Case Processing Times in Three Courts

The time it takes a court to process its cases, a critical aspect of court performance, varies widely from case to case, from court to court, and within a given court as the court and its environment change over time. The sources of this variation, however, have remained largely obscure.
This paper examines the structural and case-level influences on processing times in three criminal courts experiencing structural changes, including a number of administrative reforms designed to reduce processing times. We find both similarities and differences between courts, but in general both many structural arrangements and many case characteristics affect processing times, with some case characteristics having different effects under different structural arrangements, and some structural arrangements affecting different sorts of cases differently. We conclude with a discussion of implications for court management.     

Market Volatility Is the Highest Since Hoover-FDR Transition

As British Prime Minister Gordon Brown notes in this section, we are witnessing the birth pangs of a new global order in this deepest financial and economic crash since the Great Depression. There will be plenty of pain all around for a while. And when the quarter-century leveraged-debt bubble of the United States—the explosion of which detonated the crash—is finally unwound, the new global balance will favor an Asia flush with cash. The G-20 will replace the G-7 as the executive committee of globalization. And, if wise leadership stays the course, there will be a "green lining" to the recovery as the fiscal stimulus is imbued with an environmental sensibility.     

REASSESSING RACE DISPARITIES IN MARYLAND CAPITAL CASES*

A generation of research studies that were conducted in multiple states and covered different time periods has found evidence that individuals who kill white victims encounter a greater risk of facing the death penalty than killers of black victims. More recently, research has also examined the likelihood of death penalty processing for black defendants who kill white victims in comparison with other defendant–victim race groups. In particular, a study in Maryland conducted by Paternoster et al. (2003) found evidence that offenders in black defendant–white victim cases were more likely to be death noticed by prosecutors and to receive a death sentence than other offenders. A recent analysis by Berk, Li, and Hickman (2005) raised questions about some of these findings. In this article, we conduct new analyses and conclude that black defendants who kill white victims face a greater risk of adverse treatment than other types of defendants.     

THE STRUCTURAL SIMILARITY OF PROCESSES GENERATING CRIMINAL AND ANALOGOUS BEHAVIORS*

It is possible to distinguish between broad-domain theories that offer an explanation for all phenomena of interest to a discipline and narrow-domain theories that attempt to explain a subset of those phenomena. In criminology, this distinction has prompted theorists and researchers to confront the question of whether the same etiological process can explain variation in all types of criminal offending behavior or whether it will be necessary to adopt different theories to explain variation in different kinds of criminal behavior. One broad-domain theory, advanced by Gottfredson and Hirschi (1990), contends that a variable called “self-control” can account for variation in all kinds of criminal conduct as well as variation in many acts that are “analogous” to crime in some ways but are not actually criminal. Analogous behaviors include, among other things, smoking, drinking, involvement in accidents, gambling, and loitering. Using data from the Cambridge Study in Delinquent Development (N = 369 males), we attempt to define operationally the concept of self-control with a set of variables measured at ages 8–9. We then examine the empirical association between this self-control measure and self-reported involvement in a variety of criminal and analogous acts during adolescence. In support of Gottfredson and Hirschi's position, our results indicate that self-control is associated with both outcomes and that the strength of the association is approximately equal. Contrary to the expectations of their theory, however, was our finding that the covariance between criminal and analogous behaviors could not be explained entirely by variations in self-control. This finding suggests that factors other than time-stable differences in criminal propensity do matter for criminal and legal, but, risky behaviors.     

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.