PREFERENCES FOR REDISTRIBUTION AND PERCEPTION OF FAIRNESS: AN EXPERIMENTAL STUDY

We conduct a laboratory experiment to study how demand for redistribution of income depends on self‐interest, insurance motives, and social concerns relating to inequality and efficiency. Our choice environments feature large groups of subjects and real‐world framing, and differ with respect to the source of inequality (earned or arbitrary), the cost of taxation to the decision maker, the dead‐weight loss of taxation, uncertainty about own pretax income, and whether the decision maker is affected by redistribution. We estimate utility weights for the different sources of demand for redistribution, with the potential to inform modeling in macroeconomics and political economy     

EXPLAINING GIFT‐EXCHANGE—THE LIMITS OF GOOD INTENTIONS

This paper explores the limitations of intention‐based social preferences as an explanation of gift‐exchange between a firm and a worker. In a framework with one self‐interested and one reciprocal player, gift‐giving never arises in equilibrium. Instead, any equilibrium in a large class of multistage games must involve mutually unkind behavior of both players. Besides gift‐exchange, this class of games also includes moral hazard models and the rotten kid framework. Even though equilibrium behavior may appear positively reciprocal in some of these games, the self‐interested player never benefits from reciprocity. We discuss the relation of these results to the theoretical and empirical literature on gift‐exchange in employment relations.     

Management of Energy Technology for Sustainability: How to Fund Energy Technology Research and Development

Operations management methods have been applied profitably to a wide range of technology portfolio management problems, but have been slow to be adopted by governments and policy makers. We develop a framework that allows us to apply such techniques to a large and important public policy problem: energy technology R&D portfolio management under climate change. We apply a multi‐model approach, implementing probabilistic data derived from expert elicitations into a novel stochastic programming version of a dynamic integrated assessment model. We note that while the unifying framework we present can be applied to a range of models and data sets, the specific results depend on the data and assumptions used and therefore may not be generalizable. Nevertheless, the results are suggestive, and we find that the optimal technology portfolio for the set of projects considered is fairly robust to different specifications of climate uncertainty, to different policy environments, and to assumptions about the opportunity cost of investing. We also conclude that policy makers would do better to over‐invest in R&D rather than under‐invest. Finally, we show that R&D can play different roles in different types of policy environments, sometimes leading primarily to cost reduction, other times leading to better environmental outcomes.     

莫言小说《丰乳肥臀》中的3D细节描写

3D细节描写是一种立体的3D视角,能将平面的画面直观地显现成3维的立体画面。《丰乳肥臀》中有大量的细节描写,有一部分是明显的3D描写,让读者融入情景、直观感受。尽管莫言不是有意识进行3D描写,但有意识地立体地细节描写写出了3D效果。     

The Evolution of Knowledge and the Dynamics of an Industry Network

The paper moves a step forward in the direction of establishing a connection between the structure and evolution of knowledge bases and the structure and evolution of organizational forms in innovative activities in a science-intensive industry. The paper has an explicit focus on the dynamics of the network of collaborative agreements in R&D in the pharma/biotech industry after the “molecular biology revolution”. Using a comprehensive dataset, built by the authors integrating several sources in the industry, the dynamics of the network over time is extensively analyzed. With regards to network structure, it is found that, while the size of the network increases over time due to net flows of entry, its topological properties remain relatively unchanged. The evolution of the network has occurred without relevant deformations in the core-periphery profile. With regards to age-dependent propensity to collaborate, the paper finds that the extent of inter-generational collaboration is much more significant than intra-generational collaboration. In addition, the propensity of firms of a given generation to enter into collaboration with firms of a different generation increases with the distance between the two, while the total number of intra-generational collaborations decreases over time and, moreover, tends to decrease for most recent generations. In the paper a unitary and coherent explanation of the evidence is developed, coming to reveal the existence of a striking isomorphism between structural properties of the dynamics of knowledge and of the evolution of network structure. This revised version was published online in July 2006 with corrections to the Cover Date.     

