timely interpretations of whatis done in this paper we attempt to follow Literate Statistical Programmingprocedures For documenting our results we have used Sweave by Leisch(2002) in combination with the LATEX family of programs using the packagesinputenc fontenc natbib Sweave fancyvrb color url hyperref andmultirow

All code (R code LATEX code and BibTEX data base code) used to do theeconometric estimations to produce this technical documentation includingall tables and to produce the companion paper Arai et al (2008) is containedin the file araietal_sourceRnw

The estimations and the documentation can be reproduced in by follow-ing the instructions at the top of the file httppeoplesuse~lundhfragile_groundsaraietal_sourceRnw

Our results were obtained on a i486-pc-linux-gnu platform using R ver-sion 281 beta (2008-12-12 r47173) (R Development Core Team 2008) with

21

2516 No parents

Variable No parents means that the respondent is not living with his orher parents This variable is coded with question f14a (which takes code 1for both alive code 2 father alive code 3 for mother alive and 8 for bothdead) and f14b (which takes code 2 if both living parents do not live withthe respondent and code 6 if the only living parent does not live with therespondent else it takes one of the values 1 or 3 minus 5) No parents shouldbe coded TRUE if either both parents are dead or the respondent does notlive with any living parents However since we follow Bisin et al (2008) wecode this variable including only those who have both their parents dead orboth paprents live away from the respondent This definition implies thatthose who have a parent living away and one parent dead are assigned thevalue FALSE Code 8 and 9 are assigned NA (FNSEM 1993a p 56)

gt isna(U$f14a) lt- U$f14a==9

gt isna(U$f14b) lt- U$f14b==9

gt U$NoParents lt- U$f14a==8 | U$f14b==2

2517 Contacts with parents

The three variables measuring contacts with parents are defined via threequestions asking about the number of physical contacts (question f15bn)the number of contacts via telephone (question f15cn) and the number ofcontacts via letters (question f15dn) that the respondent has had with hisor her parents during the last four weeks conditional on not both parentsbeing dead All three takes the value of the underlaying variable if at leastone parent is alive and the value 0 if both parents are dead Code 999 onall three variables and code 997 on f15bn are assigned NA (FNSEM 1993ap 56)

gt isna(U$f15bn) lt- U$f15bn==997 | U$f15bn==999

gt isna(U$f15cn) lt- U$f15cn==999

gt isna(U$f15dn) lt- U$f15dn==999

gt U$ParentsPhysicalContacts lt- ifelse(

+ U$f14a=8 U$f15bn0)

gt U$ParentsTelephoneCalls lt- ifelse(

+ U$f14a=8 U$f15cn0)

gt U$ParentsLetters lt- ifelse(

+ U$f14a=8 U$f15dn0)

2518 English language

There are several language variables measuring whether the respondent isspeaking English with different individuals at home with older at home

15

with younger at work and with friends We construct theses variables usingquestion s12a which asks whether the respondent regularly speak to anyonein Britain in any other language than English and s12f s12g s12hand s12i which asks which language is spoken to the above mentionedcategories of individuals Each of s12f s12g s12h and s12i comesin 18 versions (eg s12f1 s12f18) where each question 1ndash15 iscoded yes if the respondent speaks the language Question 16 is ldquoNeverspeaks to these peopleNot ap[plicable]rdquo question 17 NA and question 18ldquo None of the above answered positiverdquo Question 1 is always regardingEnglish

The respondent is coded as English speaker if either s12a is answerednegatively or s12aX1 where X=fghi is answered positively Onlyrespondents for which either s12a is NA or all of s12X1 s12X16 areanswered negatively are coded as NA Below is the code for English SpokenAt Home With Older

gt isna(U$s12a) lt- U$s12a== 8 | U$s12a == 9

gt s12flist lt- c(paste(s12f 216 sep=))

gt U$oOLD lt- apply(U[s12flist]==yes 1 sum)

gt U$EnglishSpokenatHomewithOlder lt-

+ ((U$s12a==1 | isna(U$s12a)) amp

+ U$s12f1==yes) | U$s12a==2

gt isna(U$EnglishSpokenatHomewithOlder) lt-

+ U$oOLD==0 amp

+ U$EnglishSpokenatHomewithOlder==FALSE

gt U$EnglishSpokenatHomewithOlder lt-

+ replace(U$EnglishSpokenatHomewithOlder

+ U$oOLDgt0 amp

+ isna(U$EnglishSpokenatHomewithOlder)FALSE)

gt U$DONOTSPEAKWITHOLDER lt- ifelse(U$s12f16==no01)

The codes for English Spoken At Home With Younger English SpokenAt Work and English Spoken With Friends are equivalent These codesare in araietal_sourceRnw but not shown here It is also available inaraietal_sourceR

2519 Household income

The question hh40 provides information in which interval the householdincome of the respondentrsquos household is We assign the midpoints in theseintervals as the household income For the lowest bracket this income is themidpoint of [0 77] For the highest bracket we assign the income which is thelowest income in the bracket plus the income interval down to the midpintof the second highest bracket ie 789 + 788minus731

2 = 8175 This method

16

underestimates the range of the highest bracket but to a lesser extent thanthe lower limit 789

gt isna(U$hh40) lt- U$hh40==refused |

+ U$hh40==cant say | U$hh40==na

gt U$HouseholdIncome lt- c(3859651350

+ 173523052600318036604140

+ 471552955870649570207595

+ 8175)[U$hh40]

2520 Ward variables

Ward density of own ethnic group is measured by the variable wown in theoriginal data This variable is coded 1 minus 7 depending on the density of therespondentrsquos own ethnic group is in the ward of the respondent (FNSEM1993b)

1 Up to 1992 2-4993 5-9994 10-14995 15-24996 25-32997 33 or more

We recode the variable to take the midpoints of the density intervals in thesame fashion as the household income variable was recoded This meansthat if the respondent is the lowest interval the density is set to be 1 etc Inthe highest interval we set the density to be the lowest density in the intervalplus the density distance down to the midpoint of the second highest densityinterval ie 33 + 33minus25

2 = 37The variable wunemp is coded 1minus 6 depending on unemployment rate in

the ward of the respondent (FNSEM 1993b)

1 Up to 1992 2-4993 5-9994 10-14995 15-206 20 or more

We recode this variable to instead take the midpoints of the intervals aswe did for the household income This means that if the respondent is thelowest interval the the rate is set to be 1 etc In the highest interval we setthe rate to be the lowest rate in the interval plus the distance down to themidpoint of the second highest rate interval ie 20 + 20minus15

2 = 225

17

gt U$WardDensityOwnEthnicity lt- (c(1035

+ 75125200290370)[U$wown])100

gt U$WardUnemploymentRate lt- c(103575125

+ 175225)[U$wunemp]

26 Discrimination own ethnicity

Finally we define a variable describing the discrimination against the ownethnic group It is defined as the average of the variable Discrimination overethnic groups after the removal of nonndashavailables

gt GroupDiscrimination lt-

+ tapply(U$DiscriminationU$ethnic

+ function(x) mean(x narm=TRUE))

gt U$DiscriminationOwnEthnicity lt-

+ GroupDiscrimination[U$ethnic]

27 Defining the subset

We define dummy variables for labour market status to have the same vari-able labels as in Bisin et al (2008)

gt U$Employee lt- asnumeric(U$LabourMarketStatus==Employee)

gt U$Manager lt- asnumeric(U$LabourMarketStatus==Manager)

gt U$SelfEmployed lt-

+ asnumeric(U$LabourMarketStatus==SelfEmployed)

gt U$OUTOFLABOURFORCE lt-

+ asnumeric(U$LabourMarketStatus==OutOfLabourForce)

gt U$Unemployed lt-

+ asnumeric(U$LabourMarketStatus==Unemployed)

We save a data set keeping all observations containing nonndashavailables

gt UOriginal lt- U

We then choose the variables to keep in U This code is in araietal_sourceRnwbut not shown here It is also available in araietal_sourceR

Finally we remove all observations containing nonndashavailables from U

gt U lt- naomit(U)

28 Sample statistics

Table 1 compares the number of observations in this sample before (seesection 27 ie data frame UOriginal) and after (see section 27 iedata frame U) nonndashavailables are removed with the numbers of observations

18

reported by the Bisin et al (2008) study The number of observations forvarious groups in the nonndashMuslim category are not reported in the Bisinet al (2008) paper These numbers are therefore missing in the table Thecategory definitions are from the original dataset and involves no recodingon our part

After removing observations with missing values on all variables of inter-est (ldquoAfterrdquo in Table 1) we are left with 330 Muslims and 488 non-MuslimsThe sample selections induced by the choice of variables and the missingvalues in these variables lead to a loss of 57 percent of the relevant sampleof the original data3

The sample means reported in Bisin et al (2008) seem to be unweightedSince data instructions says that the data should always be weighted tables2 and 3 report weighted and unweighted sample means before and afterremoval of non-availables and the Bisin et al (2008) data4 Comparingmeans the Bisin et al (2008) data seem to be different from the originalsample The variables Attitude Towards Inter Marriage and Importance ofRacial Composition in Schools in Bisin et al (2008) data deviate largelyfrom corresponding averages in the original data The deviation is extremein case of Importance of Racial Composition in Schools The original samplehas a mean for this variable that is 2 percent for non-Muslims (compare with33 percent in Bisin et al (2008)) and 6 percent for Muslims (compare with65 percent in Bisin et al (2008)) Due to this extremely skewed distributionit is hardly meaningfull to run a regression on this variable

Notice that also the distribution of the variable Importance of Religionwould be extremely skewed using standard coding of this type of variablesSuch a coding would imply that religion is important when the respondentanswer ldquoVery Importantrdquo and ldquoFairly Importantrdquo to the question ldquoHow im-portant is religion to the way you live your liferdquo

The sample means in our data after removing accumulated missing val-ues due to all variables in the estimations deviate marginally in general fromthe original data The similarities here are partly due to the fact that thestatistics are based exactly on the same variable definition in our implemen-tation In some respects the deviations are larger For further comparisonswe refer to Tables 2 and 3

Due to the fact that the large majority of observations from the originaldata are lost the remaining sample is likely to be contaminated with sampleselection bias To compare the characteristics of the remaining sample withthe original sample says something about systematic attrition with respectto observables The sample selection bias with respect to unobservablescannot however be resolved

3The variables written in capital letters are created to ensure wellndashdefined referencecategories They are included in our regressions but we cannot say whether they areincluded in the regressions of Bisin et al (2008)

4See FNSEM (1993b)

19

3 Regression Results

We use linear probability models (LPM)5 Our results are presented in Tables4 and 5 Bisin et al (2008) write that

1 ldquoMuslims integrate less and more slowly than non-Muslimsrdquo (abstractp 445) and

2 ldquo there is no evidence that segregated neighbourhoods breed intensereligious and cultural identities On the contrary intense identitiesin our data are more prominent in relatively mixed neighbourhoodsrdquo(p 446)

The first claim is based on their reported results concerning the variableYears Since Arrival In this way Bisin et al (2008) compare cohorts ofMuslims and non Muslims and attempt to say something about the evolutionof values over time They do not follow individuals over time but nonethelesscall these cohort differences ldquoIntegration over timerdquo They report negativecoefficients for Years Since Arrival but the estimates are smaller in absolutevalue for Muslims than for non-Muslims In our case the coefficients forYears Since Arrival reported in Tables 4 and 5 are insignificant in all casesexcept in the regression for Importance of Religion for Muslims where it isnegative This is opposite to what Bisin et al (2008) claim

The second claim is based on their reported results concerning the vari-able Ward Density Own Ethnicity Bisin et al (2008) report negative andsignificant estimates for Ward Density Own Ethnicity in all six specifica-tions Their negative coefficient for this variable would imply that ethnicminorities put more weight on religion mind more about inter-ethnic mar-riage and have stronger taste for ethnically profiled schools as we move fromneighbourhoods (Wards) with high density of their own ethnicity to neigh-bourhoods where people from their own ethnicity are scarce This is not atall what we find in our replication

5Bisin et al (2008) use probit estimations Our attempts to use probit run into con-vergence problems The convergence problems are severe for the model using Importanceof Racial Composition in Schools as dependent variable Hence our choice of LPM

Another issue is that Bisin et al (2008) should have included dummy variables indicatingreligious affiliation Christians Sikhs and others in the non-Muslim category to checksimilarities and differences among non-Muslims as well In this respect we follow theirmodel specification

Moreover Bisin et al (2008) should have adjusted for within ward correlations Thismight matter for their standard errors which might be underestimated In our case withalmost no significant results this would not matter much The variable is not available inthe data set and we did not make much effort to obtain it

All estimated models include 7 UK-region dummies and the variables DONOT

SPEAKWITHOLDER DONOTSPEAKATWORK and DONOTSPEAKWITHFRIENDS It turnedout that the variable DONOTSPEAKWITHYOUNGER is TRUE for few observations and can-not be included in the model

20

In our estimations the estimated coefficients for this variable are allpositive but far from significant The P-values are 069 097 and 059 forMuslims and 015 014 and 03 for non-Muslims contradicting the Bisin et al(2008) results

Inspecting the results presented in Tables 4 and 5 there are many sim-ilarities and few differences in the estimated coefficients for Muslims andnon-Muslims Our results are generally very different from results reportedby Bisin et al (2008) We are however doubtful whether it is possibleto draw any reliable inference from these results due to great loss of ob-servations and possible sample selection bias together with the problem ofendogeneity (also mentioned by Bisin et al (2008))

