Disaggregating and Defeating Terrorist Safe Havens

Terrorist organizations’ physical safe havens continue to shape the terrorist threat to the United States by extending the groups’ longevity and increasing the threat they pose. As a result, eliminating terrorist safe havens has been a key component of U.S. counterterrorism policy since at least 2001. However, some scholars challenged the post-9/11 policy consensus that terrorists find sanctuary in weak states and so-called ungoverned spaces. This article seeks to bridge this gap between scholarship and policy by offering a typology for disaggregating different kinds of terrorist safe havens. Our typology operates on two axes based on host government will (i.e., the host government's posture toward each group with haven inside its borders), as well as government capability, (specifically whether the host government possesses the specific capabilities needed to oust each group). This intersection of will and capability produces three types of havens. We briefly illustrate each type of haven using the exemplar case study of Pakistan—a location often described as an overarching safe haven, but which is actually home to several sanctuaries—and offer policy recommendations for addressing them. A need exists to disaggregate and identify how the United States can approach haven elimination. This typology and the analysis that stems from it offer a starting point for devising such strategies.     

Al Qaeda and the Islamic State's Break: Strategic Strife or Lackluster Leadership?

Employing counterfactuals to assess individual and systemic explanations for the split between Al Qaeda and the Islamic State of Iraq and al-Sham (ISIS), this article concludes that individual leaders factor greatly into terrorist alliance outcomes. Osama bin Laden was instrumental in keeping Al Qaeda and ISIS allied as he prioritized unity and handled internal disputes more deftly than his successor, Dr. Ayman al-Zawahiri. Although a troubled alliance, strategic differences between Al Qaeda and ISIS were not sufficient to cause the split. Rather, the capabilities of Al Qaeda's leader determined the group's ability to prevent alliance ruptures.     

Crime and Residential Choice: A Neighborhood Level Analysis of the Impact of Crime on Housing Prices

Crime serves as an important catalyst for change in the socio-economic composition of communities. While such change occurs over a long period of time, crime is capitalized into local housing markets quickly and thus provides an early indicator of neighborhood transition. Using hedonic regression, we quantify this “intangible cost” of crime and extend the crime-housing price literature in several important ways. First, we disaggregate crime to the census tract level. Second, using longitudinal data, we examine changes in crime in addition to the neighborhood levels of crime. Third, we differentiate between the effects of property crime and violent crime. Fourth, we also disaggregate our sample into groups based on per capita income of the census tract. Finally, we show that it is vital to account for the measurement error that is endemic in reported crime statistics. We address this with an instrumental variable approach. Our results indicate that the average impacts of crime rates on house prices are misleading. We find that crime is capitalized at different rates for poor, middle class and wealthy neighborhoods and that violent crime imparts the greatest cost.

Consistency and Accuracy of Batterer Typology Identification

Researchers in the field of domestic violence have derived “typologies” to better conceptualize the differences found among batterers (e.g., Holtzworth-Monroe and Stuart [1994]. Psychol. Bull. 116: 476–497). Efforts have since been made to evaluate clinicians' accuracy in classifying batterers into subtypes (Langhinrichsen-Rohling, Huss, and Ramsey [2000]. J. Fam. Violence 15: 37–53). The present study investigated the accuracy and consistency with which experienced clinicians could sort profiles into an empirically derived MCMI-based batterer typology (Hamberger, Lohr, Bonge, and Tolin [1996] Violence Vict. 11: 277–292). Seven PhD level psychologists with experience in the field of domestic violence were asked to sort 36 MCMI profiles into three piles. Each pile was represented by each of the three prototypical cluster types described in Hamberger et al. (1996) using the Basic 8 MCMI subscales. Overall, expert raters were able to sort most profiles into the three clusters accurately. However, the expert raters had the most difficulty correctly sorting some of the “nonpathological” profiles, as 40% were placed into the antisocial cluster and (6%) were sorted into the negativistic-dependent cluster. There are a number of possible explanations for the lower accuracy in sorting the nonpathological cluster. Results suggest that psychologists with domestic violence training can accurately sort MCMI profiles of batterers into the main three subtypes derived from empirically-based typology research. Clinical implications for typology assessment are discussed.