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Dropout prevention measures in the Netherlands, anevaluation
Kristof De Wittey
TIER, Maastricht University
Kapoenstraat 2, 6200 Maastricht
and
KU Leuven
Naamsestraat 69
3000 Leuven, Belgium
Soe J. Cabus
TIER, Maastricht University
Kapoenstraat 2, 6200 Maastricht
April 26, 2010
Abstract
In line with the Lisbon Agenda, set by the European Council in the year 2000,
European governments formulated ambitious plans to half the level of early school leavers
by 2012. This paper outlines the dropout prevention measures in the Netherlands and
analyzes their eect at both the individual level and school level. Using a panel probit
model, we nd little inuence of policy at the individual level. By means of quantile
regressions, we observe that schools with a relatively high dropout rate benet the most
from dropout prevention measures.
Keywords: Eectiveness, Dropout prevention, Secondary education, Logit, Quan-
tile regression
JEL-classication: I21, C35
Corresponding author: [email protected] would like to thank Wim Groot, Henritte Maassen van den Brink, Chris van Klaveren, Marton
Csillag, participants of the TIER seminar at the U niversity of Groningen, m embers of the feedback committee
at the Dutch Ministry of Education, Culture and Sciences and The Scientic Review Commission at NICISInstitute for insightful comments.
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1 Introduction
"All Dutch municipalities should register (potential) dropout students and make sure that
by following a suited educational track, they obtain a starters qualication".(OCW, 2010, p.1)
Over the last decades, societies have developed from an industrial towards a knowledge-
driven economy. The economic welfare of individuals and the competitive advantage of
nations have come to depend on knowledge, skills and enterprise of the workforce" (Brown et
al., 2003). Investment in human capital plays a key-role in economic prosperity. The human
capital theory suggests that schooling raises productivity and earnings (Becker, 1992, 1993)
and can serve as ones signal of productivity (Spence, 1973). Nelson and Phelps (1966) and
Schultz (1967) treat human capital of the workforce as a crucial factor for adoption of -
innovative - productive technologies.
Every year, many students drop out of school without obtaining a higher secondary edu-
cation diploma. This is not desirable in a knowledge-driven economy, not only for societys
productiveness, but also for individual development. These so-called dropout students or
early school leavers constitute a group that is heavily at risk (Psacharopoulos, 2007). They
have a relatively higher risk of (1) entering a vicious circle in which on turn their children ob-
tain lower education levels (e.g., Bowles, 1972; McLanahan, 1985; Anger and Heineck, 2009),
(2) long-term unemployment or failing to secure productive employment (e.g., Rumberger
and Lamb, 2003; OECD, 2008), (3) suering from health problems (e.g., Groot and Maassen
van den Brink, 2007) or (4) lack of social cohesion (e.g., Milligan et al., 2004; van der Steeg
and Webbink, 2006).
At the Lisbon 2000 summit, the European council decided to aim for a lower dropout rate,
among other benchmarks. The average rate of early school leavers should be no more than
10% by 2012. Following the European council, we dene an early school leaver (or dropout)
as a person younger than 23 who leaves education without a higher secondary education
degree. Thanks to political eagerness to tackle the problem, the European member states
developed various programs to reduce dropping out at secondary education. From Figure
1, we could deduce a declining trend in the dropout rates in EU countries. Since 1992, the
European dropout rate has fallen from about 35% to about 17%.1
Determining the most eective way of tackling the dropout problem is not straightforward
as students do not dropout at secondary education because of one specic drawback. They
often are piling up problems, both at home, in their neighborhood or at school, before they
1 Dropout is also a major issue in other continents. For instance, consider the following citation out of the
inauguration speech of U.S. president Obama. "Every American will need to get more than a high school
diploma. And dropping out of high school is no longer an option" (Obama, 2009).
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0
5
10
15
20
25
30
35
40
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
PeopleinEU-12aged18-24withonlylowerse
condaryeducation
notineducation(in%)
Figure 1: Dropout rates of high schools in the EU
actually make the dropout decision (Rumberger, 2001). The literature indicates, for instance,
that dropout students change school more often (Rumberger and Larson, 1998), have more
retentions in grade (Roderick, 1994; Jimerson, 1999), struggle through their study curriculum
(Garnier et al., 1997), are more often involved in criminal activities (Elliot and Voss, 1974;
Phillips and Kelly, 1979), use more often cannabis, alcohol or other drugs (Fergusson et
al., 2003; ter Borgt et al., 2009), and are more likely to live in disadvantaged neighborhoods
(Bobonis and Finan, 2009) and in poorer families (Nelson et al., 1996). It is the accumulation
of small and large problems which pushes the pupil eventually towards the dropout decision.
This paper discusses the dropout prevention policy in the Netherlands and analyzes its
eectiveness. Numerous measures and actions have been taken nationwide. Because of the
underlying population, dierent regions and cities have often dierent needs. Therefore, the
Ministry of Education created 39 regional dropout authorities (RMC) in 2002. Each of those
regions can take dierent actions towards policy goal settings. As this is not desirable for
working up to an integral approach (Holter and Bruinsma, 2009), the Ministry of Education
decided to outline a general framework, known as the covenants (gentlemen agreements).
