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They will get there! Studies on educational performance of immigrant youth in theNetherlandsvan Welie, E.A.A.M.
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Download date: 04 May 2018
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4 Patterns in secondary school selection in
the context of unlimited choice
I should also add further that this liberty (i.e. the liberty of
judgment) is absolutely essential to the advancement of the arts
and sciences; for they can be cultivated only by those with a free
and unfettered judgment.
Baruch de Spinoza, 1670
4.1 Introduction
Considering the stubborn problem of unequal access to higher levels of
education for different socio‐economic groups, offering broader options for school
choice is a policy measure that generally raises high expectations. Interestingly, in the
Netherlands, free school choice has been practically unlimited for almost a century, by
the constitutional law of 1917. This law also gives parents the right to found a new
school according to, for example, a specific religious or pedagogical concept, on the
condition that a sufficient number of pupils will attend the new school, and no
comparable school is available within reasonable distance. In practice, because of the
density of schools in the Netherlands, the constitutional right to found a new school is
nowadays rarely exercised. Free school choice is, moreover, guaranteed because all
This chapter is based on: van Welie, E.A.A.M., Hartog, J. and Cornelisz, I. (2013). Forthcoming.
68
schools are funded equally by the state, both schools with a board based on private law
or those with a board based on public law. Additionally, in elementary and secondary
schools no tuition fee is required, except for a contribution for certain activities outside
the curriculum, like school festivities. This contribution is, however, not obligatory; when
parents cannot afford to pay, the child is still entitled to participate. Finally, schools are
independent of local taxes (since all funding comes from the Ministry of Education), and
all children in elementary education growing up in low SES families are additionally
funded through a voucher system, the money following the pupil. Secondary schools
receive extra funding when the student population crosses a threshold percentage of
pupils who live in low SES neighbourhoods (the threshold percentage is lower for
vocational tracks and higher for academic tracks). This results in schools with relatively
more low SES pupils actually receiving more funding than schools with more children
from affluent parents. In summary, the long history of free school choice, equal funding
by the state, no tuition fees, and extra funding for low SES pupils, offers interesting
opportunities to study possible differences in school choice between distinct groups in
society.
In the current chapter we compare patterns of school choice among native Dutch
youth and youth with a migrant family history. In most cases, these migrant pupils'
grandfathers came to the Netherlands as unskilled workers in the 1969s and 1970s. The
average level of education of the children and grandchildren of these labour immigrants
has shown a steady increase over the past decades. However, considering the
importance of attainment levels that fit pupils' capacities– both for the individual and
for society at large– we still need a deeper understanding of the actual choices migrant
pupils make, in order to develop policies that may accelerate the process of finding
equal access to high levels of schooling. According to the OECD17 (2011), a knowledge‐
based economy may lose part of the population's potential to expand scientific and
cultural knowledge, and may receive lower tax revenues over the lifetime of individuals,
when substantial numbers of citizens remain under‐schooled.
In this chapter we focus on secondary schools, and diversify between secondary
vocational tracks (VMBO) on the one hand, and two secondary academic levels (HAVO
17 OECD: Organization for Economic Cooperation and Development.
4 Patterns in secondary school selection in the context of unlimited choice
69
and VWO), on the other18. In the Dutch educational system VMBO is the lowest level,
and VWO the highest level. The achievement gap in Dutch secondary education is
typically described as the lower enrolment levels in academic tracks among youth with a
migrant background, as compared to their native Dutch peers. Both academic tracks
offer access to higher education; in the text we refer to these two levels combined as
the academic level; in some tables we diversify between HAVO and VWO.
In Europe and the U.S. alike, the debate on raising educational opportunities for
underrepresented groups in the higher strata of education seems to concentrate on
extending school choice, assuming that this will enhance the desegregation of schools
along the lines of race, ethnicity, or social status; the underlying assumption for
desegregation policies in general, is that low SES children may benefit from peers
belonging to other social strata.
Interestingly, geographical distance measurements offer opportunities to
disentangle the effects of school choice, school composition, neighbourhood
composition, school density, and urbanicity19. Using the right to select a school freely,
which may result in choosing another school than the one closest to the pupils'
residence, unavoidably involves the extra effort of a longer distance to travel to school.
In the present chapter, we use measurements of the distance from home to the chosen
secondary school, relative to the nearest available school, controlling for indicators at
the individual, neighbourhood and school level, in order to be able to analyse whether
and how patterns of school choice differ between ethnic groups.
Analysing the effect of secondary school choice is complicated by the fact that
measuring school success– as the combined effect of individual, neighbourhood, school
quality, and educational system variables– requires data on prior achievement in
elementary education to start with. Interestingly, in 2001, the Dutch Ministry of
18 Tracks in the Dutch system for secondary education, plus the ISCED translation (International Standard Classification of Education by UNESCO, update1997):
VMBO: pre‐vocational secondary education, 4 years, ISCED 2, qualifying for senior secondary vocational education.
HAVO: senior general education, 5 years, grade 1‐3 ISCED 2, grade 4‐5 ISCED 3, qualifying for higher education (professional universities).
VWO: pre‐university education, 6 years, grade 1‐3 ISCED 2, grade 4‐6 ISCED 3, qualifying for higher education (research universities).
19 Urbanicity: a measure for the intensity of human activity in a given area, based on the number of addresses per km² (definition Statistics Netherlands).
70
Education, Culture and Science started to create a longitudinal educational data set
based on individual social security numbers, covering all students and their complete
educational history. In 2008, all pupils in the final year of elementary education were
recorded for the first time in this new database (students in other strata in the
educational system had preceded them). In the coming years, when the total school
history of all individuals will be recorded, performance in elementary education can
actually be linked to the exit‐exam outcomes of secondary education at the individual
level20. At the time of our research, the cohort that was enrolled in the final year of
elementary education in 2008 has reached the Year 3 in secondary school. Therefore,
we could relate prior achievement in elementary education to achievements at the
entrance of the third secondary year.
Among policy makers and researchers alike, positive effects of equal
opportunities in education are expected from: 1) free school choice; 2) equal
(government) funding of schools; 3) independence of local taxes; and 4) extra funding
for low SES pupils. These four conditions have all been met system‐wide in Dutch
schools. Although the outcomes of the Dutch system of education rank among the top‐
ten in international comparisons (PISA, 2009), the pattern of unequal access to higher
levels of schooling among low SES youth per se, resembles patterns in countries without
the four above mentioned conditions.
While we use distance to the preferred non‐nearest school as a measure for the
selectivity of choice, we acknowledge that choosing the proximity allocation may also be
based on selective choice; however, in this case we do not know whether choice was
based on qualitative considerations, or whether parents and pupils simply chose the
nearest school because it was close to their home. Both high SES and low SES pupils,
notably youth with a migrant background, may have reasons to choose the school
closest to the residence‐ or prefer another school, albeit these reasons may be different
between both groups. Obviously, choosing the nearest school takes the least effort in
terms of travelling time and costs. Since there is no school bus system in the
20 We are indebted to Cees Vermeulen at DUO (the Dutch government organisation that collects all educational data), who provided us with an extended data set of great quality. We acknowledge his unique professionalism in designing and loading this new database. Moreover, we valued his advice in finding our way in the database. We are also deeply grateful for the support of Erik Smits and Rob Kerstens (Director‐ General of DUO).
4 Patterns in secondary school selection in the context of unlimited choice
71
Netherlands, avoiding the dangers of traffic may also be a consideration for parents.
There may be other reasons though: native Dutch pupils living in affluent areas may
prefer a school close to home, because this school presumably mirrors the high SES
composition of the residential area. Migrant students, on the other hand, may choose
the nearest school because this school may be more specialized in migrant education,
for example with an extra emphasis on language proficiency. Migrant students may also
have reasons to choose a school where they form the majority. In summary, preferring
the nearest school may, or may not be, a deliberate choice; however, on the basis of our
available data we cannot diversify between different motives behind the choice for the
nearest school. We assume, however, that making the extra effort to travel further to
school is the result of active choice.
We explore the following research questions:
1. Does distance to school, as a measure of selectivity of school choice, differ
between ethnic groups, and does the group of pupils who choose the nearest
school differ from those who opt for a school further away?
2. How is school choice associated with ethnic segregation?
3. Do pupils benefit from selective school choice?
The outline of the chapter is as follows: previous research is reviewed in Section
4.2; the data sources we used are described in Section 4.3; the results are presented in
Section 4.4; conclusions, discussion and possible policy implications for policies are
presented in Section 4.5. The results show that, of all pupils in secondary schools living
in the four largest cities, 88.7 % are not enrolled in the school nearest to their residence.
Pupils of Dutch origin, living in poorer neighbourhoods, have a stronger tendency to
choose a school at a further distance than migrant students in such neighbourhoods.
Differences between the group of pupils who choose the nearest school and those who
choose the non‐nearest school are marginal. Prior achievement in elementary
education, secondary school average exam scores, and school‐level upward mobility are
only marginally associated with distance travelled to school. The school's percentage of
students with a non‐Western immigrant background and the school population's
average SES, however, do significantly drive school choice. Choice patterns seem to
reveal ethnic segregation by choice among migrant pupils.
72
4.2 Previous research
Our data allow for measurements of socio‐economic pupil, neighbourhood, and
school characteristics; for qualitative characteristics on the individual and school level;
and for distance measurements. In the literature reviewed in this section, we have made
an attempt to address this wide array of factors related to the patterns and geographic
distribution of school choice. Segregation, neighbourhood schools, differential effects
related to distinct pupil characteristics, postponed tracking, distance from home to
school and extended school choice are the key issues we explored in academic
literature: 1) We studied: 1) research that points out the benefits of mixed desegregated
schools, identifying peer effects and parental support by better‐educated parents as
main mechanisms behind higher expected outcomes; but 2) in contrast, research that
finds positive effects of segregated neighbourhood schools in the case of migrant pupils,
that indicates the importance of close cooperation between parents and school; 3) yet
other research findings that demonstrate differential effects for different groups. For
example, students in vocational trajectories who might benefit from neighbourhood
schools, and, on the other hand, low SES, talented students, who might have better
opportunities in a school with a higher average SES; 4) scientific research that considers
the effect of postponement of the age of tracking in secondary schools, as well as the
availability of comprehensive schools that offer all tracks (thus enhancing chances for
upward mobility); 5) research that considers the distance to school specifically and
different patterns of school choice between ethnic groups. And, finally, 6) the expected
effects of extending school choice.
School composition
The expected mechanism behind desegregation policies (typically involving
greater distances to school) is often based on: 1) the assumed positive peer effects
between pupils from various backgrounds; 2) assumed positive effects of more support
in school activities by higher SES parents; and 3) a probable teacher selection effect,
because high quality teachers might prefer high SES schools. Desegregation in this
context involves the reduction of ethnic segregation between schools. If residential
areas are ethnically segregated, this implies aiming for a school composition that
deviates from the ethnic composition of the neighbourhood where the school is located.
4 Patterns in secondary school selection in the context of unlimited choice
73
Research outcomes on the effects of school composition appear, however, to be
ambiguous. While, according to some authors, the overall effect may be limited, other
authors warn that assumedly underestimated compositional effects or differential
effects should be treated with caution (e.g. Palardy, 2008, Vigdor and Ludwig, 2010).
Thrupp, Lauder and Robinson (2003), in their comparison of school compositional and
peer effects in the United States, the United Kingdom, Belgium and New Zealand, argue
that the little consensus over these effects might be due to inadequate theorizing and
research design. In order to be able to attribute differences in school quality and
outcomes to school or pupil characteristics, it is a prerequisite, so they argue, to be able
to distinguish between the effects of school leadership and instructional quality on the
one hand, and pupil composition and peer effects on the other hand. The authors
emphasize that peer and school effects can, in fact, only be measured adequately, when
prior achievement at the individual pupil level is known. This means in the Dutch context
that the pupils' CITO21 score at the end of elementary education, plus the elementary
teacher's advice for the appropriate track level in secondary education, should be linked
to later actual achievements in secondary education, in order to assess the secondary
school's added value.
Ho Sui‐Chu and Willms (1996) find that children perform better in schools with a
high mean SES of parents, and illustrate that this positive effect is indeed mediated by
parental involvement. The educational systems in the United States and in the
Netherlands differ, however, in important characteristics related to these research
outcomes: although in the United States many schools with low SES pupils receive extra
funding schools also depend on local tax revenues; poor neighbourhoods may more
often have poor schools with a high turnover of teachers, which further burdens poor
children. In the Netherlands all children from low SES parents receive extra funding, and
all schools receive equal standard funding from the government. Schools in the
Netherlands, therefore, hardly depend on financial support by parents. However,
parental assistance in school processes and projects may vary also in the Netherlands,
depending on average levels of schooling of parents.
21 The final test of elementary education; scores range between 500‐ and 550; the national average is 535.
74
Opdenakker and van Damme (2001) suggest that composition effects may be
larger than many researchers assume, but the mechanism behind these effects might
not so much be a peer effect, but also the result from the influence of school
composition on school processes, notably an orderly learning environment and
structural and effective cooperation between teachers. Therefore, the authors suggest
that school effectiveness measurements might be biased in favour of high SES schools,
where high standard outcomes might wrongly be attributed to high standard school
processes, while in fact these are the emergent effects of school composition.
