Religion,Age, and Crime: Do Religious Traditions Differentially Impact Juvenile Versus Adult Violence?

A growing body of empirical research demonstrates that the relative presence of religious adherents at the community-level has important relationships with rates of crime and violence. Less understood is how adherence to specific religious traditions (e.g., evangelical Protestant, Catholic, mainline Protestant) is associated with rates of crime, especially across particular age groups toward which religious traditions devote varying degrees of structural and cultural resources. Using data from the Religious Congregations and Membership Survey and age-specific arrest data from the Uniform Crime Reporting program in 2010, the current study finds that the impact of religious adherence on crime varies by religious tradition and across juvenile versus adult crime. Specifically, evangelical Protestant adherence is negatively associated with juvenile but not adult violence, while Catholic adherence is associated with reduced adult but not juvenile violence, net of controls. Implications for research on religious contexts and crime, as well as policy, are discussed.     

Morality,Relationality, and Listening Pedagogy in Language Education

Listening pedagogy in language education treats listening proficiency almost exclusively as a function or skill, the purpose of which is to generate products or outcomes desired by language users. Though listening is rhetorically acknowledged to be an active and complex process of making meanings within contexts and relationships, in practice teacher education and pedagogical discourse treat listening simply as a linguistic transaction and listening pedagogy as a technical and instrumental process of skill building, with the goal of enabling learners fluently to perform such transactions. Such a means-to-ends orientation, however, is inadequate or insufficient to encompass holistic moral and relational dimensions inherent in listening. These dimensions might be added to language listening pedagogy by, for example, broadening the idea of “dialogue” to include more open-ended perspectives from other disciplines, contextualizing the relationality of typical coursebook listening passages, and working with metaphors such as hospitality that encourage a high attentiveness to communication accompanied by a high moral respect for persons.     

Conditional Desirability

Conditional attitudes are not the attitudes an agent is disposed to acquire in event of learning that a condition holds. Rather they are the components of agent's current attitudes that derive from the consideration they give to the possibility that the condition is true. Jeffrey's decision theory can be extended to include quantitative representation of the strength of these components. A conditional desirability measure for degrees of conditional desire is proposed and shown to imply that an agent's degrees of conditional belief are conditional probabilities. Rational conditional preference is axiomatised and by application of Bolker's representation theorem for rational preferences it is shown that conditional preference rankings determine the existence of probability and desirability measures that agree with them. It is then proven that every conditional desirability function agrees with an agent's conditional preferences and, under certain assumptions, every desirability function agreeing with an agent's conditional preferences is a conditional desirability function agreeing with her unconditional preferences.     

Exploring Gender Differences on Internalizing and Externalizing Behavior Among Maltreated Youth: Implications for Social Work Action

This purpose of this study was to explore the moderating influence of gender on the relationship between child maltreatment and internalizing symptoms (e.g., affective and somatic problems) and externalizing behavior (e.g., rule breaking behavior and aggression) among children aged 7–12 years old. Using a longitudinal comparison group design and a sample of 300 youth of which 56% (n = 168) had substantiated cases of child maltreatment, results of a structural equation modeling revealed that internalizing symptoms exerted a mediating influence that was conditioned by gender. Only girls’ internalizing symptoms were found to mediate the link between child maltreatment and externalizing behavior while a direct relationship between maltreatment and externalizing behavior was found among boys. These findings provide evidence for gender differences in the pathways between being child maltreatment and maladaptive thoughts, feelings, and behaviors. Thus, adapting evidence-based strategies that target gender specific internalizing behaviors and externalizing behaviors among maltreated youth may significantly reduce the risk of short and long-term maladaptive behavior.     

IV Quantile Regression for Group‐Level Treatments,With an Application to the Distributional Effects of Trade

We present a methodology for estimating the distributional effects of an endogenous treatment that varies at the group level when there are group‐level unobservables, a quantile extension of Hausman and Taylor, 1981. Because of the presence of group‐level unobservables, standard quantile regression techniques are inconsistent in our setting even if the treatment is independent of unobservables. In contrast, our estimation technique is consistent as well as computationally simple, consisting of group‐by‐group quantile regression followed by two‐stage least squares. Using the Bahadur representation of quantile estimators, we derive weak conditions on the growth of the number of observations per group that are sufficient for consistency and asymptotic zero‐mean normality of our estimator. As in Hausman and Taylor, 1981, micro‐level covariates can be used as internal instruments for the endogenous group‐level treatment if they satisfy relevance and exogeneity conditions. Our approach applies to a broad range of settings including labor, public finance, industrial organization, urban economics, and development; we illustrate its usefulness with several such examples. Finally, an empirical application of our estimator finds that low‐wage earners in the United States from 1990 to 2007 were significantly more affected by increased Chinese import competition than high‐wage earners.     

