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Excessive Uniformity in Federal Drug Sentencing
Article in Journal of Quantitative Criminology · June 2009
DOI: 10.1007/s10940-009-9064-z
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J Quant Criminol (2009) 25:155–180
DOI 10.1007/s10940-009-9064-z
ORIGINAL PAPER
Excessive Uniformity in Federal Drug Sentencing
Eric L. Sevigny
Published online: 21 March 2009
Springer Science+Business Media, LLC 2009
Abstract The ideal of fair and proportionate punishment was a major impetus for federal
sentencing reform. Observers of the current federal drug sentencing regime contend that
the sentencing guidelines and mandatory minimums lead to the problem of ‘‘excessive
uniformity’’ in which offenders of widely differing culpability receive similar sentences
due to the dominance of drug quantity as a sentencing factor. This study investigates this
phenomenon using the 1997 Survey of Inmates in Federal Correctional Facilities. Controlling for relevant offense, offender, and case processing variables, the analysis finds that
the quantity-driven sentencing fails to account for important differences in offender culpability—resulting in excessively uniform sentences for offenders with highly dissimilar
roles in the offense. The main policy implication of this research is that the central,
organizing role of drug quantity in federal drug sentencing needs to be rethought. Indeed,
effectively dealing with the problem of excessive uniformity will likely require the
wholesale restructuring of how federal sentences for drug offenders are determined.
Keywords Excessive uniformity Drug sentencing Survey of Inmates in Federal Correctional Facilities Truncated regression
Introduction
The ideal of fair and proportionate punishment was a major impetus for federal sentencing
reform efforts spearheaded in the 1970s (Frankel 1972; Stith and Koh 1993; United States
Sentencing Commission [USSC] 1991a). Inequities in punishment, which often fell along
race and class lines, were believed to be rooted in the indeterminate sentencing system of
the time in which federal judges and regional parole boards possessed virtually unlimited
discretion to impose sentences and grant conditional release, respectively (Feinberg 1993;
Wilkins et al. 1993). Congress enacted the Sentencing Reform Act of 1984 (SRA) with the
E. L. Sevigny (&)
Department of Criminology and Criminal Justice, University of South Carolina,
1305 Greene Street, Columbia, SC 29208, USA
e-mail: sevigny@mailbox.sc.edu
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primary objective of eliminating these unwarranted disparities. The SRA abolished the
federal parole system and mandated the United States Sentencing Commission (the
‘‘Commission’’) to develop sentencing guidelines that would ensure ‘‘similar treatment for
similar offenders and different treatment for different offenders’’ (USSC 2004:79). Congress instructed the Commission to fulfill this mandate by structuring sentencing decisions
around clearly specified legally relevant offense and offender characteristics (Feinberg
1993; Stith and Cabranes 1998; USSC 1991a, 2004), while remaining ‘‘entirely neutral as
to the race, sex, national origin, creed, and socioeconomic status of offenders’’ (28 U.S.C.
§994(d)).
Less than two years after passing the SRA and just months before the Commission
submitted its initial guidelines for congressional review, Congress passed the Anti-Drug
Abuse Act of 1986 (‘‘1986 Act’’) establishing five- and ten-year mandatory minimum
penalties for drug trafficking offenses based solely on the type and amount of drugs
involved in the offense.1 Because they were established by an act of Congress, the mandatory minimums would always trump the guidelines in controlling the final sentence
(General Accounting Office 2003). Thus, to maintain consistency with the statutory provisions of the 1986 Act, the Commission was obliged to frame the yet-to-be-completed
drug sentencing guidelines around the penalty structure of the mandatory minimums,
which included the notorious 100-to-1 crack/powder cocaine quantity ratio2 (Hofer 2001;
Scotkin 1990). In November 1987, the Commission ultimately promulgated sentencing
guidelines that reached well beyond the mandatory minimums in severity by, in effect,
placing the bottom of the guideline range atop the mandatory minimum threshold and
extrapolating sentences upward and downward based on the amount of drugs involved in
the offense. In this way, penalties for federal drug offenders came to be expressly unified
around a single, dominant sentencing factor: drug quantity.
Critics of this quantity-driven approach contend that it fails to assure proportionality
because the influence of other important sentencing factors, such as the offender’s role in
the offense, are given relatively little weight (Alschuler 1991; Goodwin 1992; Hofer and
Allenbaugh 2003; Osler 2007; Schulhofer 1992a, b; Wasserman 1995; Weinstein and
Bernstein 1994; Young 1990). In particular, one of the major criticisms of the current
regime is that it leads to the problem of ‘‘excessive uniformity’’ in which offenders of
widely differing culpability receive unreasonably similar sentences. Excessive uniformity
in drug sentencing has its genesis in guideline-based rules of sentencing, including an
overemphasis on drug quantity, the ‘‘relevant conduct’’ standard, and the narrow scope and
applicability of culpability-based sentencing adjustments (Schulhofer 1992a, b).
Notwithstanding the need to maintain consistency with the mandatory minimums, the
original Commission elevated quantity, as a measure of harm, over culpability-based
distinctions because the amount of drugs was more easily quantifiable than role in the
offense and, it was thought, quantity would serve as a good proxy for role (USSC 2002).
The available evidence indicates, however, that drug quantity is at best a ‘‘crude surrogate’’
1
Higher mandatory minimums of twenty years and life were possible for repeat drug offenders and those
who committed a drug trafficking offense that resulted in death or serious injury. The Anti-Drug Abuse Act
of 1988 expanded the scope of these penalties to include simple crack possession and drug conspiracies.
2
It is noteworthy that the Sentencing Commission’s ‘‘crack minus two’’ amendment, which reduced the
guidelines’ crack/powder quantity ratio from 100-to-1 to 20-to-1, took effect on November 1, 2007 (and was
subsequently made retroactive to previously convicted crack offenders on December 11, 2007). However,
the action applies only to crack offenders sentenced under the guidelines, since changing the mandatory
minimum penalty structure will require an explicit act of Congress.
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for role in the offense and that it is an especially poor proxy for low-level drug trade
participants such as couriers, mules, and loaders (DOJ 1994; USSC 1995, 2002).
The dominance of drug quantity as a sentencing factor is further problematized by the
sentencing guidelines’ ‘‘relevant conduct’’ rule. Relevant conduct exposes all participants
of jointly undertaken criminal activity to sentences based on the total amount of drugs that
can be foreseeably attributed to the conspiracy, irrespective of any particular member’s
low-level or mitigating role (Schulhofer 1992a). The relevant conduct rule also exposes
offenders to sentences based on the aggregate amount of drugs they may have sold or
trafficked over an extended period of time (even if acquitted of that prior conduct). In short,
the relevant conduct rule often exposes minor participants in the drug trade to sentences
intended for higher-level actors.
Moreover, the narrow scope and applicability of the guidelines’ mitigating role and
‘‘safety valve’’ adjustments hardly counterbalance the upside quantity-based sentencing
potential. Not only are the culpability-based sentencing adjustments relatively insubstantial
in effect, but their use is severely constrained by case law and official Commission policy.
For example, quantity alone can produce a sentence anywhere between probation and life
in prison, but a mitigating role adjustment can decrease this sentence by only about 25%
(Hofer and Allenbaugh 2003). Moreover, the culpability-based guideline adjustments are
restricted to coconspirators involved in ‘‘jointly undertaken criminal activity’’ (USSC
1990, 1992, 1997a). Thus, a small-time freelance street dealer is afforded no sentence
mitigation under the guidelines. Likewise, application of the ‘‘safety valve’’ departure,
which allows certain low-level offenders to escape sentencing under the mandatory minimums, is also tightly circumscribed to select low-level offenders who meet stringent
criteria (Froyd 2000; Oliss 1995).
To summarize, by basing sentences primarily on drug quantity and disallowing more
nuanced culpability distinctions, the sentencing guidelines and mandatory minimums
effectively ‘‘mandate uniformity’’ for offenders of widely varying roles and responsibility
in the drug trade (Schulhofer 1992a, b). Equal treatment of this sort is demonstrably unjust.