Interestingly, Opdenakker and van Damme (ibid.) find that low SES, high ability students
might be twice as sensitive to school composition as low SES, low‐ability students.
Neighbourhood schools
Ryan Wells (2010) raises the issue that policies aimed at the enhancement of the
levels of schooling of low SES youth, may in fact work out differently for migrant pupils,
than for low SES native‐born pupils. Ryan Wells (ibid.), in accordance with often‐
reported research findings in the Unites States, reports that migrant youths on average
express higher expectations of their future attainment levels than native‐born pupils.
Whether this is also the case in the Dutch context, we do not know. Interestingly, Wells
(ibid.) also reports, seemingly in contrast with these higher expectations among migrant
youth, that immigrant status is associated with (self‐reported) lower expectations in
high SES schools‐ all else being equal. He assumes that the rationale behind these
findings is that migrant pupils may rather benefit from the presence of successful
migrant students as a reference and role model, in schools with high percentages of
migrant students, and may feel isolated in high SES schools.
Especially in the tracked and fairly complex system for secondary education in
the Netherlands, access to information is an important prerequisite for parents in order
to be able to choose the best school for their children. Parents need to be
knowledgeable specifically about the consequences of the choice for a particular
secondary track, considering their children's future options for tertiary education.
According to Cabrera and La Nasa (2001): "SES gaps are reduced, if not eliminated, once
a number of school‐based and family‐oriented factors are taken into account… [these]
practices are as important, if not more, than is family's SES in becoming college
4 Patterns in secondary school selection in the context of unlimited choice
75
qualified…" Following this line of reasoning, primary schools in migrant neighbourhoods
may play an important role in informing parents about the options when choosing a
secondary school. In practice, schools with large numbers of migrant pupils may put
family‐based policies that involve cooperation with parents much higher on the agenda
than schools with more affluent, well‐educated parents.
Finally, Bauder (2002) severely criticizes policies to send migrant pupils to schools
outside their residential area, for the sole purpose of desegregating schools: this may
wrongly stigmatize students attending neighbourhood schools in the view of future
employers, because by implication the message appears to be that neighbourhood
schools in migrant communities are of insufficient quality.
Mixed and differential effects
Cullen, Jacob and Levitt (2000) investigated the effect of expanded parental
choice within the Chicago Public School System. This educational reform resulted in
about half of all the students opting out to attend another school. The authors describe
how this intervention, in the first place, dramatically increased sorting. Disproportionally
more motivated students opted out, and this indeed raised the odds of graduating in
their school of choice. They found no evidence of a negative effect on students who
remained in their assigned neighbourhood school. The overall effect on the
desegregation of schools, however, turned out to be limited. In sum, the authors
demonstrate that expanded choice affects students and their parents differently,
resulting in more motivated students to opt for a new school. With the exception of
what are called the "Career Academies"22, the increased chance to graduate for those
who opt for another school may be correlated with motivation, rather than with the
quality of the chosen school. Finally, in the words of the authors, the "greatest puzzle" is
why so many students opt for another school, while the academic benefits turn out to
be limited. This is an interesting question in the Dutch context as well, where parental
choice is free, but especially migrant students tend to choose a school on average closer
to their residence address than native‐born students.
22 Career Academies promote a college‐preparatory curriculum and career‐focused education in different fields. Students get the opportunity to visit local business, and shadow business professionals in various career areas.
76
Rumberger and Palardy (2005) raise the question of (reversed) causality when
finding that schools with mostly lower‐income students tend to be organized differently
from schools with affluent students: Should school reform introduce effective school
characteristics in schools with many low‐income students? Or are effective school
characteristics the emergent result of having affluent students? In the first option,
school characteristics and processes may make the difference; in the second option, the
composition of students affects outcomes. Furthermore, Rumberger and Palardy (ibid.)
explore whether school policies and organizational characteristics may have a
differential effect on black and white students. Their study demonstrates that teacher
expectations (albeit raising teacher expectations structurally may be a complicated
endeavour) and an academic school climate foster the success of low SES students; if
schools with many low SES students were to focus on these quality aspects, the authors
do not expect that desegregation would offer any extra educational advantages.
Finally, Konstantopoulos and Borman (2011) gained new interesting insights from
their replicated analyses of Coleman's famous data (1966), with current, more advanced,
statistical instruments. Coleman (ibid.) found that the pupils' background was a stronger
predictor for student achievement than school quality. While these findings were
reaffirmed by Konstantopoulos and Borman (ibid.), because of the current availability of
multi‐level models for statistical analyses, they could also demonstrate, however,
significant between‐school variance: "Our results also indicated that schools play
meaningful roles in distributing equality or inequality of educational outcomes to
females, minorities, and the disadvantaged." Similar to Coleman, they found that within‐
school variance is in fact larger than in‐between school variance; but notwithstanding
this, they could also demonstrate that "40 % of the total variability in achievement was
attributable to differences among schools, and ...that schools have nontrivial effects [ ...]
on the achievement gap."
Opportunities for upward mobility
Migrant students may need more time to discover their ambitions and capacities,
for example, because they may lack role models in their own family. Especially when
Dutch is not the language spoken at home, they may also need some extra time to
acquire the level of Dutch language proficiency that is needed for academic tracks in
4 Patterns in secondary school selection in the context of unlimited choice
77
secondary education. This would imply that broad secondary schools that offer all tracks
(vocational and academic), and which are specialized in assessing the capacities of
migrant students repeatedly during their school career, would be an advantage for
migrant students. Recent studies seem to support this hypothesis.
Pekkarinen, Uusitalo and Kerr (2009) made use of the unique opportunity of a
system‐wide school reform in Finland to investigate the effect of postponed tracking on
the correlation between the labour market position of parents and the level of schooling
of their children. Between 1972 and 1977, the former Finnish two‐tracked system was
replaced by a comprehensive secondary school system that shifted the age of choosing
between a professional or an academic track, from age 11 to 16. This later selection
resulted in a substantial decrease of 23 % in intergenerational income elasticity.
Comparable research outcomes (Hanushek and Woessman, 2006, Brunello and
Checchi, 2007, Bauer and Riphahn, 2006) demonstrate that early selection and tracking
negatively affects educational outcomes of low SES students. Considering the
intergenerational elasticity of high‐ and middle‐income students in the Swiss educational
system, Bauer and Riphahn (2006) find that "early tracking increases the absolute
benefit of having highly vs. mid‐way educated parents and magnifies the relative
advantage of highly educated parents." Finally, also Bjorklund and Salvanes (2010) also
find that postponement of tracking may potentially reduce intergenerational
correlations with parental schooling.
Interestingly, Van Elk et al. (2011) make use of the opportunity that in the
Netherlands early tracking at age 12, and postponed tracking at age 13/14, exist in
parallel. As described above, schools, furthermore, may offer all secondary tracks or may
be specialized in either vocational or academic tracks. Considering schools that offer all
tracks (lower vocational and academic), and that start with one or two comprehensive
years, they find that pupils starting in the lower secondary vocational track, have a 26 %
chance to complete higher education later on. Note that without upward mobility to an
academic track, the secondary lower professional track does not directly qualify pupils
for access to higher education. In schools that only offer the secondary professional
tracks (and no academic tracks), however, this chance is 21 %. The findings of van Elk et
al. (ibid.) suggest that upward mobility is more feasible, when all tracks are offered
within the same school. A 5 percentage point increase in the chance to complete higher
78
education, after attending broad secondary schools in the case of students starting on
the pre‐vocational level, seems of extra importance for low SES migrant students, who
may need some more time to discover their ambitions and capacities.
The distance between home and school
Interestingly, Nihad Bunar (2010) explored the question why the children of
immigrants in Sweden would prefer the (segregated) neighbourhood school even when
they know the school is low‐performing and located in a high‐poverty area, while– as is
the case in the Netherlands– school choice is free. Bunar (ibid.) interviewed migrant
pupils in two urban schools in Malmö and Stockholm, and found that neither school
quality, nor a lack of information on school quality, nor the costs of travelling to a school
at greater distance, can fully explain the choice to stay in an urban school in the
proximity of the residential area. Bunar (ibid.) writes: "The answer is to be found in the
process of negotiation taking place within the realms of families and peer groups
oscillating around the importance of relationships that provide safety, the feeling of
belonging and cultural recognition, on the one hand. On the other hand, there are
detrimental effects of categorization and stigmatization attached to immigrants,
neighbourhoods and schools."
Harris, Johnston and Burgess (2007) report in their geodemographic analysis of
ethnicity and school choice in Birmingham (England) that the likelihood of attending the
nearest state‐funded secondary school indeed varies with the ethnic composition of the
neighbourhood. They find that white pupils are always more likely not to attend the
nearest school, and that this likelihood is further increased by greater exposure to other
ethnic groups in the residential area. Harris et al. (ibid.) find evidence that pupils may
prefer to attend a school that is more representative for a pupils' own ethnic group; this
may result in a stronger segregation at the school level, as compared to the
neighbourhood level. Allen (2007) reports similar findings that demonstrate that
choosing another school than the nearest school tends to increase social and ability
sorting. Allen (ibid.) developed a new model, based on the availability of the pupils' zip‐
codes, to compare actual school choice data to the proximity counterfactual. Allen (ibid.)
provides us with a telling illustration of how choosing another school than the nearest
one implies a larger average distance to school as a consequence: "The proximity
4 Patterns in secondary school selection in the context of unlimited choice
79
allocation indicates that the typical journey currently made by a pupil is 60% longer than
the minimum necessary. In fact, over 5 million kilometres of additional travel are made
by 11 ‐ 16 year‐olds every school day…"
Finally, Andersson, Malmberg and Östh (2012) describe how the liberalization of
school choice in Sweden (20 years ago) indeed increased the average distance travelled
to school, but that foreign‐born students travel shorter distances. They find,
furthermore, that Swedish‐born students choose more distant schools, and tend to do
so more often when residential areas have larger proportions of foreign‐born students,
or larger proportions of socio‐economically disadvantaged families.
Extended school choice
Overall, the better match of pupils and schools is the rationale behind, the
expected educational gains of extended school choice (Gibbons, Machin, Silva, 2008).
Secondly, extended school choice is often thought of as an important strategy to reduce
segregation along lines of SES, ethnicity or race (e.g. Cullen et al., 2000). Ladd et al.
(2009) find, however, that schools are to a large extent segregated in the larger Dutch
cities, notwithstanding the fact that school choice has been free and universal for almost
a century. They suggest that free choice may lead to Dutch parents to avoid multi‐
cultural schools, thus increasing segregation.
In summary, the literature reviewed above predicts that school choice may be
related to ethnicity and segregation, to school‐ and neighbourhood socio‐economic
composition and to school quality; furthermore, those effects may be different
depending on pupil characteristics. This has inspired us to choose distance
measurements as the core of our research: first, distance measurements allow for a
comparison of school choice patterns between native Dutch pupils and youth with a
migrant background, relative to the socio‐economic characteristics of their residential
area. Second, measuring distance to the more distant schools as a proxy for the
selectivity of choice, allows for analyses of school characteristics that may drive school
choice, like average exam scores and the percentage of upward mobility to a higher
track, but also school ethnic composition and school average SES. Third, distance
measurements may shed a light on the possible benefits of deliberately choosing a
school at further distance.
80
4.3 Data and Methods
4.3.1 BRON data and additional data sources
Starting in 2001 (based on a new law), DUO23, the government agency that
collects all educational data linked to the individual social security number, is in the
process of constructing a new educational database called BRON24. This database covers
all relevant socio‐economic background characteristics of pupils, including their
complete educational history. From 2008 on, all pupils in elementary education will be
recorded in the BRON data, starting with pupils in the final grade of elementary school in
that year; at the time of our research this first cohort had reached the third grade of
secondary education. In the Dutch tracked secondary system, many schools are (partly)
comprehensive for the first two years. In the third year, however, the majority of pupils
have been placed in a specific track. This offered us the opportunity to relate the track
level in the third year at secondary school, to prior achievement in elementary
education. We have added a list with the definitions of all variables we used in this
chapter in Section 4.6.1.
4.3.2 Ethnic diverse populations in the four major cities
In the current chapter we focus on a comparison of school choice patterns of
pupils with a Dutch background, and those with a migrant family history. Since the
majority of migrant students live in the four major cities (Amsterdam, Rotterdam,
Utrecht, and The Hague), we based our analyses on youths living in these cities. Another
consideration for our choice to concentrate on these four cities was our assumption that
in smaller cities, and especially in the countryside, school choices may structurally differ
from those taken in urban environments. For example, considering the much lower
density of schools in the countryside, school choice could be driven to a large extent by
the sheer presence of a school.
Our total data set contains in total 170.465 individuals living in the Netherlands,
who were enrolled in the last grade of elementary school in 2008. The data cover
information from 2008 up to and including 2011. From this total data set we selected
pupils living in the four major Dutch cities throughout the years 2008‐2011, 17.192
23 Dutch acronym: Dienst Uitvoering Onderwijs. 24 Dutch acronym: Basis Register Onderwijs; official educational database.
