Layered Sites of Environmental Justice: Considering the Case of Prisons*

A growing body of scholarship highlights the merits of fusing green criminology and environmental justice frameworks to better understand intersections among carceral systems, race- and class-based stratification, and environmental harm. This paper explores how correctional institutions (CIs) with known histories of federal environmental law violations compare against other previously established environmentally harmful facilities and land uses. In this article, we ask: are prisons and other CIs that have violated federal environmental laws located proximate to areas where there is evidence of existing high-pollution facilities? Relatedly, are CIs that have established noncompliant histories with federal environmental laws located in similarly marginalized and disadvantaged communities compared to other traditionally defined sites of environmental injustice and harm? To answer these questions, we utilize data from the EPA's Enforcement and Compliance History Online (ECHO) database. Our findings provide evidence that, within our sample of facilities that have recorded noncompliance with federal environmental laws, CIs are significantly more likely to be located proximate to Superfund sites than most of the other facility types/land uses and more likely to be located in communities with racially minoritized populations. Our findings have important implications for further research on carceral systems and environmental justice.     

Are economists overconfident? Ideology and uncertainty in expert opinion

Economics frequently serves as an advisory discipline to policymakers, bolstered in part by its claims to a unified intellectual framework and high disciplinary consensus. Recent research challenges this perspective, providing empirical evidence that economists' professional opinions are divided by ideological commitments to either free markets on one hand or state intervention on the other. We investigate the influence of ideology in economics by examining the relation between economists' ideological commitments and the certainty with which they express their expert opinions. To examine this relationship, we analyze data from the Initiative on Global Markets Economic Experts Panel, a unique survey of 51 economists at seven elite American universities. Our results suggest that economists with ideologically patterned views report higher levels of certainty in their opinions than their less ideologically consistent peers, but this boost in confidence is limited to topics that closely pertain to the free market versus interventionism divide.     

Variance estimation of the risk difference when using propensity-score matching and weighting with time-to-event outcomes

Observational studies are increasingly being used in medicine to estimate the effects of treatments or exposures on outcomes. To minimize the potential for confounding when estimating treatment effects, propensity score methods are frequently implemented. Often outcomes are the time to event. While it is common to report the treatment effect as a relative effect, such as the hazard ratio, reporting the effect using an absolute measure of effect is also important. One commonly used absolute measure of effect is the risk difference or difference in probability of the occurrence of an event within a specified duration of follow-up between a treatment and comparison group. We first describe methods for point and variance estimation of the risk difference when using weighting or matching based on the propensity score when outcomes are time-to-event. Next, we conducted Monte Carlo simulations to compare the relative performance of these methods with respect to bias of the point estimate, accuracy of variance estimates, and coverage of estimated confidence intervals. The results of the simulation generally support the use of weighting methods (untrimmed ATT weights and IPTW) or caliper matching when the prevalence of treatment is low for point estimation. For standard error estimation the simulation results support the use of weighted robust standard errors, bootstrap methods, or matching with a naïve standard error (i.e., Greenwood method). The methods considered in the article are illustrated using a real-world example in which we estimate the effect of discharge prescribing of statins on patients hospitalized for acute myocardial infarction.