In this respect, excessive uniformity represents a form of policy-driven disparity that runs
against the goals of federal sentencing reform (Feinberg 1993; USSC 2004).
Despite these equity concerns, few empirical studies have investigated the problem of
excessive uniformity. Much of the scholarly literature addressing this issue is limited to
legal analyses and case law anecdotes, and the limited empirical research suffers from
serious methodological shortcomings (e.g., sample selection, model misspecification).
Thus, using data from the 1997 Survey of Inmates in Federal Correctional Facilities
(SIFCF) (Bureau of Justice Statistics and Bureau of Prisons [BJS/BOP] 2000), this study
addresses two substantive research questions:
• How much influence does drug quantity have on sanctioning severity relative to other
legally relevant offense and offender characteristics?
• To what extent is excessive uniformity evident in federal drug sentencing outcomes?
In answering these questions, the study analyzes self-report survey data from guidelineera sentenced federal drug inmates.
Review of Empirical Research
An extensive body of case-law analysis and legal scholarship highlights how quantitydriven guideline and mandatory minimum sentencing can lead to excessive sentencing
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uniformity for defendants of widely varying culpability (Alschuler 1991; Froyd 2000;
Gaskins 2004; Goodwin 1992, Lutjen 1996; Nagel and Johnson 1994; Oliss 1995; Osler
2007; Schulhofer 1992a, b, 1993; Tobin 1999; Wasserman 1995; Weinstein 2003; Young
1990). In particular, this body of literature highlights the concern that federal drug sentences fail to account for important differences in culpability because drug quantity is the
single, overriding sentencing factor—what one commentator refers to as ‘‘the problem of
weight-centric guidelines’’ (Osler 2007). Yet, the empirical basis for the claim of excessive
uniformity is not well established. Indeed, despite the attention of legal scholars and
commentators, this issue has gone largely unaddressed by social scientists. In fact, only
three empirical studies of federal drug sentencing outcomes were located in the literature
that can speak to the issue of quantity-driven guidelines and excessive sentencing
uniformity.
In a noteworthy study, the Department of Justice (DOJ 1994) analyzed sentence length
outcomes for a sample of 767 ‘‘low-level’’ drug offenders sentenced in 1992. The results
showed that drug quantity was the strongest predictor of sentence length. Importantly, the
study correctly operationalized drug quantity using marijuana equivalencies to account for
the differential sentencing liability across drug types.3 The study also found that upperlevel distributors and launderer/manufacturers were sentenced more severely on average
than street dealers, couriers, and other minor role offenders (i.e., enablers, lookouts, users),
but that sentences within each of these two groupings were statistically indistinguishable.
While these results suggest meaningful sentencing differentiation between the most and
least culpable roles, statistically significant differences within these two broad groupings
were not evident. Unfortunately, serious bias in these estimates cannot be ruled out because
DOJ excluded certain high-level offenders from the sampling frame (e.g., offenders with
an aggravating role adjustment) (Roth 1994).
Semisch (2000) used stepwise regression to analyze, among other things, sentence
length outcomes from a sample of 2,657 drug offenders sentenced in 1995. The original
data were collected by the Sentencing Commission employing a stratified sampling plan
with disproportionate selection across drug types.4 Semisch’s results revealed that trial
disposition and criminal history were the strongest predictors of sentence length, respectively, with the relative effect of drug quantity ranking a relatively modest ninth. Indicators
for marijuana and crack cocaine were actually stronger predictors in the model because
drug quantity was not operationalized properly to account for marijuana equivalencies,
causing much of the variation to be ‘‘taken up’’ by the individual drug type measures.
Concerning role in the offense, which was operationalized as a 20-level ordinal variable
and was the fifth strongest predictor in the model, each level increase in culpability
predicted a net three-month increase in sentence length, or a predicted five-year net sentencing differential between the least and most culpable roles (ranging from user-only to
importer/high-level supplier).
Unfortunately, in addition to the poor operationalization of drug quantity, confidence in
Semisch’s (2000) results is diminished by certain methodological limitations in her analysis. First, she failed to account for the sampling design. Ignoring stratification (and
3
In practice, marijuana equivalencies are used to establish commensurate offense levels among the various
drugs and to obtain a single offense level for offenders trafficking in multiple drugs, each with its own
penalty scale. The sentencing guideline’s conversion rules stipulate, for instance, that 1 g of powder cocaine
equals 200 g of marijuana and 1 g of heroin equals 1,000 g of marijuana for sentencing purposes.
4
In particular, methamphetamine offenders were oversampled (representing 50% of the sample), with
marijuana (20%), heroin (20%), crack cocaine (20%), and powder cocaine (10%) offenders rounding out the
sample.
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159
clustering) can produce erroneous standard errors and confidence intervals; omitting
sampling weights can result in biased regression coefficients (Kreuter and Valliant 2007;
Lee and Forthofer 2006). Second, she used an ill-advised stepwise regression procedure.
Had she accounted for the sampling design in her analysis, it is likely that her final
stepwise model would have retained an alternate set of variables and/or produced different
relative effect sizes among the significant variables.
Finally, the Sentencing Commission (USSC 1991b) examined outcomes for 1,165
offenders sentenced in FY1990 who were facing a mandatory minimum drug and/or gun
charge. Specifically, the agency conducted a probit analysis predicting the probability of
receiving the applicable mandatory minimum sentence, while controlling for multiple roles
in the offense (high-level distributor, above-street dealer, street dealer, or low-level
helper), drug quantity (operationalized as the amount in excess of the quantity threshold for
the mandatory minimum), criminal history, and certain demographic characteristics.
Standardized coefficients were not reported, so it is not possible to discern relative effects.
Concerning the issue of unwarranted uniformity, low-level helpers were relatively less
likely than offenders with higher level roles to receive a mandatory sentence. However, the
above-street dealers and high-level distributors were less likely than the street dealers to be
sentenced to the requisite mandatory minimum. This is suggestive of the so-called
‘‘cooperation paradox,’’ which occurs when a knowledgeable high-level defendant provides substantial assistance to the prosecutor in exchange for a downward sentence
departure (Hrvatin 2002; Simons 2002). Unfortunately, USSC’s (1991b) estimates likely
suffer from omitted variable bias since relevant predictors such as the use of a firearm, drug
type, and plea status were eliminated from the final model due to either a lack of significance or problems with multicollinearity (see Langan 1992 for a similar critique and
reanalysis of the Commission’s data).
From the above review, it is apparent that the few studies that offer insight into the
problem of excessive uniformity suffer from serious methodological flaws, producing
inconsistent and potentially unreliable estimates of the relative effects of drug quantity and
role in the offense on sentencing outcomes. Given the extent of legal scholarship on the
issue, there is a glaring absence of relevant empirical research. The reason, in large part,
stems from data limitations in the Sentencing Commission’s main monitoring files, which
do not routinely capture key data elements that are critical to answering the questions
raised in this paper—namely, detailed information on the offender’s role in the offense.
Thus, the present study aims to overcome these limitations by analyzing self-report data
from the SIFCF, which represents a novel and rich data source for purposes of federal
sentencing research.
Theoretical Perspective and Research Hypotheses
Hofer and Allenbaugh’s (2003) ‘‘rationally reconstructed’’ theory of guideline sentencing,
harm-based modified just deserts, provides perhaps the most coherent theoretical framework for understanding the problem of excessive uniformity. According to this perspective,
federal sentences are explained first and foremost by a retributive rationale emphasizing
offense seriousness and proportionate punishment. In traditional desert theory, offense
seriousness is a function of the amount of harm caused by the offender and the offender’s
degree of culpability for that harm (von Hirsch 1985). Thus, within the context of federal
drug sentencing, harm-based modified just deserts asserts that harm (as measured by drug
quantity) is central to the guideline’s version of just deserts, whereas culpability (as
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measured by role in the offense) plays but a secondary role. Limited ‘‘modifications’’ to
this harm-based calculation are subsequently justified by a utilitarian rationale emphasizing
the incapacitation of higher risk offenders. In short, the federal drug sentencing guidelines
are based primarily on a retributive rationale that calls for proportionate punishment, in a
strict liability sense, to the quantity of drugs involved in the offense. A secondary incapacitation rationale then serves to extend sentences for the more high risk and dangerous
offenders.