4 Patterns in secondary school selection in the context of unlimited choice
81
individuals. Next we dropped 997 individuals with a distance from home to school ≥ 10
km (elementary school) or ≥ 20 km (secondary school). These eliminations resulted in
16.195 individuals. We dropped, furthermore, 124 individuals because of incomplete
distance data.
This resulted in N = 16.071 individuals we kept for analyses. This includes 2624
pupils living in one of the four cities, who were enrolled in a school outside the city (but
within 20 km). The four major cities differ in size, urbanicity and unobserved
characteristics. Therefore, we added city dummies in our estimates to account for such
aggregate effects. When we introduced the characteristics of the nearest school as an
explaining variable, this reduced the number of individuals to 11.023, since we do not
have relative exam scores for the nearest school in the case of all individuals.
In our analysis of the difference between pupils who choose the nearest school
to their residence, and those who do not, our counts demonstrate that 1824 pupils
chose the nearest school, while 14.247 chose a school at further distance (total 16.071).
For measuring individual upward mobility to a higher track in secondary school,
we selected all pupils who were enrolled in Year 1 of the lower vocational track (VMBO),
and enrolled in Year 3 at the time of our research: 4343 pupils.
Unless clearly indicated otherwise, we distinguish five ethnic groups: 1) native
Dutch pupils; pupils with a 2) Surinamese/Antillean‐, 3) Turkish‐, or 4) Moroccan
background; and 5) the combined group of all other non‐Dutch backgrounds, referred to
in the tables and figures as "other background". The last group is very diverse and
includes many nationalities and a wide variance in educational attainment and in
motives for immigration (e.g. the children of high skilled labour immigrants, but also the
children of low educated refugees). The data set includes 6368 pupils of Dutch origin,
2450 of Moroccan descent, 1995 of Turkish descent, and 2269 came from Suriname or
the Dutch Antilles. 2928 individuals had another type of immigrant background; for 43
individuals we did not know their ethnicity. The vast majority of pupils with a Turkish or
Moroccan background were born in the Netherlands; they belong to the second
generation immigrants. For the sake of sufficient statistical power, in some tables a
82
distinction was made between the combined group of all immigrants with a non‐
Western migrant background25 and others.
4.3.3 Distance measurements
For our distance measurements we used the BRON information on the pupils'
residence (4‐digit postal code) and the school address (6‐digit postal code). A student's
geographic coordinates refer to the centroid of the neighbourhood of residence;
neighbourhoods are defined by their corresponding 4‐digit postcode. In compliance with
privacy laws, the data do not allow the student to be located at the 6‐digit postal code
and the individual household level. The average number of individuals per 4 digit
postcode area is 100.7 (SD 52.3)26. Correspondingly, in our data set clusters of around
100 students on average, residing in the same 4‐digit zip‐code area, share the same
residential location. For each school, geographic coordinates are available at the exact 6‐
digit zip‐code school location level.
Using these geographic coordinates for students and schools, Euclidian distances
were calculated in order to derive the distance‐to‐school measures. For each student,
the school‐distance measure is thus defined as the Euclidian distance between the
centroid of the student's 4‐digit zip‐code neighbourhood, and the 6‐digit zip‐code school
location he/she attends, measured in kilometres. We constructed a data set that covers
the distance to the nearest relevant school (i.e. the nearest school that indeed offers the
track the pupil is enrolled in) and the distance to the actually attended school (if
different), for every individual.
We used three types of distance measurements: 1) the absolute distance from
home to school; 2) the difference in distance between the nearest and the actual school;
3) a dummy for not attending the nearest school.
25 The following definitions by Statistics Netherlands (CBS, the national statistics office, www.cbs.nl) have been used:
Western immigrant: someone originating from a country in Europe (exclusive of Turkey), North America, Oceania, Indonesia or Japan.
Non‐Western immigrant: someone origination from Africa (in the Netherlands the majority group of immigrants from Morocco), South America, Asia (exclusive of Indonesia and Japan) or Turkey.
26 Percentiles of the numbers of individuals in our data set living in one 4‐digit postal code area: 41 (10%); 61 (25%); 90 (50%); 125 (75%); 174 (90%).
4 Patterns in secondary school selection in the context of unlimited choice
83
For some analyses, we also make use of density measures, capturing the number
of schools within a reasonable distance of the pupils' residence. We used the data by
Statistics Netherlands (CBS) on the number of schools within a 5 km radius.
4.3.4 SES indices
We included information on the student's residential area characteristics. In
particular, we make use of data collected by the Netherlands Institute for Social
Research (SCP). These data provide us with a socio‐economic index for every
neighbourhood, known as the "status scores".27 Information for these status scores is
collected through household (telephone) surveys, one household per 6‐digit zip‐code.
Next, these data are aggregated at the 4‐digit zip‐code level. The data include
neighbourhood mean income, employment and level of schooling (all self‐reported by
inhabitants). In our figures we present poverty indices in the reverse order, compared to
SCP: in all our figures on neighbourhood SES and distance to school, the standardized
poverty score ranges from ‐4 (poor) to +4 (affluent). Lastly, we merged our data with
additional data on neighbourhood characteristics that are updated annually (e.g.
demographics and urbanicity), provided by Statistics Netherlands (CBS).28
4.3.5 Indicators of pupils' prior achievement and secondary school quality
Prior achievement in elementary education is of crucial importance for the
assessment of results in secondary schools. At the end of elementary education, children
have a final test (the CITO test). The score on this test, plus the recommendation of the
pupils' elementary teacher, typically determine the level of enrolment in a specific track
in Dutch secondary education. The BRON database we used contains both the score on
the final elementary CITO test, and the teacher's recommendation, but unfortunately,
we only had the results of the CITO test in the case of 3208 individuals and the
elementary teacher's advice in the case of 6081 pupils. We tested these subsets of the
data on all control variables we use; we found that the regression coefficients of the
controls remained stable. Therefore, we trusted that the subsets did not differ from the
total set of 16.071 individuals in major ways. Furthermore, we compared the subsample
with only the CITO score, and the group with only the teacher's advice. The pupils
27 SCP data set "Statusscores Postcodegebieden 2006". 28 CBS data set "Buurtkaart met Cijfers 2008" (update 2).
84
without CITO scores had a slightly lower mean teacher's advice score, but similar
individual and neighbourhood characteristics. We acknowledge the limitation of
introducing a smaller subsample with information on prior achievement of pupils;
however, we considered this additional information relevant in combination with the
data on school quality we use in our analyses, in our search for quality‐driven elements
(at the individual and school level) of school preference. The Inspectorate of Education
(2011) found no evidence that children with a migrant background receive a lower
advice from their elementary teacher than native Dutch children.
We used data provided by the Inspectorate of Education for our measurements
of school mean exam scores and upward mobility to higher tracks, as indicators of
secondary school quality (see Section 4.6.1 for definitions of the variables).
In the tables we present below, we diversified between three different main
tracks in Dutch secondary education: VMBO (lower secondary vocational education),
HAVO (senior general education), and VWO (pre‐university education), plus the
combined advice HAVO/VWO. We used VMBO (the lowest level) as a reference, and the
other levels as dummy variables. We included pupils who are eligible for extra funding
because of special educational needs; these pupils can, in principle, enrol in every
school. In the Netherlands only pupils with specific special needs (e.g. blind children)
attend special schools; these pupils are not listed in our data set.
4.3.6 Limitation
We had to accept some limitations, because, as we described earlier, the
development of the BRON data set is currently in progress. As a consequence, for the
time being, we could not link results on (future) final secondary exams to prior
achievement in elementary education. We attempted to bypass this barrier somewhat,
by using mean exam results at the school level (collected by the Inspectorate of
Education) to estimate whether distance to the preferred school, controlling for
individual achievement in elementary school, is associated with school quality, as
expressed in the average exam score.
4.4 Results
In this Section we start with a general overview of summary statistics of the total
data set of 16071 pupils living in the four Dutch major cities, who were enrolled in the
4 Patterns in secondary school selection in the context of unlimited choice
85
last grade of elementary education in 2008; most of them were enrolled in Year 3 of
secondary school (2011) at the time of our research (Table 4‐1). Next we present a first
overall analysis of the association between distance to school and control variables at
the individual, neighbourhood, and school level; we added the characteristics of the
nearest school as an explaining variable for school choice (Table 4‐2). We continue with
a comparison of the group of pupils who choose the nearest school, and those who opt
out for a school at greater distance; we present the summary statistics in Table 4‐3, and
regression analyses in Table 4‐4. In Table 4‐5 we consider the distance difference
between the nearest and the actual school, as a proxy for the selectivity of choice,
assuming that pupils who take the extra effort to travel further made a more deliberate
choice. In Table 4‐6 we present a comparison between the nearest school and the actual
school, with a focus on patterns of ethnic segregation. We investigate these patterns
specifically by considering the SES difference and the difference in percentages of
migrant pupils, when comparing the nearest (not chosen) and the actual school (Tables
4‐7 and 4‐8). Finally we look at upward mobility to a higher track, as a specific example
of a possible benefit of selective school choice in Table 4‐9.
4.4.1 A general overview of socio‐economic measurements and distance to school
As a visualization of our data, we have added in Appendix 4‐1 the maps of the
four cities, which present a view of the ethnic composition of neighbourhoods, the
average distance travelled to school, and the distribution of secondary schools. The
maps give a first impression of the association between the ethnic composition of an
area and the distance travelled to school.
In Table 4‐1, we set the scene and present the summary statistics for the four
ethnicities (including native Dutch) that we distinguish in this chapter, plus the
combined group "other immigrants". Counts were carried out on the pupil‐, school‐ and
neighbourhood level. In addition to socio‐economic variables, we present school quality
indicators (mean exam score and percentage of pupils with upward mobility to a higher
track) and indicators of pupils' prior achievement in elementary school (the CITO score
and elementary teacher's advice for the track level in secondary school).
86
Table 4‐1: Summary statistics for pupils living in the four major Dutch cities, at the individual, neighbourhood and school level
Dutch Sur./Ant. Turkish Moroccan Other Imm. Total mean mean mean mean mean mean
VARIABLE (SD) (SD) (SD) (SD) (SD) (SD) individual level distance to primary school (km) 1.16 1.29 0.93 0.91 1.23 1.12 (1.21) (1.44) (1.17) (1.15) (1.4) (1.27) distance to secondary school (km) 3.49 3.30 2.92 2.67 3.17 3.21 (2.4) (2.48) (2.02) (1.89) (2.20) (2.28) school outside municipality border 0.21 0.17 0.13 0.10 0.12 0.16 (0.41) (0.38) (0.34) (0.30) (0.33) (0.37) CITO‐score 537.44 529.32 528.77 528.29 533.51 533.16 (9.93) (10.59) (9.68) (10.64) (11.03) (11.03) VMBO advice 0.42 0.72 0.75 0.76 0.54 0.58 (0.49) (0.45) (0.43) (0.43) (0.50) (0.49) HAVO advice 0.15 0.11 0.12 0.12 0.15 0.14 (0.36) (0.31) (0.32) (0.32) (0.36) (0.34) HAVO/VWO advice 0.18 0.10 0.07 0.08 0.14 0.13 (0.39) (0.30) (0.26) (0.26) (0.35) (0.34) VWO advice 0.24 0.07 0.06 0.05 0.17 0.15 (0.43) (0.26) (0.23) (0.22) (0.37) (0.36) individual upward mobility in Year 3 0.18 0.09 0.07 0.09 0.17 0.14 (0.39) (0.29) (0.26) (0.29) (0.38) (0.35) male 0.50 0.50 0.51 0.47 0.51 0.50 (0.50) (0.50) (0.50) (0.50) (0.50) (0.50) non‐western background 0.00 1.00 1.00 1.00 0.60 0.53 (.) (.) (.) (.) (0.49) (0.50) 2nd generation non‐western background 0.00 0.85 0.91 0.93 0.81 0.52 (.) (0.36) (0.28) (0.26) (0.39) (0.50) weighted student funding 0.03 0.33 0.52 0.57 0.32 0.27 (0.10) (0.43) (0.44) (0.43) (0.42) (0.40) one‐parent household 0.06 0.26 0.02 0.02 0.12 0.09 (0.24) (0.44) (0.15) (0.12) (0.32) (0.28) nearest school chooser 0.12 0.14 0.10 0.10 0.11 0.11 (0.32) (0.34) (0.30) (0.30) (0.31) (0.32) distance difference chosen and nearest school 2.40 2.39 2.09 1.85 2.29 2.26 (2.26) (2.44) (1.96) (1.79) (2.12) (2.17) neighbourhood level neighbourhood SES index 0.84 ‐0.47 ‐0.91 ‐0.90 ‐0.12 0.00 (1.33) (1.33) (1.16) (1.14) (1.44) (1.50) urbanicity 3.42 3.60 3.81 3.81 3.67 3.60 (0.76) (0.57) (0.40) (0.39) (0.53) (0.63) distance to nearest relevant school (km) 1.08 0.91 0.83 0.82 0.88 0.95 (0.84) (0.64) (0.63) (0.64) (0.65) (0.74) # of relevant schools within 5 km 15.44 17.36 21.19 20.85 18.47 17.81 (8.00) (9.52) (9.07) (9.26) (9.35) (9.10) school level # of tracks offered at secondary school 2.76 2.93 2.99 3.07 2.86 2.88 (1.18) (1.30) (1.41) (1.28) (1.30) (1.27) relative exam scores secondary school 0.09 ‐0.04 ‐0.10 ‐0.13 0.02 0.01 (0.26) (0.25) (0.27) (0.26) (0.29) (0.28) % upward mobility at school level 15.50 19.10 21.05 22.23 17.28 17.93 (9.26) (8.73) (9.78) (10.27) (9.51) (9.79) N 6386 2269 1995 2450 2928 16071
The mean average distance to secondary school is lowest for Moroccan youth at
2.67 km. Native Dutch pupils more often attend the nearest school (12%) than migrant
groups, with the exception of youth with a Surinamese/Antillean background (14%). Like
native Dutch students, Surinamese and Antillean students travel a larger mean distance
to school, but at the same time more often choose the nearest school. Presumably
Surinamese and Antillean pupils prefer a school with a large share of pupils with the
same background; consider for example Amsterdam: a large share of Surinamese and
Antillean pupils live in the Bijlmer district in the south‐eastern part of Amsterdam, and
4 Patterns in secondary school selection in the context of unlimited choice
87
tend to go to local schools with large percentages of pupils belonging to this group; this
might explain why they more often attend the nearest school than is the case for other
groups. At the same time, Surinamese and Antillean pupils who do not live in the Bijlmer
district, may still prefer to attend a school with many Surinamese and Antillean students
in the Bijlmer district; this could explain the higher mean distance to school.
