Absenteeism and peer interaction effects: Evidence from an Italian Public Institute

Using microdata for a sample of about 350 workers employed by an Italian public institute, we explain individual absence rates considering both variables that may be related to health conditions and variables that may suggest shirking behaviour. Among these variables we especially focus our attention on the influence produced by the behaviour of randomly assigned peers. In order to handle reflection problems, we combine random assignment with the use of instrumental variables. The proportion of females in the peer group is used as an instrument of peer absence behaviour. From Two-Stage Least Square estimates it emerges that social and group interaction plays an important role in shaping individual absence behaviour.     

Six‐ and 9‐Month‐Old Infants Discriminate Between Goals Despite Similar Action Patterns

Behne, Carpenter, Call, and Tomasello (2005) showed that 9‐ to 18‐month‐olds, but not 6‐month‐olds, differentiated between people who were unwilling and unable to share toys. As the outcome of the two tasks is the same (i.e., the toy is not shared), the infants must respond to the different goals of the actor. However, visual habituation paradigms have shown an earlier onset of goal awareness. The present study reconciles this disparity by replicating the findings of Behne et al. with both 6‐ and 9‐month‐olds, using similar tasks and additional response measures.     

Quality and Demand of Child Care and Female Labour Supply in Italy

This paper provides a micro‐econometric evaluation of the effects of child care rationing on the household expenditures on child care and on the female participation decision, in Italy. A sample of households is used with at least one pre‐school child, selected from the Bank of Italy Survey of Household Income and Wealth (SHIW); this is complemented with ISTAT statistics on nursery and crèche access rates. It is found that both child care rationing and the availability of alternative forms of non‐market child care (relatives etc.) affect in various ways the household decisions over child care expenditure and labour supply.     

Slice sampling mixture models

We propose a more efficient version of the slice sampler for Dirichlet process mixture models described by Walker (Commun. Stat., Simul. Comput. 36:45–54, 2007). This new sampler allows for the fitting of infinite mixture models with a wide-range of prior specifications. To illustrate this flexibility we consider priors defined through infinite sequences of independent positive random variables. Two applications are considered: density estimation using mixture models and hazard function estimation. In each case we show how the slice efficient sampler can be applied to make inference in the models. In the mixture case, two submodels are studied in detail. The first one assumes that the positive random variables are Gamma distributed and the second assumes that they are inverse-Gaussian distributed. Both priors have two hyperparameters and we consider their effect on the prior distribution of the number of occupied clusters in a sample. Extensive computational comparisons with alternative “conditional” simulation techniques for mixture models using the standard Dirichlet process prior and our new priors are made. The properties of the new priors are illustrated on a density estimation problem.     

Finite mixture modeling of censored regression models

A finite mixture of Tobit models is suggested for estimation of regression models with a censored response variable. A mixture of models is not primarily adapted due to a true component structure in the population; the flexibility of the mixture is suggested as a way of avoiding non-robust parametrically specified models. The new estimator has several interesting features. One is its potential to yield valid estimates in cases with a high degree of censoring. The estimator is in a Monte Carlo simulation compared with earlier suggestions of estimators based on semi-parametric censored regression models. Simulation results are partly in favor of the proposed estimator and indicate potentials for further improvements.     

Estimation of causal effects in observational studies with interference between units

Causal effects are usually estimated under the assumption of no interference between individuals. This assumption means that the potential outcomes for one individual are unaffected by the treatments received by other individuals. In many situations, this is not reasonable to assume. Moreover, not taking interference into account could result in misleading conclusions about the effect of a treatment. For two-stage observational studies, where treatment assigment is randomized in the first stage but not in the second stage, we propose IPW estimators of direct and indirect causal effects as defined by Hudgens and Halloran (J Am Stat Assoc 103(482):832–842, 2008) for two-stage randomized studies. We illustrate the use of these estimators in an evaluation study of an implementation of Triple P (a parenting support program) within preschools in Uppsala, Sweden.     

The potential power of social policy programmes: income redistribution,economic resources and health

Lundberg O, Fritzell J, Åberg Yngwe M, Kölegård ML. The potential power of social policy programmes: income redistribution, economic resources and health Int J Soc Welfare 2010: ??: ??–??© 2010 The Author(s), Journal compilation © 2010 Blackwell Publishing Ltd and International Journal of Social Welfare. This Supplement includes a number of articles dealing with the role of social policy schemes for public health across the life course. As a key social determinant of health, poverty and its consequences have historically been at the forefront of the public health discussion. But also in rich countries today, economic resources are likely to be important for health and survival, both on an individual and an aggregate level. This introductory article serves as a background for the more specific analyses that follow. The focus is on why income and income inequality could have an effect on individual and population health. We discuss relationships between the individual and population levels and between income and health, and some of the possible mechanisms involved. We also present arguments for why welfare state institutions may matter.     

Electricity consumption prediction with functional linear regression using spline estimators

A functional linear regression model linking observations of a functional response variable with measurements of an explanatory functional variable is considered. This model serves to analyse a real data set describing electricity consumption in Sardinia. The interest lies in predicting either oncoming weekends’ or oncoming weekdays’ consumption, provided actual weekdays’ consumption is known. A B-spline estimator of the functional parameter is used. Selected computational issues are addressed as well.     

Sharp bounds for the mean of the total time on test for distributions with increasing generalized failure rate

The results of this paper are the continuation of the research presented by Bieniek [Optimal bounds for the mean of the total time on test for distributions with decreasing generalized failure rate. Statistics. 2016;50:1206–1220]. We consider the remaining total time on test after a given failure in a life test experiment. We derive sharp upper bounds on the mean of the total time on test optimal in the class of distributions with increasing generalized failure rate with respect to generalized Pareto distributions. Specific results are obtained for distributions with increasing density and increasing failure rate. We also provide exemplary numerical values of the obtained bounds and we compare them with the corresponding bounds for distributions with decreasing generalized failure rate.