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Crime & Delinquency
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DOI: 10.1177/0011128714551406
published online 26 September 2014Crime & Delinquency
VanNostrand and Timothy P. Cadigan
James C. Oleson, Christopher T. Lowenkamp, John Wooldredge, Marie
The Sentencing Consequences of Federal Pretrial Supervision
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DOI: 10.1177/0011128714551406
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Article
The Sentencing
Consequences of Federal
Pretrial Supervision
James C. Oleson
1
, Christopher T. Lowenkamp
2
,
John Wooldredge
3
, Marie VanNostrand
4
,
and Timothy P. Cadigan
5
Abstract
Legal variables, such as offense severity and criminal history, principally shape
sentencing decisions, but extralegal factors such as race, gender, and age
also influence sentencing outcomes. Studies focusing on the effect of pretrial
detention on sentencing outcomes usually associate pretrial detention with
negative sentencing outcomes. The current study followed 90,037 federal
defendants from indictment through sentencing, and measured the effects
of pretrial detention on sentencing decisions. Detention (and, to a lesser
degree, revocation of pretrial release) was associated with increased
likelihood of receiving a prison sentence and greater sentence length, even
when controlling for offense severity and criminal history scores.
Keywords
pretrial services, supervision, detention, federal courts, sentencing
[P]retrial decisions determine mostly everything.
—Sacks and Ackerman (2012, p. 72)
1
The University of Auckland, New Zealand
2
University of Missouri–Kansas City, USA
3
University of Cincinnati, OH, USA
4
Luminosity Solutions, St. Petersburg, FL, USA
5
Administrative Office of the United States Courts, Washington, DC, USA (ret.)
Corresponding Author:
James C. Oleson, Senior Lecturer in Criminology, Research Director for the School of Social
Sciences, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.
Email: j.oleson@auckland.ac.nz
551406CADXXX10.1177/0011128714551406Crime & DelinquencyOleson et al.
research-article2014
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2 Crime & Delinquency
Many United States jurisdictions dramatically expanded their prison popula-
tions between 1980 and 2010 (Austin & Irwin, 2012), incurring increasing
corresponding economic and social costs (Clear, 2007; Frost & Clear, 2012;
Western, 2006). Although several state prison populations have decreased
since the financial crisis of 2008 (Harcourt, 2012), the United States never-
theless remains an outlier in terms of incarceration rates. Indeed, the United
States has the highest reported rate of incarceration in the world (Walmsley,
2013), incarcerating its citizens at a rate 5 to 10 times that of other Western
industrialized nations (Berman, 2009). In 2009, the Pew Center on the States
reported that 1 in 31 adult U.S. citizens is either incarcerated or under com-
munity supervision. Given the fiscal and social impacts of modern mass
incarceration, scholarly efforts to understand the variables that influence
judicial sentencing decisions are particularly important (Fitzmaurice &
Pease, 1986; Hogarth, 1971; Myers & Talarico, 1987; Posner, 2008; Spohn,
2009a; Tonry, 1996; Ulmer, 1997).
Research suggests that legal factors, such as the severity of the offense and
the offender’s criminal history, are chiefly determinative in deciding whether
an offender will be incarcerated and for how long (Gottfredson & Gottfredson,
1988; Klein, Petersilia, & Turner, 1990; Kramer & Steffensmeier, 1993;
Neubauer, 2002; Reitler, Sullivan, & Frank, 2013; Spohn & Halleran, 2000).
However, extralegal variables including race (e.g., Mitchell, 2005; Spohn,
2009b; Steffensmeier & Demuth, 2000; Western, 2006), gender (e.g., Daly &
Bordt, 1995; Doerner, 2012; Freiburger, 2011), and age (e.g., Doerner &
Demuth, 2010; Steffensmeier & Motivans, 2000), also appear to influence
sentencing outcomes, and exert a greater influence when interacting than
when functioning in isolation (Doerner & Demuth, 2010; Leiber & Fox,
2005; Steffensmeier, Ulmer, & Kramer, 1998; Wooldredge, 2012).
