The impact of immigrant concentration in Spanish schools: school, class, and composition effects

The Impact of ImmigrantConcentration in SpanishSchools: School, Class, andComposition EffectsHector Cebolla-Boado and Luis Garrido Medina

The existence of a negative correlation between the concentration of immigrants in certain

schools and the school attainment of its students is a well-documented empirical conclusion

in the American and European sociology of education, yet sociologists of education are

still debating why the migrant (and ethnic) composition of schools has such an impact

on results above and beyond individual characteristics. Using a Spanish survey study

(INECSE, Evaluacion de la Educacion Primaria 2003, Madrid, 2005), this article intends to

confirm and measure the poorer results of students in schools where immigrants are more

highly represented. It also seeks to identify potential explanations to account for variation

in attainment due to the concentration of immigrant origin students in some schools. In

the literature, the empirical analyses that seek to explain this empirical regularity are far

less frequent, despite the fact that the list of its potential causes includes several mediating

mechanisms. We deal with three main groups of causes that could be responsible for this

regularity: (i) micro-interactions or peer group effects; (ii) compositional effects—the pupil

population is not randomly distributed on the school map but according to important

features that determine their future school attainment (such as socio-economic status); and

(iii) school/classroom effects—given their contextual characteristics, the quantity and

quality of their human and material resources, some schools offer a less stimulating

learning environment.

Introduction

Immigrants’ geographical concentration in developed

countries is a frequent source of public concern.

Spatial segregation is thought to delay (and in some

cases to block) the proper integration of immigrants.

Regarding education, the concentration in schools of

immigrants and ethnic minorities is thought to

damage the school performance of both the children

of immigrant and autochthonous families. As a result,

host families in immigrant receiving societies are

normally concerned with the concentration of immi-

grants in certain parts of the school map, and try to

avoid stigmatized schools, which in turn increases the

over-representation of immigrants. In this article, we

intend to make a 2-fold contribution. On the one

hand, by focusing on Spain, a new case study is added

to the existing empirical literature. On the other, we

try to disentangle the effect of several potential causal

factors that could account for the negative correlation

between immigrant concentration and individual

performance.

European Sociological Review VOLUME 27 NUMBER 5 2011 606–623 606

DOI:10.1093/esr/jcq024, available online at www.esr.oxfordjournals.org

Online publication 21 June 2010

� The Author(s) 2010. Published by Oxford University Press.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-CommercialLicense (http://creativecommons.org/licenses/by-nc/2.5/uk), which permits unrestricted non-commercial use, distribution,and reproduction in any medium, provided the original work is properly cited.

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The empirical analysis here presented focuses onSpain, a case that has previously been absent from theempirical literature on the school concentration ofimmigrants for two main reasons: (i) it has onlyrecently been considered an immigration destination;and (ii) until INECSE (2005) became available therewas no appropriate dataset to conduct empiricalresearch on this topic. As a case for this study, Spainshares key characteristics with several other immigra-tion destinations in Western European: because of thenetwork-driven nature of labour immigration, thedistribution of immigrants in Spanish cities is quiteuneven; immigrants’ average school attainment is lessthan that of natives; and the Spanish school systemhas highly segmented ownership. As a consequence,we develop our analysis from the assumption that ourexplanations could be generalized to other relevantcases in the literature.

The only significant distinctive feature of theSpanish case derives from its rapid transformationinto a key immigration-destination in Western Europe.In the past 10 years, Spain has received high migrationinflows. In fact, its fast transformation from anemigration to an immigration country is unprecedent-ed. In 1996, the UN Population Report (UNFPA,1997) did not mention Spain among the main inter-national destinations for immigrants. Yet, in 2006, thesame report highlighted Spain as the 10th country bythe number of international migrants, hosting some2.5 per cent of the total world stock (UNFPA, 2007–2008). This list is headed by the United States and onlyincludes three other EU countries before Spain(Germany, France, and the United Kingdom), withItaly being well behind in 16th place.

Little is known about the specific reasons why theover-representation of immigrants impacts negativelyon attainment. Yet, it is known that the widely docu-mented existence of a negative correlation between theconcentration of immigrants and school attainmentis the consequence of various causal mechanisms.In this article we propose three main types ofexplanations that, it is assumed, could equally applyto other country studies: peer group effects, class orsocio-economic compositional effects, and group-leveldifferences with respect to resources and learningstrategies in schools.

The article is organized as follows. We first quantifythe concentration problem in the Spanish context.After that we explore the broader theoretical explan-ations given to the lower average performance ofstudents in schools where immigrants are concen-trated. After describing the data we proceed with theempirical analysis, which is divided into two blocks.

The first is an aggregate exploration of the impact ofconcentration that has schools as the unit of analysis.This study reveals a significant negative effect asso-ciated with immigrant concentration in school net ofother relevant aggregate-level school characteristics.Next, the article proceeds with the individual-levelanalysis which suggests that the explanation of theconcentration effect combines composition and class-room effects suggesting that the presence of low-performing students in any school decreases theattainment of the student body as a whole.

Some ConsiderationsRegarding the Spanish Context

In recent years Spain has passed from being anemigration country to a major immigration destination(Izquierdo, 2003; Garrido, 2005; Aja and Arango,2006). This change has been so rapid and, to someextent also unexpected, that in only 6 years, from 2000to 2006, the number of foreign-born residents in thecountry increased by more than 3 million—from 1.5million to nearly 5 million.1 As a consequence, theSpanish school system faced important challenges. Tobegin with it had to accommodate and react to thegrowing number of children of immigrant parentsenrolled as students in compulsory education. TheSpanish school system was relatively successful inadapting itself to this changing context because untilthe end of the 1990s, the size of the cohorts ofSpaniards in compulsory education was still decreasing(see Figure 1) rendering part of the material andhuman resources available in the school system obsoleteand unnecessary (Arango and Carabana, 1981). Thisleft enough empty space to host the newcomers.

