A calibration-based approach to sensitive data: a simulation study

In this paper, we discuss the use of auxiliary information to estimate the population mean of a sensitive variable when data are perturbed by means of three scrambled response devices, namely the additive, the multiplicative and the mixed model. Emphasis is given to the calibration approach, and the behavior of different estimators is investigated through simulated and real data. It is shown that the use of auxiliary information can considerably improve the efficiency of the estimates without jeopardizing respondent privacy.     

A class of estimators for quantitative sensitive data

Randomized response methods for quantitative sensitive data are treated in an unified approach which includes the use of auxiliary information at the estimation stage. A class of estimators for the mean of a sensitive variable is proposed under a generic randomization model and the optimum estimator is obtained. Some special models are discussed in detail. To evaluate the degree of respondents’ confidentiality in models using auxiliary variables, a new measure of privacy protection is introduced. Different models are then compared both from the perspective of efficiency and privacy protection.     

Putting Words to the Experience: An Italian Group Intervention for Children of Separated Parents

This article presents an Italian group intervention for children of separated parents and describes preliminary findings about its efficacy. After briefly discussing the state of art for interventions of children of separated parents, the core characteristics of the group intervention are explained and each meeting is described. Then, pre–post research is presented to provide empirical support for this approach. Results reported that participation in the group strengthens the social and interpersonal self-concept dimension in the majority of children. Results are discussed considering both empirical and clinical implications.     

Social time preference

The observed practice of discounting the future should not be rationalised on the grounds of myopia or selfishness. A positive rate of pure time preference is necessary to ensure that heterogeneous generations are treated in an egalitarian fashion. A zero social discount rate would yield intertemporal allocations which are biased against the current generations. Endogenous productivity growth requires that the social discount rate be set above the subjective rate of pure time preference. Positive social time preference, far from discriminating against future generations, is essential for a fairer intertemporal allocation of resources. Received: 3 July 1999/Accepted: 6 October 1999     

Time Horizon and Cooperation in Continuous Time

We study social dilemmas in (quasi‐) continuous‐time experiments, comparing games with different durations and termination rules. We discover a stark qualitative contrast in behavior in continuous time as compared to previously studied behavior in discrete‐time games: cooperation is easier to achieve and sustain with deterministic horizons than with stochastic ones, and end‐game effects emerge, but subjects postpone them with experience. Analysis of individual strategies provides a basis for a simple reinforcement learning model that proves to be consistent with this evidence. An additional treatment lends further support to this explanation.     

Regional Infrastructure and Convergence: Growth Implications in a Spatial Framework

In this paper we contribute to the debate on convergence, by presenting an overview of the catch up process of the European regions between 1995 and 2006, focusing on both absolute and conditional β convergence. Our focus is on the role of infrastructure stocks in shaping the growth and convergence process between EU regions and to what extent the spatial dimension of the data affects results. We also explicitly examine the link between infrastructure evolution and regional economic growth with a spatial panel data approach. Our results confirm an ongoing convergence process at the EU regional level, and assess the important role of transport and telecommunication infrastructure, with traditional and spatial estimation techniques. We also confirm, in a panel setting, the strong positive correlation between transport and TLC indicators and GDP growth at the regional level.     

Social time preference: a rejoinder

This note argues that positive discounting is necessary for intergenerational equity, and that the choice of the discount rate should be based on the principle that individuals of different generations should be treated in a symmetrical fashion.      

Two tests for monotone failure rate with incomplete data

Two statistics are proposed for testing for the exponential distribution against monotone failure rate alternatives when ran-domly right censored data are available. One of them is a general-ization of the Billmann, Antle and Bain test based on the MLE of the shape parameter of the Weibull distribution. The second has the advantage of being given in closed form. For this test the asymptotic null distribution is given. Consistency of the two tests is proved starting from an expected value inequality characterizing monotone failure rate.     

Linearization of inequality indices in the design-based framework

Linearization methods are customarily adopted in sampling surveys to obtain approximated variance formulae for estimators of statistical functionals under the design-based approach. In the present paper, following the Deville [Variance estimation for complex statistics and estimators: linearization and residual techniques. Surv Methodol. 1999;25:193–203] approach stemming from the concept of design-based influence function, we provide a general result for linearizing a large family of population functionals which includes many of the inequality measures considered in social, economic and statistical studies, such as the Gini, Amato, Zenga, Atkinson and Generalized Entropy indices. The feasibility of our theoretical results is assessed by some simulation studies involving real and artificial data.