Reinforced urns and the subdistribution beta‐Stacy process prior for competing risks analysis

In this paper, we introduce the subdistribution beta‐Stacy process, a novel Bayesian nonparametric process prior for subdistribution functions useful for the analysis of competing risks data. In particular, we (i) characterize this process from a predictive perspective by means of an urn model with reinforcement, (ii) show that it is conjugate with respect to right‐censored data, and (iii) highlight its relations with other prior processes for competing risks data. Additionally, we consider the subdistribution beta‐Stacy process prior in a nonparametric regression model for competing risks data, which, contrary to most others available in the literature, is not based on the proportional hazards assumption.     

A note on multiple testing for composite null hypotheses

Multiple hypothesis testing literature has recently experienced a growing development with particular attention to the control of the false discovery rate (FDR) based on p-values. While these are not the only methods to deal with multiplicity, inference with small samples and large sets of hypotheses depends on the specific choice of the p-value used to control the FDR in the presence of nuisance parameters. In this paper we propose to use the partial posterior predictive p-value [Bayarri, M.J., Berger, J.O., 2000. p-values for composite null models. J. Amer. Statist. Assoc. 95, 1127–1142] that overcomes this difficulty. This choice is motivated by theoretical considerations and examples. Finally, an application to a controlled microarray experiment is presented.     

Does Immigration Raise Blue and White Collar Wages of Natives? The Case of Italy

This paper analyses both theoretically and empirically the effects of immigration on the wage rate of native workers. There is rare evidence in empirical literature that immigration generates a fall in the wages of manual workers. By hypothesizing an economic system where advanced firms buy an intermediate good from traditional firms, which employ manual workers in both clean and dirty tasks, the latter being more disliked by native workers, we present a theoretical model that justifies these results. We conclude that native skilled wages always increase whereas native unskilled wages can both increase or decrease with immigration. An empirical analysis of the Italian labour market follows, showing that native workers' wages always rise with immigration.     

Power and inequality in global value chains: Advancing the research agenda

Power is a central, but largely undertheorized, concept for scholars of global value chains (GVCs). In this introduction to a special issue on power and inequality in GVCs, the authors summarize the key insights from the articles gathered here and explain how the collection advances our understanding of the types and forms of power operating in GVCs and their effect on different dimensions of inequality.     

Different Predictors of Quality of Life in Urban Environment

The present study aimed at comparing the effects of different sets of predictors on quality of life in an urban environment. We used secondary data collected by means of a self-report questionnaire on a sample of 343 residents of a big Italian city. The questionnaire included a multidimensional scale elaborated by the World Health Organization (WHOQoL brief scale) assessing quality of life in terms of four different evaluations concerning distinct aspects of life: physical health; psychological status; social relationships; environment. Four different types of predictors were considered: (1) socio-demographic characteristics; (2) quality of social relations (perceived social support); (3) place attachment; (4) healthy lifestyle. To test the influence of different groups of predictors on the dimensions of WHOQoL we performed four hierarchical regression analyses. Several significant influences were found. In particular the results pointed out the great role of perceived social support and place attachment in promoting quality of life. That result suggests the importance of community interventions in urban environment.     

Probabilistic spatial power indexes

In this study, we present a generalization of spatial power indexes able to overcome their main limitations, namely (i) the excessive concentration of power measures; (ii) the too high sensitivity to players’ location in the ideological space. Voters’ propensity to support an issue is modeled via a random utility function with two additive terms: the deterministic term accounts for voters’ preference-driven/predictable behavior; the random one is a catch-all term that accounts for all the idiosyncratic/unpredictable factors. The relative strength of the two terms gives rise to a continuum of cases ranging from the Shapley value, where all aggregation patterns are equally probable, to a standard spatial value, like the Owen–Shapley index, where instead the conditional order is fully deterministic. As an illustrative application, we analyze the distribution of power in the Council of Ministers under three different scenarios: (i) EU15 Pre-Nice; (ii) EU27 Nice Treaty; (iii) EU27 Lisbon Treaty.     

The Dimensions of Agricultural Diversification: A Spatial Analysis of Italian Municipalities

Diversification in agriculture could ensure the survival of farming by broadening the income base of farms and encouraging agricultural sustainability. The diversification of on-farm activities may depend on socioeconomic and environmental characteristics. Although multifunctionality is a hotly debated topic, few papers have focused on the farm characteristics that influence diversification and none have focused on the influence of the spatial pattern. This paper examines the relationship between farm characteristics and the adoption of a specific type of diversification. An econometric model is presented that uses a spatial autoregressive lag model at the municipal scale. The results demonstrate that the activation of each diversification dimension is influenced by both internal and external factors, encompassing farmers’ characteristics, the farm structure, and territorial features, including regional and spatial patterns. Deepening is mainly influenced by the farmer’s age, education, and the presence of small and labor-intensive farms. The key factors for broadening diversification are the presence of small farms and farms with mixed production (breeding and crop cultivation). Regrounding is most affected by the labor-intensive farms in the area and the farms with mixed production.     

Reliability and Validity of the Pathological Gambling Adaptation of the Yale-Brown Obsessive-Compulsive Scale (PG-YBOCS)

The Yale Brown Obsessive Compulsive Scale adapted for Pathological Gambling (PG-YBOCS) was developed to measure the severity and change in severity of pathological gambling symptoms. The PG-YBOCS is a 10-item clinician-administered questionnaire that measures the severity of PG over a recent time interval (usually within the past one/two week(s)). In order to assess and validate the scale, it was administered to 337 subjects: 188 pathological gamblers and 149 healthy controls. Internal consistency and correlations between individual items and total score were assessed for various permutations of the sample. Other scales were administered to assess convergent, discriminant and content validity. Sensitivity to change was evaluated in treatment studies with fluovoxamine, lithium, and valproate. Each item was frequently endorsed across a range of severity. Good inter-rater reliability and internal consistency were obtained. The PG-YBOCS showed high validity and reliability for total score, item-total correlations, and for each subscale (Thoughts/Urges and Behavior). PG-YBOCS scores correlated with global severity and South Oaks Gambling Screen (SOGS) scores. The scale was also sensitive to change in pathological gambling severity. PG-YBOCS thus appears to be a reliable and valid measure of pathological gambling severity, and can be regarded as an important tool for clinicians and researchers treating pathological gamblers.     

Presenting Results from Model Based Studies to Decision-Makers: Can Sensitivity Analysis be a Defogging Agent?

The motivation of the present work is to provide an auxiliary tool for the decision-maker (DM) faced with predictive model uncertainty. The tool is especially suited for the allocation of R&Dresources. When taking decisions under uncertainties, making use of the output from mathematical or computational models, the DM might be helped if the uncertainty in model predictions be decomposed in a quantitative-rather than qualitativefashion, apportioning uncertainty according to source. This would allow optimal use of resources to reduce the imprecision in the prediction. For complex models, such a decomposition of the uncertainty into constituent elements could be impractical as such, due to the large number of parameters involved. If instead parameters could be grouped into logical subsets, then the analysis could be more useful, also because the decision maker might likely have different perceptions (and degrees of acceptance) for different kinds of uncertainty. For instance, the decomposition in groups could involve one subset of factors for each constituent module of the model; or one set for the weights, and one for the factors in a multicriteria analysis; or phenomenological parameters of the model vs. factors driving the model configuratiodstructure aggregation level, etc.); finally, one might imagine that a partition of the uncertainty could be sought between stochastic (or aleatory) and subjective (or epistemic) uncertainty. The present note shows how to compute rigorous decomposition of the output's variance with grouped parameters, and how this approach may be beneficial for the efficiency and transparency of the analysis.