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The Direct and Indirect Effects of Offender Drug Use on Federal Sentencing Outcomes

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Abstract

Objectives

The federal sentencing guidelines constrain decision makers’ discretion to consider offenders’ life histories and current circumstances, including their histories of drug use and drug use at the time of the crime. However, the situation is complicated by the fact that judges are required to take the offender’s drug use into account in making bail and pretrial detention decisions and the ambiguity inherent in decisions regarding substantial assistance departures allows consideration of this factor. In this paper we build upon and extend prior research examining the impact of an offender’s drug use on sentences imposed on drug trafficking offenders.

Methods

We used data from three U.S. District Courts and a methodologically sophisticated approach (i.e., path analysis) to test for the direct and indirect (i.e., through pretrial detention and receipt of a substantial assistance departure) effects of an offender’s drug use history and use of drug at the time of the crime, to determine if the effects of drug use varies by the type of drug, and to test for the moderating effect of type of crime.

Results

We found that although the offender’s history of drug use did not affect sentence length, offenders who were using drugs at the time of the crime received longer sentences both as a direct consequence of their drug use and because drug use at the time of the crime increased the odds of pretrial detention and increased the likelihood of receiving a substantial assistance departure. We also found that the effects of drug use varied depending on whether the offender was using crack cocaine or some other drug and that the type of offense for which the offender was convicted moderated these relationships.

Conclusions

Our findings illustrate that there is a complex array of relationships between drug use and key case processing decisions in federal courts.

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Notes

  1. The data used in this study are from the pre-Booker time period, when the guidelines were mandatory. However, our analysis of the effect of drug use on sentencing is quite relevant to the post-Booker era, given that judges now have more discretion to consider offenders’ life circumstances and backgrounds.

  2. Our data do not contain information on clinical diagnoses of drug abuse or dependence, only whether the defendant had used illegal drugs. We therefore use the term “drug use” throughout the paper. This limitation is discussed further in the "Discussion" section.

  3. Moreover, if some judges adopt the first perspective (and, as a result, sentence offenders who use or abuse drugs more harshly) but others adopt the second (and sentence those who use or abuse drugs more leniently), the positive and negative effects of the drug use variables on case outcomes may cancel each other out.

  4. We do not model the prison/no prison decision because the two drug use variables did not have statistically significant effects on the likelihood of imprisonment in our model without the two mediating variables. According to the classic strategy on mediation analysis (see Baron and Kenny 1986), the first step in modeling a mediated relationship is to estimate a model without possible mediating variables. The analysis only proceeds to the next step if there is a direct relationship between the variables of interest and the dependent variable. Because there were no direct effects on the likelihood of imprisonment, we could not logically test for the mediating effects of pretrial detention or substantial assistance departure.

  5. The decision to exclude offenders not sentenced to prison raises a potential sample selection bias issue. To correct for this potential bias, we followed suggestions by Bushway et al. (2007) regarding the use of the Heckman two-step procedure. However, consistent with most sentencing research, we failed to locate the exclusion restriction and estimated the inverse Mills ratio without the exclusion criteria. When we attempted to model our outcome with the Lambda term included, we found that the Lambda term was highly correlated with the presumptive sentence (r = −0.87, p = .000) and more importantly the condition index was 36.18, which goes well beyond the recommended range. For these reasons, we use the uncorrected estimates and care must be taken in interpreting the results in that respect.

  6. Offenders who were using hard drugs at the time of the crime by definition have a history of hard drug use, which was measured as evidence that the offender had “ever” used the specified drugs. Our history of hard drug use variables therefore differentiate among offenders who had used hard drugs at some point in their lives but were not using drugs at the time of the crime, offenders who were using hard drugs at the time of the crime, and offenders who had never used hard drugs.

  7. We first ran the models using a set of dummy variables (violent, drug, fraud, weapons, immigration, and other offenses). However, a series of model specifications indicated that this was unnecessarily specific and therefore undermined the principle of model parsimony, which is an important concern of path analysis.