4 Concluding remarks

The Bisin et al (2008) paper rests on fragile grounds Our examinationof the data using their variable definitions and the same set-up indicatesthat their claims about differences between Muslims and non-Muslims andtheir conclusion that strong ReligiousEthnic identities are found in mixedneighbourhoods does not hold There is no systematic relation betweenethnic minoritiesrsquo views on religion inter-ethnic marriage or ethnic profile ofschools and the density of their own ethnic minority in their neighbourhoodHowever we hesitate to draw inference from these results since the great lossof observations (57 percent) implies that the remaining sample is most likelynot representative

5 Production notes

To facilitate reproducibility and save others timely interpretations of whatis done in this paper we attempt to follow Literate Statistical Programmingprocedures For documenting our results we have used Sweave by Leisch(2002) in combination with the LATEX family of programs using the packagesinputenc fontenc natbib Sweave fancyvrb color url hyperref andmultirow

All code (R code LATEX code and BibTEX data base code) used to do theeconometric estimations to produce this technical documentation includingall tables and to produce the companion paper Arai et al (2008) is containedin the file araietal_sourceRnw

The estimations and the documentation can be reproduced in by follow-ing the instructions at the top of the file httppeoplesuse~lundhfragile_groundsaraietal_sourceRnw

Our results were obtained on a i486-pc-linux-gnu platform using R ver-sion 281 beta (2008-12-12 r47173) (R Development Core Team 2008) with

21

with younger at work and with friends We construct theses variables usingquestion s12a which asks whether the respondent regularly speak to anyonein Britain in any other language than English and s12f s12g s12hand s12i which asks which language is spoken to the above mentionedcategories of individuals Each of s12f s12g s12h and s12i comesin 18 versions (eg s12f1 s12f18) where each question 1ndash15 iscoded yes if the respondent speaks the language Question 16 is ldquoNeverspeaks to these peopleNot ap[plicable]rdquo question 17 NA and question 18ldquo None of the above answered positiverdquo Question 1 is always regardingEnglish

The respondent is coded as English speaker if either s12a is answerednegatively or s12aX1 where X=fghi is answered positively Onlyrespondents for which either s12a is NA or all of s12X1 s12X16 areanswered negatively are coded as NA Below is the code for English SpokenAt Home With Older

gt isna(U$s12a) lt- U$s12a== 8 | U$s12a == 9

gt s12flist lt- c(paste(s12f 216 sep=))

gt U$oOLD lt- apply(U[s12flist]==yes 1 sum)

gt U$EnglishSpokenatHomewithOlder lt-

+ ((U$s12a==1 | isna(U$s12a)) amp

+ U$s12f1==yes) | U$s12a==2

gt isna(U$EnglishSpokenatHomewithOlder) lt-

+ U$oOLD==0 amp

+ U$EnglishSpokenatHomewithOlder==FALSE

gt U$EnglishSpokenatHomewithOlder lt-

+ replace(U$EnglishSpokenatHomewithOlder

+ U$oOLDgt0 amp

+ isna(U$EnglishSpokenatHomewithOlder)FALSE)

gt U$DONOTSPEAKWITHOLDER lt- ifelse(U$s12f16==no01)

The codes for English Spoken At Home With Younger English SpokenAt Work and English Spoken With Friends are equivalent These codesare in araietal_sourceRnw but not shown here It is also available inaraietal_sourceR

2519 Household income

The question hh40 provides information in which interval the householdincome of the respondentrsquos household is We assign the midpoints in theseintervals as the household income For the lowest bracket this income is themidpoint of [0 77] For the highest bracket we assign the income which is thelowest income in the bracket plus the income interval down to the midpintof the second highest bracket ie 789 + 788minus731

2 = 8175 This method

16

underestimates the range of the highest bracket but to a lesser extent thanthe lower limit 789

gt isna(U$hh40) lt- U$hh40==refused |

+ U$hh40==cant say | U$hh40==na

gt U$HouseholdIncome lt- c(3859651350

+ 173523052600318036604140

+ 471552955870649570207595

+ 8175)[U$hh40]

2520 Ward variables

Ward density of own ethnic group is measured by the variable wown in theoriginal data This variable is coded 1 minus 7 depending on the density of therespondentrsquos own ethnic group is in the ward of the respondent (FNSEM1993b)

1 Up to 1992 2-4993 5-9994 10-14995 15-24996 25-32997 33 or more

We recode the variable to take the midpoints of the density intervals in thesame fashion as the household income variable was recoded This meansthat if the respondent is the lowest interval the density is set to be 1 etc Inthe highest interval we set the density to be the lowest density in the intervalplus the density distance down to the midpoint of the second highest densityinterval ie 33 + 33minus25

2 = 37The variable wunemp is coded 1minus 6 depending on unemployment rate in

the ward of the respondent (FNSEM 1993b)

1 Up to 1992 2-4993 5-9994 10-14995 15-206 20 or more

We recode this variable to instead take the midpoints of the intervals aswe did for the household income This means that if the respondent is thelowest interval the the rate is set to be 1 etc In the highest interval we setthe rate to be the lowest rate in the interval plus the distance down to themidpoint of the second highest rate interval ie 20 + 20minus15

2 = 225

17

gt U$WardDensityOwnEthnicity lt- (c(1035

+ 75125200290370)[U$wown])100

gt U$WardUnemploymentRate lt- c(103575125

+ 175225)[U$wunemp]

26 Discrimination own ethnicity

Finally we define a variable describing the discrimination against the ownethnic group It is defined as the average of the variable Discrimination overethnic groups after the removal of nonndashavailables

gt GroupDiscrimination lt-

+ tapply(U$DiscriminationU$ethnic

+ function(x) mean(x narm=TRUE))

gt U$DiscriminationOwnEthnicity lt-

+ GroupDiscrimination[U$ethnic]

27 Defining the subset

We define dummy variables for labour market status to have the same vari-able labels as in Bisin et al (2008)

gt U$Employee lt- asnumeric(U$LabourMarketStatus==Employee)

gt U$Manager lt- asnumeric(U$LabourMarketStatus==Manager)

gt U$SelfEmployed lt-

+ asnumeric(U$LabourMarketStatus==SelfEmployed)

gt U$OUTOFLABOURFORCE lt-

+ asnumeric(U$LabourMarketStatus==OutOfLabourForce)

gt U$Unemployed lt-

+ asnumeric(U$LabourMarketStatus==Unemployed)

We save a data set keeping all observations containing nonndashavailables

gt UOriginal lt- U

We then choose the variables to keep in U This code is in araietal_sourceRnwbut not shown here It is also available in araietal_sourceR

Finally we remove all observations containing nonndashavailables from U

gt U lt- naomit(U)

28 Sample statistics

Table 1 compares the number of observations in this sample before (seesection 27 ie data frame UOriginal) and after (see section 27 iedata frame U) nonndashavailables are removed with the numbers of observations

18

reported by the Bisin et al (2008) study The number of observations forvarious groups in the nonndashMuslim category are not reported in the Bisinet al (2008) paper These numbers are therefore missing in the table Thecategory definitions are from the original dataset and involves no recodingon our part

After removing observations with missing values on all variables of inter-est (ldquoAfterrdquo in Table 1) we are left with 330 Muslims and 488 non-MuslimsThe sample selections induced by the choice of variables and the missingvalues in these variables lead to a loss of 57 percent of the relevant sampleof the original data3

The sample means reported in Bisin et al (2008) seem to be unweightedSince data instructions says that the data should always be weighted tables2 and 3 report weighted and unweighted sample means before and afterremoval of non-availables and the Bisin et al (2008) data4 Comparingmeans the Bisin et al (2008) data seem to be different from the originalsample The variables Attitude Towards Inter Marriage and Importance ofRacial Composition in Schools in Bisin et al (2008) data deviate largelyfrom corresponding averages in the original data The deviation is extremein case of Importance of Racial Composition in Schools The original samplehas a mean for this variable that is 2 percent for non-Muslims (compare with33 percent in Bisin et al (2008)) and 6 percent for Muslims (compare with65 percent in Bisin et al (2008)) Due to this extremely skewed distributionit is hardly meaningfull to run a regression on this variable

Notice that also the distribution of the variable Importance of Religionwould be extremely skewed using standard coding of this type of variablesSuch a coding would imply that religion is important when the respondentanswer ldquoVery Importantrdquo and ldquoFairly Importantrdquo to the question ldquoHow im-portant is religion to the way you live your liferdquo

The sample means in our data after removing accumulated missing val-ues due to all variables in the estimations deviate marginally in general fromthe original data The similarities here are partly due to the fact that thestatistics are based exactly on the same variable definition in our implemen-tation In some respects the deviations are larger For further comparisonswe refer to Tables 2 and 3

Due to the fact that the large majority of observations from the originaldata are lost the remaining sample is likely to be contaminated with sampleselection bias To compare the characteristics of the remaining sample withthe original sample says something about systematic attrition with respectto observables The sample selection bias with respect to unobservablescannot however be resolved

3The variables written in capital letters are created to ensure wellndashdefined referencecategories They are included in our regressions but we cannot say whether they areincluded in the regressions of Bisin et al (2008)

4See FNSEM (1993b)

19

3 Regression Results

We use linear probability models (LPM)5 Our results are presented in Tables4 and 5 Bisin et al (2008) write that

1 ldquoMuslims integrate less and more slowly than non-Muslimsrdquo (abstractp 445) and

2 ldquo there is no evidence that segregated neighbourhoods breed intensereligious and cultural identities On the contrary intense identitiesin our data are more prominent in relatively mixed neighbourhoodsrdquo(p 446)

The first claim is based on their reported results concerning the variableYears Since Arrival In this way Bisin et al (2008) compare cohorts ofMuslims and non Muslims and attempt to say something about the evolutionof values over time They do not follow individuals over time but nonethelesscall these cohort differences ldquoIntegration over timerdquo They report negativecoefficients for Years Since Arrival but the estimates are smaller in absolutevalue for Muslims than for non-Muslims In our case the coefficients forYears Since Arrival reported in Tables 4 and 5 are insignificant in all casesexcept in the regression for Importance of Religion for Muslims where it isnegative This is opposite to what Bisin et al (2008) claim

The second claim is based on their reported results concerning the vari-able Ward Density Own Ethnicity Bisin et al (2008) report negative andsignificant estimates for Ward Density Own Ethnicity in all six specifica-tions Their negative coefficient for this variable would imply that ethnicminorities put more weight on religion mind more about inter-ethnic mar-riage and have stronger taste for ethnically profiled schools as we move fromneighbourhoods (Wards) with high density of their own ethnicity to neigh-bourhoods where people from their own ethnicity are scarce This is not atall what we find in our replication

5Bisin et al (2008) use probit estimations Our attempts to use probit run into con-vergence problems The convergence problems are severe for the model using Importanceof Racial Composition in Schools as dependent variable Hence our choice of LPM

Another issue is that Bisin et al (2008) should have included dummy variables indicatingreligious affiliation Christians Sikhs and others in the non-Muslim category to checksimilarities and differences among non-Muslims as well In this respect we follow theirmodel specification

Moreover Bisin et al (2008) should have adjusted for within ward correlations Thismight matter for their standard errors which might be underestimated In our case withalmost no significant results this would not matter much The variable is not available inthe data set and we did not make much effort to obtain it

All estimated models include 7 UK-region dummies and the variables DONOT

SPEAKWITHOLDER DONOTSPEAKATWORK and DONOTSPEAKWITHFRIENDS It turnedout that the variable DONOTSPEAKWITHYOUNGER is TRUE for few observations and can-not be included in the model

20

In our estimations the estimated coefficients for this variable are allpositive but far from significant The P-values are 069 097 and 059 forMuslims and 015 014 and 03 for non-Muslims contradicting the Bisin et al(2008) results

Inspecting the results presented in Tables 4 and 5 there are many sim-ilarities and few differences in the estimated coefficients for Muslims andnon-Muslims Our results are generally very different from results reportedby Bisin et al (2008) We are however doubtful whether it is possibleto draw any reliable inference from these results due to great loss of ob-servations and possible sample selection bias together with the problem ofendogeneity (also mentioned by Bisin et al (2008))

4 Concluding remarks

The Bisin et al (2008) paper rests on fragile grounds Our examinationof the data using their variable definitions and the same set-up indicatesthat their claims about differences between Muslims and non-Muslims andtheir conclusion that strong ReligiousEthnic identities are found in mixedneighbourhoods does not hold There is no systematic relation betweenethnic minoritiesrsquo views on religion inter-ethnic marriage or ethnic profile ofschools and the density of their own ethnic minority in their neighbourhoodHowever we hesitate to draw inference from these results since the great lossof observations (57 percent) implies that the remaining sample is most likelynot representative

5 Production notes

To facilitate reproducibility and save others timely interpretations of whatis done in this paper we attempt to follow Literate Statistical Programmingprocedures For documenting our results we have used Sweave by Leisch(2002) in combination with the LATEX family of programs using the packagesinputenc fontenc natbib Sweave fancyvrb color url hyperref andmultirow

All code (R code LATEX code and BibTEX data base code) used to do theeconometric estimations to produce this technical documentation includingall tables and to produce the companion paper Arai et al (2008) is containedin the file araietal_sourceRnw

The estimations and the documentation can be reproduced in by follow-ing the instructions at the top of the file httppeoplesuse~lundhfragile_groundsaraietal_sourceRnw

Our results were obtained on a i486-pc-linux-gnu platform using R ver-sion 281 beta (2008-12-12 r47173) (R Development Core Team 2008) with

