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Dropout Prevention Measures in the Netherlands, an Explorative Evaluation

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Title: Dropout Prevention Measures in the Netherlands, an Explorative Evaluation
Language: English
Authors: De Witte, Kristof, Cabus, Sofie J.
Source: Educational Review. 2013 65(2):155-176.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 22
Publication Date: 2013
Document Type: Journal Articles
Reports - Evaluative
Descriptors: Dropouts, Foreign Countries, Coaching (Performance), Dropout Prevention, Dropout Rate, Strategic Planning, Correlation, Mentors, Decision Making, Regression (Statistics)
Geographic Terms: Netherlands
DOI: 10.1080/00131911.2011.648172
ISSN: 0013-1911
Abstract: In line with the Lisbon Agenda, set by the European Council in the year 2000, European governments formulated ambitious plans to halve the level of early school-leavers by 2012. This paper outlines the dropout prevention measures in the Netherlands and analyzes their influence at both the individual and school level. While most policy measures correlate negatively with the individual dropout decision, only "mentoring and coaching" (i.e., matching of students with a coach from public or private organizations), "optimal track or profession" (e.g., work placement) and "dual track" (i.e., re-entering education for dropout students) have a significant negative impact on the individual dropout decision. By means of quantile regressions, we observe that schools with a relatively high dropout rate benefit the most from dropout prevention measures. (Contains 4 tables, 1 figure, and 13 notes.)
Abstractor: As Provided
Number of References: 78
Entry Date: 2013
Accession Number: EJ999515
Database: ERIC
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  Value: <anid>AN0086994690;edi01may.13;2019Feb20.14:10;v2.2.500</anid> <title id="AN0086994690-1">Dropout prevention measures in the Netherlands, an explorative evaluation. </title> <p>In line with the Lisbon Agenda, set by the European Council in the year 2000, European governments formulated ambitious plans to halve the level of early school-leavers by 2012. This paper outlines the dropout prevention measures in the Netherlands and analyzes their influence at both the individual and school level. While most policy measures correlate negatively with the individual dropout decision, only "mentoring and coaching" (i.e., matching of students with a coach from public or private organizations), "optimal track or profession" (e.g., work placement) and "dual track" (i.e., re-entering education for dropout students) have a significant negative impact on the individual dropout decision. By means of quantile regressions, we observe that schools with a relatively high dropout rate benefit the most from dropout prevention measures.</p> <p>Keywords: school dropout prevention; secondary education; probit; quantile regression</p> <hd id="AN0086994690-2">Introduction</hd> <p>In EU-27, on average 20% of students younger than 23 years of age left education without a higher secondary education degree in 2000 (European Commission [<reflink idref="bib24" id="ref1">24</reflink>]). These so-called "dropout students" or "early school-leavers" constitute a group that is heavily at risk (Rumberger [<reflink idref="bib60" id="ref2">60</reflink>]; Psacharopoulos [<reflink idref="bib54" id="ref3">54</reflink>]). They have a relatively high risk of (<reflink idref="bib1" id="ref4">1</reflink>) entering a vicious circle in which in turn their children obtain lower education levels (e.g., Bowles [<reflink idref="bib14" id="ref5">14</reflink>]; McLanahan [<reflink idref="bib39" id="ref6">39</reflink>]; Anger and Heineck [<reflink idref="bib2" id="ref7">2</reflink>]), (<reflink idref="bib2" id="ref8">2</reflink>) having long-term unemployment (e.g., Rumberger and Lamb [<reflink idref="bib61" id="ref9">61</reflink>]; Organization for Economic Cooperation and Development [OECD] [<reflink idref="bib48" id="ref10">48</reflink>]), (<reflink idref="bib3" id="ref11">3</reflink>) suffering from health problems (e.g., Groot and Maassen van den Brink [<reflink idref="bib31" id="ref12">31</reflink>]) or (<reflink idref="bib4" id="ref13">4</reflink>) feeling a low social cohesion (e.g., Milligan, Moretti, and Oreopoulos [<reflink idref="bib49" id="ref14">49</reflink>], [<reflink idref="bib50" id="ref15">50</reflink>]; van der Steeg and Webbink [<reflink idref="bib77" id="ref16">77</reflink>]).</p> <p>At the Lisbon 2000 summit, the European council decided among other benchmarks to aim for a lower dropout rate. The average rate of early school-leavers should be no more than 10% by 2012. Thanks to political eagerness to tackle the problem, the European member states developed various programs to reduce dropping out of secondary education. Since 1992, the European dropout rate has fallen from about 35% to about 14.4% in 2009. Similar objectives have been formulated in the US by the "No Child Left Behind" Act in 2001. This has recently been restated in the inauguration speech of US 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 [<reflink idref="bib44" id="ref17">44</reflink>]).</p> <p>Determining the most effective way of tackling the dropout problem is not straightforward as students do not dropout of secondary education because of one specific drawback. They often are piling up problems, both at home, in their neighborhood or at school, before they actually make the dropout decision (Rumberger [<reflink idref="bib59" id="ref18">59</reflink>]). The literature indicates, for instance, that dropout students change school more frequently (Rumberger and Larson [<reflink idref="bib62" id="ref19">62</reflink>]; Strand and Demie [<reflink idref="bib71" id="ref20">71</reflink>]), have more retentions in grade (Roderick [<reflink idref="bib56" id="ref21">56</reflink>]; Jimerson [<reflink idref="bib35" id="ref22">35</reflink>]), struggle through their study curriculum (Garnier, Stein, and Jacobs [<reflink idref="bib29" id="ref23">29</reflink>]), are more often involved in criminal activities (Elliot and Voss [<reflink idref="bib23" id="ref24">23</reflink>]; Phillips and Kelly 1979), use more often cannabis, alcohol or other drugs (Fergusson, Horwood, and Beautrais [<reflink idref="bib27" id="ref25">27</reflink>]; ter borgt et al. [<reflink idref="bib73" id="ref26">73</reflink>]), and are more likely to live in disadvantaged neighborhoods (Bobonis and Finan [<reflink idref="bib10" id="ref27">10</reflink>]) and in poorer families (Nelson, Simoni, and Adelman [<reflink idref="bib43" id="ref28">43</reflink>]). It is the accumulation of small and large problems which pushes the pupil eventually towards the dropout decision.</p> <p>This paper discusses the dropout prevention policy in a European country and explores its effectiveness. We focus on the Netherlands, which is an interesting case study. Before the academic year 2002–03, a coherent policy towards early school-leaving was lacking. Because of large differences in the underlying population, different regions and cities require different policy measures. Therefore, a decentralized policy has been developed. A task force on early school-leaving within the Ministry of Education (the so-called "Projectdirectie Voortijdig Schoolverlaten" [Ministry of Education [<reflink idref="bib42" id="ref29">42</reflink>]]) created 39 regional dropout authorities (RMC) in 2002. At that time, each of the RMC regions could take (or could not take) different actions towards policy goal settings.</p> <p>To facilitate the policy, the Ministry of Education outlined a general framework, known as the "covenants" (Holter and Bruinsma [<reflink idref="bib34" id="ref30">34</reflink>]). A covenant is a written agreement between the Ministry on the one hand, and the RMC and the schools on the other hand. It stipulates the interventions of the RMC region. Examples of interventions in the covenants are improved truancy registration, increased flexibility in changing educational tracks, intensified counseling for students or increased possibilities for apprenticeships. In the academic year 2005–06, 14 regions with the highest dropout levels signed a first round of covenants. The first covenant agreements consist of a financial incentive of 2,000 euro per dropout less compared to the reference school year 2005–06.[<reflink idref="bib1" id="ref31">1</reflink>] van der Steeg, van Elk, and Webbink ([<reflink idref="bib76" id="ref32">76</reflink>]) exploited this dual implementation by a difference-in-differences design. They observed that the immediate impact of the covenant (i.e., evaluation only one year after the implementation) was not effective in reducing early school-leaving. A second round of covenants has been signed by all 39 RMCs in the school year 2007–08. The 2007–08 covenants replaced the first covenant agreements and increased the performance bonus to 2,500 euro per dropout less in comparison to academic year 2005–06.[<reflink idref="bib2" id="ref33">2</reflink>] The contributions of this paper are threefold. First, we examine the effectiveness of the early school-leaving incentives by analyzing which of the incentives significantly correlate with a lower probability of student dropout in secondary education. We use an exceptionally rich panel data set (BRON [Basis Register Onderwijsnummer]) which covers all students in the Netherlands. Thanks to postcode information, the data are enriched with neighborhood characteristics obtained from Statistics Netherlands. We start by an analysis at the individual level. In particular, a panel probit model examines the probability of a student dropping out. While controlling for student characteristics (e.g., gender, school track, migrant status), neighborhood 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 fixed effects, we correlate the dropout prevention measures to the individual probability of dropping out. We argue that the obtained outcome provides a lower bound of the effect.