A covenant is a signed written agreement between the Ministry on the one hand and the
RMC and the schools at the other hand. The covenants contain a list of measures (menu-
items) and actions to ght dropout, e.g., to improve the registration of non-attendance and
dropout, to improve exibility of educational participation, to intensify the care for potential
dropouts and to increase attention for a good preparation for apprenticeships. In this way,
there are 39 covenants. The Ministry of Education signed covenants in all RMC-regions in
the Netherlands over the period 2007-2008. Nevertheless, in 2006-2007 14 regions with the
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highest dropout levels have been signing a previous round of covenants.
The covenant includes a performance bonus to schools that reduce the number of dropouts
at secondary education over the period 2009-2012. The nancial incentive consists of a xed
fee of 2,000 euro per dropout less in comparison to base year 2005-2006 .2 This should enhancethe proper implementation of the covenants. Van der Steeg et al. (2008) evaluated the 2006
covenant, however, and concluded that the 2006 covenant policy (as a bundle of activities)
was not eective in reducing early school-leaving.
The contributions of this paper are threefold. Firstly, we examine the eectiveness of the
gentlemen agreements by analyzing which of the incentives signicantly correlate with a
lower probability of student dropout at secondary education. We use an exceptionally rich
panel data set (BRON; Basis Register Onderwijsnummer) which covers all students in the
Netherlands from 2004 to 2008. Thanks to postcode information, the data are enriched with
neighborhood characteristics obtained from Statistics Netherlands. We start by an analysis
at the individual level. We estimate by a panel probit model the probability that a student
drops out. While controlling for student characteristics (e.g., gender, school track, etnicity),
neigborhood characteristics (e.g., income per capita, green areas, employment in the area),
a time trend (controlling for the increased awareness of obtaining a diploma) and region
xed eects, we correlate the inuence of dropout prevention measures to the individual
probability of dropping out.
Secondly, at the individual level, various unobserved exogenous variables can inuence
the dropout decision. Therefore, we aggregate all data at the school level. This provides an
indication of schools with many and few dropout rates. Using a quantile analysis, and con-trolling for the student, neighborhood, time and regional inuence, we examine the inuence
of dropout prevention policy measures for schools with few (i.e. 25th quantile), average (i.e.,
50th quantile) and many (i.e., 75th quantile) dropouts. As selecting quantiles is rather arbi-
trarily, we estimate also the inuence of the dropout measures for a continuum of quantiles.
Thirdly, this paper is to our best knowledge the rst to describe the dropout prevention
incentives in one of the EU member states. The Dutch Ministry of Education spends 313
million a year (anno 2008) on dropout prevention policy, which implies 0.83% of its total
budget (Dutch Agency for Statistics). It has been foreseen that this budget will increase to
400 million euro a year by 2011 (Ceulenaere et al., 2009 and Statistics Netherlands). Not a
negligible budget, which eectiveness is worth analyzing.
Although this paper focusses on the Netherlands, its impact goes far beyond this specic
country. Firstly, dropout policy is high on the political agenda in about all industrialized
countries. Secondly, our analysis reveals some best practice policy, what might be interesting
2 Recently, the fee has been raised to 2,500 euro per dropout less to reduce the Financial risks coupled
with the schools investments in ghting dropout.
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Mentoring & Coaching EVC or Dual TracksCare & Advisory Teams Frequent Intakes
Smoot hing Transition
Extended School
Registration & Commu nication
Reporting Truants (" verzuimloket")
Apprenticeships
Starter's QualificationNational Measures
Dropout prevention
policy
Compulsory
Education
Preventive
MeasuresCurative Measures
Figure 2: Policy measures in the Netherlands to reduce dropout (The broken border line
refers to the measures and actions included in the RMC-region specic covenants)
also for other countries.
The paper proceeds as follows. The Dutch dropout prevention policy is described in
Section 2. Section 3.1 briey presents the data, its structure and some descriptive statistics.
We examine the inuence of the Dutch dropout prevention measures at the individual level
in Section 3.2 and at the school level in Section 3.3. A nal section provides some policy
advice.
2 Dropout Prevention Measures in the Netherlands
This section describes briey the dropout policy in the Netherlands. We summarize the policy
measures systematically in Figure 2. Although the Ministry of Education did not make a
distinction between the measures, we can distinguish four types of measures: compulsoryeducation measures, preventive measures, curative measures and national measures. The
former three arise from the so-called covenant. We discuss each of the policy measures next
and link them to the international literature.
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Table 1: Summary: convenant dropout prevention measuresMeasure Implementation
A. Compulsory Education measures1 R ep orting truants R ep orting and tackling truancy at a very early stag e
2 Changing sub ject A tailored track for students who cho ose a wrong sub ject or whoprefers another subject.
3 Guidance towards to the students op-timal track or profession
Work placement, writing a letter of application, apprenticeship pro-grams, creating a portfolio
4 A pprenticeship C oo rdinatio n with lo cal private rm s and adha nced apprenticeshipprograms for students who prefer to do manual jobs.
B. Preventive measures5 M ent or in g and coach in g S tu dent s are m at ch ed w it h a coach f rom p ub lic or p rivate organ isa-
tions6 C ar e and adv isor y t eam C oord in at ion of stu dent car e by social workers, y ou th assistance,
school attendance ocers, health services and police.7 Smoothing the transition from the pre-
vocational level to the vocational levelIntake talks at the vocational school, providing more information onthe educational tracks, and checking wheter the students eectivelyenroll at and start in the new vocational school
8 Extended scho ol A dd m ore sp orts and culture to scho ols in order to m ake scho ol m oreattractive.
C. Curative measure9 Dual track Oering the posibility for drop out students to re-enter education by
a tailered educational track.1 0 Fr eq ue nt inta kes I ncr ea si ng t he nu mb er o f m om ents th at s tu de nts m ay e nte r se co nd ar y
education.