21

underestimates the range of the highest bracket but to a lesser extent thanthe lower limit 789

gt isna(U$hh40) lt- U$hh40==refused |

+ U$hh40==cant say | U$hh40==na

gt U$HouseholdIncome lt- c(3859651350

+ 173523052600318036604140

+ 471552955870649570207595

+ 8175)[U$hh40]

2520 Ward variables

Ward density of own ethnic group is measured by the variable wown in theoriginal data This variable is coded 1 minus 7 depending on the density of therespondentrsquos own ethnic group is in the ward of the respondent (FNSEM1993b)

1 Up to 1992 2-4993 5-9994 10-14995 15-24996 25-32997 33 or more

We recode the variable to take the midpoints of the density intervals in thesame fashion as the household income variable was recoded This meansthat if the respondent is the lowest interval the density is set to be 1 etc Inthe highest interval we set the density to be the lowest density in the intervalplus the density distance down to the midpoint of the second highest densityinterval ie 33 + 33minus25

2 = 37The variable wunemp is coded 1minus 6 depending on unemployment rate in

the ward of the respondent (FNSEM 1993b)

1 Up to 1992 2-4993 5-9994 10-14995 15-206 20 or more

We recode this variable to instead take the midpoints of the intervals aswe did for the household income This means that if the respondent is thelowest interval the the rate is set to be 1 etc In the highest interval we setthe rate to be the lowest rate in the interval plus the distance down to themidpoint of the second highest rate interval ie 20 + 20minus15

2 = 225

17

gt U$WardDensityOwnEthnicity lt- (c(1035

+ 75125200290370)[U$wown])100

gt U$WardUnemploymentRate lt- c(103575125

+ 175225)[U$wunemp]

26 Discrimination own ethnicity

Finally we define a variable describing the discrimination against the ownethnic group It is defined as the average of the variable Discrimination overethnic groups after the removal of nonndashavailables

gt GroupDiscrimination lt-

+ tapply(U$DiscriminationU$ethnic

+ function(x) mean(x narm=TRUE))

gt U$DiscriminationOwnEthnicity lt-

+ GroupDiscrimination[U$ethnic]

27 Defining the subset

We define dummy variables for labour market status to have the same vari-able labels as in Bisin et al (2008)

gt U$Employee lt- asnumeric(U$LabourMarketStatus==Employee)

gt U$Manager lt- asnumeric(U$LabourMarketStatus==Manager)

gt U$SelfEmployed lt-

+ asnumeric(U$LabourMarketStatus==SelfEmployed)

gt U$OUTOFLABOURFORCE lt-

+ asnumeric(U$LabourMarketStatus==OutOfLabourForce)

gt U$Unemployed lt-

+ asnumeric(U$LabourMarketStatus==Unemployed)

We save a data set keeping all observations containing nonndashavailables

gt UOriginal lt- U

We then choose the variables to keep in U This code is in araietal_sourceRnwbut not shown here It is also available in araietal_sourceR

Finally we remove all observations containing nonndashavailables from U

gt U lt- naomit(U)

28 Sample statistics

Table 1 compares the number of observations in this sample before (seesection 27 ie data frame UOriginal) and after (see section 27 iedata frame U) nonndashavailables are removed with the numbers of observations

18

reported by the Bisin et al (2008) study The number of observations forvarious groups in the nonndashMuslim category are not reported in the Bisinet al (2008) paper These numbers are therefore missing in the table Thecategory definitions are from the original dataset and involves no recodingon our part

After removing observations with missing values on all variables of inter-est (ldquoAfterrdquo in Table 1) we are left with 330 Muslims and 488 non-MuslimsThe sample selections induced by the choice of variables and the missingvalues in these variables lead to a loss of 57 percent of the relevant sampleof the original data3

The sample means reported in Bisin et al (2008) seem to be unweightedSince data instructions says that the data should always be weighted tables2 and 3 report weighted and unweighted sample means before and afterremoval of non-availables and the Bisin et al (2008) data4 Comparingmeans the Bisin et al (2008) data seem to be different from the originalsample The variables Attitude Towards Inter Marriage and Importance ofRacial Composition in Schools in Bisin et al (2008) data deviate largelyfrom corresponding averages in the original data The deviation is extremein case of Importance of Racial Composition in Schools The original samplehas a mean for this variable that is 2 percent for non-Muslims (compare with33 percent in Bisin et al (2008)) and 6 percent for Muslims (compare with65 percent in Bisin et al (2008)) Due to this extremely skewed distributionit is hardly meaningfull to run a regression on this variable

Notice that also the distribution of the variable Importance of Religionwould be extremely skewed using standard coding of this type of variablesSuch a coding would imply that religion is important when the respondentanswer ldquoVery Importantrdquo and ldquoFairly Importantrdquo to the question ldquoHow im-portant is religion to the way you live your liferdquo

The sample means in our data after removing accumulated missing val-ues due to all variables in the estimations deviate marginally in general fromthe original data The similarities here are partly due to the fact that thestatistics are based exactly on the same variable definition in our implemen-tation In some respects the deviations are larger For further comparisonswe refer to Tables 2 and 3

Due to the fact that the large majority of observations from the originaldata are lost the remaining sample is likely to be contaminated with sampleselection bias To compare the characteristics of the remaining sample withthe original sample says something about systematic attrition with respectto observables The sample selection bias with respect to unobservablescannot however be resolved

3The variables written in capital letters are created to ensure wellndashdefined referencecategories They are included in our regressions but we cannot say whether they areincluded in the regressions of Bisin et al (2008)

4See FNSEM (1993b)

19

3 Regression Results

We use linear probability models (LPM)5 Our results are presented in Tables4 and 5 Bisin et al (2008) write that

1 ldquoMuslims integrate less and more slowly than non-Muslimsrdquo (abstractp 445) and

2 ldquo there is no evidence that segregated neighbourhoods breed intensereligious and cultural identities On the contrary intense identitiesin our data are more prominent in relatively mixed neighbourhoodsrdquo(p 446)

The first claim is based on their reported results concerning the variableYears Since Arrival In this way Bisin et al (2008) compare cohorts ofMuslims and non Muslims and attempt to say something about the evolutionof values over time They do not follow individuals over time but nonethelesscall these cohort differences ldquoIntegration over timerdquo They report negativecoefficients for Years Since Arrival but the estimates are smaller in absolutevalue for Muslims than for non-Muslims In our case the coefficients forYears Since Arrival reported in Tables 4 and 5 are insignificant in all casesexcept in the regression for Importance of Religion for Muslims where it isnegative This is opposite to what Bisin et al (2008) claim

The second claim is based on their reported results concerning the vari-able Ward Density Own Ethnicity Bisin et al (2008) report negative andsignificant estimates for Ward Density Own Ethnicity in all six specifica-tions Their negative coefficient for this variable would imply that ethnicminorities put more weight on religion mind more about inter-ethnic mar-riage and have stronger taste for ethnically profiled schools as we move fromneighbourhoods (Wards) with high density of their own ethnicity to neigh-bourhoods where people from their own ethnicity are scarce This is not atall what we find in our replication

5Bisin et al (2008) use probit estimations Our attempts to use probit run into con-vergence problems The convergence problems are severe for the model using Importanceof Racial Composition in Schools as dependent variable Hence our choice of LPM

Another issue is that Bisin et al (2008) should have included dummy variables indicatingreligious affiliation Christians Sikhs and others in the non-Muslim category to checksimilarities and differences among non-Muslims as well In this respect we follow theirmodel specification

Moreover Bisin et al (2008) should have adjusted for within ward correlations Thismight matter for their standard errors which might be underestimated In our case withalmost no significant results this would not matter much The variable is not available inthe data set and we did not make much effort to obtain it

All estimated models include 7 UK-region dummies and the variables DONOT

SPEAKWITHOLDER DONOTSPEAKATWORK and DONOTSPEAKWITHFRIENDS It turnedout that the variable DONOTSPEAKWITHYOUNGER is TRUE for few observations and can-not be included in the model

20

In our estimations the estimated coefficients for this variable are allpositive but far from significant The P-values are 069 097 and 059 forMuslims and 015 014 and 03 for non-Muslims contradicting the Bisin et al(2008) results

Inspecting the results presented in Tables 4 and 5 there are many sim-ilarities and few differences in the estimated coefficients for Muslims andnon-Muslims Our results are generally very different from results reportedby Bisin et al (2008) We are however doubtful whether it is possibleto draw any reliable inference from these results due to great loss of ob-servations and possible sample selection bias together with the problem ofendogeneity (also mentioned by Bisin et al (2008))

4 Concluding remarks

The Bisin et al (2008) paper rests on fragile grounds Our examinationof the data using their variable definitions and the same set-up indicatesthat their claims about differences between Muslims and non-Muslims andtheir conclusion that strong ReligiousEthnic identities are found in mixedneighbourhoods does not hold There is no systematic relation betweenethnic minoritiesrsquo views on religion inter-ethnic marriage or ethnic profile ofschools and the density of their own ethnic minority in their neighbourhoodHowever we hesitate to draw inference from these results since the great lossof observations (57 percent) implies that the remaining sample is most likelynot representative

5 Production notes

To facilitate reproducibility and save others timely interpretations of whatis done in this paper we attempt to follow Literate Statistical Programmingprocedures For documenting our results we have used Sweave by Leisch(2002) in combination with the LATEX family of programs using the packagesinputenc fontenc natbib Sweave fancyvrb color url hyperref andmultirow

All code (R code LATEX code and BibTEX data base code) used to do theeconometric estimations to produce this technical documentation includingall tables and to produce the companion paper Arai et al (2008) is containedin the file araietal_sourceRnw

The estimations and the documentation can be reproduced in by follow-ing the instructions at the top of the file httppeoplesuse~lundhfragile_groundsaraietal_sourceRnw

Our results were obtained on a i486-pc-linux-gnu platform using R ver-sion 281 beta (2008-12-12 r47173) (R Development Core Team 2008) with

21

gt U$WardDensityOwnEthnicity lt- (c(1035

+ 75125200290370)[U$wown])100

gt U$WardUnemploymentRate lt- c(103575125

+ 175225)[U$wunemp]

26 Discrimination own ethnicity

Finally we define a variable describing the discrimination against the ownethnic group It is defined as the average of the variable Discrimination overethnic groups after the removal of nonndashavailables

gt GroupDiscrimination lt-

+ tapply(U$DiscriminationU$ethnic

+ function(x) mean(x narm=TRUE))

gt U$DiscriminationOwnEthnicity lt-

+ GroupDiscrimination[U$ethnic]

27 Defining the subset

We define dummy variables for labour market status to have the same vari-able labels as in Bisin et al (2008)

gt U$Employee lt- asnumeric(U$LabourMarketStatus==Employee)

gt U$Manager lt- asnumeric(U$LabourMarketStatus==Manager)

gt U$SelfEmployed lt-

+ asnumeric(U$LabourMarketStatus==SelfEmployed)

gt U$OUTOFLABOURFORCE lt-

+ asnumeric(U$LabourMarketStatus==OutOfLabourForce)

gt U$Unemployed lt-

+ asnumeric(U$LabourMarketStatus==Unemployed)

We save a data set keeping all observations containing nonndashavailables

gt UOriginal lt- U

We then choose the variables to keep in U This code is in araietal_sourceRnwbut not shown here It is also available in araietal_sourceR

Finally we remove all observations containing nonndashavailables from U

gt U lt- naomit(U)

28 Sample statistics

Table 1 compares the number of observations in this sample before (seesection 27 ie data frame UOriginal) and after (see section 27 iedata frame U) nonndashavailables are removed with the numbers of observations

18

reported by the Bisin et al (2008) study The number of observations forvarious groups in the nonndashMuslim category are not reported in the Bisinet al (2008) paper These numbers are therefore missing in the table Thecategory definitions are from the original dataset and involves no recodingon our part

After removing observations with missing values on all variables of inter-est (ldquoAfterrdquo in Table 1) we are left with 330 Muslims and 488 non-MuslimsThe sample selections induced by the choice of variables and the missingvalues in these variables lead to a loss of 57 percent of the relevant sampleof the original data3

The sample means reported in Bisin et al (2008) seem to be unweightedSince data instructions says that the data should always be weighted tables2 and 3 report weighted and unweighted sample means before and afterremoval of non-availables and the Bisin et al (2008) data4 Comparingmeans the Bisin et al (2008) data seem to be different from the originalsample The variables Attitude Towards Inter Marriage and Importance ofRacial Composition in Schools in Bisin et al (2008) data deviate largelyfrom corresponding averages in the original data The deviation is extremein case of Importance of Racial Composition in Schools The original samplehas a mean for this variable that is 2 percent for non-Muslims (compare with33 percent in Bisin et al (2008)) and 6 percent for Muslims (compare with65 percent in Bisin et al (2008)) Due to this extremely skewed distributionit is hardly meaningfull to run a regression on this variable

Notice that also the distribution of the variable Importance of Religionwould be extremely skewed using standard coding of this type of variablesSuch a coding would imply that religion is important when the respondentanswer ldquoVery Importantrdquo and ldquoFairly Importantrdquo to the question ldquoHow im-portant is religion to the way you live your liferdquo

The sample means in our data after removing accumulated missing val-ues due to all variables in the estimations deviate marginally in general fromthe original data The similarities here are partly due to the fact that thestatistics are based exactly on the same variable definition in our implemen-tation In some respects the deviations are larger For further comparisonswe refer to Tables 2 and 3