Native Dutch pupils prefer more often prefer the nearest school; the odds are
that these students more often live in more affluent areas than is the case for migrant
students, and tend to attend a local school with a population that mirrors the affluent
neighbourhood. For example 57% of Moroccan pupils are eligible for weighted student
funding in elementary education, against 3% among native Dutch pupils.
The mean neighbourhood SES index differs substantially between the different
ethnic groups, by more than 1 Standard Deviation, and between native Dutch pupils and
all other groups. The share of Dutch pupils enrolled in a school outside the city
boundaries, at 21% differs considerably from, for example, students with a Moroccan
background (10%). This alludes to our further findings further below, which illustrate
that native Dutch pupils tend to prefer the nearest school when they live in an affluent
neighbourhood, but leave lower SES residential areas to go to school elsewhere more
often than migrant pupils living in the same area.
Considering pupils' prior achievement, the mean CITO score of native Dutch
pupils is close to 1 Standard Deviation higher than, for example, it is for pupils of
Moroccan descent. Substantially more often, migrant children are advised to proceed in
secondary vocational education (the ranking order of secondary tracks is VWO (highest),
HAVO, VMBO); 24% of native Dutch students are advised to proceed to the highest
secondary track (pre‐university track) from their elementary school teacher, against 5%
and 6% of pupils with, respectively, a Moroccan or Turkish background.
Successful progress in secondary school, as measured by upward mobility to a
higher track, differs substantially between native Dutch and migrant students: among
immigrant groups, 7‐9% move up to a higher secondary track at the start of Year 3 of
88
secondary school, whereas this is the case for 18% of the Dutch students29. Besides the
percentage of upward mobility, we considered the school's relative exit‐exam score to
be an important indicator of school quality; we defined this indicator as the
performance at each track level, measured by the mean exit‐exam score, compared with
the mean exit‐exam score of all other schools in the four major cities which offer this
same track. The school mean exam score is higher in the case of native Dutch pupils.
4.4.2 Distance to school
In Table 4‐2 we present OLS regressions for the individual distance travelled to
school. Except for Surinamese/Antillean students, immigrant students travel less far to
school than native Dutch students. This conclusion still holds with the inclusion of
additional controls. The magnitude of the difference declines if we add school and
neighbourhood characteristics, but is restored if we add school advice and CITO score.
Specification 1 (individual student characteristics) demonstrates that poorer students
(who were eligible for weighted student funding in elementary education), among
whom there are many Turkish and Moroccan migrant students, on average travel less
far to school.
In Specification 2 we introduce neighbourhood characteristics: an increase in
neighbourhood SES decreases distance to school. This may seem in contrast with what
we find at the individual level: poorer students (as measured by the mark‐up on funding
per pupil in elementary education– weighted student funding) travel less far to school.
However, while poorer migrant students tend to attend their neighbourhood school, the
same also appears to be the case for affluent native Dutch youth in higher SES
neighbourhoods.
29 Data files of the Inspectorate of Education show, as a reference, that native Dutch pupils are enrolled in schools with a mean 15.5 % upward mobility (measured after the completion of final exams), while Turkish pupils are enrolled in schools with a mean 21.1 % upward mobility, and Moroccan pupils in schools with a mean 22.2 % upward mobility. This indicates that migrant students more often attend a school with a larger upward mobility, but do so less themselves (as table 1 shows) than native‐born students.
4 Patterns in secondary school selection in the context of unlimited choice
89
Table 4‐2: Regression results (OLS) for distance to secondary school in km, on individual‐, school‐ and neighbourhood variables (standard errors in parentheses).
(1) (2) (3) (4) (5) distance to distance to distance to distance to distance to VARIABLES sec. school sec. school sec. school sec. school sec. school individual level male 0.10*** 0.08** 0.05 0.16** 0.20** (0.038) (0.038) (0.047) (0.063) (0.081) weighted student funding ‐0.30*** ‐0.18*** ‐0.19** ‐0.28*** ‐0.09 (0.076) (0.056) (0.074) (0.106) (0.155) Surinamese/Antillean ‐0.11 ‐0.13 ‐0.12 ‐0.21 ‐0.18 (0.124) (0.089) (0.097) (0.137) (0.216) Turkish ‐0.41*** ‐0.18* ‐0.23 ‐0.35** ‐0.48* (0.120) (0.102) (0.138) (0.158) (0.258) Moroccan ‐0.65*** ‐0.52*** ‐0.54*** ‐0.65*** ‐0.65*** (0.111) (0.082) (0.107) (0.148) (0.243) Other Immigrant Background ‐0.24*** ‐0.18*** ‐0.21*** ‐0.30*** ‐0.34** (0.091) (0.064) (0.076) (0.097) (0.161) one‐parent household 0.08 0.12* 0.12 0.07 ‐0.02 (0.088) (0.069) (0.083) (0.102) (0.133) HAVO advice track dummy ‐0.84*** (0.109) HAVO/VWO advice track dummy ‐0.89*** (0.122) VWO advice track dummy ‐0.61*** (0.168) CITO test score ‐0.02*** (0.007) Non‐western x CITO test score 0.00 (0.000) neighbourhood level neighbourhood SES index ‐0.21*** ‐0.14** ‐0.04 0.05 (0.052) (0.068) (0.078) (0.125) Surinamese/Antillean x SES 0.14** 0.13* 0.12 0.14 (0.059) (0.072) (0.096) (0.124) Turkish x SES 0.19*** 0.19** 0.16* 0.17 (0.064) (0.076) (0.090) (0.113) Moroccan x SES 0.19*** 0.21** 0.15 0.04 (0.069) (0.085) (0.097) (0.169) Other Immigrant Background x SES 0.08** 0.09* 0.06 ‐0.02 (0.041) (0.046) (0.058) (0.094) urbanicity ‐0.33*** ‐0.42*** ‐0.19 ‐0.28 (0.113) (0.157) (0.155) (0.187) # of relevant schools within 5 km ‐0.03*** ‐0.04*** ‐0.05*** ‐0.05*** (0.008) (0.009) (0.009) (0.011) distance to nearest relevant school (km) 0.59*** 0.47*** 0.45*** 0.51*** (0.117) (0.139) (0.143) (0.163) Utrecht municipality dummy ‐0.13 0.12 0.10 0.07 (0.193) (0.250) (0.255) (0.303) The Hague municipality dummy ‐0.55*** ‐0.67*** ‐0.49** ‐0.88*** (0.139) (0.189) (0.193) (0.257) Rotterdam municipality dummy ‐0.51*** ‐0.50*** ‐0.32* ‐0.53*** (0.137) (0.174) (0.177) (0.201) school level relative exam scores nearest school ‐0.32 ‐0.30 ‐0.17 (0.266) (0.262) (0.322) # of tracks offered at nearest school ‐0.02 ‐0.01 ‐0.10 (0.051) (0.054) (0.080) % upward mobility at nearest school ‐0.49 0.09 ‐0.38 (0.606) (0.570) (0.704) average SES index at nearest school ‐0.25*** ‐0.27*** ‐0.42*** (0.093) (0.104) (0.130) Constant 3.44*** 4.89*** 5.48*** 5.08*** 18.22*** (0.117) (0.600) (0.828) (0.805) (3.695) Observations 16,071 16,060 11,023 6,081 3,208 R‐squared 0.02 0.14 0.14 0.16 0.18 Adj. R‐squared 0.0200 0.139 0.139 0.158 0.171
Notes: Robust clustered standard errors (at neighbourhood level) in parentheses *** p<0.01, ** p<0.05, * p<0.1
90
In Specification 3 we introduce the characteristics of the nearest relevant school
as an explaining variable. The two school quality indicators we consider in this chapter–
mean exam score and percentage upward mobility– do not seem to drive school choice.
A higher average SES of the nearest school, however, decreases distance to school, in
accordance with what we described above: native Dutch pupils in richer areas tend to
attend their neighbourhood school.
In Specification 4 we add the elementary teacher's advice for the pupils'
appropriate track in secondary school. Pupils who are advised to follow the academic
secondary tracks (HAVO and VWO), relative to the lowest professional track (VMBO),
travel less far to school. This again seems related to what we described above: native
Dutch pupils more often have affluent parents and live in higher SES residential areas;
the children of more affluent, better‐educated parents more often enrol in academic
tracks (e.g. 58% among native Dutch, and 25% among pupils of Moroccan descent, Table
4‐1). We have seen that affluent native Dutch pupils tend to attend their neighbourhood
school, hence the lower mean distance to school among pupils in academic tracks.
More migrant than native Dutch students start in secondary vocational tracks
(VMBO); on average, enrolment in a vocational track may require somewhat longer
travel distances: in contrast to academic tracks, at the vocational level a wide range of
professional programmes is offered (that correspond to professions on the labour
market), but not all these options are offered in every school. Therefore, enrolling on
the preferred vocational programme may imply further travelling to school.
Finally, in Specification 5, we add the CITO test score; unfortunately, similar to
the elementary teacher's advice, these analyses are based on a substantially smaller
subsample. We decided, however, to include these regressions, because we found
hardly any instability to controls. We trusted, therefore, that the subsample may not
differ in major ways from the total data set. The effect of the pupils' CITO score on
distance to school is marginal but statistically significant.
As we presented in Table 4‐1, only 10‐14% (depending on the ethnicity) of pupils
chooses the nearest school to their residence. For this reason, we looked further into
possible differences between the two groups, those who choose the nearest school, and
those who do not.
4 Patterns in secondary school selection in the context of unlimited choice
91
Figure 4‐1 demonstrates these two effects. The interaction of ethnicity with
neighbourhood SES, furthermore, diminishes the effect of ethnicity on distance to
school: when neighbourhood SES increases, the odds are that Turkish and Moroccan
students travel further to school. We see an opposite effect between native Dutch and
migrant students here: while the first group tends to choose a school closer to home in a
high SES area, our analyses estimate the opposite effect for migrant students.
A greater distance to the nearest relevant school: a greater distance to the
nearest school may point at a lower population density and, accordingly, lower school
density. In that case, a preferred school may tend to be further away than in a densely
populated area.
Figure 4‐1: Linearly fitted lines (OLS), one for each of the 5 ethnic groups on the correlation between absolute distance to secondary school and relative neighbourhood SES
4.4.3 Choosing the nearest school or not
Only 1824 (11.3%) pupils chose the nearest school to their residence, while
14.247 (88.7%) pupils chose another school, illustrating that the right to choose freely is
largely exercised (Table 4‐3). Note that preferring the nearest school may also be a
deliberate choice, however, in this case we cannot know; therefore, the percentage of
pupils who actively choose a school could in reality even be higher. In these descriptive
92
measurements we used the higher aggregate level of the combined group of non‐
Western immigrants, in order to avoid losing too much statistical power if we were to
specify 1824 pupils (nearest school choosers) into five ethnicities.