2.1 Compulsory Education Measures
Compulsory education measures are supported by Dutch law. In this way, every RMC-region
is forced to undertake actions in order to obey the law. We briey discuss three compulsory
education measures: registration and communication, reporting truants, and apprenticeships.
2.1.1 Registration and Communication
A good measurement instrument is indispensable when it comes to the evaluation of dropout
policies. In the past, registration of early school leavers was inaccurate and unreliable. There
was denitely a lack of transparency (Expertisecentrum, 2006). Therefore, the program
Aanval op de uitval (or ghting dropout) has been launched in the year 2006, which implied
an upgrade of the registration system. Nowadays, we can use nearly complete and reliable
data on dropout levels in the Netherlands.
The registration of dropouts takes place as follows. Every pupil who attends school in
the Dutch educational system gets a personal identication number (or education number
record). All schools register students using this personal identication number. In the end, allregistrations end up in one large nationwide database called het Basisregister Onderwijs or
BRON. Since 2007, BRON can be used to evaluate the regional and national policy measures
for reducing dropout in secondary education.
An incentive based on naming and shaming of schools with superior and inferior dropout
rates can boost competition among schools. Information on early school leavers has been
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published on the World Wide Web, e.g., VSV-Atlas, VSV-verkenner and the website voorti-
jdigschoolverlaten.nl.
Another important role of communication is to inform youngsters about the relevance
of obtaining a starters qualication. In the Netherlands, a VSV-jongerenteam3 has beenlaunched in the summer of 2009 (cf. stayinthegame.nu). Youngsters who have (a bad)
experience with dropping out at school give information on the relevance of obtaining a
starters qualication (which is a minimal higher secondary diploma) to other youngsters.
2.1.2 Reporting Truants
A second policy measure aims at reporting non-attendance by registering truants in a central
database, the so-called digital oce. It oers the opportunity to signal better potential
dropouts (Auditdienst OCW, 2007). There is only one digital oce in the Netherlands
(Expertise Centrum, 2006).
An important feature can be attributed to the digital oce of non-attendance: to dis-
courage risk-averse pupils. It is possible that pupils do not want to run the risk "to be
caught" at not attending classes. For example, they dont want their parents to know about
skipping classes. Therefore, augmenting the chance to be caught can discourage those pupils
to undertake outside school activities during school time. Further research on the eect of
the digital oce of non-attendance on pupils behavior is desirable: does this digital oce
increase the probability to be caught?
Some previous work on truancy has been done in the literature. Attwood and Croll (2006)
have used the British Household Panel Survey and in-depth interviews to ask persistent tru-
ants about the extent, consequences and explanations for truancy from secondary schools.Poor relationships with teachers, bullying and a more general dislike of the schools at-
mosphere are considered as triggers for the dropout decision. In contrast to existing liter-
ature (e.g., Beekhoven and Dekkers, 2005), socio-economic factors, such as status, parental
involvement and the value of education, do not play a key role in non-attendance rates of
pupils. With this, Attwood and Croll suggest a distinction between socio-economic and atti-
tudinal factors. Davis and Lee (2004) also adhere to above ndings. They rst collected all
existing material on attending or not attending school in large cities of England, then went
into discussion with truants, as well as attendees and some parents. Davis and Lee argue
that, in contrast to professionals and existing literature, the curriculum is not considered as
a dropout trigger. This fact has been armed by Attwood and Croll (2006) and weakened
by Beekhoven and Dekkers (2005) who, in contrast, put emphasis on learning problems, lack
of motivation and problems arising from choosing the wrong vocational track.
3 VSV-jongerenteam or ESL-team composed out of youngsters.
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2.1.3 Apprenticeships
Apprenticeships are shown to be interesting learning methods to develop transferable, inter-
personal skills (cf. Lucas and Lammont, 1998) and to increase employability, especially in
vocational tracks. A lack of workplaces for apprentices is an important trigger of the dropout
decision. Finding better matches between apprenticeship and labor organization and improv-
ing information and support for pupils can make the problem less persistent (Onstenk, 2004;
Onstenk and Blokhuis, 2007).
More evidence from abroad can be found in the book of Bosch and Charest (2009). It
deals with various aspects of national vocational training systems and oers an in-depth com-
parative analysis of the following countries: Australia, Canada, Denmark, France, Germany,
Korea, Mexico, Morocco, the United Kingdom and the United States of America.
2.2 Preventive measures
Preventive measures try to keep youngsters in school, i.e., before the dropout decision has
been taken. Four preventive measures will dealt with: mentoring and coaching, care and
advisory teams, smoothing transition, and extended school.
2.2.1 Mentoring and Coaching
One of the main reasons of dropping out at secondary education is making the wrong study
choice. About 20% of all dropouts in the Netherlands indicate to leave school because of a
bad study choice (ROA, 2009/1). A professional approach of managing the study curriculum
can enhance school attendance. The Dutch government provides subsidies to stimulate the
realization of common educational programs over the period 2008-2011.
Back 2 Your Future is an example of improving study choice. It is a course that aims at
discovering your (study or work) capacities through, for instance, workshops or e-learning.