Due to the fact that the large majority of observations from the originaldata are lost the remaining sample is likely to be contaminated with sampleselection bias To compare the characteristics of the remaining sample withthe original sample says something about systematic attrition with respectto observables The sample selection bias with respect to unobservablescannot however be resolved

3The variables written in capital letters are created to ensure wellndashdefined referencecategories They are included in our regressions but we cannot say whether they areincluded in the regressions of Bisin et al (2008)

4See FNSEM (1993b)

19

3 Regression Results

We use linear probability models (LPM)5 Our results are presented in Tables4 and 5 Bisin et al (2008) write that

1 ldquoMuslims integrate less and more slowly than non-Muslimsrdquo (abstractp 445) and

2 ldquo there is no evidence that segregated neighbourhoods breed intensereligious and cultural identities On the contrary intense identitiesin our data are more prominent in relatively mixed neighbourhoodsrdquo(p 446)

The first claim is based on their reported results concerning the variableYears Since Arrival In this way Bisin et al (2008) compare cohorts ofMuslims and non Muslims and attempt to say something about the evolutionof values over time They do not follow individuals over time but nonethelesscall these cohort differences ldquoIntegration over timerdquo They report negativecoefficients for Years Since Arrival but the estimates are smaller in absolutevalue for Muslims than for non-Muslims In our case the coefficients forYears Since Arrival reported in Tables 4 and 5 are insignificant in all casesexcept in the regression for Importance of Religion for Muslims where it isnegative This is opposite to what Bisin et al (2008) claim

The second claim is based on their reported results concerning the vari-able Ward Density Own Ethnicity Bisin et al (2008) report negative andsignificant estimates for Ward Density Own Ethnicity in all six specifica-tions Their negative coefficient for this variable would imply that ethnicminorities put more weight on religion mind more about inter-ethnic mar-riage and have stronger taste for ethnically profiled schools as we move fromneighbourhoods (Wards) with high density of their own ethnicity to neigh-bourhoods where people from their own ethnicity are scarce This is not atall what we find in our replication

5Bisin et al (2008) use probit estimations Our attempts to use probit run into con-vergence problems The convergence problems are severe for the model using Importanceof Racial Composition in Schools as dependent variable Hence our choice of LPM

Another issue is that Bisin et al (2008) should have included dummy variables indicatingreligious affiliation Christians Sikhs and others in the non-Muslim category to checksimilarities and differences among non-Muslims as well In this respect we follow theirmodel specification

Moreover Bisin et al (2008) should have adjusted for within ward correlations Thismight matter for their standard errors which might be underestimated In our case withalmost no significant results this would not matter much The variable is not available inthe data set and we did not make much effort to obtain it

All estimated models include 7 UK-region dummies and the variables DONOT

SPEAKWITHOLDER DONOTSPEAKATWORK and DONOTSPEAKWITHFRIENDS It turnedout that the variable DONOTSPEAKWITHYOUNGER is TRUE for few observations and can-not be included in the model

20

In our estimations the estimated coefficients for this variable are allpositive but far from significant The P-values are 069 097 and 059 forMuslims and 015 014 and 03 for non-Muslims contradicting the Bisin et al(2008) results

Inspecting the results presented in Tables 4 and 5 there are many sim-ilarities and few differences in the estimated coefficients for Muslims andnon-Muslims Our results are generally very different from results reportedby Bisin et al (2008) We are however doubtful whether it is possibleto draw any reliable inference from these results due to great loss of ob-servations and possible sample selection bias together with the problem ofendogeneity (also mentioned by Bisin et al (2008))

4 Concluding remarks

The Bisin et al (2008) paper rests on fragile grounds Our examinationof the data using their variable definitions and the same set-up indicatesthat their claims about differences between Muslims and non-Muslims andtheir conclusion that strong ReligiousEthnic identities are found in mixedneighbourhoods does not hold There is no systematic relation betweenethnic minoritiesrsquo views on religion inter-ethnic marriage or ethnic profile ofschools and the density of their own ethnic minority in their neighbourhoodHowever we hesitate to draw inference from these results since the great lossof observations (57 percent) implies that the remaining sample is most likelynot representative

5 Production notes

To facilitate reproducibility and save others timely interpretations of whatis done in this paper we attempt to follow Literate Statistical Programmingprocedures For documenting our results we have used Sweave by Leisch(2002) in combination with the LATEX family of programs using the packagesinputenc fontenc natbib Sweave fancyvrb color url hyperref andmultirow

All code (R code LATEX code and BibTEX data base code) used to do theeconometric estimations to produce this technical documentation includingall tables and to produce the companion paper Arai et al (2008) is containedin the file araietal_sourceRnw

The estimations and the documentation can be reproduced in by follow-ing the instructions at the top of the file httppeoplesuse~lundhfragile_groundsaraietal_sourceRnw

Our results were obtained on a i486-pc-linux-gnu platform using R ver-sion 281 beta (2008-12-12 r47173) (R Development Core Team 2008) with

21

reported by the Bisin et al (2008) study The number of observations forvarious groups in the nonndashMuslim category are not reported in the Bisinet al (2008) paper These numbers are therefore missing in the table Thecategory definitions are from the original dataset and involves no recodingon our part

After removing observations with missing values on all variables of inter-est (ldquoAfterrdquo in Table 1) we are left with 330 Muslims and 488 non-MuslimsThe sample selections induced by the choice of variables and the missingvalues in these variables lead to a loss of 57 percent of the relevant sampleof the original data3

The sample means reported in Bisin et al (2008) seem to be unweightedSince data instructions says that the data should always be weighted tables2 and 3 report weighted and unweighted sample means before and afterremoval of non-availables and the Bisin et al (2008) data4 Comparingmeans the Bisin et al (2008) data seem to be different from the originalsample The variables Attitude Towards Inter Marriage and Importance ofRacial Composition in Schools in Bisin et al (2008) data deviate largelyfrom corresponding averages in the original data The deviation is extremein case of Importance of Racial Composition in Schools The original samplehas a mean for this variable that is 2 percent for non-Muslims (compare with33 percent in Bisin et al (2008)) and 6 percent for Muslims (compare with65 percent in Bisin et al (2008)) Due to this extremely skewed distributionit is hardly meaningfull to run a regression on this variable

Notice that also the distribution of the variable Importance of Religionwould be extremely skewed using standard coding of this type of variablesSuch a coding would imply that religion is important when the respondentanswer ldquoVery Importantrdquo and ldquoFairly Importantrdquo to the question ldquoHow im-portant is religion to the way you live your liferdquo

The sample means in our data after removing accumulated missing val-ues due to all variables in the estimations deviate marginally in general fromthe original data The similarities here are partly due to the fact that thestatistics are based exactly on the same variable definition in our implemen-tation In some respects the deviations are larger For further comparisonswe refer to Tables 2 and 3

Due to the fact that the large majority of observations from the originaldata are lost the remaining sample is likely to be contaminated with sampleselection bias To compare the characteristics of the remaining sample withthe original sample says something about systematic attrition with respectto observables The sample selection bias with respect to unobservablescannot however be resolved

3The variables written in capital letters are created to ensure wellndashdefined referencecategories They are included in our regressions but we cannot say whether they areincluded in the regressions of Bisin et al (2008)

4See FNSEM (1993b)

19

3 Regression Results

We use linear probability models (LPM)5 Our results are presented in Tables4 and 5 Bisin et al (2008) write that

1 ldquoMuslims integrate less and more slowly than non-Muslimsrdquo (abstractp 445) and

2 ldquo there is no evidence that segregated neighbourhoods breed intensereligious and cultural identities On the contrary intense identitiesin our data are more prominent in relatively mixed neighbourhoodsrdquo(p 446)

The first claim is based on their reported results concerning the variableYears Since Arrival In this way Bisin et al (2008) compare cohorts ofMuslims and non Muslims and attempt to say something about the evolutionof values over time They do not follow individuals over time but nonethelesscall these cohort differences ldquoIntegration over timerdquo They report negativecoefficients for Years Since Arrival but the estimates are smaller in absolutevalue for Muslims than for non-Muslims In our case the coefficients forYears Since Arrival reported in Tables 4 and 5 are insignificant in all casesexcept in the regression for Importance of Religion for Muslims where it isnegative This is opposite to what Bisin et al (2008) claim

The second claim is based on their reported results concerning the vari-able Ward Density Own Ethnicity Bisin et al (2008) report negative andsignificant estimates for Ward Density Own Ethnicity in all six specifica-tions Their negative coefficient for this variable would imply that ethnicminorities put more weight on religion mind more about inter-ethnic mar-riage and have stronger taste for ethnically profiled schools as we move fromneighbourhoods (Wards) with high density of their own ethnicity to neigh-bourhoods where people from their own ethnicity are scarce This is not atall what we find in our replication

5Bisin et al (2008) use probit estimations Our attempts to use probit run into con-vergence problems The convergence problems are severe for the model using Importanceof Racial Composition in Schools as dependent variable Hence our choice of LPM

Another issue is that Bisin et al (2008) should have included dummy variables indicatingreligious affiliation Christians Sikhs and others in the non-Muslim category to checksimilarities and differences among non-Muslims as well In this respect we follow theirmodel specification

Moreover Bisin et al (2008) should have adjusted for within ward correlations Thismight matter for their standard errors which might be underestimated In our case withalmost no significant results this would not matter much The variable is not available inthe data set and we did not make much effort to obtain it

All estimated models include 7 UK-region dummies and the variables DONOT

SPEAKWITHOLDER DONOTSPEAKATWORK and DONOTSPEAKWITHFRIENDS It turnedout that the variable DONOTSPEAKWITHYOUNGER is TRUE for few observations and can-not be included in the model

20

In our estimations the estimated coefficients for this variable are allpositive but far from significant The P-values are 069 097 and 059 forMuslims and 015 014 and 03 for non-Muslims contradicting the Bisin et al(2008) results

Inspecting the results presented in Tables 4 and 5 there are many sim-ilarities and few differences in the estimated coefficients for Muslims andnon-Muslims Our results are generally very different from results reportedby Bisin et al (2008) We are however doubtful whether it is possibleto draw any reliable inference from these results due to great loss of ob-servations and possible sample selection bias together with the problem ofendogeneity (also mentioned by Bisin et al (2008))

4 Concluding remarks

The Bisin et al (2008) paper rests on fragile grounds Our examinationof the data using their variable definitions and the same set-up indicatesthat their claims about differences between Muslims and non-Muslims andtheir conclusion that strong ReligiousEthnic identities are found in mixedneighbourhoods does not hold There is no systematic relation betweenethnic minoritiesrsquo views on religion inter-ethnic marriage or ethnic profile ofschools and the density of their own ethnic minority in their neighbourhoodHowever we hesitate to draw inference from these results since the great lossof observations (57 percent) implies that the remaining sample is most likelynot representative

5 Production notes

To facilitate reproducibility and save others timely interpretations of whatis done in this paper we attempt to follow Literate Statistical Programmingprocedures For documenting our results we have used Sweave by Leisch(2002) in combination with the LATEX family of programs using the packagesinputenc fontenc natbib Sweave fancyvrb color url hyperref andmultirow

All code (R code LATEX code and BibTEX data base code) used to do theeconometric estimations to produce this technical documentation includingall tables and to produce the companion paper Arai et al (2008) is containedin the file araietal_sourceRnw

The estimations and the documentation can be reproduced in by follow-ing the instructions at the top of the file httppeoplesuse~lundhfragile_groundsaraietal_sourceRnw

Our results were obtained on a i486-pc-linux-gnu platform using R ver-sion 281 beta (2008-12-12 r47173) (R Development Core Team 2008) with

21

3 Regression Results

We use linear probability models (LPM)5 Our results are presented in Tables4 and 5 Bisin et al (2008) write that

1 ldquoMuslims integrate less and more slowly than non-Muslimsrdquo (abstractp 445) and

2 ldquo there is no evidence that segregated neighbourhoods breed intensereligious and cultural identities On the contrary intense identitiesin our data are more prominent in relatively mixed neighbourhoodsrdquo(p 446)

The first claim is based on their reported results concerning the variableYears Since Arrival In this way Bisin et al (2008) compare cohorts ofMuslims and non Muslims and attempt to say something about the evolutionof values over time They do not follow individuals over time but nonethelesscall these cohort differences ldquoIntegration over timerdquo They report negativecoefficients for Years Since Arrival but the estimates are smaller in absolutevalue for Muslims than for non-Muslims In our case the coefficients forYears Since Arrival reported in Tables 4 and 5 are insignificant in all casesexcept in the regression for Importance of Religion for Muslims where it isnegative This is opposite to what Bisin et al (2008) claim

The second claim is based on their reported results concerning the vari-able Ward Density Own Ethnicity Bisin et al (2008) report negative andsignificant estimates for Ward Density Own Ethnicity in all six specifica-tions Their negative coefficient for this variable would imply that ethnicminorities put more weight on religion mind more about inter-ethnic mar-riage and have stronger taste for ethnically profiled schools as we move fromneighbourhoods (Wards) with high density of their own ethnicity to neigh-bourhoods where people from their own ethnicity are scarce This is not atall what we find in our replication

5Bisin et al (2008) use probit estimations Our attempts to use probit run into con-vergence problems The convergence problems are severe for the model using Importanceof Racial Composition in Schools as dependent variable Hence our choice of LPM

Another issue is that Bisin et al (2008) should have included dummy variables indicatingreligious affiliation Christians Sikhs and others in the non-Muslim category to checksimilarities and differences among non-Muslims as well In this respect we follow theirmodel specification

