Top Banner

of 25

Dropout Prevention Measures in the Netherlands, An Evaluation

Apr 08, 2018

Download

Documents

Proiectul SOS
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
  • 8/7/2019 Dropout Prevention Measures in the Netherlands, An Evaluation

    1/25

    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.

    1

  • 8/7/2019 Dropout Prevention Measures in the Netherlands, An Evaluation

    2/25

    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).

    2

  • 8/7/2019 Dropout Prevention Measures in the Netherlands, An Evaluation

    3/25

    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

    3

  • 8/7/2019 Dropout Prevention Measures in the Netherlands, An Evaluation

    4/25

    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.

    4

  • 8/7/2019 Dropout Prevention Measures in the Netherlands, An Evaluation

    5/25

    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.

    5

  • 8/7/2019 Dropout Prevention Measures in the Netherlands, An Evaluation

    6/25

    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

    6

  • 8/7/2019 Dropout Prevention Measures in the Netherlands, An Evaluation

    7/25

    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.

    7

  • 8/7/2019 Dropout Prevention Measures in the Netherlands, An Evaluation

    8/25

    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.

    8

  • 8/7/2019 Dropout Prevention Measures in the Netherlands, An Evaluation

    9/25

    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

    9

  • 8/7/2019 Dropout Prevention Measures in the Netherlands, An Evaluation

    10/25

    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

    10

  • 8/7/2019 Dropout Prevention Measures in the Netherlands, An Evaluation

    11/25

    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

    11

  • 8/7/2019 Dropout Prevention Measures in the Netherlands, An Evaluation

    12/25

    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).

    12

  • 8/7/2019 Dropout Prevention Measures in the Netherlands, An Evaluation

    13/25

    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

    13

  • 8/7/2019 Dropout Prevention Measures in the Netherlands, An Evaluation

    14/25

    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.

    14

  • 8/7/2019 Dropout Prevention Measures in the Netherlands, An Evaluation

    15/25

    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

    15

  • 8/7/2019 Dropout Prevention Measures in the Netherlands, An Evaluation

    16/25

    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.

    16

  • 8/7/2019 Dropout Prevention Measures in the Netherlands, An Evaluation

    17/25

    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

    17

  • 8/7/2019 Dropout Prevention Measures in the Netherlands, An Evaluation

    18/25

    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.

    References

    [1] Anger, S. and Heineck, G. (2009). Do smart parents raise smart Children? The inter-

    generational transmission of cognitive abilities. Working paper series, SOEPpaper, No.

    156.

    [2] Angrist, J.D.and Krueger, A.B. (1991). Does compulsory school attendance aect school-

    ing and earnings? The Quarterly Journal of Economics, vol. 106, No.4, pp. 979-1014.

    18

  • 8/7/2019 Dropout Prevention Measures in the Netherlands, An Evaluation

    19/25

  • 8/7/2019 Dropout Prevention Measures in the Netherlands, An Evaluation

    20/25

    [3] Astone, N.M. and McLanahan, S.S. (1991). Family structure, parental practices and high

    school completion. American Sociological Review, 56,309-320.

    [4] Astone, N.M. and McLanahan, S.S. (1994). Family Structure, Residential Mobility, and

    School Dropout: A research note, Vol.31, No.4, pp.575-584.

    [5] Attwood, G.and Croll, P. (2006).Truancy in secondary school pupils: prevalence, trajec-

    tories and pupil perspectives. Research papers in Education, Vol.21, No.4, pp.467-484.

    [6] Auditdienst Ministerie OCW. Rapport van bevindingen over Thema-Onderzoek VSV

    2007, auditdienst OCW, Inspectie van het Onderwijs, Den Haag.

    [7] Becker, G.S. (1992), Human Capital and the Economy. Proceedings of the American

    Philosophical Society Vol.136, No.1, pp.85-92.

    [8] Becker, G.S. (1993). Human Capital. A theoretical and empirical analysis with specialreference to education Third Edition. National Bureau of Economic Research, The Uni-

    versity of Chicago Press, Ltd. London.

    [9] Beekhoven, S. and Dekkers, H. (2005). Early school leaving in the lower vocational track:

    triangulation of qualitative and quantitative data. Adolescence, Vol.40, No.157, pp.197-

    213.

