LAW AND ECONOMICS RESEARCH PAPER SERIES
PAPER NO. 12-018

AUGUST 2012

ESTIMATING GENER DISPARITIES IN FEDERAL
CRIMINAL CASES
SONJA B. STARR

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Estimating Gender Disparities in Federal Criminal Cases
Sonja B. Starr*
University of Michigan Law School
sbstarr@umich.edu
August 29, 2012
This paper assesses gender disparities in federal criminal cases. It finds large
gender gaps favoring women throughout the sentence length distribution (averaging over
60%), conditional on arrest offense, criminal history, and other pre-charge observables.
Female arrestees are also significantly likelier to avoid charges and convictions entirely,
and twice as likely to avoid incarceration if convicted. Prior studies have reported much
smaller sentence gaps because they have ignored the role of charging, plea-bargaining,
and sentencing fact-finding in producing sentences. Most studies control for endogenous
severity measures that result from these earlier discretionary processes and use samples
that have been winnowed by them. I avoid these problems by using a linked dataset
tracing cases from arrest through sentencing. Using decomposition methods, I show that
most sentence disparity arises from decisions at the earlier stages, and use the rich data
to investigate causal theories for these gender gaps.

	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
*

Thanks to Ing-Haw Cheng, John DiNardo, Nancy Gertner, Sam Gross, Jim Hines, JJ Prescott, Eve
Brensike Primus, Adam Pritchard, and Marit Rehavi for helpful comments and conversations, to Ryan
Gersowitz, Michael Farrell, Seth Kingery, and Adam Teitelbaum for research assistance, and to participants
in the Law and Economics Lunch, Fawley Lunch Workshop, and Criminal Justice Roundtable at the
University of Michigan Law School, the University of Michigan Labor Lunch, and the Ninth Circuit
Judicial Conference.

Electronic copy available at: http://ssrn.com/abstract=2144002

Starr—Estimating	
  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

Estimating Gender Disparities in Federal Criminal Cases
Introduction
In the United States, men are fifteen times as likely to be incarcerated as women are.
But can this gap be explained by differences in criminal behavior or circumstances, or are
courts or prosecutors treating genuinely equivalent cases differently on the basis of gender?
The latter would violate the Constitution, undercut the criminal justice system’s punishment
objectives, and contribute to the social consequences of demographically concentrated mass
incarceration. So the reasons for the gender gap are of considerable legal and policy interest.
This study explores them using a dataset that traces federal criminal cases from arrest
through sentencing. I find that gender gaps widen at every stage of the justice process and
that men and women ultimately receive dramatically different sentences.
Existing studies of demographic disparities in criminal justice focus on narrow slices
of the justice process in isolation. Most assess the judge’s final sentencing decision,
controlling for conviction severity or “presumptive sentence” measures that are themselves
produced by discretionary decisions and negotiations. Ignoring disparities in those earlier
stages could bias sentencing disparity estimates, both because the key control variable is
endogenous and because of sample selection from the winnowing of cases at each procedural
stage. Current sentencing literature typically ignores this “funnel.” There is a small literature
addressing disparities in prosecutorial decisons, but it addresses only certain pieces of the
process and does not estimate their ultimate sentencing consequences.
These limitations represent a surprising gulf between the quantitative empirical
scholarship and the theoretical literature on the criminal justice system, which widely
recognizes that sentencing is heavily shaped by prosecutors’ capacious charging and
bargaining discretion. This study seeks to close this gap, using a multi-agency linked dataset
that traces cases from arrest through sentencing. I estimate sentence outcomes conditioned
on characteristics that are fixed near the beginning of the justice process, rather than near the
end of it: the arrest offense, criminal history, and other prior characteristics. This approach
generates a measure of the aggregate gender disparity introduced in the post-arrest justice
process. I then use sequential decomposition methods to assess how much of this gap
appears to be explainable by decision-making at each procedural stage. See Altonji,
Bharadwaj, and Lange 2008; DiNardo, Fortin, and Lemieux 1996.
In short, I ask: do otherwise-similar men and women who are arrested for the same
crimes end up with the same punishments, and if not, at what points do their fates diverge?
Although the arrest offense is not a perfect proxy for underlying criminal conduct, it is a big
improvement on the highly endogenous controls used in current research. I also use
estimation strategies—reweighting of the mean and the distribution—that offer a useful
solution to a problem with which sentencing researchers have long struggled: how to treat
non-prison sentences. The leading approach is a Two-Part Model that separates the
incarceration decision from the length decision, but that introduces serious sample selection
concerns if there is disparity in the first stage. The best solution is simply to treat sentencing
as a single process and estimate disparities in all sentences, including the zeros. Doing so
with reweighting rather than regression obviates the functional form concerns that underlie
many researchers’ preference for the Two-Part Model.

	
  

