Spillover effects of studying with immigrant
students in the same classroom:Evidence from
Quantile regression analysis
Asako Ohinata∗ and Jan C. van Ours†
Preliminary version: October 1, 2012
Abstract
We analyze how the share of immigrant children in the classroom affects the educational attainment of native Dutch children. As opposed
to the previous studies that mainly focus on the immediate spill-over
effects at the conditional mean, we investigate the spill-over effects at
different parts of the test score distribution for students in the 8th
grade. Our analysis uses a rich Dutch panel data, PRIMA, which
allow us to characterize educational attainment in many dimensions.
After taking classroom characteristics, parental education and school
characteristics into account, we do not find strong evidence of negative
spill-over effects from immigrant children to native Dutch boys. For
native Dutch girls some negative spillover effects persist in particular
if their parents have a low educational attainment.
JEL classification: I21, J15
Keywords: Immigrant children; Peer effects; Educational attainment
∗
Department of Economics and CentER, Tilburg University, The Netherlands;
[email protected]
†
Department of Economics, CentER, Tilburg University, The Netherlands; Department of Economics, University of Melbourne, Parkville, Australia and CEPR;
[email protected]
The authors gratefully acknowledge financial support from the NORFACE research
program on Migration in Europe - Social, Economic, Cultural and Policy Dynamics.
1
1
Introduction
Now that immigrants are a substantial part of the European population,
research is shifting towards assessing educational performance of first and
second-generation immigrant children sometimes in comparison to the native children. However, the question of whether immigrant children affect
native children’s educational outcomes remains largely unanswered. Our
paper contributes to the small literature on educational spillover effects by
analyzing whether studying with immigrant children in the same classroom
affects the educational attainment of the 8th grade (approximately 12 years
old) native Dutch children.
Educational spill-over effects between immigrant children and native children have been studied in the United States by Hoxby (1998) and Borjas
(2004), who present evidence of immigrant crowding out effects on the native students in the graduate and postgraduate schools. Other US studies
focus on peer effects due to interactions with immigrant students in the
neighborhood and their impact on academic performance of native students
Betts (1998); Betts and Lofstrom (2000).
Evidence from outside US include Gould et al. (2009), who use the large
influx of Jewish immigrants from the former Soviet Union to investigate peer
effects on the native Israeli students. Brunello and Rocco (2011) present
cross country evidence from 27 European and Anglo-Saxon countries by using the 2000, 2003, 2006 and 2009 Program for International Student Assessment (PISA). PISA assesses the cognitive abilities (reading, mathematics,
and science) of the 15 year old students in OECD member countries. Jensen
and Rasmussen (2011) study the immigrant peer effects in Denmark using
the 2000 and 2005 PISA and Danish administrative register data. They address the non-random allocation of immigrant families to certain regions by
using the population size of the residence of children as an IV. Geay et al.
(2012) use data from the British National Pupil Database between 2003 and
2009 to relate the percentage of non-English speaking children aged 12 in
England to the educational performance reading, writing, math of native
children within the same school.
2
Educational spillover effects of immigrant children have been investigated in the Netherlands in a couple of studies. Van der Silk et al. (2006)
study the effect of ethnic composition on language proficiency of children
in grade 4 to 6. They find that pupils in school classes with a high share
of ethnic minorities perform worse but this effect disappears once parental
characteristics are taken into account. Maestri (2011) studies the effect of
ethnic diversity in a class room finding that this has a positive effect on test
scores of minority students. Finally, Ohinata and van Ours (2011) analyze
how the share of immigrant children in the classroom affects the educational attainment of native Dutch children. In our previous study we do not
find strong evidence of negative spillover effects from immigrant children to
native Dutch children on reading, maths and science skills.1
The existing literature all examine the spill-over effects of immigrant
students at the conditional mean of the test score distribution. However,
students struggling academically may suffer more/less from studying with
immigrant students than those who are at the top of the test score distribution. On the one hand, a larger share of immigrant students in the same
classroom may lead teachers to provide more focus to weaker students if the
immigrant students are also performing poorly. On the other hand, weak
Dutch students may see reduction in the teaching resources allocated when
they study with a large share of immigrants. Providing an answer to such
a question requires the spill-over effects to be evaluated at different parts of
the test score distribution. The present paper, therefore, employs a Quantile
regression approach and extends our previous analysis by studying differential spill-over effects of immigrant students on the native Dutch students at
different parts of the test score distribution.
In our analysis we use the so called PRIMA data which are discussed
in more detail below. In the PRIMA data information from students at
primary schools in the Netherlands is collected, including a range of educational performance tests. In the current paper we focus on the so called
1
Our previous study used data from PIRLS (Progress in International Reading Literacy
Study)and TIMSS (Trends in International Mathematics and Science Study). The studies
by Van der Silk et al. (2006) and Maestri (2011) use PRIMA data, as we do in the current
paper. These data are discussed in more detail below.
3
CITO test scores. The CITO test is a national test providing an overall
assessment of educational performance. Following primary education, all
children in the Netherlands go on to secondary education - to one of the
educational tracks of preparatory intermediate vocational education, senior
general secondary education or pre-university education. The CITO test is
used for the allocation of students to post-primary schools.
One major obstacle researchers face when studying the present topic is
that immigrant students are not randomly allocated to schools. A simple
Quantile regressions would report biased estimates due to the endogeneity
problem arising from the non-random allocation of students. We, therefore,
propose to overcome this problem by exploiting the variation in the number
of immigrant students in the 8th grade from the same school over the years.
