Economic sanctions research suggests that sanctioned countries’ overall economic costs tend to be low. This article argues that, despite this, sanction costs can force the governments of these countries to reallocate budget resources from low-priority spending categories to other categories in an effort to minimize their political costs. One such low-priority category is disaster preparedness and mitigation. The authors show that economic sanctions lead to reduced disaster preparedness spending and, as a result, increase the scale of economic and human losses generated by natural disasters in sanctioned countries.

Quantitative comparative social scientists have long worried about the performance of multilevel models when the number of upper-level units is small. Adding to these concerns, an influential Monte Carlo study by Stegmueller (2013) suggests that standard maximum-likelihood (ML) methods yield biased point estimates and severely anti-conservative inference with few upper-level units. In this article, the authors seek to rectify this negative assessment. First, they show that ML estimators of coefficients are unbiased in linear multilevel models. The apparent bias in coefficient estimates found by Stegmueller can be attributed to Monte Carlo Error and a flaw in the design of his simulation study. Secondly, they demonstrate how inferential problems can be overcome by using restricted ML estimators for variance parameters and a t-distribution with appropriate degrees of freedom for statistical inference. Thus, accurate multilevel analysis is possible within the framework that most practitioners are familiar with, even if there are only a few upper-level units.