On temperance and risk spreading

Theory and Decision - On page 599 in the original publication of the article.     

Body image,satisfaction with sexual life,self-esteem,and anxiodepressive symptoms: A comparative study between premenopausal,perimenopausal, and postmenopausal women

Menopause is an important life transition in women, and it is associated with significant physical and psychological changes. This study aimed at examining how the menopausal transition influences body image and satisfaction with sexual life. To this aim, body image dissatisfaction, sexual dissatisfaction, and self-esteem were compared in three groups of women with distinct menopausal status (premenopausal n = 142, perimenopausal n = 66, or postmenopausal n = 149), while controlling for depressive and anxiety symptoms and BMI. Using ANOVA between these three groups, we observed that dissatisfaction with body image was significantly higher in the perimenopausal sample compared with its premenopausal counterpart. In the postmenopausal sample, body image improved, as this group displayed body image scores close to the premenopausal level. In addition, sexual dissatisfaction was significantly increased in the perimenopausal and postmenopausal samples, which is compatible with the concept that higher body appreciation positively predicted sexual function. We conclude that body image dissatisfaction reaches a maximum during the perimenopausal phase, before returning to a level nearly identical between the premenopausal and the postmenopausal phases.     

Productive organizational energy mediates the impact of organizational structure on absorptive capacity

The ability of an organization to cope with radical technological change is regarded to be heavily dependent on its ability to absorb and apply knowledge from its environment. This study investigates the role of organizational structure in driving absorptive capacity and uncovers the role of the emergent phenomenon of organizational energy as the enabler of this relationship. A field study was conducted among firms that are challenged by the disruptive nature of Cloud computing. Our results show that organizational design affects the degree of mobilization of an organization's affective, cognitive and behavioral resources, which in turn influence the effectiveness of learning processes related to the absorption and exchange of knowledge within the organization. Furthermore, they reveal the positive relationship between the enactment of absorptive capacity and the successful adoption of Cloud technology for incumbent firms. The findings contribute to our understanding of the micro-foundations of absorptive capacity and how positive organizational phenomena facilitate effective adoption and implementation of emerging technologies.     

Optimal exact design of double acceptance sampling plans by attributes

The design of double acceptance sampling (AS) plans for attributes based on the operating characteristic curve paradigm is usually addressed by enumeration algorithms. These AS plans may be non optimal regarding the sample size to inspect as they were obtained without the requirement that the constraints at the OC curve controlled points are not violated for minimum Average Sample Number (ASN) scenarios. An approach based on mathematical programming is proposed to systematically design double AS plans for attributes, where the characteristics controlled are modelled by binomial or Poisson distributions. Specifically, Mixed Integer Nonlinear Programming (MINLP) formulations are developed and combined with an enumeration algorithm that allows finding ASN minimax optimal plans. A theoretical result is developed with the purpose of assuring the global optimum design is reached by iteration where a convenient solver is used to find local optima. To validate the algorithm, we compare our results with those of tables commonly used for practical purposes, consider different rates of risk, and setups commonly used in Lot Quality Assurance Plans (LQAS) for health monitoring programmes. Finally, we compare AS plans determined for processes described by binomial and Poisson distributions.     

On the one parameter unit-Lindley distribution and its associated regression model for proportion data

In this paper considering an appropriate transformation on the Lindley distribution, we propose the unit-Lindley distribution and investigate some of its statistical properties. An important fact associated with this new distribution is that it is possible to obtain the analytical expression for bias correction of the maximum likelihood estimator. Moreover, it belongs to the exponential family. This distribution allows us to incorporate covariates directly in the mean and consequently to quantify their influences on the average of the response variable. Finally, a practical application is presented to show that our model fits much better than the Beta regression.     

Identification of average marginal effects under misspecification when covariates are normal

A previously known result in the econometrics literature is that when covariates of an underlying data generating process are jointly normally distributed, estimates from a nonlinear model that is misspecified as linear can be interpreted as average marginal effects. This has been shown for models with exogenous covariates and separability between covariates and errors. In this paper, we extend this identification result to a variety of more general cases, in particular for combinations of separable and nonseparable models under both exogeneity and endogeneity. So long as the underlying model belongs to one of these large classes of data generating processes, our results show that nothing else must be known about the true DGP—beyond normality of observable data, a testable assumption—in order for linear estimators to be interpretable as average marginal effects. We use simulation to explore the performance of these estimators using a misspecified linear model and show they perform well when the data are normal but can perform poorly when this is not the case.     

Welfare gains of the poor: An endogenous Bayesian approach with spatial random effects

We introduce a Bayesian instrumental variable procedure with spatial random effects that handles endogeneity, and spatial dependence with unobserved heterogeneity. We find through a limited Monte Carlo experiment that our proposal works well in terms of point estimates and prediction. We apply our method to analyze the welfare effects generated by a process of electricity tariff unification on the poorest households. In particular, we deduce an Equivalent Variation measure where there is a budget constraint for a two-tiered pricing scheme, and find that 10% of the poorest municipalities attained welfare gains above 2% of their initial income.     

Focused estimation for noisy and small data sets: a Bayesian minimum expected loss estimator approach

Central to many inferential situations is the estimation of rational functions of parameters. The mainstream in statistics and econometrics estimates these quantities based on the plug‐in approach without consideration of the main objective of the inferential situation. We propose the Bayesian Minimum Expected Loss (MELO) approach focusing explicitly on the function of interest, and calculating its frequentist variability. Asymptotic properties of the MELO estimator are similar to the plug‐in approach. Nevertheless, simulation exercises show that our proposal is better in situations characterised by small sample sizes and/or noisy data sets. In addition, we observe in the applications that our approach gives lower standard errors than frequently used alternatives when data sets are not very informative.     

Pilot study of a Web-based acceptance and commitment therapy intervention for university students to reduce academic procrastination

Objective: This pilot study pursued two aims. The first was to investigate the feasibility and acceptability of a Web-based acceptance and commitment therapy (ACT) intervention to reduce academic procrastination among university students. The second aim was to test the effectiveness of the intervention on procrastination and committed actions. Participants: The sample was comprised of Canadian university students (n?=?36) that participated in the intervention between September 2016 and April 2017. Methods: The study relied on a prepost research design. Results: The intervention appears feasible, acceptable, and valuable to students. A significant reduction in procrastination and a significant improvement in committed actions were found between pre and postintervention. The effect sizes for these results were medium. Conclusions: Results provide preliminary support for the feasibility and effectiveness of a Web-based ACT intervention for academic procrastination. Results also highlight some aspects that need to be improved for further development.     

Refugees,not Economic Migrants ‐ Why do Asylum‐Seekers Register in Hungary?

The article analyses why asylum‐seekers choose Hungary as an entry point to the European Union. Among the Central and Eastern European countries Hungary has been by far the most popular choice for asylum‐seekers between 2002 and 2016, yet surprisingly, it has been neglected by the literature. Using a panel dataset and fixed effects regressions, the article finds that beyond being ‘conveniently’ located on the Balkans migration route, variables related to Hungary's immigration policy are the most significant determinants of asylum‐seeker choices. The article finds no evidence to support recent claims by the Hungarian government that arrivals to the country are actually economic migrants and not asylum‐seekers; quite the contrary, the results indicate that on average asylum‐seekers entering Hungary are fleeing violent conflict in their countries of origin.