</p> <p>Second, we aggregate all data at the school level. This provides an indication of schools with low and high dropout rates. Using a quantile analysis, and controlling for the student, neighborhood, time and regional influence, we examine the influence 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 arbitrary, we estimate also the influence of the dropout measures for a continuum of quantiles.</p> <p>Third, this paper is to our best knowledge the first to describe the dropout prevention incentives in one of the EU member states. The Dutch Ministry of Education spends 313 million euro a year (anno 2007–08) on dropout prevention policy, which implies 0.83% of its total budget (Statistics Netherlands [<reflink idref="bib69" id="ref34">69</reflink>]). It has been foreseen that this budget will increase to 400 million euro a year by 2011 (Ceulenaere et al. [<reflink idref="bib16" id="ref35">16</reflink>]; Ministry of Education [<reflink idref="bib6" id="ref36">6</reflink>]; Statistics Netherlands [<reflink idref="bib69" id="ref37">69</reflink>]). Not a negligible budget, whose effectiveness is worth analyzing.</p> <p>The remainder of the paper is structured as follows. First, the Dutch dropout prevention policy is described. Then, we briefly present the data, its structure and some descriptive statistics and examine the influence of the Dutch dropout prevention measures at the individual level and at the school level. The conclusion provides some policy advice.</p> <hd id="AN0086994690-3">Dropout prevention measures in the Netherlands</hd> <p>Students do not leave school too early because of one specific drawback. They are often piling up problems before the actual dropout decision is taken. Consequently, a wide variety of dropout prevention or curative measures and actions have to be considered to tackle the complexity of the school dropout problem. This section briefly describes the Dutch policy on early school-leaving. We start from the Dutch education system, and continue by a conceptual framework to classify the policy measures. Subsequently, we discuss the policy measures, and link them to the academic literature.</p> <hd id="AN0086994690-4">The Dutch education system</hd> <p>The Dutch education system consists of three main streams: pre-university education (VWO), general upper secondary education (HAVO) and vocational secondary education (VMBO-MBO). There is a strong ability tracking at the end of primary education. Based on standard testing, students are assigned one of the three main streams. A student who wants to obtain a school-leaving certificate enrolls in a pre-vocational, general secondary or pre-university education stream at the age of 12. Between the ages of 16 and 18, a student has the option to enroll in school based (full-time) or work based (part-time) educational arrangements. Only after completion of vocational education with level-2 (graduation age 18), general secondary education (graduation age 17), or pre-university education (graduation age 18), does one obtain a valid school-leaving certificate (and will not be registered as a school dropout) (for an extensive discussion, see Cabus and De Witte [<reflink idref="bib15" id="ref38">15</reflink>]; Tieben and Wolbers 2010).</p> <hd id="AN0086994690-5">Programmatic versus systemic approach</hd> <p>Rumberger ([<reflink idref="bib59" id="ref39">59</reflink>]) suggests two perspectives to categorize the triggers for school dropout: (<reflink idref="bib1" id="ref40">1</reflink>) a framework based on the individual perspective, and (<reflink idref="bib2" id="ref41">2</reflink>) a framework based on an institutional perspective that consists of family, school, communities and peers. Sutphen, Ford, and Flaherty ([<reflink idref="bib72" id="ref42">72</reflink>]) discuss a similar classification of policy measures for school attendance interventions: namely measures aimed at (<reflink idref="bib1" id="ref43">1</reflink>) the individual level, (<reflink idref="bib2" id="ref44">2</reflink>) the family level, (<reflink idref="bib3" id="ref45">3</reflink>) the school level and (<reflink idref="bib4" id="ref46">4</reflink>) the community level. A fifth category for interventions combine these four levels and are appropriately called: multi-model interventions (Bell, Rosen, and Dynlacht [<reflink idref="bib8" id="ref47">8</reflink>]).</p> <p>Accordingly, policy interventions from the individual perspective aim at identifying at-risk students by, for example, providing them increased student counseling or guidance through their study curriculum. Conversely, policy interventions from the institutional perspective aim at creating a new institutional environment or a fundamental change, for example, in the way school programs or educational settings are organized. They do not affect one individual, but rather aim at all students going to school in this institutional setting. The former kind of measures follow a programmatic approach, whereas the latter a systemic approach (Rumberger [<reflink idref="bib59" id="ref48">59</reflink>]).</p> <hd id="AN0086994690-6">Programmatic interventions</hd> <p>Most Dutch dropout prevention measures follow a programmatic approach as they "[...] do not attempt to change the institutional setting, but rather create alternative programs or institutions to target students who are somehow identified as at-risk of dropping out" (Rumberger [<reflink idref="bib59" id="ref49">59</reflink>], 23). The dropout prevention measures are systematically summarized in Table 1, namely (<reflink idref="bib1" id="ref50">1</reflink>) reporting truants, (<reflink idref="bib2" id="ref51">2</reflink>) changing subject, (<reflink idref="bib3" id="ref52">3</reflink>) guidance towards the students' optimal track or profession, (<reflink idref="bib4" id="ref53">4</reflink>) apprenticeship, (<reflink idref="bib5" id="ref54">5</reflink>) mentoring and coaching, (<reflink idref="bib6" id="ref55">6</reflink>) care and advisory team, (<reflink idref="bib7" id="ref56">7</reflink>) smoothing the transition from the pre-vocational to the vocational level, (<reflink idref="bib8" id="ref57">8</reflink>) extended school, (<reflink idref="bib9" id="ref58">9</reflink>) dual track, and (<reflink idref="bib10" id="ref59">10</reflink>) frequent intakes. Examples of the programmatic approach are, from Table 1, mentoring and coaching, guidance through the study curriculum, and care and advisory teams.</p> <p>Table 1. Summary: covenant dropout prevention measures.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="top"><td>Measure</td><td>Implementation</td></tr></thead><tbody><tr valign="top"><td>1 Reporting truants</td><td>Reporting and tackling truancy at a very early stage.</td></tr><tr valign="top"><td>2 Changing subject</td><td>A tailored track for students who choose a wrong subject or who prefer another subject.</td></tr><tr valign="top"><td>3 Guidance towards to the students' optimal track or profession</td><td>Work placement, writing a letter of application, apprenticeship programs, and creating a portfolio.</td></tr><tr valign="top"><td>4 Apprenticeship</td><td>Coordination with local private firms and advanced apprenticeship programs for students who prefer to do manual jobs.</td></tr><tr valign="top"><td>5 Mentoring and coaching</td><td>Students are matched with a coach from public or private organizations.</td></tr><tr valign="top"><td>6 Care and advisory team</td><td>Coordination of student care by social workers, youth assistance, school attendance officers, health services and police.</td></tr><tr valign="top"><td>7 Smoothing the transition from the pre-vocational level to the vocational level</td><td>Intake talks at the vocational school, providing more information on the educational tracks, and checking whether the students effectively enroll at and start in the new vocational school.</td></tr><tr valign="top"><td>8 Extended school</td><td>Add more sports and culture to schools in order to make school more attractive.</td></tr><tr valign="top"><td>9 Dual track</td><td>Offering the possibility for dropout students to re-enter education by a tailored educational track.</td></tr><tr valign="top"><td>10 Frequent intakes</td><td>Increasing the number of moments that students may enter secondary education.</td></tr></tbody></table> </ephtml> </p> <p>First, consider the interventions "mentoring and coaching" and "changing subject". Previous literature argues that the enrollment reason for a particular educational track (e.g., vocational education and training versus pre-university education) is a strong indication for school dropout. Students with a too heavy study load, a wrong track choice, or without peer or family support have a higher probability for school dropout (Pittman [<reflink idref="bib52" id="ref60">52</reflink>]; Phinney, Dennis, and Osorio [<reflink idref="bib51" id="ref61">51</reflink>]). About 21% of all dropouts in the Netherlands indicate a wrong study choice as the main trigger for leaving school (Allen and Meng [<reflink idref="bib1" id="ref62">1</reflink>]). The interventions "mentoring and coaching" and "changing subject" aim to tackle these problems by more professional advice and follow up of students.</p> <p>Second, and in addition to the guidance through the study curriculum, care and advisory teams have been established to align internal (i.e., at the school) and external (i.e., from outside the school) care for potential dropouts. Although different settings are possible, a care and advisory team typically consists of psychologists, pedagogues, social workers, a representative of the region and a policy officer.</p> <p>Third, the intervention "frequent intakes" aims at continuous enrollment moments. This actually means that pupils can enter the academic year after 1 October (which is one month after the official start of the school year). The "Centraal Toegangsloket voor het Onderwijs" (or Central Entry Office for Education) organizes frequent intakes for pupils who are following vocational tracks. Furthermore, 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, students can continue another track as soon as possible.