1	
  
Electronic copy available at: http://ssrn.com/abstract=2144002

Starr—Estimating	
  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

The estimated gender disparities are strikingly large, conditional on observables.
Most notably, treatment as male is associated with a 63% average increase in sentence
length, with substantial unexplained gaps throughout the sentence distribution. These gaps
are much larger than those estimated by previous research. This is because, as the sequential
decomposition demonstrates, the gender gap in sentences is mostly driven by decisions
earlier in the justice process—most importantly sentencing fact-finding, a prosecutor-driven
process that other literature has ignored.
But why do these disparities exist? Despite the rich set of covariates, unobservable
gender differences are still possible, so I cannot definitively answer the causal question.
However, several plausible theories have testable implications, and I take advantage of the
unusually rich dataset to explore them. I find substantial support for some theories
(particularly accommodation of childcare responsibilities and perceived role differences in
group crimes), but that these appear only to partially explain the observed disparities.
1. Discretion and Gender Disparity in Criminal Justice
1.1. Sources of Discretion in the Federal Criminal Justice Process
Just as the states do, the federal justice system gives enormous power to prosecutors.
The United States in effect has a system of negotiated justice, and prosecutors hold most of
the chips. They have broad discretion to choose charges from numerous overlapping
criminal statutes, and then to determine the terms of plea deals. Plea-bargaining does not
necessarily focus mainly on dropping of charges—indeed, the lead charge was dropped only
17% of the time in this study’s sample. The parties also often negotiate stipulations to key
“sentencing facts”—for instance, the quantity of drugs trafficked or the defendant’s major or
minor role in a conspiracy. The prosecutor also may make non-binding sentencing
recommendations or request special leniency to reward cooperators.
Federal sentencing is guided by two main legal frameworks. First, each criminal
statute specifies a sentencing range. Most are broad and start at zero (for instance, 0-20
years), but some specify a “mandatory minimum.” Second, since 1987, the statutory
sentencing constraints have been supplemented by much narrower ranges (for instance, 27 to
33 months) found in the U.S. Sentencing Guidelines. The Guidelines sought to reduce
unwarranted disparities in sentencing, including gender disparities (see Breyer 1988), by
constraining judicial discretion. They were mandatory until 2005, when the Supreme Court’s
decision in United States v. Booker (543 U.S. 220) rendered them advisory. But advisory
does not mean unimportant—judges are still required to calculate the Guidelines sentence,
and most sentences are still within the Guidelines range (U.S. Sentencing Commission 2010).
The Guidelines sentencing ranges are found in the cells of a grid, the two axes of
which are the “offense level” and the defendant’s criminal history. Judges determine the
offense level based on the crime(s) of conviction and the “sentencing facts.” Although
judges have independent factfinding authority, in practice they usually defer to the plea
agreement’s stipulations (Stith 2008; Schulhofer and Nagel 1997; Powell and Cimino 1995).
One survey found that 92% of judges said their findings of fact diverge from the plea
agreement either “infrequently” or “never” (Gilbert and Johnson 1996).
Legal scholars widely agree that the Guidelines greatly empowered prosecutors
because the sentence was now far more constrained by the charges of conviction and
especially by the negotiated “sentencing facts” (Stith 2008; Bibas 2009). Prosecutors thus
	
  

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Electronic copy available at: http://ssrn.com/abstract=2144002

Starr—Estimating	
  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

could both threaten long sentences and virtually promise much lower ones in exchange for
guilty pleas, and plea rates rose from 87% to 97%, where they remain today (Alschuler 2005;
Miller 2004). Although Booker expanded judicial discretion, the continued high rate of
Guidelines compliance means these sources of prosecutorial influence have not disappeared.
In addition, prosecutors can still firmly bind judges using mandatory minimums.
Prosecutors have a variety of incentives to balance, including career incentives that
push toward maximizing sentences and resource constraints that discourage going to trial
(see, for example, Baker and Mezzetti 2001; Easterbrook 1983). In addition, prosecutors
may be affected by sympathy or a sense of fairness. Schulhofer and Nagel (1997) review
federal prosecutors’ case files and find evidence of deliberate charge manipulation to avoid
excessive sentences. Prosecutorial discretion is often described as the power not to seek to
maximize punishment—to be selectively lenient (see Stith 2008). Although there may be
good policy reasons for allowing such discretion, it is a potential source of unwarranted
disparity if it is influenced by legally irrelevant factors such as gender.
1.2. Existing Empirical Research
Existing studies of demographic disparities in criminal justice have typically focused
on single stages of the criminal process in isolation—usually, the judge’s final sentencing
decision. In the federal-court literature, the usual approach is to estimate gaps in sentence
outcomes when controlling for the Guidelines offense level and the defendant’s criminal
history. These two key controls are often combined into a “presumptive sentence,” usually
the lower end of the Guidelines range (U.S. Sentencing Commission 2010), or into dummies
for the Guidelines grid cell (see, for example, Mustard 2001). Similarly, state-level studies
generally control for some measure of conviction severity as well as criminal history (see, for
example, Steffensmeier, Kramer, and Streifel 1993).
Studies of gender disparity that take this approach have usually found that women
receive shorter sentences, conditional on observables. The size of this effect has varied
considerably, even among studies that use federal data. Sarnikar et al. (2007) find about a
30% unexplained gender gap in sentence length, as did a prominent recent U.S. Sentencing
Commission (2010) study. Many studies, however, have estimated considerably smaller
disparities—for instance, Stacey and Spohn (2006), Schanzenbach (2005), and Mustard
(2001) all find average gender gaps in sentence length of around 10%.
The problem with the dominant approach is that the key control variable is itself the
result of a host of discretionary decisions made earlier in the justice process, which these
studies ignore. The resulting sentencing disparity estimates are potentially biased by the
endogeneity of the key control variable as well as sample selection introduced by the
dismissal of cases prior to sentencing. Although there have been occasional studies of pleabargaining disparities (see, for example, Spohn and Spears 1997; Shermer and Johnson
2010), they concern only certain bargaining outcomes, such binary measures of whether any
charges were dropped, and ignore negotiation over sentencing facts, which is the key aspect
of bargaining in the modern federal system. Moreover, without assessing disparities in
prosecutor’s initial choice of charges, the charge-bargaining results are not very meaningful.1
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
1

Spohn, Gruhl, and Welch (1987) found gender disparities favoring women in the rate of filing felony charges
in Los Angeles County, but did not analyze charge severity as an outcome.