The following hypotheses draw from this theoretical framework and the above-cited
empirical research. First, it is expected that drug quantity, as the primary measure of
offense seriousness, will have the strongest impact on sentence length, followed by indicators of dangerousness such as criminal history and the use of a firearm. Second, because
the guidelines and mandatory minimums do not adequately account for culpability differences, it is expected that sentences will be expressly uniform across different functional
roles in the offense.
Data and Methods
Data
The empirical questions outlined above were addressed using data from the 1997 Survey of
Inmates in Federal Correctional Facilities (SIFCF), a nationally representative survey of
federal inmates administered every 5–6 years.5 The 1997 SIFCF, fielded between June and
October 1997, collected self-report information from 4,041 federal inmates on a wide array
of topics, including conviction and sentencing information, offense characteristics, criminal history, socioeconomic status, and alcohol/drug use and treatment history (BJS/BOP
2000).
Sample
The analytic sample of interest for the present study includes guideline-era sentenced drug
offenders. Identification of this subgroup proceeded as follows. First, BJS/BOP identified
1,520 sample cases as involving primary drug offenses. Second, 210 cases were added to
this number based upon the author’s determination that BJS/BOP classified certain crimes
as nondrug offenses even though the underlying offense conduct was drug-related.6 For
example, BJS/BOP categorized drug-related money laundering and the use of a firearm
while trafficking drugs as public order offenses even though the sentencing guidelines
handle these offenses under the drug trafficking guideline (USSG §2D1.17). For instance,
the guidelines stipulate that the sentence for a defendant convicted of money laundering
should be based on ‘‘the underlying offense from which the laundered funds were derived’’
(USSG §2S1.1(a)(1)). Finally, 59 cases were excluded in which the inmates were either
5
The 1991 SIFCF was the first in the series, and the 2004 SIFCF was publicly released in 2007 (after this
research was initiated). This study used the restricted access version of the 1997 SIFCF, as it contains
necessary sampling design variables not available in the unrestricted version.
6
Notably, interviewers flagged three-quarters of these 210 inmates as drug offenders during the course of
the interview only to be subsequently reclassified, supporting the decision to include these cases in the
subpopulation of interest.
7
This and all subsequent references to specific United States Sentencing Guidelines (USSG) refer to those
published in the 1997 Guidelines Manual (USSC 1997a).
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161
awaiting sentencing or arrested prior to the effective guidelines date of November 1,
1987—leaving a final analytic sample of 1,671 drug inmates.8
Variables
Weighted descriptive statistics for all variables are presented in Table 1. The Appendix
provides detailed operational definitions of the variables discussed briefly here. The
dependent variable, sentence length, is a measure of the maximum sentence imposed (in
months).
The independent variables encompass a range of offense, offender, and case processing
characteristics relevant to sentencing research. The legally relevant offense factors include
measures for drug quantity expressed in marijuana equivalent grams, the primary drug
type involved in the offense, and the offender’s functional role in the offense. Also
included are indicators for the receipt of an aggravating or mitigating role adjustment, the
safety valve departure for certain low-level defendants, and either of the two firearm
sentence enhancements (FSEs) for using a gun while trafficking drugs (i.e., a concurrent
mandatory minimum 924(c) firearm conviction or the guideline FSE). Finally, there is a
measure of the offender’s criminal history category.
Case processing variables include indicators for case disposition (i.e., a plea, bench
trial, or jury trial), a plea-based charge bargain to a reduced charge or fewer counts, and
pretrial release from custody. Sociodemographic variables include the usual suspects in
sentencing research (i.e., race/ethnicity, gender, and age at offense), as well as measures of
noncitizen status and educational level.
Analytic Approach
The analysis for this study proceeded in several stages. First, a design-based truncated
regression model was estimated predicting sentence length from the vector of offense, case
processing, and sociodemographic independent variables. This model accounts for the
survey’s complex sampling design and truncated response distribution. Second, residual
analysis was performed to ensure key model assumptions were met, namely, that errors
were normally distributed with constant variance (Cong 1999; StataCorp 2007b). Third,
model predictions of sentence length, stratified by role in the offense and adjusted for other
covariates, were generated to investigate the presence of excessive uniformity.
Design-Based Analysis
The 1997 SIFCF sample was selected using a stratified, two-stage cluster sampling design,
first selecting prisons and then inmates within the selected prisons (BJS/BOP 2000). In the
first stage, one male and two female facilities were selected with certainty. The remaining
facilities were grouped into seven strata based on gender and security level. Within each
stratum, facilities were systematically selected with probability proportional to size. In this
first stage, 40 of 135 correctional facilities were selected as primary sampling units (PSUs).
In the second stage, systematic random samples of inmates were selected from within each
of the 40 sampled facilities. Since drug offenders account for roughly six in ten federal
prisoners, they were undersampled by one-third to enable efficient parameter estimation for
8
In actuality, this cutoff only excluded a handful of cases sentenced over a sporadic number of prior years,
as 1989 represents the first year of admission in the analytic sample of 1,671.
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Table 1 Weighted descriptive statistics: federal drug inmates, 1997 (N = 55,481)
Variables
%
Mean (median)
SE
Base N
Dependent variable
Sentence length in months
124.70 (97)
2.30
52,962
Offense factors
Marijuana equivalent grams (71,000)
53,835 (800)
16,446.13
Drug type
Heroin
7.7
Methamphetamine
11.0
Crack cocaine
26.9
Powder cocaine
37.1
Marijuana
14.9
Other drugs
2.4
Role in the offense
53,228
Importing
10.4
Producing
5.2
Money laundering
48,388
54,537
3.0
Wholesaling
19.8
Retailing
20.6
Possessing
13.5
Unspecified role
27.6
Aggravating role adjustment
6.0
54,457
Mitigating role adjustment
7.6
54,457
924(c) firearm conviction
9.3
55,448
10.1
55,143
Guideline FSE
Criminal history category
Safety valve
2.07 (1)
0.06
9.1
55,481
55,481
Case processing characteristics
Case disposition
Plea
Bench trial
55,096
73.0
4.3
Jury trial
22.7
Charge bargain
53.4
55,000
Pretrial release
36.3
55,307
Sociodemographic characteristics
Race/ethnicity
55,481
White
22.7
Black
41.6
Hispanic
32.5
Other
Male
3.2
91.5
55,481
Age at offense
33.08 (32)
0.42
55,466
Educational level
10.91 (12)
0.11
55,061
Noncitizen
123
22.2
55,430
J Quant Criminol (2009) 25:155–180
163
less prevalent offense types. The final response rate was 90.2%, with all 40 correctional
facilities and 4,041 of 4,479 selected inmates agreeing to participate (BJS/BOP 2000).
Survey methodologists caution that failure to account for a survey’s design elements
(i.e., weights, stratification, and clustering) can produce biased and misleading results
(Chambers and Skinner 2003; Korn and Graubard 1999; Kreuter and Valliant 2007; Lee
and Forthofer 2006; Skinner et al. 1989). Therefore, design-based analytic methods were
employed using Stata’s -svy- features to account for the SIFCF’s stratified two-stage
design. Specifically, first stage design parameters were set to account for 10 strata and 40
PSUs, with the three certainty PSUs serving as their own strata (StataCorp 2007a, b).
Individual inmates represented second stage sampling units. The final weights reflect the
base probabilities of selection, adjusted for nonresponse, drug offense undersampling, and
other auxiliary design factors (BJS/BOP 2000). Finite population corrections (FPCs) were
not used because the necessary auxiliary variables were not readily available in the SIFCF
dataset. However, this is a conservative approach since FPCs reduce the sampling variance
of statistics (Groves et al. 2004). Finally, design-based subgroup estimation procedures
were used to compute variance estimates using the full sample and point estimates using
the target subsample of drug offenders (Korn and Graubard 1999; Lee and Forthofer 2006).