</p> <hd id="AN0086994690-7">Multi-model interventions</hd> <p>Dropout prevention measures beyond the individual level consist of reporting truants, apprenticeships, dual tracks and smoothing transition. They are part of multi-model interventions (Bell et al. [<reflink idref="bib8" id="ref63">8</reflink>]), which are dropout prevention measures aiming at both the individual level and the institutional level. Consequently, they are more difficult to realize in a short time-span.</p> <p>First, consider truancy reporting. Truancy or school absenteeism is considered one of the best predictors of early school-leaving (e.g., Bos, Ruijters, and Visscher [<reflink idref="bib12" id="ref64">12</reflink>]; Attwood and Croll [<reflink idref="bib5" id="ref65">5</reflink>]; Henry [<reflink idref="bib32" id="ref66">32</reflink>]). Truancy is defined as one or more days' absenteeism from school for students who did not obtain a higher secondary diploma (Schaefer and Millman [<reflink idref="bib64" id="ref67">64</reflink>]).[<reflink idref="bib3" id="ref68">3</reflink>] It is positively associated with juvenile crime (e.g., Garry [<reflink idref="bib30" id="ref69">30</reflink>]), teenage pregnancy (e.g., Hibbert and Fogelman [<reflink idref="bib33" id="ref70">33</reflink>]), drugs or alcohol use (Roebuck et al. [<reflink idref="bib57" id="ref71">57</reflink>]), and eventually may lead to school dropout (Rumberger [<reflink idref="bib59" id="ref72">59</reflink>]; DesJardins, Ahlburg, and McCall [<reflink idref="bib21" id="ref73">21</reflink>]; Henry [<reflink idref="bib32" id="ref74">32</reflink>]). Truancy rates are highest among students enrolled in vocational tracks (Shavit and Müller [<reflink idref="bib65" id="ref75">65</reflink>], 550; Gangl [<reflink idref="bib28" id="ref76">28</reflink>]; Weltz [<reflink idref="bib78" id="ref77">78</reflink>]). Research on truancy as an indicator for school dropout discusses and describes the nature and extent of school absenteeism. For example, Bos, Ruijters, and Visscher ([<reflink idref="bib12" id="ref78">12</reflink>]) analyze a 1980s government experiment in the Netherlands. An improved computer assisted registration system was launched in 36 schools in four major dropout cities: Haarlem, Amsterdam, Utrecht and Rotterdam.[<reflink idref="bib4" id="ref79">4</reflink>] On average, they find a truancy rate of 16%, where heterogeneity was observed among different school curricula (e.g., vocational education and training versus general secondary or pre-university education), study subjects, and ethnic groups. However, the experiment suffered from multiple definitions of truancy and, as a consequence, a lack of transparency. Attwood and Croll ([<reflink idref="bib5" id="ref80">5</reflink>]) and Henry ([<reflink idref="bib32" id="ref81">32</reflink>]) also stress definitional problems due to the concept of "illicit" or "unauthorized" school absenteeism. Researchers often rely on self-reported school absence, where the answers may differ upon the survey question asked to classify the extent they skip class.[<reflink idref="bib5" id="ref82">5</reflink>] The dropout prevention measure "reporting truants" aims at improved registration of unauthorized truancy, using a uniform definition and registration of truants and school dropouts in a central database, the so-called "digital office" (for an extensive description, see: Data and definition). A central database offers the opportunity to better detect potential dropouts (Auditdienst Ministry of Education [<reflink idref="bib6" id="ref83">6</reflink>]). Furthermore, if truancy reporting is accompanied with adequate truancy policy (for a discussion, see: De Witte and Csillag [<reflink idref="bib19" id="ref84">19</reflink>]), an important feature can be attributed to this truancy reporting: the discouraging of potential truants or dropouts. It is likely that pupils are afraid to "be caught" as their parents will know about their truancy behavior. Therefore, increasing the probability of truancy detection can discourage students from truancy.</p> <p>Second, consider the intervention on apprenticeships. Apprenticeships are interesting learning methods, where students develop interpersonal skills and increase employability (Lucas and Lammont [<reflink idref="bib37" id="ref85">37</reflink>]). A lack of workplaces for apprentices is considered an important incentive for school dropout. Finding better matches between apprenticeship and labor organizations, and improving information and support for students can make the dropout problem less persistent (for an extensive discussion on various aspects of national vocational training systems and the importance of apprenticeships, see: Gangl [<reflink idref="bib28" id="ref86">28</reflink>]; Onstenk [<reflink idref="bib46" id="ref87">46</reflink>]; Onstenk and Blokhuis [<reflink idref="bib47" id="ref88">47</reflink>]; Bosch and Charest [<reflink idref="bib13" id="ref89">13</reflink>], 324).</p> <p>Third, some students do not intend to follow a full-time education program, instead they would rather opt for an employment contract and a work based pathway at school. By allowing for a combination of learning and working, a dual track may trigger students to obtain a higher secondary degree (ROA [<reflink idref="bib55" id="ref90">55</reflink>]). For instance, part-time learning on construction techniques and part-time working in construction. In a similar program, educational vocational coordination (EVC) is a certificate that can be obtained if a student passes a learning module. EVC and dual track measures also aim at (unemployed) dropouts (as a curative measure). In the Netherlands, unemployment rates increased at the end of 2008: for pre-vocational dropouts from 6% in 2007 to 9% in 2008 and for dropouts in the first year of vocational education from 10% in 2007 to 16% in 2008 (ROA [<reflink idref="bib55" id="ref91">55</reflink>]). The dual tracks and EVC should be attractive for these dropouts.</p> <p>Fourth, a necessary condition for a better connection between school and labor market consists of a successful transition into vocational education and training. In the Netherlands, enrollment in the first year of senior vocational education and training is considered difficult due to a long summer break of four months after graduation from pre-vocational education.[<reflink idref="bib6" id="ref92">6</reflink>] Students have to physically enroll in another school, as the pre-vocational school does not offer senior vocational study subjects, and may lose contact with their teachers and school. Therefore, there was a shift in Dutch policy from horizontal alignment to crossing system boundaries or "smoothing transition" (Onstenk and Blokhuis [<reflink idref="bib47" id="ref93">47</reflink>]). Policy towards smoothing transition consists of increased care for at-risk students. For example, they are followed during the summer break, go through an intake procedure and communication between the pre-vocational and senior vocational school is enhanced. In some cases, students could attend classes in their pre-vocational school to follow classes with familiar peers and teachers.[<reflink idref="bib7" id="ref94">7</reflink>] In summary, the policy measure aimed at enhanced student commitment to the school, peers and teachers by simplifying the transition between the pre-vocational and the senior vocational level. Enhanced student commitment to school, peers, and teachers may effectively reduce school dropout, as conceptually discussed by Spady ([<reflink idref="bib67" id="ref95">67</reflink>]), Spady ([<reflink idref="bib68" id="ref96">68</reflink>]) and Tinto ([<reflink idref="bib75" id="ref97">75</reflink>]) in theories on student attrition. Evidence on its effectiveness has been found by Felner, Primavera, and Cause ([<reflink idref="bib26" id="ref98">26</reflink>]) and Felner, Ginter, and Primavera ([<reflink idref="bib25" id="ref99">25</reflink>]) who conducted a randomized experiment in the US, called the Transition Project. The experiment had two goal settings: restructuring the role of teachers and reorganizing the school's environment. Students with improved transition reported significantly higher levels of teacher support, teacher affiliation and involvement than students without the additional transition. As a result, students belonging to the treatment group had better scores on the assessments instruments. The experiment indicates that primary preventive community based programs may help pupils during school transitions and may actually reduce dropout rates.</p> <p>Student commitment and motivation may be further enhanced beyond school time. One particular policy measure aimed at extended school (time) by offering a range of additional services and activities to students. For example, schools may offer sport and leisure activities to augment their attractiveness for students (de Zwart et al. [<reflink idref="bib20" id="ref100">20</reflink>]). It offers the chance to motivate students to take part in sport activities, to combat the problem of overweight and to develop talented youngsters towards professional sport careers. By keeping students longer at school, they can be better followed, given a more comprehensive education and a more attractive study program. In turn, it is expected that the students' well-being and motivation will be enhanced, which should reduce early school-leaving.</p> <hd id="AN0086994690-8">Results: effects of dropout policy</hd> <p>The previous section described the dropout interventions in the Netherlands. Similar to previous literature (Rumberger [<reflink idref="bib59" id="ref101">59</reflink>]), first we examine their influence at the individual level, and second we perform an analysis at the school level.</p> <hd id="AN0086994690-9">Data and definition</hd> <p>A measurement instrument is indispensable when it comes to the evaluation of policy. In the past, registration of early school-leavers was inaccurate and unreliable (see: Dropout prevention measures). As from academic year 2005–2006, the program "Aanval op de uitval" (or fighting dropout) improved the registration system remarkably. Thanks to the Dutch program, complete and reliable data on dropout levels are available. The registration of school dropout takes place as follows. Every pupil who attends school in the Dutch educational system obtains a personal identification number. All schools register students using this personal identification number and provide the registration to the ministry. Finally, all registrations end up in one nationwide database called "het Basisregister Onderwijs" (or BRON).