The above-described process happened differentlyfor private and public schools. The Spanish schoolmarket is significantly segmented. According to theMinistry of Education, the private sector accounts for26.3 per cent of the students enrolled in compulsoryeducation in 2007–2008 (this percentage has beenreasonably stable in previous years). Since 2000, thegrowing size of the cohorts of students attendingcompulsory education was far more acute in thepublic than in private sector because of the over-representation of immigrant-origin children in thepublic school sector or large urban areas. Indeed, theirdifficulty to access private schools (including publiclyfunded private schools) is a constant concern in thepublic debate. Today, the distribution of immigrant-origin children and natives in the private and publicschool sector is fairly uneven (see Figure 2). While,

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up until 2001–2002, the immigrant-origin studentswere evenly distributed across public and privateinstitutions, from that time on their weight in thepublic sector increased significantly until 2006–2007,when they represented 10.5 per cent of their studentbody (in contrast to only 4.6 per cent in the privatesector).2

Because immigrants arrived some years after thecohort of Spaniards began to shrink, the early logisticconcerns provoked in other countries by the intensi-fication of migration inflows were rather rare in theSpanish educational debate. This explains why thepublic focus rapidly turned to the problems derivedfrom existence of immigrant-native differentials in

Figure 1. The changing size of the student-population in compulsory education

Figure 2. Distribution of immigrant and native students in Spanish public and private schools

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school attainment. Of course this is not a Spanishspecificity. Both international surveys of learningachievement (Schnepf, 2004; Stanat and Gaylen,2006) and case studies (Heath and Brinbaum, 2007)have revealed notable differences between the schoolperformance of immigrants—including the native-bornchildren of immigrant families—and native children.However, the empirical literature has suggested that alarge part of these differentials is explained by acompositional effect that is due to the unequal classstratification of immigrants and natives (Kao andThompson, 2003; Schnepf, 2004). In Spain, the immi-grant disadvantage in educational attainment hasalready been well documented (Aparicio and Tornos,2003; Defensor del Pueblo, 2003; Cebolla andGonzalez, 2008). According to our estimations, theaverage test scores of the children of immigrants inmathematics and Spanish are some 10 per cent lowerthan those obtained by the offspring of Spanishfamilies (see Figure 3).

Spanish public opinion seems to be aware of thisempirical fact. One of the most polemic debatesaround the integration of the immigrant populationin the country has to do with the concentration of thechildren of immigrant families in certain publicschools, particularly in large cities. Although thisdebate is not new, it is now receiving more publicattention than ever, especially in Catalonia where theacademic and political discussion around the negativeeffects of the concentration of immigrants is especiallydynamic (Ponce, 2007), particularly since the Catalan

government decided to create specific schools for

hosting newcomer children of immigrant families,

while the Catalan ombudsman backed the application

of de-segregation measures to fight an excessive

concentration of immigrant origin students on the

school map (Sindic de Greuges, 2008). Yet, the

empirical studies available in Catalonia or the rest of

Spain do not quantify how negative the impact of this

concentration is. Furthermore, they do not focus on

the potential causes that negatively correlate concen-

tration of immigrant children and average school

attainment.

School Concentration ofImmigrants and AverageAttainment

Ever since the publication of the ‘Coleman Report’

(Coleman, 1966), sociologists of education have been

interested in understanding the reasons why school

context, with a special reference to school socio-

economic status (SES) and ethnic composition, has an

impact on attainment above and beyond individual

characteristics. The existence of a negative correlation

between the concentration of immigrants and the

school attainment of its students is a well-documented

empirical conclusion in the American and European

sociology of education (Felouzis, 2003; Portes and

Hao, 2004; Fekjær and Birkelund 2007; Szulkin and

-12 -10 -8 -6 -4 -2 0

Mathematics

Sources: Primary school INECSE (2005); Secondary school, PISA 2003

Spanish

Primary schoolSecondary school

Figure 3. Gap in test scores between immigrants and natives

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Jonsson, 2007). However, the number of empiricalanalyses that seek to explain this regularity is far lessfrequent, and there are a long list of potential causesand unexplained mediating mechanisms. There arethree main groups of causes that could be responsiblefor this regularity.

1. Micro-interactions: Some scholars refer to the sort

of micro-interactions that take place within schools

where immigrants are overrepresented. This argu-

ment seems to assume that immigrants are less

inclined towards educational success, and that the

concentration of migrants in the student-body

results in micro-interactions that act as disincen-

tives to investments in education. These types of

explanations have been fuelled by the popularity of

a burgeoning literature on social capital-related

causal mechanisms in the study of the social

mobility of immigrants and natives, mainly coming

from the United States (Portes and Zhou, 1993;

Portes and Rumbaut, 2001). However, if indivi-

dual-level socio-economic and demographic vari-

ables are properly controlled for, the unexplained

variation associated with contextual variables de-

creases significantly (Evans, Oates and Schwab,

1992; Dietz, 2002).

2. School effects: This argument suggests that the

specific characteristics of schools where immigrants

are more highly represented could differ signifi-

cantly from the rest. If immigrant students attend

schools in the most deprived environments, they

might rely on fewer and worse material and/or

human resources, which together represent a less

effective learning environment. In this same group

of factors, some sociologists of education have

suggested the importance of classroom-level effects,

possibly linked to the fact that some teachers

might adapt their demands to the average level of

the student-body, in other words, forcing teachers

to set different thresholds for evaluating who

passes and who fails (Duru Bellat and Mingat,

1997). This adaption could result in a less

demanding and motivating learning environment.