Analysis of Truncated Data
Limited response distributions are defined by nonrandom selection on the dependent
variable in which the realized distribution is not fully representative of the true range of
values in the original population (Berk 1983; Berk and Ray 1982). In the present study, the
realized SIFCF sample is a nonrandom subset of more severely sanctioned offenders
relative to the population of all imprisoned offenders. That is, since inmates with shorter
sentences are released earlier from prison, they are systematically underrepresented in the
SIFCF sample. Figure 1 demonstrates this nonrandom selection process as a function of
the prison admission year. It can be shown, for example, that three-quarters of the
remaining inmates admitted in 1997, half of those admitted in 1993, and just one-quarter
admitted in 1991 had sentences of ten years or less.
Several mechanisms characterize the data generating process in samples with limited
response distributions: truncation, censoring, and incidental selection9 (Breen 1996;
Bushway et al. 2007; Maddala 1983). The SIFCF sample suffers from left-truncation, or
truncation from below, because data is not available on convicted offenders sentenced to
probation or those sentenced to short prison terms and since released. Employing ordinary
least squares (OLS) estimation in this instance would overestimate the true effects of
offense and offender factors on sentence length. To see why, take the regression equation
yi ¼ bXi þ ui
ð1Þ
where yi is sentence length, Xi is a vector of independent variables, and the error term
u * N(0, r2). For left-truncated data characteristic of the SIFCF, we would observe yi only
if yi C c, where c is a given threshold on sentence length. This constraint implies that
9
Truncation occurs when a sample is drawn from a population that has been restricted at some threshold
value, c, of the response variable, y. Thus, sample data for all x and y variables are available only for cases in
which y does not exceed c. Censoring, on the other hand, occurs when a sample is drawn from the full
population, but the values of the response variable y have been constrained at some threshold value, c of y.
Truncation and censoring are sometimes referred to as explicit selection mechanisms, because whether y is
observed depends on the values of y itself. In contrast, incidental selection occurs when a sample observation for y is observed only when another variable, z, achieves some threshold value, c.
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Sentence Length in Years
100
10
1
0
1989
1990
1991
1992
1993
1994
1995
1996
1997
Controlling Admission Year
Fig. 1 Distribution of sentence length by admission date, federal drug inmates, 1997
bXi ? ui C c, or equivalently that ui C c - bXi. Therefore, the expectation, E(ui | ui C
c - bXi), cannot equal zero; in fact, the residual will be correlated with the set of independent variables, Xi, producing inconsistent b estimates through the method of OLS
(Breen 1996; Maddala 1983). In the present context, b will be positively biased.
In order to counter this bias, the maximum likelihood-based truncated regression model
(-truncreg- in Stata) is employed for the analysis (Breen 1996; Maddala 1983; Cong
1999). Parameter estimates in truncated regression are derived from the observed part of
the distribution, accounting for the probability that an observation falls within a specified
range (StataCorp 2007a). The general log likelihood that is maximized by this model is
n n
2 X
n
1 X
d xj b
c xj b
2
yj xj b log U
U
ð2Þ
ln L ¼ logð2pr Þ 2
2
2r j¼1
r
r
j1
where U is the distribution function of the standard normal distribution, c is the lower limit,
and d is the upper limit (StataCorp 2007b). In the current context, there is no upper limit d
and, as Fig. 1 demonstrates, the lower limit c is not constant, occurring at greater limits the
earlier the prison admission date.
Accordingly, this uneven left-truncation was modeled as an observation-specific indicator of time served. In other words, this specification accounts for the differential
probabilities of individual cases being observed given the amount of time already served.
This likelihood will differ, for example, for two offenders with 60-month sentences
(y = 60) where one has already served half his time (c = 30) and the other just five
months (c = 5) (Cong 1999). Under this specification, 24 cases in which the inmates
served more time than their court-imposed sentence (e.g., due to additional violations in
custody) will not factor into the estimates because they will fall in the ‘‘unobserved’’ part
of the distribution. For example, an offender who was sentenced to 60 months in prison,
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165
but has already served 65 months due to committing new infractions while in custody will
be dropped from the estimation sample. This loss of information is minor relative to the
noted statistical advantages.
Standardized Regression Coefficients
One of the central objectives of the present study is to assess the relative importance of the
independent variables in explaining variation in the dependent variable, sentence length.
Answering this type of question commonly involves computing fully standardized
regression (i.e., ‘‘beta’’) coefficients for all independent variables, where bx ¼ rx bx =ry .
The interpretation is that for a one standard deviation change in x, there is a corresponding
bx standard deviation change in y. Although this method is commonly employed in sentencing research, indiscriminate use of standardization can cause its own problems (Berk
2004; Gelman 2008). While this study does not purport to address all the problems of
standardization, it does focus on two particular areas of concern: (1) the standardization of
truncated regression coefficients and (2) the standardization of multicategory nominal
variables.
The general formula for computing beta coefficients does not hold for limited dependent
variable models such as truncated regression because y* is a latent variable and it is not
possible to compute ry* directly. Roncek (1992) proposed an analogue to the standardized
regression coefficient that uses the standard deviation of y* conditional on x. However,
Long (1997) argued against the use of Roncek’s approximation, asserting that the
d ^
^2e .
^2y ¼ b^0 VarðxÞ
bþr
unconditional variance of y* should be computed instead, where r
Accordingly, Long’s method is used here to compute fully standardized truncated
regression coefficients.10
Standardization methods also do not routinely report the combined effects of a multicategory nominal variable with k groups, entered into a regression equation as k - 1
dummy variables (e.g., race/ethnicity). Thus, to assess the relative influence of a single
concept modeled as a ‘‘compound of dummy variables,’’ this study follows the method of
Eisinga et al. (1991). This procedure involves running an initial full model regression and
predicting a new composite variable T as the linear combination of the concept’s individual
dummy variables. The composite variable T is then entered into a second regression
equation given by
y ¼ a þ b5 ðb1 D1 þ b2 D2 þ þ bk1 DK1 Þ þ b4 Z þ e
¼ a þ b5 T þ b4 Z þ e
ð3Þ
ð4Þ
Equation 3 is identical to Eq. 4 in the intercept a, the coefficient for Z, and the proportion of variance explained. More importantly, the standardized regression coefficients
will provide an estimate of the absolute relative importance of T and Z.11 Notably the
procedure can be extended to account for several multicategory nominal variables within a
single regression.
10
Note, however, that whether one uses Roncek’s (1992) or Long’s (1997) method has no affect on the
relative rankings of the standardized regression coefficients. They will simply differ in effect size by an
order of magnitude that is defined by the ratio between ry*|x and ry*.
11
The standardized regression coefficient for T differs from an ordinary standardized regression coefficient
in an important way. Since T predicts Dk perfectly, the unstandardized regression coefficient for T will equal
1, which reduces the standardized regression coefficient equation for T to bT ¼ rT =ry . Thus, as an artifact of
the procedure, the standardized regression coefficient for T will always be positive.
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Additional Model Specifications
The model employed a double-log functional form. Specifically, both the dependent variable, sentence length in months, and the key independent variable, marijuana equivalent
grams, were natural log transformed prior to analysis to achieve normal distribution
assumptions and mitigate the effect of outliers. The statistical and theoretical advantages of
this functional form are well documented in the sentencing literature (Bushway and Piehl
2001; McDonald and Carlson 1993).