The identification assumption requires that once year and school fixed characteristics are controlled for, students in the 8th grade in a particular year is
comparable to those who are in the same grade in another year. Moreover,
the number of immigrant students found in the 8th grade in a particular
year is random (i.e. share of immigrant students in exogenously given, since
it is mainly determined due to date of birth of the students).
One thing is to note, however, is that Quantiles are not linear operators.
This implies that a simple de-meaning approach often employed in a linear
setting cannot be applied. Instead, we employ an estimator proposed by
Canay (2011). Canay (2011)’s approach treats the unobserved school characteristics as a simple location shift that does not depend on each quantile.
This, in turn, implies that the school fixed effects can first be estimated at
the conditional mean and the predicted fixed effect values can then be deducted from the dependent variable before estimating the spill-over effects.
Our current paper is set-up as follows. Section ?? provides information
about immigrants in the Netherlands and the Dutch educational system.
Section 3 describes our data and the set-up of our analysis. Section 4 discusses preliminary parameter estimates. Section 5 concludes.
4
2
Background information
2.1
Immigrants in the Netherlands
After the second World War, migrants to the Netherlands moved broadly
for the following three reasons. Firstly, large groups of immigrants came
from the former Dutch colonies between the middle of 1940s to 1970s. These
include migrants from Indonesia and Molucca, Surinam, and Antilles. Secondly, foreign workers were recruited in the 1960s and 1970s as guest workers
to combat the shortages of labor in the Netherlands. Lastly, in recent years,
some entered as asylum seekers.
A large number of Dutch-Indonesian repatriates and Moluccans moved
to the Netherlands after the independence of Indonesia in 1949. Approximately 300,000 repatriates migrated to the Netherlands during the two
decades. Half of these were Eurasians, and 12,500 were Moluccans . Moreover, approximately 40,000 Surinamese moved to the Netherlands in 1975
when Surinam was decolonized and became independent. Another large
flow of migration occurred around 1979 and 1980 when the mandatory entry visa for Surinamese was introduced. This was due to fear that entry
to the Netherlands would become more restricted Ersanilli (2007); Lucassen
and Penninx (1997). Finally, there has been a continuous flow of immigrants
from the Netherlands Antilles over the past years.
In the 1960s, the Dutch economy saw a large boom, which resulted in a
severe shortage of labor. As a result, the major hiring of guest workers from
Southern Europe, Yugoslavia, and particularly from Morocco and Turkey
started Van Ours and Veenman (2005). The number of these immigrants
reached up to approximately 235,000 in 1970 Penninx et al. (1994). Although recruitment stopped in 1973, further migration from Morocco and
Turkey continued even in 1980s, for the purpose of family formation or unification Ersanilli (2007).
In addition, the Netherlands has been an active host for political refugees
and asylum seekers. After the fall of the Soviet Union, many immigrants
from the eastern Europe moved to the Netherlands. In the more recent
5
years, economic and political crisis has increased immigrants from diverse
backgrounds such as Iraq, Iran, Afghanistan, and Somalia.
An important distinction for the purpose of this paper is between firstgeneration and second-generation immigrants. First-generation immigrants
include those who were born outside of the Netherlands with at least one
parent also born abroad. Second-generation immigrants are those who were
born in the Netherlands with at least one of the parents born outside the
Netherlands. Among the non-Western immigrants both the number of firstgeneration immigrants as well as the number of second-generation immigrants increased substantially. Although Indonesians are one of the major
groups of immigrants in the Netherlands, they are unlikely to represent a
significant portion of the students in this paper. Due to their entry mainly
in the 1950s and 1960s, the Indonesian and Moluccan first and secondgeneration immigrants are likely to be too old to be in primary schools.
2.2
The Dutch educational system
Immigrant population is not evenly distributed across the Netherlands.
In fact, the highest concentration of immigrant households are found in the
four largest cities, i.e. Amsterdam, the Hague, Rotterdam, and Utrecht.
This has two important implications for the purpose of the analysis in this
paper. Firstly, since Dutch parents have always enjoyed the freedom to
choose a school that they want to send their child to, the probability of racial
and socioeconomic segregations in these big cities is likely to be higher, due
to the large number of schools from which to choose. Secondly, schools located in the four large cities were likely to have received more funding from
the Dutch government, at least until 2006. The Weighted Student Funding
(WSF) was in operation between 1985 until 2006 in order to promote equal
educational quality among schools and also to assist schools with a larger
number of disadvantaged students Ladd and Fiske (2009). The scheme calculates a weighting index for each school by taking account of the number
of immigrant students as well as disadvantaged Dutch students. This index
ranges between 1 and 1.9, where schools with an index of 1 until 1.09 were
6
not given any extra funding. Schools with the index above 1.09 were offered
the extra funding, whose amount reflected the index. For example, those
with 1.9 received 90 percent more funding per student. The system is made
slightly more complex by the fact that money was not directly paid to each
school but rather was given to school boards that had the control over the
distribution of the allocated funding across the schools. Nonetheless, Ladd
and Fiske (2009) show evidence that extra funding was allocated mainly
to schools in the four largest cities. The implication of such a treatment
is that school principals may have allocated additional resources towards
classes with larger numbers of immigrant students. If this is the case, and
classes with a high share of immigrant children were being taught by more
able teachers or if these classes had better teaching resources, the size of
the potentially negative peer effects of immigrant students may have been
reduced. Below, we investigate to what extent the allocation of educational
resources is correlated with the share of immigrant children in a classroom.