In international literature, curriculum dierentiation, or streaming and/or ability group-
ing is the most persistent issue in managing the study curriculum (for some examples, see
Keitel, 1987; Oakes et al.,1992; Gamoran et al., 1995; Terwel, 2004, 2005).
2.2.2 Care and Advisory Teams
In the Netherlands, care and advisory teams have been set up to connect internal and external
care for potential dropouts. It is the ambition to have full coverage in all schools by August
2011 (OCW). Although dierent settings are possible, a care and advisory team typically
consists of a pshycologist, pedagogues, social workers of the school, a representative of the
region and a policy ocer.
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2.2.3 Smoothing Transition
A long summer break of four months (het zomerlek or summer leakage) is often referred to
as the main reason of dicult school enrolment in the rst year track of vocational educa-
tion. Besides losing connection with their teachers and schools, in the Netherlands, students
have to physically go to another school in the transition from pre-vocational education to
vocational education. During this transition, students often lost track, which suggests a tran-
sition problem between pre-vocational and vocational education (van der Steeg and Webbink,
2006). This covenant measure tries to smooth the transition from pre-vocational to vocational
education by staying in touch with the students.
Evidence has been found in the literature by Felner et al. (1981, 1982) who conducted
a randomized experiment in the US, called the Transition Project. The experiment had
two goal settings: restructuring of the role of teachers and reorganizing the schools envi-
ronment. Students with improved transition reported signicantly higher levels of teachersupport, teacher aliation and involvement than students without the additional transition.
As a result, students belonging to the treatment group had better scores on the assessments
instruments. In sum, the experiment indicates that primary preventive community based
programs may help pupils during school transitions and may actually reduce dropout rates.
2.2.4 Extended School
Extended school(-time) refers to a range of services and activities, often beyond the school
day, to meet the needs of pupil and their families. For instance, schools oer sport and leisure
activities to augment their attractiveness for pupils.
Time-In is a such a project in the Netherlands (de Zwart et al., 2009). The initial goal
of the project is to teach skills to pupil by means of education or work, such that pupils do
not leave school without a diploma or job. It also oers the chance to motivate youngsters to
do sport activities, to combat the problem of overweight and to develop talented youngsters
to professional sport careers.
2.3 Curative Measures
Curative measures aim at taking dropouts back in school, i.e. after the dropout decision
has been taken. We discuss two educational measures in this eld: EVC or dual tracks and
frequent intakes.
2.3.1 EVC or Dual Tracks
Curative interventions deal with early school leavers already working in the labor market
(ROA, 2009/4). The Dutch government wants to support these youngsters in attaining
their starters qualication by means of EVC or dual tracks. EVC stands for a learning
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certicate which can be obtained if a student passes a learning module. Dual tracks refer
to the combination of labor and study. For instance, part-time learning on construction
techniques and part-time working in construction.
Borghans et al. (2000) oer more insight into the demand and supply of Dutch vocationalstudents on the labor market. Both measures aim at (unemployed) dropouts in times of eco-
nomic crisis. Unemployment rates have been increasing at the end of 2008: for pre-vocational
dropouts from 6% in 2007 to 9% in 2008 and for dropouts in the rst year of vocational edu-
cation from 10% in 2007 to 16% in 2008 (ROA, 2009/4). Temporary arrangements can oer
those youngsters a perspective on a (long term) job.
2.3.2 Frequent Intakes
With frequent intakes, there is more than one moment during the academic year to enter
secondary education. This actually means that pupils can enter the academic year after
October 1th (which is one month after the ocial start of the school year). The Centraal
Toegangsloket voor het Onderwijs (ROC) or Central Entry Oce for Education organizes
frequent intakes for pupils following vocational tracks. Moreover, early school leavers can
also enter a reception class, which is a special class for previous dropout students. After
a possible revision of the study choice, youngsters can continue another track as soon as
possible.
2.4 Non-covenant measures
The dropout prevention measures of the previous subsections arise from the covenant (i.e.,
the agreement between the Ministry of Education on the one hand, and the regions andschools on the other hand). However, the policy measures go beyond the covenant. The
Ministry of Education created a so-called starters qualication, which is a minimal degree
before one can leave school. In other words, Dutch pupils are compelled to go to school
until the age of 18 or until a starters qualication is obtained. A starters qualication is
considered as the minimum level of education a student needs to be equipped for labor market
entrance (Eimers et al., 2009). In the literature, a starters qualication corresponds to a
higher secondary diploma. Students who only obtained a lower secondary diploma do hence
not fulll the requirements for obtaining a starters qualication. There are three types of
qualications one can obtain before entering the labor market (Dutch abbreviation between
brackets): pre-university education (vwo), senior general secondary education (havo) and
vocational secondary education (mbo). This paper examines the inuence of the starters
qualication, but only summarizes below some evidence from the literature.
Indications on its eectiveness A starters qualication implies mandatory school at-
tendance. The eect of compulsory schooling on dropout rates in school has only little
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attention in evidence-based literature (for some examples, see Angrist and Krueger, 1991;
Oosterbeek and Webbink, 2004; Pischke and von Wachter, 2005; Oreopoulos, 2006, 2007).
van der Steeg and Webbink (2006) stress the importance of the additional years of education
gained through compulsory school attendance even if the pupil drops out of school tooearly. They promote the use of a sliding scale: more education leads to better labor market
outcomes irrespectively of actually obtaining the school-leaving certicate. They also oer
dierent labor market perspectives to dierent types of vocational education and orientation.