Given the size and complexity of the federal judiciary, more study of the
U.S. federal courts is sorely needed (e.g., Reitler et al., 2013; Stith &
Cabranes, 1998). The federal courts process an immense criminal docket. In
2011, approximately 110,000 criminal defendants moved through federal dis-
trict courts; 91,938 defendants were convicted and sentenced (Hogan, 2012,
Tables D-1 and D-5). Approximately 14% of those who were sentenced
received non-custodial sentences (~2.5% were fined, ~11.3% were placed on
probation), but 86% were sentenced to federal prison with an average term of
52.9 months (Hogan, 2012, Table D-5). These judicial figures correspond
with dramatic increases in the federal prison population. In 1980, there were
only 21,000 prisoners in Bureau of Prisons custody; today, there are more
than 218,000—a 10-fold increase (La Vigne & Samuels, 2012). Forecasts
indicate that this growth will continue and the inmate population will grow
by another 11,000 during the next 2 years (Government Accountability
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Oleson et al. 3
Office, 2012). Sentencing in federal courts is also procedurally complex
(Oleson, 2011). Sentencing decisions are made under obligations imposed by
statute (see 18 U.S.C. § 3551 et seq. and 28 U.S.C. §§ 991 to 998), now-
advisory sentencing guidelines (see United States v. Booker, 2005), and con-
trolling federal case law (see, for example, Gall v. United States, 2007;
Kimbrough v. United States, 2007; Rita v. United States, 2007).
There are many reasons for researchers to empirically study the drivers of
sentencing in the federal courts. Constitutional questions of disparity and
equality under law are fundamental: Criminal justice scholars should strive to
ascertain why similarly situated offenders might receive different sentences
(e.g., Albonetti, 1991; Frankel, 1973; Spohn, 2009a; Ulmer, Light, & Kramer,
2011; U.S. Sentencing Commission, 2010; Wu & Spohn, 2010). In an era of
budget sequestration (Federal Judiciary Braces for Broad Impact of Budget
Sequestration, 2013), cost–benefit analyses are important, as well. It costs
between US $21,006 (minimum security) and US $33,930 (high security) to
incarcerate a federal prisoner for 1 year, while it costs only US $3,433 per year
to supervise a federal offender in the community (La Vigne & Samuels, 2012).
These costs, multiplied by the numbers of defendants processed annually in the
federal courts, are enormous. The current annual budget for the Bureau of
Prisons is US $6.6 billion (Department of Justice, 2013). If researchers can
identify the variables that drive federal sentencing decisions, allowing judges
and other relevant stakeholders to make evidence-based sentencing judgments,
scarce resources can be diverted from being concentrated where they are
useless—or even contraindicated—to where they will do the most good
(Oleson, 2011; Wolff, 2008). To that end, we asked whether—after controlling
for offense, criminal history, and other relevant variables—successfully com-
pleting a term of pretrial supervision might influence the likelihood of impris-
onment and the length of imprisonment in the U.S. district courts.
Examining data from the U.S. Sentencing Commission, Reitler et al.
(2013) examined a range of variables and found that detention after convic-
tion but before sentencing was most related to legal factors (e.g., length of
criminal history, commission of a violent or otherwise serious offense, or
commission of a crime while under criminal justice supervision) and less
related to extralegal factors such as age, race, and ethnicity. The Reitler study
constitutes a very important piece of scholarship on federal sentencing, but
was constrained by the limitations of the extant data set of 2007 information
employed. In our own study, described here, we have used more recent 2010-
2011 sentencing data, drawn directly from the U.S. district courts, to follow
90,037 cases longitudinally, from indictment through sentencing. In this way,
we were able to examine the effects of both pretrial release and the revocation
of pretrial release on sentencing outcomes. We were also able to assess the
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4 Crime & Delinquency
influence of these decisions on judicial sentencing decisions to incarcerate or
to impose a non-custodial sentence, as well as—as is more commonly done—
on sentence length.
The Effects of Pretrial Release/Detention on
Sentencing
Although pretrial detention and release are commonly controlled for in
empirical studies of sentencing, they are typically employed as a control vari-
able (Williams, 2003). Although a large study conducted 50 years ago by the
Vera Foundation found that defendants who were detained before trial in
New York were significantly more likely to be convicted and incarcerated
(Ares, Rankin, & Sturz, 1963; Rankin, 1964), the effects of pretrial release
and detention on subsequent sentencing decisions remain under-researched.
Only a few studies have examined the relationship (e.g., Feeley, 1992; Free,
2004; Philips, 2007, 2008, 2012; Sacks & Ackerman, 2012; Tartaro &
Sedelmaier, 2009; Williams, 2003).
After examining more than 50,000 cases from the New York metropolitan
region, the New York City Criminal Justice Agency replicated the findings of
the Vera study, reporting that pretrial detention is significantly and positively
related to conviction, incarceration, and sentence length (Philips, 2012). The
relationships between pretrial detention and increased conviction rates,
increased likelihood of incarceration, and increased sentence length appear to
hold true for both felony cases (Philips, 2008) and non-felony cases (Philips,
2007).
Similarly, in a study of 412 Florida cases, “pretrial detention was a strong,
significant predictor of both incarceration and length of sentence” (Williams,
2003, p. 313). Detention was the strongest predictor of incarceration in the
model, even controlling for legal variables such as offense seriousness and
prior record and extralegal variables such as age, race, and gender.
