The influence of different dimensions of social comparison on performance measure choice and team composition

Empirical research in the economic literature is increasingly addressing the implications of social comparison on incentive contracts by using analytical principal-agent models. Contrary to the existing investigations, which are primarily based on the assumption that individuals exclusively compare monetary income, theories of behavioral science suggest that monetary income and effort represent different dimensions of social comparison, which are weighted individually. Using a LEN framework, the present study focuses on this aspect of social comparisons and discusses how these dimensions and their individual weights affect optimal contract design and contract efficiency. I consider status-seeking agents who compare themselves to each other and differ with regard to their intensity of social preferences and the specific relevance of dimensions. Finally, I draw conclusions for the drafting of contracts with respect to (1) the choice of performance measures and (2) an optimal team composition.     

Sequential Monte Carlo on large binary sampling spaces

A Monte Carlo algorithm is said to be adaptive if it automatically calibrates its current proposal distribution using past simulations. The choice of the parametric family that defines the set of proposal distributions is critical for good performance. In this paper, we present such a parametric family for adaptive sampling on high dimensional binary spaces. A practical motivation for this problem is variable selection in a linear regression context. We want to sample from a Bayesian posterior distribution on the model space using an appropriate version of Sequential Monte Carlo. Raw versions of Sequential Monte Carlo are easily implemented using binary vectors with independent components. For high dimensional problems, however, these simple proposals do not yield satisfactory results. The key to an efficient adaptive algorithm are binary parametric families which take correlations into account, analogously to the multivariate normal distribution on continuous spaces. We provide a review of models for binary data and make one of them work in the context of Sequential Monte Carlo sampling. Computational studies on real life data with about a hundred covariates suggest that, on difficult instances, our Sequential Monte Carlo approach clearly outperforms standard techniques based on Markov chain exploration.     

Collective Action and Property Rights for Poverty Reduction: A Review of Methods and Approaches

This article provides a review of literature on the relationship between poverty and the institutions of collective action and property rights, as outlined in the conceptual framework of Di Gregorio et al. (2008). Using the elements of the framework as a guide, it offers an overview of how researchers and practitioners identify and evaluate these concepts. The article emphasises the multidimensionality of poverty and the necessity of applying various approaches and tools to conceptualising and measuring it. In addition to highlighting the crucial role that institutions play in poverty reduction, it shows power relations and the political context to be of fundamental importance in poverty‐related studies.     

A method for consideration of conditional dependencies in the Fellegi and Sunter model of record linkage

An objective of Record Linkage is to link two data files by identifying common elements. A popular model for doing the separation is the probabilistic one from Fellegi and Sunter. To estimate the parameters needed for the model usually a mixture model is constructed and the EM algorithm is applied. For simplification, the assumption of conditional independence is often made. This assumption says that if several attributes of elements in the data are compared, then the results of the comparisons regarding the several attributes are independent within the mixture classes. A mixture model constructed with this assumption has been often used. Within this article a straightforward extension of the model is introduced which allows for conditional dependencies but is heavily dependent on the choice of the starting value. Therefore also an estimation procedure for the EM algorithm starting value is proposed. The two models are compared empirically in a simulation study based on telephone book entries. Particularly the effect of different starting values and conditional dependencies on the matching results is investigated.     

Evaluating the effects of full-time vs part-time educational programs for the gifted: affective outcomes and policy considerations

This study evaluates the effects of student participation in two different educational programs/settings for the gifted, i.e., full time homogeneous classes vs mixed ability classes (with a part-time extension program), on a variety of affective outcome. Data on academic self-concept, test anxiety, achievement motivation, perceptions of giftedness, school attitudes, and satisfaction with school were gathered on a sample of 1020 Israeli gifted elementary school children in grades 4–6. Analyses of the data pointed to a less positive personal-social profile in students partaking in special full-time homogeneous classes, compared to their mainstreamed counterparts, as evidenced by lower evaluative anxiety, higher academic self-concept, and more positive labeling in the former setting. However, compared to their mainstreamed counterparts in mixed ability classes, students in special homogeneous classes for the gifted held more favorable attitudes towards various facets of the school/classroom environment (school atmosphere, level of instruction, teacher-student relations, teacher characteristics) and were also more satisfied with school in general when compared to their gifted counterparts partaking in mixed ability classes. The tradeoff between a more positive perception of the school environment and less effective personal-social adjustment for students in special gifted classes should be given due consideration by program planners and evaluators in any cost-benefit analysis of educational programs for gifted students.     

Receptivity toward Immigrants in Rural Pennsylvania: Perceptions of Adult English as Second Language Providers*

This article uses interview and questionnaire data to examine how adult English as a second language (ESL) providers in rural Pennsylvania perceive community receptivity toward immigrants and the factors they believe foster or hinder receptivity and immigrants' integration. ESL providers' depictions of local responses to immigrants ranged from welcoming to hostile. They identified four constellations of factors that influenced receptivity: national and local politics, the labor market and immigrant occupations, immigrants' ability to look or act like native-born residents, and community institutions. This study reveals how differing contexts of reception are believed to influence immigrants' incorporation into rural communities. It also highlights the role of educators and educational institutions in creating a welcoming atmosphere that supports immigrants' socioeconomic well-being.     

Assessing goodness-of-fit for evaluation of dose-proportionality

For the clinical development of a new drug, the determination of dose-proportionality is an essential part of the pharmacokinetic evaluations, which may provide early indications of non-linear pharmacokinetics and may help to identify sub-populations with divergent clearances. Prior to making any conclusions regarding dose-proportionality, the goodness-of-fit of the model must be assessed to evaluate the model performance. We propose the use of simulation-based visual predictive checks to improve the validity of dose-proportionality conclusions for complex designs. We provide an illustrative example and include a table to facilitate review by regulatory authorities.