3. Composition effects: The last block of explanations

refers to the existence of a SES composition effect

of student bodies across schools. Because of the

cost of housing, the existence of segmented labour

markets and the influence of ethnic social networks,

immigrant families tend to concentrate in areas

of residence where the average socio-economic

profiles of the households is less advantaged. This

over-representation of students from disadvan-

taged family backgrounds may also explain why

students enrolled in schools with more immigrants

are worse off.

Disentangling each of these potential causal mech-anisms is complicated, yet from an analytical point ofview—as well as from a policy-oriented perspective—itis required, since the nature of the explanatoryprocesses that could produce the negative correlationthat we are interested in could be divergent. While inthe first case, desegregating student bodies acrossmigration status or ethnicity axes could be an efficientsolution, it might be a worthless strategy under thesecond scenario. If differences across schools happenbecause of their unequal access to educational re-sources, redistributing human and material reservesacross schools is probably a better option. Finally, ifthe correlation between the representation of immi-grants and average school attainment is the result ofthe concentration of a generally deprived student body,standard policies aiming at reducing class differentialsin educational attainment could be enough to solve theinter school differences seen between establishments inwhich the children of immigrants are more or lessrepresented.

The empirical analyses that follow have a 2-foldobjective. In the first place, to measure and confirm theexistence of a lower attainment in schools where thepresence of immigrant children is greater. Secondly,to identify which of the three alternative above-listedexplanations is more pertinent in explaining the impactof immigrants’ concentration in schools.

The lack of appropriate datasets for studying thecauses of educational disadvantage in Spain is almost astructural constraint. In particular, little is knownabout the dynamics accounting for migrant status andethnic differentials in school attainment. In this articlewe use the only available survey that samples the entirepopulation of students in primary education in Spain:the National Survey for the Evaluation of PrimaryEducation 2003 produced by the National Institute forthe Evaluation and Quality of the Educational System(Instituto Nacional para la Evaluacion y Calidad delSistema Educativo, henceforth INECSE).3 This surveyincludes interviews with the students, their parents,their tutor teachers, and the school directors. Thestudents answered three tests to evaluate their profi-ciency in different subjects: mathematics, Spanish

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language, and Social Sciences (conocimiento del medio).

The results range from 0 to 500 and are normally

distributed (mean 250 and standard deviation 50). The

sampling included two stages: a random selection of

schools and a sample of students within each school.

The school’s sample was stratified by region (comuni-

dades autonomas) and school ownership (private and

public schools). Unfortunately, given the sensitivity of

regional issues and comparisons in Spain, the survey

information made available to researchers does not

include any reference to the quality of the local

environment of the schools.4

The final sample includes 450 schools with a class of

five or more students in the sixth year of primary (the

average number of students per schools is over 22),

and 9,814 students, 428 tutor teachers, 420 school

directors, and 8,538 families. The sample includes 424

children of mixed immigrant parental couples (an im-

migrant and a Spanish-born) and 430 children of two

immigrants (or one if in a single parent household).

Because of the fast transformation of Spain into an

immigration destination since the end of the 1990s and

because of the changing ethnic composition of migra-

tion inflows in these early years, the sample does

not allow us to disentangle the effect of ethnic origin

from migration status. Given that national origin

is collapsed into large geographical areas (Europe,

Africa, Asia, Spanish-speaking Americans, and other

Americans), our analysis is forced to ignore the ethnic

composition of the schools and concentrates on the

migrant status composition of the establishments.

Results

In the sample, the distribution of immigrants acrossschools is fairly uneven. Dissimilarity indexes suggest

that 54 per cent of the children of non-mixedimmigrant families should attend a different schoolso as to ensure an even distribution of immigrant-

origin students.5 This figure drops to 41 per cent if welook at both the children of mixed and non-mixedimmigrant parents.

The children of mixed parental couples are knownto be more successful than the children of immigrantparents in terms of education attainment. Further-more, in Spain they represent a particularly differ-

entiated population given that mixed marryingbetween Spaniards and Latin Americans is far morecommon than among other ethnic backgrounds. Thus,

including this population in our measure of concen-tration could have misleading effects in the finalconclusions. In the following pages only the children oftwo immigrants are used to estimate the impact of

concentration. Figure 4 shows the overall distributionof immigrants in the schools included in our sample.Bear in mind that this graph was constructed using anation-wide representative sample of Spanish schools

(the dataset does not allow us to segment the sampleinto regional or urban samples). While in 2003, 30 percent of Spanish schools did not have any immigrant-

origin student, today there are a significant number ofschools where the percentage of immigrants is wellabove 10 per cent.

Figure 4. Percentage of children of immigrant households in Spanish schools

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As was said at the beginning of this article, the

relative weight of immigrants and natives in the public

and private sectors is becoming increasingly different.

Form our data we know that 59 per cent of the

Spanish private schools do not have immigrant

students. The majority of schools where immigrant

students represent more than 10 per cent are in the

public sector (where 12 per cent of schools have in the

range of 11–20 per cent immigrant students, as

opposed to 3 per cent of the private institutions).Figure 5 puts this in clearer terms by distinguishing

between public and private schools. Its vertical axis

shows the proportion of students in each school whose

final score in the mathematics test was in the highest

tercile of the distribution (scoring 270–500). The

horizontal axis registers the percentage of children of

non-mixed immigrant households.There are a minority of schools with a very high

concentration of immigrants and a low percentage of

students in the most successful tercile of the scores

distribution. Furthermore, none of the schools with

more than 20 per cent of immigrant-origin students

host more than 50 per cent of best students in the

sample (only a single school reaches this percentage,

and it is a private institution). It is also worth noting

that only two private schools have more than 20 per

cent of immigrant-origin students.

The remainder of the empirical section is organizedinto two main blocks. The first presents an aggregateanalysis at group level to show whether performance canbe explained through a reference to school characteris-tics. Secondly an individual-level analysis will expand ourfocus to micro-level causal mechanisms.