Relationships between pretrial detention and sentencing dispositions were
reported by Leiber and Fox (2005), and Kellough and Wortley (2002) found
that, among a dozen variables, pretrial detention appeared to be the strongest
predictor of guilty pleas. Other researchers have identified significant rela-
tionships between pretrial detention and increased rates of conviction (Cohen
& Reaves, 2007; Hart & Reaves, 1999), pretrial detention and the increased
probability of a prison sentence (Harrington & Spohn, 2007), and pretrial
detention and increased sentence length (Tartaro & Sedelmaier, 2009;
Willison, 1984). Although Goldkamp (1980) found little relationship between
pretrial detention and conviction, he reported a significant relationship
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Oleson et al. 5
between pretrial detention and receiving a prison sentence. On the other
hand, Sacks and Ackerman (2012) did not find evidence that pretrial deten-
tion affected the decision to incarcerate, but did report an association between
pretrial detention and increased sentence length. Examining sentencing in
two federal districts (New Jersey and Pennsylvania Eastern), we found that,
after controlling for a number of variables, being detained before trial—and,
to a lesser degree, being revoked from pretrial supervision—were associated
with increased sentence length, while defendants who are released before
trial and successfully complete their terms of supervision appeared to receive
shorter sentences (Oleson, Lowenkamp, Cadigan, VanNostrand, &
Wooldredge, n.d.).
Several researchers have speculated about the reason for the links between
pretrial detention and increased risk of conviction, increased risk of impris-
onment, and increased sentence length. For example, Williams (2003) sug-
gests that the explanation can be found in the released defendant’s ability to
demonstrate good behavior: “[A] defendant who is out on bail has the ability
to demonstrate to the sentencing judge that he or she is not a danger to the
community” (p. 314). Williams also notes that defendants are often detained
before trial for the same reasons that influence sentencing decisions: serious
and harmful crimes, lengthy criminal histories, and perceived risk of further
offending. Many have prior convictions, lack employment and education,
and suffer from deficits such as illiteracy, mental illness, physical disability,
and drug and/or alcohol addiction (Petersilia, 2003; VanNostrand & Keebler,
2009). Research shows that defendants who are detained before trial are often
indigent, and can neither afford privately retained counsel nor post bail
(Holmes, Daudistel, & Farrell, 1987). Detained defendants may find it diffi-
cult to contribute meaningfully to their own defense (Foote, 1954; Reitler
et al., 2013), a problem exacerbated by the fact that attorneys spend less time
with defendants detained before trial than with defendants who are released
(Allan, Allan, Giles, Drake, & Froyland, 2005). In sum, the specific mecha-
nism remains unclear, but pretrial detention appears to increase the risk of
conviction, incarceration, and sentence length. Until now, however, little
research on these relationships has been conducted in the federal courts.
Pretrial Services Supervision in the United States
Courts
Pretrial services supervision can be traced to the early probation and bail
efforts of John Augustus (Panzarella, 2002). The Speedy Trial Act of 1974
created 10 demonstration pretrial services offices within the federal courts,
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6 Crime & Delinquency
but the federal pretrial services system was not formed until the Pretrial
Services Act of 1982 was passed (Byrne & Stowell, 2007; Cadigan, 2007).
The 1982 Act identified four principal goals: ensuring pretrial services inves-
tigations and reports for all defendants, reducing unnecessary detention,
reducing crime and absconding while on bail, and reducing reliance on surety
bonds (Cadigan, 2007).
Although establishment of a coordinated federal pretrial services system
with offices in all 94 judicial districts has done much to realize the goals of
the Act, the system has not yet fully realized pretrial justice (VanNostrand &
Keebler, 2007). There are more federal crimes than when the pretrial services
system was established (Baker, 2008). Today, there are more federal defen-
dants (Hogan, 2012), more federal prisoners (La Vigne & Samuels, 2012),
more non-citizens (Scalia, 1996), and increasing—not decreasing—rates of
pretrial detention (Byrne & Stowell, 2007; Cadigan, 2007; VanNostrand &
Keebler, 2007). The contemporary federal pretrial services system has devel-
oped and implemented a program of risk assessment (Cadigan & Lowenkamp,
2011a; Lowenkamp & Whetzel, 2009) and is increasingly evidence-based
(Cadigan, 2009). The federal pretrial system is larger and more sophisticated
than it was 30 years ago.
Federal law outlines the process for judges to follow when deciding
whether to release or detain a defendant. United States Code 18 U.S.C. §
3142(g) directs judicial officers to consider specific factors when making
determinations about pretrial release, including (a) the nature and circum-
stances of the offense, (b) the weight of the evidence, (c) the history and
characteristics of the person, and (d) the nature and seriousness of danger to
any person or the community that would be posed by the defendant’s release.
