Les transformations de l'intégration par l'emploi: Regards croisés : France métropolitaine - île de La Réunion

Against a background of mass unemployment and the increasing precariousness of employment, the current changes in the labour market demand that we examine the previously central role of paid employment in social integration. If the individual now deprived of employment or having a temporary contract is by dint of this fact less included in society or is less secure by not having a permanent salary, is it to say that he embodies the face of the de-affiliated, of those who miss out on social benefits? Taking the example of the Reunion Island which has the highest level of unemployment and population covered by the minimum income (RMI: Revenu Minimum d'Insertion) in the country, this article endeavours to explain and understand how, in the context of a protective society and increasingly flexible employment market, new methods of social integration without and resulting from paid employment are being formed.     

Using a mixture model for multiple imputation in the presence of outliers: the 'Healthy for life' project

Summary.  We consider the problem of obtaining population-based inference in the presence of missing data and outliers in the context of estimating the prevalence of obesity and body mass index measures from the 'Healthy for life' study. Identifying multiple outliers in a multivariate setting is problematic because of problems such as masking, in which groups of outliers inflate the covariance matrix in a fashion that prevents their identification when included, and swamping, in which outliers skew covariances in a fashion that makes non-outlying observations appear to be outliers. We develop a latent class model that assumes that each observation belongs to one of K unobserved latent classes, with each latent class having a distinct covariance matrix. We consider the latent class covariance matrix with the largest determinant to form an 'outlier class'. By separating the covariance matrix for the outliers from the covariance matrices for the remainder of the data, we avoid the problems of masking and swamping. As did Ghosh-Dastidar and Schafer, we use a multiple-imputation approach, which allows us simultaneously to conduct inference after removing cases that appear to be outliers and to promulgate uncertainty in the outlier status through the model inference. We extend the work of Ghosh-Dastidar and Schafer by embedding the outlier class in a larger mixture model, consider penalized likelihood and posterior predictive distributions to assess model choice and model fit, and develop the model in a fashion to account for the complex sample design. We also consider the repeated sampling properties of the multiple imputation removal of outliers.     

Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations

Summary.  Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalized) linear models, (generalized) additive models, smoothing spline models, state space models, semiparametric regression, spatial and spatiotemporal models, log-Gaussian Cox processes and geostatistical and geoadditive models. We consider approximate Bayesian inference in a popular subset of structured additive regression models, latent Gaussian models , where the latent field is Gaussian, controlled by a few hyperparameters and with non-Gaussian response variables. The posterior marginals are not available in closed form owing to the non-Gaussian response variables. For such models, Markov chain Monte Carlo methods can be implemented, but they are not without problems, in terms of both convergence and computational time. In some practical applications, the extent of these problems is such that Markov chain Monte Carlo sampling is simply not an appropriate tool for routine analysis. We show that, by using an integrated nested Laplace approximation and its simplified version, we can directly compute very accurate approximations to the posterior marginals. The main benefit of these approximations is computational: where Markov chain Monte Carlo algorithms need hours or days to run, our approximations provide more precise estimates in seconds or minutes. Another advantage with our approach is its generality, which makes it possible to perform Bayesian analysis in an automatic, streamlined way, and to compute model comparison criteria and various predictive measures so that models can be compared and the model under study can be challenged.     

The UN Global Compact for Safe,Orderly and Regular Migration: Its Impact on Asia

This article focuses on the Global Compact for Safe, Orderly and Regular Migration (GCM), a landmark international document that builds on the momentum of a decade of initiatives at the international level on migration governance, and its impact on Asia from the perspective of South Asia, Southeast Asia, China, and South Korea. While none of these countries voted against the adoption of the GCM, there are significant differences in how individual countries approached the commitments made in the GCM. These perspectives were articulated in a webinar hosted by Metropolis Asia on 20 June 2018 and in governmental statements made at the UN international conference when the GCM was adopted. This article also discusses the challenges confronting the implementation of the GCM. While the Compact resulted from inclusive and intensive negotiations and many compromises, the road towards realizing its goals will be at least as challenging as the road that led to its adoption. In spite of these challenges, the GCM is a ground‐breaking representation of the first multilateral framework for migration governance. Its success rests upon the recognition that addressing the challenges and maximizing the benefits of migration require true international cooperation, renewed commitment and meaningful action on the commitments agreed to.     

In Vino Pecunia? The Association Between Beverage-Specific Drinking Behavior and Wages

The positive association between moderate alcohol consumption and wages is well documented in the economic literature. Positive health effects as well as networking mechanisms serve as explanations for the “alcohol–income puzzle.” Using individual-based microdata from the SOEP for 2006, we confirm that this relationship exists for Germany as well. More importantly, we shed light on the alcohol–income puzzle by analyzing, for the first time, the association between beverage-specific drinking behavior and wages. In our analysis, we disentangle the general wage effect of drinking into diverse effects for different types of drinkers. Mincerian estimates reveal significant and positive relationships between wine drinkers and wages as well as between multiple beverage drinkers and wages. When splitting the sample into age groups, the “drinking gain” disappears for employees under the age of 35 and increases in size and significance for higher age groups. We also find a “beer gain” for the oldest age group and male residents of rural areas as well as a “cocktail gain” for residents of urban areas. Several explanations for our empirical results are discussed in view of the likelihood that the alcohol–income puzzle is a multicausal phenomenon.