	
  

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Starr—Estimating	
  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

Further, the plea-bargaining studies tend to assess that stage in isolation too, rather than
assessing its ultimate sentencing-disparity consequences.
1.3. The Dataset
This study uses data from four different federal sources: the U.S. Marshals’ Service
(USMS), the Executive Office of U.S. Attorneys (EOUSA), the Administrative Office of the
U.S. Courts (AOUSC), and the U.S. Sentencing Commission (USSC); the Bureau of Justice
Statistics provided inter-agency linking files that allow cases to be traced from arrest through
sentencing. The main sample consists of federal property and fraud crimes, drug crimes,
regulatory offenses, and violent crimes sentenced between FY 2001 and FY 2009.2
Immigration cases, which have different stakes centering on deportation, were excluded. To
reduce common support concerns, offense categories that were over 95% male were dropped:
weapons, sex and pornography, conservation, and family offenses.
The data include rich offense and offender information, including arrest offense
(which USMS identifies with 430 codes),3 gender, race, age, marital status, district,
citizenship, a string field describing the offense, criminal history, number of dependents,
education, Hispanic ethnicity, counsel type, co-defendant information, and county. AOUSC
also lists the initial and final charges ; these statutory sections then had to be coded on a
numeric charge severity scale. I constructed three such scales based on combined severity of
all charges: the statutory maximum, the statutory minimum, and a Guidelines-based measure.
If the statute prescribed varying sentences depending on case facts, I used default
assumptions grounded in legal research. For further details, see the Data Appendix.
2. Analysis and Results
2.1. Filing and Conviction-Stage Disparity
This study principally focuses on whether male and female arrestees ultimately
receive the same sentences, but a threshold question is whether they are equally likely to be
sentenced at all. Disparities in charging and conviction rates are important outcomes in their
own right, and also are potential sources of sample selection bias in the sentencing analysis.
To be included in the sentencing data, defendants must first face charges before a district
court judge—a close proxy for felony charges because misdemeanors are usually handled by
magistrates. Second, defendants must be convicted of a non-petty offense: a felony or a Class
A misdemeanor. Accordingly, I begin by estimating the probability of these events.
Columns 1 and 2 of Table 2 report the “male” odds ratios from logistic regressions.4
Conditional on arrest offense, district, race, citizenship, and age (the variables observed for
all arrested defendants), male arrestees face a modestly but significantly higher probability of
a charge before a district judge: 92.2% for the average male and 90.7% for the equivalent

	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
2

For the filing and conviction analyses, the sample consists of cases charged or disposed of during that period.
grouped certain closely related codes and subdivided certain drug codes based on a separate drug-type field.
There were 123 arrest offenses after this recoding, and the results are robust to use of the original codes.	
  
4	
  Except where other clustering is noted, all standard errors are clustered on arrest offense and district
(combined), due to concern that local crime patterns or the U.S. Attorney’s Office’s priorities might introduce
correlations. Results are robust to clustering on arrest offense or district alone.	
  
3	
  I

	
  

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Starr—Estimating	
  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

female.5 Conditional on the same variables plus multi-defendant case structure, male district
court defendants are also significantly more likely to be convicted of a non-petty offense
(93.2% versus 91.4%; Table 2, Col. 2).6 Sample selection bias from filing and conviction are
likely to downward-bias the sentencing disparity estimates reported below, but fairly slightly,
because these initial disparities affect relatively few cases. I therefore do not correct the
sentencing-stage estimates below for sample selection at these threshold stages.
2.2. The “Two-Part Model” of Incarceration Probability and Sentence Length
When estimating sentencing disparity, a threshold question is how to treat non-prison
sentences such as probation or fines (18% of the sample). This question has been hotly
debated in sentencing research. The leading practice is to break sentencing into two decision
processes, each estimated parametrically: whether to order incarceration and, if so, for how
long (see, for example, Berk 1983). The theory is that non-prison sentences are have no
obvious “prison equivalent,” and moreover, some covariates might be more influential in the
incarceration decision than the length decision or vice versa. A practical advantage is that
constraining the length sample to positive-length cases allows log transformation without
having to assign some arbitrary small value to the zeros.7 This is ideal because sentencing
law is structured so that inputs to sentencing will generally have multiplicative effects—each
Guidelines grid cell is a multiplier of the ones adjacent to it.
Although I prefer a different approach (discussed below), for comparability to the
current literature, I begin with estimates for this “Two-Part Model” (TPM). Table 2, Column
3 shows the results of a logistic regression of an incarceration indicator on gender, arrest
offense, criminal history, district, race, age, education level, U.S. citizenship, and the multidefendant case flag. The average male in the sample faces an 86% probability of
incarceration; comparable females are nearly twice as likely to avoid incarceration (74%).
Conditional on incarceration, men receive sentences that are approximately 34% longer.
The complication is that the gender disparity in the incarceration decision almost
surely means that the length estimates are downward biased by sample selection.8
Criminologists have often responded to this problem with Heckman-style corrections (see
Heckman et al. 1988; see Ulmer and Bradley [2006] for sentencing examples), but this
approach is not ideal because there is no plausible exclusion restriction.9 In addition, the
approach assumes that the estimand is the average treatment effect (the “ATE”) on the
underlying population. In this context, that is a strange object: the gender disparity in prison
sentence length that would be observed in a hypothetical world in which all defendants had
to go to prison. This thought exercise is of improbable interest to policymakers.
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
5

This sample consisted of arrestees facing some charge. Cases that were entirely declined were dropped
because they often represent unknown outcomes (transfers to other authorities or districts). When declinations
citing a favorable reason (such as lack of evidence) are included as zeros, the gender disparity stays significant.
6
Petty offense convictions and jury acquittals are rare, so this disparity is driven by dismissals by prosecutors. 	
  
7
The resulting estimates would be extremely sensitive to the choice of small value. Note that there are also a
very small number of life sentences, which I code as 540 months based on life expectancy data.
8
The direction of bias is clear because of the incarceration decision and the prison length decision are both
driven by observable and unobservable factors affecting case severity. If selection-on-observables holds in the
full sample, it almost surely will not hold in the sample of nonzero prison cases, because the incarceration
regression indicates that conditional on the observed covariates, men are more likely to be incarcerated—that is,
it takes less severe unobservables to push a given male case into the incarceration sample.
9
As Bushway, Johnson, and Slocum (2007) point out, the sentencing literature tends to ignore this problem.

	
  

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Starr—Estimating	
  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

If one is to follow the Two-Part Model at all, it is better instead to ask: If we went
from treating everyone like women to treating everyone like men,
(1)
(2)

what percentage of non-prison sentences would be replaced with prison, and
among cases that already would have received prison sentences, how would the
average length of those sentences change?