3
Data and set-up of the analysis
3.1
Data
Our paper employs a rich Dutch dataset, PRIMA. PRIMA is longitudinal
data which was commissioned by the Ministry of Education and coordinated
by the Institute for Research of Education (DME). The implementation of
the research rested with the Institute for Applied Social Sciences (ITS) of
the Catholic University Nijmegen and the SCO-Kohnstamm Institute of the
University of Amsterdam. PRIMA collects detailed information from approximately 600 primary schools and test scores and individual characteristics were collected from students and their parents. Moreover, information
at the classroom, teachers school administrators were also made available.
The surveys were conducted every two years since the 1994-1995 academic year. Although there are 6 waves in PRIMA (1994/1995, 1996/1997,
1998/1999, 2001/2002, 2002/2003, 2004/2005) we only use the last 3 waves.
This is because the first wave has a rather different set of questions and I
7
need to look in more detail while the second an third wave do not seem to
have identifiers that are comparable over the years.
From each school, information on the 2nd, 4th, 6th and 8th grade students are collected at the student, parent, teacher (classroom), school (Principal) levels. All students took tests specially designed by PRIMA on reading
and algebra. The results from the CITO tests for the 8th grade students
are also included. In addition to the CITO test scores we also analyze educational attainment in terms of language test scores, maths test scores,
reading test scores and IQ test scores of the 8th grade students. In the
analysis the main explanatory variables are the share of immigrant students
in the classroom, the gender of the teacher, the experience of the teacher,
the number of students in the classroom, the composition of the classroom
in terms of grades, the age of the students, the educational attainment of
the parents and the cohort of the students (see the appendix for definitions
and descriptive statistics of all variables).
PRIMA data have been used in various studies investigating a wide range
of educational issues in the Netherlands including the educational spillover
effects of immigrant children by Van der Silk et al. (2006) and Maestri
(2011), discussed in section 1. Dobbelsteen et al. (2002) for example analyses the causal effect of class size on educational performance finding that
after correcting for endogeneity, pupils in larger classes do no worse than
identical pupils in small classes. Driessen (2007) studies whether teachers’
gender affects achievement, attitudes and behavior of pupils finding no effects. Leuven et al. (2010) find that early schooling enrollment has positive
effects on language scores and math scores of disadvantages pupils, but has
effects on the educational attainment of non-disadvantaged pupils. Rønning
(2011) studies the effect of homework assignments finding that these assignments only improves the achievement of pupils from advantaged family
backgrounds.
8
3.2
Descriptive analysis
Figure 1 shows the distribution of our main explanatory variables. As
shown in the top graph in almost 70 percent of the students in 8th grade
classes had no first-generation immigrant students in their classroom. The
bottom graph shows that in a little over 20 percent of all students were in
classes without no second-generation immigrant students but there are also
many Dutch students who had more than 40 percent of second-generation
immigrants in their classroom. The bottom graph shows the distribution of
the share of all immigrant students which is to a large extent driven by the
presence of second-generation immigrant students.
Figure 2 shows that there is a lot of variation between the presence
of first-generation and second-generation immigrant students. This should
allow us to estimate the separate effects on the educational attainment of
Dutch students of both types of immigrant students.
Figure 3 gives an impression of the relationship at the level of the individual students between the share of immigrants in the classroom and the
educational attainment in terms of the overall CITO score.
Figure 4 shows the relationship between the share of immigrant children
in the eighth grade and the share of immigrant children in the sixth grade.
Clearly there is a strong variation but this correlation is far from perfect.
This should allow us to estimate the separate short run effect of the share
of immigrants in the eighth grade on educational performance in the eighth
grade as well as the long run effect of the share of immigrants in the sixth
grade on educational performance in the eighth grade.
Figure 5 groups the scatter plot of Figure 3 in 5 percent intervals of
the share of immigrants. Then, it is clear that there is a strong negative
relationship between the share of immigrants and the CITO score.
Figure 6, on the other hand, shows the cumulative test score distributions
by gender and by the share of immigrant students. In particular, similarly to
Figure 7, students studying with zero immigrant students are compared with
those studying in the 8th grade with 20% or more immigrant students in the
same classroom. The plotted distribution functions for boys show that the
9
two distributions are almost identical regardless of the share of immigrant
students in the same classroom. For girls, however, it is clear that larger
proportion of the students who study in a classroom with a share of 20%
or more immigrant students attain less test scores in comparison to those
studying with no immigrant students.
Figure 7 indicates evidence that the effect of immigrant students is not
uniform across the distribution. In these figures, Kernel Density estimations
of test scores are calculated separately for boys and girls. Moreover, students
studying with zero immigrant students are compared with those studying in
the 8th grade with 20% or more immigrant students. These figures indicate
that whilst the average test scores do not change regardless of the proportion
of immigrant students in the 8th grade, boys studying with no immigrant
students exhibit heavier tails at the lower end of the test scores. The opposite
story is true for the girls.
3.3
Set-up of the analysis
If students were randomly allocated to schools, then following Buchinsky
(1998), Eq. 1 below specifies the θth conditional quantile of the test score
distribution for the ith individual in 8th grade classroom c, kth cohort and
sth school.