The § 3142(g) process appears straightforward, almost mechanical, but they
are enormously important. According to research about the effects of pretrial
detention on sentencing, early judicial decisions to detain or release influence
almost all downstream determinations (e.g., guilt, imprisonment, and sen-
tence length).
Method
To determine whether the associations between pretrial detention, increased
likelihood of a prison sentence, and increased sentence length previously
observed in other jurisdictions (e.g., Philips, 2012) also exists within the fed-
eral criminal justice system, a national sample of 94,229 pretrial cases, all
disposed of between October 1, 2010 and September 30, 2011, was drawn
from the U.S. district courts. This sample represented all defendants sen-
tenced in fiscal year 2011 with a case-closure code of “execution of
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Oleson et al. 7
sentence,” which eliminated all dismissed cases. Eliminating misdemeanor
cases from the analyses further reduced the sample size to 90,037. The num-
ber of cases available for our analyses is further reduced, depending on miss-
ing data and the model estimated (see Table 1 for information on the
percentage of missing values by measure).
Variables
We examined four categories of variables: outcome measures (related to sen-
tencing outcomes) and three groups of control variables: pretrial factors, defen-
dant demographics, and legally relevant factors. Our outcome measures included
sentence of imprisonment (measured dichotomously, in which a prison term was
coded as 1, and a non-prison sentence was coded as 0) and sentence length
(measured in both months and years). Recoding of sentence length in years was
necessary because when rounding to years, 6-month prison sentences would be
rounded down to 0. To correct for this, sentences of less than 6 months but
greater than 0 months were assigned a value of 1 year, and (to correct for this
adjustment), we added 1 year to all cases of 1 or more years in prison. This pro-
duced whole number counts that could be employed in hierarchical Poisson
analyses. Data on recoded years in prison were right-censored: a small number
of extreme values were coded back to 33 years (the sentence representing three
standard deviations above the mean sentence length). Sentence in months, sen-
tence in years, and recoded sentence in years were all ratio scales.
The pretrial measures employed in our analyses included preventive deten-
tion status, pretrial release status (detained or released), pretrial release viola-
tions (if released, violations or not), pretrial release revocation (if released,
revoked or not), and risk score (the continuous total risk score from the Pretrial
Risk Assessment instrument in use in U.S. Pretrial Services system). Many of
these variables were coded dichotomously. Coding followed the variable
name, where each label reflects Category 1. Specifically, released pretrial (1 =
pretrial release and 0 = not released), pretrial release revoked (1 = pretrial
release revoked and 0 = not revoked), and violations (1 = pretrial release viola-
tions and 0 = no violations). The defendant demographic control variables
included sex (male or female), race (White, Black, and Hispanic), citizenship
status (U.S. citizen or non-citizen), age (in years), marital status, and employ-
ment status. Age was coded in years, but most of these variables were coded
dichotomously. Coding followed the variable name, where each label reflects
Category 1. Specifically, for the variable labeled female (1 = female and 0 =
male); non-citizen status (1 = legal/illegal alien and 0 = United States citizen);
married (1 = married and 0 = single/not married); employed (1 = employed
and 0 = not employed). Because the racial composition of the sample was
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8 Crime & Delinquency
overwhelmingly Black and White, race was coded into two separate dichoto-
mous variables, White and Black. For the variable “White,” 0 = non-White, 1 =
White. For the variable “Black,” 0 = White/Other, 1 = Black.
Table 1. Descriptive Statistics.
Measure n M SD Minimum Maximum
%
missing
Sentence in months 90,034 39.158 98.161 0 3,480 <1
Sentence in years 90,034 3.300 8.189 0 290 <1
Recoded sentence in
years
90,034 3.802 4.802 0 33 <1
Pretrial measures
Preventive detention 90,037 0.056 0.231 0 1 0
Released pretrial 90,037 0.322 0.467 0 1 0
Pretrial release
revoked
29,025 0.057 0.232 0 1 0
Violations 29,025 0.184 0.388 0 1 68
Pretrial risk score 90,037 6.876 1.877 1 13 0
Defendant demographics
Female 89,997 0.122 0.328 0 1 <1
Non-White 89,097 0.199 0.399 0 1 1
White 89,097 0.801 0.399 0 1 1
Hispanic 90,037 0.624 0.484 0 1 0
Non-citizen 89,724 0.546 0.498 0 1 <1
Age 89,742 34.202 10.507 18 89 <1
Married 47,653 0.288 0.453 0 1 47
Employed 90,037 0.220 0.414 0 1 0
Legally relevant factors
Offense severity
score
74,581 17.502 8.853 0 43 17
Criminal history
score
73,874 3.986 4.934 0 50 18
Minimum sentence 73,211 49.197 65.521 0 600 19
Offense categories
Drug offense 89,885 0.280 0.449 0 1 <1
Immigration offense 89,885 0.458 0.498 0 1 <1
Theft offense 89,885 0.112 0.315 0 1 <1
Firearm offense 89,885 0.073 0.260 0 1 <1
Violent offense 89,885 0.024 0.152 0 1 <1
Sex offense 89,885 0.028 0.164 0 1 <1
Other 89,885 0.026 0.158 0 1 <1
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Oleson et al. 9
The legally relevant variables included offense severity score (measured
as a continuous score calculated as part of the federal sentencing process),
criminal history score (measured as a continuous score calculated as part of
the federal sentencing process), and minimum sentence (in months). The
offense charged was coded into seven separate dichotomous variables: drug,
immigration, theft, firearm, violent, sex offense, and other, following the pri-
mary offense categories employed by the U.S. Courts, with 1 = offense cat-
egory cited and 0 = all other offense categories.
