Dropout from secondary education: All's well that begins well

Despite the increased attention to students leaving secondary education without a diploma, numerous students dropout yearly. This paper makes a distinction between the 'individual perspective' and the 'institutional perspective' of dropping out. The individual perspective considers the probability of an individual student to drop out. It is explored by multinominal logit models, with and without accounting for unobserved heterogeneity. We observe that particularly motivation of the student and interest in schooling of his/her parents are crucial predictors of the individual dropout decision. The institutional perspective focusses on contextual factors and is examined by ordered logit models, both with and without accounting for unobserved heterogeneity. In particular, we discuss the influence of the first year of secondary education by analysing the large di¤erences in the number of dropouts in Dutch …rst year classes. We observe that, more than motivation, conditions in the …rst year of secondary education are crucial in shaping the dropping out decision.Dropout decision, Secondary education, Motivation, Logit, Unobserved heterogeneity

COOPER-framework: A Unified Standard Process for Non-parametric Projects

Practitioners assess performance of entities in increasingly large and complicated datasets. If non-parametric models, such as Data Envelopment Analysis, were ever considered as simple push-button technologies, this is impossible when many variables are available or when data have to be compiled from several sources. This paper introduces by the ‘COOPER-framework’ a comprehensive model for carrying out non-parametric projects. The framework consists of six interrelated phases: Concepts and objectives, On structuring data, Operational models, Performance comparison model, Evaluation, and Result and deployment. Each of the phases describes some necessary steps a researcher should examine for a well defined and repeatable analysis. The COOPER-framework provides for the novice analyst guidance, structure and advice for a sound non-parametric analysis. The more experienced analyst benefits from a check list such that important issues are not forgotten. In addition, by the use of a standardized framework non-parametric assessments will be more reliable, more repeatable, more manageable, faster and less costly.DEA, non-parametric efficiency, unified standard process, COOPER-framework.

Estimating and explaining efficiency in a multilevel setting: A robust two-stage approach

Various applications require multilevel settings (e.g., for estimating fixed and random effects). However, due to the curse of dimensionality, the literature on non-parametric efficiency analysis did not yet explore the estimation of performance drivers in highly multilevel settings. As such, it lacks models which are particularly designed for multilevel estimations. This paper suggests a semi-parametric two-stage framework in which, in a first stage, non-parametric a effciency estimators are determined. As such, we do not require any a priori information on the production possibility set. In a second stage, a semiparametric Generalized Additive Mixed Model (GAMM) examines the sign and significance of both discrete and continuous background characteristics. The proper working of the procedure is illustrated by simulated data. Finally, the model is applied on real life data. In particular, using the proposed robust two-stage approach, we examine a claim by the Dutch Ministry of Education in that three out of the twelve Dutch provinces would provide lower quality education. When properly controlled for abilities, background variables, peer group and ability track effects, we do not observe differences among the provinces in educational attainments.Productivity estimation; Multilevel setting; Generalized Additive Mixed Model; Education; Social segregation

On estimating the effectiveness of resources. A local maximum likelihood frontier approach on care for students

To study education as a complex production process in a noisy and heterogeneous setting, this paper suggests to using a stochastic frontier model estimated by a local maximum likelihood approach (LMLSF). The LMLSF smoothly combines the virtues of the non-parametric Data Envelopment Analysis model and the semi-parametric Stochastic Frontier model. Additionally, by the LMLSF approach one can deduce the eectiveness of resources by examining the impact of inputs on the frontier. Indeed, while eciency estimations (i.e., doing the things right) received considerable attention in the literature, the analysis of effectiveness (i.e., doing the right things) is less explored. The approach is illustrated on a sample of Dutch primary education pupils. We examine the eectiveness of instruction time, experience of the teacher, and student care (both social worker and psychologist) on educational attainments of native and non-native students.Stochastic Frontier Analysis; Data Envelopment Analysis; Local Maximum Likelihood; Education; Student care

Accounting for exogenous influences in a benevolent performance evaluation of teachers