Quantθ (y icks |X icks ) = X icks 0 β θ
(1)
where Quantθ (θicks |X icks ) = 0 and β θ shows a vector of parameters at
θth quantile. Furthermore, y icsk is the national CITO test scores of Dutch
students, and X icsk captures the student, classroom, teacher and parent
level characteristics. Although student interactions and, therefore, the spillover effects is likely to be stronger at the class-level, students or teaching
resources may not have been allocated to classes randomly. X icsk also includes various information on the gender of the student, parental education,
cohort specific fixed effects, single motherhood and fatherhood households.
Furthermore, teacher’s teaching experience and gender are included. The
key variable of interest for the purpose of our paper is the proportion of
immigrant students in grade 8 classroom as the coefficient shows us the
10
spill-over effects of immigrant students.
However, evidence from Ladd and Fiske (2009) and Ladd et al. (2010) indicate that the students are selectively allocated to schools. This, therefore,
implies that we need to alter our conditional quantile regression as shown
below.
Quantθ (y icks |X icks ) = X icks 0 β θ + αs
(2)
αs represents school fixed characteristics. The specification exploits the
variation in the number of immigrant students in the same grade over the
years. The identification assumption requires that once cohort and school
fixed characteristics are controlled for, students in a grade in a particular
year is comparable to those who are in the same grade in another year.
Moreover, the number of immigrant students found in a grade in a particular
year is random (i.e. 2nd graders in 2000 are comparable to those from
2002 and the number of immigrant students in each cohort is randomly
determined due to date of birth).
However, αs is unobserved and is likely to be correlated with other covariates. When estimating a quantile model, we cannot apply the standard demeaning approach, which is frequently employed in the linear setting. This
is because quantiles are not linear operators. The present paper, therefore,
employs the two-step estimator proposed by Canay (2011). This approach
treats αs as a simple location shift and, therefore, it does not depend on the
quantiles. This implies that the school fixed effects affects the test scores of
all students within the same school in the same way regardless of where the
students are located along the test score distribution. In other words, this
assumption allows us to estimate the school fixed effects at the conditional
mean, since the same school fixed effects are assumed to affect all students
regardless of the quantiles.
Therefore, intuitively speaking, Canay (2011) two-step estimator involves the following two steps. Firstly, we need to estimate the school fixed
effects by estimating the test score regression at the conditional mean. Once
the predicted values are obtained, they can be deducted from the observed
test scores so as to eliminate the school fixed effects. The second step in11
cludes estimating a quantile regression with the new test score variable.
More specifically, the Canay (2011) two-step estimator involves the following steps.
• Step 1: Estimate the conditional mean equation for y icsk . Let β̂θ̄ be
√
a n consistent estimator of βθ̄ where βθ̄ is a vector of parameters at
the conditional mean. Then, obtain
n
α̂s ≡
1X
[y icsk − X icsk 0 β̂θ̄ ]
n
(3)
i=1
• Step 2: Define ŷ icsk ≡ y icsk − αˆs . Given ρq (u) = u[q − I(u < 0)],
where I(.) is an indicator function, the estimator β̂θ is
n
β̂θ = argmin
θ∈Θ
4
1X
[ρq (ŷ icsk − X icsk 0 θ)]
n
(4)
i=1
Preliminary results
4.1
Conditional mean estimates
Before we present our quantile estimates, Table 1 shows our baseline
OLS estimates of the effects of the classroom share of immigrants on the
CITO scores as a point of comparison. When interpreting the magnitude of
the parameter estimates one has to keep in mind that the standard deviation
in the CITO scores for boys is 9.86 while for girls this is 9.91.
The first estimates are unconditional estimates indicating the effect of
the share of immigrants on the CITO-scores. For boys there is a significant negative effect of 4.36, which is equivalent to an immigrant share of
10 percent causing a drop in CITO-score of about 0.05 standard deviation,
for girls the effect is also negative equivalent to an immigrant share of 10
percent causing a drop in CITO-score of about 0.07 standard deviation.
The negative effects become somewhat smaller after classroom information
is introduced and reduces substantially if in addition to that parental educational attainment variables are introduced.
12
Once we introduce school fixed effects, the significance of the parameter
estimate for boys disappears. Findings for girls also show the same pattern,
and in addition, the magnitude of the estimated spill-over effect is also
reduced substantially. The fifth estimates shows that adding a lagged effect
of immigrant share does not influence the main parameter estimates.
Table 2 presents the parameter estimates of Table 1a estimates (4) in
more detail, to illustrate the effects of the classroom characteristics and the
parental education variables. Classroom characteristics turn out to be not
very important. On the other hand, not surprisingly, the higher the parental
education the higher the CITO-score of their children. The estimate indicate
that the older the child the lower the CITO-score. This may be due to the
fact that older children are those who typically tool the same grade at some
point during their primary school education due to their weak academic
performance.
4.2
Quantile estimates
The quantile estimates are presented in Table 3. Just as before, the results are separately estimated for boys and girls. Columns (1)-(5) and (6)(10) each show the estimates for boys and girls, respectively. All columns
clearly present an identical pattern, namely the Dutch students who are in
lower quantiles are affected more severely by the presence of immigrant students in the same classroom. Similarly to the results in Table 1, controlling
for classroom characteristics does little to the estimates for both boys and
girls. Taking account of parental education, however, drastically reduces the
size of the negative spillover effects experienced by the male students. Controlling for the school fixed effects further reduces the size of the estimates.
