RANKING THE SCHOOLS: HOW SCHOOL‐QUALITY INFORMATION AFFECTS SCHOOL CHOICE IN THE NETHERLANDS

This paper analyzes whether information about the quality of high schools published in a national newspaper affects school choice in the Netherlands. We find that negative (positive) school‐quality scores decrease (increase) the number of first‐year students who choose a school after the year of publication. These effects are only large for the college‐preparatory track, such that a school receiving the most positive score for its most academic track sees 16–18 more first‐year students enroll. We find that parents respond to the most recent and most prominently displayed information. The effects of information about school quality do not seem to be greater in regions with larger relevant newspaper circulation, suggesting that direct exposure to news about school quality does not explain the response to this information.     

Variational Bayes model averaging for graphon functions and motif frequencies inference in <Emphasis Type="Italic">W</Emphasis>-graph models

W-graph refers to a general class of random graph models that can be seen as a random graph limit. It is characterized by both its graphon function and its motif frequencies. In this paper, relying on an existing variational Bayes algorithm for the stochastic block models (SBMs) along with the corresponding weights for model averaging, we derive an estimate of the graphon function as an average of SBMs with increasing number of blocks. In the same framework, we derive the variational posterior frequency of any motif. A simulation study and an illustration on a social network complete our work.     

A Note on the Hyperbolic-Secant Distribution

This note discusses briefly some basic properties of the hyperbolic-secant distribution (e.g., Johnson and Kotz 1970; Manoukian 1986; Perks 1932; Talacko 1956), which has not received sufficient attention in the published literature, and may be useful for students and practitioners.     

Differentiating transformational and non-transformational leaders on the basis of neurological imaging

The purpose of this study was to evaluate the viability of using neurological imaging to classify transformational leaders, versus non-transformational leaders, as identified through existing psychometric methods. Specifically, power spectral analysis measures based on electroencephalograms (EEG) were used to develop and validate a discriminant function that can classify individuals according to their transformational leadership behavior. Resting, eyes closed EEG was recorded from 19 scalp locations for 200 civilian and military leaders. We also assessed follower or peer perceptions of transformational leadership through the use of the Multifactor Leadership Questionnaire (MLQ). Our discriminant analysis, which involved a two-step, neural variable reduction and selection process, was 92.5% accurate in its classification of leaders. Patterns in the spectral measures of the brain of leaders, including activity and network dynamic metrics, are discussed as potential correlates of transformational leadership behavior. The current work provides a better understanding of the latent and dynamic neurological mechanisms that may underpin the transformational leadership qualities of individuals.     

A generalized linear model approach to seasonal aspects of wind speed modeling

The aim of the article is to identify the intraday seasonality in a wind speed time series. Following the traditional approach, the marginal probability law is Weibull and, consequently, we consider seasonal Weibull law. A new estimation and decision procedure to estimate the seasonal Weibull law intraday scale parameter is presented. We will also give statistical decision-making tools to discard or not the trend parameter and to validate the seasonal model.     

Asymptotic Normality of Extreme Quantile Estimators Based on the Peaks-Over-Threshold Approach

The POT (Peaks-Over-Threshold) approach consists of using the generalized Pareto distribution (GPD) to approximate the distribution of excesses over thresholds. In this article, we establish the asymptotic normality of the well-known extreme quantile estimators based on this POT method, under very general assumptions. As an illustration, from this result, we deduce the asymptotic normality of the POT extreme quantile estimators in the case where the maximum likelihood (ML) or the generalized probability-weighted moments (GPWM) methods are used. Simulations are provided in order to compare the efficiency of these estimators based on ML or GPWM methods with classical ones proposed in the literature.     

