Anatomy of a government massacre: Abuses of hostage‐barricade protocols during the Waco standoff

A systematic evaluation of the FBI's crisis negotiations with the Branch Davidians during a 51‐day standoff in 1993 is conducted. The analysis uncovers extensive violations of basic hostage‐barricade standards and protocols. The violations appear shortly after negotiations began indicating a premature disregard for the publicly declared goal of a peaceful resolution. Failed negotiations subsequently were cited by FBI officials as a rationale for organizing a violent and dangerous CS gas assault on the barricaded group, resulting in the destruction of the religious community and the deaths of 74 people. The violations do not appear to be random, incidental, or the result of disorganization, as officials claim. The data indicate that the FBI's on‐scene commanders and tactical component of the Hostage‐Rescue Team (HRT) contributed largely to the failed negotiations through methods of increased tactical pressure and psychological warfare providing justification for the high‐risk assault. The thesis is advanced that the HRT command may have pursued furtively what some insiders call a ‘Western’ view of hostage‐barricade incidents ‐ i.e., that negotiations should be used as a means of manipulating people into positions where a tactical solution can be executed. In this context, the standoff is analyzed as a government massacre. Possible motives for this state violence are linked to the sect's defiant posture, aspects of police culture, effective demonization of the sect, and the disturbing trend of ‘militarization’ within law enforcement.     

Uncertainty and disaster‐avoidance behaviour in peasant farming: Evidence from Bangladesh

A sample of smallholding farmers in the Pabna region of Bangladesh provide data to test predictions of a safety‐first model and of a profit maximisation model of farmer behaviour. Of the two sets of predictions, the data tend to support those of the safety‐first model more frequently than they support the predictions of the profit maximisation model. While the degree to which the former model ‘out‐performs’ the latter is not great, it is important to note that when the major inputs (land and human labour) and the major outputs (rice and jute), are focused upon, the safety‐first model performs much better than the profit maximisation model.     

Using the Results from Rigorous Multisite Evaluations to Inform Local Policy Decisions

Evidence‐based policy at the local level requires predicting the impact of an intervention to inform whether it should be adopted. Increasingly, local policymakers have access to published research evaluating the effectiveness of policy interventions from national research clearinghouses that review and disseminate evidence from program evaluations. Through these evaluations, local policymakers have a wealth of evidence describing what works, but not necessarily where. Multisite evaluations may produce unbiased estimates of the average impact of an intervention in the study sample and still produce inaccurate predictions of the impact for localities outside the sample for two reasons: (1) the impact of the intervention may vary across localities, and (2) the evaluation estimate is subject to sampling error. Unfortunately, there is relatively little evidence on how much the impacts of policy interventions vary from one locality to another and almost no evidence on the implications of this variation for the accuracy with which the local impact of adopting an intervention can be predicted using findings from an evaluation in other localities. In this paper, we present a set of methods for quantifying the accuracy of the local predictions that can be obtained using the results of multisite randomized trials and for assessing the likelihood that prediction errors will lead to errors in local policy decisions. We demonstrate these methods using three evaluations of educational interventions, providing the first empirical evidence of the ability to use multisite evaluations to predict impacts in individual localities—i.e., the ability of “evidence‐based policy” to improve local policy.     

Mass Media and Electoral Preferences During the 2016 US Presidential Race

Political Behavior - This paper uses analyses of commercial polls alongside content-analytic measures of sentiment in the content of nine newspapers to explore the relationship between voter...     

Policy attribute framing: A comparison between three policy instruments for personal emissions reduction

A comparative experiment in the UK examined people's willingness to change energy consumption behavior under three different policy framings: energy tax, carbon tax, and personal carbon allowances (PCA). PCA is a downstream cap‐and‐trade policy proposed in the UK, in which emission rights are allocated to individuals. We hypothesized that due to economic, pro‐environmental and mental accounting drivers PCA would have greater potential to deliver emissions reduction than taxation. Participants (n = 1,096) received one version of a survey with the same energy‐behavior–related questions and identical incurred costs under one of the following framings: energy tax (where carbon was not mentioned), carbon tax, and PCA. Results suggest that policies that draw people's attention to carbon (PCA and carbon taxation) could have greater impact on their stated willingness to reduce energy consumption, and on the reduction amounts prompted, than would a non‐overt price signal (energy tax). There is mixed evidence, however, as to whether PCA or carbon taxation would produce the largest energy demand reductions. Some indication was found for a spillover toward wider carbon conservation under the PCA framing. © 2011 by the Association for Public Policy Analysis and Management.     

Reading the State as a Multi-Identity Formation: The Touch and Feel of Equality Governance

How does a sense of touch, figuratively and practically, get deployed within equality governance, and to what questions and ways of thinking about the state does this direct us? Taking 2009–2010 as a snap-shot moment in the development of British equality reform—the year leading up to passage of the Equality Act 2010—this article explores the relationship between touch (the haptic) and equality governance from three angles. First, how have governmental bodies used touch language and imagery, including in geometrical representations of disadvantage? Second, what other, more challenging encounters and actions are imaginable; specifically, can touch mobilise the feeling state as a critical form of active citizenship? Third, what re-conceptualisations of the state does the touching, feeling state invoke, and with what effects? Specifically, does conceiving of the state as a multi-identity formation reframe the risks associated with a haptic state, thereby opening up new strategies for political action?     

Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference

Kosuke Imai Department of Politics, Princeton University, Princeton, NJ 08544 e-mail: kimai{at}princeton.edu Gary King Department of Government, Harvard University, 1737 Cambridge Street, Cambridge, MA 02138 Elizabeth A. Stuart Departments of Mental Health and Biostatistics, Johns Hopkins Bloomberg School of Public Health, 624 North Broadway, Room 804, Baltimore, MD 21205 e-mail: estuart{at}jhsph.edu e-mail: king{at}harvard.edu (corresponding author) Although published works rarely include causal estimates frommore than a few model specifications, authors usually choosethe presented estimates from numerous trial runs readers neversee. Given the often large variation in estimates across choicesof control variables, functional forms, and other modeling assumptions,how can researchers ensure that the few estimates presentedare accurate or representative? How do readers know that publicationsare not merely demonstrations that it is possible to find aspecification that fits the author's favorite hypothesis? Andhow do we evaluate or even define statistical properties likeunbiasedness or mean squared error when no unique model or estimatoreven exists? Matching methods, which offer the promise of causalinference with fewer assumptions, constitute one possible wayforward, but crucial results in this fast-growing methodologicalliterature are often grossly misinterpreted. We explain howto avoid these misinterpretations and propose a unified approachthat makes it possible for researchers to preprocess data withmatching (such as with the easy-to-use software we offer) andthen to apply the best parametric techniques they would haveused anyway. This procedure makes parametric models producemore accurate and considerably less model-dependent causal inferences. Authors' note: Our thanks to Dan Carpenter and Jeff Koch fordata; Alberto Abadie, Neal Beck, Sam Cook, Alexis Diamond, BenHansen, Guido Imbens, Olivia Lau, Gabe Lenz, Paul Rosenbaum,Don Rubin, and Jas Sekhon for many helpful comments; and theNational Institutes of Aging (P01 AG17625-01), the NationalInstitute of Mental Health (MH066247), the National ScienceFoundation (SES-0318275, IIS-9874747, SES-0550873), and thePrinceton University Committee on Research in the Humanitiesand Social Sciences for research support. Software to implementthe methods in this paper is available at http://GKing.Harvard.edu/matchitand a replication data file is available as Ho et al. (2006).