The level of starters qualication is hence no critical boundary for labor market success.
Nonetheless, to leave secondary school without a diploma means to have fewer chances
on the labor market to be successful (Spence, 1973; Becker, 1992; Card, 1999; Rumberger
and Lamb, 2003; among others). Providing pupils education and skills needed to enhance
success on todays labor market, has long been a key policy goal in the Netherlands (OCW).
In further research, we want to nd out the schooling and labor market consequences of a
starters qualication on dropping out at secondary education in the Netherlands.
3 Results: Eects of dropout policy
In the previous section, we have described educational measures within the scope of dropout
prevention policy in the Netherlands. We have distinguished two broad categories: covenant
and non-covenant measures. The research results of this paper only deal with analyses on
the eectiveness of covenant measures. We perform these analyses at the individual level (cf.
section 3.2) and the school level (cf. section 3.3), but start with a description of the data in
the next section 3.1.
3.1 The data
This paper uses the BRON-data, an unique registered data set of all Dutch students. The
database contains pupil specic information about his/her personal characteristics (e.g., gen-
der, ethnicity and receives special care at school), schooling (e.g., school type, school track
and major subjects) and about the parents (e.g., single parent household). Because of match-
ing variables (as postcode or identication numbers) government institutions can further link
this BRON data to neighborhood characteristics, tax information on the parents, work status,
and basically any other ocial Dutch data set.
Thanks to the Ministry of Education, this research could benet from the full BRONdata set, which comprises all Dutch students enrolled between 2004 and 2008. Using post-
code information, we linked this data set to neighborhood characteristics as obtained from
Statistics Netherlands. By carefully analyzing the agreements between the government on
the one hand and the regional dropout authorities and schools on the other hand, we con-
structed dummy variables which capture the particular implementation of the agreements
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at regional level. As schools and municipalities are collaborating extensively within each of
the 39 regions, and due to lack of information at the local level, we assume that all schools
within a region are implementing the agreements in a similar way. Some summary statistics
on the data are presented in Table 2.To examine the eectiveness of the dropout prevention, we analyze the data at two levels:
(1) at individual level and (2) at school level.
We follow the Ministry of Education in dening dropout students as follows. In the
Netherlands, dropout students are determined by comparing the students younger than 22
on the rst of October of a given year, with the students at the rst of October of the next
year. Students who did not obtain a diploma (i.e., a starters qualication) and left the
database are considered as dropouts.4 As such, students between the age of 12 and 23 who
do not have a starters qualication at the time that they drop out at secondary education,
are dened as early school leavers.
We estimate by means of a panel probit model the probability that a student will dropout
at secondary education. With this, we estimate two specications: a random eect panel
probit model and population averaged panel probit model. Controlling for (1) student, (2)
neighborhood, and (3) regional characteristics, we relate the dropout probability to the pre-
vention mechanisms for dropout reduction. We remark, however, that due to data limitations
(e.g., it is to our best knowlegde impossible to nd proper instrumental variables), we do
not attempt to estimate the causal impact of the dropout prevention measures. Hence, we
rather look for strong correlations within the sample. As we rigorously control for various
background characteristics of the students, the neighborhood and the schools, and as we
allow for a time trend in the data (i.e. control for potential time eects), our results give aclear indication on the impact of menu-items within the covenants.
3.2 Analysis at individual level
We start analyzing the eectiveness of the Dutch dropout prevention policy by considering
the impact on the individual student. In other words, while controlling for various inuences,
we correlate by a panel probit model the probability of dropping out with the introduction
of particular prevention measures. The results are presented in Table 3.
Following the literature, gender, ethnicity and family background are indicated as triggers
of the individual dropout decision (Rumberger, 1983; Astone and Mclanahan, 1991, 1994;
Mayer, 1991; Steinberg et al., 1992; Berktold et al., 1998; Pong and Ju, 2000; among others).
We observe that neighborhood characteristics play an important role. Students living in
poor and high density areas have a higher probability of dropping out, as well as students
4 We consider all students living in the Netherlands, who are taking courses in secondary education (includesvwo, havo, vmbo, mbo) and vavo. We do not consider students living abroad, in an international Baccalaureat,or in an apprenticeship program (praktijkonderwijs).