    [10] Berktold, J., Geis, S. and Kaufman, P. (1998). Subsequent educational attainment of

    high school dropouts. Washington, DC : U.S. Dept. of Education, Oce of Educational

    Research and Improvement, National Center for Education Statistics.

    [11] Bobonis, G.J.and Finan, F. (2009). Neighborhood peer eects in secondary school enroll-

    ment decisions. Review of Economics and Statistics, Vol. 91, No. 4, pp.695-716.

    [12] Borghans , L., Smits, W., Vlasblom, J.D.and Jacobs, A. (2000). Leren en werken in het

    Nederlandse beroepsonderwijs. Vraag- en aanbodontwikkeling voor de BBL 1999-2004.

    ROA, Maastricht.

    [13] Bosch, G. and Charest, J. (2009). Vocational training: international perspectives. Rout-

    ledge, pp.324.

    [14] Bowles, S. (1972). Schooling and inequality from generation to generation Journal of

    Political Economy, Vol.80, pp.219-251.

    [15] Brown, P., Hesketh, A. and Williams, S. (2003). Employability in a knowledge-driven

    economy. Journal of Education and Work, Vol.16, No.2, pp.107-126.

    [16] Card, D. (1999). The causal eect of education on earnings. Handbook of labor eco-

    nomics, Vol.3, pp.1801-1863.

    20

  • 8/7/2019 Dropout Prevention Measures in the Netherlands, An Evaluation

    21/25

    [17] Ceulenaere, B., Willemsen, A., van der Aa, R., van Zuthpen, F. and Groen, C. (2009).

    Case studies MKBA voortijd schoolverlaten. Analyse van Rebound Centre Rotterdam,

    Delfshaven en de Amstersamse School. Ecorys.

    [18] Davies, J.D. and Lee, J. (2004). To attend or not to attend? Why some students chose

    school and others reject it. Support for Learning, Vol.21, No.4, pp.204-209.

    [19] de Zwart, S., Zwaneveld, F., Hamdam, Y., Schenk, S. and Hamdam, M. (2009).

    Maatschappelijke kosten-batenanalyse Time In en Sportklassen. Berenschot and Rebel-

    Group

    [20] Elliot, D.and Voss, H. (1974). Delinquency and drop out. Toronto-Londen, Lexington.

    [21] Eimers, T., van Amelsvoort, J., Roelofs, M. and Schuit, H. (2009). Tussentijdse invoering

    van de kwalicatieplicht. Kenniscentrum Beroepsonderwijs Arbeidsmarkt, pp.17.

    [22] Expertise Centrum (2006). En meld- en registratiepunt voor leerplicht en voortijdig

    schoolverlaten. s-Gravenhage.

    [23] Felner, R.D., Primavera, J. and Cause, A.M. (1981). The impact of school transitions: A

    focus for preventive eorts. American Journal for Community Psychology, Vol.9, pp.449-

    459.

    [24] Felner, R.D., Ginter, M. and Primavera, J. (1982). Primary prevention during school

    transitions: social support and environmental structure. American Journal of Commu-

    nity Psychology, Vol.10, No.3, pp.277.

    [25] Fergusson, D.M., Horwood, L.J. and Beautrais, A.L. (2003). Cannabis and educational

    achievement. Society for the study of addiction to alcohol and other drugs, Vol.98,

    pp.1681-1692.

    [26] Gamoran, A., Nystrand, M., Berends, M. and Lepore, P.C. (1995). An organizational

    analysis of the eects of ability grouping. American Educational Research Journal,

    Vol.32, No.4, pp.687-715.

    [27] Garnier, H., Stein, J. and Jacobs, J. (1997). The process of dropping out of high school:

    a 19-year perspective. American Educational Research Journal, Vol.34, pp.395-419.

    [28] Groot, W. and Maassen van den Brink, H. (2007). The health eects of education. Eco-

    nomic of Education Review, Vol.26, pp.186-200.

    [29] Holter, N. and Bruinsma, W. (2009). Wat werkt bij het voorkomen van voortijdige

    schoolverlaters? Nederlands Jeugdinstituut (NJI).

