RUTA: A framework for assessing and selecting additive value functions on the basis of rank related requirements

In this paper, we present a new preference disaggregation method, called RUTA, which infers a set of additive value functions from the preference information referring to the desired ranks of some reference alternatives. Real-life experience indicates that people willingly refer to the range of allowed ranks that a particular alternative should attain, or to constraints on the final scores of the alternatives. We develop a mathematical model for incorporating such preference information via mixed-integer linear programming (MILP). Then, we discuss how decision making could be supported with the use of the already proposed extreme ranking analysis (ERA), which indicates the best and worst ranks gained by each alternative over the set of compatible preference model instances. We also introduce a new interactive UTA-like technique, which aims at selecting a single value function representing the outcomes of ERA. In the interactive process, the decision maker (DM) is assigning priorities to different pre-defined targets, which are built on results of ERA, and refer to the comparison of the best and/or worst ranks for pairs of alternatives. In particular, the DM may choose to emphasize or neglect the advantage of some alternatives over the others, in terms of results of ERA. In this way, one obtains a synthetic representation of extreme ranking analysis at a higher level of abstraction.     

Religious Affiliation,Religiosity, and Attitudes Toward Same-Sex Parenting

Legalizing marriage and facilitating access to parenting for same-sex couples are controversial subjects in many countries. Based on a survey of 1,861 French heterosexual students, this study examined the effects of gender, methods gays and lesbians use to become parents, religious affiliation (Catholic vs. no religious affiliation), and religiosity (in Catholic participants) on attitudes to same-sex parenting. The participants ranged in age from 18 to 66 (M = 22.27, SD = 5.20), 67% were women, 31% described themselves as Catholic, and 69% had no religious affiliation. The results based on generalized estimating equation analyses indicate that women were more favorable to same-sex parenting than men and that participants across religious groups preferred “traditional” families composed of two, different-sex parents who do not use medically assisted procreation. Of all the methods same-sex couples use to become parents, respondents preferred adoption and rejected surrogacy. Furthermore, Catholic participants were less favorable of same-sex parenting in general than participants without religious affiliation. Higher levels of religiosity intensified that rejection. Gender does not mitigate this effect for Catholic participants, suggesting that religiosity plays a major and independent role in shaping attitudes to same-sex parenting.     

A Poisson-Based Framework for Setting Poverty Thresholds Using Indicator Lists

Poverty is one of the most important concepts in the social sciences, yet commonly-used thresholds for the operationalization of poverty have little or no conceptual basis. This is especially true of the “relative deprivation” poverty concept, where the arbitrary threshold of missing k ≥ 3 items from a list of socially defined necessities is the accepted operationalization. This paper presents a conceptual framework for meaningfully setting k based on the properties of the Poisson distribution. Data from the 2011 Swiss Household Panel are used to illustrate this approach for three poverty concepts: conventional relative deprivation based on non-affordability of items (“afford-deprivation”), simplified relative deprivation based on non-possession of items (“possess-deprivation”), and a novel approach based on the status syndrome concept (“high life burden”). Proof-of-concept analyses suggest thresholds of k ≥ 2 for conventional afford-deprivation, k ≥ 5 for possess-deprivation, and k ≥ 6 for high life burden. In addition to providing a rationale for k, the Poisson-based approach allows for the systematic variation of k in different empirical contexts. It also supports the estimation of “adjusted” poverty rates that account for the chance probability that non-poverty households are accidentally miscategorized as being in poverty.     

Corruption,lack of Transparency and the Misuse of Public Funds in Times of Crisis: An introduction

Public Organization Review -     

COMPETITIVE MARKETS WITH ENDOGENOUS HEALTH RISKS

We study an economy where agents' productivity and labor endowment depend on their health status, and indivisible occupational choices affect individual health distributions. We show that Pareto efficiency requires cross‐transfers across occupations. Moreover, workers with relatively less safe jobs must get positive transfers whenever labor supply is not very reactive to wages, a condition in line with the findings of a large empirical literature. In these instances, compensating wage differentials equalizing the utilities of ex‐ante identical workers in different jobs undermine ex‐ante efficiency. Moreover, competitive equilibria where only assets with deterministic payoffs are traded are not first‐best. Finally, we show that simple transfer schemes, implemented through linear subsidies to health insurance, enhance efficiency.     

A likelihood-based constrained algorithm for multivariate normal mixture models

It is well known that the log-likelihood function for samples coming from normal mixture distributions may present spurious maxima and singularities. For this reason here we reformulate some Hathaways results and we propose two constrained estimation procedures for multivariate normal mixture modelling according to the likelihood approach. Their perfomances are illustrated on the grounds of some numerical simulations based on the EM algorithm. A comparison between multivariate normal mixtures and the hot-deck approach in missing data imputation is also considered.Salvatore Ingrassia: S. Ingrassia carried out the research as part of the project Metodi Statistici e Reti Neuronali per lAnalisi di Dati Complessi (PRIN 2000, resp. G. Lunetta).