Analyses
To determine whether pretrial release and pretrial failure affected imprison-
ment decisions and sentence length, we analyzed the data using two models.
The first model, a hierarchical logistic regression model, was used to exam-
ine whether pretrial release and pretrial revocation affected the defendants’
likelihood of receiving a prison sentence. Hierarchical logistic regression
models provide an effective means of studying data with group structure and
a binary response variable (such as whether defendants received a prison
sentence or a non-prison sentence; Wong & Mason, 1985). This model was
estimated using hierarchical linear model (HLM) for Windows version 7.0.
Because the specific district where a case is processed as well as the specific
judge who processed the case can impact sentence length, our analyses
account for the “nested” nature of the data. That is, defendants at Level 1 (n
1
=
71,825) were nested within judges at Level 2 (n
2
= 857), and judges were
nested within districts at Level 3 (n
3
= 92).
The second model was used to examine whether pretrial release and pre-
trial revocation affected the defendants’ sentence length. Ordinary least
squares (OLS) regression is often employed to examine linear relationships
involving interval or ratio dependent variables (Seber & Lee, 2003), such as
the amount of time sentenced in months. However, this technique requires
that the dependent variable possesses a certain distributional shape (Berry,
1993; Fox, 1991). In situations where the dependent variable (sentence length
in years recoded) departs substantially from “normal” (i.e., the dependent
variable is over-dispersed in the current data, violating assumptions of nor-
mality, as indicated in Table 1), negative binomial regression or Poisson
regression with a correction for over-dispersion is an appropriate procedure
(Hilbe, 2011). The second model was a hierarchical Poisson model with cor-
rection for over-dispersion, predicting sentence length in years. Similar to the
hierarchical logistic regression model, our hierarchical Poisson model analy-
sis accounts for the “nested” nature of the data by disaggregating by judge
(“Variance Level 2”) and district (“Variance Level 3”). The final number of
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10 Crime & Delinquency
defendants, judges, and districts used in the hierarchical Poisson models is
71,821, 819, and 92, respectively.
Results
Our study includes the descriptive statistics for the federal defendants con-
victed of felony offenses, and examined information about sentencing out-
comes, pretrial factors, defendant demographics, and legally relevant factors.
Table 1 presents the descriptive statistics for the sample.
As indicated in Table 1, offenders in the sample tended to be male (88%),
non-Hispanic White (80%), Hispanic (62%) or Black (20%), non-U.S. citi-
zens (55%), young (34 years), unmarried (71%), and unemployed (78%).
Sentences of preventive detention were rare: only 6% of the sample was
denied the option of pretrial release altogether. Approximately 28% of the
cases were drug cases, 46% were immigration cases, 11% were theft cases,
7% were firearms cases, 2% were violence cases, 3% were cases involving
sex offenses, and approximately 3% involved other offenses. Approximately
32% of the sample was released on pretrial services supervision, 18% of the
sample were linked to violations of the conditions of release, and 6% of the
sample had pretrial services supervision revoked. Approximately 77% were
sentenced to a prison term and the average sentence length was 3.8 years
(recoded per the method listed above).
A hierarchical logistic regression model was employed to predict the like-
lihood of receiving a prison sentence. This model is presented in Table 2
provided below.
When controlling for offense severity and criminal history, pretrial release
was a statistically significant predictor of whether or not a prison sentence
was imposed. The coefficient for pretrial release was −1.323 which translates
into a .266 factor reduction in the ratio of the odds that a prison sentence will
be imposed. For instance, assuming the raw likelihood of receiving a prison
sentence was 70%, the raw likelihood of not receiving a prison sentence is
30%. Put more simply, the odds of receiving a prison sentence would be 7 to
3. Using the current example immediately above (7:3), if the odds are reduced
by a factor of .266 for those released pretrial as noted above, the likelihood of
a prison sentence for those who were released goes from 7:3 to 5.2:3—a sub-
stantial reduction in the likelihood of a prison sentence being imposed.