Aggregate Analyses

Our first task is to confirm the existence of unex-plained variation associated with the percentage ofimmigrants in each school, controlling for theirsocio-economic composition. For the sake of simplicitywe only present here the results that have beenestimated using the mathematics test scores.6 Yet thesame sort of conclusions could be reached if using thegrades in Spanish and social sciences.7

H0 aggregate: Schools where immigrant children andthe children of immigrants concentrate have loweraverage test scores.

Immigrants in Spain, as in other developed countries,are known for concentrating in the lowest segments ofthe stratification system (Garrido, 2005; Bernardi,Garrido and Miyar, forthcoming). This widely docu-ment regularity may of course at least partially explain

0 20 40 60 0 20 40 60

Private schools Public schools

l n. o

% of children of immigrantsGraphs by publico

Source: our calculation from INECSE, Evaluación de la Educación Primaria (2005)

Figure 5. Percentage of children of immigrant households in private and public schools and percentage of students whose

score is in the highest tercile

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why schools in which the children of immigrantfamilies concentrate, obtain poorer average results.Figure 6 shows the relation between our main inde-pendent variable and the average result obtained by theschool in mathematics. The regression line correspondsto the simplest equation specification (the averagegrade is equal to a constant and the percentage ofimmigrants). It confirms the negative slope of theconcentration although it appears to be not very steep.The circles in the figure tell us about the average levelof parental education in the school (as measured by acontinuous variable ranging from 0—no education to4—university degree).

Figure 6 suggests that those schools where immi-grants are better represented tend to have a loweraverage performance in mathematics, and also that theparents have lower levels of education.

Despite the association between the average level ofparental education and the concentration of immi-grants in schools, the empirical literature has not yetreached a consensus on whether the impact of themigrant composition of the student bodies has anyimpact beyond this composition effect (Coleman,1966; Fekjær and Birkelund, 2007).

H1 aggregate: At least part of the correlation betweenthe percentage of immigrants in a given school

and its average academic performance is due tothe over-representation of immigrant householdsamong the most deprived segments of the popu-lation, and thus has to do with processes related tosocio-economic disadvantage.

An alternative explanation to the negative correlationconcentration-outcomes has to do with school charac-teristics. School (group)-level characteristics are awell-documented source of inter-school variation inattainment (Smith and Tomlinson, 1989). One of themost prominent school-level characteristics in thisliterature is school ownership. The empirical literaturehas documented a wide gap in attainment betweenpublic and private (or publicly funded privateschools) across countries, including Spain (Dronkersand Roberts, 2005), with differences in the schoolenvironment and resources being a possible explanationfor this gap.8

H2 aggregate: Since the children of immigrants aremore likely to be enrolled in public schools, part ofthe correlation between the percentage of immi-grants in a given school and its academicperformance reflects the unequal distribution ofresources between private and public schools, anddifferences in their school environment.

0 20 40 60

% children of two immigrants in school

Note: size of the school markers is average parental education (0: no education; 4: university)

Figure 6. Average school score in mathematics by percentage of immigrants in the school sample and average level of

parental education in the school

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While disentangling the effect of school environmentand resources is an important analytical aspirationin this analysis, the INECSE dataset is not particularlyrich in describing these two aspects of schools. Wecould only include one standard variable to measurethe impact of school resources: whether the school hasa pc room for students (1,0).9

The net impact of the concentration of immigrantsin schools is estimated in the stepwise regressionanalysis presented below (Table 1). Model 1 confirmsthe significance of the concentration of immigrants tounderstand differences in attainment between schools(H0). There is almost no difference between thisestimate in Models 1 and 2 where the average level ofparental education is included in the model, so thesecond hypothesis stated above should be rejected.Thus, at least at the aggregate level, the correlationbetween concentration and attainment seems to berelated to something other than average levels ofsocio-economic deprivation. Despite the stability of theconcentration estimate, the introduction of the averagelevel of parental education in the school boosts the R2

to 24 per cent, revealing how important it is in theexplanation of inter-school inequalities.

The third model reveals that the distinction betweenpublic and private institutions is almost irrelevant inexplaining inter-school variation in attainment (H2).It has no impact on the concentration estimate andimplies no increase in the amount of variation accountedfor by this model. The model suggests that the averageresults of private and public schools are equal since theestimate is not statistically significant, although as might

have been expected, its sign is negative. Finally, the

fourth model adds the variable proxying the school

resources that appear to have the predicted positive

impact, and decreases the public school estimate, which

continues to be non-significant. Yet, this variable does

not help to explain the negative impact found on the

percentage of migrants in the school; on the contrary,

this final specification increases its size.

Individual-Level Analysis

An individual-level analysis can expand our test to

other explanatory mechanisms and may help to

confirm the robustness of our preliminary conclusions.

The combination of individual and group-level vari-

ables into a single analysis requires disentangling

individual and group-level variation for a proper

estimation of standard errors. This is what a multilevel

regression adds to the standard ordinary least squares

(Snijders and Bosker, 1999). The standard one-level

regression includes a single residual (Rij).

Yij ¼ �0j þ �1j xij þ Rij

A multiple-level regression allows us to add as many

random elements as we need to model variation

between groups. The simplest multilevel regression is

a random intercept model, which only adds a single

random parameter associated to the intercept repre-

senting the average value of a randomly chosen school

from the INECSE sample. In a multilevel regression,

the intercept is composed of an average value for the

Table 1 Aggregate analysis ordinary least squares regressionsa

Model 1 Model 2 Model 3 Model 4

Per cent children of immigrants �0.50*** �0.51*** �0.47*** �0.56***0.15 0.13 0.13 0.15

Average parental education 28.66*** 26.77*** 25.42***2.55 2.92 3.07

Public school �3.44 �1.082.59 2.76

PC room 10.98***3.22

Constant 250.20*** 162.30*** 170.24*** 153.41***1.37 7.92 9.93 11.49

N of schools 450 450 450 417F 11.14*** 70.12*** 47.41*** 35.73***R2 0.02 0.24 0.24 0.26

Values are � and standard errors.aGrades (mathematics).