Moreover Bisin et al (2008) should have adjusted for within ward correlations Thismight matter for their standard errors which might be underestimated In our case withalmost no significant results this would not matter much The variable is not available inthe data set and we did not make much effort to obtain it

All estimated models include 7 UK-region dummies and the variables DONOT

SPEAKWITHOLDER DONOTSPEAKATWORK and DONOTSPEAKWITHFRIENDS It turnedout that the variable DONOTSPEAKWITHYOUNGER is TRUE for few observations and can-not be included in the model

20

In our estimations the estimated coefficients for this variable are allpositive but far from significant The P-values are 069 097 and 059 forMuslims and 015 014 and 03 for non-Muslims contradicting the Bisin et al(2008) results

Inspecting the results presented in Tables 4 and 5 there are many sim-ilarities and few differences in the estimated coefficients for Muslims andnon-Muslims Our results are generally very different from results reportedby Bisin et al (2008) We are however doubtful whether it is possibleto draw any reliable inference from these results due to great loss of ob-servations and possible sample selection bias together with the problem ofendogeneity (also mentioned by Bisin et al (2008))

4 Concluding remarks

The Bisin et al (2008) paper rests on fragile grounds Our examinationof the data using their variable definitions and the same set-up indicatesthat their claims about differences between Muslims and non-Muslims andtheir conclusion that strong ReligiousEthnic identities are found in mixedneighbourhoods does not hold There is no systematic relation betweenethnic minoritiesrsquo views on religion inter-ethnic marriage or ethnic profile ofschools and the density of their own ethnic minority in their neighbourhoodHowever we hesitate to draw inference from these results since the great lossof observations (57 percent) implies that the remaining sample is most likelynot representative

5 Production notes

To facilitate reproducibility and save others timely interpretations of whatis done in this paper we attempt to follow Literate Statistical Programmingprocedures For documenting our results we have used Sweave by Leisch(2002) in combination with the LATEX family of programs using the packagesinputenc fontenc natbib Sweave fancyvrb color url hyperref andmultirow

All code (R code LATEX code and BibTEX data base code) used to do theeconometric estimations to produce this technical documentation includingall tables and to produce the companion paper Arai et al (2008) is containedin the file araietal_sourceRnw

The estimations and the documentation can be reproduced in by follow-ing the instructions at the top of the file httppeoplesuse~lundhfragile_groundsaraietal_sourceRnw

Our results were obtained on a i486-pc-linux-gnu platform using R ver-sion 281 beta (2008-12-12 r47173) (R Development Core Team 2008) with

21

In our estimations the estimated coefficients for this variable are allpositive but far from significant The P-values are 069 097 and 059 forMuslims and 015 014 and 03 for non-Muslims contradicting the Bisin et al(2008) results

Inspecting the results presented in Tables 4 and 5 there are many sim-ilarities and few differences in the estimated coefficients for Muslims andnon-Muslims Our results are generally very different from results reportedby Bisin et al (2008) We are however doubtful whether it is possibleto draw any reliable inference from these results due to great loss of ob-servations and possible sample selection bias together with the problem ofendogeneity (also mentioned by Bisin et al (2008))

4 Concluding remarks

The Bisin et al (2008) paper rests on fragile grounds Our examinationof the data using their variable definitions and the same set-up indicatesthat their claims about differences between Muslims and non-Muslims andtheir conclusion that strong ReligiousEthnic identities are found in mixedneighbourhoods does not hold There is no systematic relation betweenethnic minoritiesrsquo views on religion inter-ethnic marriage or ethnic profile ofschools and the density of their own ethnic minority in their neighbourhoodHowever we hesitate to draw inference from these results since the great lossof observations (57 percent) implies that the remaining sample is most likelynot representative

5 Production notes

To facilitate reproducibility and save others timely interpretations of whatis done in this paper we attempt to follow Literate Statistical Programmingprocedures For documenting our results we have used Sweave by Leisch(2002) in combination with the LATEX family of programs using the packagesinputenc fontenc natbib Sweave fancyvrb color url hyperref andmultirow

All code (R code LATEX code and BibTEX data base code) used to do theeconometric estimations to produce this technical documentation includingall tables and to produce the companion paper Arai et al (2008) is containedin the file araietal_sourceRnw

The estimations and the documentation can be reproduced in by follow-ing the instructions at the top of the file httppeoplesuse~lundhfragile_groundsaraietal_sourceRnw

Our results were obtained on a i486-pc-linux-gnu platform using R ver-sion 281 beta (2008-12-12 r47173) (R Development Core Team 2008) with

21

packages lmtest 09-22 (2008-12-09) sandwich 21-0 (2008-01-26) zoo 15-4 (2008-07-09) foreign 08-29 (2008-08-07) and xtable 15-4 (20081003)

References

Mahmood Arai Jonas Karlsson and Michael Lundholm On fragile groundsA replication of Are Muslim immigrants different in terms of cultural in-tegration Unpublished manuscript 2008

RG Berthoud T Modood P Smith and G Prior Fourth National Sur-vey of Ethnic Minorities 1993-1994 [computer file] Colchester EssexUK 1997 URL httpwwwdata-archiveacukdoc36855Cmrdoc5CUKDA5CUKDA_Study_3685_Informationhtm SN 3685

Alberto Bisin Eleonora Patacchini Thierry Verdier and Yves Zenou Aremuslim immigrants different in terms of cultural integration Journal ofthe European Economic Association 6445ndash456 2008

FNSEM P1312 Fourth National Study of Etnic Minorities Projectinstructions Social and Community Planning Research London1993a URL httpwwwdata-archiveacukdoc3685mrdocpdfa3685uabpdf Project 3685

FNSEM UK Data Archive Data Dictionary Social andCommunity Plan-ning Research London 1993b An RTF file called UKDA-3685-tabmrdocallissue3685_UKDA_Data_Dictionaryrtf available when theFNSEM data set is downloaded from the UK Data Archive

Roger Koenker and Achim Zeileis Reproducible econometric research (acritical review of the state of the art) Report 60 Department of Statisticsand Mathematics Wirtschaftsuniversitat Wien Research Report Series2007 URL httpepubwu-wienacatdynvirlibwpengmediateepub-wu-01_c75pdfID=epub-wu-01_c75

Friedrich Leisch Sweave user manual 2002 URL httpwwwcituwienacat~leischSweave

R Development Core Team R A Language and Environment for StatisticalComputing R Foundation for Statistical Computing Vienna Austria2008 URL httpwwwR-projectorg ISBN 3-900051-07-0

22

Appendix Tables

Table 1 Religious affiliation (absolute () and relative () numbers) before(columns 1 and 2) and after (columns 3 and 4) removal of NA compared withBisin et al (2008) (columns 5 and 6)

Religious affiliation (1) (2) (3) (4) (5) (6)Before After Bisin et al

n = 1901 n = 818 n = 5963

hindu 359 1888 149 1822sikh 288 1515 86 1051muslim 852 4482 330 4034 2369 3973christian 357 1878 232 2836buddhist 17 089 9 110confucian 1 005 1 012jain 7 037 3 037parsizorastrian 3 016 2 024rastafarian 2 011 1 012jewish 1 005 0 000other 10 053 5 061na 0 000 0 000NArsquos 4 021 0 000All non-Muslims 1045 5509 488 5966 3594 6027NOTE The row names shows exactly how the original data is coded sothat eg lsquoNArsquosrsquo are true missing values whereas lsquonarsquo is coded as religiousaffiliation lsquonarsquo On the last line nonndashMuslims are calculated excluding naand NA

23

Tab

le2

Wei

ghte

dan

dU

nwei

ghte

dM

eans

for

Mus

lims

and

nonndash

Mus

lims

befo

rean

daf

ter

rem

oval

ofNA

com

pare

dw

ith

Bis

inet

al

(200

8)

Mus

limN

on-M

uslim

We

ig

ht

ed

Un

we

ig

ht

ed

We

ig

ht

ed

Un

we

ig

ht

ed

Bef

ore

Aft

erB

efor

eA

fter

Bis

inB

efor

eA

fter

Bef

ore

Aft

erB

isin

etal

et

al

Impo

rtan

ceof

Rel

igio

n0

790

810

800

850

790

490

480

480

480

42A

ttit

ude

Tow

ards

Inte

rM

arri

age

045

039

045

039

070

019

015

021

017

037

Impo

rtan

ceof

Rac

ial

Com

posi

tion

inSc

hool

s0

060

080

060

070

650

020

010

020

020

33A

geat

Arr

ival

224

920

73

223

620

32

391

822

31

178

522

83

187

142

57

Age

404

242

01

401

840

56

444

043

98

449

044

59

Fem

ale

050

047

050

045

047

052

053

052

052

048

Bor

nin

the

UK

008

008

006

006

021

013

018

012

016

028

Arr

ange

dM

arri

age

065

063

065

063

022

027

018

030

022

012

Dis

crim

inat

ion

023

020

020

016

017

038

041

035

039

019

Chi

ldre

n3

123

413

233

432

172

372

322

442

361

68Y

ears

Sinc

eA

rriv

al19

78

192

718

99

187

926

43

241

920

68

240

620

95

270

8N

oB

riti

shE

duca

tion

080

079

083

082

081

056

050

059

053

052

Bri

tish

Bas

icE

duca

tion

014

013

012

011

006

030

036

029

035

013

Bri

tish

Hig

her

Edu

cati

on0

050

060

030

030

080

100

120

090

120

16Fo

reig

nE

duca

tion

025

025

023

022

025

026

024

028

025

029

Em

ploy

ee0

190

210

190

220

380

400

420

400

420

59M

anag

er0

020

020

020

020

020

070

080

060

070

04

24

Tab

le3

Tab

le2

cont

inue

dW

eigh

ted

and

Unw

eigh

ted

Mea

nsfo

rM

uslim

san

dno

nndashM

uslim

sbe

fore

and

afte

rre

mov

alof

NA

com

pare

dw

ith

Bis

inet

al

(200

8)

Mus

limN

on-M

uslim

We

ig

ht

ed

Un

we

ig

ht

ed

We

ig

ht

ed

Un

we

ig

ht

ed

Bef

ore

Aft

erB

efor

eA

fter

Bis

inB

efor

eA

fter

Bef

ore

Aft

erB

isin

etal

et

al

Self

Em

ploy

ed0

090

080

070

060

090

120

120

100

100

14O

UT

OF

LA

BO

UR

FO

RC

E0

500

490

520

480

330

290

360

32U

nem

ploy

ed0

190

190

200

220

190

080

100

080

090

08N

oP

aren

ts0

650

700

670

700

340

640

650

660

660

32P

aren

tsP

hysi

cal

Con

tact

s2

692

722

802

883

052

753

022

832

973

87P

aren

tsT

elep

hone

Cal

ls2

102

512

282

853

383

343

343

153

264

74P

aren

tsL

ette

rs0

470

410

480

420

670

250

240

250

270

37E

nglis

hSp

oken

atH

ome

wit

hO

lder

012

012

010

009

003

043

053

042

052

008

DO

NO

TSP

EA

KW

ITH

OL

DE

R0

020

040

010

020

020

010

020

02E

nglis

hSp

oken

atH

ome

wit

hY

oung

er0

460

460

410

400

200

770

810

750

790

25D

ON

OT

SPE

AK

WIT

HY

OU

NG

ER

000

000

000

000

000

000

000

000

Eng

lish

Spok

enat

Wor

k0

460

530

410

470

190

790

850

770

820

27D

ON

OT

SPE

AK

AT

WO

RK

045

039

048

044

016

011

018

014

Eng

lish

Spok

enW

ith

Frie

nds

050

055

046

051

022

079

086

077

083

027

DO

NO

TSP

EA

KW

ITH

FR

IEN

DS

012

007

012

006

004

002

005

002

Hou

seho

ldIn

com

e21

619

215

4819

540

194

9420

074

334

6633

383

307

6931

346

330

26W

ard

Den

sity

Ow

nE

thni

city

013

013

016

016

015

011

010

012

011

011

War

dU

nem

ploy

men

tR

ate

160

715

72

179

718

05

165

712

19

123

513

44

135

212

60

Dis

crim

inat

ion

Ow

nE

thni

city

023

022

021

019

021

034

037

034

036

018

25

Tab

le4

Reg

ress

ion

Res

ults

for

Mus

lims

330

and

non-

Mus

lims

488

tobe

com

pare

dw

ith

Tab

le2

inB

isin

etal

(2

008)

H

eter

oske

dast

icit

yco

rrec

ted

(HC

1)St

anda

rdE

rror

sar

ein

pare

nthe

ses

P-v

alue

slt

005

are

mar

ked

wit

h

Impo

rtan

ceof

Inte

rE

thni

cE

thni

cC

ompo

siti

onR

elig

ion

Mar

riag

eof

Scho

ols

Mus

lims

nonndash

Mus

lims

Mus

lims

nonndash

Mus

lims

Mus

lims

nonndash

Mus

lims

(Int

erce

pt)

092

0

94

083

0

270

04-0

02

(03

0)(0

26)

(03

7)(0

26)

(01

8)(0

04)

Age

atA

rriv

al0

000

010

000

000

000

00(0

00)

(00

0)(0

00)

(00

0)(0

00)

(00

0)Fe

mal

e-0

04

011

0

140

05-0

04

-00

1(0

08)

(00

5)(0

09)

(00

3)(0

05)

