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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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Citation for published version (APA):van Welie, E. A. A. M. (2013). They will get there! Studies on educational performance of immigrant youth in theNetherlands Amsterdam: TIER

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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).


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.


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


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


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


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.


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

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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%).


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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).

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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


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).

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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


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

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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.


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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

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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.


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

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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


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


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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.


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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


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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.


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.


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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


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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


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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


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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


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,


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).


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.

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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.


111


113


115

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)

116

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



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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


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