More formally, the quantities of interest are:
(1) E(PM|X) – E(PF|X)
(2) E(YM|X, PF=1) - E(YF|X, PF=1)
where P indicates a prison sentence, Y is prison sentence length, M and F denote the male
and female treatment conditions, and X is the covariate distribution for the population
noted.10 Object (2), in my view, is of more policy interest than the full-population ATE,
requiring no speculation about a world in which probation and fines were not possible.
With the estimand framed this way, the selection bias problem is not that the
estimation sample contains too few females, but that it contains “extra” males who would not
have been incarcerated if they were female. If it were possible to identify who those extra
males were, OLS regression in a sample excluding them would be an unbiased estimator of
object (2). Unfortunately, while the number of extra males can be readily estimated based on
the incarceration logit,11 they cannot be identified; PF is unobserved for males (see Lee
[2009], who discusses an analogous problem). In Table 3, I apply varying assumptions as to
which males were marginal to produce different trimmed-sample estimates.
Table 3, Column 1 replicates the “male” coefficient on log prison sentence length
from the full-sample OLS regression. Because sample selection bias is almost surely
downward, this should be treated as a lower bound on the true sentence length disparity
within the pool of cases that would have been subject to incarceration regardless of gender.
Column 2 provides something roughly approximating an upper bound, based on a nearworst-case assumption about selection bias. The Column 2 sample has trimmed the males
with the lowest (most negative) individual influence on the “male” coefficient.12 In this case,
the Column 2 length-disparity estimate is about 67%—approximately double the estimate for
the untrimmed sample. Columns 3 and 4 of Table 3 show results for samples trimmed based
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
10

This notation assumes monotonicity, such that PM=1 whenever PF=1.
This assumes gender monotonically affects incarceration probability, a reasonable assumption: being male
greatly increased that probability in every one out of dozens of analyzed subsamples.
12
Lee (2009) proposes a similar trimming method for estimating bounds on the effect of a randomly assigned
treatment when treatment monotonically affects attrition. In that case worst-case bounds can be more readily
estimated; the trim that will raise the treatment effect estimate by the most is just the lower tail of the treated
outcome distribution (see Lee 2009). The trim I conduct in Table 3, Column 2 is based on the same intuition.
But rather than assuming random treatment, I assume selection on observables within the full sentenced sample,
and use regression to estimate the number of “extra males” and to model the outcome. This assumption could
certainly be challenged, as I discuss below, but it already underlies both parts of the TPM; my method simply
gives a near-worst-case adjustment for the second-part estimate assuming that the first part is correct.
When there are covariates, one cannot just trim the lower tail; rather, the trim is based on the
observations’ influence on the partial effect of being male. Estimating a true upper bound would require
trimming the group with the most negative joint influence on the “male” coefficient. Identifying that group is
computationally impossible. But ranking observations by individual influence is easy and is, in practice,
probably a “bad enough” assumption about sample selection to provide useful guidance as to its possible scope.
11

	
  

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Starr—Estimating	
  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

on a plausibly realistic (rather than worst-case) assumption about who the marginal males
are. The assumption is simply that they are those with short sentences—that is, that gender is
likelier to be the deciding factor in closer cases. The Column 3 sample trims the males with
the very shortest nonzero sentences (one year or less), while the Column 4 sample picks them
randomly from the bottom quarter of the distribution (two years or less). The estimates for
these two trimmed samples are 63% and 47%, respectively.
This trimming exercise is not meant to “correct” sample selection bias, but rather to
provide a general sense of its possible magnitude. Unfortunately, the potential bias here is
large, rendering the TPM not ideally informative. The TPM remains appealing when the
disparity in incarceration probability is small, such that selection bias is likely minor; for this
reason, Rehavi and Starr (2012a) used it to assess racial disparity. In the gender context,
however, more useful guidance can be found using other methods.
2.3. Inverse Propensity-Score Weighting Estimates of Gender Disparities
The sample selection problem described above would not exist but for the choice to
model the determination of sentences as two distinct decision processes, a choice that is not
compelled by theory.13 I propose a simpler approach: keeping non-prison sentences in the
sample for the length-disparity estimates, and treating them as zeros.
While the Two-Part Model dominates the sentencing literature, a substantial minority
of the literature rejects it. Researchers following the minority approach typically instead treat
sentencing as a single process in which the non-prison cases are censored, applying a Tobit
model that estimates average disparity in an underlying latent variable (see Tobin 1958; see
Sarnikar et al. [2007]; Bushway and Piehl [2001]; Kurlychek and Johnson [2004]; and
Albonetti [1997] for sentencing examples). This approach avoids the sample selection
concern, but raises other practical problems. The Tobit is not robust to violations of its
assumptions of normality and homoskedasticity (see, for example, Arabmazar and Schmidt
1982; Cameron and Trivedi 2010)—and in this sample, specification tests for the Tobit are
decisively failed. Moreover, while the Tobit allows researchers to avoid assigning a specific
value to the non-prison sentences, they still must choose a censoring point below which their
value is assumed to fall. This choice is arguably equally arbitrary, and if the length variable
is log-transformed, it will have a big effect on the Tobit estimates.14
The approach I propose is conceptually simpler than either the Tobit approach or the
Two-Part Model, and avoids the practical weaknesses of both. If incarceration disparities are
the outcome of policy interest, then there is nothing unknown about the value of non-prison
sentences: they are correctly valued at zero. The main practical drawback of including them
is that it precludes log transformation, but this functional form concern is only a problem for
parametric estimation. I instead estimate the average length disparity in months by inverse
propensity score weighting (“IPW”), without specifying any functional relationship between
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
13

Bushway and Piehl (2001) provide strong reasons that a single-decision model (in particular, the Tobit) is a
better fit to the Guidelines process, in which zeros are just values in the lower end of the sentencing grid.
14
For instance, using a lower limit of half a day in the the Tobit log prison model (and the same covariates as in
the TPM above) produces a gender disparity estimate of 128%, while a limit of one month produces an estimate
of 72%. Either limit is theoretically defensible, as are many others. While the very lowest observed nonzero
sentence is one day, only 0.3% are below one month. One might reasonably set the limit to censor these cases,
to avoid giving excessive weight to large multiplicative differences between trivially short sentences.