</p> <p>We follow the Dutch definition of a dropout student.[<reflink idref="bib8" id="ref102">8</reflink>] If a youth below the age of 23 was enrolled in school on 1 October of a given year, but is not enrolled in the following year on 1 October and has not obtained a higher secondary degree, then the youth is designated as an early school-leaver. Despite the difficulties with this definition (e.g., it looks only at one date in time), it is a clear and uniform definition.</p> <p>This paper uses the BRON-data, a unique administrative data set that covers all students going to school in the Netherlands. The data include information on the student (e.g., gender, ethnicity), educational support at school (i.e., receives additional student counseling at school), school track (e.g., pre-university, general secondary or vocational school type) and on the parents (e.g., single-parent-household).[<reflink idref="bib9" id="ref103">9</reflink>] Using postcode information, we can further match the data to neighborhood characteristics. The data comprise all Dutch students enrolled between school years 2005–06 and 2007–08.</p> <p>By carefully reading the covenants, we constructed dummy variables which capture the implementation of the agreements at regional level. As schools and municipalities are collaborating extensively within each of the 39 regions, and as information at the local level is lacking, 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.</p> <p>Table 2. Descriptive statistics – total number, unless otherwise stated in second column.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="top"><td /><td /><td>2005</td><td>2006</td><td>2007</td><td>2008</td></tr></thead><tbody><tr valign="top"><td><bold>Number of students</bold></td><td char=".">1,347,162</td><td char=".">1,370,886</td><td char=".">1,399,068</td><td char=".">1,401,265</td></tr><tr valign="top"><td /></tr><tr valign="top"><td><bold>Student characteristics</bold></td><td /><td /><td /><td /></tr><tr valign="top"><td>dropout</td><td /><td char=".">56,790</td><td char=".">54,954</td><td char=".">51,156</td><td char=".">47,082</td></tr><tr valign="top"><td>School type</td><td>Senior vocational education</td><td char=".">424,776</td><td char=".">442,177</td><td char=".">453,383</td><td char=".">459,851</td></tr><tr valign="top"><td /><td>pro (practice-oriented education)</td><td char=".">10,140</td><td char=".">10,196</td><td char=".">27,083</td><td char=".">26,879</td></tr><tr valign="top"><td /><td>brug (first class)</td><td char=".">153,987</td><td char=".">150,777</td><td char=".">149,105</td><td char=".">146,459</td></tr><tr valign="top"><td /><td>lwoo (supported education)</td><td char=".">98,652</td><td char=".">99,996</td><td char=".">101,820</td><td char=".">100,462</td></tr><tr valign="top"><td /><td>vmbo (pre-vocational education)</td><td char=".">302,934</td><td char=".">294,221</td><td char=".">281,030</td><td char=".">271,687</td></tr><tr valign="top"><td /><td>havo (general secondary education)</td><td char=".">160,962</td><td char=".">166,244</td><td char=".">170,425</td><td char=".">172,617</td></tr><tr valign="top"><td /><td>vwo (pre-university education)</td><td char=".">195,711</td><td char=".">204,038</td><td char=".">211,686</td><td char=".">216,498</td></tr><tr valign="top"><td /><td>other</td><td char=".">0</td><td char=".">3,237</td><td char=".">4,536</td><td char=".">6,812</td></tr><tr valign="top"><td>Care for student</td><td>vmbo, geen lwoo</td><td char=".">302,934</td><td char=".">294,221</td><td char=".">281,030</td><td char=".">271,687</td></tr><tr valign="top"><td /><td>care</td><td char=".">98,652</td><td char=".">99,996</td><td char=".">101,820</td><td char=".">100,462</td></tr><tr valign="top"><td>City</td><td>Amsterdam</td><td char=".">48,391</td><td char=".">48,764</td><td char=".">49,799</td><td char=".">49,132</td></tr><tr valign="top"><td /><td>Rotterdam</td><td char=".">47,578</td><td char=".">48,312</td><td char=".">48,938</td><td char=".">48,705</td></tr><tr valign="top"><td /><td>The Hague</td><td char=".">33,588</td><td char=".">34,668</td><td char=".">35,832</td><td char=".">35,981</td></tr><tr valign="top"><td /><td>Utrecht</td><td char=".">16,123</td><td char=".">16,618</td><td char=".">17,280</td><td char=".">17,315</td></tr><tr valign="top"><td /><td>Average sized city</td><td char=".">1,199,830</td><td char=".">1,220,709</td><td char=".">1,245,034</td><td char=".">1,247,388</td></tr><tr valign="top"><td>Gender</td><td>Female</td><td char=".">656,707</td><td char=".">669,051</td><td char=".">681,305</td><td char=".">682,131</td></tr><tr valign="top"><td>Ethnicity</td><td>Autochton</td><td char=".">1,044,608</td><td char=".">1,061,931</td><td char=".">1,080,004</td><td char=".">1,080,467</td></tr><tr valign="top"><td /><td>Suriname</td><td char=".">40,323</td><td char=".">40,777</td><td char=".">41,417</td><td char=".">40,668</td></tr><tr valign="top"><td /><td>Aruba</td><td char=".">17,267</td><td char=".">17,957</td><td char=".">18,900</td><td char=".">19,168</td></tr><tr valign="top"><td /><td>Turkey</td><td char=".">46,693</td><td char=".">48,972</td><td char=".">51,513</td><td char=".">52,242</td></tr><tr valign="top"><td /><td>Morocco</td><td char=".">43,613</td><td char=".">45,122</td><td char=".">46,713</td><td char=".">46,592</td></tr><tr valign="top"><td /><td>non-western migrant</td><td char=".">67,987</td><td char=".">70,308</td><td char=".">72,544</td><td char=".">73,565</td></tr><tr valign="top"><td /><td>Western migrant</td><td char=".">82,044</td><td char=".">83,043</td><td char=".">84,274</td><td char=".">84,431</td></tr><tr valign="top"><td /><td>Unknown</td><td char=".">4,627</td><td char=".">2,776</td><td char=".">3,703</td><td char=".">4,132</td></tr><tr valign="top"><td>Generation of migrant</td><td>Autochton</td><td char=".">1,044,608</td><td char=".">1,061,931</td><td char=".">1,080,004</td><td char=".">1,080,467</td></tr><tr valign="top"><td /><td>First generation</td><td char=".">87,338</td><td char=".">86,455</td><td char=".">85,342</td><td char=".">82,469</td></tr><tr valign="top"><td /><td>Second generation</td><td char=".">210,589</td><td char=".">219,724</td><td char=".">230,019</td><td char=".">234,197</td></tr><tr valign="top"><td /><td>Unknown</td><td char=".">4,627</td><td char=".">2,776</td><td char=".">3,703</td><td char=".">4,132</td></tr><tr valign="top"><td>Living in poor area</td><td char=".">545,589</td><td char=".">555,578</td><td char=".">569,329</td><td char=".">568,062</td></tr><tr valign="top"><td /></tr><tr valign="top"><td><bold>Characteristics municipality</bold></td><td /><td /><td /><td /></tr><tr valign="top"><td>Number of inhabitants</td><td>mean (std)</td><td char=".">3387 (3776)</td><td char=".">3380 (3763)</td><td char=".">3393 (3771)</td><td char=".">3390 (3758)</td></tr><tr valign="top"><td>Population density</td><td>mean (std)</td><td char=".">3934 (3951)</td><td char=".">3939 (3953)</td><td char=".">4022 (4012)</td><td char=".">4069 (4043)</td></tr><tr valign="top"><td>% one person household</td><td>mean (std)</td><td char=".">28.888 (13.892)</td><td char=".">28.762 (13.806)</td><td char=".">29.371 (14.415)</td><td char=".">29.801 (14.815)</td></tr><tr valign="top"><td>% ethnic minorities</td><td>mean (std)</td><td char=".">9.194 (13.256)</td><td char=".">9.140 (13.183)</td><td char=".">9.243 (13.155)</td><td char=".">9.298 (13.139)</td></tr><tr valign="top"><td>Average income</td><td>mean (std)</td><td char=".">16.395 (2.566)</td><td char=".">16.412 (2.569)</td><td char=".">16.399 (2.579)</td><td char=".">16.392 (2.586)</td></tr><tr valign="top"><td>Green areas (km2)</td><td>mean (std)</td><td char=".">40.649 (5.365)</td><td char=".">40.663 (5.350)</td><td char=".">40.547 (5.382)</td><td char=".">40.462 (5.390)</td></tr><tr valign="top"><td>Number of households moving</td><td>mean (std)</td><td char=".">95.996 (24.035)</td><td char=".">95.882 (24.012)</td><td char=".">96.542 (24.248)</td><td char=".">96.956 (24.351)</td></tr><tr valign="top"><td>Average house value</td><td>mean (std)</td><td char=".">134.998 (32.472)</td><td char=".">135.255 (32.483)</td><td char=".">135.039 (32.387)</td><td char=".">134.911 (32.292)</td></tr><tr valign="top"><td>Employment in the area</td><td>mean (std)</td><td char=".">64.107 (109.889)</td><td char=".">63.030 (108.497)</td><td char=".">64.996 (110.175)</td><td char=".">66.289 (111.327)</td></tr><tr valign="top"><td /></tr><tr valign="top"><td><bold>Dropout prevention</bold></td><td /><td /><td /><td /></tr><tr valign="top"><td>Initial implementer</td><td char=".">0</td><td char=".">0</td><td char=".">774,770</td><td char=".">774,770</td></tr><tr valign="top"><td>Number of prevention items</td><td>mean (std)</td><td char=".">0</td><td char=".">0</td><td char=".">2.354</td><td char=".">4.886</td></tr><tr valign="top"><td>items</td><td /><td /><td /><td char=".">(2.458)</td><td char=".">(1.443)</td></tr><tr valign="top"><td>Care and advisory team</td><td char=".">0</td><td char=".">0</td><td char=".">667,198</td><td char=".">1,406,188</td></tr><tr valign="top"><td>Smoothing the transition</td><td char=".">0</td><td char=".">0</td><td char=".">646,830</td><td char=".">1,305,316</td></tr><tr valign="top"><td>Mentoring and coaching</td><td char=".">0</td><td char=".">0</td><td char=".">397,911</td><td char=".">752,125</td></tr><tr valign="top"><td>Changing subject</td><td char=".">0</td><td char=".">0</td><td char=".">128,053</td><td char=".">276,347</td></tr><tr valign="top"><td>optimal track or profession</td><td char=".">0</td><td char=".">0</td><td char=".">346,551</td><td char=".">766,340</td></tr><tr valign="top"><td>Apprenticeship</td><td char=".">0</td><td char=".">0</td><td char=".">127,940</td><td char=".">246,567</td></tr><tr valign="top"><td>Frequent intakes</td><td char=".">0</td><td char=".">0</td><td char=".">452,063</td><td char=".">835,898</td></tr><tr valign="top"><td>Extended school</td><td char=".">0</td><td char=".">0</td><td char=".">0</td><td char=".">202,601</td></tr><tr valign="top"><td>Reporting truants</td><td char=".">0</td><td char=".">0</td><td char=".">657,507</td><td char=".">1,228,348</td></tr><tr valign="top"><td>Dual track</td><td char=".">0</td><td char=".">0</td><td char=".">0</td><td char=".">149,715</td></tr><tr valign="top"><td>Number of schools</td><td char=".">723</td><td char=".">728</td><td char=".">728</td><td char=".">734</td></tr></tbody></table> </ephtml> </p> <p>We estimate by a panel probit model the probability that a student will drop out of secondary education. Controlling for (<reflink idref="bib1" id="ref104">1</reflink>) student, (<reflink idref="bib2" id="ref105">2</reflink>) neighborhood, and (<reflink idref="bib3" id="ref106">3</reflink>) regional characteristics, we relate the dropout probability to the dropout prevention measures.