Likewise, the revocation of pretrial release was statistically significant; how-
ever, the coefficient was positive (.698) which translates into a factor of 2.01,
or roughly a doubling in the likelihood a prison sentence will be imposed if
pretrial supervision is revoked. Again, assuming raw odds of 7:3 in favor of
receiving a sentence to prison, the odds would increase to 14:3.
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Oleson et al. 11
Other significant predictors in the logistic regression model included
being White, which had a negative coefficient (−.146) that translated into
a .866 reduction in the odds ratio of a prison sentence being imposed; using
the example above, the likelihood would go from 7:3 to 1:3. Likewise, the
logistic regression coefficient for being female was negative (−.406), which
translates into a .666 reduction in the odds a prison sentence will be imposed
(odds of 7:3, would reduce to 2.4:3), while the coefficient for being a non-
citizen (.765) was positive, increasing the odds ratio by a factor of 2.15 (7:3
to more than 14:3).
The y intercept, the effects of pretrial release and revocation of pretrial
supervision were allowed to vary across judges and districts. The model
results indicate that there is significant variation in the y intercept and the
strength of the relationship between pretrial release and sentencing. These
effects are evident at both the judge and district level. The strength of the
relationship between revocation of pretrial supervision and sentencing did
not differ across judges or districts.
Table 2. Hierarchical Logistic Regression Model Predicting Prison Sentence
Imposed (Yes/No).
Variable Coefficient SE
Variance
Level 2
Variance
Level 3
Released pretrial −1.323* 0.101 2.355* 0.844*
Pretrial release revoked 0.698* 0.081 1.860 0.518
Offense severity 0.155* 0.008
Criminal history 0.111* 0.008
Risk score 0.076* 0.016
Drug offense 0.062 0.071
Immigration offense −0.049 0.185
Theft offense −0.004 0.081
Firearm offense −0.182 0.089
Violent offense −0.002 0.092
Hispanic 0.241* 0.065
Age 18-29 0.056 0.058
Age 30-49 0.096 0.038
White −0.146* 0.041
Female −0.406* 0.050
Non-citizen 0.765* 0.090
Employed −0.225* 0.053
Note. The three entries with variance estimates were the only random effects in the model.
*p ≤ .001.
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12 Crime & Delinquency
In short, although offense severity and criminal history are the two pri-
mary items used to determine the imposition of a prison sentence, other fac-
tors appear to be at work (while controlling for the effects of offense severity
and criminal history). Specifically, being released pretrial had a negative (i.e.,
decreasing) effect on the likelihood of a prison sentence, while being revoked
pretrial had a positive (increasing) effect on the likelihood of a prison sen-
tence. Being White had a negative (suppressing) effect on the likelihood of a
prison sentence, as did being female, whereas being a non-citizen had a posi-
tive (increasing) effect on the likelihood of prison sentence imposition.
Furthermore, the effects of pretrial release on sentencing varied across judges
and districts.
A hierarchical Poisson model with correction for over-dispersion was used
to predict sentence length in years. This model is presented in Table 3 pro-
vided below.
Similar to the model presented above in Table 2, pretrial release was a
statistically significant predictor in the Poisson model that utilized sentence
length (years) as a dependent variable. Specifically, the coefficient was −.375,
which means being released pretrial had a negative (suppressing) effect on
sentence length. This coefficient translated into a factor reduction to .687 of
the expected sentence for those not released. If the pretrial supervision is
revoked, however, the opposite effect on sentence length is observed. The
variable that measures pretrial revocation resulted in a coefficient of .281.
Even so, those defendants that released pretrial and subsequently have their
pretrial supervision revoked still have, on average, sentences that are lower
than those of detained defendants.
The coefficient for being White was also a statistically significant predic-
tor of sentence length. The coefficient was negative (−.038) which means
being White had a negative (suppressing) effect on sentence length, and
translates into a factor reduction of .963. In other words, assuming a normal
sentence length of 3 years, being White reduces that length to less than 35
months. Likewise, being female has a suppressing effect on sentence length.
Being female resulted in a negative coefficient (−.250), which translates into
a factor reduction of .779. Assuming a normal sentence length of 3 years,
being female reduces that sentence length to under 28 months.
The Levels 2 and 3 analyses included the y intercept and the effects of
pretrial release, pretrial revocation, and the effects of minimum sentence on
sentence length. As is indicated in Table 3, the relationship between pretrial
release, revocation of pretrial supervision, minimum sentence length, and the
actual sentence length varies significantly across judges and districts.
