Demande et emploi dans les chaînes d'approvisionnement mondiales

L'essor des chaînes d'approvisionnement mondiales (CAM) éloigne l'emploi de la demande, géographiquement et sectoriellement. Pour mieux comprendre le phénomène, les auteurs mesurent l'emploi lié aux CAM selon sa localisation et celle de la demande, en se fondant sur les données de la base WIOD et en complétant les estimations déjà présentées par le BIT. L'analyse porte sur la période 1995–2013 et sur 40 économies. Elle montre que la Chine, principale bénéficiaire de la demande liée aux CAM autrefois, contribue maintenant à l'alimenter, tout comme les relations de production entre économies émergentes. En outre, le poids des services liés à une demande manufacturière augmente.     

L'effet des obstacles aux échanges sur l'emploi généré par les chaînes d'approvisionnement mondiales

Les auteurs étudient l'effet des obstacles aux échanges internationaux de biens et de services sur les marchés du travail des pays producteurs en partant des estimations du BIT sur l'emploi imputable aux chaînes d'approvisionnement mondiales. L'analyse empirique confirme largement les prédictions d'un modèle théorique calibré avec des données de la base WIOD (2000 et 2011). Les obstacles au commerce de produits manufacturés ou de services ont un effet transfrontalier, intrasectoriel et intersectoriel, sur l'emploi, qui reflète l'interdépendance croissante entre pays et secteurs. L'effet intersectoriel s'affirme avec le temps. Les politiques commerciales semblent donc pouvoir avoir des effets externes importants sur les marchés du travail étrangers.     

Symptoms of Mental Health Problems: Children’s and Adolescents’ Understandings and Implications for Gender Differences in Help Seeking

Amidst concerns that young people’s mental health is deteriorating, it is important to explore their understandings of symptoms of mental health problems and beliefs around help seeking. Drawing on focus group data from Scottish school pupils, we demonstrate how they understood symptoms of mental health problems and how their characterisations of these symptoms as ‘rare’ and ‘weird’ informed participants’ perceptions that peers, teachers and parents would respond to disclosure in stigmatising ways. Consequently, participants suggested that they would delay or avoid disclosing symptoms of mental health problems. We highlight subtle gender and age differences and outline implications for policy and practice.     

Making predictions from complex longitudinal data, with application to planning monitoring intervals in a national screening programme

When biological or physiological variables change over time, we are often interested in making predictions either of future measurements or of the time taken to reach some threshold value. On the basis of longitudinal data for multiple individuals, we develop Bayesian hierarchical models for making these predictions together with their associated uncertainty. Particular aspects addressed, which include some novel components, are handling curvature in individuals' trends over time, making predictions for both underlying and measured levels, making predictions from a single baseline measurement, making predictions from a series of measurements, allowing flexibility in the error and random-effects distributions, and including covariates. In the context of data on the expansion of abdominal aortic aneurysms over time, where reaching a certain threshold leads to referral for surgery, we discuss the practical application of these models to the planning of monitoring intervals in a national screening programme. Prediction of the time to reach a threshold was too imprecise to be practically useful, and we focus instead on limiting the probability of exceeding the threshold after given time intervals. Although more complex models can be shown to fit the data better, we find that relatively simple models seem to be adequate for planning monitoring intervals.     

Application of a predictive distribution formula to Bayesian computation for incomplete data models

We consider exact and approximate Bayesian computation in the presence of latent variables or missing data. Specifically we explore the application of a posterior predictive distribution formula derived in Sweeting And Kharroubi (2003), which is a particular form of Laplace approximation, both as an importance function and a proposal distribution. We show that this formula provides a stable importance function for use within poor man’s data augmentation schemes and that it can also be used as a proposal distribution within a Metropolis-Hastings algorithm for models that are not analytically tractable. We illustrate both uses in the case of a censored regression model and a normal hierarchical model, with both normal and Student t distributed random effects. Although the predictive distribution formula is motivated by regular asymptotic theory, it is not necessary that the likelihood has a closed form or that it possesses a local maximum.     

Linking jobs in global supply chains to demand

The expansion of global supply chains (GSCs) has increasingly disconnected the location of jobs from the demand supporting them, both geographically and in terms of sector. Using data from the World Input–Output Database, the authors examine these linkages across 40 countries over the period 1995–2013, expanding on earlier analysis published by the ILO, and provide evidence of the number of GSC‐related jobs in terms of job location–export destination combinations. Their findings point to changing patterns in demand and supply of GSC‐related jobs, increasing the role of China as a demand generator, reinforcing production linkages between emerging economies and increasing the number of service jobs dependent on manufacturing GSCs.     

Selecting the precision parameter prior in Dirichlet process mixture models

We consider Dirichlet process mixture models in which the observed clusters in any particular dataset are not viewed as belonging to a finite set of possible clusters but rather as representatives of a latent structure in which objects belong to one of a potentially infinite number of clusters. As more information is revealed the number of inferred clusters is allowed to grow. The precision parameter of the Dirichlet process is a crucial parameter that controls the number of clusters. We develop a framework for the specification of the hyperparameters associated with the prior for the precision parameter that can be used both in the presence or absence of subjective prior information about the level of clustering. Our approach is illustrated in an analysis of clustering brands at the magazine Which?. The results are compared with the approach of Dorazio (2009) via a simulation study.     

On the construction of Bayes–confidence regions

We obtain approximate Bayes–confidence intervals for a scalar parameter based on directed likelihood. The posterior probabilities of these intervals agree with their unconditional coverage probabilities to fourth order, and with their conditional coverage probabilities to third order. These intervals are constructed for arbitrary smooth prior distributions. A key feature of the construction is that log-likelihood derivatives beyond second order are not required, unlike the asymptotic expansions of Severini.     

Misspecification of at‐risk periods and distributional assumptions in estimating COPD exacerbation rates: The resultant bias in treatment effect estimation

In trials comparing the rate of chronic obstructive pulmonary disease exacerbation between treatment arms, the rate is typically calculated on the basis of the whole of each patient's follow‐up period. However, the true time a patient is at risk should exclude periods in which an exacerbation episode is occurring, because a patient cannot be at risk of another exacerbation episode until recovered. We used data from two chronic obstructive pulmonary disease randomized controlled trials and compared treatment effect estimates and confidence intervals when using two different definitions of the at‐risk period. Using a simulation study we examined the bias in the estimated treatment effect and the coverage of the confidence interval, using these two definitions of the at‐risk period. We investigated how the sample size required for a given power changes on the basis of the definition of at‐risk period used. Our results showed that treatment efficacy is underestimated when the at‐risk period does not take account of exacerbation duration, and the power to detect a statistically significant result is slightly diminished. Correspondingly, using the correct at‐risk period, some modest savings in required sample size can be achieved. Using the proposed at‐risk period that excludes recovery times requires formal definitions of the beginning and end of an exacerbation episode, and we recommend these be always predefined in a trial protocol.