This, however, is not the case for the girls. The estimates are still negative
and significant even after controlling for parental characteristics, albeit the
smaller sized estimates. School fixed effects do seem to contribute to further
reduction of the size of the estimates, but students from the lower quantiles are still significantly negatively affected by studying with immigrant
students.
13
Table 4 illustrates whether the spill-over effects differ depending on the
immigrant parents’ educational attainment. When the highest educational
attainment of the immigrant parents is lower secondary education or less,
these immigrant students are considered to have been born from low educated parents. On the other hand, immigrant parents are considered to have
high education if they have upper secondary or more education. The results
indicate that it is the immigrant children born from less educated parents
that severely reduces the academic achievements of Dutch peers. The large
negative spill-over effects is particularly prevalent at the lower end of the
test score distribution. This holds both for the boys and the girls. On the
other hand, as columns (2) and (5) show, the results become less significant
and the magnitude of the negative effects is also reduced. Results for boys
exhibit no negative spillover effects once the school fixed effects is controlled
for (column (3)). However, the negative effects still persist for girls (column
(6)). For girls, controlling for school fixed effects does little to reduce the
size of the effects.
Finally, Table 5 shows differential spill-over effects by first and second
generation immigrant students. Given that the first-generation immigrants
are less likely to have assimilated to the Dutch population and are less
likely to have acquired the linguistic skills, these students are more likely
to affect the native Dutch students negatively. Our findings confirm this
prediction. The estimated results consistently show that the size of the
negative spill-over effects of first-generation immigrants is larger than that
of second-generation immigrant students. As before, the negative effects
persist for girls even after controlling for various covariates and school fixed
effects. Unlike the results in previous Tables, the negative spill-over effects
of first-generation immigrant students are observed also for boys even after
controlling for school fixed effects.
4.3
Sensitivity analysis
Table 6 presents the main parameter estimates for a wide variety of
indicators of educational attainment. We present both the unconditional
14
estimate of the share of immigrant students on each of the educational attainment scores as well as the estimate after taking into account the effects of
parental education, classroom characteristics and school fixed effects. Overall results suggest that negative spillover effects experienced by boys seem
to disappear once we control for various factors. However, the negative peer
effects are still found for girls even when we control for school fixed effects.
5
Preliminary conclusions
We analyze how the share of immigrant children in the classroom affects
the educational attainment of native Dutch children in the 8th grade. Unlike the existing literature that all study the spill-over effects of immigrant
students at the conditional mean of the test score distribution, our analysis investigate differential impacts at different parts of the distribution by
employing a Quantile regression approach.
In order to address the non-random allocation of students to schools,
we control for school fixed effects and exploit the variation in the share of
immigrant students across cohorts. Our assumption is that once the school
specific characteristics are controlled for, the share of immigrant students in
a particular cohort is determined purely by the date of birth of the students.
In all cases, unconditional estimates generally suggest significantly negative spillover effects of immigrant students. This is true for both boys
and girls. The size of this negative effect becomes somewhat smaller once
parental education is controlled for, although some estimates still persist
to be significantly negative. Controlling for unobserved school fixed effects
reduces the size of the spillover effects even more, and for most of the cases,
the spill-over effects experienced by Dutch boys are no longer statistically
significant. This is not true for the girls. In fact, estimates suggest that girls
persistently experience negative and significant spill-over effects even when
we control for the school fixed effects.
Looking at the results at each of the estimated quantiles show that academically weak students (those with test scores below 50 % quantile) are
more severely affected by studying with immigrant students. Moreover, the
15
spillover effects of first-generation immigrant students is more negative than
those of second-generation immigrants. Finally, studying with immigrants
born from parents with limited amount of education negatively affects Dutch
female students.
16
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longitudinal multilevel examination in dutch elementary schools. European Sociological Review 22, 293–308.
Van Ours, J. C. and J. Veenman (2005). The netherlands: Old emigrantsyoung immigrant country. In K. Zimmermann (Ed.), European migration:
what do we know?, pp. 173–196. Oxford University Press.
18
Appendix – Definition of variables
Dependent variables
1. CITO test: Dutch national test that are taken by the 8th grade students.
2. 8th grade language test scores
3. 8th grade maths test scores
4. 8th grade reading test scores
5. 8th grade IQ test
Explanatory variables
1. Share of immigrant students
2. 1 if the teacher is male
3. Years of teaching experiences of the 8th grade teacher
4. Number of students in the 8th grade
5. Dummy=1 if students are studying with students from other grades.
6. cohort2: Students who are in the 8th grade in year 2002.
7. cohort3: Students who are in the 8th grade in year 2004.