[<reflink idref="bib10" id="ref107">10</reflink>] A probit model is appropriate in the setting at hand as the dependent variable is a dichotomous variable and the estimated standard error has a normal distribution.[<reflink idref="bib11" id="ref108">11</reflink>] Students who leave education without a higher secondary degree are designated as early school-leavers and receive a value 1; non-dropout students obtain a value 0. The model allows us to examine which of the policy interventions correlate with a lower probability of early school-leaving.</p> <hd id="AN0086994690-10">Analysis at individual level</hd> <p>We start analyzing the effectiveness of the policy measures by considering the correlation at the micro level (i.e., student level). While controlling for observed heterogeneity, we estimate by a panel probit model the probability of dropping out and the influence of particular prevention measures. The results are presented in Table 3.</p> <p>Table 3. Menu-items at the individual level.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="top"><td /><td>Coefficient</td><td>St. error</td><td>t-statistic</td><td>p-value</td><td /></tr></thead><tbody><tr valign="top"><td><bold>Student characteristics</bold></td></tr><tr valign="top"><td>Gender</td><td char=".">−0.1426</td><td char=".">0.0064</td><td char=".">−22.4000</td><td char=".">0.0000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Ethnicity (native = reference)</td><td /><td /><td /><td /><td /></tr><tr valign="top"><td>Suriname</td><td char=".">0.7051</td><td char=".">0.0276</td><td char=".">25.5800</td><td char=".">0.0000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Dutch Antilles</td><td char=".">0.7165</td><td char=".">0.0284</td><td char=".">25.2300</td><td char=".">0.0000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Turkey</td><td char=".">0.6408</td><td char=".">0.0270</td><td char=".">23.7400</td><td char=".">0.0000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Morocco</td><td char=".">0.6930</td><td char=".">0.0268</td><td char=".">25.8300</td><td char=".">0.0000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Non-western migrant</td><td char=".">0.6874</td><td char=".">0.0212</td><td char=".">32.3600</td><td char=".">0.0000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Western migrant</td><td char=".">0.6686</td><td char=".">0.0238</td><td char=".">28.1200</td><td char=".">0.0000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Unknown</td><td char=".">3.2450</td><td char=".">0.1173</td><td char=".">27.6600</td><td char=".">0.0000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Generation of migrant</td><td char=".">−0.2629</td><td char=".">0.0122</td><td char=".">−21.5500</td><td char=".">0.0000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td /></tr><tr valign="top"><td><bold>Postcode characteristics</bold></td></tr><tr valign="top"><td>Poor area</td><td char=".">0.0804</td><td char=".">0.0106</td><td char=".">7.5600</td><td char=".">0.0000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Number of inhabitants</td><td char=".">0.0000</td><td char=".">0.0000</td><td char=".">−0.8200</td><td char=".">0.4120</td><td /></tr><tr valign="top"><td>Population density</td><td char=".">0.0000</td><td char=".">0.0000</td><td char=".">−2.2100</td><td char=".">0.0270</td><td><sup>∗∗</sup></td></tr><tr valign="top"><td>One person household</td><td char=".">0.0007</td><td char=".">0.0003</td><td char=".">2.3600</td><td char=".">0.0180</td><td><sup>∗∗</sup></td></tr><tr valign="top"><td>Number of migrants</td><td char=".">0.0008</td><td char=".">0.0004</td><td char=".">2.1800</td><td char=".">0.0290</td><td><sup>∗∗</sup></td></tr><tr valign="top"><td>Income per capita</td><td char=".">−0.0047</td><td char=".">0.0016</td><td char=".">−2.8500</td><td char=".">0.0040</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Green areas</td><td char=".">−0.0007</td><td char=".">0.0011</td><td char=".">−0.7100</td><td char=".">0.4770</td><td /></tr><tr valign="top"><td>Frequency of moving</td><td char=".">0.0014</td><td char=".">0.0003</td><td char=".">4.9800</td><td char=".">0.0000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Average housing cost</td><td char=".">−0.0005</td><td char=".">0.0002</td><td char=".">−2.8100</td><td char=".">0.0050</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Employment in the area</td><td char=".">−0.0003</td><td char=".">0.0001</td><td char=".">−5.3000</td><td char=".">0.0000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td /></tr><tr valign="top"><td><bold>Dropout prevention measures</bold></td></tr><tr valign="top"><td>Early implementer</td><td char=".">0.0095</td><td char=".">0.0117</td><td char=".">0.8100</td><td char=".">0.4150</td><td /></tr><tr valign="top"><td>Number of implemented prevention items</td><td char=".">0.0203</td><td char=".">0.0172</td><td char=".">1.1800</td><td char=".">0.2380</td><td /></tr><tr valign="top"><td>Care and advisory team</td><td char=".">−0.0083</td><td char=".">0.0280</td><td char=".">−0.3000</td><td char=".">0.7670</td><td /></tr><tr valign="top"><td>Smoothing the transition</td><td char=".">−0.0130</td><td char=".">0.0382</td><td char=".">−0.3400</td><td char=".">0.7320</td><td /></tr><tr valign="top"><td>Mentoring and coaching</td><td char=".">−0.0403</td><td char=".">0.0244</td><td char=".">−1.6500</td><td char=".">0.0990</td><td><sup>∗</sup></td></tr><tr valign="top"><td>Changing subject</td><td char=".">−0.0275</td><td char=".">0.0299</td><td char=".">−0.9200</td><td char=".">0.3580</td><td /></tr><tr valign="top"><td>Optimal track or profession</td><td char=".">−0.0434</td><td char=".">0.0226</td><td char=".">−1.9200</td><td char=".">0.0550</td><td><sup>∗</sup></td></tr><tr valign="top"><td>Apprenticeship</td><td char=".">−0.0264</td><td char=".">0.0347</td><td char=".">−0.7600</td><td char=".">0.4470</td><td /></tr><tr valign="top"><td>Frequent intakes</td><td char=".">−0.0243</td><td char=".">0.0207</td><td char=".">−1.1700</td><td char=".">0.2410</td><td /></tr><tr valign="top"><td>Extended school</td><td char=".">0.0315</td><td char=".">0.0345</td><td char=".">0.9100</td><td char=".">0.3620</td><td /></tr><tr valign="top"><td>Reporting truants</td><td char=".">−0.0221</td><td char=".">0.0246</td><td char=".">−0.9000</td><td char=".">0.3700</td><td /></tr><tr valign="top"><td>Curative projects</td><td char=".">−0.0626</td><td char=".">0.0329</td><td char=".">−1.9000</td><td char=".">0.0570</td><td><sup>∗</sup></td></tr><tr valign="top"><td /></tr><tr valign="top"><td>Constant</td><td char=".">147.9593</td><td char=".">10.8351</td><td char=".">13.6600</td><td char=".">0.0000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Region fixed effects</td><td>Yes</td><td /><td /><td /><td /></tr><tr valign="top"><td>Time fixed effect</td><td>Yes</td><td /><td /><td /><td /></tr><tr valign="top"><td>School type fixed effects</td><td>Yes</td><td /><td /><td /><td /></tr><tr><td>Note: Significance levels are provided using <sup>∗∗∗</sup>1%-significance level, <sup>∗∗</sup>5%-significance level and <sup>∗</sup>10%-significance level.</td></tr></tbody></table> </ephtml> </p> <p>In line with previous literature, gender, ethnicity and family background are indicated as triggers of the individual dropout decision (Rumberger [<reflink idref="bib58" id="ref109">58</reflink>]; Astone and McLanahan [<reflink idref="bib4" id="ref110">4</reflink>]; Mayer [<reflink idref="bib38" id="ref111">38</reflink>]; Steinberg, Dornbusch, and Brown [<reflink idref="bib70" id="ref112">70</reflink>]; Berktold, Geis, and Kaufman [<reflink idref="bib9" id="ref113">9</reflink>]; Pong and Ju [<reflink idref="bib53" id="ref114">53</reflink>]; 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 living in neighborhoods with more single-parent-households and migrant families. Better labor market opportunities also increase the students' probability to dropout. This confirms earlier research by McNeal ([<reflink idref="bib40" id="ref115">40</reflink>]).</p> <p>To control better for unobserved heterogeneity, various levels of fixed effects are included. First, school type fixed effects relate to the educational track of the pupil (i.e., pre-university, pre-vocational or vocational education). The estimates indicate that students in tracks with lower ability levels (i.e., students with additional counseling) have a relatively higher probability to drop out of secondary education. These results are in line with van der Steeg and Webbink ([<reflink idref="bib77" id="ref116">77</reflink>]), who argue that early school-leavers are concentrated in the lowest level of pre-vocational and vocational education.</p> <p>Second, we consider region fixed effects. Most of the region fixed effects are significant. This is intuitive as there are large differences in dropout rates among the regions.