Table 4‐3: Summary statistics of the comparison between the group of students who choose the nearest relevant school (i.e. a school that indeed offers a desired track) to their home, and those who do not (standard deviations in parentheses)
Non‐Nearest Nearest mean meanVARIABLE (SD) (SD)individual level distance to primary school (km) 1.13 1.08 (1.28) (1.19) distance to secondary school (km) 3.49 1.04 (2.26) (.85) CITO test score 532.96 534.53 (11.06) (10.77) VMBO advice 0.59 0.47 (.49) (.5) HAVO advice 0.13 0.18 (.34) (.39) HAVO/VWO advice 0.13 0.15 (.34) (.36) VWO advice 0.15 0.19 (.35) (.4) individual upward mobility in Year 3 0.14 0.15 (.34) (.36) male 0.50 0.51 (.5) (.5) non‐western background 0.53 0.53 (.5) (.5) 2nd generation non‐western background 0.52 0.52 (.5) (.5) weighted student funding 0.27 0.25 (.4) (.39) one‐parent household 0.09 0.09 (.28) (.29) nearest school chooser 0.00 1.00 (.) (.) distance difference chosen and nearest school 2.55 0.00 (2.14) (.)neighbourhood level neighbourhood SES index ‐0.03 0.17 (1.49) (1.57) urbanicity 3.62 3.42 (.61) (.76) # of relevant schools within 5 km 18.17 15.01 (9.04) (9.17) distance to nearest relevant school (km) 0.94 1.04 (.72) (.85)school level # of tracks offered at secondary school 2.86 3.04 (1.28) (1.2) relative exam scores secondary school 0.01 0.00 (.28) (.27) % upward mobility at school level 17.89 18.32 (9.87) (9.15) N 14247 1824
Measurements overall demonstrate only marginal differences between the
groups of nearest and non‐nearest school choosers. The main differences we found
concern the elementary teacher's advice to follow the vocational track (VMBO) and
neighbourhood SES (richer native Dutch pupils who choose the nearest school). As we
described above, attending the preferred VMBO programme may require further
4 Patterns in secondary school selection in the context of unlimited choice
93
travelling, since not all schools offer all vocational programmes. We found virtually no
difference between the two groups considering immigrant family background or
eligibility for weighted student funding in elementary school.
Table 4‐4: Regression results (OLS) for choosing the nearest school (i.e. comparing pupils who choose the nearest school with those who choose another school), with choosing the nearest school = 1
(1) (2) (3) (4) (5)VARIABLES nearest nearest nearest nearest nearest male 0.00 0.00 0.01 0.01 0.02 (0.006) (0.006) (0.007) (0.009) (0.012) weighted student funding ‐0.01 0.01 0.01 ‐0.00 ‐0.01 (0.009) (0.009) (0.011) (0.015) (0.023) Surinamese/Antillean 0.03 0.03* ‐0.03 ‐0.03 ‐0.02 (0.017) (0.014) (0.022) (0.024) (0.036) Turkish ‐0.01 0.01 ‐0.03 ‐0.03 0.01 (0.014) (0.014) (0.022) (0.025) (0.040) Moroccan ‐0.01 0.01 ‐0.03 ‐0.01 0.01 (0.015) (0.013) (0.024) (0.028) (0.037) Other Immigrant Background ‐0.00 0.01 ‐0.02 ‐0.01 ‐0.01 (0.011) (0.009) (0.014) (0.018) (0.027) one‐parent household ‐0.01 ‐0.01 ‐0.02 0.00 ‐0.00 (0.009) (0.008) (0.011) (0.016) (0.027) neighbourhood SES index 0.00 ‐0.01 ‐0.01 ‐0.01 (0.007) (0.010) (0.012) (0.016) Non‐western x SES ‐0.01 0.01 0.00 0.00 (0.008) (0.010) (0.012) (0.015) urbanicity ‐0.05*** ‐0.04** ‐0.06** ‐0.05* (0.017) (0.022) (0.025) (0.030) # of relevant schools within 5 km ‐0.00*** ‐0.00*** ‐0.00*** ‐0.00*** (0.001) (0.001) (0.001) (0.001) distance to nearest relevant school (km) ‐0.02 ‐0.00 ‐0.00 ‐0.00 (0.017) (0.026) (0.028) (0.033) Utrecht municipality dummy ‐0.04* ‐0.06* ‐0.09** ‐0.09** (0.024) (0.034) (0.037) (0.041) The Hague municipality dummy ‐0.01 ‐0.01 ‐0.02 ‐0.03 (0.018) (0.025) (0.027) (0.032) Rotterdam municipality dummy ‐0.03 ‐0.02 ‐0.03 ‐0.01 (0.019) (0.023) (0.024) (0.034) relative exam scores nearest school 0.06 0.06 0.05 (0.036) (0.042) (0.056) # of tracks offered at nearest school ‐0.01 ‐0.01 ‐0.00 (0.006) (0.007) (0.011) % upward mobility at nearest school 0.00 ‐0.04 ‐0.06 (0.076) (0.091) (0.121) average SES index at nearest school 0.02 0.02 0.04 (0.017) (0.018) (0.025) % non‐western at nearest school 0.00 ‐0.02 ‐0.06 (0.059) (0.053) (0.084) % non‐western at nearest school x non‐western 0.09*** 0.09*** 0.17*** (0.026) (0.030) (0.064) HAVO advice track dummy 0.05** (0.023)HAVO/VWO advice track dummy 0.01 (0.027)VWO advice track dummy 0.02 (0.028)CITO test score 0.00 (0.001) Non‐western x CITO test score ‐0.00 (0.000) Constant 0.11*** 0.37*** 0.39*** 0.47*** ‐0.24 (0.011) (0.072) (0.096) (0.108) (0.576) Observations 16,071 16,017 10,993 6,065 3,208 R‐squared 0.00 0.02 0.03 0.04 0.05 Adj. R‐squared 0.000901 0.0189 0.0287 0.0400 0.0436
Notes: Robust clustered standard errors (at neighbourhood level) in parentheses *** p<0.01, ** p<0.05, * p<0.1
94
Table 4‐4 lists the regressions (OLS) on these differences between nearest and
non‐nearest school choosers, and we used again the characteristics of the nearest
school as explanatory variables for the choice for the proximity school or a school
further from the residence. Similar to the summary statistics in Table 4‐3, regressions in
Table 4‐4 demonstrate hardly any dissimilarity between pupils choosing the nearest
school, and those who do not. The key result is a significant effect of interaction
between ethnicity and ethnic school composition: non‐Western pupils are more likely to
choose the nearest school if this school holds more non‐Western pupils. The coefficients
(not statistically significant) we found considering the student's ethnic background are
not sensitive to additional controls. Understandably, urbanicity– a measure for human
activity in a neighbourhood based on the number of addresses– drives the choice for a
more distant school: there are more schools to choose from in densely populated areas,
and, as a consequence, the distance difference between the nearest and the preferred
school tends to be smaller; the same can be said for the number of schools within 5 km.
Recapitulating the above, we have seen that socio‐economic indicators influence
distance to school, but that the comparison between pupils who attend the nearest
school and those who prefer another school does not demonstrate major differences
between these two groups. Next, we introduce measurements of the distance difference
between the nearest and the actual school as a proxy for the selectivity of choice.
4.4.4 Selectivity of choice
Unlike in Table 4‐2, where we measured the absolute distance from home to
school, in Table 4‐5 we consider the distance difference between the nearest and the
actual school, as a measure for the selectivity of choice, assuming that selecting a school
at greater distance implies a more deliberate choice, and has a higher cost. In
accordance with the measurements we presented above, an increase in neighbourhood
SES decreases this distance difference significantly, indicating that pupils living in higher
SES areas, more often choose the nearest school (i.e. more pupils with distance
difference = 0). As we also found earlier (Table 4‐2), the interaction term non‐Western
migrant background x SES neighbourhood reduces the effect of ethnicity considerably.
4 Patterns in secondary school selection in the context of unlimited choice
95
Table 4‐5: Regression results (OLS) for the distance difference (i.e. between the actual and the nearest school), with distance difference = 0 for pupils who choose the nearest school
(1) (2) (3) (4) (5) (6)VARIABLES Dist. Diff Dist. Diff. Dist. Diff. Dist. Diff. Dist. Diff. Dist. Diff.
male 0.08** 0.08** 0.05 0.08* 0.16** 0.20** (0.038) (0.038) (0.047) (0.047) (0.063) (0.080)weighted student funding ‐0.22*** ‐0.20*** ‐0.20*** ‐0.22*** ‐0.30*** ‐0.11 (0.064) (0.055) (0.073) (0.068) (0.105) (0.149)Surinamese/Antillean 0.01 ‐0.10 ‐0.02 ‐0.06 ‐0.10 ‐0.24 (0.126) (0.086) (0.122) (0.124) (0.164) (0.205)Turkish ‐0.20** ‐0.18** ‐0.17 ‐0.26* ‐0.26 ‐0.59*** (0.090) (0.085) (0.147) (0.158) (0.175) (0.218)Moroccan ‐0.43*** ‐0.52*** ‐0.50*** ‐0.65*** ‐0.55*** ‐0.66*** (0.087) (0.070) (0.138) (0.156) (0.158) (0.207)Other Immigrant Background ‐0.07 ‐0.13** ‐0.12 ‐0.19** ‐0.20* ‐0.33** (0.079) (0.062) (0.087) (0.091) (0.119) (0.150)one‐parent household 0.20** 0.13* 0.14* 0.11 0.09 ‐0.03 (0.088) (0.069) (0.082) (0.105) (0.103) (0.133)neighbourhood SES index ‐0.19*** ‐0.11 ‐0.16** ‐0.01 0.07 (0.050) (0.067) (0.076) (0.077) (0.119)Non‐western x SES 0.13*** 0.12** 0.17*** 0.07 0.06 (0.044) (0.057) (0.058) (0.066) (0.096)urbanicity ‐0.32*** ‐0.43*** ‐0.72*** ‐0.20 ‐0.32* (0.112) (0.155) (0.197) (0.151) (0.181)# of relevant schools within 5 km ‐0.03*** ‐0.04*** ‐0.05*** ‐0.05*** ‐0.05*** (0.008) (0.009) (0.011) (0.009) (0.011)distance to nearest relevant school (km) ‐0.40*** ‐0.52*** ‐0.66*** ‐0.55*** ‐0.51*** (0.117) (0.141) (0.162) (0.145) (0.162)relative exam scores nearest school ‐0.30 ‐0.11 ‐0.25 ‐0.21 (0.260) (0.309) (0.252) (0.309)# of tracks offered at nearest school ‐0.01 ‐0.04 ‐0.00 ‐0.08 (0.052) (0.060) (0.055) (0.077)% upward mobility at nearest school ‐0.49 ‐0.57 0.09 ‐0.51 (0.602) (0.711) (0.556) (0.686)average SES index at nearest school ‐0.32*** ‐0.33** ‐0.33** ‐0.55*** (0.114) (0.135) (0.131) (0.167)% non‐western at nearest school ‐0.20 ‐0.19 ‐0.07 ‐0.05 (0.302) (0.332) (0.336) (0.457)% non‐western at nearest school x non‐western ‐0.10 0.18 ‐0.17 ‐0.75* (0.193) (0.207) (0.195) (0.407)Utrecht municipality dummy ‐0.12 0.14 ‐0.03 0.12 0.04 (0.194) (0.247) (0.274) (0.256) (0.299)The Hague municipality dummy ‐0.55*** ‐0.67*** ‐0.85*** ‐0.49*** ‐0.90*** (0.138) (0.183) (0.210) (0.187) (0.254)Rotterdam municipality dummy ‐0.51*** ‐0.53*** ‐0.72*** ‐0.33** ‐0.65*** (0.137) (0.162) (0.185) (0.168) (0.197)HAVO advice track dummy ‐0.85*** (0.106) HAVO/VWO advice track dummy ‐0.90*** (0.122) VWO advice track dummy ‐0.63*** (0.170) CITO test score ‐0.03*** (0.007)Non‐western x CITO test score 0.00* (0.001)Constant 2.36*** 4.84*** 5.58*** 7.62*** 5.10*** 19.03*** (0.082) (0.600) (0.829) (0.955) (0.794) (3.714) Observations 16,071 16,017 10,993 9,504 6,065 3,208R‐squared 0.01 0.05 0.07 0.13 0.09 0.10Adj. R‐squared 0.0100 0.0528 0.0708 0.127 0.0913 0.0949
Notes: Robust clustered standard errors (at neighbourhood level) in parentheses *** p<0.01, ** p<0.05, * p<0.1
96
Table 4‐6: Comparisons of nearest (non‐chosen) school and the actual school, considering non‐nearest school choosers only (14247 individuals, 88.7 % of the data set)
Non‐nearest school choosing Dutch Sur./Ant. Turkish Moroccan Other Imm. Total mean mean mean mean mean meanVARIABLE (SD) (SD) (SD) (SD) (SD) (SD) NEAREST (NON‐CHOSEN) SCHOOL % non‐western at school 54.35 66.81 66.49 63.9 60.74 60.25 (29.18) (27.96) (29.27) (30.) (29.95) (29.75)school level average SES index ‐0.08 ‐0.69 ‐0.73 ‐0.52 ‐0.46 ‐0.38 (1.) (.86) (.85) (.86) (.95) (.97)relative exam school average 0 ‐0.1 ‐0.16 ‐0.16 ‐0.05 ‐0.07 (.28) (.3) (.31) (.32) (.3) (.3) school level average CITO intake 529.23 526.14 524.58 526.5 527.39 527.55 (9.17) (8.96) (9.31) (9.83) (9.69) (9.49)% upward mobility at school level 0.18 0.2 0.23 0.21 0.2 0.2 (.1) (.1) (.11) (.11) (.11) (.11)# of tracks offered at secondary 2.29 2.35 1.95 2.07 2.32 2.23 (1.54) (1.6) (1.75) (1.69) (1.56) (1.61)ACTUAL (CHOSEN) SCHOOL % non‐western at school 32.56 63.24 74.09 75.53 52.82 52.4 (20.64) (25.14) (23.23) (23.2) (28.12) (29.46)school level average SES index 0.56 ‐0.33 ‐0.6 ‐0.58 ‐0.04 0 (.86) (.91) (.82) (.77) (.96) (.99)relative exam school average 0.09 ‐0.03 ‐0.08 ‐0.11 0.03 0.02 (.26) (.26) (.28) (.27) (.3) (.29)school level average CITO intake 535.81 529.37 528.63 528.71 532.95 532.4 (8.89) (9.26) (8.95) (8.85) (9.71) (9.62)% upward mobility at school level 0.15 0.19 0.21 0.22 0.17 0.18 (.09) (.09) (.1) (.1) (.1) (.1) # of tracks offered at secondary 2.75 2.9 2.96 3.06 2.84 2.86 (1.18) (1.33) (1.43) (1.29) (1.32) (1.28)ACTUAL ‐ NEAREST mean mean mean mean mean meanVARIABLE (Pr{|T| > (Pr{|T| > (Pr{|T| > (Pr{|T| > (Pr{|T| > (Pr{|T| > % non‐western at school ‐21.79 ‐3.57 7.6 11.63 ‐7.92 ‐7.85 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)school level average SES index 0.64 0.36 0.13 ‐0.06 0.42 0.38 (0.000) (0.000) (0.000) (0.002) (0.000) (0.000)relative exam school average 0.09 0.07 0.08 0.05 0.08 0.09 (0.000) (0.000) (0.000) (0.002) (0.000) (0.000)school level average CITO intake 6.58 3.23 4.05 2.21 5.56 4.85 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)% upward mobility at school level ‐0.03 ‐0.01 ‐0.02 0.01 ‐0.03 ‐0.02 (0.000) (0.004) (0.000) (0.026) (0.000) (0.000)# of tracks offered at secondary 0.46 0.55 1.01 0.99 0.52 0.63 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) N 5649 1959 1796 2198 2605 14247
Furthermore, a higher SES index of the nearest school reduces the average
distance difference, reflecting that more pupils attend the nearest school when this
school has a higher SES population. Compared with the vocational track as a reference
(VMBO), the distance difference is reduced in the case of pupils enrolled in academic
tracks (HAVO and VWO).30 While overall we found no major differences between
analyses of the absolute distance to school and of the distance difference between the
actual and the nearest school, in the latter case the magnitude seems more sensitive to
controls. As we found repeatedly in the different measurements we present in this
research, the quality of the nearest school as expressed by the relative exam score, does
30 Since the difference in distance must be ≥ 0, we also carried out a Tobit regression. However, we opted for the OLS regression because we found no major discrepancies with the Tobit regression, probably because only around 10 % of individuals choose the nearest school. OLS tables may be easier to interpret, since Tobit regressions require an extra calculation of marginal effects.