Model fit was assessed in several ways. First, the standardized residuals12 were
inspected for evidence of outliers, nonnormality, and heteroskedasticity. Failure to meet
distributional assumptions with limited dependent variable models in particular poses a
serious threat of bias (Bushway et al. 2007; Smith and Brame 2003; Sullivan et al. 2008;
Winship and Mare 1992). Second, since Stata does not report an R2 analog for the designbased truncated regression model, McKelvey and Zavoina’s pseudo-R2 was calculated per
Long and Freese (2001). Third, the ancillary truncated regression statistic, sigma, is
analogous to the standard error of the estimate in OLS regression. Thus, smaller values of
sigma are associated with better model fit (Warner 2007).
Results
A design-based truncated regression model was estimated predicting (log) sentence length
from offense, case processing, and sociodemographic factors. Analysis of the standardized
residuals from this initial model revealed 14 cases with values exceeding |3.0|. Close
inspection of the data revealed some mismeasurement in all 14 cases.13 Thus, these cases
were dropped from the analysis and a second truncated regression model was estimated.
Inspection of the residuals for this second regression revealed no apparent violations of
model assumptions. Moreover, both the pseudo-R2 (0.48 vs. 0.50) and sigma (0.61 vs. 0.57)
statistics showed slightly improved fit, so the second regression was taken as the final
analytic model. The substantive conclusions arrived at below regarding the influence of
drug quantity or the presence of excessive uniformity did not depend on the inclusion or
exclusion of these 14 cases (although indicators for safety valve and white achieved
significance in the second model).
In addition to the 14 outlier cases, 374 observations were dropped from the analysis due
to missing data and 24 observations were left-truncated due to actual time served being
longer than the original sentence—leaving a final unweighted estimation sample of
n = 1,259. The results indicate the overall model is significant, F(29, 2) = 171.17,
p \ 0.01, with 50% of the variance explained (M&Z R2 = .50). Table 2 displays statistics
12
The standardized residuals were calculated by taking the z scores of the difference between the in-sample
truncated observed and predicted values, y - y*, where y* = c if xb ? u \ c, y* = d if xb ? u [ d, and
y* = xb ? u otherwise.
13
For example, one inmate reported a sentencing range of 5–40 years, but the case characteristics do not
appear to support the maximum sentence (which was the value taken in the operationalization of sentence
length). Another case shows a sentencing range of 30 months to 20 years, which appears to be a data entry
error for 20–30 months. In other cases, there was probable mismeasurement of drug quantity. For example,
three of the fourteen offenders were sentenced for distributing LSD, but the conversion of ‘‘papers, squares,
windowpanes, or cubes’’ of LSD into marijuana equivalencies is inherently difficult, partly because actual
case law has varied through the years with respect to calculating the weight of LSD when it is embedded in a
carrier medium such as blotter paper or sugar cubes (Quivey 1993).
123
0.068
0.074
Retailing
Unspecified role
0.061
Criminal history category
0.536
Jury trial
1.000
0.142
Bench trial
Case dispositionb (reference: plea)
Case processing characteristics
-0.146
Guideline FSE
Safety valve
0.340
0.199
USC 924(c) conviction
0.145
0.092
Wholesaling
-0.039
0.065
Money laundering
Mitigating role adjustment
0.165
Producing
Aggravating role adjustment
0.036
Importing
1.000
0.064
Role in the offenseb (reference: possessing)
0.068
-0.373
Marijuana
Other drugs
0.061
0.109
0.099
0.047
0.016
0.039
0.042
0.076
0.065
0.049
0.070
0.060
0.116
0.116
0.064
0.537
0.136
0.051
-0.056
Crack cocaine
0.068
0.087
0.037
-0.054
Methamphetamine
Heroin
0.151
0.082
1.000
0.009
SE
Drug typeb (reference: powder cocaine)
b
ln(marijuana equivalent grams)
Offense factors
Independent variables
Table 2 Truncated regression results predicting ln(sentence length in months), federal drug inmates, 1997a
8.84***
1.30
10.08***
-3.12**
3.80**
5.14***
8.10***
-0.51
2.23*
1.53
0.98
1.54
0.69
1.42
0.56
1.86
0.47
-5.45***
-1.09
0.54
-0.62
6.64***
9.62***
t-Value
0.337
0.042
0.336 [2]
-0.070 [13]
0.140 [7]
0.093 [9]
0.143 [6]
-0.017 [17]
0.055 [16]
0.051
0.043
0.057
0.016
0.056
0.018
0.057 [15]
0.012
-0.208
-0.039
0.017
-0.022
0.204 [4]
0.394 [1]
b [rank]
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167
123
123
0.570
Sigma
0.017
0.185
0.054
0.005
0.002
0.089
34.39***
16.91***
-1.90
-3.28**
3.28**
3.73**
-1.34
-2.78**
-2.59*
3.50**
-8.00***
-2.57*
t-Value
–
–
-0.066 [14]
-0.082 [12]
0.084 [11]
0.143 [5]
-0.052
-0.119
-0.095
0.124 [8]
-0.253 [3]
-0.092 [10]
b [rank]
The row statistics reported for these composite variables are derived from a second regression model that substituted the predicted linear combination of the compound of
dummy variables estimated by the initial regression model. The statistics for the individual dummy variables reflect the initial regression estimation
b
A total of 374 subgroup sample cases were dropped from the analysis due to missing data, 14 cases were dropped as outliers, and another 24 cases were left-truncated due to
actual time served being longer than the original sentence
a
* p \ 0.05; ** p \ 0.01; *** p \ 0.001
Model: F(29, 2) = 171.17; p \ 0.01; McKelvey and Zavoina’s pseudo-R = .50. Number of strata = 10; Number of PSUs = 40; Estimates weighted by the ‘final weight’
variable in the SIFCF: Subpopulation: n = 1,259, N = 42,937
2
3.129
-0.103
Non-U.S. citizen
Constant
-0.018
0.006
Age at offense
Educational level
0.334
Male
0.060
-0.165
-0.207
Hispanic
Other race
0.155
0.057
0.286
0.043
0.047
SE
-0.148
White
Raceb (reference: Black)
1.000
-0.120
-0.343
Charge bargain
Pretrial release
Sociodemographic characteristics
b
Independent variables
Table 2 continued
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J Quant Criminol (2009) 25:155–180
169
for the unstandardized regression coefficients (b), standard errors, t-values, and standardized regression coefficients (b). Ranks of effect size are also denoted in the table; for
composite variables, only the ranking for the global effect size is reported.
The Relative Influence of Offense, Offender, and Case Processing Factors on Sentence
Length
As expected, drug quantity was the strongest predictor in the model (b = 0.39). By contrast, the effects of drug type were relatively weak and insignificant. The exception was for
marijuana offenders, who received 37% shorter sentences on average than the reference
group of powder cocaine offenders. This outcome indicates that, all else equal, marijuana
offenders are dealt with relatively less harshly in federal courts—even after controlling for
the unequal quantity-based penalties associated with different controlled substances.
Overall, the linear combination of drug type was significant in the model, although the
effect of marijuana drives this result. Thus, with a composite effect size ranking fourth
(b = 0.20), drug type (operating mainly by way of the more lenient treatment of marijuana
offenders) joins drug quantity as one of the two strongest offense-related predictors of
sentence length.
Not unexpectedly, the offender’s role in the offense had relatively little impact on
sentence length, as sentences for all roles were statistically indistinguishable from the
reference group of possessors. Adjusted Wald tests also revealed no significant sentencing
differences between any other matched pair of roles (tests not shown). The combined effect
for role is also insignificant, and the standardized coefficient ranks near the bottom (15th
out of 17). Thus, by all measures, the effect of role on sentence length is insignificant and
weak. These outcomes are consistent with the predictions of harm-based modified just
deserts.
Consistent with prior sentencing research (e.g., Albonetti 1997, 2002; Everett and
Wojtkiewicz 2002; McDonald and Carlson 1993), several other legally relevant offense
factors had comparatively strong and significant effects on sentence length. These include a
924(c) conviction (b = 0.14, rank 6th), criminal history (b = 0.14, rank 7th), and the
guideline FSE (b = 0.09, rank 9th). Other significant but less influential legal predictors
included the safety valve (b = -0.07, rank 13th) and the aggravating role adjustment
(b = 0.06, rank 16th). Despite having an effect in the expected direction, the only legally
relevant guideline sentencing factor that was not significant in the model was the mitigating role adjustment—a finding that is likely due to poor measurement of this construct.