</p> <p>Third, we include a time trend. This serves two purposes. First, it captures a general trend in which, since the 1960s, education becomes more important. As pupils are more aware of the importance of obtaining a higher secondary diploma, the dropout rate can fall over time without any influence of dropout prevention measures. The results, presented in Table 3, confirm that this kind of sensitization takes place in the Netherlands. Second, the time trend allows us to interpret the obtained estimates as an under bound. As the time trend captures general policy influences and sensitization, one obtains estimates which have a stronger influence than general policy/sensitization. In other words, the resulting estimates provide a lower bound of the estimated influence – without the time trend, the estimated coefficients would be larger.</p> <p>Focusing on the correlation coefficients of the ten menu-items used in the dropout prevention policy delivers interesting insights. Out of 10 prevention measures, only three turn out to have a significant impact on the individual's dropout decision: (<reflink idref="bib1" id="ref117">1</reflink>) mentoring and coaching (estimated coefficient of -0.0403), (<reflink idref="bib2" id="ref118">2</reflink>) optimal track or profession (estimated coefficient of -0.0434), and (<reflink idref="bib3" id="ref119">3</reflink>) dual tracks (estimated coefficient of -0.0626). Not unexpectedly, those three measures correspond to preventions which regions cannot implement overnight. They are innovative, in a way that it is impossible for the school to re-label existing procedures, and require a clear follow-up of the student. Moreover, we do not observe evidence that doing more is better. The number of items that regions are implementing does not have a significant impact.</p> <p>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 implementer regions" were the 14 regions with the highest dropout rate in 2005–06 (as such, there was not a random selection).[<reflink idref="bib12" id="ref120">12</reflink>] This is in line with previous results of van der Steeg et al. ([<reflink idref="bib76" id="ref121">76</reflink>]), who analyzed the effectiveness of the covenant based on a difference-in-differences approach in those two regions. However, their model considered the general influence of the covenant, and not the various menu-items which constitute them.</p> <hd id="AN0086994690-11">Analysis at school level</hd> <p>The number of early school-leavers significantly differs across schools. Some schools have few dropouts, while the number of dropouts is high in other schools. To account for this heterogeneity across schools, a quantile analysis is estimated on school level data (Koenker and Bassett [<reflink idref="bib36" id="ref122">36</reflink>]). 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 quantile analysis, and in contrast to before, it is necessary to consider aggregated data on the school level. The aggregated data set includes therefore one observation per school per year.</p> <p>Aggregation of data yields an additional advantage. Various unobserved exogenous variables may influence the dropout decision at the individual level.[<reflink idref="bib13" id="ref123">13</reflink>] Therefore, an evaluation at the individual level may fail to indicate program effectiveness (e.g., Dynarski and Gleason [<reflink idref="bib22" id="ref124">22</reflink>]; Slavin and Fashola [<reflink idref="bib66" id="ref125">66</reflink>]). At the aggregated school level, the influence of multi-model interventions can be better estimated.</p> <p>In the school level analysis, we consider three kinds of schools: (<reflink idref="bib1" id="ref126">1</reflink>) schools with a low dropout rate, (<reflink idref="bib2" id="ref127">2</reflink>) schools with a median dropout rate, and (<reflink idref="bib3" id="ref128">3</reflink>) schools with a high dropout rate. They are decided on the first (25%), second (50%) and third quantile (75%), respectively. Besides a time trend, we control for school type and region fixed effects. Table 4 reports the results of the quantile analysis.</p> <p>Table 4. Effect of dropout prevention in first, second, and third quartile of the school.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="top"><td /><td>25%</td><td>Quart.</td><td /><td /><td /><td>50%</td><td>Quart.</td><td /><td /><td /><td>75%</td><td>Quart.</td><td /><td /><td /></tr><tr valign="top"><td /><td>coeff.</td><td>St. error</td><td>t-stat.</td><td>p-value</td><td /><td>coeff.</td><td>St. error</td><td>t-stat.</td><td>p-value</td><td /><td>coeff.</td><td>St. error</td><td>t-stat.</td><td>p-value</td><td /></tr></thead><tbody><tr valign="top"><td>% of females at school</td><td char=".">−0.004</td><td char=".">0.002</td><td char=".">−1.540</td><td char=".">0.123</td><td /><td char=".">−0.007</td><td char=".">0.002</td><td char=".">-3.480</td><td char=".">0.001</td><td><sup>∗∗∗</sup></td><td char=".">0.000</td><td char=".">0.001</td><td char=".">0.300</td><td char=".">0.766</td><td /></tr><tr valign="top"><td>% of care students at school</td><td char=".">0.065</td><td char=".">0.002</td><td char=".">40.51</td><td char=".">0.000</td><td><sup>∗∗∗</sup></td><td char=".">0.075</td><td char=".">0.001</td><td char=".">51.12</td><td char=".">0.000</td><td><sup>∗∗∗</sup></td><td char=".">0.083</td><td char=".">0.001</td><td char=".">95.710</td><td char=".">0.000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>% of natives at school</td><td char=".">−0.004</td><td char=".">0.002</td><td char=".">−2.290</td><td char=".">0.022</td><td><sup>∗∗</sup></td><td char=".">−0.014</td><td char=".">0.002</td><td char=".">-8.050</td><td char=".">0.000</td><td><sup>∗∗∗</sup></td><td char=".">−0.025</td><td char=".">0.001</td><td char=".">−25.310</td><td char=".">0.000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>% of single parents at school</td><td char=".">−0.072</td><td char=".">0.008</td><td char=".">−8.630</td><td char=".">0.000</td><td><sup>∗∗∗</sup></td><td char=".">−0.050</td><td char=".">0.008</td><td char=".">-6.440</td><td char=".">0.000</td><td><sup>∗∗∗</sup></td><td char=".">−0.006</td><td char=".">0.004</td><td char=".">−1.640</td><td char=".">0.100</td><td><sup>∗</sup></td></tr><tr valign="top"><td>school in poor area</td><td char=".">0.008</td><td char=".">0.002</td><td char=".">4.480</td><td char=".">0.000</td><td><sup>∗∗∗</sup></td><td char=".">0.003</td><td char=".">0.002</td><td char=".">2.000</td><td char=".">0.045</td><td><sup>∗∗</sup></td><td char=".">−0.003</td><td char=".">0.001</td><td char=".">−3.530</td><td char=".">0.000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Number of inhabitants</td><td char=".">0.000</td><td char=".">0.000</td><td char=".">1.050</td><td char=".">0.293</td><td /><td char=".">0.000</td><td char=".">0.000</td><td char=".">-1.420</td><td char=".">0.156</td><td /><td char=".">0.000</td><td char=".">0.000</td><td char=".">−5.230</td><td char=".">0.000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Population density</td><td char=".">0.000</td><td char=".">0.000</td><td char=".">−1.240</td><td char=".">0.216</td><td /><td char=".">0.000</td><td char=".">0.000</td><td char=".">-0.770</td><td char=".">0.440</td><td /><td char=".">0.000</td><td char=".">0.000</td><td char=".">1.750</td><td char=".">0.080</td><td><sup>∗</sup></td></tr><tr valign="top"><td>% one person household</td><td char=".">0.000</td><td char=".">0.000</td><td char=".">0.860</td><td char=".">0.392</td><td /><td char=".">0.000</td><td char=".">0.000</td><td char=".">2.250</td><td char=".">0.025</td><td><sup>∗∗</sup></td><td char=".">0.000</td><td char=".">0.000</td><td char=".">3.530</td><td char=".">0.000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>% Migrant</td><td char=".">0.000</td><td char=".">0.000</td><td char=".">4.310</td><td char=".">0.000</td><td><sup>∗∗∗</sup></td><td char=".">0.000</td><td char=".">0.000</td><td char=".">3.410</td><td char=".">0.001</td><td><sup>∗∗∗</sup></td><td char=".">0.000</td><td char=".">0.000</td><td char=".">4.200</td><td char=".">0.000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Average income</td><td char=".">0.000</td><td char=".">0.000</td><td char=".">0.110</td><td char=".">0.909</td><td /><td char=".">0.000</td><td char=".">0.000</td><td char=".">3.040</td><td char=".">0.002</td><td><sup>∗∗∗</sup></td><td char=".">0.000</td><td char=".">0.000</td><td char=".">5.990</td><td char=".">0.000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Green areas (km2)</td><td char=".">0.000</td><td char=".">0.000</td><td char=".">−1.900</td><td char=".">0.057</td><td><sup>∗</sup></td><td char=".">0.000</td><td char=".">0.000</td><td char=".">0.440</td><td char=".">0.661</td><td /><td char=".">0.000</td><td char=".">0.000</td><td char=".">−0.360</td><td char=".">0.718</td><td /></tr><tr valign="top"><td>Number of households moving</td><td char=".">0.000</td><td char=".">0.000</td><td char=".">−1.890</td><td char=".">0.059</td><td><sup>∗</sup></td><td char=".">0.000</td><td char=".">0.000</td><td char=".">−0.190</td><td char=".">0.849</td><td /><td char=".">0.000</td><td char=".">0.000</td><td char=".">2.010</td><td char=".">0.044</td><td><sup>∗∗</sup></td></tr><tr valign="top"><td>Average house value</td><td char=".">0.000</td><td char=".">0.000</td><td char=".">0.130</td><td char=".">0.894</td><td /><td char=".">0.000</td><td char=".">0.000</td><td char=".">2.600</td><td char=".">0.010</td><td><sup>∗∗∗</sup></td><td char=".">0.000</td><td char=".">0.000</td><td char=".">15.530</td><td char=".">0.000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Employment in the area</td><td char=".">0.000</td><td char=".">0.000</td><td char=".">2.560</td><td char=".">0.011</td><td><sup>∗∗</sup></td><td char=".">0.000</td><td char=".">0.000</td><td char=".">1.100</td><td char=".">0.273</td><td /><td char=".">0.000</td><td char=".">0.000</td><td char=".">1.250</td><td char=".">0.212</td><td /></tr><tr valign="top"><td>Initial implementer</td><td char=".">0.001</td><td char=".">0.002</td><td char=".">0.210</td><td char=".">0.832</td><td> </td><td char=".">0.002</td><td char=".">0.003</td><td char=".">0.720</td><td char=".">0.469</td><td> </td><td char=".">0.009</td><td char=".">0.001</td><td char=".">6.660</td><td