    21

  • 8/7/2019 Dropout Prevention Measures in the Netherlands, An Evaluation

    22/25

    [30] Jimerson, S.R. (1999). On the failure of failure: Examining the association between

    early grade retention and education and employment outcomes during late adolescence.

    Journal of School Psychology, Vol.37, pp.243-272.

    [31] Keitel, C. (1987). What are the goals of mathematics for all? Journal of Curriculum

    Studies, Vol.19, No.5, pp.392-407.

    [32] Koenker, R. and Basset, G. (1978). Regression Quantiles. Econometrica, Vol.46, No.1,

    pp.33-50.

    [33] Lucas, R. and Lammont, N. (1998) Combining Work and Study: An Empirical Study of

    Full-Time Students in School, College and University. Journal of Education and Work,

    1469-9435, Vol.11, No.1, pp.41-56.

    [34] Luyten, H., Bosker, R., Dekkers, H. and Derks, A. (2003). Dropout in the lower secondary

    tracks of Dutch secondary education: predictor variables and variation among schools.

    School eectiveness and school improvement. Vol.14, No.4, pp.373-411.

    [35] Mayer, S. (1991). How much does a high schools racial and socioeconomic mix aect

    graduation and teenage fertility rates? In C. Jencks & P. Peterson (Eds.), The Urban

    Underclass, pp.321-341. Washington, D.C.: Brookings Institution.

    [36] Mclanahan, S.S. (1985). Family structure and the intergenerational transmission of

    poverty. American Journal of Sociology, Vol.90, pp.873-901.

    [37] McNeal, R.B. (1997). Are students being pulled out of high school? The eect of adoles-

    cent employment on dropping out. Sociology of Education, Vol.70, pp.206-220.

    [38] Milligan, K., Moretti, E. and Oreopoulos, P. (2004). Does education improve citizenship?

    Evidence from the United States and the United Kingdom. Journal of Public Economics,

    Vol.88, No.9, pp.1667-1695.

    [39] Ministry of Education (2010). Voortijdig schoolverlaten: cijfers en beleid. Letter to the

    Chairman of the Tweede Kamer der Staaten-Generaal. 17.02.2010.

    [40] Ministry of Education, Kerncijfers 2003-2007 OCW, Ministerie van Onderwijs Cultuur

    en Wetenschap, Den Haag.

    [41] Nelson, P.S., Simoni, J. M., and Adelman, H. S. (1996). Mobility and school functioning

    in the early grades. Journal of Educational Research, Vol.89, No.6, pp.365-369.

    [42] Nelson, R. and Phelps, E. (1966). Investment in humans, technological diusion, and

    economic growth. American Economic Review, Vol.51, No.2, pp. 69-75.

    22

  • 8/7/2019 Dropout Prevention Measures in the Netherlands, An Evaluation

    23/25

    [43] Oakes, J., Gamoran, A. and Page, R.N. ( 1992). Curriculum dierentiation. Opportuni-

    ties, outcomes and meanings. Handbook of Research on Curriculum, pp.570-608.

    [44] OECD (2008). Jobs for youth. The Netherlands.

    [45] Onstenk, J. (2004). Innovation in vocational education in the Netherlands. Vocal, Vol.5,

    pp.17-21.

    [46] Onstenk, J. and Blokhuis, F. (2007). Apprenticeship in the Netherlands: connecting

    school- and work-based learning. Education + Training, Vol.49, No.6 pp.489-499.

    [47] Oosterbeek, H. and Webbink, D. (2004). Wage eects of an extra year of lower vocational

    education: Evidence from a simultaneous change of compulsory school leaving age and

    program length. Department of Economics, University of Amsterdam and NOW-pragram

    Scholar.

    [48] Oreopoulos, P. (2006). The compelling eects of compulsory schooling: evidence from

    Canada. Canadian Journal of Economics, Vol.39, No.1, pp.23-52.

    [49] Oreopoulos, P. (2007). Do dropouts drop out too soon? Wealth, health and happiness

    from compulsory schooling. Journal of Public Economics, Vol.91, Issues 11-12, pp.2213-

    2229.

    [50] Phillips, J. and Kelly, D. (1979). School failure and delinquency: which causes which?

    Criminology, pp.194-207.

    [51] Pischke, J-S. and von Wachter, T. (2005). Zero returns to compulsory schooling in Ger-many: evidence and interpretation. Working Paper 11414, National Bureau of Economic

    Research, Cambridge: pp.48.

