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.     

The Changing Safety Net for Low-Income Parents and Their Children: Structural or Cyclical Changes in Income Support Policy?

Refundable tax credits and food assistance are the largest transfer programs available to able-bodied working poor and near-poor families in the United States, and simultaneous participation in these programs has more than doubled since the early 2000s. To understand this growth, we construct a series of two-year panels from the 1981–2013 waves of the Current Population Survey Annual Social and Economic Supplement to estimate the effect of state labor-market conditions, federal and state transfer program policy choices, and household demographics governing joint participation in food and refundable tax credit programs. Overall, changing policy drives much of the increase in the simultaneous, biennial use of food assistance and refundable tax credits. This stands in stark contrast from the factors accounting for the growth in food assistance alone, where cyclical and structural labor market factors account for at least one-half of the growth, and demographics play a more prominent role. Moreover, since 2000, the business cycle factors as the leading determinant in biennial participation decisions in food programs and refundable tax credits, suggesting a recent strengthening in the relationship between economic conditions and transfer programs.     

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.     

The Effects of Revealed Information on Catastrophe Loss Projection Models’ Characterization of Risk: Damage Vulnerability Evidence from Florida

We examine whether the risk characterization estimated by catastrophic loss projection models is sensitive to the revelation of new information regarding risk type. We use commercial loss projection models from two widely employed modeling firms to estimate the expected hurricane losses of Florida Atlantic University's building stock, both including and excluding secondary information regarding hurricane mitigation features that influence damage vulnerability. We then compare the results of the models without and with this revealed information and find that the revelation of additional, secondary information influences modeled losses for the windstorm‐exposed university building stock, primarily evidenced by meaningful percent differences in the loss exceedance output indicated after secondary modifiers are incorporated in the analysis. Secondary risk characteristics for the data set studied appear to have substantially greater impact on probable maximum loss estimates than on average annual loss estimates. While it may be intuitively expected for catastrophe models to indicate that secondary risk characteristics hold value for reducing modeled losses, the finding that the primary value of secondary risk characteristics is in reduction of losses in the “tail” (low probability, high severity) events is less intuitive, and therefore especially interesting. Further, we address the benefit‐cost tradeoffs that commercial entities must consider when deciding whether to undergo the data collection necessary to include secondary information in modeling. Although we assert the long‐term benefit‐cost tradeoff is positive for virtually every entity, we acknowledge short‐term disincentives to such an effort.     

‘Full‐time is a Given Here’: Part‐time Versus Full‐time Job Quality*

This study explores full‐time workers' understanding of and assumptions about part‐time work against six job quality components identified in recent literature. Forty interviews were conducted with employees in a public sector agency in Australia, a study context where part‐time work is ostensibly ‘good quality’ and is typically long term, voluntary, involving secure contracts (i.e. permanent rather than casual) and having predictable hours distributed evenly over the week and year. Despite strong collective bargaining arrangements as well as substantial legal and industrial obligations, the findings revealed some serious concerns for part‐time job quality. These concerns included reduced responsibilities and lesser access to high status roles and projects, a lack of access to promotion opportunities, increased work intensity and poor workplace support. The highly gendered, part‐time labour market also means that it is women who disproportionately experience this disadvantage. To foster equity, greater attention needs to focus on monitoring and enhancing job quality, regardless of hours worked.     

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.     

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.