	
  

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Starr—Estimating	
  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

the covariates and the outcome variable. I then extend this method to the distribution,
allowing assessment of disparities in incarceration probability as well as other possible
heterogeneity in gender effects on sentences of different lengths.
The IPW estimates of average gender disparities in sentence length are given in Table
4. The probability of being male (E(M|Xi) for each observation (the “propensity score”) is
first estimated by a logistic regression of “male” on the covariates X: gender, arrest offense,
criminal history, race, age, education level, U.S. citizenship, and the multi-defendant case
indicator.15 Estimates of average gender disparities are then produced via weighted
regression where the weights are inverse functions of the propensity score. To refer to the
estimands, I use the common language of “treatment effects,” where “treatment” refers to
being male. But note that for these “effects” to be given a causal interpretation, one must
assume there are no confounding variables; I return to this point below.
In Column 1 of Table 4, I estimate the overall average gender disparity in sentence
length conditional on the pre-charge covariates. This “average treatment effect” (ATE)
represents the difference between two counterfactuals: the mean sentence if everybody were
treated like males and the mean sentence if everybody were treated like females (see
DiNardo 2002).16 Table 4, Columns 4 and 7 reflect separate estimates of the average effects
of gender disparity on male and female sentences. The “average treatment effect on the
treated” (TOT) reflects the estimated effect of being male on male sentences, and is
estimated by comparing the observed male average to a reweighted female average (Col.
4).17 After this reweighting, the female endowments of covariates are similar to those of the
males, so the reweighted female mean can be interpreted as a counterfactual mean if males
were treated like females. The “average treatment effect on the untreated” (TUT) is
conversely estimated by reweighting the males, and represents the counterfactual increase in
sentence if females were treated like males (Col. 7).
As Table 4 shows, even after reweighting, the average gender gaps in sentence length
are strikingly large. The overall average disparity (the ATE) in Column 1 is 23 months,
which translates into a 63% increase in sentence length. When measured in months, gender
appears to have a bigger effect on males than females (compare Columns 4 and 7): being
male increases male sentences by 25 months, and would increase female sentences by 15
months. But this difference is mostly because of a higher baseline average: in percentage
terms, the TOT and TUT are not very different (64% versus 61%).
A drawback of propensity score reweighting is its vulnerability to the problem of
limited overlap between the male and female samples (see Busso, DiNardo, and McCrary
2008). Although the large sample size reduces this concern, women are only 19% of the
sample and are thinly represented in certain offenses and high criminal history categories.18
The reweighting of the female distribution risks giving unduly high weight to women with
unusual covariate values. In Table 4, Columns 2 and 5, I report the ATE and TOT for a
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
15

District fixed effects, which were included in the Two-Part Model, are not included in the weights. When
reweighting, parsimony makes it easier to balance the most important variables, and gender composition does
not vary much by district in any event. The results are robust to including the districts.
16
The weights are given by 1/(1-E(M|Xi)) for female observations and 1/ E(M|Xi) for males, before rescaling to
average 1 (see Busso, DiNardo, and McCrary 2008).
17
The weights are E(M|Xi )/(1-E(M|Xi) for female observations, before rescaling to average 1.
18
See Figure 1a for the propensity score distribution.

	
  

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  Criminal	
  Cases	
  

sample that eliminates those problematic covariate combinations by trimming extreme
propensity score values (see, for example, Heckman et al. 1998).19 The drawback with this
method is that the sample to which the estimates apply is not very intuitively or transparently
defined. In Columns 3 and 6, I report the ATE and TOT for an alternate sample that
excludes the highest three criminal history categories.20 Both trimming strategies produce
gender disparity estimates that are fairly similar in percentage terms to the full-sample
estimates (compare Columns 1 through 3 and Columns 4 through 6).
I report only the full-sample results for the TUT (the effect of gender on women),
because estimating it depends on reweighting only the males, and no males have propensity
scores anywhere near zero. For this reason, as I proceed below to analyze the gender
disparity in more detail, I focus on the counterfactual effects if women were treated like men.
The effects of gender on men and women are of equal policy interest, but analyzing the TUT
is simpler because the full sample can be used without limited-overlap concerns.
Table 5 accordingly shows TUT estimates for subsamples and alternate
specifications. Column 1 replicates the main estimate from Table 4 for comparison purposes.
Columns 2 and 3 show estimates for two large offense-type categories: drug offenses
(Column 2) and property, fraud, and regulatory offenses (Column 3). In percentage terms the
effects are similar. The disparity is likewise almost identical in percentage terms before and
after the watershed Booker decision (Columns 4 and 5).21 It is smallest for non-parents and
largest for single parents (51.6% versus 67.3%; compare Columns 6-8). It is larger for
defendants in multi-defendant cases than for sole defendants (66% vs. 51.2%, Columns 910), much larger among blacks than non-blacks (74% vs. 51.1%, Columns 11-12), and
slightly larger in states without federal women’s prisons (Columns 13-14). Many of these
subsample comparisons are useful in assessing possible causal theories for the unexplained
gender gap, and they will be further addressed in the Discussion.
The remainder of Table 5 shows the robustness of the TUT estimates to alternate
specifications of the gender-propensity model. Columns 15 and 16 show that the TUT is
unchanged by the addition of a set of flags for case characteristics mentioned in a text field
based on the arresting officers’ notes (in 2001-2007, the years the field is available). The
flags are for mentions of guns, other weapons, drug seizures, official victims, minor victims,
conspiracy and racketeering. Columns 17 through 20 show that the estimates are robust to
adding controls for marital and parental status and defense counsel type. Disparities decline
slightly when controlling for pleas and time elapsed before conviction (Col. 21). The gender
disparities in drug cases decline slightly when drug quantity seized at arrest, as recorded in
the EOUSA investigation files, is added to the controls. This check could only be performed
for arrests before 2004 because of data limitations (compare Columns 22 and 23).22
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
19