  8. It is important to note that our models do not control for whether the offender received an upward departure or a judge-initiated downward departure. Because there were only 13 upward departure cases (and thus upward departures did not constitute a meaningful outcome), we eliminated these cases from the analysis. Our decision to use substantial assistance departure but not judicial departure as a mediating variable reflects the fact that the decision to file a motion for a substantial assistance departure is an important earlier case processing outcome; prosecutors have sole discretion to file a motion for this type of departure (in many cases as a part of plea negotiations) and therefore judges have limited control over this outcome (although they must approve the motion). By contrast, a judicial departure typically is given during the final sentencing process; as such, it is not necessarily an earlier case processing outcome. The fact that we do not control for judicial departure status means that the effects of the drug use/history variables are the effects estimated at the final sentencing decision both within and outside the guideline ranges.

  9. Path analysis is a more appropriate analytical technique than a regression-based mediation technique. It provides a specific indirect effect size, which is difficult to calculate in the regression-based mediation technique. In addition, our fifth hypothesis, which focuses on moderated mediation, adds more complexity and therefore requires a more sophisticated analytical approach.

  10. Maximum likelihood (ML) estimation is one of the most widely used estimators in the path analytic approach; it assumes that all the variables in the model will follow multivariate normal distributions. As a result, binary variables typically were not included in path models, before WLS (Weighted Least Squares) appeared as an alternative model estimator. However, Muthén et al. (1997) reported that WLS was found to be inferior to WLSMV estimation. With regard to the use of the theta parameterization, M-plus provides two tests for the significance of indirect effects: the delta and the theta tests. Theta parameterization must be selected when categorical endogenous variables are employed along with a continuous dependent variable (Muthen and Muthen 1998: 2011).

  11. The determination of model fit in path models or SEM is not as straightforward as it is with other statistical approaches. It is recommended that various model-fit criteria be used in combination to assess overall model fit. Model fit criteria and acceptable fit interpretation are as follows; the probability of Chi square test should be p > 0.05. RMSEA should have at least a value of 0.05 to 0.08. CFI and TLI should have values close to 0.90 or 0.95. The chi-square model-fit criterion is sensitive to sample size because as sample size increases, the chi-square statistic has a tendency to indicate a significant probability level. Furthermore, the chi-square statistic is also sensitive to departures from multivariate normality of the observed variables. For these reasons, studies which analyze large samples with ordinal endogenous variables in the model rely on more robust model fit indicators such as RMSEA (Bollen 1989).

  12. One anonymous reviewer pointed out that our measure of non-drug offense might be problematic, since several different offenses comprise the non-drug category and there may be important differences in the ways that drug use/abuse affects these crimes. Following this reviewer’s suggestion, we attempted to compare drug offenses with a non-drug category that excluded violent crimes. One of the consistent results was that the indirect effects of both current crack use and other drug use via pretrial detention were substantially reduced for non-drug offenders. This finding raises a possibility that some of the statistically significant indirect effects through pretrial detention for non-drug offenders in Table 5 resulted from the effects of violent crime. The other comparisons (i.e. drug v. violent offense or drug v. fraud) were not feasible due to the small number of offenders in these categories who received substantial assistance departures.

  13. One anonymous reviewer pointed out that our estimates for the effects of drug use variables on pretrial detention may be biased due to an omitted variable bias. Even though our main measures are largely consistent with those of prior studies on pretrial detention, there still exists a possibility that the absence of some of the factors considered relevant to pretrial decision-making (i.e. evidence, health, length of residence) may have resulted in biased estimates. For that reason, our estimates need to be interpreted with caution.

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Authors and Affiliations

  1. School of Criminology and Criminal Justice, Arizona State University, 411 N. Central Ave., Phoenix, AZ, 85004, USA

    Cassia C. Spohn & Byungbae Kim

  2. Department of Criminal Justice, Temple University, Philadelphia, PA, 19122, USA

    Steven Belenko

  3. School of Criminology and Criminal Justice, University of Nebraska at Omaha, Omaha, NE, 68182, USA

    Pauline K. Brennan

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  1. Cassia C. Spohn
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  2. Byungbae Kim
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  3. Steven Belenko
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  4. Pauline K. Brennan
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Correspondence to Cassia C. Spohn.

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Spohn, C.C., Kim, B., Belenko, S. et al. The Direct and Indirect Effects of Offender Drug Use on Federal Sentencing Outcomes. J Quant Criminol 30, 549–576 (2014). https://doi.org/10.1007/s10940-014-9214-9

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