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Table 2: Descriptive statistics - total number, unless otherwise stated in second column2004 2005 2006 2007
Student characteristicsdropout 58,600 52,700 50,900 46,800
School type General education 911,421 922,062 928,563 945,605Vocational 373,770 417,257 427,954 431,998Adult education 6,462 12,288 10,869 9,566Higher education 5,820 78,538 158,392 236,206
schtyp pro 9,628 10,133 10,195 27,080First class 245,707 153,946 150,761 149,098Student care 92,910 98,616 99,966 101,812pre-vocational training 234,773 302,784 294,166 280,997h igher gen er al sec-ondary
152,089 160,903 166,213 170,406
pre-university training 176,314 195,680 204,025 211,676Care for student vmbo, geen lwoo 234,773 302,784 294,897 281,912
care 92,910 98,616 99,966 101,812City Amsterdam 47,816 50,916 56,882 62,653
Rotterdam 47,488 50,355 54,235 57,217The Hague 31,408 35,596 38,621 41,248
Utrecht 15,879 17,451 23,051 27,912Average sized city 313,125 340,236 378,668 414,049
Gender Female 643,450 701,762 752,802 802,909Etnicity Autochton 1,025,540 1,111,011 1,188,187 1,265,127
Suriname 39,802 42,440 44,506 46,363Aruba 16,806 17,753 18,721 19,870Turkey 44,914 49,237 52,796 56,355Moroco 42,425 45,806 48,314 50,817non-western migrant 64,802 70,050 74,853 79,071Western migrant 84,958 90,768 96,568 102,183Unknown 3,676 4,376 2,702 3,589
Generation of migrant Autochton 1,025,540 1,111,011 1,188,187 1,265,127First generation 90,849 94,085 95,104 94,383Second generation 202,858 221,969 240,654 260,276Unknown 3,676 4,376 2,702 3,589
Living in poor area 212,858 227,814 252,192 273,349
Characteristics munici-pality
Number of inhabitants mean (std) 3387 (3776) 3380 (3763) 3393 (3771) 3390 (3758)Population density mean (std) 3934 (3951) 3939 (3953) 4022 (4012) 4069 (4043)% one person house-hold
m ean (std) 28.8 88 (13.892) 28.762 (13 .806) 29.371 (1 4.41 5) 29 .801 (14.8 1
% Allochton mean (std) 9.194 (13.256) 9.140 (13.183) 9.243 (13.155) 9.298 (13.139Average income mean (std) 16.395 (2.566) 16.412 (2.569) 16.399 (2.579) 16.392 (2.586Green areas (km2) mean (std) 40.649 (5.365) 40.663 (5.350) 40.547 (5.382) 40.462 (5.390Number of householdsmoving
m ean (std) 95.9 96 (24.035) 95.882 (24 .012) 96.542 (2 4.24 8) 96 .956 (24.3 5
A ve ra ge h ou se va lu e m ea n (st d) 1 34 .9 98 ( 32 .4 72 ) 1 35 .2 55 (3 2.4 83 ) 1 35 .0 39 (3 2. 38 7) 1 34 .9 11 (3 2.2E mp loy ment in t hearea
m ea n (st d) 6 4. 10 7 (1 09 .8 89 ) 6 3.0 30 ( 10 8.4 97 ) 6 4.9 96 (1 10 .1 75 ) 6 6.2 89 (1 11 .3
Dropout prevention
Initial implementor 0 0 774,770 774,770
Number of preventionitems
mean (std) 0 0 2.354 (2.458) 4.886 (1.443
C are a nd a dv is oryteam
0 0 667,198 1,406,188
Smoothing the transi-tion
0 0 646,830 1,305,316
Mentoring and coach-ing
0 0 397,911 752,125
Changing sub ject 0 0 128,053 276,347optimal track or profes-sion
0 0 346,551 766,340
Apprenticeship 0 0 127,940 246,567Frequent intakes 0 0 452,063 835,898Extended school 0 0 0 202,601Reporting truants 0 0 657,507 1,228,348Dual track 0 0 0 149,715
Number of schools 594 606 596 690
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living in neighborhoods with more one person households and migrant families. More op-
portunities to nd work in the area, in addition, also increase the students probability to
dropout (this fact has also been found in, McNeal, 1997).
To control better for unobserved heterogeneity, we have estimated xed eects at regionand school type level. In addition, we have also estimated a time trend. Firstly, school type
xed eects relates to the educational track a pupil follows in the Dutch educational system
(e.g., pre-university, pre-vocational or vocational education). The estimates (available upon
request) indicate that students following tracks with lower ability levels (i.e. lwoo or care
students in vocational track) have a relatively higher probability to drop out at secondary
education. These results are in line with van der Steeg and Webbink (2006), who argue
that early school leavers are concentrated in the lowest level of pre-vocational and vocational
education.
Secondly, we also consider region xed eects. As expected (referring to above analyses on
neighborhood characteristics), we deal with signicant region xed eects. Further research
is needed to disentangle these eects.
Thirdly, the time trend takes sensitization into account. As pupils get more informed on
the relevance of obtaining a higher secondary diploma, the dropout rate can fall over time
without any inuence of dropout prevention measures. The results, presented in Table 3,
conrm that this kind of sensitization takes place in the Netherlands. This was also clear
from Figure 1, which indicated a decrease in dropout since 1992. We remark that, to our
best knowledge, no further research can be found on the impact of time eects on dropout
rates.
Focussing on the correlation coecients of the ten menu-items used in the dropout pre-vention policy delivers interesting insights. Out of 10 prevention measures, only 3 turn out to
have a signicant impact on the individuals dropout decision: (1) mentoring and coaching,
(2) optimal track or profession, and (3) dual tracks. Not unexpectedly, those 3 measures
correspond to preventions which regions cannot implement overnight. They are innovative,
in a way that it is not possible for the school to re-label existing procedures, and require a
clear follow-up of the student. Moreover, the number of items that regions are implementing
does not have a signicant impact.
Finally, we observe that the individual dropout decision did not alter in regions that
implemented dropout prevention programs one year before other regions. These early im-
plementor regions were the 14 regions with the highest dropout rate in 2005-2006 (as such,
there was not a random selection). Our results are in line with previous results of van der
Steeg et al. (2008), who analysed the eectiveness of the covenant based on a dierence in
dierence approach in those two regions.