The propensity-score cutoff (approximately 0.93) is optimized to minimize variance (see Crump et al. 2009).
The trim drops about 4% of women and 21% of men from the sample.
20
The main sample already excludes the most male-dominated crime categories. Adding the criminal history
constraint does not entirely eliminate the limited overlap problem, but mitigates it considerably (see Figure 1b).
21
This does not preclude the possibility that Booker changed disparities; this analysis does not seek to
disentangle Booker’s causal effects from longer-term trends.
22
Results are also robust to the use of the original ungrouped arrest codes; the addition of district controls,
Hispanic ethnicity, and county-level controls for poverty rate, unemployment, per capita income, and crime

	
  

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  Criminal	
  Cases	
  

Finally, a comparison of Column 1 and Column 24 of Table 5 illustrates the
importance of the choice to condition on arrest offense rather than on the end result of
sentencing fact-finding. The Column 24 reweighting substitutes the final Guidelines offense
level instead of the arrest offense, and the estimated disparity is reduced by 63%. This
comparison suggests that by conditioning on an endogenous variable and ignoring gender
disparities introduced earlier in the justice process, the current literature may have
substantially understated the size of the gender gap.
In Figure 2, I extend the reweighting method to estimate the effect of gender on the
distribution of sentences for females following the method proposed by DiNardo, Fortin, and
Lemieux (1996). The white and black bars reflect the observed distribution of sentence
lengths for male and female defendants, respectively; non-prison sentences have their own
bin and need not be assigned a numeric value. The checkered bars represent the
counterfactual distribution if females were treated like males. Comparison of the checkered
to the black bars shows large unexplained gaps throughout the distribution. The unexplained
gap in the share sentenced to non-prison sentences (about 11 percentage points) is similar to
the regression estimate in Table 2. The gap is not confined to the low end—the whole
reweighted male distribution is shifted to the right relative to the female distribution.
2.4. Decomposing the Gender Gaps
The estimates presented above represent the aggregate disparities introduced
throughout the post-arrest justice process, raising the further question of when in the justice
process those disparities emerge. Table 6 shows a sequential decomposition of the observed
average gender disparity into components explainable by pre-charge covariates and by each
subsequent stage of the process: charging, charge-bargaining, sentencing fact-finding, and
sentencing. The method is a sequence of inverse-propensity score reweightings, in which
new variables are added to the propensity score estimation at each step (see, for example,
Altonji, Bharadwaj, and Lange 2008; DiNardo, Fortin, and Lemieux 1996).
In this part of the analysis, data limitations require separate assessment of drug and
non-drug cases. For non-drug crimes, the initial and final charges were coded with the
statutory minimum, maximum, and Guidelines measures described above. But in drug cases,
the AOUSC charge data are too ambiguous to permit that coding; the same statutory
subsections encompass a vast array of drug types, quantities, and sentences. The only usable
measure of statutory severity available for drug cases is the mandatory minimum for the
crime of conviction, which the Sentencing Commission records. Thus, in drug cases I cannot
disentangle the effects of initial charging and subsequent charge-bargaining. The mandatory
minimum variable represents the combined effect of those stages.
The non-drug decomposition is shown in Panel A of Table 6. Column 1 shows the
raw observed gender gap to be decomposed. In Column 2, the men have been weighted
based on pre-charge covariates. Columns 3, 4 and 5 sequentially add the initial charge
severity measures, the conviction measures, and the final offense level (the product of
sentencing fact-finding). The drug decomposition (Panel B) has one stage fewer: the
conviction mandatory minimum substitutes for the separate charging and conviction
variables. The explanatory value attributed to each stage is the change in the unexplained
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
rate; and various exclusions from the sample: cases in which the indictment was issued before the arrest, cases
from the South, and arrests by each of the two enforcement agencies (the FBI and the DEA).

	
  

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gender gap when one adds that stage’s measures. What remains after the final reweighting is
attributed to the sentencing decision. In the last two lines of each panel, I express each
component as percentages of the raw observed gender gap and of the gender gap that was
unexplained by the pre-charge covariates. That is, the last line decomposes the gender
disparity that appears to be introduced during the criminal justice process.
This method of decomposition is path-dependent: explanatory value is preferentially
attributed to the covariates that are added first. Path-dependence is often a drawback to
sequential decomposition, because in many contexts, when multiple correlated covariates
together explain a certain portion of an outcome gap, there is no theoretical reason to
“blame” one over the others (see Fortin, Lemieux, and Firpo 2011; DiNardo, Fortin, and
Lemieux 1996). But here path-dependence is desirable, because the justice process is itself
path-dependent: earlier decisions constrain later ones.23 The decomposition tracks the
divergence of men’s and women’s fates as the process advances, so it would not make sense
to attribute to a later stage a disparity that already existed. When there is a natural ordering
like this, sequential decomposition is appropriate (see Altonji, Bharadwaj, and Lange 2008).
The decompositions show that significant new disparity favoring women is
introduced at every stage of the justice process, but sentencing fact-finding is especially
crucial. In non-drug cases, an eight-month gender gap remained unexplained after
reweighting by arrest offense and the other pre-charge covariates—this is the gap attributed
to the justice process as a whole. Initial charging and charge-bargaining contribute about 9%
and 4% of the gap, respectively; Guidelines fact-finding explains 60%, leaving 27% for the
final sentencing stage to explain. In drug cases, the mandatory minimum can explain one
third of the 23-month gender gap attributed to the justice process. Guidelines fact-finding
can explain 29.5%, leaving 37% attributed to the final sentencing decision.
In Figures 3a through 3d, I show a similar sequential decomposition of the sentencing
distributions (see DiNardo, Fortin, and Lemieux 1996). Figure 3a shows the distribution of
non-drug sentences observed for males and females and, between them, the distributions
produced by the same series of reweightings described above. Each step in the sequence
makes the male distribution look somewhat more like the female. Figure 3b presents these
results in a way that (while it does not show the underlying distributions) allows the
procedural sources of the gaps in the distribution to be more readily discerned. The full
height of each bar represents the gap in the cumulative distribution at the denoted sentence
threshold after reweighting by the pre-charge covariates—that is, the gap in the probability of
getting a sentence exceeding the threshold. The patterned sections decompose these gaps
into charging, charge-bargaining, fact-finding, and sentencing components. Figures 3c and
3d repeat these exercises for drug cases. The decompositions again show the central role of
sentencing fact-finding, especially in explaining gaps higher in the length distribution.
Judges’ final sentencing decisions appear to be more important in explaining disparities at
the lower end, particularly in the incarceration decision (Figs. 3b, 3d).
Because fact-finding and Guidelines departures are both stages in which men’s and
women’s outcomes appear to diverge substantially, it is worth inquiring whether any
particular findings of fact and departures appear to be key factors. Table 7 shows the
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
23