Order-free co-regionalized areal data models with application to multiple-disease mapping

With the ready availability of spatial databases and geographical information system software, statisticians are increasingly encountering multivariate modelling settings featuring associations of more than one type: spatial associations between data locations and associations between the variables within the locations. Although flexible modelling of multivariate point-referenced data has recently been addressed by using a linear model of co-regionalization, existing methods for multivariate areal data typically suffer from unnecessary restrictions on the covariance structure or undesirable dependence on the conditioning order of the variables. We propose a class of Bayesian hierarchical models for multivariate areal data that avoids these restrictions, permitting flexible and order-free modelling of correlations both between variables and across areal units. Our framework encompasses a rich class of multivariate conditionally autoregressive models that are computationally feasible via modern Markov chain Monte Carlo methods. We illustrate the strengths of our approach over existing models by using simulation studies and also offer a real data application involving annual lung, larynx and oesophageal cancer death-rates in Minnesota counties between 1990 and 2000.     

The minimum wage and productivity differentials

A firm’s ability to adjust its production process to economize on low-skilled labor when faced with a minimum wage increase will differ greatly depending on industry or occupation. For example, more capital-intensive means of cleaning hotel rooms or serving customers at restaurants may not be readily available without degrading service quality. In such situations, the productivity of labor is essentially capped, and firms have few options when the minimum wage increases. This simple observation has implications for studies that rely on microdata to examine the effects of minimum wage increases. If firms only increase prices in response to a minimum wage increase, employment effects are likely small. If the goal of the minimum wage is to redistribute income from firms and consumers to workers, minimum-wage increases targeted at industries and occupations where such rigidities result in an inelastic demand for labor may achieve the desired goal at a lower cost than across-the-board increases. However, such a scheme causes an inefficient allocation of labor and would be subjected to substantial political pressures that may lead to anomalous results. Additionally, it is unreasonable to conclude that policy makers have the necessary information to skillfully set the minimum wage. I thank Brian E. Chezum and Jeff Waddoups for helpful comments. All mistakes, of course, are my own.     

Do Flow Rates Respond Asymmetrically to Water Level? Evidence from the Edwards Aquifer

This research examines the time series relationship between the Comal Springs flow rate and the water level in the Edwards Aquifer (Well J-17). The empirical methodology utilizes threshold autoregression (TAR) and momentum-TAR models that allow for asymmetry in responses and adjustments to a disequilibrium in the long-run cointegrating relationship. Based on the results, an asymmetric error-correction model (AECM) is proposed to characterize the short-run and long-run dynamic relationship between spring flow and water level. The results have implications for the management of water resources, water demand, and ecosystems.     

Bayesian measures of model complexity and fit

Summary. We consider the problem of comparing complex hierarchical models in which the number of parameters is not clearly defined. Using an information theoretic argument we derive a measure p _D for the effective number of parameters in a model as the difference between the posterior mean of the deviance and the deviance at the posterior means of the parameters of interest. In general p _D approximately corresponds to the trace of the product of Fisher's information and the posterior covariance, which in normal models is the trace of the 'hat' matrix projecting observations onto fitted values. Its properties in exponential families are explored. The posterior mean deviance is suggested as a Bayesian measure of fit or adequacy, and the contributions of individual observations to the fit and complexity can give rise to a diagnostic plot of deviance residuals against leverages. Adding p _D to the posterior mean deviance gives a deviance information criterion for comparing models, which is related to other information criteria and has an approximate decision theoretic justification. The procedure is illustrated in some examples, and comparisons are drawn with alternative Bayesian and classical proposals. Throughout it is emphasized that the quantities required are trivial to compute in a Markov chain Monte Carlo analysis.