Nonexclusive Competition in the Market for Lemons

A seller can trade an endowment of a perfectly divisible good, the quality of which she privately knows. Buyers compete by offering menus of nonexclusive contracts, so that the seller can privately trade with several buyers. In this setting, we show that an equilibrium exists under mild conditions and that aggregate equilibrium allocations are generically unique. Although the good for sale is divisible, in equilibrium the seller ends up trading her whole endowment or not trading at all. Trades take place at a price equal to the expected quality of the good, conditional on the seller being ready to trade at that price. Our model thus provides a novel strategic foundation for Akerlof's (1970) results. It also contrasts with competitive screening models in which contracts are assumed to be exclusive, as in Rothschild and Stiglitz (1976). Latent contracts that are issued but not traded in equilibrium play an important role in our analysis.     

The Access/Impact Problem and the Green and Gold Roads to Open Access

The research access/impact problem arises because journal articles are not accessible to all of their would-be users; hence, they are losing potential research impact. The solution is to make all articles Open Access (OA; i.e., accessible online, free for all). OA articles have significantly higher citation impact than non-OA articles. There are two roads to OA: the “golden” road (publish your article in an OA journal) and the “green” road (publish your article in a non-OA journal but also self-archive it in an OA archive). Only 5% of journals are gold, but over 90% are already green (i.e., they have given their authors the green light to self-archive); yet only about 10–20% of articles have been self-archived. To reach 100% OA, self-archiving needs to be mandated by researchers' employers and funders, as the United Kingdom and the United States have recently recommended, and universities need to implement that mandate.     

Non-linear regression models for Approximate Bayesian Computation

Approximate Bayesian inference on the basis of summary statistics is well-suited to complex problems for which the likelihood is either mathematically or computationally intractable. However the methods that use rejection suffer from the curse of dimensionality when the number of summary statistics is increased. Here we propose a machine-learning approach to the estimation of the posterior density by introducing two innovations. The new method fits a nonlinear conditional heteroscedastic regression of the parameter on the summary statistics, and then adaptively improves estimation using importance sampling. The new algorithm is compared to the state-of-the-art approximate Bayesian methods, and achieves considerable reduction of the computational burden in two examples of inference in statistical genetics and in a queueing model.     

Applications and asymptotic power of marginal-free tests of stochastic vectorial independence

Fully nonparametric tests for the independence between random vectors are studied in this paper. The test statistics are functionals of an empirical process defined as the difference between the joint empirical copula and the product of the empirical copulas associated to the vectors that are suspected to be independent. The validity of a weighted bootstrap procedure is established, which allows for a quick computation of p-values. A special attention is given to the asymptotic behavior of the tests under contiguous sequences of distributions. Finally, a characteristic of the copulas in the Archimedean class in terms of independence of vectors is exploited in order to propose a new goodness-of-fit procedure.     

Tree-structured subgroup analysis for censored survival data: Validation of computationally inexpensive model selection criteria

The performance of computationally inexpensive model selection criteria in the context of tree-structured subgroup analysis is investigated. It is shown through simulation that no single model selection criterion exhibits a uniformly superior performance over a wide range of scenarios. Therefore, a two-stage approach for model selection is proposed and shown to perform satisfactorily. Applied example of subgroup analysis is presented. Problems associated with tree-structured subgroup analysis are discussed and practical solutions are suggested.     

A multistate approach for estimating the incidence of human immunodeficiency virus by using data from a prevalent cohort study

Summary.  Cohort studies of individuals infected with the human immunodeficiency virus (HIV) provide useful information on the past pattern of HIV diagnoses, progression of the disease and use of antiretroviral therapy. We propose a new method for using individual data from an open prevalent cohort study to estimate the incidence of HIV, by jointly modelling the HIV diagnosis, the inclusion in the cohort and the progression of the disease in a Markov model framework. The estimation procedure involves the construction of a likelihood function which takes into account the probability of observing the total number of subjects who are enrolled in the cohort and the probabilities of passage through the stages of disease for each observed subject conditionally on being included in the cohort. The estimator of the HIV infection rate is defined as the function which maximizes a penalized likelihood, and the solution of this maximization problem is approximated on a basis of cubic M -splines. The method is illustrated by using cohort data from a hospital-based surveillance system of HIV infection in Aquitaine, a region of south-western France. A simulation study is performed to study the ability of the model to reconstruct the incidence of HIV from prevalent cohort data.     

Low wages and high unemployment rates: The role of social interactions in hiring discrimination

The purpose of this paper is to explain why low-wage workers with identical qualifications to higher-wage workers are more exposed to unemployment. Each worker is considered to belong to a social group (defined according to his/her gender, age, and nationality). We assume that workers experience both productive interdependencies and social interactions within the firm. Also inter- and intra-group interactions determine worker productivity, and frictions on the labor market limit the hiring of the most productive workers. Consequently, externalities acting both within the firm and in the labor market can lead to a higher rate of unemployment for low-wage workers.     

When Passion Leads to Problematic Outcomes: A Look at Gambling

Vallerand et al. (2003) have proposed that individuals can have two distinct types of passion toward an activity. Harmonious passion, an internal force leading one to choose to engage in the activity, is proposed to be associated with positive consequences. Obsessive passion, an internal pressure forcing one to engage in an activity, is posited to be associated with negative consequences. The present study sought to determine the role of the two types of passion in various cognitive and affective states associated with dependence and problems with gambling. Participants (n = 412) were recruited at the Montréal Casino and given a questionnaire measuring passion toward gambling, as well as consequences associated with dependence and problem gambling. Results showed that obsessive passion for gambling predicted poorer vitality and concentration in daily tasks, as well as increased rumination, anxiety, negative mood, guilt, and problem gambling. These relations were not found for harmonious passion for gambling. Results are discussed in light of the motivational approach to passion (Vallerand et al., 2003).