Industrial relations and adjustments to the crisis: A comparative micro‐statistical analysis of France and Great Britain

In this comparative study, the authors analyse the relationships between industrial relations and workforce or wage adjustments in response to the 2007–08 crisis, using two highly comparable establishment‐level surveys conducted in Great Britain (WERS) and France (REPONSE) in 2010–12. Notwithstanding contextual differences in the countries’ productive systems and the timing and impact of the crisis, the relationships between industrial relations and adjustment strategies appear to have been similar (trade union presence not preventing adjustments). Differences in industrial relations are therefore not found to provide an explanation for the different modes of adjustment observed at the macroeconomic level.     

Construction Safety Risk Modeling and Simulation

By building on a genetic‐inspired attribute‐based conceptual framework for safety risk analysis, we propose a novel approach to define, model, and simulate univariate and bivariate construction safety risk at the situational level. Our fully data‐driven techniques provide construction practitioners and academicians with an easy and automated way of getting valuable empirical insights from attribute‐based data extracted from unstructured textual injury reports. By applying our methodology on a data set of 814 injury reports, we first show the frequency‐magnitude distribution of construction safety risk to be very similar to that of many natural phenomena such as precipitation or earthquakes. Motivated by this observation, and drawing on state‐of‐the‐art techniques in hydroclimatology and insurance, we then introduce univariate and bivariate nonparametric stochastic safety risk generators based on kernel density estimators and copulas. These generators enable the user to produce large numbers of synthetic safety risk values faithful to the original data, allowing safety‐related decision making under uncertainty to be grounded on extensive empirical evidence. One of the implications of our study is that like natural phenomena, construction safety may benefit from being studied quantitatively by leveraging empirical data rather than strictly being approached through a managerial perspective using subjective data, which is the current industry standard. Finally, a side but interesting finding is that in our data set, attributes related to high energy levels (e.g., machinery, hazardous substance) and to human error (e.g., improper security of tools) emerge as strong risk shapers.     

Inference in Targeted Group‐Sequential Covariate‐Adjusted Randomized Clinical Trials

This article is devoted to the construction and asymptotic study of adaptive, group‐sequential, covariate‐adjusted randomized clinical trials analysed through the prism of the semiparametric methodology of targeted maximum likelihood estimation. We show how to build, as the data accrue group‐sequentially, a sampling design that targets a user‐supplied optimal covariate‐adjusted design. We also show how to carry out sound statistical inference based on such an adaptive sampling scheme (therefore extending some results known in the independent and identically distributed setting only so far), and how group‐sequential testing applies on top of it. The procedure is robust (i.e. consistent even if the working model is mis‐specified). A simulation study confirms the theoretical results and validates the conjecture that the procedure may also be efficient.     

Can preferences for catastrophe avoidance reconcile social discounting with intergenerational equity?

A social welfare function treating all generations equally is derived from a set of axioms that allow for preferences for catastrophe avoidance or risk equity. Implications for the case where there is a risk of world extinction are studied. We show that substantial time discounting can arise from the planner’s taste for catastrophe avoidance, even if the probability of the world ending is infinitesimally small. We wish to thank Marc Fleurbaey, Thibault Gajdos, two anonymous referees and an associate editor for many valuable comments.     

Conditions for Exercising Residents’ Voting Rights in Long-Term Care Residences: A Prospective Multicenter Study

To assess voting conditions in long-term care settings, we conducted a multicenter survey after the 2009 European elections in France. A questionnaire about voting procedures and European elections was proposed in 146 out of 884 randomized facilities. Sixty-four percent of facilities answered the questionnaire. Four percent of residents voted (national turnout: 40%), by proxy (58%) or at polling places (42%). Abstention related to procedural issues was reported in 32% of facilities. Sixty-seven percent of establishments had voting procedures, and 53% declared that they assessed residents’ capacity to vote. Assistance was proposed to residents for voter registration, for proxy voting, and for voting at polling places, respectively, in 33%, 87%, and 80% of facilities. This survey suggests that residents may be disenfranchised and that more progress should be made to protect the voting rights of residents in long-term care facilities.     

Using repeated-prevalence data in multi-state modeling of renal replacement therapy

Multi-state models help predict future numbers of patients requiring specific treatments but these models require exhaustive incidence data. Deriving reliable predictions from repeated-prevalence data would be helpful. A new method to model the number of patients that switch between therapeutic modalities using repeated-prevalence data is presented and illustrated. The parameters and goodness of fit obtained with the new method and repeated-prevalence data were compared to those obtained with the classical method and incidence data. The multi-state model parameters’ confidence intervals obtained with annually collected repeated-prevalence data were wider than those obtained with incidence data and six out of nine pairs of confidence intervals did not overlap. However, most parameters were of the same order of magnitude and the predicted patient distributions among various renal replacement therapies were similar regardless of the type of data used. In the absence of incidence data, a multi-state model can still be successfully built with annually collected repeated-prevalence data to predict the numbers of patients requiring specific treatments. This modeling technique can be extended to other chronic diseases.     

The impact of risk aversion and ambiguity aversion on annuity and saving choices

Journal of Risk and Uncertainty - We analyze the impact of risk aversion and ambiguity aversion on the competing demands for annuities and bequeathable savings using a lifecycle recursive utility...     

Sharing beliefs and the absence of betting in the Choquet expected utility model

Choquet expected utility maximizers tend to behave in a more “cautious” way than Bayesian agents, i. e. expected utility maximizers. We illustrate this phenomenon in the particular case of betting behavior. Specifically, consider agents who are Choquet expected utility maximizers. Then, if the economy is large, Pareto optimal allocations provide full insurance if and only if the agents share at least on prior, i. e., if the intersection of the core of the capacities representing their beliefs is non empty. In the expected utility case, this is true only if they have a common prior. Received: July 2000; revised version: May 2001     

Clustering transformed compositional data using K-means,with applications in gene expression and bicycle sharing system data

Although there is no shortage of clustering algorithms proposed in the literature, the question of the most relevant strategy for clustering compositional data (i.e. data whose rows belong to the simplex) remains largely unexplored in cases where the observed value is equal or close to zero for one or more samples. This work is motivated by the analysis of two applications, both focused on the categorization of compositional profiles: (1) identifying groups of co-expressed genes from high-throughput RNA sequencing data, in which a given gene may be completely silent in one or more experimental conditions; and (2) finding patterns in the usage of stations over the course of one week in the Velib' bicycle sharing system in Paris, France. For both of these applications, we make use of appropriately chosen data transformations, including the Centered Log Ratio and a novel extension called the Log Centered Log Ratio, in conjunction with the K-means algorithm. We use a non-asymptotic penalized criterion, whose penalty is calibrated with the slope heuristics, to select the number of clusters. Finally, we illustrate the performance of this clustering strategy, which is implemented in the Bioconductor package coseq, on both the gene expression and bicycle sharing system data.