8. Age of students in the 8th grade
9. Parental education
19
Means of variables:
Boys
Mean Min Max
Dependent variables – 8th grad test scores
CITO
535
501
550
Language
1123 1003 1261
Maths
119
63
160
Reading
56
2
100
26
6
34
IQ
Explanatory variables
Share immigrants (%)
21
0
96
Classroom characteristics
Teacher male
0.57
0
1
Teacher experience
19
0
77
Number of students
25
7
38
Combined class
0.24
0
1
Parental education
Father
Lower vocational
0.40
0
1
Intermediate vocational 0.32
0
1
Higher education
0.23
0
1
Mother
Lower vocational
0.36
0
1
0
1
Intermediate vocational 0.40
Higher education
0.19
0
1
Student characteristics
Age student
11
10
20
Cohort 2
0.40
0
1
Cohort 3
0.33
0
1
Students
5614
Girls
Mean Min
Max
535
1124
117
58
26
500
984
58
14
6
550
1261
160
100
34
21
0
96
0.56
19
25
0.24
0
0
7
0
1
77
38
1
0.40
0.32
0.22
0
0
0
1
1
1
0.38
0.38
0.18
0
0
0
1
1
1
11
0.41
0.33
9
0
0
5586
14
1
1
Note: Due to some missing observations the number of observations on the
language, match, reading and IQ tests are somewhat smaller; see also Table
6.
20
21
Education
Parents
x
x
x
x
x
x
Info
Classroom
x
x
x
x
x
x
x
x
x
x
x
x
School
f.e.
389
370
385
363
6488
5840
5586
5586
4550
Number of
Schools
Number of
Observations
6584
5878
5614
5614
4534
Note: see table 2 for an overview of classroom characteristics and parental education variables; absolute t-values in parentheses; the **
(*) indicate significance at a 5% (10%) level.
a. Classroom share immigrant students
Share
Difference shares
th
Boys
8 grade
6th -8th grade
1.
-4.36 (7.6)**
2.
-3.65 (5.8)**
3.
-1.46 (2.4)**
4.
-1.53 (0.8)
5.
-1.53 (0.7)
2.94 (1.5)
Girls
1.
-6.50 (11.4)**
2.
-5.18 (8.2)**
3.
-2.70 (4.4)**
4.
-1.36 (0.8)
5.
-1.07 (0.7)
0.50 (0.3)
Table 1: Parameter estimates effects share of immigrant students on CITO-scores 8th grade
Table 2: Parameter estimates CITO-score
Share immigrants
Classroom characteristics
Teacher male
Teacher experience
Number of students
Combined class
Parental education
Father
Lower vocational
Intermediate vocational
Higher education
Mother
Lower vocational
Intermediate vocational
Higher education
Student characteristics
Age student
Cohort 2
Cohort 3
Students
Classroom
-1.53
Boys
(0.8)
-1.36
Girls
(0.8)
0.42
0.03
0.00
0.98
(1.1)
(1.4)
(0.0)
(1.5)
0.16
0.06
-0.08
0.11
(0.4)
(0.6)
(2.2)
(0.2)
0.34
2.81
4.51
(0.5)
(4.1)**
(6.3)**
2.09
4.42
6.52
(3.3)**
(6.5)**
(9.1)**
1.29
3.91
6.56
(1.9)*
(5.5)**
(8.6)
0.23
3.41
5.48
(0.3)
(4.9)**
(7.3)**
-2.68
0.32
-0.90
(12.5)**
(1.0)
(2.5)**
5614
389
-3.15
-0.68
-1.87
(13.9)**
(2.1)**
(5.1)**
5586
385
Note: The parameter estimates presented here are the same as in Table 1a, estimates 4; constants are included but not reported; absolute t-values in parentheses;
the ** (*) indicate significance at a 5% (10%) level.
22
23
6488
x
5840
-5.66(1.09)***
-5.41(0.98)***
-5.41(0.95)***
-5.18(0.73)***
-3.28(0.71)***
(7)
(6)
-8.44(0.98)***
-8.33(0.87)***
-6.82(0.87)***
-5.97(0.90)***
-3.07(1.00)***
5878
-4.65(1.28)***
-5.33(0.95)***
-3.64(0.92)***
-3.15(0.66)***
-1.64(0.72)**
(2)
6584
-5.00(1.42)***
-6.22(0.90)***
-5.00(0.76)***
-3.86(0.85)***
-1.63(0.70)**
(1)
x
x
5586
-2.40(1.34)*
-2.72(1.20)**
-2.71(0.74)***
-1.95(0.68)***
-2.20(0.68)***
(8)
5614
0.12(1.19)
-1.06(0.94)
-1.74(0.88)**
-1.03(0.86)
-1.05(0.65)
(3)
x
x
x
5586
385
-2.67(1.15)**
-1.99(1.05)*
-1.86(0.82)**
-0.73(0.67)
-1.31(0.69)*
5614
389
(9)
-0.02(1.15)
0.24(0.95)
-0.25(0.72)
-0.18(0.72)
0.88(0.76)
(4)
x
x
x
3.39(3.14)
2.83(2.52)
0.27(1.51)
-1.50(1.96)
-0.55(1.45)
4550
363
-2.12(1.26)*
-1.82(1.13)
-1.96(0.91)**
-0.49(0.74)
-1.65(0.64)***
-0.78(3.14)
-0.01(3.13)
0.91(1.87)
0.98(1.64)
2.73(1.49)*
4534
370
(10)
-1.38(1.13)
-1.30(1.09)
-1.09(0.81)
-1.61(0.76)**
0.48(0.84)
(5)
Note: In this table, spill-over effects of studying with immigrant children are estimated at five of the chosen quantiles. The effects
are estimated separately by gender of Dutch students. see table 2 for an overview of classroom characteristics and parental education
variables; absolute t-values in parentheses; the ** (*) indicate significance at a 5% (10%) level.