*P50.05; **P50.01; ***P50.001.

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groups (�00) and a random one which reflects thevariation across groups (U0j)

�0j ¼ �00 þ U0j,

To this basic formulation, one can add group-levelvariables to explain variation in the intercept:

�0j ¼ �00 þ �10x1j þ . . .þ �q0xqj þ U0j:

Thus, our final model specification will be as follows:

Yij ¼ �00 þ �1jx1j þ �1j Xij þ Rij þ Uij,

where the random effects are Rij—the unexplainedindividual-level residual, and U0j—the group level one.�1j is a fixed effect that can be interpreted as a regularcoefficient in a standard regression. Accordingly, Xij isthe vector of student and group-level fixed effects thatwill be used to explain the relative over-ratings used asdependent variables.

The first set of micro-level models tries to confirmand quantify the negative impact of attending a schoolwhere the percentage of immigrant-origin studentsis higher (thus, it replicates the analysis previouslyconducted for H0 at the aggregated level). It exploresthe functional form of this expected association. Somescholars of contextual effects have suggested that theeffect of contextual variables is not linear, but ratherhas what might be described as an epidemic effect,having a more negative impact the larger the percent-age of immigrants in the school is.

H1 individual: The impact of the concentration ofimmigrants in schools is bigger the larger thepercentage of migrants in the student-body is.

The curvilinear form of the association betweenconcentration and attainment will be tested introdu-cing a quadratic term.

Besides, the impact of immigrants’ concentrationcould only be significant above a given threshold. Thishypothesis seems rather intuitive since it is difficult toexpect a negative impact from the first immigrantattending a given school. In other cases, this thresholdhas been identified to be around 40 per cent (Statnat,2006).

H2 individual: There is a threshold below which theconcentration of immigrants has no impact onattainment.

The threshold hypothesis will be tested by divid-ing the concentration into dummies. The models inTable 2 confirm a negative correlation between theweight of immigrants in the student body and the

grades in mathematics, which is statistically significant(H0). The empty model’s (Model 1) interclass correl-ation coefficient {ICC¼ �(u)/[�(u) þ �(e)]}, suggeststhat some 34 per cent of the total variation in testscores could be related to group-level factors, the restbeing explained by individual-level characteristics. Fewchanges happen by adding the percentage of immigrantorigin children in the school. The drop in �(u) is notappreciable. As expected the amount of variation inattainment explained by the percentage of immi-grants is really low, since the concentration problemis only particularly important in a small number ofschools (most of them public and located in largeurban areas).

The quadratic term introduced in Model 2 to testthe non-linearity of this association is not significant.By introducing the division of the concentration intodummies (no immigrants, less than 10 per cent,between 11 and 20 per cent and more than 21 percent), Model 3 allows us to check whether the impactof immigrants’ over-representation is significant de-parting from low levels of concentration and to qualifyour test of the non-linearity functional shape of theassociation we are interested in. The concentrationof immigrants seems to be irrelevant (or has nosignificant impact) below 20 per cent.10 Thus, athreshold has been identified confirming H2 individual.Unfortunately the number of schools above thisthreshold does not allow us to divide the concentrationinto more segments so as to confirm that the impactof concentration above 20 per cent is non-linear.However, we can suspect that this is the case bylooking at the non-significant parameters estimated forconcentration below 10 and 11–20 per cent incomparison to schools with no immigrants.

To sum up, given that our dependent variable rangesfrom 0 to 500 with a mean of 250, the estimationhere presented indicates that the loss in test scores ofschools where all the students have an immigrantorigin is 50 points, which represents a 20 per centaverage score in contrast to the one obtained by arandom school with no immigrants.

We now proceed to explore different explanationsgiven to the negative correlation between the per-centage of immigrants in schools and the averageattainment of the student body. If the correlation isprovoked by negative micro-level interactions,immigrant-origin students would not be expected tobe motivated for education, and this lack of enthusi-asm would have to be passed on to the students withwhom they interact. This argument does not fit awidely documented regularity in the empirical litera-ture on migration and educational attainment, namely

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that immigrant-origin students normally have moreambitious expectations than their native counterparts,controlling for class of origin (Muller and Kerbow,1993; Kao and Tienda, 1995; Kao and Thompson,2003). The INECSE dataset allows us to check whetherthis is also the case in Spain. Next, Figure 7 shows theanswers given by students from migrant andnon-migrant origin to the following question: ‘Whatis the highest level of education that you would like toattain?’ Answers were recoded into three values:university, other option or does not know yet. It canbe seen that in Spain, migrant-origin students aremore ambitious and expect to attain a universitydegree more often than their native colleagues whentheir parents have a low education (no education, orprimary and secondary). Yet, the opposite happensamong the children of highly educated families sincethe percentage of those aspiring to a university degreeis 78 per cent among the natives and only 27 per centamong the children of non-mixed immigrant parents.A point should be raised about this last statement: thechildren of highly educated migrant parents are notmore inclined towards other options in the schoolsystem but just less certain about their plans.