A Note on Sufficient Conditions for Valid Unmodified t Testing in Correlation Analysis with Autocorrelated and Heteroscedastic Sample Data

Traditionally, sphericity (i.e., independence and homoscedasticity for raw data) is put forward as the condition to be satisfied by the variance–covariance matrix of at least one of the two observation vectors analyzed for correlation, for the unmodified t test of significance to be valid under the Gaussian and constant population mean assumptions. In this article, the author proves that the sphericity condition is too strong and a weaker (i.e., more general) sufficient condition for valid unmodified t testing in correlation analysis is circularity (i.e., independence and homoscedasticity after linear transformation by orthonormal contrasts), to be satisfied by the variance–covariance matrix of one of the two observation vectors. Two other conditions (i.e., compound symmetry for one of the two observation vectors; absence of correlation between the components of one observation vector, combined with a particular pattern of joint heteroscedasticity in the two observation vectors) are also considered and discussed. When both observation vectors possess the same variance–covariance matrix up to a positive multiplicative constant, the circularity condition is shown to be necessary and sufficient. “Observation vectors” may designate partial realizations of temporal or spatial stochastic processes as well as profile vectors of repeated measures. From the proof, it follows that an effective sample size appropriately defined can measure the discrepancy from the more general sufficient condition for valid unmodified t testing in correlation analysis with autocorrelated and heteroscedastic sample data. The proof is complemented by a simulation study. Finally, the differences between the role of the circularity condition in the correlation analysis and its role in the repeated measures ANOVA (i.e., where it was first introduced) are scrutinized, and the link between the circular variance–covariance structure and the centering of observations with respect to the sample mean is emphasized.     

Too Big to Learn: The Effects of Major Acquisition Failures on Subsequent Acquisition Divestment

We examine whether firms learn from their major acquisition failures. Drawing from a threat‐rigidity theoretical framework, we suggest that firms do not learn from their major acquisition failures. Furthermore, we hypothesize that host‐country experience reinforces the negative effects of major acquisition failures. Our research hypotheses are tested using an event history analysis of 741 acquisitions undertaken by French listed and non‐listed firms in the USA between January 1988 and December 2008. We use failure divestment (divestment resulting from acquisition failure) as a proxy for acquisition performance. Consistent with our theoretical framework, we find that major acquisition failures have a negative impact on future acquisition performance. Furthermore, we find that such negative effects are reinforced by firms’ host‐country experience.     

La démocratisation de l'enseignement supérieur au Canada: la face cachée de la massification

The increase in available student placements at colleges and universities, the implementation of provincial and federal postsecondary education policies, and the rise of the educational aspirations of families and individuals have all led to the massification of Canadian higher education. Based on Merle's typology of the forms of democratization, this article attempts to revisit the theory of equality of opportunities by critically analyzing the link between massification of higher education and social equity. The results of an analysis of longitudinal data from the (YIT) Youth in Transition Survey show that at the age of 24 in 2008, approximately 77% of young Canadians have pursued studies in a college or university. If access to postsecondary education is now higher, to what extent has it improved social equity? The article shows, in light of the Merle's typology, that mass university education is achieved in part under the seal of a segregative democratization, while college education tends to be egalitarian. L'augmentation de la capacité d'accueil des collèges et des universités, la mise en ?uvre de politiques provinciales et fédérales de développement de l'enseignement postsecondaire et la montée des aspirations scolaires des familles et des individus ont contribué à massifier l'enseignement supérieur canadien. En se basant sur la typologie des formes de démocratisation de Merle, le présent article tente de revisiter la théorie de l'égalité des chances en s'interrogeant sur le lien entre la massification de l'enseignement supérieur et l'équité sociale. Les résultats obtenus à partir de données longitudinales provenant de l'Enquête auprès de jeunes en transition (EJET) montrent qu’à l'âge de 24 ans en 2008, environ 77 % des jeunes Canadiens ont poursuivi des études dans un collège ou une université. Si l'accès aux études postsecondaires est aujourd'hui plus élevé, dans quelle mesure celui‐ci a‐t‐il amélioré l’équité sociale? L'article montre, à la lumière de la typologie de Merle, que la massification de l'enseignement universitaire se réalise en partie sous le sceau d'une démocratisation ségrégative, tandis que l'enseignement collégial a tendance à être égalisateur.