Info Classroom
Education Parents
School f.e.
Difference share 6th-8th grade
10th
30th
50th
70th
90th
Number of students
Number of schools
Difference share 6th-8th grade
10th
30th
50th
70th
90th
Number of students
Number of schools
Girls
Share 8thgrade
10th
30th
50th
70th
90th
Share 8thgrade
10th
30th
50th
70th
90th
Boys
Table 3: Quantile estimates: Effects of the share of immigrant students
24
Low
x
Info Classroom
Education Parents
School f.e.
-3.43(2.63)
-3.53(2.20)
-1.72(1.65)
-0.98(1.50)
-1.93(1.36)
High
2.16(2.22)
3.50(1.91)*
0.02(1.45)
0.02(1.45)
-0.27(1.31)
High
Low
Low
x
x
5589
-1.95(1.88)
-2.77(1.78)
-3.49(1.65)***
-2.98(1.30)**
-2.39(1.03)**
(5)
5601
-0.95(1.77)
-4.01(1.39)***
-3.19(1.43)**
-2.42(1.26)*
-1.36(1.05)
(2)
-2.64(2.35)
-0.48(2.07)
-0.84(1.72)
2.60(1.35)*
-0.90(1.52)
High
1.47(2.11)
2.54(1.78)
0.16(1.49)
-0.13(1.69)
1.64(1.24)
High
Low
Low
x
x
x
5589
385
-2.63(1.72)
-3.35(1.70)**
-2.65(1.18)**
-2.69(0.99)***
-1.98(1.03)*
(6)
5601
389
-1.19(1.74)
-1.19(1.37)
-1.13(1.24)
-0.75(1.16)
0.30(1.21)
(3)
Note: In this table, spill-over effects of studying with immigrant children born from low/highly educated parents are estimated at five
of the chosen quantiles. The effects are estimated separately by gender of Dutch students. Parents whose highest qualification is upper
secondary or more are considered highly educated. see table 2 for an overview of classroom characteristics and parental education
variables; absolute t-values in parentheses; the ** (*) indicate significance at a 5% (10%) level.
6491
-10.91(1.46)***
-13.64(1.24)***
-13.06(1.20)***
-10.70(1.21)***
-7.38(1.28)***
Number of students
Number of schools
-2.58(2.85)
-0.61(1.57)
1.86(1.95)
0.26(1.23)
1.86(1.20)
High
(4)
Girls
Share 8thgrade
10th
30th
50th
70th
90th
Low
-10.38(1.72)***
-11.00(1.94)***
-9.54(1.19)***
-7,32(1.12)***
-3,75(1.53)**
6569
6.23(2.60)***
-0.00(2.24)
0.00(1.45)
0.56(1.67)
0.75(1.05)
High
(1)
Number of students
Number of schools
Share 8thgrade
10th
30th
50th
70th
90th
Boys
Table 4: Quantile estimates: Effects of the share of immigrant students by parental education
25
Second gen
x
x
Second gen
-1.35(1.43)
-1.81(1.26)
-2.34(0.90)***
-1.37(0.76)*
-2.15(0.86)**
5589
-8.93(4.72)*
-8.32(3.04)***
-3.90(2.16)*
-4.54(1.70)***
-2.38(1.84)
First gen
(5)
Second gen
1.22(1.35)
-0.59(1.10)
-1.02(0.94)**
–0.68(0.87)*
-0.66(0.76)
5601
-1.12(2.99)
-3.87(1.94)**
-6.25(2.15)***
-3.72(2.24)*
-3.17(1.81)*
First gen
(2)
x
x
x
5589
385
-11.92(3.56)***
-9,56(2.75)***
-5.55(1.99)***
-2.80(1.74)*
-1.34(2.43)
First gen
(6)
5601
389
-7.26(3.89)*
-3.61(2.18)*
-5.73(2.13)***
-2.82(2.04)
-4.42(1.95)**
First gen
(3)
-0.06(1.44)
-0.76(1.02)
-0.72(0.95)
0.14(0.76)
-1.31(0.79)*
Second gen
1.03(1.18)
0.76(1.04)
0.95(0.88)
0.37(0.86)
1.75(0.92)*
Second gen
Note: In this table, spill-over effects of studying with first/second generation immigrant children are estimated at five of the chosen
quantiles. The effects are estimated separately by gender of Dutch students. see table 2 for an overview of classroom characteristics and
parental education variables; absolute t-values in parentheses; the ** (*) indicate significance at a 5% (10%) level.
x
Info Classroom
Education Parents
School f.e.
-7.15(1.18)***
-7.44(1.07)***
-6.78(1.17)***
-5.62(0.95)****
-2.09(1.15)*
6491
-15.81(3.93)***
-13.49(2.96)***
-6.89(2.86)**
-6.52(1.72)***
-7.06(2.58)***
Number of students
Number of schools
10th
30th
50th
70th
90th
First gen
(4)
Girls
Share 8thgrade
Second gen
-4.62(1.63)***
-5.97(1.10)***
-3.87(0.95)***
-3.67(0.85)***
-1.66(0.71)**
6569
-8.67(3.95)**
-9.51(2.46)***
-11.50(2.24)***
-5.33(2.43)**
-2.37(2.02)
First gen
(1)
Number of students
Number of schools
10th
30th
50th
70th
90th
Share 8thgrade
Boys
Table 5: Quantile estimates: Effects of the share of first/second generation immigrant students
26
CITO test
Language test
Maths test
Reading test
IQ test
Boys
Conditional
Number
estimate
students
-1.53 (0.8)
5614
-4.65 (2.3)**
7643
-0.95 (1.4)
7494
-0.73 (0.7)
7609
-0.42 (1.3)
7650
Number
schools
389
470
465
468
468
1 s.d.