4 Patterns in secondary school selection in the context of unlimited choice
97
not demonstrate a significant effect on the distance difference. We added Appendix 4‐2,
to illustrate that the exam score at the proximity school is indeed only marginally
associated with the distance difference, in the case of all ethnic groups.
4.4.5 School choice and segregation
In this paragraph we take a closer look at links between school choice and
segregation. We have already seen that notably Turkish and Moroccan migrant students
travel less far to school. In Table 4‐6 we consider the subset of pupils who do not choose
the nearest school (14247 individuals, 88.7% of the total data set), and compare the
characteristics of their chosen school with the nearest school they did not choose.
Considerable dissimilarities appear between choice patterns of native Dutch pupils,
compared with pupils who have a migrant background. Native Dutch students, relative
to the not‐chosen nearest school, choose a school with lower percentages of migrant
pupils, a higher school average SES , a higher relative exam score, and a higher average
CITO score intake. In contrast, consider, for example students with a Moroccan
background: they choose a school with a higher percentage of pupils with a non‐
Western migrant background, about the same school SES index as the nearest school, a
slightly better relative exam score and more often a broader secondary school, offering
more tracks. We carried out a paired t‐test and found that the characteristics of the
chosen more distant school in all cases differed significantly from those of the nearest
school.
The different choice patterns between native Dutch and migrant pupils
apparently lead to more segregation, and may reveal segregation by choice among
migrant parents. Only in the case of native Dutch students we did find a school quality
induced movement.
We carried out regression analyses for these different choice patterns, and
analysed the difference between the nearest and the actual school for all choosers of
more distant schools, concerning four different indicators at school level. Note that
these four tables are based on different numbers of observations; in some cases, the
information on all school quality indicators was not listed in our data set. We carried out
tests to check for unobserved selection mechanisms, and found that coefficients on
98
socio‐economic variables remained stable between the different subsamples. We
analysed the following school characteristics:
The school's mean exam score (Appendix 4‐3);
The school's percentage of upward mobility ("pupward") to a higher track
(Appendix 4‐4);
The average SES of the school population (Table 4‐7);
The school's percentage of pupils with a non‐western background (Table 4‐8).
We found the most significant coefficients for the difference in school SES (Table
4‐7) and school percentage of pupils with a non‐Western migrant background, as a
further Specification of school SES (Table 4‐8). Tables on relative exam score and upward
mobility have been included in the Appendices.
Looking at the difference in the average school SES between the nearest and the
actual school (Table 4‐7), the intercept in Specification (1) indicates that the reference
group of native Dutch gains from not choosing the nearest school. This outcome
reiterates our finding that native Dutch pupils tend to opt for a school with less migrant
pupils when their nearest school has many migrant pupils. Immigrant students gain less,
and, in fact, Moroccan students do not gain SES status at all. Neighbourhood SES has no
effect on the gain, but the percentage of non‐Western pupils at the nearest school
clearly increases the gain, by some 0.15 SES score (about one‐tenth of a standard
deviation) for a 10 percentage‐point increase. Weighted student funding in the
elementary school also reduces the SES gain between the nearest and the actual school
significantly. Finally, a recommendation by the primary school teacher for one of the
academic secondary tracks also increases the SES difference between the nearest and
the actual school significantly: the mean SES among pupils in academic tracks tends to
be higher than among pupils in vocational tracks. In The Hague and Rotterdam, the gain
is substantially higher than in the other two cities.
4 Patterns in secondary school selection in the context of unlimited choice
99
Table 4‐7: Regression results (OLS) for SES difference (i.e. average SES of chosen school minus average SES of nearest school), considering non‐nearest school choosers only (14197 individuals)
(1) (2) (3) (4) (5)VARIABLES SES diff SES diff SES diff SES diff SES diff Distance difference to nearest school 0.03*** 0.06*** 0.05*** 0.05*** 0.05*** (0.010) (0.013) (0.010) (0.010) (0.013) male 0.01 0.01 0.01 0.01 0.02 (0.013) (0.013) (0.013) (0.016) (0.022) weighted student funding ‐0.16*** ‐0.15*** ‐0.12*** ‐0.16*** ‐0.17*** (0.048) (0.033) (0.029) (0.040) (0.051) Surinamese/Antillean ‐0.20*** ‐0.26*** ‐0.18** ‐0.05 ‐0.06 (0.045) (0.041) (0.073) (0.073) (0.065) Turkish ‐0.41*** ‐0.52*** ‐0.41*** ‐0.25*** ‐0.26*** (0.082) (0.057) (0.083) (0.084) (0.082) Moroccan ‐0.59*** ‐0.58*** ‐0.46*** ‐0.31*** ‐0.28*** (0.091) (0.057) (0.088) (0.091) (0.090) Other Immigrant Background ‐0.16*** ‐0.20*** ‐0.13** ‐0.06 ‐0.04 (0.050) (0.034) (0.050) (0.054) (0.058) one‐parent household ‐0.12** ‐0.12*** ‐0.15*** ‐0.07* ‐0.09* (0.045) (0.030) (0.028) (0.038) (0.048) neighbourhood SES index ‐0.02 0.08* 0.05 0.05 (0.054) (0.047) (0.053) (0.042) Non‐western x SES 0.00 0.01 0.02 0.04 (0.034) (0.031) (0.034) (0.035) urbanicity 0.36*** 0.33*** 0.22*** 0.17* (0.110) (0.075) (0.075) (0.087) # of relevant schools within 5 km ‐0.00 ‐0.00 0.00 ‐0.00 (0.005) (0.004) (0.005) (0.005) distance to nearest relevant school (km) 0.29** 0.17*** 0.15*** 0.13** (0.112) (0.057) (0.056) (0.053) # of tracks offered at nearest school ‐0.02 ‐0.02 ‐0.03 (0.038) (0.039) (0.043) % non‐western at nearest school 1.55*** 1.43*** 1.60*** (0.273) (0.296) (0.296) % non‐western at nearest school x non‐western ‐0.22* ‐0.22 ‐0.18 (0.124) (0.137) (0.226) Utrecht municipality dummy 0.03 0.11 0.10 0.08 (0.166) (0.116) (0.133) (0.127) The Hague municipality dummy 0.66*** 0.56*** 0.70*** 0.73*** (0.166) (0.112) (0.124) (0.125) Rotterdam municipality dummy 0.30** 0.38*** 0.41*** 0.49*** (0.134) (0.134) (0.142) (0.154) HAVO advice track dummy 0.25*** (0.057)HAVO/VWO advice track dummy 0.36*** (0.066)VWO advice track dummy 0.51*** (0.079)CITO test score 0.02*** (0.003) Non‐western x CITO test score ‐0.00 (0.000) Constant 0.56*** ‐1.25*** ‐1.86*** ‐1.70*** ‐11.12*** (0.090) (0.474) (0.409) (0.392) (1.525) Observations 14,197 14,147 14,147 7,912 4,145 R‐squared 0.08 0.18 0.35 0.37 0.43 Adj. R‐Squared 0.0747 0.181 0.344 0.371 0.427
Notes: Robust clustered standard errors (at neighbourhood level) in parentheses *** p<0.01, ** p<0.05, * p<0.1
100
Table 4‐8: Regression results (OLS) for the difference in the percentage of pupils with a non‐Western migrant background (i.e. percentage at the actual school minus the percentage at the nearest school), considering non‐nearest school choosers only (14197 individuals)
(1) (2) (3) (4) (5)VARIABLES % NW diff % NW diff % NW diff % NW diff % NW diff Distance difference to nearest school ‐1.70*** ‐1.99*** ‐1.30*** ‐1.77*** ‐1.03** (0.372) (0.374) (0.289) (0.328) (0.417) male 0.05 0.10 0.39 0.73 0.14 (0.554) (0.529) (0.512) (0.670) (0.914) weighted student funding 8.90*** 8.62*** 9.26*** 10.06*** 7.11** (2.119) (1.882) (1.567) (1.836) (2.998) Surinamese/Antillean 14.19*** 15.06*** 17.64*** 14.83*** 7.45*** (1.837) (1.603) (1.763) (1.835) (2.521) Turkish 24.26*** 26.39*** 26.87*** 22.34*** 17.81*** (3.309) (2.914) (2.698) (2.059) (3.307) Moroccan 27.48*** 28.51*** 27.18*** 22.23*** 16.84*** (3.513) (2.672) (2.524) (2.139) (3.325) Other Immigrant Background 10.44*** 10.66*** 11.15*** 8.94*** 3.89* (1.537) (1.238) (1.288) (1.321) (2.103) one‐parent household 5.38*** 5.20*** 6.67*** 4.74*** 4.61** (1.489) (1.281) (1.103) (1.355) (2.128) neighbourhood SES index 1.27 ‐4.49*** ‐3.13 ‐3.10* (1.713) (1.590) (1.954) (1.871) Non‐western x SES 0.88 0.94 1.52 1.06 (1.450) (1.513) (1.546) (1.656) urbanicity ‐3.29 1.74 4.81 5.68 (4.322) (3.123) (3.523) (3.760) # of relevant schools within 5 km 0.08 0.16 ‐0.01 0.16 (0.264) (0.213) (0.269) (0.235) distance to nearest relevant school (km) ‐8.44* ‐5.05 ‐5.60 ‐3.50 (5.072) (3.818) (3.518) (3.267) # of tracks offered at nearest school ‐3.48** ‐3.89*** ‐4.08*** (1.349) (1.451) (1.519) SES index at nearest school 21.34*** 22.66*** 24.76*** (2.282) (2.652) (2.631) SES index at nearest school x non‐western 1.14 ‐1.18 ‐0.79 (2.062) (1.865) (2.042) Utrecht municipality dummy ‐2.21 ‐15.52** ‐17.86*** ‐19.07*** (8.542) (6.666) (6.415) (5.864) The Hague municipality dummy ‐10.35* ‐8.35* ‐9.30* ‐9.52** (6.164) (4.341) (4.885) (4.524) Rotterdam municipality dummy 0.88 11.92** 11.33** 16.87*** (6.120) (5.076) (5.534) (5.781) HAVO advice track dummy ‐10.91*** (2.381)HAVO/VWO advice track dummy ‐13.43*** (2.948)VWO advice track dummy ‐16.69*** (2.729)CITO test score ‐0.65*** (0.117) Non‐western x CITO test score 0.01*** (0.005) Constant ‐17.57*** 3.92 ‐7.36 ‐4.42 327.73*** (2.775) (17.828) (14.916) (16.965) (63.260) Observations 14,197 14,147 14,147 7,912 4,145 R‐squared 0.14 0.18 0.38 0.41 0.47 Adj. R‐Squared 0.137 0.174 0.375 0.408 0.465
Notes: Robust clustered standard errors (at neighbourhood level) in parentheses *** p<0.01, ** p<0.05, * p<0.1
101
In Table 4‐8 we chose the difference between the percentages of pupils with a
non‐western immigrant background of the actual minus the nearest school as the
outcome variable. As indicated by the intercept in Specification (1), native Dutch
reference students, on average, travel to a school with lower percentages of migrant
pupils. Turkish and Moroccan students travel to schools with higher percentages of non‐
Western immigrants, whereas the difference for the other immigrant groups is smaller.
