Can Psychosocial Intervention Improve Peer and Sibling Relations Among War‐affected Children? Impact and Mediating Analyses in a Randomized Controlled Trial

Social resources are considered important protectors in traumatic conditions, but few studies have analyzed their role in psychosocial interventions among war‐affected children. We examined (1) whether a psychosocial intervention (teaching recovery techniques, TRT) is effective in improving peer and sibling relations, and (2) whether these potentially improved relations mediate the intervention's impacts on children's mental health. Participants were 428 Palestinian children [10–13 (mean = 11.29, standard deviation SD = .68)‐year‐old girls (49.4 percent) and boys (50.6 percent)], who were cluster‐randomized into the TRT and wait‐list control groups. They reported the quality of peer (friendship and loneliness) and sibling (intimacy, warmth, conflict, and rivalry) relations, and posttraumatic stress, depressive and psychological distress symptoms, as well as psychosocial well‐being at baseline (T1), postintervention (T2), and six month follow‐up (T3). Results showed gender‐specific TRT intervention effects: Loneliness in peer relations reduced among boys and sibling rivalry reduced among girls. The TRT prevented the increase in sibling conflict that happened in the control group. The mediating hypothesis was partially substantiated for improved peer relations, and beneficial changes in sibling relations were generally associated with improved mental health.     

Que faut‐il voir dans les écarts de rémunération entre les travailleurs immigrés des pays du Golfe: l'effet d'a priori patronaux ou la simple conséquence de coûts d'opportunité différents?

Les auteurs s'intéressent aux disparités salariales entre les différents groupes de migrants qualifiés qui travaillent en grand nombre pour le secteur privé dans les États du Conseil de coopération du Golfe. Ils se demandent notamment si la prime salariale des Occidentaux (par rapport aux travailleurs venus d'Asie ou d'autres États arabes) découle d'éventuels a priori des employeurs. L'analyse, qui repose sur des données de 2012–2014 et sur deux méthodes de décomposition classiques, infirme l'hypothèse: les caractéristiques observables relatives à la productivité expliquent entre un tiers et trois quarts des écarts; le coût d'opportunité de la migration, supérieur pour les Occidentaux, explique la portion restante.     

Bayesian estimation for the Pareto income distribution

The Bayes estimators of the Gini index, the mean income and the proportion of the population living below a prescribed income level are obtained in this paper on the basis of censored income data from a pareto income distribution. The said estimators are obtained under the assumptions of a two-parameter exponential prior distribution and the usual squared error loss function. This work is also extended to the case when the income data are grouped and the exact incomes for the individuals in the population are not available. The method for the assessment of the hyperparameters is also outlined. Finally, the results are generalized for the doubly truncated gamma prior distribution. Now deceased.     

Bayesian classification with multivariate autoregressive sources that might have different orders

The main objective of this paper is to develop an exact Bayesian technique that can be used to assign a multivariate time series realization to one of several autoregressive sources, with unknown coefficients and precision, that might have different orders. The foundation of the proposed technique is to develop the posterior mass function of a classification vector, in an easy form, using the conditional likelihood function. A multivariate time series realization is assigned to the multivariate autoregressive source with the largest posterior probability. A simulation study, with uniform prior mass function, is carried out to demonstrate the performance of the proposed technique and to test its adequacy in handling the multivariate classification problems. The analysis of the numerical results supports the adequacy of the proposed technique in solving the classification problems with multivariate autoregressive sources.     

Bayesian inferences and forecasts with moving averages processes

This paper will develop Bayesian inferential and forecasting techniques which can be used with any moving average process. By employing the conditional likelihood function, at-approximation to the predictive distribution and the marginal posterior distribution of the moving average parameters is developed. Several examples demonstrate posterior and predictive inferences.     

Facility Location with Dynamic Distance Functions

Facility location problems have always been studied with theassumption that the edge lengths in the network are static anddo not change over time. The underlying network could be used to model a city street networkfor emergency facility location/hospitals, or an electronic network for locating information centers. In any case, it is clear that due to trafficcongestion the traversal time on links changes with time. Very often, we have estimates as to how the edge lengths change over time, and our objective is to choose a set of locations (vertices) ascenters, such that at every time instant each vertex has a center close to it (clearly, the center close to a vertex may change over time). We also provide approximation algorithms as well as hardness results forthe K-center problem under this model. This is the first comprehensive study regarding approximation algorithmsfor facility location for good time-invariant solutions.     

Response to the COVID-19: Understanding implications of government lockdown policies

The rising number of COVID-19 cases and economic implications of lockdown measures indicate the tricky balancing act policy makers face as they implement the subsequent phases of 'unlock'. We develop a model to examine how lockdown and social distancing measures have influenced the behavioral conduct of people. The current situation highlights that policy makers need to focus on bringing awareness and social restraint among people rather than going for stringent lockdown measures. We believe this work will help the policy makers gain insights into the troubled COVID-19 times ahead, and based on the estimates, they can frame policies to navigate these wild waves in the best possible way.     

On the Computation of Non-Central Chi-Square Distribution Function

The cumulative distribution function of the non-central chi-square is very important in calculating the power function of some statistical tests. On the other hand it involves an integral which is difficult to obtain. In literature some workers discussed the evaluation and the approximation of the c.d.f. of the non-central chi-square [see references (2)]. In the present work two computational formulae for computing the cumulative distribution function of the non-central chi-square distribution are given, the first one deals with the case of any degrees of freedom (odd and even), and the second deals with the case of odd degrees of freedom. Numerical illustrations are discussed.     

An interactive decision support system for an aggregate production planning model based on multiple criteria mixed integer linear programming

In this paper, we present an aggregate production planning (APP) model applied to a Portuguese firm that produces construction materials. A multiple criteria mixed integer linear programming (MCMILP) model is developed with the following performance criteria: (1) maximize profit, (2) minimize late orders, and (3) minimize work force level changes. It includes certain operational features such as partial inflexibility of the work force, legal restrictions on workload, work force size (workers to be hired and downsized), workers in training, and production and inventory capacity. The purpose is to determine the number of workers for each worker type, the number of overtime hours, the inventory level for each product category, and the level of subcontracting in order to meet the forecasted demand for a planning period of 12 months. Additionally, a decision support system (DSS) based on the MCMILP model is proposed. It will help practitioners find the “best” solution for an APP problem without having to familiarize themselves with the mathematical complexities associated with the model. An example to illustrate the use of the DSS is also included.     

Family firms and high technology Mergers & Acquisitions

We examine whether family firms undertake value creating high technology M&A. We also examine whether level of ownership, diversification, agency issues and CEO type matter. Our sample consists of high-technology M&A undertaken by Canadian firms over the period 1997–2006. Canada offers a setting with many family firms and the use of control enhancing mechanisms such as dual class shares and pyramid structures. We find a positive relationship between family ownership and announcement period abnormal returns. This relationship, however, starts to decrease at higher levels of ownership but remains overall positive. We also show that the agency conflict between shareholders and professional managers has a detrimental impact on announcement period abnormal returns whereas the conflict between controlling and minority shareholders via control enhancing mechanisms does not. Finally, we document that founder CEO undertake better high tech M&A than descendant or hired CEO.