Restricted maximum likelihood estimation of joint mean‐covariance models

The class of joint mean‐covariance models uses the modified Cholesky decomposition of the within subject covariance matrix in order to arrive to an unconstrained, statistically meaningful reparameterisation. The new parameterisation of the covariance matrix has two sets of parameters that separately describe the variances and correlations. Thus, with the mean or regression parameters, these models have three sets of distinct parameters. In order to alleviate the problem of inefficient estimation and downward bias in the variance estimates, inherent in the maximum likelihood estimation procedure, the usual REML estimation procedure adjusts for the degrees of freedom lost due to the estimation of the mean parameters. Because of the parameterisation of the joint mean covariance models, it is possible to adapt the usual REML procedure in order to estimate the variance (correlation) parameters by taking into account the degrees of freedom lost by the estimation of both the mean and correlation (variance) parameters. To this end, here we propose adjustments to the estimation procedures based on the modified and adjusted profile likelihoods. The methods are illustrated by an application to a real data set and simulation studies. The Canadian Journal of Statistics 40: 225–242; 2012 © 2012 Statistical Society of Canada     

A generalisation of T‐optimality for discriminating between competing models with an application to pharmacokinetic studies

The T‐optimality criterion is used in optimal design to derive designs for model selection. To set up the method, it is required that one of the models is considered to be true. We term this local T‐optimality. In this work, we propose a generalisation of T‐optimality (termed robust T‐optimality) that relaxes the requirement that one of the candidate models is set as true. We then show an application to a nonlinear mixed effects model with two candidate non‐nested models and combine robust T‐optimality with robust D‐optimality. Optimal design under local T‐optimality was found to provide adequate power when the a priori assumed true model was the true model but poor power if the a priori assumed true model was not the true model. The robust T‐optimality method provided adequate power irrespective of which model was true. The robust T‐optimality method appears to have useful properties for nonlinear models, where both the parameter values and model structure are required to be known a priori, and the most likely model that would be applied to any new experiment is not known with certainty. Copyright © 2012 John Wiley & Sons, Ltd.     

Generalized quasi-likelihood

The quasi-score function, as defined by Wedderburn (1974) and McCullagh (1983) and so on, is a linear function of observations. The generalized quasi-score function introduced in this paper is a linear function of some unbiased basis functions, where the unbiased basis functions may be some linear functions of the observations or not, and can be easily constructed by the meaning of the parameters such as mean and median and so on. The generalized quasi-likelihood estimate obtained by such a generalized quasi-score function is consistent and has an asymptotically normal distribution. As a result, the optimum generalized quasi-score is obtained and a method to construct the optimum unbiased basis function is introduced. In order to construct the potential function, a conservative generalized estimating function is defined. By conservative, a potential function for the projected score has many properties of a log-likelihood function. Finally, some examples are given to illustrate the theoretical results. This paper is supported by NNSF project (10371059) of China and Youth Teacher Foundation of Nankai University.     

Nonparametric regression with cross‐classified responses

In this article, we develop regression models with cross‐classified responses. Conditional independence structures can be explored/exploited through the selective inclusion/exclusion of terms in a certain functional ANOVA decomposition, and the estimation is done nonparametrically via the penalized likelihood method. A cohort of computational and data analytical tools are presented, which include cross‐validation for smoothing parameter selection, Kullback–Leibler projection for model selection, and Bayesian confidence intervals for odds ratios. Random effects are introduced to model possible correlations such as those found in longitudinal and clustered data. Empirical performances of the methods are explored in simulation studies of limited scales, and a real data example is presented using some eyetracking data from linguistic studies. The techniques are implemented in a suite of R functions, whose usage is briefly described in the appendix. The Canadian Journal of Statistics 39: 591–609; 2011. © 2011 Statistical Society of Canada