For instance, the initial charges define the range of possible outcomes to charge-bargaining; charges are
almost never added (and in most cases are not dropped).

	
  

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  Criminal	
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explanatory value attributed to each of several findings and departures when they are added
to the mean decompositions from Table 6. These variables were not added sequentially with
one another because there is no natural ordering among them; each was added independently.
If they are correlated, the sum of the shares reported likely overstates their collective
importance.24 Each share is thus best interpreted as the maximum the variable can explain.	
  
The factors listed in Table 7 were assessed because they are factors that one might
expect to vary by gender. Their relevance to possible causal theories for gender disparity are
addressed in the Discussion below. Other than the factors analyzed here, sentencing factfinding involves a vast array of context-specific inquiries. Likewise, other stated reasons for
departures vary widely, and are often vague, such as “the interests of justice.”
3. Discussion
The unexplained gender disparities identified above are large—much larger than
those estimated via the prevailing method of conditioning on presumptive sentence. The key
interpretive question is why these gaps exist—and, in particular, whether unobserved
differences between men and women might justify them. One cannot instrument for inborn
traits or manipulate them, so estimation of demographic disparities always risks omitted
variables bias, and one must be cautious about inferring gender discrimination. Still, some
often-advanced causal theories have testable implications. In this Part, I consider the leading
theories suggested in the literature and in my informal conversations with criminal lawyers.
3.1. Unobserved differences in offense severity.
One obvious question is whether the crimes differ in ways not captured by the arrest
offense codes. The arrest offense is not a perfect proxy for underlying criminal conduct, and
if it overstates the severity of female conduct relative to that of men, that might explain some
of the observed disparity. In particular, one might wonder whether the disparities introduced
at sentencing fact-finding merely represent the process’s proper accounting for nuance
differences in facts within offense categories, which is, after all, fact-finding’s purpose.
Unobserved differences naturally cannot be ruled out, but there are good reasons to
doubt that they explain much of the observed disparity. First, the observable covariates are
detailed, capturing considerable nuance. They include not just the 430 arrest codes and the
multi-defendant flag (a proxy for group criminality, an important severity criterion), but also
additional flags based on the written offense description (see Table 4, Rows 15-16). Second,
the disparities are similar across all case types (and across arresting agencies), suggesting it is
not a matter of a few crimes being “worse” when men commit them. Such differences would
have to be prevalent across a variety of crimes and agencies to explain the result.
Third, there is some reason to believe unobserved divergences between the arrest
offense and actual criminal conduct may bias disparity estimates downward. If police tend to
treat men more harshly, one might expect them to record arrest offenses that overstate men’s
culpability relative to women’s. The empirical evidence on gender and policing is limited.
Traffic stop studies reach divergent conclusions about whether there is bias against men
(compare Rowe 2009 with Persico and Todd 2006), but at least do not suggest bias against
women. A study covering a wider range of crimes (Stolzenberg and D’Alessio (2004)) found
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
24

This is almost surely the case with the fact-finding results in drug cases, where the shares reported in Table 7
add up to slightly more than the total months of disparity attributed to fact-finding in Table 6.

	
  

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that other factors equal, reported crimes with female offenders are substantially less likely to
lead to arrests, results that they interpret to show police leniency toward women.
Nonetheless, there are some easily imaginable differences between male and female
cases that might not be observed. For instance, men might well commit violent crimes with
greater force, a difference not fully captured by the arrest code (beyond the labeling of some
assaults as “aggravated”). There are fewer obvious potential differences in property,
regulatory, or drug offenses, but perhaps women might commit smaller-scale offenses. Scale
is captured to some degree by the arrest offense codes (for instance, pickpocketing versus
vehicle theft), but not entirely—for instance, wire fraud could be in any amount. Findings of
fact on loss value appear capable of explaining up to 20% of the otherwise-unexplained gap
in non-drug crimes (Table 7). Unfortunately, there is no way to tell how much of that factfinding difference reflects true underlying differences in the facts.
With respect to drug quantity, the data are more informative. Drug quantity and type
determine eligibility for mandatory minimums, which explain 29.5% of the post-arrest
gender gap in drug cases (Table 6); related Guidelines adjustments can explain a further 3%
(Table 7).25 For arrests before FY 2004, the drug quantity and type seized at arrest is
recorded in the EOUSA investigation file. Within that pool, there are substantial gender
disparities in the drug quantity found at the sentencing stage, even after controlling for drug
quantity at arrest and the other standard covariates. The estimated gender gap in sentences
in pre-2004 drug cases is only slightly reduced by adding arrest-stage drug quantity controls
to the reweighting (Table 5, Cols. 22-23). These findings suggest that quantity findings at
sentencing diverge from the underlying facts in ways that differ by gender.
Another key factor affecting drug sentencing is the “safety valve” loophole built into
the drug mandatory minimum statutes and the related Guidelines safety valve. The safety
valves can explain up to 9% of the sentence gap in drug cases, and one might wonder
whether this reflects “real” case differences. Eligibility for the safety valve is defined by
statute, and cases can be coded as seemingly eligible or not based on the case’s observed
characteristics: criminal history, certain offense features, lack of aggravating role, and lack of
obstruction. Conditional on apparent eligibility, women are significantly more likely to get
safety-valve reductions. This is only suggestive evidence of disparate treatment, however,
because the observables do not perfectly track the eligibility requirements.26
3.2. The “girlfriend theory.”
In group offenses, another factor affecting culpability is relative role. Women might
be viewed as minor players—perhaps mere accessories of their male romantic partners.
Prosecutors and judges may consider such women less dangerous, less morally culpable, or
useful sources of testimony. While leniency may be appropriate in such cases (see Raeder
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
25