Furthermore, the y intercept for the Poisson models predicting sentence
length also varies across judges and districts.
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Oleson et al. 13
Discussion
Our research indicates that, after controlling for a range of legal and extrale-
gal variables, pretrial detention is itself associated with increased likelihood
of a prison sentence and with increased sentence length (although the effect
varies greatly across judges and districts). Similarly, being released on pre-
trial services supervision was associated with a decrease in the likelihood of
being sentenced to prison, and a decrease in sentence length (although, again,
the effect varied greatly across judges and districts). Not surprisingly, having
one’s pretrial release revoked had a statistically significant increase on the
likelihood of being sentenced to prison, as well as prison length (and this
effect appeared to be consistent across judges and districts).
This research builds upon extant research (Goldkamp, 1980; Reitler et al.,
2013; Sacks & Ackerman, 2012; Tartaro & Sedelmaier, 2009; Williams,
2003; Willison, 1984) and constitutes an important contribution to the
Table 3. Hierarchical Poisson Model With Correction for Over-Dispersion
Predicting Sentence Length in Years.
Variable Coefficient SE
Variance
Level 2
Variance
Level 3
Released pretrial −0.375* 0.021 0.028* 0.014*
Pretrial release revoked 0.281* 0.022 0.036* 0.014*
Preventative detention 0.025 0.017
Minimum sentence 0.007* 0.000 4.75E-06* 1.35E-06*
Criminal history 0.007* 0.002
Risk score 0.018* 0.003
Drug offense 0.040 0.018
Immigration offense −0.355* 0.043
Theft offense −0.231* 0.032
Firearm offense 0.021 0.030
Violent offense 0.227* 0.023
Hispanic −0.008 0.014
Age 18-29 −0.083* 0.017
Age 30-49 −0.032 0.011
White −0.038* 0.010
Female −0.250* 0.016
Non-citizen −0.061 0.021
Employed −0.027 0.009
Note. The four entries with variance estimates were the only random effects in the model.
*p ≤ .001.
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14 Crime & Delinquency
under-studied subject of pretrial detention’s effects on sentencing. It extends
the existing body of work in three important ways. First, it examines empiri-
cally the link between pretrial detention and sentencing in the federal crimi-
nal justice system. Federal sentencing is more complex than many state
sentencing systems (Oleson, 2011). Although some equivalent research has
been conducted in state courts, and while Reitler and her colleagues (2013)
examined the legal and extralegal drivers of federal presentence detention,
our study followed federal defendants all the way from indictment to sentenc-
ing, breaking new ground in analyzing the sentencing effects of pretrial
detention in federal cases. Second, it examined a very large sample of cases
(N = 90,037). Although most studies of state sentencing have analyzed sam-
ples of 1,000 cases or less, we have presented a large, multi-faceted study that
represents a wide range of federal offenses across a geographically heteroge-
neous jurisdiction (i.e., all 94 federal judicial districts in the federal system).
Third, instead of looking only at the effects of release and detention on sen-
tencing, our research also examines the decision to incarcerate or impose a
non-custodial sentence and looks at the effects of revocation while on pretrial
release (whether for technical violations, minor violations, or major viola-
tions). Philips (2012) studied misconduct while on release, but this issue has
received little scholarly attention to date.
Despite these strengths, our research is limited in several ways. First, our
sample was drawn from a limited period of time (one fiscal year). Second,
because it employs a national sample, the data might obscure important
regional differences among criminal dockets across the U.S. courts (Hagan,
Nagel, & Albonetti, 1980; Hogan, 2012; Wu & Spohn, 2010). Our study con-
trolled for a number of variables that might influence sentencing decisions,
including judicial circuit and district, but it controlled neither for sentencing
judge nor § 5K1.1 departures (in which judges, upon prosecutor’s motion,
impose sentences outside the normal sentencing guideline range to reward
the defendant’s substantial assistance in prosecution). We hope that future
research might follow pretrial cases longitudinally from pretrial to trial to
sentence to supervised release, the post-conviction term of community super-
vision imposed at the back end of many federal sentences (Baer, 1996).
The empirical results of the study are important. The effects of pretrial
detention and release on sentencing remain under-researched, especially in
federal courts, and our study extends the line of existing research. The greater
value of our study, however, may lie in its implications for evidence-based
policy in the federal pretrial system (Cadigan, 2009). Because pretrial deten-
tion itself increases sentence length, policies that reduce detention rates of
defendants who are unlikely to commit new crimes may decrease federal
prison crowding and corrections costs (La Vigne & Samuels, 2012). And it
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Oleson et al. 15
may do so while maintaining or enhancing public safety, because, research
indicates that federal defendants who are detained before trial are twice as
likely as released defendants to fail on post-conviction supervised release
(Cadigan & Lowenkamp, 2011b).