All this suggests that the argument linking concen-tration and attainment through micro-interactionsis implausible. The remainder of this analysis

concentrates on the other two sets of explanations:

school and composition effects.Explanations arguing that schools offer different

qualities of learning environments are generally of two

types. First, schools may differ in the amount and

quality of material and human resources they have at

their disposal. As was undertaken in the aggregate-level

analysis, we can here proxy this argument by distin-

guishing between public and private schools, and by

using a proxy of the school resources (pc room

available for students).A second school-effect argument is that an over-

representation of low-performing students (independ-

ently of whether they are of migrant-origin or not)

forces teachers to lower the threshold used to set

learning goals and to evaluate the students’ overall

academic performance. This is probably a too complex

theory to test empirically since in the available datasets

teachers’ behaviour remains unobserved. Nonetheless,

there are two empirical strategies to provide a rough

test to our theory:

1. The INECSE questionnaire answered by the school

directors included a question that asked how

students were distributed across classrooms. In

schools where students are sorted into groups that

ensure a heterogeneous composition in terms of

Table 2 Random intercept multilevel regressions

Model 1 Model 2 Model 3 Model 4

Per cent children of immigrants �0.57* �0.140.23 0.57

(Per cent children of immigrants)2�0.02

0.02Less than 10 per cent 0.18

2.67From 11 to 20 per cent �3.69

4.30More than 21 per cent �18.68**

6.61Constant 248.38*** 250.99*** 250.27*** 249.29***

1.82 2.08 2.26 1.57�(e) (individual level) 43.11 44.39 44.39 44.39�(u) (group level) 24.97 23.24 23.26 23.37Q 0.25 0.22 0.22 0.22N of schools 450 450 450 450N 9,441 9,441 9,441 9,441�2 0.00 11.09*** 11.38 9.23*Within school R2 0.00 0.00 0.00 0.00Between school R2 0.00 0.02 0.02 0.02Overall R2 0.000 0.01 0.01 0.01

Values are � and standard errors. *P50.05; **P50.01; ***P50.001.

nloaded from

performance, best and worse-performing students

are mixed. If the most common teachers’ strategy

is to maximize the number of their students

passing to the next level, they may concentrate on

the least successful ones. As a result, dispersion in

the distribution of grades could have an average

negative impact overall, since it will lower the

requisites demanded of all students, including the

most successful ones.

H3 individual: If teachers adapt their level of

exigency to low-performing students in each

group, students sorted into heterogeneous class-

rooms may obtain (on average) lower grades,

since dispersion in academic outcomes would

mean that on average the threshold defining who

passes and who fails is lower.

2. Alternatively, an objective indicator of the perform-

ance composition of schools can be used to check

whether observed dispersion around the mean

decreases average performance. If our theory is

correct, a disperse distribution (a larger standard

deviation) of the school performance could be

negative for the best students. In order to test this

hypothesis, we distinguished schools according to

the percentage of best students/total number of

pupils. The criteria used to define who is a good

student are to some extent random. We can define

the best students as those scoring above 270 in

the mathematics test (this is the upper tercile of the

distribution). Schools type A have more than two-

thirds of good students; type B between one-third

and two-third, and type C less than one-third

(this last group is used as a reference category).11

School type A and B dummies are interacted with

the schools’ standard error in mathematics. This

will allow us to see if on average attainment is lower

in schools with more dispersion, and if this is

specially the case of schools with more good

students.12

H4 individual: If teachers lower their threshold toadapt to low-performing students in the school,dispersion in the distribution of grades shouldhave a more negative impact in schools wherethe most successful students are betterrepresented.

0% 20% 40% 60% 80% 100%

No education (inmigrant)

No education native

Primary (inmigrant)

Primary (native)

Secondary (inmigrant)

Secondary (native)

University (inmigrant)

University (native)

Percentage of students wishing a university degree

Does not know

Other option

Source: our calculation from INECSE (2005)

Figure 7. Educational expectations

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These two complementary tests are the best empir-ical strategy to proxy our argument. As we said before,we cannot observe real teacher behaviour butour predictions are implied from our theory, so if weconfirm them, our results should be interpretedas offering indirect support to our argument. Theliterature has already provided some empirical sup-port to similar arguments to ours (a literature reviewand an empirical test is available in Duru-Bellat, 1997).

Testing this hypothesis imposes a complicatedequation specification, which artificially boosts the‘between schools R2’. It is also difficult to see separatelyhow much of the expected decrease in the effect of theconcentration estimate from Models 1 to 3 is due tothe substantive process described in these lines or fromthe artificial division of schools into the three groupsdescribed above.

The third block of explanations refers to a class-composition effect underlying the negative correlationbetween the concentration of immigrants and attain-ment. The sociology of education has identified a longlist of mediating parental socio-economic characteris-tics that may be related to the attainment of theiroffspring. Many of them are available in our dataset,so the list of pertinent independent variables couldbe endless. For the sake of simplicity, after a number ofsensitivity tests we decided to use a short number ofthem, including the highest level of parental education(0 ¼ ‘no studies’ to 4 ¼ ‘university degree’) and analternative proxy of cultural capital—the amount ofbooks at home (1 ‘from 0 to 50’ to 3 ‘more than 100’).Parental education and the stock of cultural capitalat the household level are known to be importantdeterminants of cognitive abilities and one of the bestindividual-level predictors of test scores (Schnepf,2004). So as to complete the list of individual-levelvariables that could have a significant impact onindividual attainment we decided to include sex (boysare known to perform better in mathematics thangirls), whether the household belongs to the groupslabelled non-mixed immigrants and the student’snumber of siblings as a proxy of the degree of compe-tition for limited resources in the household.

H5 individual: Part of the significant correlationbetween immigrants’ concentration and attain-ment is spurious and will disappear when thestock of cultural capital accumulated at the house-hold level, parental education and the number ofsiblings are controlled for.

Our fifth model specification also controls for sex(girls are known to be less successful in mathematics

than boys) and for family migrant status (one ifneither of the parents are native and zero in the rest ofthe cases). The final model (Model 6) represents ajoint test of all our arguments.

The models presented in Table 3 include significantinteractions if necessary. Note that, because ofthe identification of a significant threshold effectabove 21 per cent, the indicator on concentration isnow a dummy variable set to one if more thanone-fifth of the students are of immigrant-origin andzero otherwise. Only the final model (Model 7)includes the continuous version of concentration toreassure the reader that no changes in the conclusionsderive from the operationalization of the concentrationvariable.