char=".">0.000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Number of implemented prevention items</td><td char=".">0.000</td><td char=".">0.001</td><td char=".">0.130</td><td char=".">0.898</td><td /><td char=".">0.001</td><td char=".">0.001</td><td char=".">0.790</td><td char=".">0.427</td><td /><td char=".">0.002</td><td char=".">0.000</td><td char=".">4.410</td><td char=".">0.000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Care and advisory team</td><td char=".">0.002</td><td char=".">0.002</td><td char=".">0.950</td><td char=".">0.341</td><td /><td char=".">0.002</td><td char=".">0.002</td><td char=".">1.160</td><td char=".">0.246</td><td /><td char=".">0.000</td><td char=".">0.001</td><td char=".">−0.490</td><td char=".">0.624</td><td /></tr><tr valign="top"><td>Smoothing the transition</td><td char=".">−0.003</td><td char=".">0.002</td><td char=".">−1.290</td><td char=".">0.197</td><td /><td char=".">−0.004</td><td char=".">0.002</td><td char=".">−1.840</td><td char=".">0.067</td><td><sup>∗</sup></td><td char=".">−0.011</td><td char=".">0.001</td><td char=".">−8.750</td><td char=".">0.000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Mentoring and coaching</td><td char=".">0.000</td><td char=".">0.001</td><td char=".">0.150</td><td char=".">0.879</td><td /><td char=".">−0.001</td><td char=".">0.001</td><td char=".">−0.470</td><td char=".">0.638</td><td /><td char=".">−0.002</td><td char=".">0.001</td><td char=".">−2.990</td><td char=".">0.003</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Changing subject</td><td char=".">0.000</td><td char=".">0.002</td><td char=".">0.130</td><td char=".">0.896</td><td /><td char=".">0.000</td><td char=".">0.002</td><td char=".">−0.140</td><td char=".">0.889</td><td /><td char=".">−0.002</td><td char=".">0.001</td><td char=".">−2.580</td><td char=".">0.010</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Optimal track or profession</td><td char=".">0.000</td><td char=".">0.001</td><td char=".">−0.300</td><td char=".">0.762</td><td /><td char=".">−0.001</td><td char=".">0.001</td><td char=".">−0.450</td><td char=".">0.654</td><td /><td char=".">−0.002</td><td char=".">0.001</td><td char=".">−2.790</td><td char=".">0.005</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Apprenticeship</td><td char=".">−0.002</td><td char=".">0.002</td><td char=".">−1.270</td><td char=".">0.206</td><td /><td char=".">−0.005</td><td char=".">0.002</td><td char=".">−2.620</td><td char=".">0.009</td><td><sup>∗∗∗</sup></td><td char=".">−0.011</td><td char=".">0.001</td><td char=".">−10.300</td><td char=".">0.000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Frequent intakes</td><td char=".">0.000</td><td char=".">0.001</td><td char=".">−0.230</td><td char=".">0.820</td><td /><td char=".">−0.001</td><td char=".">0.001</td><td char=".">−0.660</td><td char=".">0.509</td><td /><td char=".">−0.002</td><td char=".">0.001</td><td char=".">−2.550</td><td char=".">0.011</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Extended school</td><td char=".">0.003</td><td char=".">0.002</td><td char=".">1.590</td><td char=".">0.113</td><td /><td char=".">0.001</td><td char=".">0.002</td><td char=".">0.610</td><td char=".">0.542</td><td /><td char=".">−0.005</td><td char=".">0.001</td><td char=".">−5.280</td><td char=".">0.000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Reporting truants</td><td char=".">0.001</td><td char=".">0.001</td><td char=".">0.640</td><td char=".">0.522</td><td /><td char=".">−0.002</td><td char=".">0.001</td><td char=".">−1.480</td><td char=".">0.138</td><td /><td char=".">−0.005</td><td char=".">0.001</td><td char=".">−7.170</td><td char=".">0.000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td>Curative projects</td><td char=".">0.001</td><td char=".">0.001</td><td char=".">0.830</td><td char=".">0.408</td><td /><td char=".">0.000</td><td char=".">0.002</td><td char=".">0.110</td><td char=".">0.914</td><td /><td char=".">0.000</td><td char=".">0.001</td><td char=".">0.440</td><td char=".">0.657</td><td /></tr><tr valign="top"><td>Constant</td><td char=".">0.009</td><td char=".">0.004</td><td char=".">2.020</td><td char=".">0.043</td><td><sup>∗∗</sup></td><td char=".">0.012</td><td char=".">0.004</td><td char=".">2.790</td><td char=".">0.005</td><td><sup>∗∗∗</sup></td><td char=".">0.012</td><td char=".">0.002</td><td char=".">5.040</td><td char=".">0.000</td><td><sup>∗∗∗</sup></td></tr><tr valign="top"><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr valign="top"><td>Region fixed effects</td><td>Yes</td><td /><td /><td /><td /><td>Yes</td><td /><td /><td /><td /><td>Yes</td><td /><td /><td /><td /></tr><tr valign="top"><td>Time fixed effect</td><td>Yes</td><td /><td /><td /><td /><td>Yes</td><td /><td /><td /><td /><td>Yes</td><td /><td /><td /><td /></tr><tr valign="top"><td>School type fixed effects</td><td>Yes</td><td /><td /><td /><td /><td>Yes</td><td /><td /><td /><td /><td>Yes</td><td /><td /><td /><td /></tr><tr><td>Note: Significance levels are provided using <sup>∗∗∗</sup>1%-significance level, <sup>∗∗</sup>5%-significance level and <sup>∗</sup>10%-significance level.</td></tr></tbody></table> </ephtml> </p> <p>We see that schools with relatively high dropout rates benefit most from dropout prevention measures. All dropout prevention measures, except for advisory team and dual track projects, are associated with lower dropout rates. In contrast, we do not find a significant impact of the dropout prevention measures on schools with low or median dropout rates.</p> <p>Obviously, schools cannot simply be divided into three groups. The distribution of dropout rates is more a continuum (Rumberger and Thomas [<reflink idref="bib63" id="ref129">63</reflink>]). Therefore, we estimate the impact of each dropout prevention measure on the dropout level of schools for all centiles (i.e., a continuous distribution). The corresponding graphs are plotted in Figure 1. We observe a negative slope in almost all graphs. This indicates that the higher the dropout levels of the school, the larger the impact of the dropout prevention measure.</p> <p>Graph: Figure 1 Quantile estimates of dropout prevention measures.</p> <hd id="AN0086994690-12">Conclusion and policy advice</hd> <p>In line with the Lisbon Agenda, which aims for a reduction of school dropout by half, the Dutch government created a policy framework to reduce dropout of secondary education. Regions can select intervention measures from a list provided by the central government. This paper analyzed the impact of the policy measures at both the individual level (i.e., do the selected policy measures of the covenant change the dropout decision of the student?) and at the school level (i.e., do the selected prevention measures change the number of students dropping out of schools?).</p> <p>First consider the micro level – the individual perspective in the conceptual framework of Rumberger ([<reflink idref="bib59" id="ref130">59</reflink>]). While most policy measures correlate negatively with the individual dropout decision, only "mentoring and coaching" (i.e., matching of students with a coach from public or private organizations), "optimal track or profession" (e.g., work placement) and "dual track" (i.e., re-entering education for dropout students) have a significant negative impact on the individual dropout decision. This might not be coincidence, as these measures are difficult to implement overnight and require a change in the process (see also De Bruijn et al. [<reflink idref="bib18" id="ref131">18</reflink>]). Second, we observe that the number of policy measures implemented by a region does not have a significant impact. More is not necessarily better (and vice versa).</p> <p>An analysis at the individual level hides a significant heterogeneity across schools as some schools have few dropouts while others have many early school-leavers. 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 different quantiles of schools (e.g., the schools with the 25% lowest or 25% highest percentage of students) also different impacts of prevention measures arise. While only few policy measures items have a significant effect in schools with a relatively low percentage of dropouts, schools with a relatively high percentage of dropouts benefit 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 benefit the most from dropout prevention measures.</p> <p>Three remarks are relevant at this point. First, despite the rich data set, we are unable to draw conclusions on the causal process. Causal identification is impossible due to simultaneous implementation of policy actions. Nevertheless, 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 effects), our results give a clear indication on the lower bound of the influence of the policy measures on dropout reduction. Second, the covenants between regions and government were signed in the academic year 2005–06 for 14 regions and in 2007–08 for all 39 regions. Our analysis starts in the academic year 2005–06 as the registration system improved remarkably from 2005–06 onwards and the first incentives were agreed on at the start of this academic year. It should, however, be noted that the interventions agreed on in the covenant are intended interventions by the regions. To our best knowledge, there is no information on the degree of realization of these intended policy measures. Moreover, the implementation of some measures might take some time or might only yield effects after some years. Nevertheless, qualitative research pointed out that the assessed measures are effectively implemented in regions and schools soon after they were established (De Bruijn et al. [<reflink idref="bib18" id="ref132">18</reflink>]). This enables us to gauge, for the first time, the influence of the policy interventions. Third, although the central focus of the paper is on the Netherlands, its impact goes far beyond this specific country. On the one hand, dropout policy is high on the political agenda in about all industrialized countries. Given that economies are increasingly knowledge-driven, the ensuing penalty of dropping out is increasingly large. Hence, the further reducing of dropout rates is an important policy issue. On the other hand, our analysis reveals some best practice policy, what might also be insightful for other countries.