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Table 3: Menu-items at individual levelcoe. S t. err or t -statist ic p -valu e
Student characteristicsGender -0.1426 0.0064 -22.4000 0.0000 ***
Etnicity (native = reference)Suriname 0.7051 0.0276 25.5800 0.0000 ***
Dutch Antilles 0.7165 0.0284 25.2300 0.0000 ***Turkey 0.6408 0.0270 23.7400 0.0000 ***
Marocco 0.6930 0.0268 25.8300 0.0000 ***Non-western m igrant 0.6874 0.0212 32.36 00 0.00 00 ***Western migrant 0.6686 0.0238 28.1200 0.0000 ***
Unknown 3.2450 0.1173 27.6600 0.0000 ***G eneration of m igrant -0.2629 0.0122 -21.55 00 0.00 00 ***
Postcode characteristicsPoor area 0.0804 0.0106 7.5600 0.0000 ***
Numb er o f inhabitants 0.0000 0.0000 -0.82 00 0.41 20Population density 0.0000 0.0000 -2.2100 0.0270 **
One person household 0.0007 0.0003 2.3600 0.0180 **Number of migrants 0.0008 0.0004 2.1800 0.0290 **
Income per capita -0.0047 0.0016 -2.8500 0.0040 ***Green areas -0.0007 0.0011 -0.7100 0.4770
Frequency of moving 0.0014 0.0003 4.9800 0.0000 ***A verage ho using co st -0.0005 0.0002 -2.8 100 0.00 50 ** *
E mploym ent in the area -0.0003 0.0001 -5.30 00 0.00 00 ***
Dropout prevention measuresEarly implementor 0.0095 0.0117 0.8100 0.4150
N umb er o f im ple ment ed pr event io n ite ms 0 .0 20 3 0 .0 17 2 1 .1 80 0 0 .2 38 0Care and a dvisory tea m -0.0083 0.0280 -0.30 00 0.76 70Sm oo thi ng t he tra ns iti on -0 .0 13 0 0 .0 38 2 -0 .3 40 0 0 .7 32 0
Mentoring and coaching -0.0403 0.0244 -1.6500 0.0990 *Cha nging sub ject -0.0275 0.0299 -0.92 00 0.35 80
O ptim al track or profession -0.0434 0.0226 -1.92 00 0.05 50 *A pprenticeship -0.0264 0.0347 -0.76 00 0.44 70
Frequent intakes -0.0243 0.0207 -1.17 00 0.24 10Extended scho ol 0.0315 0.0345 0.9100 0.3620
Rep orting truants -0.0221 0.0246 -0.90 00 0.37 00Dual tracks -0.0626 0.0329 -1.9000 0.0570 *
Co nstant 147.9593 10.8351 13.6 600 0.00 00 ** *
Region xed eects YesTime xed eect Yes
S ch oo l ty pe xe d e e cts Ye s
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3.3 Eectiveness of dropout incentives at school level
Various unobserved exogenous variables may inuence the dropout decision at the individual
level. Therefore, we examine the eectiveness of the dropout prevention measures by aggre-
gating all data at the school level. As the BRON database is a registered dataset of all Dutch
students (and not a sample), the model is not vulnerable to any selection eect. 5 We estimate
the eectiveness of the dropout prevention measures by means of a nonparametric quantile
regression (Koenker and Bassett, 1978). Quantile regressions are convenient to estimate the
impact on other levels than the mean (i.e., other quantiles). In this way, we can estimate the
whole conditional distribution of the dependent variable y.
In the school level analysis, we consider three kinds of schools: (1) schools with a low
dropout rate, (2) schools with a median dropout rate, and (3) schools with a high dropout
rate. They are decided on the rst (25%), second (50%) and third quantile (75%), respec-
tively. Besides a time trend, we control for school type and region xed eects. Table 4report the results of the quantile analysis.
We see that schools with relatively high dropout rates benet most from dropout preven-
tion measures. All dropout prevention measures, except for advisory team and dual track
projects, are associated with lower dropout rates. In contrast, we do not nd a signicant
impact of the dropout prevention measures on schools with low or median dropout rates.
Obviously, schools cannot simply be divided into three groups. The distribution of
dropout rates is more a continuum (Rumberger and Thomas, 2000). Therefore, we esti-
mate the impact of each dropout prevention measure on the dropout level of schools for all
quantiles. We plot the corresponding graphs in Figure 3. We see a negative slope in about
all graphs (but not all of them are signicant). This indicates that the higher the dropout
level of the school, the larger the impact of the dropout prevention measure.
5 We observe all Dutch students and all Dutch schools.
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Table4:Eectofdropoutpreventioninrst,secondandthird
quartileofschool
25%
quartile
50%
quartile
75%
quartile
coe.
St.error
t-statistic
coe.
St.error
t-stati
stic
coe.