Model 2 generally rejects that the criteria used tosort students across schools has any significant impacton attainment (H2). Yet, the interaction between thisvariable and the percentage of immigrants per schoolappears to be negative and statistically significant.Thus, using sorting criteria that preserve the hetero-geneity of the student body seems to be associated withlower performance in contexts where immigrant-originstudents are better represented.

Dispersion also seems to be a relevant group-levelfactor to explain individual attainment if an objectivemeasure of school performance composition is used(Model 3). The interaction between the dummiescorresponding to school types A and B with the schooldeviation in mathematics are significantly negative(and almost identical in terms of coefficient size).Therefore we can confirm Hypothesis 3. Under thismodel specification, the interaction between the het-erogeneous grouping of students and the deviationdecreases importantly although it remains statisticallysignificant. Thus, the adjustment of the teachers’threshold level seems to be a function of the schoolcomposition in terms of performance, rather than aclassroom-level effect.

The public school hypothesis can also be con-firmed since this estimate is negative and highlystatistically significant in Model 3.13 By itself it isable to reduce the size of the concentration estimatefrom �19.5 to �15.9, which remains to capture someunexplained variation in terms of statistical signifi-cance. Note that this school-level variable appears tohave little potential in explaining inter-school vari-ation since the �(u) only drops to 21.63 in this thirdmodel.

Controlling for the list of above-mentioned indi-vidual variables, the concentration estimate significant-ly decreases (from �19.5 to �5.7) and appears to benon-statistically significant. All the individual-level

nloaded from

factors behave as expected. Girls obtain on averagelower grades in mathematics than boys; parentaleducation and parental cultural capital have a positiveimpact on performance, while the number of siblingsis negatively associated with the dependent variable.Finally, migrant status (as measured by being the childof a non-mixed immigrant parental couple) is alsonegatively affecting the average score obtained inmathematics. The interaction between the concentra-tion estimate and migrant status is not significant,which means that this effect is equal for migrant andnative-origin students.

A final joint test of the hypotheses reveals that theconcentration effect measured both as threshold(Model 6) or as a continuous variable (Model 7) isnot only non-significant, but also positive.14 Theinteraction between the sorting criteria that preserveheterogeneity and the percentage of immigrants losesits statistical power, as also happens in the case of theinteraction between the best schools (A) and ourmeasure of dispersion (Models 6 and 7). The publicschool dummy is now also non-significant, and evenpositive, which means that its brute effect is mediatedby a composition effect accounted for by the list of

Table 3 Random intercept multilevel regressions

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

More than 21 per centimmigrants

�19.46** �13.71* �8.95* �15.91* �5.69 2.086.57 6.80 3.76 6.34 5.18 3.69

Per cent children ofimmigrants

0.050.19

Heterogeneous grouping �0.13 �0.74 �0.682.93 1.47 1.50

Percentage � grouping �1.00** �0.30 �0.320.31 0.16 0.18

School type A 68.84*** 58.42*** 58.52***9.52 8.76 8.75

School type B 47.13*** 41.47*** 41.29***8.71 8.04 8.01

School SD in maths 0.58*** 0.54*** 0.54***0.13 0.12 0.12

School A � dispersion �0.39þ �0.33 �0.340.22 0.20 0.20

School B � dispersion �0.40* �0.36* �0.360.19 0.17 0.17

Public school �15.42*** 0.38 0.312.44 1.32 1.33

Sex �5.41*** �5.52*** �5.51***1.01 0.99 0.99

Parental education 6.92*** 5.71*** 5.71***0.66 0.63 0.63

Siblings �4.43*** �4.11*** �4.10***0.49 0.47 0.47

Cultural capital (n. books) 9.27*** 8.01*** 8.01***0.68 0.67 0.67

Non-mixed �17.58*** �18.07*** �18.18***2.57 2.53 2.58

Constant 251.61*** 252.95*** 199.90*** 261.54*** 225.38*** 179.98*** 180.02***1.23 1.48 5.98 1.96 3.05 6.16 6.16

Sigma (e) 44.12 44.12 44.12 44.03 42.76 42.76 42.76Sigma (u) 23.04 22.67 8.74 21.63 16.17 7.22 7.20Rho 0.21 0.21 0.04 0.19 0.13 0.03 0.03N 7779 7779 7779 7779 7779 7779 7779No. of schools 450 450 450 450 450 450 450�2 8.77*** 23.03*** 1063.63*** 61.77*** 631.20*** 1663.63*** 1666.01***

Values are � and standard errors. þP50.10; *P50.05; **P50.01; ***P50.001.

nloaded from

independent variables and controls added in our finalmodel specification.

Conclusions and Discussion

In this article we have found no effect on achieve-ment of the concentration of immigrants, once socialindividual characteristics are controlled for. Beforecompleting the model specification, the effect ofconcentration is only significant if immigrants repre-sent at least one fifth of the student body. On average,students whose schools are in this situation score20/500 points less in the mathematics tests. There aregrounds to suspect that the effect of concentrationabove the threshold is non-linear. Unfortunately thesample of schools with a high concentration ofimmigrants used in this article did not allow this tobe confirmed.

The article has also tested the relevance of alterna-tive competing explanations to account for theconcentration effect. As was argued, the identificationof specific causal mechanisms is a mandatory require-ment in this literature since the concentration canresult from several different processes. The existence ofpeer-pressures operating at the micro level was dis-carded since immigrant-origin students do appear to beless inclined than natives to education. Contrary to whatis normally expected, the concentration effect does notseem to result from differences in the school-levelresources, although these are clearly important inexplaining inter-school differences in attainment.