</p> <p>Given the importance of the theme, early school-leaving will definitely attract further research. From a methodological point of view, further research could examine the influence of the policy measures by a multi-level model. From a policy point of view, potential research avenues could arise from the policy on truancy, the additional years of compulsory education, or from long term policy evaluations.</p> <hd id="AN0086994690-13">Acknowledgements</hd> <p>We would like to thank Wim Groot, Henriette Maassen van den Brink, Chris van Klaveren, Marton Csillag, participants of the TIER seminar at the University of Groningen, two anonymous referees, and members of the feedback committee at the Dutch Ministry of Education, Culture and Sciences and The Scientific Review Commission at NICIS Institute for insightful comments. We are grateful to the Dutch Ministry of Education for providing the data. The authors acknowledge financial support of NICIS. The usual caveat applies.</p> <hd id="AN0086994690-14">Notes</hd> <ref id="AN0086994690-15"> <title> Footnotes </title> <blist> <bibl id="bib1" idref="ref4" type="bt">1</bibl> <bibtext> 1. On 1 August 2008, a total of 20 schools were selected for a government experiment. If students were going to school in a selected school, they had the possibility to attend classes in their pre-vocational school. As such, they did not have to switch from a pre-vocational to a senior vocational school. Most schools were located in Amsterdam and Rotterdam (rijksoverheid.nl).</bibtext> </blist> <blist> <bibl id="bib2" idref="ref7" type="bt">2</bibl> <bibtext> 2. The decentralized policy is accommodated with a large accountability. Well performing schools and regions are "named", while poorly performing regions and schools are "shamed". The naming and shaming incentive is implemented by the website <ulink href="http://www.aanvalopschooluitval.nl">http://www.aanvalopschooluitval.nl</ulink>.</bibtext> </blist> <blist> <bibl id="bib3" idref="ref11" type="bt">3</bibl> <bibtext> 3. In this paper, truancy and school absenteeism are used interchangeably.</bibtext> </blist> <blist> <bibl id="bib4" idref="ref13" type="bt">4</bibl> <bibtext> 4. The authors mainly use descriptive statistics and multiple regressions to point out that average class size and ethnic minority groups are responsible for about 56% of the variance in the observed truancy rate.</bibtext> </blist> <blist> <bibl id="bib5" idref="ref54" type="bt">5</bibl> <bibtext> 5. Attwood and Croll (2006) have used the British Household Panel Survey and in-depth interviews to ask persistent truants about the extent, consequences and explanations for truancy at secondary schools. Poor relationships with teachers, bullying and a more general dislike of the school's atmosphere are considered as triggers for the dropout decision. Attwood and Croll (2006) suggest a distinction between socio-economic and attitudinal factors. Davis and Lee (2004) also adhere to above findings. They went into discussion with truants, as well as attendees and some parents. Davis and Lee (2004) add to the discussion that, in contrast to professionals, the study curriculum is not considered as a dropout trigger. This finding has been weakened by Beekhoven and Dekkers (2005) who put emphasis on learning problems, lack of motivation and problems arising from choosing the wrong vocational track.</bibtext> </blist> <blist> <bibl id="bib6" idref="ref36" type="bt">6</bibl> <bibtext> 6. In line with the European definition, senior vocational education is denoted by ISCED (International Standard Classification of Education) 3.</bibtext> </blist> <blist> <bibl id="bib7" idref="ref56" type="bt">7</bibl> <bibtext> 7. On 1 August 2008, a total of 20 schools were selected for a government experiment. If students were going to school in a selected school, they had the possibility to attend classes in their pre-vocational school. As such, they did not have to switch from a pre-vocational to a senior vocational school. Most schools were located in Amsterdam and Rotterdam (rijksoverheid.nl).</bibtext> </blist> <blist> <bibl id="bib8" idref="ref47" type="bt">8</bibl> <bibtext> 8. We realize a general accepted definition of dropout (in all its nuances of, for example, event and status dropouts) does not exist. It is beyond the scope of this paper to discuss the various definitions, however, we follow the "official" European and Dutch definition.</bibtext> </blist> <blist> <bibl id="bib9" idref="ref58" type="bt">9</bibl> <bibtext> 9. Some of these variables (e.g., school type) are endogenous with respect to the risk of dropping out. An instrumental variable (IV) approach would be appropriate to deal with this endogeneity. However, due to data restrictions, the appropriate instruments are as yet unavailable. We consider the endogeneity issue as scope for further research.</bibtext> </blist> <blist> <bibtext> 10. Extensive additional robustness checks were performed. First, some measures may have been implemented in very few regions. Their effects may be confounded with autonomous time trends in these regions. Given that dropout rates have been falling substantially in recent years, we included the interaction effect of the region dummy with the time trend into the regression model: if the autonomous time trend in the fall in dropout rate differs between regions, between region differences in time trends may be confounded with effects of certain measures that were implemented in these regions. The analysis delivered robust results. Second, some measures are implemented by almost all regions. Both removing these measures, and clustering of the measures at a higher level, delivered robust outcomes.</bibtext> </blist> <blist> <bibtext> 11. Using an ordinary least squares regression model could yield probabilities outside the (0.1) boundary. As an alternative econometric technique multi-level modeling could be employed. We consider this as scope for further research.</bibtext> </blist> <blist> <bibtext> 12. 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  Data: Routledge. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 22
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2013
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Evaluative
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Dropouts%22">Dropouts</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Coaching+%28Performance%29%22">Coaching (Performance)</searchLink><br /><searchLink fieldCode="DE" term="%22Dropout+Prevention%22">Dropout Prevention</searchLink><br /><searchLink fieldCode="DE" term="%22Dropout+Rate%22">Dropout Rate</searchLink><br /><searchLink fieldCode="DE" term="%22Strategic+Planning%22">Strategic Planning</searchLink><br /><searchLink fieldCode="DE" term="%22Correlation%22">Correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Mentors%22">Mentors</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+Making%22">Decision Making</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+%28Statistics%29%22">Regression (Statistics)</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Netherlands%22">Netherlands</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1080/00131911.2011.648172
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0013-1911
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In line with the Lisbon Agenda, set by the European Council in the year 2000, European governments formulated ambitious plans to halve the level of early school-leavers by 2012. This paper outlines the dropout prevention measures in the Netherlands and analyzes their influence at both the individual and school level. While most policy measures correlate negatively with the individual dropout decision, only "mentoring and coaching" (i.e., matching of students with a coach from public or private organizations), "optimal track or profession" (e.g., work placement) and "dual track" (i.e., re-entering education for dropout students) have a significant negative impact on the individual dropout decision. By means of quantile regressions, we observe that schools with a relatively high dropout rate benefit the most from dropout prevention measures. (Contains 4 tables, 1 figure, and 13 notes.)
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: Ref
  Label: Number of References
  Group: RefInfo
  Data: 78
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2013
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ999515
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ999515
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      – Type: doi
        Value: 10.1080/00131911.2011.648172
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      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 22
        StartPage: 155
    Subjects:
      – SubjectFull: Dropouts
        Type: general
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Coaching (Performance)
        Type: general
      – SubjectFull: Dropout Prevention
        Type: general
      – SubjectFull: Dropout Rate
        Type: general
      – SubjectFull: Strategic Planning
        Type: general
      – SubjectFull: Correlation
        Type: general
      – SubjectFull: Mentors
        Type: general
      – SubjectFull: Decision Making
        Type: general
      – SubjectFull: Regression (Statistics)
        Type: general
      – SubjectFull: Netherlands
        Type: general
    Titles:
      – TitleFull: Dropout Prevention Measures in the Netherlands, an Explorative Evaluation
        Type: main
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          Name:
            NameFull: De Witte, Kristof
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            NameFull: Cabus, Sofie J.
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              Y: 2013
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            – TitleFull: Educational Review
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