Notably, two case processing factors—case disposition (b = 0.34) and pretrial release
(b = -0.25)—were the second and third strongest factors, respectively, after drug quantity. The outcome for the composite variable, case disposition, is driven by the strong effect
of conviction by a jury versus a judge (relative in each case to pleading guilty). These
results demonstrate both a substantial ‘‘trial penalty’’ and the serious disadvantage of
pretrial detention.14 Charge bargaining with the prosecutor, in contrast, bestowed a significant sentencing advantage (b = -0.09, rank 10th). Finally, it is noteworthy that even
after controlling for relevant offense and case processing factors, significant and influential
14
One reviewer was surprised by this latter finding, and suggested that pretrial status might be mediating
the relationship between role and sentence length. While role and pretrial release are significantly related
[Design-based v2 = F(4.14, 124.16) = 2.50, p \ 0.05], rerunning the regression with pretrial status
removed as a predictor changed none of the substantive results.
123
Quantity-Based Offense Level
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38
36
34
32
30
28
26
24
22
20
18
16
14
12
10
8
6
30
40
50
60
70
80
90
100
110
120
130
140
150
Predicted Mean Sentence with 95% C.I.
Fig. 2 Predicted sentence by quantity-based offense level, federal drug inmates, 1997
disparities remained across race, age, gender, and education despite the guidelines’ putative neutrality along these dimensions.
Evidence for Excessive Uniformity in Federal Drug Sentencing
This section examines additional graphical evidence for the main hypotheses concerning
the effects of drug quantity and role in the offense on sentence length. Figure 2 shows the
adjusted mean sentence length (and 95% confidence intervals) from the full regression
model presented in Table 2, stratified by quantity-based offense level.15 The difference
between adjacent drug offense levels is not that marked since the top of the guideline range
for any given offense level generally serves as the bottom of the range for the next offense
level. However, the graph clearly depicts that the predicted mean sentence increases
substantially over the 17 drug offense levels. Specifically, holding all other variables at
their means, the predicted mean sentence increases from 34 to 129 months between offense
levels 6 and 38—a difference of almost eight years.
In contrast, Fig. 3 shows the adjusted mean sentence length stratified by the offender’s
role in the offense. As portrayed in the graph by the overlap of the 95% confidence bands,
the predicted mean sentences across all roles are statistically indistinguishable. Less than
14 months in the average sentence separates all functional roles (ranging from roughly
77 months for possessing to 91 months for producing). Moreover, for wholesalers and
retailers who together account for about 40% of incarcerated drug offenders, just a twomonth average sentencing difference exists between the former who distribute to other
dealers and the latter who sell to end-users.
15
For drug offenses, the base offense levels range from 6 to 38 where level 6 corresponds to less than 250
marijuana equivalent grams and level 38 corresponds to 30,000 or more marijuana equivalent kilograms.
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171
Importing
Role in the Offense
79.5
Producing
90.5
Laundering
81.8
Wholesaling
84.1
Retailing
82.1
Possessing
76.7
Unspecified Role
82.6
60
70
80
90
100
110
120
Predicted Mean Sentence with 95% C.I.
Fig. 3 Predicted sentence by role in the offense, federal drug inmates, 1997
One possible explanation for this lack of effect is that drug quantity is implicitly
controlling for role in the offense. Indeed, one of the original rationales for Congress and
the Sentencing Commission placing primary emphasis on drug quantity as a sentencing
factor was that it would serve as a measurable, objective proxy for a trafficker’s role in the
offense (Hofer 2001; USSC 1995). This proposition does not hold in the current sample,
however, as drug quantity is not significantly correlated with role in the offense (Spearman’s rho = -0.03, p = 0.22). Moreover, a graphical examination of the distribution of
drug quantity by role in the offense (not shown) confirmed this lack of association. Overall,
these findings are consistent with the predictions of harm-based modified just deserts and
offer fairly robust support of the claim of unwarranted or excessive uniformity in federal
drug sentencing.
This finding raises other important concerns. If role is not independent of race and
gender, for example, then guideline restrictions on the ability of judges to fully account for
culpability might actually contribute to the observed race and gender biases. Indeed, a post
hoc analysis revealed significant associations between both role and race [Design-based
v2 = F(11.28, 338.27) = 12.59, p \ 0.001] and role and gender [Design-based
v2 = F(3.10, 93.05) = 4.63, p \ 0.01]. In particular, among those with a clearly specified
role, 55% of Blacks were either retailing or in simple possession of drugs at the time of
their arrest compared to 38% of Whites and 42% of Hispanics. Similarly, 48% of males
versus 36% of females were either retailing or in possession of drugs.16 This suggests that
an indirect consequence of excessive uniformity is the exacerbation of racial and gender
disparities.
16
While this might seem rather surprising, the modal group among females was actually importing (28%),
which speaks to their more likely roles as couriers and mules.
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Discussion
This study addressed the central problem that the rules and procedures of federal drug
sentencing fail to ensure similar treatment for similar offenders and different treatment for
different offenders. Data from the 1997 Survey of Inmates in Federal Correctional
Facilities were used to investigate the premise that an overemphasis on quantity in the drug
sentencing guidelines, in concert with quantity-based mandatory minimums, leads to the
problem of excessive uniformity in which offenders of differing culpability receive similar
sentences. Toward this end, a design-based truncated regression model was estimated
predicting sentence length from an array of relevant offense, offender, and case processing
variables.
Methodologically, this study used a novel data source for purposes federal sentencing
research. To the best of the author’s knowledge, this is the first study to use the SIFCF to
examine sentencing outcomes with multivariate regression methods. As such, the SIFCF
and its companion data series at the state and local level (i.e., Survey of Inmates in State
Correctional Facilities and the Survey of Inmates in Local Jails) represent untapped
sources of data for sentencing research. Thus, the Inmate Surveys can be used to triangulate the findings of more commonly analyzed datasets, such as the Sentencing
Commission’s monitoring files, or to provide state-level estimates where no similar data
exist.17 This study also highlighted advanced analytic methods for analyzing a truncated
response distribution using complex survey data. Moreover, this research used an appropriate method for standardizing truncated regression coefficients, and also dealt with the
problem of standardizing multicategory nominal variables.
Substantively, the results revealed that drug quantity was the primary determinant of
sentence length, with the other legally relevant offense factors significantly influencing
sentences to lesser degrees and in expected ways. That drug quantity was the strongest
predictor of federal drug sentences was expected given its central role under the guidelines
and mandatory minimums. Prior research has also found that quantity is a significant and
important predictor of sentence length (McDonald and Carlson 1993; Peterson and Hagan
1984; Rhodes 1991; Semisch 2000), but this study substantiates an earlier Department of
Justice (1994) study that found drug quantity plays the strongest role.
As predicted by the harm-based modified just deserts theory of guideline sentencing,
this study also demonstrated that quantity-driven sentencing, coupled with culpabilitybased adjustments that are too limited in scope, leads to excessively uniform sentences for
offenders of widely differing culpability and responsibility in the drug trade. This is not
surprising given the overall narrow applicability of both the safety valve (Froyd 2000) and
aggravating and mitigating role adjustments (USSC 1990, 1992). The issue of excessive
uniformity warrants further investigation, however, since the data used for this study could
not be used to make reliable distinctions between, for example, high-level importers and
low-level courier/mules. In this respect, question construction and phrasing in future
Inmate Surveys could be greatly improved.18
17
The sampling methodology used in the 1997 Survey of Inmates in State Correctional Facilities, for
instance, allows independent analyses to be conducted for California, New York, and Texas.
18
For example, the role in the offense question could be revised to read ‘‘For the [CONTROLLING
OFFENSE] for which you are incarcerated, were you…’’ instead of ‘‘At the time of your arrest for
[CONTROLLING OFFENSE] were you…’’ (see Appendix). The question could also be made open-ended
to allow data to be collected on unspecified roles.