In the case of Moroccan pupils, the percentage non‐western pupils increases at the
actual school by 9.9 percentage points (‐17.57 + 27.48), minus 1 percentage point per
km increase in distance difference (Specification 5). Since distances to school are
relatively small in the Netherlands (Table 4‐1), we may assume that the different
patterns found in school choice between native Dutch and pupils of Moroccan descent
are indeed the effect of different ethnic preferences. We found comparable patterns of
choice between Turkish and Moroccan students; also Surinamese/Antillean pupils and
those with another migrant background, on average, prefer a school with a higher
percentage of migrants, but to a lesser degree than pupils of Turkish and Moroccan
descent. Introducing the CITO test score (at the cost of substantially lower numbers of
observations) is also associated with a lower percentage of migrant pupils at the chosen
school. These findings confirm our earlier outcomes: migrant pupils and their parents
appear to choose further segregation.
4.4.6 Benefits of school choice?
Upward mobility to a higher track may be a particularly important extra
opportunity for migrant pupils. They are relatively more often the first one in their
family with the option to enrol in academic secondary tracks, and may need extra time
to arrive at this level and later on in higher education. At the start of secondary
education, far higher percentages of migrant pupils enter the lower secondary
vocational track: e.g. 76 % of Moroccan youths, against 42 % of native Dutch youngsters
(Table 4‐1). In fact, this is the width of the educational achievement gap, which indeed
could be reduced by offering migrant students extra opportunities to improve their track
level while in secondary school. The Dutch educational system has two trajectories for
upward mobility: 1) after finishing one track with a diploma, pupils can re‐enrol in a
higher track and obtain a second diploma at this higher level. 2) Pupils can also move up
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between grades, and finish with a diploma on a higher level than their level of entrance
in Year 1. In Table 4‐9 we consider this second trajectory for upward mobility between
grades. We included only pupils who started on the lower vocational track in Year 1,
since notably this lowest level leaves open the possibility of opportunities for upward
transfer to a higher track. We counted only individuals who chose the more distant
school, in order to be able to assume active choice. 4343 individuals in our data set
started in the vocational track when they entered secondary school. This number was
further reduced to 2730 individuals when we introduced characteristics of the nearest
school (which are not in all cases listed in the data set). Other socio‐economic variables,
however, remained stable in comparison with the total number of individuals in our data
set.
The coefficients in Table 4‐9 show a small, but significant, negative correlation
between a pupils' upward mobility and the distance difference to the nearest school: the
predicted probability for a student to have moved up to a higher track in Year 3 reduces
by 0.01 for every kilometre increase in the distance difference between the actual and
the nearest school. Notably migrant pupils seem to lose opportunities to move up to a
higher track, when they travel further to school. Furthermore, the neighbourhood SES
index is positively associated with upward mobility, indicating that pupils living in a more
affluent residential area also have a higher chance to move up while in secondary
education. The positive neighbourhood effect is reduced for immigrant pupils.
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Table 4‐9: OLS regressions of individual upward mobility at the entrance of Year 3, considering only pupils who started at the lower vocational secondary level in Year 1 (4343 individuals); non‐nearest school choosers only
Non‐nearest choosing VMBO advice students (1) (2) (3)VARIABLES level‐up in Year 3 level‐up in Year 3 level‐up in Year 3 Distance difference to nearest school ‐0.01*** ‐0.01*** ‐0.01*** (0.002) (0.003) (0.003) male ‐0.03*** ‐0.03*** ‐0.04*** (0.010) (0.010) (0.013) weighted student funding ‐0.06*** ‐0.06*** ‐0.06*** (0.014) (0.014) (0.018) Surinamese/Antillean ‐0.06*** ‐0.05*** ‐0.05** (0.017) (0.017) (0.020) Turkish ‐0.06*** ‐0.04** ‐0.05* (0.019) (0.018) (0.024) Moroccan ‐0.04** ‐0.03 ‐0.03 (0.019) (0.018) (0.024) Other Immigrant Background 0.01 0.02 0.02 (0.018) (0.018) (0.023) one‐parent household ‐0.03* ‐0.02 ‐0.01 (0.015) (0.014) (0.016) neighbourhood SES index 0.05*** 0.05*** (0.010) (0.013) Non‐western x SES ‐0.02** ‐0.03*** (0.010) (0.012) urbanicity ‐0.02 ‐0.01 (0.017) (0.023) # of relevant schools within 5 km 0.00* 0.00** (0.001) (0.001) distance to nearest relevant school (km) ‐0.03** ‐0.04** (0.013) (0.017) relative exam scores nearest school 0.02 (0.033) # of tracks offered at nearest school 0.00 (0.006) % upward mobility at nearest school ‐0.05 (0.086) average SES index at nearest school 0.01 (0.020) % non‐western at nearest school ‐0.00 (0.034) Utrecht municipality dummy ‐0.03 0.00 (0.025) (0.035) The Hague municipality dummy ‐0.04** ‐0.00 (0.019) (0.022) Rotterdam municipality dummy ‐0.04** ‐0.01 (0.019) (0.022) Constant 0.22*** 0.29*** 0.25** (0.020) (0.075) (0.098) Observations 4,343 4,325 2,730 R‐squared 0.03 0.04 0.05 Adj. R‐Squared 0.0253 0.0401 0.0462
Notes: Robust clustered standard errors (at neighbourhood level) in parentheses *** p<0.01, ** p<0.05, * p<0.1
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4.5 Conclusions and discussion
In the current research we explored 1) how the distance travelled to school is
related to ethnicity and other socio‐economic variables on the individual‐,
neighbourhood‐ and school level; 2) the differences between the group that chooses the
nearest school, and the group that prefers another school; 3) the effects of free school
choice on ethnic segregation; and 4) upward mobility to a higher track as a possible
benefit of school choice.
4.5.1 Who travels further to school?
All over, migrant pupils travel less distance to school than native Dutch pupils,
except for native Dutch pupils in affluent residential areas. Average distance to school is
obviously influenced by population density and the corresponding larger number of
schools in densely populated areas. The number of schools to choose from in the four
Dutch major cities is, for example, 15 schools within a radius of 5 km in the case of
native Dutch students, to 21 schools in the case of students of Turkish descent (Table
4‐1). Since more students with a migrant background at present live in less affluent,
more densely‐populated neighbourhoods, this may partly explain why they travel, on
average, less far to school. However, urbanicity, according to our findings, cannot solely
explain the lower mean distance travelled to school by migrant pupils.
Students of Dutch origin travel the largest mean absolute distance, 3.49 km.
However, the absolute distance to school is inversely correlated with residential SES: in
the poorest residential areas, native Dutch youths travel further than Turkish and
Moroccan youths, while in contrast, the average distance to school is lower among
native Dutch students in affluent areas. This may also explain our findings that the
elementary teacher's recommendation for the academic secondary track is associated
with a lower distance to school: substantially higher percentages of native Dutch
students obtain this advice, and they more often live in an affluent area where the
preferred school may be nearby.
4.5.2 Differential sorting, mobility increases segregation
The literature predicts strong effects on ability sorting of extended school choice
(e.g. Cullen et al., 2000). In the context of unlimited choice in the Netherlands, however,
we found, somewhat to our surprise, virtually no systematic differences between the
4 Patterns in secondary school selection in the context of unlimited choice
105
group of choosers for the nearest school and those who chose a school further from
their home address. We did find, however, differential sorting when we compared the
characteristics of the nearest school and the selected school, that was not induced by
pupils' abilities, but related to the pupils' ethnicity: native Dutch pupils seem to weigh
other aspects when selectively choosing a school at a further distance than is the case
among migrant pupils. When migrant students prefer a school further outside their
residential area, they tend to choose a school with even higher percentages of migrant
pupils than the neighbourhood school. Native Dutch pupils, on the other hand, tend to
make a quality‐driven choice for a school with a higher mean exam score and a higher
mean CITO intake. They prefer, furthermore, a school with a higher SES population and a
lower percentage of migrant students than the nearest school. Remarkably, migrant
students, in contrast, more often choose schools with higher percentages of migrant
students, than the often already high percentages at the nearest school, while at the
same time, they show only a marginal preference for higher school quality. In fact, we
may have revealed here the segregation of migrant pupils by choice. Our findings bring
to mind research by Bunar in Sweden (2010), who learned from interviews with migrant
students in Stockholm and Malmö how important the sense of belonging and
recognition within the pupils' own ethnic group seems to be.
4.5.3 Upward mobility, an important opportunity for migrant students
Finally, although the effect we found is fairly small, we still consider as important
the outcome that the slightly higher odds of migrant students to move up to a higher
track when they attend a school closer to their home. In the domain of academic
research into the achievement gap between the children of low SES and high SES
parents, the question whether disadvantaged pupils should be bussed to better schools,
or better schools should be brought to poorer neighbourhoods, seems centre stage. Our
findings rather support the second strategy.
4.5.4 Policy implications and further research
We plan to follow up on the current research, and investigate more in‐depth why
migrant parents and pupils seem to prefer a school with high percentages of migrant
pupils. For the moment we reflect on two possible explanations: Over the years, schools
with high percentages of migrant students (up to 90%), have largely invested in, for
106
example, the development of tailor‐made strategies to support first‐time academic
learners, effective cooperation with parents, and the encouragement of upward
mobility. There may be, therefore, reasons for migrant parent to choose a school with
large numbers of migrant children; 2) we cannot exclude, however, that migrant pupils
tend to wish to avoid stigmatization in a school with predominantly native Dutch pupils,
and prefer a school where they are the absolute majority. In the Dutch context, as we
described earlier, it is important to keep in mind that no financial concerns limit school
choice in any way; neither do schools with lower SES pupils, in general, have less
qualified teachers, or struggle with a high turnover of teachers. This may enhance
opportunities to identify other motives behind school choice, for example, choices
related to ethnicity, as mentioned above.
Furthermore, we reflected on our finding that the average school exam scores
only marginally drive school choice, even though this information is freely available on
the internet (Inspectorate of Education). Since the average school exam score is strongly
correlated to the average level of schooling of parents, it is complicated for parents to
identify the school's added value which best fits their child. Supported by our finding
that the school's upward mobility does not drive school choice whereas this could be
expectedly an important element in the closing of the achievement gap– we suggest
that the Inspectorate of Education publishes on the Internet both exam results and the
upward mobility of all schools, specified for the separate ethnic groups. More in general,
informing migrant parents is, according to, for example Douglas Archbald (2004), a
requirement for free school choice as a Liberation Model.
We contemplated on the overall marginal associations we found with school
quality indicators. It is imaginable that almost a century of free school choice, the
absence of tuition fees, and the publication of the Inspectorate's assessment reports of
all schools on the Internet have all together resulted in a transparent, fairly
homogeneous school market. Out of about 650 secondary schools nationally, around 30
schools are under intensified supervision of the Inspectorate because of severe
underperformance. Currently only two of these 30 schools are located in the major four
cities (www.onderwijsinspectie.nl). Moreover, underperforming schools typically
improve within one or two years, under intensified supervision of the Inspectorate.
Taking this thought further, in the Netherlands, perhaps less so than in other countries,
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107
choosing one school or another may not result in serious divergences in opportunities
for pupils.
Finally, another aspect of school choice that may inspire further research is the
fact that pupils do have an important say themselves in the choice of a secondary school
in the Netherlands. They may, for example, want to go to the same secondary school as
their classmates in elementary education, they may feel inspired by the extra
programmes a secondary school offers, or they may feel attracted to a new well‐
equipped school building. Free school choice and the absence of tuition fees make it
possible for pupils to follow such preferences. How pupil preferences influence patterns
of choice and school results would be an interesting further research question.
We look forward to be able to use the data on final exams in the coming years, as
the BRON database is further developing. We acknowledge that assembling data sets for
the purpose of academic research is time‐consuming, and that the government institute
that collects these data (DUO) does currently not have the extra personnel to carry out
this task. This has deepened our gratitude towards Cees Vermeulen, Erik Smits and Rob
Kerstens, who have already invested their time and support. We do hope that our work
has illustrated the high quality and the possibilities of the BRON data, and its potential to
improve the quality of education at large, through a better understanding of these
complex processes.
