On the Output Orientation of the Benefit-of-the-Doubt-Model

While Benefit-of-the-Doubt (BoD) models, as used in the context of composite indicator construction, seek to aggregate outputs only, they are formally equivalent to an input-oriented Data Envelopment Analysis (DEA) model in multiplier form. Strictly read, this introduces conceptually ambiguous results, as input adjustments have no real significance in the context at hand. At heart of this ambiguity lies a double interpretation of what is essentially a binding constraint in BoD’s underlying linear-fractional program. Moreover, there is a direct, reciprocal relation between the BoD-model and an earlier output-oriented DEA model introduced by Lovell et al. (Eur J Oper Res 87:507–518, 1995) for similar purposes. Although these models are essentially similar, I also show that there are instances (i.e. when adding additional weight restrictions) in which the results of one alternative are easier to communicate. The models surveyed in this paper are complemented with a short application on human development data for females living in Brussels municipalities.     

Modeling human-bed interaction: the predictive value of anthropometric models in choosing the correct bed support

The sleep system (i.e. the combination of mattress and bed base) is an important factor of the sleep environment since it allows physical recuperation during sleep by providing proper body support. However, various factors influence the interaction between the human body and the sleep system. Contributing factors include body dimensions, distribution of body weight and stiffness of the sleep system across the mattress surface. During the past decade, the rise of several new bedding technologies has made it increasingly difficult for the consumer to select a proper sleep system. Therefore, this study presents a method to model human-bed interaction in order to objectively predict the ideal sleep system for a particular individual. The proposed method combines a personalized anthropometric model with standardized load-deflection characteristics of mattress and bed base. Results for lateral sleep positions show a root mean square deviation of 11.9 ± 6.1 mm between modeled spine shapes and validation shapes, derived from 3D surface scans of the back surface. The method showed to be a reliable tool to individually identify the sleep system providing superior support from a variety of possible mattress-bed base combinations.     

The Covariation of Antisocial Behavior and Substance Use in Adolescence: A Behavioral Genetic Perspective

Multivariate genetic studies have revealed genetic correlations between antisocial behavior (ASB) and substance use (SU). However, ASB is heterogeneous, and it remains unclear whether all forms are similarly related to SU. The present study examines links between cannabis use, alcohol consumption, and aggressive and delinquent forms of ASB using a behavioral genetic approach. Participants were 1,688 adolescents (482 monozygotic twins, 852 dizygotic twins, and 354 nontwin siblings) aged 15–23 years old (M = 16.91), sampled from the community in the U.K. Multivariate model fitting revealed that the genetic components of alcohol consumption and cannabis use correlated with those of both aggression (.21 and .49, respectively) and delinquency (.35 and .69, respectively). Results suggest that both aggression and delinquency have genetic effects in common with alcohol consumption and cannabis use.     

How social movements survive: the Treatment Action Campaign and the South African state, 2009–2016

This paper seeks to explain the survival of the Treatment Action Campaign (TAC) and to identify lessons it may offer on how social movements may resist demobilisation. It considers the role the TAC plays in the creation of social accountability dynamics around primary health care but with broader implications for strengthening rights-based notions of citizenship. The paper examines the TAC’s relative success in democratising public health care provision and explores shifts in the relationship between this social movement organisation and the South African state.     

Why targeting matters: The apprenticeship program for youth in the Czech Republic

In this paper, we analyze the targeting and outcomes of the apprenticeship program implemented under the Youth Guarantee/YG scheme in the Czech Republic. We examine the outcomes and targeting using counterfactual impact evaluation (quasi-experimental design) of the apprenticeship program on the basis of administrative data from the Czech Employment Office. The implementation strategy is analyzed using various policy documents. The findings indicate that the program is apparently targeted at those groups of young people who are less disadvantaged as regards education level and previous unemployment experience. At the same time, paradoxically, the effects in terms of outflows from the unemployment register are weak for the short-term and medium-term unemployed, as well as for low-skilled and high-skilled youth, and stronger effects are evident in the case of long-term unemployed and medium-skilled youth. The failures in targeting and in adjusting the program to the needs of more vulnerable groups of youth are due to an inconsistent implementation strategy of Czech Public Employment Services.     

Inference for cluster point processes with over- or under-dispersed cluster sizes

Cluster point processes comprise a class of models that have been used for a wide range of applications. While several models have been studied for the probability density function of the offspring displacements and the parent point process, there are few examples of non-Poisson distributed cluster sizes. In this paper, we introduce a generalization of the Thomas process, which allows for the cluster sizes to have a variance that is greater or less than the expected value. We refer to this as the cluster sizes being over- and under-dispersed, respectively. To fit the model, we introduce minimum contrast methods and a Bayesian MCMC algorithm. These are evaluated in a simulation study. It is found that using the Bayesian MCMC method, we are in most cases able to detect over- and under-dispersion in the cluster sizes. We use the MCMC method to fit the model to nerve fiber data, and contrast the results to those of a fitted Thomas process.