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Another important finding of this study is the apparent linkage between role and both
race and gender. In effect, because role is related to these extralegal factors, the guideline’s
promotion of culpability-based uniformity contributes indirectly to racial and gender
disparities. This mechanism differs from that suggested by the prominent focal concerns
perspective on judicial decision making, which posits that in the absence of reliable
information on the focal concerns of offender blameworthiness and dangerousness,
unwarranted disparity is introduced when judges adopt a ‘‘perceptual shorthand’’ based on
stereotypes that are themselves linked to ‘‘race, ethnicity, sex, and age’’ (Hartley et al.
2007:59). The findings of this study suggest that, for federal drug offenders at least, race
and gender disparities are linked in part to legal sentencing rules that ‘‘mandate equality’’
along a theoretically important but undervalued sentencing factor.
The finding that discretionary plea and charge bargaining practices have stronger
independent effects on sentence length than many legally relevant offense factors may
further compound disparate sentencing outcomes. For example, case disposition was
second only to drug quantity in effect, and charge bargaining had a similar effect to
receiving the guideline FSE. Although empirical research suggests that prosecutorial
practices in drug cases are often framed by a desire to achieve fair sentences for low-level
or otherwise sympathetic defendants (Bowman and Heise 2002; Nagel and Schulhofer
1992; Schulhofer and Nagel 1989; USSC 1991a), legitimate concerns of reintroduced
disparity arise when prosecutors grant and judges accede to plea and charge bargains that
are conducted in an ad hoc and largely unreviewable manner (Stith and Cabranes 1998;
Wilmot and Spohn 2004).
In summary, these findings suggest that the dominance of drug quantity as a sentencing
factor, when coupled with guideline-based sentencing adjustments that fail to adequately
account for important offender differences in culpability, leads to excessive uniformity
where ‘‘the big fish, the minnows, and the superminnows wind up in the same sentencing
boat’’ (Schulhofer 1992b:170). Consistency and proportionality in sentencing appear to be
further compromised by the interaction between role and race/gender, as well as the effects
of ad hoc plea and charge bargaining practices.
Conclusion
Perhaps the clearest and most far-reaching policy implication of this research is that the
central, organizing role of drug quantity in federal drug sentencing needs to be rethought.
Indeed, effectively dealing with the problem of quantity-driven sentencing will likely
require the wholesale restructuring of how sentences for drug offenders are determined.
Numerous proposals in the literature suggest how such reforms might be structured in
practice (e.g., O’Dowd 1999; Osler 2007; Wasserman 1995)—some of which call for
abolishing the drug mandatory minimums, and all of which call for deemphasizing the
influence of drug quantity by placing greater importance on culpability in sentencing.
The results of the current study provide empirical backing for such proposals, which are
particularly timely given recent sentencing law developments. These include Supreme
Court decisions striking down the mandatory provisions of the SRA (Booker v. United
States 2005) and affirming greater judicial discretion to sentence outside the guideline range
in order to avoid unwarranted disparity (United States v. Kimbrough 2007), as well as the
2007 guideline amendment reducing the notorious 100:1 quantity ratio for crack and powder
cocaine to 20:1 (USSC 2007b). All of these developments hint that additional reforms to
federal drug sentencing laws may be more politically viable than even a few years ago.
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The study also provides strong support for Hofer and Allenbaugh’s (2003) harm-based
modified just deserts theory of guidelines sentencing. The framework predicts that
excessive uniformity results from guideline rules that place relatively little emphasis on
culpability-based distinctions in offender behavior. That excessive uniformity might
contribute to race and gender disparities warrants further theoretical elaboration and
empirical investigation.
Future research should attempt to replicate this study’s findings with different data
sources. For example, the 2004 Survey of Inmates in Federal Correctional Facilities has
recently become publicly available, and the Commission’s 2005 Drug Sample—a special
USSC data collection—is available to researchers upon request and approval (USSC
2007a). Each has its own limitations: the 2004 Inmate Survey is subject to all the errors of
reporting and data collection inherent in survey research, and the 2005 Drug Sample
includes only data on crack and powder cocaine offenders. However, given the disparate
impacts of excessive uniformity and the current policy environment, greater empirical and
theoretical attention to this issue is warranted.
Appendix: Operational Definitions and Criterion Validity of SIFCF Variables
Sentence length is a measure of the maximum sentence imposed (in months) derived from
the following question: ‘‘What is the total maximum sentence length to prison for ALL the
consecutive sentences you are serving?’’ In cases where a range was provided, the maximum value was taken. Also, data for inmates with a life or death sentence (n = 50) were
coded as missing.
For each drug involved in the offense, respondents were asked ‘‘Approximately what
amount of [DRUG] was involved?’’ Following guideline practice, drug amounts were
converted into marijuana equivalent grams. The various reported metrics (e.g., ounces,
pounds) were first converted into grams. Amounts reported in nonstandard metrics were
converted using criteria from either the sentencing guidelines (USSG §2D1.1(c)) or supplementary information (e.g., Office of National Drug Control Policy 1995-2002). For
example, one bag of heroin was assumed to be 0.1 g; one rock or vial of crack cocaine to be
0.2 g; and one marijuana joint to be 0.5 g. Marijuana equivalencies were then calculated for
each drug type according to the guideline’s Drug Equivalency Tables (USSG §2D1.1(c))
and then summed to produce a single measure of marijuana equivalent grams.
The primary drug type involved in the offense was identified from the following
question: ‘‘You said that you were serving time for [CURRENT OFFENSES]. What drugs
were involved?’’ The data were collapsed into the following six drug categories: heroin,
methamphetamine (including amphetamines), crack cocaine, powder cocaine, marijuana
(including hashish), and other drugs (methaqualone, barbiturates, tranquilizers, PCP, LSD
or other hallucinogens, etc.). For the 11% of drug inmates reporting involvement with
multiple drugs, the primary drug was coded as the one with the greatest sentencing
potential based on quantity.
The inmate’s functional role in the offense was derived from the following question:
At the time of your arrest for [CONTROLLING OFFENSE] were you: (1) Importing
or helping others import illegal drugs into the United States? (2) Illegally manufacturing, growing or helping others manufacture or grow drugs? (3) Laundering
drug money? (4) Distributing or helping to distribute drugs to dealers? (5) Selling or
helping to sell drugs to others for their use? (6) Using or possessing illegal drugs?
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175
Respondents were asked to report all applicable roles.19 Notably, about one-quarter of
the respondents directly answered ‘no’ to all six roles. Question phrasing (i.e., ‘‘At the time
of your arrest…’’) largely explains why so many respondents failed to acknowledge a role,
since conspiratorial acts are often completed well before an arrest takes place. Not representing missing data, these cases were coded into a generic ‘‘unspecified role’’ category.
Following prior research by the Sentencing Commission, the most serious functional role
was selected according to the following rank order: importing, producing, money laundering, wholesaling, retailing, possessing, and unspecified role (Miller and Freed 1994;
USSC 2002, 2007a).
The following two survey items were used to create two dummy variables representing
aggravating and mitigating role adjustments: ‘‘In the year before your arrest on [DATE],
were you a part of any group or organization that engaged in drug manufacturing,
importing, distribution or selling?’’ and, if yes, ‘‘Which of these best describes your role in
that group or organization: a leader or organizer; a middle man; an underling, such as a
carrier, runner, etc.; a seller; other?’’ Thus, for offenders involved in a drug distribution
organization, an aggravating role adjustment was indicated for leader/organizers or middlemen and a mitigating role adjustment was indicated for underlings, sellers, and other
peripheral participants.
Federal drug offenders are subject to two different firearm sentence enhancements
(FSEs) for using or possessing a gun while trafficking drugs. The first, a 924(c) firearm
conviction—a separate provable offense carrying a mandatory five year sentence
enhancement—was indicated for drug offenders convicted of a concurrent weapon offense.