St.error
t-statistic
%
offemales
atschool
-0.0037
0.0024
-1.5400
-0.0073
0.0021
-3.4
800
***
0.0004
0.0013
0.3
000
%
ofcarestudents
atschool
0.0646
0.0016
40.5100
***
0.0755
0.0015
51.1
200
***
0.0825
0.0009
95.7
100
***
%
ofnatives
atschool
-0.0044
0.0019
-2.2900
**
-0.0138
0.0017
-8.0
500
***
-0.0249
0.0010
-25.3
100
***
%
ofsingleparents
atschool
-0.0721
0.0084
-8.6300
***
-0.0498
0.0077
-6.4
400
***
-0.0063
0.0039
-1.6
400
*
schoolinpoorarea
0.0077
0.0017
4.4800
***
0.0034
0.0017
2.0
000
**
-0.0035
0.0010
-3.5
300
***
Numberofin
habitants
0.0000
0.0000
1.0500
0.0000
0.0000
-1.4
200
0.0000
0.0000
-5.2
300
***
Populatio
ndensity
0.0000
0.0000
-1.2400
0.0000
0.0000
-0.7
700
0.0000
0.0000
1.7
500
*
%
onepersonhousehold
0.0000
0.0000
0.8600
0.0000
0.0000
2.2
500
**
0.0000
0.0000
3.5
300
***
%
Migrant
0.0001
0.0000
4.3100
***
0.0001
0.0000
3.4
100
***
0.0001
0.0000
4.2
000
***
Averageincome
0.0000
0.0001
0.1100
0.0003
0.0001
3.0
400
***
0.0003
0.0000
5.9
900
***
Greenareas(km2)
-0.0001
0.0000
-1.9000
*
0.0000
0.0001
0.4
400
0.0000
0.0000
-0.3
600
Numberofhousehold
smoving
0.0000
0.0000
-1.8900
*
0.0000
0.0000
-0.1
900
0.0000
0.0000
2.0
100
**
Averageho
usevalue
0.0000
0.0000
0.1300
0.0000
0.0000
2.6
000
***
0.0001
0.0000
15.5
300
***
Employmentin
thearea
0.0000
0.0000
2.5600
**
0.0000
0.0000
1.1
000
0.0000
0.0000
1.2
500
Initialimp
lementor
0.0005
0.0024
0.2100
0.0018
0.0025
0.7
200
0.0095
0.0014
6.6
600
***
Numbermenu-items
0.0001
0.0009
0.1300
0.0007
0.0009
0.7
900
0.0022
0.0005
4.4
100
***
Careandadvisoryteam
0.0015
0.0016
0.9500
0.0020
0.0017
1.1
600
-0.0005
0.0010
-0.4
900
Smoothingthetransition
-0.0029
0.0022
-1.2900
-0.0041
0.0023
-1.8
400
*
-0.0110
0.0013
-8.7
500
***
Mentoringand
coaching
0.0002
0.0012
0.1500
-0.0006
0.0012
-0.4
700
-0.0020
0.0007
-2.9
900
***
Changin
gsubject
0.0002
0.0015
0.1300
-0.0002
0.0016
-0.1
400
-0.0023
0.0009
-2.5
800
***
Optimaltrackorprofession
-0.0004
0.0012
-0.3000
-0.0005
0.0012
-0.4
500
-0.0018
0.0007
-2.7
900
***
Apprenticeship
-0.0025
0.0019
-1.2700
-0.0053
0.0020
-2.6
200
***
-0.0112
0.0011
-10.3
000
***
Frequentintakes
-0.0003
0.0011
-0.2300
-0.0008
0.0012
-0.6
600
-0.0016
0.0006
-2.5
500
***
Extend
edschool
0.0027
0.0017
1.5900
0.0011
0.0018
0.6
100
-0.0052
0.0010
-5.2
800
***
Reportin
gtruants
0.0008
0.0013
0.6400
-0.0020
0.0014
-1.4
800
-0.0053
0.0007
-7.1
700
***
Dualtracks
0.0012
0.0015
0.8300
0.0002
0.0015
0.1
100
0.0004
0.0008
0.4
400
Constant
0.0085
0.0042
2.0200
**
0.0121
0.0043
2.7
900
***
0.0119
0.0024
5.0
400
***
Regionx
edeects
Yes
Yes
Yes
Timexedeect
Yes
Yes
Yes
Schooltypex
edeects
Yes
Yes
Yes
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4 Conclusion
In line with the targets of the Lisbon Agenda, the Dutch goverment created a broad policy
agenda to reduce dropout at secondary education. This paper analyzed the impact of the
policy measures at both the individual level (i.e., do the menu-items of the covenant change
the dropout decision of the student) and at the school level (i.e., do the menu-items change
the number of students dropping out at schools?). Firstly consider the individual impact.
While most of the menu-items correlate negatively with the individual dropout decission,
only mentoring and coaching, optimal track or profession and dual track have a signcant
negative impact on the individual dropout decision. Secondly, we nd that the number of
menu-items implemented by the RMC does not have a signicant impact. Thirdly, regions
that implemented the covenant one year before the other regions are not obtaining lower
dropout rates. We remark, however, that the early implementors were regions with the highest
dropout levels in the Netherlands.
As an analysis at the individual level might be intricate due to unobserved heterogeneity
in the estimates, we have analysed the eect of the covenants at an aggregated school level.
By means of quantile regressions, we have estimated the correlation between the menu-items
and the percentage of dropouts in school. It has been observed that for dierent quantiles
of schools (e.g., the schools with the 25% lowest or 25% highest percentage of students) also
dierent impacts of menu-items arise. While only few menu-items have a signicant eect in
schools with a relatively low percentage of dropouts, schools with a relatively high percentage
of dropouts indicate to benet from all but two dropout prevention measures. These two
educational measures are advisory teams and dual tracks. We observe that schools with a
relatively higher dropout level benet the most from dropout prevention measures as outlined
in the covenant.
Given the importance of the theme, and the availability of larger and more detailed data
sets, early school leaving will denitely attract further research. Potential research avenues
could arise from the policy on truancy, the additional years of compulsory education, or from
long term policy evaluations.
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