Diversity,Trust, and Social Learning in Collaborative Governance

Scholarship on collaborative governance identifies several structural and procedural factors that consistently influence governance outcomes. A promising next step for collaborative governance research is to explore how these factors interact. Focusing on two dimensions of social learning—relational and cognitive—as outcomes of collaboration, this article examines potential interacting effects of participant diversity and trust. The empirical setting entails 10 collaborative partnerships in the United States that provide advice on marine aquaculture policy. The findings indicate that diversity in beliefs among participants is positively related to relational learning, whereas diversity in participants' affiliations is negatively related to relational learning, and high trust bolsters the positive effects of belief diversity on both relational and cognitive learning. In addition, high trust dampens the negative effects of affiliation diversity on relational learning. A more nuanced understanding of diversity in collaborative governance has practical implications for the design and facilitation of diverse stakeholder groups.     

Gone with the Wind: Federalism and the Strategic Location of Air Polluters

In federal systems, both state governments and firms have incentives to strategically locate polluting facilities where the environmental and health consequences will be borne as much as possible by residents of other jurisdictions. We analyze air polluter location in the United States using a spatial point pattern model, which models where events occur in latitude and longitude. Our analyses indicate that major air polluters are significantly more likely to be located near a state's downwind border than a control group of other industrial facilities, results that are robust to a wide variety of model specifications and measurement strategies. This effect is particularly pronounced for facilities with toxic air emissions. The observed pattern of polluter location varies systematically across states and time in ways that suggest it is responsive to public policy at both the national and state levels.     

How Stable is the Condition of Family Homelessness?

Although research has documented the majority of homelessness experienced by individuals and families in the US to be transitional rather than chronic, the mechanisms by which some families successfully escape homelessness while other do not remains relatively unclear. To provide more information to this area of inquiry, this study analyzed the transitional nature of homelessness for a sample of families with school-aged children who were homeless under the McKinney-Vento definition. By conducting a survey with over 1000 homeless families with children enrolled in two public school systems in Central Florida, we find that over a quarter of families exit homelessness in less than six months. We compare these families to those that remained homeless to examine the potential importance of demographic differences and other potential barriers to securing housing. Many of the commonly assumed barriers were not significant predictors of continued homelessness; the most salient difference between the groups was income underscoring the importance of employment opportunities for families to allow them to exit homelessness.     

At the Nexus of Observational and Experimental Research: Theory,Specification, and Analysis of Experiments with Heterogeneous Treatment Effects

Experimentalists are increasingly examining heterogeneous treatment effects, in which observed individual-level characteristics are hypothesized to moderate an experimental treatment effect. Such work places researchers at the nexus of experimental and observational approaches. In this paper, we discuss the theoretical and statistical issues that can arise in testing such hypotheses. We note that inclusion of an observed (as opposed to randomly-assigned) moderator introduces the possibility of confounds that are commonplace in observational data analysis but too-easily ignored in experimental data analysis. We simulate several different data generating processes that include heterogeneous treatment effects, and we discuss the implications of various statistical models. We aim to provide researchers who examine heterogeneous treatment effects with background and advice that enable them to identify where common issues may arise and to develop research designs and implement statistical tests that will mitigate them.