The impact of brain injury on within-individual changes in moral disengagement: implications for criminal and antisocial behavior

Journal of Experimental Criminology - The current study aims to further unpack the link between head injury and criminal behavior by examining the association between brain injury and changes in...     

Tempest in a teapot? Toward new collaborations between mainstream policy process studies and interpretive policy studies

Policy Sciences - “Tempest in a teapot” is an idiom that refers to a problem that has been blown out of proportion, which is how we see the supposedly divisive relationship between two...     

Do Pilot and Demonstration Projects Work? Evidence from a Green Building Program

Pilot and demonstration (P&D) projects are commonly deployed to catalyze early adoption of technology but are poorly understood in terms of mechanism and impact. We conceptually distinguish unique functions of pilots and demonstrations, then examine whether they accelerate adoption in the case of green building technology. To identify effects on adoption, we develop a difference-in-difference-in-differences strategy, exploiting variation in timing, location, and technologies of green building P&Ds. Results indicate local quarterly green building adoption rates double following completion of a P&D project. Further analyses examine mechanisms driving this effect. The results suggest green building demonstration projects create learning externalities, proliferating technology diffusion in local markets and through building owner networks. Together, these results suggest that investments in P&D projects by public and private actors can lower costs for subsequent adoption.     

The welfare cost of terrorism

Data from 117 countries over the period 2006 to 2011 are used to estimate a macroeconomic cross-country system of equations that examines the association between terrorism, self-reported life satisfaction, and national income. Results indicate that terrorism is negatively associated with life satisfaction, whereas no such association is found between terrorism and real GDP per worker. Stark contrasts are found, however, between OECD and non-OECD members. In all, our results suggest that the social costs of terrorism are potentially much higher than the economic costs, and measuring only the conventional economic costs of terrorism significantly underestimates the true costs.     

Multinational Corporations in World Development: 40 years on

In light of the 2014 Ecuador-sponsored resolution at the UN Human Rights Council to examine the link between Transnational Corporations and Human Rights, in this paper I review the first major discussion at the United Nations of the role of multinational corporations. The report on Multinational Corporations in World Development (1973) for the UN Department of Economic and Social affair launched the (then) new UN Centre on Transnational Corporations. I examine the report in some detail, compare and contrast this with the Ecuadorian resolution from 2014, and reflect on the continuities and changes in attempts to regulate the conduct of global corporations over the 40 years between these two moments.     

How learning aggregates: a social network analysis of learning between Swedish municipalities

Using a unique data set of learning among all 290 Swedish municipalities, we use social network analysis to analyse how learning networks aggregate nationally. To facilitate this analysis, we describe five ideal-typical patterns of aggregation: core-periphery, small world, top-down regionalism, bottom-up regionalism and urban hierarchy. Each of these ideal-types has important implications for how ideas, information and innovation will circulate among municipalities. Social network analysis allows us to both isolate these patterns and to appreciate composite patterns. The analysis indicates that Swedish municipalities are a small-world network with regional and hierarchical elements. County seats serve an important role as network hubs.     

Perceptions of Relatedness with Classroom Peers Promote Adolescents’ Behavioral Engagement and Achievement in Secondary School

Secondary school is a vulnerable time where stagnation or declines in classroom behavioral engagement occur for many students, and peer relationships take on a heightened significance. We examined the implications of adolescents’ perceptions of relatedness with classroom peers for their academic learning. Participants were 1084 adolescents (53% female) in 65 middle and high school classrooms. Multilevel cross-lagged path analyses found that adolescents’ perceived relatedness with classroom peers subsequently predicted their increased self-reported behavioral engagement in that classroom from fall to winter and again from winter to spring. Higher engagement in spring predicted higher end of year objective achievement test scores after statistical control of prior year test scores. Implications are discussed for increasing classroom peer relatedness to enhance adolescents’ achievement.     

The Balance‐Sample Size Frontier in Matching Methods for Causal Inference

We propose a simplified approach to matching for causal inference that simultaneously optimizes balance (similarity between the treated and control groups) and matched sample size. Existing approaches either fix the matched sample size and maximize balance or fix balance and maximize sample size, leaving analysts to settle for suboptimal solutions or attempt manual optimization by iteratively tweaking their matching method and rechecking balance. To jointly maximize balance and sample size, we introduce the matching frontier, the set of matching solutions with maximum possible balance for each sample size. Rather than iterating, researchers can choose matching solutions from the frontier for analysis in one step. We derive fast algorithms that calculate the matching frontier for several commonly used balance metrics. We demonstrate this approach with analyses of the effect of sex on judging and job training programs that show how the methods we introduce can extract new knowledge from existing data sets.