(00

1)B

orn

inth

eU

K-0

24

026

-02

30

08-0

03

-00

4(0

16)

(01

5)(0

15)

(00

9)(0

08)

(00

3)A

rran

ged

Mar

riag

e0

11-0

08

009

020

-0

06

001

(00

6)(0

07)

(00

7)(0

07)

(00

5)(0

03)

Dis

crim

inat

ion

-00

90

040

010

010

000

00(0

07)

(00

5)(0

08)

(00

4)(0

05)

(00

1)C

hild

ren

002

0

02-0

01

001

-00

10

00(0

01)

(00

2)(0

01)

(00

1)(0

01)

(00

0)Y

ears

Sinc

eA

rriv

al-0

01

001

000

000

000

000

(00

0)(0

00)

(00

0)(0

00)

(00

0)(0

00)

No

Bri

tish

Edu

cati

on0

01-0

14

-00

60

040

080

01(0

12)

(00

9)(0

13)

(00

5)(0

07)

(00

1)B

riti

shB

asic

Edu

cati

on0

05-0

12

-01

3-0

09

000

000

(01

4)(0

10)

(01

7)(0

06)

(00

7)(0

01)

Bri

tish

Hig

her

Edu

cati

on-0

16

-01

3-0

19

001

-00

20

00(0

18)

(00

8)(0

15)

(00

6)(0

08)

(00

1)Fo

reig

nE

duca

tion

000

005

-01

5-0

01

001

-00

2(0

07)

(00

7)(0

07)

(00

5)(0

05)

(00

2)E

mpl

oyee

-01

4-0

04

001

002

-00

4-0

04

(01

0)(0

08)

(01

0)(0

05)

(00

6)(0

02)

Man

ager

-02

9-0

06

009

-00

1-0

17

-00

3(0

24)

(01

2)(0

22)

(00

7)(0

08)

(00

2)

26

Tab

le5

Tab

le4

cont

inue

dR

egre

ssio

nR

esul

tsfo

rM

uslim

s33

0an

dno

n-M

uslim

s48

8to

beco

mpa

red

wit

hT

able

2in

Bis

inet

al

(200

8)

Het

eros

keda

stic

ity

corr

ecte

d(H

C1)

Stan

dard

Err

ors

are

inpa

rent

hese

sP

-val

ues

lt0

05ar

em

arke

dw

ith

Im

port

ance

ofIn

ter

Eth

nic

Eth

nic

Com

posi

tion

Rel

igio

nM

arri

age

ofSc

hool

s

Mus

lims

nonndash

Mus

lims

Mus

lims

nonndash

Mus

lims

Mus

lims

nonndash

Mus

lims

Self

Em

ploy

ed0

00-0

06

011

006

-00

4-0

05

(01

3)(0

10)

(01

5)(0

07)

(00

9)(0

03)

Une

mpl

oyed

-00

70

070

120

07-0

07

-00

4(0

08)

(01

0)(0

10)

(00

8)(0

07)

(00

2)N

oP

aren

ts-0

07

009

004

006

009

0

00(0

05)

(00

5)(0

07)

(00

4)(0

03)

(00

1)P

aren

tsP

hysi

cal

Con

tact

s0

000

000

000

000

000

00

(00

0)(0

00)

(00

1)(0

00)

(00

0)(0

00)

Par

ents

Tel

epho

neC

alls

000

000

-00

10

000

000

00(0

00)

(00

0)(0

00)

(00

0)(0

00)

(00

0)P

aren

tsL

ette

rs-0

02

007

0

030

01-0

01

000

(00

3)(0

03)

(00

3)(0

02)

(00

2)(0

00)

Eng

lish

Spok

enat

Hom

ew

ith

Old

er-0

34

-00

8-0

05

-00

90

00-0

05

(01

2)(0

07)

(01

0)(0

07)

(00

6)(0

03)

Eng

lish

Spok

enat

Hom

ew

ith

You

nger

-00

1-0

20

011

003

002

002

(00

7)(0

09)

(00

8)(0

09)

(00

5)(0

03)

Eng

lish

Spok

enat

Wor

k-0

04

-01

9-0

08

003

010

004

(00

8)(0

13)

(01

4)(0

12)

(00

6)(0

03)

Eng

lish

Spok

enW

ith

Frie

nds

-00

40

06-0

12

-01

6-0

02

002

(00

7)(0

09)

(01

0)(0

09)

(00

7)(0

02)

Hou

seho

ldIn

com

e0

000

000

000

00

000

000

(00

0)(0

00)

(00

0)(0

00)

(00

0)(0

00)

Dis

crim

inat

ion

Ow

nE

thni

city

026

013

007

-02

80

100

20(0

41)

(03

9)(0

43)

(03

1)(0

24)

(01

0)W

ard

Den

sity

Ow

nE

thni

city

013

-04

1-0

01

036

010

-00

6(0

32)

(02

9)(0

30)

(02

4)(0

19)

(00

6)W

ard

Une

mpl

oym

ent

Rat

e0

01

001

0

000

000

000

00(0

01)

(00

1)(0

01)

(00

0)(0

00)

(00

0)A

djus

ted

R-s

quar

e0

200

090

180

180

050

08

NO

TE

A

lles

tim

ate

dm

odel

sin

clude

7U

K-r

egio

ndum

mie

sand

the

vari

able

sDONOTSPEAKWITHOLDERDONOTSPEAKATWORK

and

DONOTSPEAKWITHFRIENDS

Ittu

rned

out

that

the

vari

able

DONOTSPEAKWITHYOUNGER

isT

RU

Efo

rfe

wobse

rvati

ons

and

cannot

be

incl

uded

inth

em

odel

27

• Introduction
• Data and variable description
• • Data
  • Reading data and selecting variables
  • Defining the relevant (ethnic minority) sample
  • Recoding of missing values
  • Variable definitions
  • • Religious affiliation
    • Importance of religion
    • Attitude towards inter-marriage
    • Importance of racial composition in schools
    • Born in the UK
    • Age at and years since arrival
    • Female
    • Arranged marriage
    • Discrimination
    • Children
    • No British education
    • British basic education
    • British higher education
    • Foreign education
    • Labour market status
    • No parents
    • Contacts with parents
    • English language
    • Household income
    • Ward variables
    • • Discrimination own ethnicity
      • Defining the subset
      • Sample statistics
      • • Regression Results
        • Concluding remarks
        • Production notes

Tab

le2

Wei

ghte

dan

dU

nwei

ghte

dM

eans

for

Mus

lims

and

nonndash

Mus

lims

befo

rean

daf

ter

rem

oval

ofNA

com

pare

dw

ith

Bis

inet

al

(200

8)

Mus

limN

on-M

uslim

We

ig

ht

ed

Un

we

ig

ht

ed

We

ig

ht

ed

Un

we

ig

ht

ed

Bef

ore

Aft

erB

efor

eA

fter

Bis

inB

efor

eA

fter

Bef

ore

Aft

erB

isin

etal

et

al

Impo

rtan

ceof

Rel

igio

n0

790

810

800

850

790

490

480

480

480

42A

ttit

ude

Tow

ards

Inte

rM

arri

age

045

039

045

039

070

019

015

021

017

037

Impo

rtan

ceof

Rac

ial

Com

posi

tion

inSc

hool

s0

060

080

060

070

650

020

010

020

020

33A

geat

Arr

ival

224

920

73

223

620

32

391

822

31

178

522

83

187

142

57

Age

404

242

01

401

840

56

444

043

98

449

044

59

Fem

ale

050

047

050

045

047

052

053

052

052

048

Bor

nin

the

UK

008

008

006

006

021

013

018

012

016

028

Arr

ange

dM

arri

age

065

063

065

063

022

027

018

030

022

012

Dis

crim

inat

ion

023

020

020

016

017

038

041

035

039

019

Chi

ldre

n3

123

413

233

432

172

372

322

442

361

68Y

ears

Sinc

eA

rriv

al19

78

192

718

99

187

926

43

241

920

68

240

620

95

270

8N

oB

riti

shE

duca

tion

080

079

083

082

081

056

050

059

053

052

Bri

tish

Bas

icE

duca

tion

014

013

012

011

006

030

036

029

035

013

Bri

tish

Hig

her

Edu

cati

on0

050

060

030

030

080

100

120

090

120

16Fo

reig

nE

duca

tion

025

025

023

022

025

026

024

028

025

029

Em

ploy

ee0

190

210

190

220

380

400

420

400

420

59M

anag

er0

020

020

020

020

020

070

080

060

070

04

24

Tab

le3

Tab

le2

cont

inue

dW

eigh

ted

and

Unw

eigh

ted

Mea

nsfo

rM

uslim

san

dno

nndashM

uslim

sbe

fore

and

afte

rre

mov

alof

NA

com

pare

dw

ith

Bis

inet

al

(200

8)

Mus

limN

on-M

uslim

We

ig

ht

ed

Un

we

ig

ht

ed

We

ig

ht

ed

Un

we

ig

ht

ed

Bef

ore

Aft

erB

efor

eA

fter

Bis

inB

efor

eA

fter

Bef

ore

Aft

erB

isin

etal

et

al

Self

Em

ploy

ed0

090

080

070

060

090

120

120

100

100

14O

UT

OF

LA

BO

UR

FO

RC

E0

500

490

520

480

330

290

360

32U

nem

ploy

ed0

190

190

200

220

190

080

100

080

090

08N

oP

aren

ts0

650

700

670

700

340

640

650

660

660

32P

aren

tsP

hysi

cal

Con

tact

s2

692

722

802

883

052

753

022

832

973

87P

aren

tsT

elep

hone

Cal

ls2

102

512

282

853

383

343

343

153

264

74P

aren

tsL

ette

rs0

470

410

480

420

670

250

240

250

270

37E

nglis

hSp

oken

atH

ome

wit

hO

lder

012

012

010

009

003

043

053

042

052

008

DO

NO

TSP

EA

KW

ITH

OL

DE

R0

020

040

010

020

020

010

020

02E

nglis

hSp

oken

atH

ome

wit

hY

oung

er0

460

460

410

400

200

770

810

750

790

25D

ON

OT

SPE

AK

WIT

HY

OU

NG

ER

000

000

000

000

000

000

000

000

Eng

lish

Spok

enat

Wor

k0

460

530

410

470

190

790

850

770

820

27D

ON

OT

SPE

AK

AT

WO

RK

045

039

048

044

016

011

018

014

Eng

lish

Spok

enW

ith

Frie

nds

050

055

046

051

022

079

086

077

083

027

DO

NO

TSP

EA

KW

ITH

FR

IEN

DS

012

007

012

006

004

002

005

002

Hou

seho

ldIn

com

e21

619

215

4819

540

194

9420

074

334

6633

383

307

6931

346

330

26W

ard

Den

sity

Ow

nE

thni

city

013

013

016

016

015

011

010

012

011

011

War

dU

nem

ploy

men

tR

ate

160

715

72

179

718

05

165

712

19

123

513

44

135

212

60

Dis

crim

inat

ion

Ow

nE

thni

city

023

022

021

019

021

034

037

034

036

018

25

Tab

le4

Reg

ress

ion

Res

ults

for

Mus

lims

330

and

non-

Mus

lims

488

tobe

com

pare

dw

ith

Tab

le2

inB

isin

etal

(2

008)

H

eter

oske

dast

icit

yco

rrec

ted

(HC

1)St

anda

rdE

rror

sar

ein

pare

nthe

ses

P-v

alue

slt

005

are

mar

ked

wit

h

Impo

rtan

ceof

Inte

rE

thni

cE

thni

cC

ompo

siti

onR

elig

ion

Mar

riag

eof

Scho

ols

Mus

lims

nonndash

Mus

lims

Mus

lims

nonndash

Mus

lims

Mus

lims

nonndash

Mus

lims

(Int

erce

pt)

092

0

94

083

0

270

04-0

02

(03

0)(0

26)

(03

7)(0

26)

(01

8)(0

04)

Age

atA

rriv

al0

000

010

000

000

000

00(0

00)

(00

0)(0

00)

(00

0)(0

00)

(00

0)Fe

mal

e-0

04

011

0

140

05-0

04

-00

1(0

08)

(00

5)(0

09)

(00

3)(0

05)

(00

1)B

orn

inth

eU

K-0

24

026

-02

30

08-0

03

-00

4(0

16)

(01

5)(0

15)

(00

9)(0

08)

(00

3)A

rran

ged

Mar

riag

e0

11-0

08

009

020

-0

06

001

(00

6)(0

07)

(00

7)(0

07)

(00

5)(0

03)

Dis

crim

inat

ion

-00

90

040

010

010

000

00(0

07)

(00

5)(0

08)

(00

4)(0

05)

(00

1)C

hild

ren

002

0

02-0

01

001

-00

10

00(0

01)

(00

2)(0

01)

(00

1)(0

01)

(00

0)Y

ears

Sinc

eA

rriv

al-0

01

001

000

000

000

000

(00

0)(0

00)

(00

0)(0

00)

(00

0)(0

00)

No

Bri

tish

Edu

cati

on0

01-0

14

-00

60

040

080

01(0

12)

(00

9)(0

13)

(00

5)(0

07)

(00

1)B

riti

shB

asic

Edu

cati

on0

05-0

12

-01

3-0

09

000

000

(01

4)(0

10)

(01

7)(0

06)

(00

7)(0

01)

Bri

tish

Hig

her

Edu

cati

on-0

16

-01

3-0

19

001

-00

20

00(0

18)

(00

8)(0

15)

(00

6)(0

08)