score
9.8
35.1
9.2
16.3
4.4
Unconditional
estimate
-6.50 (11.4)**
-10.86 (6.2)**
-4.41 (9.4)**
-7.90 (9.6)**
-1.69 (7.9)**
Girls
Conditional
Number
estimate
students
-1.36 (0.8)
5586
-1.74 (1.7)*
7609
-1.79 (2.5)**
7367
-3.09 (3.2)**
7551
-0.72 (2.5)**
7577
Number
schools
385
464
463
465
462
Note: see table 2 for an overview of classroom characteristics and parental education variables; absolute t-values in parentheses; the **
(*) indicate ignificance at a 5% (10%) level.
1.
2.
3.
4.
5.
Unconditional
estimate
-4.36 (7.6)**
-9.58 (5.4)**
-3.24 (6.9)**
-5.41 (6.6)**
-1.05 (4.7)**
Table 6: Parameter estimates: The effects of the share of immigrant students on other educational attainment variables 8th
grade
1 s.d.
score
9.9
34.6
9.1
16.3
4.2
Figure 1: Distribution shares of immigrant students within the 8th grade
a. First-generation immigrants
b. Second-generation immigrants
c. Total immigrant students
27
Figure 2: Shares of first-generation and second-generation immigrants
within the 8th grade
28
Figure 3: Shares of immigrant students and average CITO test scores in the
8th grade
a. All immigrant students
b. First-generation immigrants
c. Second-generation immigrants
29
Figure 4: Shares of immigrant students in the 6th and 8th grade
Figure 5: Average CITO-score by share of immigrants in class
-> immi8 = 0
-> immi8 = 0
Variable
Obs
Mean
Std. Dev.
Min
Variable
Obs
Mean
Std. Dev.
Min
citoeind_8
1553535.4733
9.383
citoeind_8
1553535.4733
9.383
538
Max
Max
503
503
550
550
immi8
536
-> immi8 = 1
-> immi8 = 1
Variable
Obs
Mean
Std. Dev.
Min
Variable
Obs
Mean
Std. Dev.
Min
citoeind_8
812 535.9815
9.577043
citoeind_8
812 535.9815
9.577043
Average Cito score
534
Max
Max
501
501
550
550
532
-> immi8 = 2
-> immi8 = 2
Variable
Obs
Mean
Std. Dev.
Min
Variable
Obs
Mean
Std. Dev.
Min
citoeind_8
1433535.0391
9.895114
citoeind_8
1433535.0391
9.895114
500
500
-> immi8 = 3
-> immi8 = 3
Variable
Obs
Mean
Std. Dev.
Min
Variable
Obs
Mean
Std. Dev.
Min
citoeind_8
1258535.6677
9.477483
citoeind_8
1258535.6677
9.477483
506
506
-> immi8 = 4
-> immi8 = 4
Variable
Obs
Mean
Std. Dev.
Min
Variable
Obs
Mean
Std. Dev.
Min
citoeind_8
736 535.0041
9.248128
citoeind_8
736 535.0041
9.248128
507
507
Max
Max
530
528
526
524
520
550
550
0
0.025
0.075
0.125
0.175
0.225
0.275
0.325
0.375
0.425
0.475
0.525
0.575
0.625
0.675
0.725
0.775
0.825
0.875
0.925
0.975
535.4733
535.9815
535.0391
535.6677
535.0041
535.5375
532.5248
531.9546
531.6205
532.188
531.368
531.8715
530.5486
530.103
528.7161
528.1617
529.9572
528.2007
528.3274
526.3055
528.9555
Max
Max
550
550
0.025 0.125 0.225 0.325 0.425 0.525 0.625 0.725 0.825 0.925
0 Std.0.075
0.175Max 0.275 0.375 0.475 0.575 0.675 0.775 0.875 0.975
Dev.
Min
-> immi8 = 5
-> immi8 = 5
Variable
Obs
Mean
Variable
Obs
Mean
Std. Dev.
Min
citoeind_8
774 535.5375
9.688981
citoeind_8
774 535.5375
9.688981
-> immi8 = 6
-> immi8 = 6
Variable
Obs
Mean
550
550
Max
Max
522
Girls
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Std. Dev.
Min
Max
506
506
550
550
Max
Share of immigrants
Girls
Boys
30
Figure 6: Comparisons of estimated cumulative distributions by the proportion of immigrant students in the 8th grade classroom
a. Boys
b. Girls
Note: The yellow line shows the cumulative CITO test score distribution of students
studying with no immigrant students. The red line indicates the distribution for
students studying in a class in which the share of immigrant students is 20 % or
more.
31
Figure 7: Comparisons of Kernel Density estimates by the proportion of
immigrant students in the 8th grade classroom
a. Boys
b. Girls
Note: The thin line shows the Kernel Density estimates of CITO test scores from
students studying with no immigrant students. The thick line indicates the test
32 in which the share of immigrant students
score density of students studying in a class
is 20 % or more.