Is the Way Young People Drive a Reflection of the Way Their Parents Drive? An Econometric Study of the Relation Between Parental Risk and Their Children's Risk

This article aims to investigate parental influence on high-risk behavior by young people. Although research on the topic of perception of risk demonstrates that it is socially constructed, the role of the family in this construction has rarely been studied. Using a French national survey of more than 1,200 young drivers between the ages of 18 and 25, and their parents, we attempt to understand the transmission of risk within families. Our econometric study shows that parents influence both the practices and representations of their children. When parental norms and values are transmitted, they are by no means accepted in a wholly passive way. Indeed, the parental model is in competition with other models that originate from both inside the family (brothers and sisters) and outside it (instructors). Furthermore, parental influence decreases over time as young people become more experienced drivers and construct their own identity with regard to risk.     

Quantitative Risk Assessment of Listeria monocytogenes in French Cold-Smoked Salmon: I. Quantitative Exposure Assessment

A quantitative assessment of the exposure to Listeria monocytogenes from cold-smoked salmon (CSS) consumption in France is developed. The general framework is a second-order (or two-dimensional) Monte Carlo simulation, which characterizes the uncertainty and variability of the exposure estimate. The model takes into account the competitive bacterial growth between L. monocytogenes and the background competitive flora from the end of the production line to the consumer phase. An original algorithm is proposed to integrate this growth in conditions of varying temperature. As part of a more general project led by the French Food Safety Agency (Afssa), specific data were acquired and modeled for this quantitative exposure assessment model, particularly time-temperature profiles, prevalence data, and contamination-level data. The sensitivity analysis points out the main influence of the mean temperature in household refrigerators and the prevalence of contaminated CSS on the exposure level. The outputs of this model can be used as inputs for further risk assessment.     

The ratio bias phenomenon: fact or artifact?

The ratio bias—according to which individuals prefer to bet on probabilities expressed as a ratio of large numbers to normatively equivalent or superior probabilities expressed as a ratio of small numbers—has recently gained momentum, with researchers especially in health economics emphasizing the policy importance of the phenomenon. Although the bias has been replicated several times, some doubts remain about its economic significance. Our two experiments show that the bias disappears once order effects are excluded, and once salient and dominant incentives are provided. This holds true for both choice and valuation tasks. Also, adding context to the decision problem does not alter this finding. No ratio bias could be found in between-subject tests either, which leads us to the conclusion that the policy relevance of the phenomenon is doubtful at best.     

Multilevel maximum likelihood estimation with application to covariance matrices

The asymptotic variance of the maximum likelihood estimate is proved to decrease when the maximization is restricted to a subspace that contains the true parameter value. Maximum likelihood estimation allows a systematic fitting of covariance models to the sample, which is important in data assimilation. The hierarchical maximum likelihood approach is applied to the spectral diagonal covariance model with different parameterizations of eigenvalue decay, and to the sparse inverse covariance model with specified parameter values on different sets of nonzero entries. It is shown computationally that using smaller sets of parameters can decrease the sampling noise in high dimension substantially.     

Tests for time series of counts based on the probability-generating function

We propose testing procedures for the hypothesis that a given set of discrete observations may be formulated as a particular time series of counts with a specific conditional law. The new test statistics incorporate the empirical probability-generating function computed from the observations. Special emphasis is given to the popular models of integer autoregression and Poisson autoregression. The asymptotic properties of the proposed test statistics are studied under the null hypothesis as well as under alternatives. A Monte Carlo power study on bootstrap versions of the new methods is included as well as real-data examples.     

Identity Dynamics, Action and Context

The aim of this article is to present an investigation of the action-representation relationship, via the processes which arise from the effects of one's action and the effects of one's capacity (normative and subjective). These effects are, in our view, of major importance, for they link social legitimation and the personal skill necessary for the preparation and carrying out of action. We look at this complex relationship, and propose a model, the capacity model, which situates action as an executory regulator of representation, which allows us to take some steps towards an explanation of the processes which link action and representation within the identity dynamic.