Certainly, the evidence suggests that, in making decisions about how to
deal with a defendant before trial, federal pretrial services officers and judges
should be aware of the many links between pretrial detention, release, con-
viction, incarceration, sentence length, and success or failure on supervised
release. Fortunately, researchers already know something about the factors
that appear to lead to failure on federal pretrial services supervision (Bechtel,
Lowenkamp, & Holsinger, 2011), and the establishment and implementation
of a federal pretrial risk assessment instrument (Cadigan & Lowenkamp,
2011a; Lowenkamp & Whetzel, 2009) should provide pretrial services offi-
cers and judges with the necessary tools to make more informed release
decisions.
Conclusion
The current study supports the broad findings from previous research in state
courts on the effects of pretrial release/detention upon conviction, incarcera-
tion, and sentence length. Even after controlling for legal variables (e.g.,
offense severity and criminal history) and extralegal variables (e.g., age, race,
and gender), the study found that defendants who were detained or who failed
while on pretrial release served longer sentences than defendants who were
released and successfully completed pretrial services supervision. In short,
detention itself appeared to increase the length of the prison sentence. These
findings indicate that additional research is needed to understand the effects
of pretrial detention, pretrial release, and pretrial failure on sentencing. Our
findings also imply that pretrial risk assessment (Cadigan & Lowenkamp,
2011a) and evidence-based practices (Cadigan, 2009) should be employed to
help determine which defendants need to be detained to protect public safety,
and which can safely be released and/or supervised in the community.
Acknowledgment
The authors wish to thank the Laura and John Arnold Foundation for their generous
financial support of the study.
Declaration of Conflicting Interests
The author(s) declared the following potential conflicts of interest with respect to the
research, authorship, and/or publication of this article: The authors collaborated on
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16 Crime & Delinquency
the basis of previous professional relationships. Previously, James Oleson, Christopher
Lowenkamp, and Timothy Cadigan were all employees at the Administrative Office
of the U.S. Courts, and Marie VanNostrand conducted research through Luminosity.
Christopher Lowenkamp was employed by University of Cincinnati, where John
Wooldredge continues to be employed.
Funding
The author(s) disclosed receipt of the following financial support for the research,
authorship, and/or publication of this article: The research was funded in part by the
Laura and John Arnold Foundation.
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Author Biographies
James C. Oleson earned his PhD in criminology from Cambridge University in 1998
and his JD from University of California–Berkeley in 2001. After his term as a 2004-
2005 U.S. Supreme Court Fellow, he served as a chief counsel to the Criminal Law
Policy Staff of the U.S. Courts until 2010. He is currently a senior lecturer in criminol-
ogy at the University of Auckland and serves as the director of research for the School
of Social Sciences. He is interested in high-IQ crime, evidence-based corrections,
sentencing, and punishment.
Christopher T. Lowenkamp received his PhD in criminal justice from the University
of Cincinnati. He has served as the director of the Center for Criminal Justice Research
and the associate director of The Corrections Institute at the University of Cincinnati.
He also held the positions of research associate and research professor at the University
of Cincinnati. He served as a probation officer and jail emergency release coordinator
in Summit County, Ohio, and as a probation administrator with the Office of U.S.
Probation and Pretrial Services. He is currently a lecturer at the University of
Missouri–Kansas City and a consultant. He has co-authored more than 65 articles and
book chapters some of which are published in top-tier academic journals.
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Oleson et al. 21
John Wooldredge is a professor in the School of Criminal Justice at the University of
Cincinnati. His research and publications focus on institutional corrections (crowding,
inmate violence, inmate adaptation), and criminal case processing (sentencing and
recidivism, extralegal disparities in case processing and outcomes). He is currently
involved in research on judicial effects on sentencing, and official responses to prison
inmate rule violations.
Marie VanNostrand is a project manager in Luminosity, Inc., a criminal justice con-
sulting firm specializing in the pretrial stage of the criminal justice system. A nation-
ally recognized expert in pretrial services, risk assessment, alternatives to detention,
and jail population management, she is the pioneer of multi-jurisdictional pretrial risk
assessment and the field of Pretrial Services Legal and Evidence-Based Practices. She
earned her bachelor of science degree in criminal justice from Syracuse University,
and master’s degrees in public administration and urban studies and a PhD in public
policy from Old Dominion University.
Timothy P. Cadigan served as the data analysis branch chief in the Office of
Probation and Pretrial Services at the Administrative Office of the United States
Courts. He earned his BA from Keane College in 1983 and his MA from Rutgers
University in 1986. After serving as a U.S. Pretrial Services Officer in the District of
New Jersey from 1988 to 1990, he worked in federal pretrial services at the
Administrative Office of the United States Courts.
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