(00

1)Fo

reig

nE

duca

tion

000

005

-01

5-0

01

001

-00

2(0

07)

(00

7)(0

07)

(00

5)(0

05)

(00

2)E

mpl

oyee

-01

4-0

04

001

002

-00

4-0

04

(01

0)(0

08)

(01

0)(0

05)

(00

6)(0

02)

Man

ager

-02

9-0

06

009

-00

1-0

17

-00

3(0

24)

(01

2)(0

22)

(00

7)(0

08)

(00

2)

26

Tab

le5

Tab

le4

cont

inue

dR

egre

ssio

nR

esul

tsfo

rM

uslim

s33

0an

dno

n-M

uslim

s48

8to

beco

mpa

red

wit

hT

able

2in

Bis

inet

al

(200

8)

Het

eros

keda

stic

ity

corr

ecte

d(H

C1)

Stan

dard

Err

ors

are

inpa

rent

hese

sP

-val

ues

lt0

05ar

em

arke

dw

ith

Im

port

ance

ofIn

ter

Eth

nic

Eth

nic

Com

posi

tion

Rel

igio

nM

arri

age

ofSc

hool

s

Mus

lims

nonndash

Mus

lims

Mus

lims

nonndash

Mus

lims

Mus

lims

nonndash

Mus

lims

Self

Em

ploy

ed0

00-0

06

011

006

-00

4-0

05

(01

3)(0

10)

(01

5)(0

07)

(00

9)(0

03)

Une

mpl

oyed

-00

70

070

120

07-0

07

-00

4(0

08)

(01

0)(0

10)

(00

8)(0

07)

(00

2)N

oP

aren

ts-0

07

009

004

006

009

0

00(0

05)

(00

5)(0

07)

(00

4)(0

03)

(00

1)P

aren

tsP

hysi

cal

Con

tact

s0

000

000

000

000

000

00

(00

0)(0

00)

(00

1)(0

00)

(00

0)(0

00)

Par

ents

Tel

epho

neC

alls

000

000

-00

10

000

000

00(0

00)

(00

0)(0

00)

(00

0)(0

00)

(00

0)P

aren

tsL

ette

rs-0

02

007

0

030

01-0

01

000

(00

3)(0

03)

(00

3)(0

02)

(00

2)(0

00)

Eng

lish

Spok

enat

Hom

ew

ith

Old

er-0

34

-00

8-0

05

-00

90

00-0

05

(01

2)(0

07)

(01

0)(0

07)

(00

6)(0

03)

Eng

lish

Spok

enat

Hom

ew

ith

You

nger

-00

1-0

20

011

003

002

002

(00

7)(0

09)

(00

8)(0

09)

(00

5)(0

03)

Eng

lish

Spok

enat

Wor

k-0

04

-01

9-0

08

003

010

004

(00

8)(0

13)

(01

4)(0

12)

(00

6)(0

03)

Eng

lish

Spok

enW

ith

Frie

nds

-00

40

06-0

12

-01

6-0

02

002

(00

7)(0

09)

(01

0)(0

09)

(00

7)(0

02)

Hou

seho

ldIn

com

e0

000

000

000

00

000

000

(00

0)(0

00)

(00

0)(0

00)

(00

0)(0

00)

Dis

crim

inat

ion

Ow

nE

thni

city

026

013

007

-02

80

100

20(0

41)

(03

9)(0

43)

(03

1)(0

24)

(01

0)W

ard

Den

sity

Ow

nE

thni

city

013

-04

1-0

01

036

010

-00

6(0

32)

(02

9)(0

30)

(02

4)(0

19)

(00

6)W

ard

Une

mpl

oym

ent

Rat

e0

01

001

0

000

000

000

00(0

01)

(00

1)(0

01)

(00

0)(0

00)

(00

0)A

djus

ted

R-s

quar

e0

200

090

180

180

050

08

NO

TE

A

lles

tim

ate

dm

odel

sin

clude

7U

K-r

egio

ndum

mie

sand

the

vari

able

sDONOTSPEAKWITHOLDERDONOTSPEAKATWORK

and

DONOTSPEAKWITHFRIENDS

Ittu

rned

out

that

the

vari

able

DONOTSPEAKWITHYOUNGER

isT

RU

Efo

rfe

wobse

rvati

ons

and

cannot

be

incl

uded

inth

em

odel

27

• Introduction
• Data and variable description
• • Data
  • Reading data and selecting variables
  • Defining the relevant (ethnic minority) sample
  • Recoding of missing values
  • Variable definitions
  • • Religious affiliation
    • Importance of religion
    • Attitude towards inter-marriage
    • Importance of racial composition in schools
    • Born in the UK
    • Age at and years since arrival
    • Female
    • Arranged marriage
    • Discrimination
    • Children
    • No British education
    • British basic education
    • British higher education
    • Foreign education
    • Labour market status
    • No parents
    • Contacts with parents
    • English language
    • Household income
    • Ward variables
    • • Discrimination own ethnicity
      • Defining the subset
      • Sample statistics
      • • Regression Results
        • Concluding remarks
        • Production notes

Tab

le3

Tab

le2

cont

inue

dW

eigh

ted

and

Unw

eigh

ted

Mea

nsfo

rM

uslim

san

dno

nndashM

uslim

sbe

fore

and

afte

rre

mov

alof

NA

com

pare

dw

ith

Bis

inet

al

(200

8)

Mus

limN

on-M

uslim

We

ig

ht

ed

Un

we

ig

ht

ed

We

ig

ht

ed

Un

we

ig

ht

ed

Bef

ore

Aft

erB

efor

eA

fter

Bis

inB

efor

eA

fter

Bef

ore

Aft

erB

isin

etal

et

al

Self

Em

ploy

ed0

090

080

070

060

090

120

120

100

100

14O

UT

OF

LA

BO

UR

FO

RC

E0

500

490

520

480

330

290

360

32U

nem

ploy

ed0

190

190

200

220

190

080

100

080

090

08N

oP

aren

ts0

650

700

670

700

340

640

650

660

660

32P

aren

tsP

hysi

cal

Con

tact

s2

692

722

802

883

052

753

022

832

973

87P

aren

tsT

elep

hone

Cal

ls2

102

512

282

853

383

343

343

153

264

74P

aren

tsL

ette

rs0

470

410

480

420

670

250

240

250

270

37E

nglis

hSp

oken

atH

ome

wit

hO

lder

012

012

010

009

003

043

053

042

052

008

DO

NO

TSP

EA

KW

ITH

OL

DE

R0

020

040

010

020

020

010

020

02E

nglis

hSp

oken

atH

ome

wit

hY

oung

er0

460

460

410

400

200

770

810

750

790

25D

ON

OT

SPE

AK

WIT

HY

OU

NG

ER

000

000

000

000

000

000

000

000

Eng

lish

Spok

enat

Wor

k0

460

530

410

470

190

790

850

770

820

27D

ON

OT

SPE

AK

AT

WO

RK

045

039

048

044

016

011

018

014

Eng

lish

Spok

enW

ith

Frie

nds

050

055

046

051

022

079

086

077

083

027

DO

NO

TSP

EA

KW

ITH

FR

IEN

DS

012

007

012

006

004

002

005

002

Hou

seho

ldIn

com

e21

619

215

4819

540

194

9420

074

334

6633

383

307

6931

346

330

26W

ard

Den

sity

Ow

nE

thni

city

013

013

016

016

015

011

010

012

011

011

War

dU

nem

ploy

men

tR

ate

160

715

72

179

718

05

165

712

19

123

513

44

135

212

60

Dis

crim

inat

ion

Ow

nE

thni

city

023

022

021

019

021

034

037

034

036

018

25

Tab

le4

Reg

ress

ion

Res

ults

for

Mus

lims

330

and

non-

Mus

lims

488

tobe

com

pare

dw

ith

Tab

le2

inB

isin

etal

(2

008)

H

eter

oske

dast

icit

yco

rrec

ted

(HC

1)St

anda

rdE

rror

sar

ein

pare

nthe

ses

P-v

alue

slt

005

are

mar

ked

wit

h

Impo

rtan

ceof

Inte

rE

thni

cE

thni

cC

ompo

siti

onR

elig

ion

Mar

riag

eof

Scho

ols

Mus

lims

nonndash

Mus

lims

Mus

lims

nonndash

Mus

lims

Mus

lims

nonndash

Mus

lims

(Int

erce

pt)

092

0

94

083

0

270

04-0

02

(03

0)(0

26)

(03

7)(0

26)

(01

8)(0

04)

Age

atA

rriv

al0

000

010

000

000

000

00(0

00)

(00

0)(0

00)

(00

0)(0

00)

(00

0)Fe

mal

e-0

04

011

0

140

05-0

04

-00

1(0

08)

(00

5)(0

09)

(00

3)(0

05)

(00

1)B

orn

inth

eU

K-0

24

026

-02

30

08-0

03

-00

4(0

16)

(01

5)(0

15)

(00

9)(0

08)

(00

3)A

rran

ged

Mar

riag

e0

11-0

08

009

020

-0

06

001

(00

6)(0

07)

(00

7)(0

07)

(00

5)(0

03)

Dis

crim

inat

ion

-00

90

040

010

010

000

00(0

07)

(00

5)(0

08)

(00

4)(0

05)

(00

1)C

hild

ren

002

0

02-0

01

001

-00

10

00(0

01)

(00

2)(0

01)

(00

1)(0

01)

(00

0)Y

ears

Sinc

eA

rriv

al-0

01

001

000

000

000

000

(00

0)(0

00)

(00

0)(0

00)

(00

0)(0

00)

No

Bri

tish

Edu

cati

on0

01-0

14

-00

60

040

080

01(0

12)

(00

9)(0

13)

(00

5)(0

07)

(00

1)B

riti

shB

asic

Edu

cati

on0

05-0

12

-01

3-0

09

000

000

(01

4)(0

10)

(01

7)(0

06)

(00

7)(0

01)

Bri

tish

Hig

her

Edu

cati

on-0

16

-01

3-0

19

001

-00

20

00(0

18)

(00

8)(0

15)

(00

6)(0

08)

(00

1)Fo

reig

nE

duca

tion

000

005

-01

5-0

01

001

-00

2(0

07)

(00

7)(0

07)

(00

5)(0

05)

(00

2)E

mpl

oyee

-01

4-0

04

001

002

-00

4-0

04

(01

0)(0

08)

(01

0)(0

05)

(00

6)(0

02)

Man

ager

-02

9-0

06

009

-00

1-0

17

-00

3(0

24)

(01

2)(0

22)

(00

7)(0

08)

(00

2)

26

Tab

le5

Tab

le4

cont

inue

dR

egre

ssio

nR

esul

tsfo

rM

uslim

s33

0an

dno

n-M

uslim

s48

8to

beco

mpa

red

wit

hT

able

2in

Bis

inet

al

(200

8)

Het

eros

keda

stic

ity

corr

ecte

d(H

C1)

Stan

dard

Err

ors

are

inpa

rent

hese

sP

-val

ues

lt0

05ar

em

arke

dw

ith

Im

port

ance

ofIn

ter

Eth

nic

Eth

nic

Com

posi

tion

Rel

igio

nM

arri

age

ofSc

hool

s

Mus

lims

nonndash

Mus

lims

Mus

lims

nonndash

Mus

lims

Mus

lims

nonndash

Mus

lims

Self

Em

ploy

ed0

00-0

06

011

006

-00

4-0

05

(01

3)(0

10)

(01

5)(0

07)

(00

9)(0

03)

Une

mpl

oyed

-00

70

070

120

07-0

07

-00

4(0

08)

(01

0)(0

10)

(00

8)(0

07)

(00

2)N

oP

aren

ts-0

07

009

004

006

009

0

00(0

05)

(00

5)(0

07)

(00

4)(0

03)

(00

1)P

aren

tsP

hysi

cal

Con

tact

s0

000

000

000

000

000

00

(00

0)(0

00)

(00

1)(0

00)

(00

0)(0

00)

Par

ents

Tel

epho

neC

alls

000

000

-00

10

000

000

00(0

00)

(00

0)(0

00)

(00

0)(0

00)

(00

0)P

aren

tsL

ette

rs-0

02

007

0

030

01-0

01

000

(00

3)(0

03)

(00

3)(0

02)

(00

2)(0

00)

Eng

lish

Spok

enat

Hom

ew

ith

Old

er-0

34

-00

8-0

05

-00

90

00-0

05

(01

2)(0

07)

(01

0)(0

07)

(00

6)(0

03)

Eng

lish

Spok

enat

Hom

ew

ith

You

nger

-00

1-0

20

011

003

002

002

(00

7)(0

09)

(00

8)(0

09)

(00

5)(0

03)

Eng

lish

Spok

enat

Wor

k-0

04

-01

9-0

08

003

010

004

(00

8)(0

13)

(01

4)(0

12)

(00

6)(0

03)

Eng

lish

Spok

enW

ith

Frie

nds

-00

40

06-0

12

-01

6-0

02

002

(00

7)(0

09)

(01

0)(0

09)

(00

7)(0

02)

Hou

seho

ldIn

com

e0

000

000

000

00

000

000

(00

0)(0

00)

(00

0)(0

00)

(00

0)(0

00)

Dis

crim

inat

ion

Ow

nE

thni

city

026

013

007

-02

80

100

20(0

41)

(03

9)(0

43)

(03

1)(0

24)

(01

0)W

ard

Den

sity

Ow

nE

thni

city

013

-04

1-0

01

036

010

-00

6(0

32)