4.6 Appendices
In the general overview depicted in Appendix 4‐1, we considered the higher
aggregate level of non‐Western background (includes the vast majority of pupils of
Turkish or Moroccan descent) versus Dutch background. The following maps give a first
impression of the association between the ethnic composition of an area, and the
distance travelled to school. For example, the southern part of the city centre in
Amsterdam has a large share of native Dutch inhabitants (only 0‐20% inhabitants with a
migrant background), and the average distance travelled to school in this area is lower
(lightest shade of blue) than in other districts in Amsterdam.
Some differences between the four cities can be observed. In Rotterdam and The
Hague, neighbourhoods in the city centre more often have a population predominantly
consisting of people with a non‐Western background than is the case for Amsterdam
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and Utrecht. This relates to differences in city structure: in Amsterdam and Utrecht, top‐
quality residential areas are located in the heart of the city.
Interestingly, in Amsterdam, more so than in the three other cities, schools tend
to be segregated more than neighbourhoods: 20 out of 84 secondary schools in
Amsterdam enrolled between 80 and 100 % pupils with a non‐Western migrant
background, whereas no neighbourhood in Amsterdam has this high percentage of
inhabitants with a migrant background; the degree of segregation of these 20 schools,
exceeds the degree of segregation of the surrounding neighbourhood. While in
Rotterdam the variety in ethnic composition of neighbourhoods reflects the ethnic
composition of schools more closely, also in this city 21 out of 113 schools enrolled
between 80‐ 100% of pupils of non‐ Western migrant descent. In comparison, The Hague
and Utrecht seem to be less segregated, although The Hague is the only city with 6
neighbourhoods in the city‐centre where between 80‐100% of inhabitants have a non‐
Western immigrant background.
We also depicted the schools outside the city boundaries which were attended
by pupils living within the city. There are remarkable differences in the percentage of
pupils attending a school outside the city boundary among the four cities:
Amsterdam: 230/5153=4.4%
Rotterdam: 964/4853=19.8%
The Hague: 791/4000=19.7%
Utrecht: 639/2065=30.9%
In the current chapter we did not investigate further the causes for these
differences.
4.6.1 Explanation of variables
Average SES index school: the average SES of (the parents of) children at a
particular school.
CITO‐score: final test in elementary education; scores range between 500‐ 550,
the national average is 535.
HAVO, HAVO/VWO and VWO advice track dummy: dummy‐coded variable
relative to VMBO (lowest vocational track).
4 Patterns in secondary school selection in the context of unlimited choice
109
Individual upward mobility: pupil who moves up to a higher secondary track.
Municipality dummy‐coded fixed effects of Utrecht, Rotterdam and The Hague,
relative to Amsterdam.
Nearest relevant school: the nearest school to the pupils' residence that offers
the pupils' chosen track.
Non‐Western immigrant: The following definitions by Statistics Netherlands (CBS,
the National Statistics Office, www.cbs.nl) have been used: Western immigrant:
someone originating from a country in Europe (exclusive of Turkey), North
America, Oceania, Indonesia or Japan. Non‐western immigrant: someone
originating from Africa (including Morocco), South America, Asia (exclusive of
Indonesia and Japan) or Turkey.
Other immigrant background: a wide range of nationalities, including highly
educated workers, refugees and people seeking asylum. The variance in SES is
large, accordingly.
Outside municipality border: pupils, who live within city boundaries, but attend a
school outside the city.
Relative exam score: the performance per track level, measured by the mean
exit‐exam score, compared with the mean exit‐exam score of all other schools in
the four major cities offering the same track level.
Relative neighbourhood SES index: Neighbourhood SES score relative to the
average in the four major cities; SES scores have been centred on zero, ‐4 (poor)
to +4 (affluent).
Secondary school advice score: advice by the elementary teacher about which
track to follow in secondary education.
Tracks offered at secondary school: the number of tracks (1 ‐ 4 main tracks) may
differ per school. Comprehensive schools offer tracks on the vocational and
academic levels. Schools may also offer either vocational or academic tracks;
schools may be diversified further and offer only one academic track.
Upward mobility at school level: the percentage of pupils at school level who
move up to a higher secondary track.
110
Urbanicity: a measure for human activity in an area, based on the number of
addresses per km².
Weighted student funding: in primary schools children qualify for extra funding
depending on the educational attainment level of the parents; the pupil weight
for funding is 0 for better‐educated parents (means only standard funding);
children of less well‐educated parents may receive 0.3 to 1.2 extra funding,
additional to standard funding.
Appendix 4‐1: Maps of Amsterdam, Rotterdam, Utrecht and The Hague.
In shades of blue, the average distance travelled to secondary school on the
neighbourhood level; in shades of red, the percentage of non‐Western immigrants at
neighbourhood level. In parentheses, the number of neighbourhoods sharing the same
percentage of non‐Western immigrants, and the number of neighbourhoods sharing the
same average distance travelled to school, respectively. The cities are not depicted to
scale, but have been formatted to fit in this text. The ranking order of size is Amsterdam
(largest), Rotterdam, The Hague, Utrecht.
4 Patterns in secondary school selection in the context of unlimited choice
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4 Patterns in secondary school selection in the context of unlimited choice
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4 Patterns in secondary school selection in the context of unlimited choice
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Appendix 4‐2: LOWESS regression (Locally Weighted Scatterplot Smoothing; bandwidth = .6) and linear regression (OLS) of the extra distance travelled to the preferred school (as compared with the nearest school) and school's mean final exam score (national standardized exam score)
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Appendix 4‐3: Regression results (OLS) for difference in the mean relative exam score at school level (i.e. the actual school exam score minus exam score at the nearest school), considering non‐nearest school choosers only
(1) (2) (3) (4) (5)VARIABLES exam diff exam diff exam diff exam diff exam diff Distance difference to nearest school 0.00 0.01* 0.01 0.00 0.00 (0.005) (0.005) (0.005) (0.005) (0.006) male ‐0.01 ‐0.01 ‐0.01 ‐0.02* 0.00 (0.009) (0.009) (0.008) (0.011) (0.014) weighted student funding ‐0.01 ‐0.04* ‐0.03 ‐0.05** ‐0.04 (0.032) (0.022) (0.021) (0.026) (0.040) Surinamese/Antillean ‐0.05 ‐0.08*** ‐0.01 ‐0.04 ‐0.00 (0.033) (0.027) (0.048) (0.042) (0.050) Turkish ‐0.04 ‐0.13*** ‐0.04 ‐0.07 ‐0.08 (0.060) (0.043) (0.065) (0.048) (0.064) Moroccan ‐0.09 ‐0.16*** ‐0.07 ‐0.08* ‐0.06 (0.058) (0.042) (0.067) (0.045) (0.063) Other Immigrant Background ‐0.05* ‐0.06*** ‐0.02 ‐0.03 ‐0.01 (0.027) (0.024) (0.033) (0.031) (0.040) 2nd generation non‐Western Immigrant 0.03 0.04** 0.05*** 0.03 0.02 (0.019) (0.018) (0.017) (0.020) (0.029) one‐parent household ‐0.04 ‐0.05** ‐0.06*** ‐0.03 ‐0.05 (0.028) (0.020) (0.021) (0.020) (0.033) neighbourhood SES index ‐0.06** ‐0.02 ‐0.02 ‐0.01 (0.021) (0.026) (0.025) (0.027) Non‐western x SES ‐0.00 ‐0.01 ‐0.01 0.01 (0.018) (0.018) (0.020) (0.023) urbanicity ‐0.05 ‐0.05 ‐0.03 ‐0.05 (0.052) (0.052) (0.053) (0.062) # of relevant schools within 5 km 0.00* 0.01* 0.00 0.00 (0.003) (0.003) (0.003) (0.003) distance to nearest relevant school (km) 0.01 ‐0.02 ‐0.03 ‐0.03 (0.040) (0.036) (0.034) (0.036) # of tracks offered at nearest school 0.02 0.00 0.00 (0.022) (0.020) (0.027) average SES index at nearest school ‐0.02 ‐0.07 ‐0.06 (0.059) (0.061) (0.071) % non‐western at nearest school 0.40** 0.33** 0.35* (0.159) (0.164) (0.193) % non‐western at nearest school x non‐western ‐0.17** ‐0.09 0.00 (0.073) (0.063) (0.115) Utrecht municipality dummy 0.28*** 0.32*** 0.29*** 0.27*** (0.094) (0.083) (0.082) (0.082) The Hague municipality dummy 0.28*** 0.27*** 0.27*** 0.20*** (0.055) (0.056) (0.059) (0.060) Rotterdam municipality dummy 0.11* 0.13* 0.12* 0.10 (0.067) (0.065) (0.077) HAVO advice track dummy 0.01 (0.032)HAVO/VWO advice track dummy 0.02 (0.034)VWO advice track dummy 0.19*** (0.041)CITO test score 0.00** (0.002) Non‐western x CITO test score ‐0.00 (0.064) (0.000) Constant 0.11*** 0.08 ‐0.18 ‐0.15 ‐2.26** (0.030) (0.236) (0.285) (0.289) (0.985) Observations 8,503 8,471 8,471 4,720 2,524 R‐squared 0.01 0.09 0.16 0.18 0.17 Adj. R‐Squared 0.00420 0.0924 0.153 0.173 0.158
Notes: Robust clustered standard errors (at neighbourhood level) in parentheses *** p<0.01, ** p<0.05, * p<0.1
4 Patterns in secondary school selection in the context of unlimited choice
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Appendix 4‐4: Regression results (OLS) for difference in school percentage of upward mobility to a higher track (i.e. upward mobility at the actual school minus upward mobility at the nearest school), considering non‐nearest school choosers only
(1) (2) (3) (4) (5)VARIABLES pupward diff pupward diff pupward diff pupward diff pupward diff Distance difference to nearest school 0.16 0.28 0.36* 0.11 0.27 (0.202) (0.207) (0.184) (0.179) (0.221)male 0.27 0.20 0.27 0.20 ‐0.11 (0.326) (0.316) (0.296) (0.358) (0.459)weighted student funding ‐0.78 0.37 0.33 ‐0.60 ‐2.39 (0.886) (0.777) (0.736) (0.917) (1.603)Surinamese/Antillean 1.34 2.81** 1.95 ‐0.49 ‐0.80 (1.186) (1.083) (1.940) (1.750) (1.925)Turkish 1.10 3.19** 2.06 0.14 ‐0.63 (1.937) (1.538) (2.269) (1.738) (2.223)Moroccan 4.07* 6.20*** 4.82** 2.62 1.64 (2.091) (1.571) (2.318) (1.625) (2.067)Other Immigrant Background 0.27 1.15 0.56 ‐0.12 ‐2.11 (0.917) (0.843) (1.189) (1.178) (1.321)2nd generation non‐Western Immigrant 0.40 0.32 0.14 0.02 1.29 (0.704) (0.643) (0.644) (0.828) (1.113)one‐parent household 1.36* 2.11*** 2.44*** 0.33 0.77 (0.735) (0.622) (0.646) (0.628) (0.870)neighbourhood SES index 1.98*** 0.42 1.18 1.47* (0.699) (0.834) (0.829) (0.868)Non‐western x SES ‐0.57 ‐0.33 ‐0.43 ‐0.85 (0.627) (0.639) (0.843) (1.022)urbanicity 0.60 0.80 0.61 0.48 (2.023) (2.185) (2.260) (2.376)# of relevant schools within 5 km 0.00 0.01 ‐0.06 ‐0.09 (0.108) (0.106) (0.118) (0.128)distance to nearest relevant school (km) ‐0.89 ‐0.15 ‐0.14 ‐0.64 (1.976) (1.743) (1.622) (1.613)# of tracks offered at nearest school ‐1.34 ‐1.77** ‐1.27 (0.889) (0.823) (1.060)average SES index at nearest school 2.68 2.21 1.84 (1.962) (2.274) (2.419)% non‐western at nearest school ‐6.55 ‐7.40 ‐12.29* (5.234) (6.366) (7.236)% non‐western at nearest school x non‐western 2.72 5.44*** 8.00* (2.646) (1.974) (4.692)Utrecht municipality dummy ‐2.50 ‐4.16 ‐7.06** ‐6.55** (4.066) (3.373) (3.342) (2.918)The Hague municipality dummy 0.10 0.20 ‐1.24 1.57 (2.178) (2.166) (2.236) (2.630)Rotterdam municipality dummy ‐1.11 ‐0.29 ‐0.59 ‐1.35 (2.415) (2.459) (2.570) (2.787)HAVO advice track dummy ‐3.16** (1.272) HAVO/VWO advice track dummy ‐3.54*** (1.334) VWO advice track dummy ‐8.34*** (1.422) CITO test score ‐0.31*** (0.104)Non‐western x CITO test score ‐0.00 (0.006)Constant ‐3.45*** ‐6.16 0.45 7.96 170.51*** (1.309) (9.372) (10.264) (10.023) (54.278) Observations 9,025 8,997 8,997 5,046 2,709R‐squared 0.01 0.04 0.10 0.14 0.18Adj. R‐Squared 0.00967 0.0396 0.0957 0.134 0.170
Notes: Robust clustered standard errors (at neighbourhood level) in parentheses *** p<0.01, ** p<0.05, * p<0.1