    [52] Pong, S.-L. and Ju, D.B. (2000). The eects of change in family structure and income

    on dropping out of middle and high school. Journal of Family Issues, 21,147-169.

    [53] Psacharopoulos, G. (2007). The cost of school failure A feasibility study. European

    Expert Network on Economics of Education.

    [54] ROA (2009/1). Zonder diploma. Aanleiding, kansen en toekomstintenties. Researchcen-

    trum voor Onderwijs en Arbeidsmarkt, Universiteit Maastricht.

    [55] ROA (2009/4). Schoolverlaters tussen onderwijs en arbeidsmarkt. Researchcentrum voor

    Onderwijs en Arbeidsmarkt, Maastricht University.

    [56] Roderick, M. (1994). Grade Retention and School Dropout: Investigating the Association.

    American Educational Research Journal, 31, 729-759.

    23

  • 8/7/2019 Dropout Prevention Measures in the Netherlands, An Evaluation

    24/25

    [57] Rumberger, R.W. (1983). Dropping out of high school: The inuence of race, sex, and

    family background. American Educational Research Journal, 20, pp.199-220.

    [58] Rumberger, R.W. and Larson, K.A. (1998). Student mobility and the increased risk of

    high school dropout. American Journal of Education, Vol.107, No.1, pp.1-35.

    [59] Rumberger, R.W. and Thomas, S.L. (2000). The distribution of dropout and turnover

    rates among urban and suburban high schools. Sociology of Education, Vol.73, pp.39-67.

    [60] Rumberger, R.W. (2001). Who drops out of school and why. Paper prepared for the

    National Research Council, Committee on Educational Excellence and Testing Equity

    Workshop, School Completion in Standards-Based Reform: Facts and Strategies, and

    incorporated into their report, Understanding Dropouts: statistics, strategies, and high-

    stakes testing, edited by A. Beatty, U. Neiser, W. Trent, and J. Heubert, Washington:

    National Academy Press.

    [61] Rumberger, R.W. and Lamb, S.P. (2003) The Early Employment and Further Education

    Experiences of High School Dropouts: A Comparative Study of the United States and

    Australia. Economics of Education Review, Vol.22, pp.353-356.

    [62] Rumberger, R.W (2004). Why students drop out of school. In Gary Ored (Ed.),

    Dropouts in America: Confronting the Graduation Rate Crisis, Cambridge, MA: Har-

    vard Education Press, (pp.131-155).

    [63] Schultz, T. (1967). The economic value of education. Columbia University Press, New

    York

    [64] Spence, M. (1973). Job Market Signaling. The Quarterly Journal of Economics, Vol.87,

    No.3, pp.355-374.

    [65] Steinberg, L., Dornbusch, S.M. and Brown, B.B. (1992). Ethnic dierences in adolescent

    achievement. American Psychologist, Vol.47, pp.723-729.

    [66] ter borgt, T., van Lieshout, M., Doornwaard, S. and Eijkemans, Y. (2009). Middelenge-

    bruik en voortijdig schoolverlaten. Twee onderzoeken naar de actuele en gepercipieerde

    rol van alcohol en cannabis in relatie tot spijbelen, schoolprestaties, motivatie en uitval.

    Universiteit Utrecht and Trimbos-instituut.

    [67] Terwel, J. (2004). Curriculum and curriculum dierentiation. In Terwel, J., Walker,

    D.F., (2004) Curriculum as a shaping force: toward a principle approach in curriculum

    theory and practice. Nova Science, pp.33-50.

    [68] Terwel, J. (2005). Curriculum dierentiation: multiple perspectives and developments in

    education. Journal of Curriculum Studies, Vol.37, No.6, pp.653-670.

    24

  • 8/7/2019 Dropout Prevention Measures in the Netherlands, An Evaluation

    25/25

    [69] van der Steeg, M., van Elk, R. and Webbink, D. (2008). Did the 2006 covenants reduce

    school dropout in the Netherlands? CPB document 177.

    [70] van der Steeg, M. and Webbink, D. (2006). Voortijdig schoolverlaten in Nederland: om-

    vang, beleid en resultaat. CPB Document, No.107.

    25