The second, the guideline FSE—a two-level enhancement to the base offense level (or
roughly a two-year increase) imposed by the judge at sentencing—was indicated for drug
offenders without a concurrent weapon conviction who answered affirmatively to the
following question: ‘‘Did you receive an increase in your sentence because of a firearms
violation?’’
More than 400 separate items in the SIFCF capture various aspects of a defendant’s
criminal history. Using relevant indicators, the criminal history category (ranging from I to
VI) was calculated per guideline criteria. This was a multistep process. First, criminal
history points were summed per USSG §4A1.1 as follows: (a) Three points were added for
each prior adult custodial sentence exceeding one year and one month for which any
portion was served within fifteen years of the defendant’s current arrest; (b) two points
were added for each prior adult custodial sentence not counted in item (a) and exceeding
sixty days for which any portion was served within ten years of the defendant’s current
arrest; (c) one point was added for each prior adult conviction not counted in items (a) or
(b), up to a total of four points; (d) two points were added if the defendant committed the
19
Since the question’s phrasing refers to both primary and supportive activities, these roles might be
thought of as representing the ‘‘average participant’’ in each category. Notably, the following contingent
question aimed to further distinguish the degree of participation among those acknowledging importing,
wholesaling, or retailing: ‘‘Were you: (1) a street-level dealer, (2) a dealer above the street-level dealer, (3) a
bodyguard, strongman or debt collector, (4) a go-between or broker, (5) a moneyrunner, (6) a courier, mule
or loader?’’ However, this item was unreliable. First, a survey programming error skipped importers over the
contingent question (if they did not also acknowledge wholesaling or retailing). Thus, for the majority of
respondents who acknowledged ‘‘importing or helping others import illegal drugs,’’ it was not possible to
discern whether the activity was limited to being a ‘‘courier, mule, or loader.’’ Second, for offenders
involved in wholesaling or retailing, the additional information did not always contribute to clear-cut
culpability distinctions. For instance, is the wholesaler who operates at the street level more culpable than
the retailer who operates above the street level? Ultimately, given these shortcomings, data from this
contingent item were not utilized in the present study.
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J Quant Criminol (2009) 25:155–180
current offense while on parole, probation, or escape status; (e) two points were added if
the defendant committed the current offense less than two years after release from prison or
while on escape status; (f) one point was added for each prior violent offense beyond the
first, up to a total of three points. Second, the total number of points was collapsed into six
criminal history categories (I–VI) as specified in USSG §5A. Third, in modeling the
guideline’s sentence enhancements for ‘‘career offenders’’ (USSG §4B1.1) and ‘‘armed
career criminals’’ (USSG §4B1.4), offenders with an initial criminal history category of IV
or V were raised to category VI if they affirmed receiving a habitual offender enhancement: ‘‘Did you receive an increase in your sentence because of a second or third strike?’’
In 1994, Congress passed the ‘‘safety valve’’ amendment enabling certain low-level
defendants to be sentenced below the applicable mandatory or guideline sentence (Froyd
2000; Oliss 1995). Thus, based upon guideline criteria (USSG §5C1.2), the safety valve
was indicated for inmates who (1) were sentenced after the effective date, (2) faced at least
a five-year mandatory or guideline sentence based on quantity, (3) had a criminal history
category no greater than I, (4) did not receive an FSE or acknowledge using a dangerous
weapon, (5) were not concurrently convicted of a violent offense, (6) were not leader/
organizers or middlemen in a drug organization, and (7) reached a plea or charge bargain
agreement with the prosecutor.
Three dummy variables representing case disposition (i.e., a plea, bench trial, or jury
trial) were derived from the following two survey items: ‘‘In your trial for the [CURRENT
OFFENSES], did you enter an Alford plea, a no contest plea, a guilty plea, or did you plead
not guilty?’’ and, if defendant pled not guilty, ‘‘Were you found guilty by a judge or a
jury?’’ A plea disposition was indicated for any type of plea.
A charge bargain involving a dismissal or reduction in charges is measured by the
following item: ‘‘Before your trial for the [CURRENT OFFENSES], did you reach an
agreement with a prosecutor to plead guilty to a lesser charge or to fewer counts?’’ This
variable is presented as a rough proxy for substantial assistance departures (USSG §5K1.1)
and other judicial downward departures granted in connection with a plea agreement
(USSG §6B1.2).
Pretrial release from custody is measured by the following item: ‘‘Were you released
between the time of your arrest (notification of charges) and the start of your trial?’’
Sociodemographic variables include race/ethnicity, gender, age at offense, and noncitizen status. Educational level was operationalized as an ordinal measure of the number
of years of education completed, ranging from none/kindergarten only (0) to two or more
years of graduate school (18).
To assess the criterion validity of these measures, Table 3 compares the aggregate
distribution of select SIFCF variables against reported USSC measures for FY1997 (USSC
1997b). The USSC statistics are based on a universe of 18,835 federal drug defendants,
whereas the SIFCF statistics are based on an estimated 9,736 federal drug inmates. The
discrepancy in N is attributable to three factors. First, the USSC data include 1,215
defendants sentenced to probation, whereas the SIFCF data include only defendants sentenced to prison. Second, due to the timing of the inmate interviews, about four months of
FY1997 data are excluded from the SIFCF. Third, a small number of FY1997 inmates
serving short sentences would have been released by the time of the inmate interviews.
Overall, the SIFCF variables closely mirror the distribution of the criterion USSC
measures, although there are a few notable exceptions where the criterion value falls
outside the 95% confidence intervals. First, the SIFCF slightly underestimates the percentage of marijuana offenders—a discrepancy likely attributable to the inclusion of lesser
probation sentences in the USSC data. Second, the SIFCF underestimates the application of
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J Quant Criminol (2009) 25:155–180
177
Table 3 Criterion validity of select Inmate Survey variables, federal drug offenders, FY1997
Variables
U.S. Sentencing Commission data
Survey of Inmates in Federal
Correctional Facilities
%
%
95% CI
Drug type
Heroin
9.6
9.3
Methamphetamine
10.3
11.0
[6.8, 17.5]
Crack cocaine
24.6
27.3
[20.9, 34.7]
Powder cocaine
25.3
30.6
[25.6, 36.1]
Marijuana
27.4
19.5
[13.8, 26.9]
2.8
2.3
[1.2, 4.4]
8.5
4.7
[2.4, 9.0]
Mitigating role adjustment
22.2
8.9
[6.1, 12.8]
924(c) or guideline FSE
12.3
11.7
[8.4, 16.2]
I
57.1
59.7
[52.5, 66.4]
II
12.5
10.4
[6.9, 15.5]
III
14.2
16.2
[10.8, 23.5]
IV
6.4
4.9
[2.7, 8.7]
V
2.7
0.9
[0.3, 2.7]
VI
7.0
7.9
[4.6, 13.1]
Safety valve
23.7
19.2
[14.8, 24.6]
Plea (vs. bench or jury trial)
91.9
89.8
[84.6, 93.4]
Substantial assistance/charge bargain
42.6
65.7
[59.5, 71.4]
White
25.7
24.6
[19.5, 30.6]
Black
32.9
36.8
[29.2, 44.2]
Hispanic
39.4
33.1
[23.9, 43.7]
1.9
5.5
[2.4, 12.0]
Male
87.3
85.1
[82.5, 87.4]
Noncitizen
28.5
21.9
[14.6, 31.6]
Other drugs
Aggravating role adjustment
[6.7, 12.7]
Criminal history category
Race/ethnicity
Other
the mitigating role adjustment, indicating that, as constructed, this variable fails to fully
capture the full scope of mitigating role adjustments. Third, the SIFCF overestimates
charge bargain outcomes. Since judges are not obliged to depart downward when a plea
agreement is reached, the SIFCF captures prosecutorial plea agreements that may not be
reflected at sentencing. In summary, the SIFCF estimates match USSC criterion measures
remarkably well, although some mismeasurement is apparent.
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