In our analysis, the negative correlation betweenconcentration of immigrants and individual schoolseems to be produced by a strong socio-economiccompositional effect. The population attending schoolswhere immigrant-origin students are better representedseems to be more deprived than the rest of the studentpopulation. It is for this reason that when properlycontrolling for household-level socio-economic vari-ables (parental education, stock of cultural capital andnumber of siblings), together with socio-demographiccharacteristics (sex and migrant status), the concen-tration estimate significantly decreases and loses itsstatistical significance. If this is correct, dispersion ofimmigrants on the school map would not be the beststrategy to reduce inter-school inequality since itderives from the strong spatial socio-economic strati-fication of more and less deprived families on theschool map.

In our article we also found evidence indicating thatsome relevant processes could also be taking place ingroups within schools. The concentration of

immigrants, just as with the concentration of lowperformers, could be forcing teachers to adapt theirthresholds to a lower group-average. Although we onlyfound indirect evidence supporting this argument,our findings confirm that group heterogeneity inschool results negatively affects individual attainment.Although real teacher behaviour remains unobserved,we would argue that this regularity backs our theory.We take this as proof of the predominance of teacherstrategies that seek to help the worse students in eachgroup. This could occur if teachers concentrate theirefforts on helping the least successful students or ifthey set thresholds that are more adapted to thelow-performing profile of students in a given group.We also believe that sociologists of education shouldbuild on a previous tradition and focus more onwithin group dynamics and teacher behaviour as apromising line of research.

1. From 1,472,458 to 4,837,622 (source: Instituto

Nacional de Estadıstica)

2. The dataset used for the empirical analyses was

done in 2003–2004, when the percentage of

immigrants in the public sector was 7 per cent

(4.3 per cent in the private one).

3. For a more detailed description of this dataset and

for a summary of its main conclusions and

implications, see INECSE (2005).

4. This could represent a problem in our analysis

since the sampling did not particularly considered

the immigrant population, that is highly concen-

trated in large urban areas mainly in Barcelona,

Madrid or Valencia. Thus, no comparison of

schools with and without immigrants placed in

the same region can be done. The reader should

consider this as one of the limitations of our

analysis.

5. Djm ¼PN

i�1ti xi�Xj j

2TXð1�XÞ, where t and x are the total

population and minority proportion of an areal

sub-unit (i), and T and X are the population size

and minority proportion of the whole geographi-

cal area (j), which is divided into N areal sub-

units.

6. Given that the data is the result of a two stage

sample (schools and students within) it may not

be correct to aggregate data using schools with

530 students. Our results indicate that this effect

nloaded from

is nonlinear, having no significant impact below

25 per cent.

7. These results are available upon request.

8. Our dataset helps to confirm that the distribution

of human and material resources is very different

across private and public schools, being the former

one better provided: they are more likely to have

better trained teachers and also more frequently

benefit from pc-rooms ready for students.

9. As it will be seen in model 4, the inclusion of this

variable (or others reflecting school resources)

represents an important loss in the number of

schools included in the sample. If using the restricted

sample to estimate all four models presented in

Table 1, no changes in the results are appreciated.

10. This threshold has been set after a number of

t-tests conducted to check its sensibility.

11. So as to avoid potential problems of endogeneity,

the same calculation was done excluding in each

case the grade obtained by each student in the

sample. There were no changes in the results.

12. The appendix includes density graphs for the overall

distribution of grades and for each school type.

13. The other proxy of school resources, pc-room is

not included here to avoid the loss of cases since

this variable is Orly observed in 417 schools. In

any case pc-room behaves just as the dummy for

public schools and looses its significance in the

final models.

14. Re-estimating the models using the continuous

version of the concentration variable, implies no

changes in the conclusions drawn from Table 3.

These models are available upon request.

Acknowledgements

We are grateful for the comments given by Leire

Salazar and Dulce Manzano Espinosa to a previous

draft of this article and to two anonymous referees.

Funding

This research was done with the financial support of

the Comision Interministerial de Ciencia y Tecnologıa

(Project: SEJ 2007-67091 ‘Inmigracion, estado de

bienestar y desigualdad social en Espana’). Funding

to pay the open access publication charges was

provided by the same body.

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Authors’ Addresses

Hector Cebolla Boado (to whom correspondence

should be addressed), Departamento de

Sociologıa II (Estructura Social) UNED, Madrid,

C/Obispo Trejo s/n, 28040 Madrid, Spain.

Email: hcebolla@poli.uned.esLuis Garrido Medina, Departamento de Sociologıa II

(Estructura Social) UNED, Madrid, C/Obispo

Trejo s/n, 28040 Madrid, Spain.

Manuscript received: April 2010

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Appendix

Table A1 List and description of variables used

Variable label N Mean Std Dev. Min Max

Per cent children of immigrants 9,814 4.38 7.80 0.00 60.71Books at home 9,343 2.17 0.80 1.00 3.00Heterogeneous grouping 9,814 0.33 0.47 0.00 1.00Mathematics 9,441 250.00 50.00 110.64 388.20More than 21 9,814 0.03 0.18 0.00 1.00Non-mixed 9,814 0.04 0.20 0.00 1.00Parental education 8,319 3.11 0.85 1.00 4.00Parental expectations 8,457 4.38 1.19 1.00 5.00Pc room 9,070 1.87 0.34 0.00 1.00Public school 9,814 0.61 0.49 0.00 1.00Sex 9,357 0.49 0.50 0.00 1.00Siblings 8,961 1.43 1.08 0.00 22.00Social sciences 9,442 250.00 50.00 107.37 384.84Spanish 9,429 250.00 50.00 107.28 38.44

100 200 300 400Grades in mathematics

Overall distribution of grades in the sample

100 200 300 400Scores in mathematics

School type A

School type B

School type c

Figure A1. Distribution of grades per school type. School type A has more than two-third of students in the highest tercile

of the general distribution of test scores in mathematics. Type B has between one-third and two-third. Type C has less than

one-third.

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The impact of immigrant concentration in Spanish schools: school, class, and composition effects

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