Influence of competition level on referees’ decision-making in handball

The influence of competition level on referees’ decision-making was investigated. Referees’ decisions in 90 handball games (30 games X 3 competition levels) were observed in different situations related to the advantage rule, and 100 referees from two different levels of expertise were subsequently asked to offer explanations for the competition-level effects from the first part of the study. Results revealed that at the highest level of competition referees intervened less frequently with sporting sanctions, but more frequently with disciplinary sanctions. These effects were apparent mainly in immediate intervention situations and unsuccessful advantage situations, but not in successful situations. Referees explained these effects of competition level in terms of a player competence stereotype, in addition to referees’ different expertise across competition level. The implications of the findings for understanding how status-related stereotypes impact on intervention behavior are discussed.     

Naturalistic observation of drivers' interactions while overtaking on an undivided road

Current models of driving behavior and designs of driver support systems are not taking adequately into account the interactions between drivers. Although a driver's intended maneuver may not be physically possible at a specific time point, it may still be safely executed if the driver's intention is successfully communicated to and interpreted by another driver, who alters the own future trajectory so as to enable the maneuver execution. This paper presents some empirical findings relevant to communicative and cooperative interactions between drivers along naturalistic overtaking maneuvers on an undivided road. The cues used by drivers to interpret other drivers' intention and the drivers' interactions were extracted through video observations of the maneuvers together with the frequency of such interactions per level of traffic risk. The findings show that drivers use formal and informal cues to anticipate other drivers' intention. A significant percentage of maneuvers were performed after facilitation by other drivers. Future studies should focus on this phenomenon while future models of driving behavior should incorporate communicative and cooperative interactions among drivers, so as to design adequate cooperative support systems to enhance road safety.     

Distance domination in graphs with given minimum and maximum degree

For an integer \(k \ge 1\), a distance k-dominating set of a connected graph G is a set S of vertices of G such that every vertex of V(G) is at distance at most k from some vertex of S. The distance k-domination number \(\gamma _k(G)\) of G is the minimum cardinality of a distance k-dominating set of G. In this paper, we establish an upper bound on the distance k-domination number of a graph in terms of its order, minimum degree and maximum degree. We prove that for \(k \ge 2\), if G is a connected graph with minimum degree \(\delta \ge 2\) and maximum degree \(\Delta \) and of order \(n \ge \Delta + k - 1\), then \(\gamma _k(G) \le \frac{n + \delta - \Delta }{\delta + k - 1}\). This result improves existing known results.     

On tree representations of relations and graphs: symbolic ultrametrics and cograph edge decompositions

Tree representations of (sets of) symmetric binary relations, or equivalently edge-colored undirected graphs, are of central interest, e.g. in phylogenomics. In this context symbolic ultrametrics play a crucial role. Symbolic ultrametrics define an edge-colored complete graph that allows to represent the topology of this graph as a vertex-colored tree. Here, we are interested in the structure and the complexity of certain combinatorial problems resulting from considerations based on symbolic ultrametrics, and on algorithms to solve them.This includes, the characterization of symbolic ultrametrics that additionally distinguishes between edges and non-edges of arbitrary edge-colored graphs G and thus, yielding a tree representation of G, by means of so-called cographs. Moreover, we address the problem of finding “closest” symbolic ultrametrics and show the NP-completeness of the three problems: symbolic ultrametric editing, completion and deletion. Finally, as not all graphs are cographs, and hence, do not have a tree representation, we ask, furthermore, what is the minimum number of cotrees needed to represent the topology of an arbitrary non-cograph G. This is equivalent to find an optimal cograph edge k-decomposition \(\{E_1,\dots ,E_k\}\) of E so that each subgraph \((V,E_i)\) of G is a cograph. We investigate this problem in full detail, resulting in several new open problems, and NP-hardness results.For all optimization problems proven to be NP-hard we will provide integer linear program formulations to solve them.     

Free energy methods for Bayesian inference: efficient exploration of univariate Gaussian mixture posteriors

Because of their multimodality, mixture posterior distributions are difficult to sample with standard Markov chain Monte Carlo (MCMC) methods. We propose a strategy to enhance the sampling of MCMC in this context, using a biasing procedure which originates from computational Statistical Physics. The principle is first to choose a “reaction coordinate”, that is, a “direction” in which the target distribution is multimodal. In a second step, the marginal log-density of the reaction coordinate with respect to the posterior distribution is estimated; minus this quantity is called “free energy” in the computational Statistical Physics literature. To this end, we use adaptive biasing Markov chain algorithms which adapt their targeted invariant distribution on the fly, in order to overcome sampling barriers along the chosen reaction coordinate. Finally, we perform an importance sampling step in order to remove the bias and recover the true posterior. The efficiency factor of the importance sampling step can easily be estimated a priori once the bias is known, and appears to be rather large for the test cases we considered.