(02

9)(0

30)

(02

4)(0

19)

(00

6)W

ard

Une

mpl

oym

ent

Rat

e0

01

001

0

000

000

000

00(0

01)

(00

1)(0

01)

(00

0)(0

00)

(00

0)A

djus

ted

R-s

quar

e0

200

090

180

180

050

08

NO

TE

A

lles

tim

ate

dm

odel

sin

clude

7U

K-r

egio

ndum

mie

sand

the

vari

able

sDONOTSPEAKWITHOLDERDONOTSPEAKATWORK

and

DONOTSPEAKWITHFRIENDS

Ittu

rned

out

that

the

vari

able

DONOTSPEAKWITHYOUNGER

isT

RU

Efo

rfe

wobse

rvati

ons

and

cannot

be

incl

uded

inth

em

odel

27

• Introduction
• Data and variable description
• • Data
  • Reading data and selecting variables
  • Defining the relevant (ethnic minority) sample
  • Recoding of missing values
  • Variable definitions
  • • Religious affiliation
    • Importance of religion
    • Attitude towards inter-marriage
    • Importance of racial composition in schools
    • Born in the UK
    • Age at and years since arrival
    • Female
    • Arranged marriage
    • Discrimination
    • Children
    • No British education
    • British basic education
    • British higher education
    • Foreign education
    • Labour market status
    • No parents
    • Contacts with parents
    • English language
    • Household income
    • Ward variables
    • • Discrimination own ethnicity
      • Defining the subset
      • Sample statistics
      • • Regression Results
        • Concluding remarks
        • Production notes

Tab

le4

Reg

ress

ion

Res

ults

for

Mus

lims

330

and

non-

Mus

lims

488

tobe

com

pare

dw

ith

Tab

le2

inB

isin

etal

(2

008)

H

eter

oske

dast

icit

yco

rrec

ted

(HC

1)St

anda

rdE

rror

sar

ein

pare

nthe

ses

P-v

alue

slt

005

are

mar

ked

wit

h

Impo

rtan

ceof

Inte

rE

thni

cE

thni

cC

ompo

siti

onR

elig

ion

Mar

riag

eof

Scho

ols

Mus

lims

nonndash

Mus

lims

Mus

lims

nonndash

Mus

lims

Mus

lims

nonndash

Mus

lims

(Int

erce

pt)

092

0

94

083

0

270

04-0

02

(03

0)(0

26)

(03

7)(0

26)

(01

8)(0

04)

Age

atA

rriv

al0

000

010

000

000

000

00(0

00)

(00

0)(0

00)

(00

0)(0

00)

(00

0)Fe

mal

e-0

04

011

0

140

05-0

04

-00

1(0

08)

(00

5)(0

09)

(00

3)(0

05)

(00

1)B

orn

inth

eU

K-0

24

026

-02

30

08-0

03

-00

4(0

16)

(01

5)(0

15)

(00

9)(0

08)

(00

3)A

rran

ged

Mar

riag

e0

11-0

08

009

020

-0

06

001

(00

6)(0

07)

(00

7)(0

07)

(00

5)(0

03)

Dis

crim

inat

ion

-00

90

040

010

010

000

00(0

07)

(00

5)(0

08)

(00

4)(0

05)

(00

1)C

hild

ren

002

0

02-0

01

001

-00

10

00(0

01)

(00

2)(0

01)

(00

1)(0

01)

(00

0)Y

ears

Sinc

eA

rriv

al-0

01

001

000

000

000

000

(00

0)(0

00)

(00

0)(0

00)

(00

0)(0

00)

No

Bri

tish

Edu

cati

on0

01-0

14

-00

60

040

080

01(0

12)

(00

9)(0

13)

(00

5)(0

07)

(00

1)B

riti

shB

asic

Edu

cati

on0

05-0

12

-01

3-0

09

000

000

(01

4)(0

10)

(01

7)(0

06)

(00

7)(0

01)

Bri

tish

Hig

her

Edu

cati

on-0

16

-01

3-0

19

001

-00

20

00(0

18)

(00

8)(0

15)

(00

6)(0

08)

(00

1)Fo

reig

nE

duca

tion

000

005

-01

5-0

01

001

-00

2(0

07)

(00

7)(0

07)

(00

5)(0

05)

(00

2)E

mpl

oyee

-01

4-0

04

001

002

-00

4-0

04

(01

0)(0

08)

(01

0)(0

05)

(00

6)(0

02)

Man

ager

-02

9-0

06

009

-00

1-0

17

-00

3(0

24)

(01

2)(0

22)

(00

7)(0

08)

(00

2)

26

Tab

le5

Tab

le4

cont

inue

dR

egre

ssio

nR

esul

tsfo

rM

uslim

s33

0an

dno

n-M

uslim

s48

8to

beco

mpa

red

wit

hT

able

2in

Bis

inet

al

(200

8)

Het

eros

keda

stic

ity

corr

ecte

d(H

C1)

Stan

dard

Err

ors

are

inpa

rent

hese

sP

-val

ues

lt0

05ar

em

arke

dw

ith

Im

port

ance

ofIn

ter

Eth

nic

Eth

nic

Com

posi

tion

Rel

igio

nM

arri

age

ofSc

hool

s

Mus

lims

nonndash

Mus

lims

Mus

lims

nonndash

Mus

lims

Mus

lims

nonndash

Mus

lims

Self

Em

ploy

ed0

00-0

06

011

006

-00

4-0

05

(01

3)(0

10)

(01

5)(0

07)

(00

9)(0

03)

Une

mpl

oyed

-00

70

070

120

07-0

07

-00

4(0

08)

(01

0)(0

10)

(00

8)(0

07)

(00

2)N

oP

aren

ts-0

07

009

004

006

009

0

00(0

05)

(00

5)(0

07)

(00

4)(0

03)

(00

1)P

aren

tsP

hysi

cal

Con

tact

s0

000

000

000

000

000

00

(00

0)(0

00)

(00

1)(0

00)

(00

0)(0

00)

Par

ents

Tel

epho

neC

alls

000

000

-00

10

000

000

00(0

00)

(00

0)(0

00)

(00

0)(0

00)

(00

0)P

aren

tsL

ette

rs-0

02

007

0

030

01-0

01

000

(00

3)(0

03)

(00

3)(0

02)

(00

2)(0

00)

Eng

lish

Spok

enat

Hom

ew

ith

Old

er-0

34

-00

8-0

05

-00

90

00-0

05

(01

2)(0

07)

(01

0)(0

07)

(00

6)(0

03)

Eng

lish

Spok

enat

Hom

ew

ith

You

nger

-00

1-0

20

011

003

002

002

(00

7)(0

09)

(00

8)(0

09)

(00

5)(0

03)

Eng

lish

Spok

enat

Wor

k-0

04

-01

9-0

08

003

010

004

(00

8)(0

13)

(01

4)(0

12)

(00

6)(0

03)

Eng

lish

Spok

enW

ith

Frie

nds

-00

40

06-0

12

-01

6-0

02

002

(00

7)(0

09)

(01

0)(0

09)

(00

7)(0

02)

Hou

seho

ldIn

com

e0

000

000

000

00

000

000

(00

0)(0

00)

(00

0)(0

00)

(00

0)(0

00)

Dis

crim

inat

ion

Ow

nE

thni

city

026

013

007

-02

80

100

20(0

41)

(03

9)(0

43)

(03

1)(0

24)

(01

0)W

ard

Den

sity

Ow

nE

thni

city

013

-04

1-0

01

036

010

-00

6(0

32)

(02

9)(0

30)

(02

4)(0

19)

(00

6)W

ard

Une

mpl

oym

ent

Rat

e0

01

001

0

000

000

000

00(0

01)

(00

1)(0

01)

(00

0)(0

00)

(00

0)A

djus

ted

R-s

quar

e0

200

090

180

180

050

08

NO

TE

A

lles

tim

ate

dm

odel

sin

clude

7U

K-r

egio

ndum

mie

sand

the

vari

able

sDONOTSPEAKWITHOLDERDONOTSPEAKATWORK

and

DONOTSPEAKWITHFRIENDS

Ittu

rned

out

that

the

vari

able

DONOTSPEAKWITHYOUNGER

isT

RU

Efo

rfe

wobse

rvati

ons

and

cannot

be

incl

uded

inth

em

odel

27

• Introduction
• Data and variable description
• • Data
  • Reading data and selecting variables
  • Defining the relevant (ethnic minority) sample
  • Recoding of missing values
  • Variable definitions
  • • Religious affiliation
    • Importance of religion
    • Attitude towards inter-marriage
    • Importance of racial composition in schools
    • Born in the UK
    • Age at and years since arrival
    • Female
    • Arranged marriage
    • Discrimination
    • Children
    • No British education
    • British basic education
    • British higher education
    • Foreign education
    • Labour market status
    • No parents
    • Contacts with parents
    • English language
    • Household income
    • Ward variables
    • • Discrimination own ethnicity
      • Defining the subset
      • Sample statistics
      • • Regression Results
        • Concluding remarks
        • Production notes

Tab

le5

Tab

le4

cont

inue

dR

egre

ssio

nR

esul

tsfo

rM

uslim

s33

0an

dno

n-M

uslim

s48

8to

beco

mpa

red

wit

hT

able

2in

Bis

inet

al

(200

8)

Het

eros

keda

stic

ity

corr

ecte

d(H

C1)

Stan

dard

Err

ors

are

inpa

rent

hese

sP

-val

ues

lt0

05ar

em

arke

dw

ith

Im

port

ance

ofIn

ter

Eth

nic

Eth

nic

Com

posi

tion

Rel

igio

nM

arri

age

ofSc

hool

s

Mus

lims

nonndash

Mus

lims

Mus

lims

nonndash

Mus

lims

Mus

lims

nonndash

Mus

lims

Self

Em

ploy

ed0

00-0

06

011

006

-00

4-0

05

(01

3)(0

10)

(01

5)(0

07)

(00

9)(0

03)

Une

mpl

oyed

-00

70

070

120

07-0

07

-00

4(0

08)

(01

0)(0

10)

(00

8)(0

07)

(00

2)N

oP

aren

ts-0

07

009

004

006

009

0

00(0

05)

(00

5)(0

07)

(00

4)(0

03)

(00

1)P

aren

tsP

hysi

cal

Con

tact

s0

000

000

000

000

000

00

(00

0)(0

00)

(00

1)(0

00)

(00

0)(0

00)

Par

ents

Tel

epho

neC

alls

000

000

-00

10

000

000

00(0

00)

(00

0)(0

00)

(00

0)(0

00)

(00

0)P

aren

tsL

ette

rs-0

02

007

0

030

01-0

01

000

(00

3)(0

03)

(00

3)(0

02)

(00

2)(0

00)

Eng

lish

Spok

enat

Hom

ew

ith

Old

er-0

34

-00

8-0

05

-00

90

00-0

05

(01

2)(0

07)

(01

0)(0

07)

(00

6)(0

03)

Eng

lish

Spok

enat

Hom

ew

ith

You

nger

-00

1-0

20

011

003

002

002

(00

7)(0

09)

(00

8)(0

09)

(00

5)(0

03)

Eng

lish

Spok

enat

Wor

k-0

04

-01

9-0

08

003

010

004

(00

8)(0

13)

(01

4)(0

12)

(00

6)(0

03)

Eng

lish

Spok

enW

ith

Frie

nds

-00

40

06-0

12

-01

6-0

02

002

(00

7)(0

09)

(01

0)(0

09)

(00

7)(0

02)

Hou

seho

ldIn

com

e0

000

000

000

00

000

000

(00

0)(0

00)

(00

0)(0

00)

(00

0)(0

00)

Dis

crim

inat

ion

Ow

nE

thni

city

026

013

007

-02

80

100

20(0

41)

(03

9)(0

43)

(03

1)(0

24)

(01

0)W

ard

Den

sity

Ow

nE

thni

city

013

-04

1-0

01

036

010

-00

6(0

32)

(02

9)(0

30)

(02

4)(0

19)

(00

6)W

ard

Une

mpl

oym

ent

Rat

e0

01

001

0

000

000

000

00(0

01)

(00

1)(0

01)

(00

0)(0

00)

(00

0)A

djus

ted

R-s

quar

e0

200

090

180

180

050

08

NO

TE

A

lles

tim

ate

dm

odel

sin

clude

7U

K-r

egio

ndum

mie

sand

the

vari

able

sDONOTSPEAKWITHOLDERDONOTSPEAKATWORK

and

DONOTSPEAKWITHFRIENDS

Ittu

rned

out

that

the

vari

able

DONOTSPEAKWITHYOUNGER

isT

RU

Efo

rfe

wobse

rvati

ons

and

cannot

be

incl

uded

inth

em

odel

27

• Introduction
• Data and variable description
• • Data
  • Reading data and selecting variables
  • Defining the relevant (ethnic minority) sample
  • Recoding of missing values
  • Variable definitions
  • • Religious affiliation
    • Importance of religion
    • Attitude towards inter-marriage
    • Importance of racial composition in schools
    • Born in the UK
    • Age at and years since arrival
    • Female
    • Arranged marriage
    • Discrimination
    • Children
    • No British education
    • British basic education
    • British higher education
    • Foreign education
    • Labour market status
    • No parents
    • Contacts with parents
    • English language
    • Household income
    • Ward variables
    • • Discrimination own ethnicity
      • Defining the subset
      • Sample statistics
      • • Regression Results
        • Concluding remarks
        • Production notes