Students’ evaluations of teacher performance (SETs) are increasingly used by universities and colleges for teaching improvement and decision making (e.g., promotion or tenure). However, SETs are highly controversial mainly due to two issues: (1) teachers value various aspects of excellent teaching differently, and, to be fair, (2) SETs should be determined solely by the teacher’s actual performance in the classroom, not by other influences (related to the teacher, the students or the course) which are not under his or her control. To account for these two issues, this paper constructs SETs using a specially tailored version of the popular non-parametric Data Envelopment Analysis (DEA) approach. In particular, in a so-called ‘Benefit of the doubt’ model we account for different values and interpretations that teachers attach to ‘good teaching’. Within this model, we reduce the impact of measurement errors and a-typical observations, and account explicitly for heterogeneous background characteristics arising from teacher, student and course characteristics. To show the potentiality of the method, we examine teacher performance for the Hogeschool Universiteit Brussel (located in Belgium). Our findings suggest that heterogeneous background characteristics play an important role in teacher performance.Teacher performance, Data envelopment analysis, Conditional efficiency, Education.

How are Teachers Teaching? A Nonparametric Approach

This paper examines which conguration of teaching activities (expressed in, e.g., problem solving, homework, lecturing) maximizes student performance. To do so, it formulates a non- parametric eciency model that is rooted in the Data Envelopment Analysis literature. In the model, we account for (1) self selection of students and teachers in better schools, and (2) complementary teaching activities. The analysis distinguishes both individual teaching (i.e., a personal teaching style adapted to the individual needs of the student) and collective teaching (i.e., a similar style for all students in a class). Exploiting the data set, we compare the actual teaching style as revealed by the teacher in the data to the model estimations. As such, we anal- yse which students in the class the teacher is targeting with his/her teaching style. The main results show that high test scores are associated with teaching styles that emphasise problem solving and homework. In addition, teachers seem to adapt their optimal teaching style on the 70 percent least performing students.Data Envelopment Analysis, Teacher Quality, Student Performance, Nonparametric estimation, Revealed teaching style

Selective Migration in New Towns: Influence on Regional Accountability in Early School Leaving

In an attempt to stop the rampant suburbanization, which countries experienced after World War II, a 'new town' policy was enrolled. As a major objective, and related to its origins, new towns were effective in attracting low and medium income households. Nowadays, cities and municipalities experience an increased accountability in which incentives are provided by 'naming and shaming'. This paper focuses on an issue where both historical and local policy come together: early school leaving. Using an iterative matching analysis, it suggests how to account for differences in population and regional characteristics. In other words, how to compare and interpret early school leaving in new towns in a more `fair' way. The results point out that (statistically) mitigating historical differences is necessary, even though this does not necessarily means that 'naming' is replaced by 'shaming'.Urban Economics; New Town; Early School Leaving; Naming and Shaming; Iterative Matching, Urban Planning

The efficiency of education in generating literacy: a stochastic frontier approach

The growing importance attached to education as a key factor to improve economic performance coupled with the persistent scarcity of resources for education makes it important that skills and literacy are produced efficiently. This paper provides an international comparison of the efficiency of literacy production. We find substantial differences between countries in levels of literacy, differences in literacy between education levels and differences in the efficiency of literacy production. There are some notable differences between more Anglo-Saxon countries and the Continental European countries. The findings suggest that in almost all countries the scope for efficiency improvements in education is large. So even without major increases in (public) funding, improvements in educational outcomes are achievable. We can get better value for the money we spend on education.

Estimating scale economies and the optimal size of school districts: A flexible form approach

This paper investigates estimation methods to model the relationship between school district size, costs per student and the organisation of school districts. We show that the assumptions on the functional form strongly affect the estimated scale economies and offer two possible solutions to allow for more flexibility in the estimation method. First, we introduce a model by adding higher-degree district size polynomials, allowing for multiple optima. Second, we develop a Fourier cost function, innovative in the literature on scale economies in education. We then compare both models to classical approaches in the literature. We illustrate how a minor change in the estimation method can alter policy conclusions significantly using Flemish school district data. In doing so, we find sizeable potential cost savings from the consolidation of school districts, especially at the lower tail of the district-size distribution. The organisational transition from small to large school districts is characterised by an interval between two optima. Beyond an apparent slowdown in cost savings in medium-sized school districts, cost savings from school district consolidation increase again, up to the optimal size of around 6,500 students. Beyond this optimum, school districts incur diseconomies of scale. The commonly used quadratic form (U'-shaped cost function) overestimates scale economies, and fails to identify the interval between both optima