TERRORISM,POLITICS, AND PUNISHMENT: A TEST OF STRUCTURAL-CONTEXTUAL THEORY AND THE “LIBERATION HYPOTHESIS”*

The sentencing literature generally has been characterized by an inability to explain Significant amounts of the variance in sentencing outcomes. Two major theoretical explanations have addressed this issue: structural-contextual theory and the “liberation hypothesis.” Structural-contextual theory suggests that the components of the justice system traditionally work somewhat independently of one another. This theory suggests that variance explained in sentence outcomes will increase appreciably when components function with greater interdependence—a so-called “tightening” or “coupling” effect. Such tightening supposedly takes place when particular cases are given high priority for investigation and prosecution. An example of this situation might be domestic terrorism. The liberation hypothesis suggests that the greater the severity of an offense, the less likely judges or juries will feel free to follow their own sentiments regarding guilt and punishment. As a consequence, the ability of legal variables to predict variation in sentence length will be greater as crime severity increases. This study compares a sample of officially designated terrorists matched with nonterrorists convicted of the same federal offenses. OLS regression and structural equation modeling procedures are used to compare the levels of explained variance for the two groups. The results indicate strong support for the basic premises of both theories. Explained variance for the terrorist sample is more than four times greater than the explained variance for the nonterrorist sample. Further analysis shows that explained variance is highest for terrorists who have committed a high-severity offense and lowest for nonterrorists who have committed a low-severity offense. The subsequent addition of other predictor variables available only for the terrorist sample further increases the explained variance and provides additional support for the liberation hypothesis.     

USING THE CORRECT STATISTICAL TEST FOR THE EQUALITY OF REGRESSION COEFFICIENTS

Criminologists are often interested in examining interactive effects within a regression context. For example, “holding other relevant factors constant, is the effect of delinquent peers on one's own delinquent conduct the same for males and females?” or “is the effect of a given treatment program comparable between first-time and repeat offenders?” A frequent strategy in examining such interactive effects is to test for the difference between two regression coefficients across independent samples. That is, does b₁= b₂? Traditionally, criminologists have employed a t or z test for the difference between slopes in making these coefficient comparisons. While there is considerable consensus as to the appropriateness of this strategy, there has been some confusion in the criminological literature as to the correct estimator of the standard error of the difference, the standard deviation of the sampling distribution of coefficient differences, in the t or z formula. Criminologists have employed two different estimators of this standard deviation in their empirical work. In this note, we point out that one of these estimators is correct while the other is incorrect. The incorrect estimator biases one's hypothesis test in favor of rejecting the null hypothesis that b₁= b₂. Unfortunately, the use of this incorrect estimator of the standard error of the difference has been fairly widespread in criminology. We provide the formula for the correct statistical test and illustrate with two examples from the literature how the biased estimator can lead to incorrect conclusions.     

Flying solo: Explaining single-party cabinets under minority presidentialism

In recent years, the comparative literature on presidential democracy has emphasised the role of coalitional politics in attenuating the ‘perils’ facing minority presidents. Yet since the beginning of the Third Wave of democratisation in 1974, a surprising number of minority presidents have eschewed cabinet coalitions (defined minimally as the awarding of at least one portfolio to a party other than the nominal party of the president). Unipartisan governments are observed just under half of the time. What explains the adoption of single-party cabinets by minority presidents? Cross-sectional time-series analysis is employed to address this question. Hypotheses are tested that relate to the size and distribution of the formateur (presidential) and largest non-formateur parties that make up the legislature; the nature of party linkages and ideological distance between the president and possible partisan allies; and the extent of reactive veto powers held by the president.