Drug quantity findings drive both the application of mandatory minimums and the more nuanced gradations
under the Guidelines. The 3% figure in Table 7 reflects only the latter component: the additional gender
disparity explained by quantity findings after mandatory minimums had already been accounted for.
26
The key source of discretion in safety valve application is the prosecutor’s choice whether to characterize the
defendant as having been fully truthful in describing the crime (see 18 U.S.C. 3553(e)). Beyond the absence of
obstruction and the presence of acceptance-of-responsibility reductions, discussed above, the data do not
provide a way to assess whether the defendant was in fact truthful.

	
  

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  Gender	
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  Criminal	
  Cases	
  

[2006]), some lawyers I spoke to suggested that such perceptions are not always justified by
the facts; in cases involving couples, it may just be assumed that the female is the “follower.”
The data provide no way to test whether role perceptions are well founded, but they
do suggest that they can partially explain the gender gap. Other than its implications for
cooperation departures, the “girlfriend theory” has two testable implications: first, the gender
gap should be larger in multi-defendant cases, and second, part of it should be attributable to
sentencing adjustments for role in the offense. Both predictions are supported by the data.
The gender gap is significantly larger in multi-defendant cases: 66% compared to 51%
(Table 5). Approximately 14% of the otherwise-unexplained disparity in non-drug cases and
20% in drug cases can potentially be explained by role adjustments (Table 7). The girlfriend
theory appears to explain part, but not most, of the gender gap; it is hard for it to explain the
large disparities that persist even in single-defendant cases.27
3.3. Parental responsibilities.
Another possibility is that prosecutors and/or judges worry about the effect of
maternal incarceration on children. The estimates are robust to controls for marital status and
number of dependents, but these variables do not capture all differences in care
responsibilities, including custody status. Other research shows that female defendants are far
more likely than men to have primary or sole custody, and incarcerating women more often
results in foster care placements (see Hagan and Dinovitzer [1999] for a review of the
literature; Koban 1983). In an experiment asking judges to give hypothetical sentences based
on short vignettes, Freiburger (2010) found that mentioning childcare reduced judges’
probability of recommending prison, but mentioning financial support for children did not.
The childcare theory suggests that one would expect to see the largest gender
disparities among single parents, and the smallest among defendants with no children. That
expectation is borne out by the data: compare Table 5, Columns 6-8. The TUT estimate is
still over 50% among childless defendants, however, so the childcare theory appears not to
fully explain the gender gap, but it probably explains part of it.28
On the other hand, the decompositions in Table 7 indicate that, at most, between 1%
and 2% of the sentencing gap can be explained by disproportionate invocation of the official
“family hardship” departure in the Sentencing Guidelines. Women in the sample receive that
departure at three times the rate of men: 2.4% of cases versus 0.8%. But because the
departure is so rare for both genders, it cannot explain much of the overall disparity. This is
presumably because it requires “extraordinary circumstances,” and judges typically hold that
single parenthood does not suffice (see U.S.S.G. 5H1.6; Raeder 2006). Likewise, the main
federal sentencing statute, 18 U.S.C. 3553, does not mention family hardship, and the
Guidelines affirmatively instruct that family ties are “not ordinarily relevant.” Federal
sentencing law is not designed to provide much accommodation for defendants’ children.
In short, the family status-gender interaction appears to be more substantial than the
one formal legal mechanism for accommodating family hardship can explain. Prosecutors
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
27

The formal departure for duress or coercion (U.S.S.G. 5K2.12), while given to women at five times the rate
of men (0.4% versus 0.08%), is far too rare to be a significant explanation for the gender gap.
28
The gender gap is also slightly smaller in states with federal women’s prisons (see Table 5, Columns 13-14),
which may suggest that judges do not want to move women far from their families, although this is not a
dramatic difference and other characteristics of those seven states might explain it.

	
  

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  Criminal	
  Cases	
  

and/or judges seem to use their discretion to accommodate family circumstances in sub rosa
ways—but not for male defendants. Among single men, conditional on observables, having
children significantly increases sentences, and among married men, children make no
significant difference. There are many competing arguments concerning whether family
status is a proper sentencing consideration (see, for example, Markel, Collins, and Leib
2007), and I will not address them here. However, if family hardship is a legitimate
consideration, one might expect it to play at least some role in men’s cases as well.
Numerous studies have suggested that paternal incarceration harms children even when the
father was already a noncustodial parent (see Hagan and Dinovitzer [1999] for a review).
3.4. Cooperativeness.
Another often-advanced theory is that female defendants receive leniency because
they are more cooperative with the government. These data provide, at best, limited support
for that theory. Conditional on observables, women are modestly but significantly more
likely to receive downward departures for cooperation in another case (20% versus 17%),
have higher guilty plea rates (97.5 vs. 96.2%), and have their cases resolved about two weeks
sooner on average (a 10% difference). But the interpretation of these differences is not clear.
Plea rates, timing, and cooperation are all endogenous, turning on the deals being offered.
Moreover, women could be being rewarded more for the same level of cooperation; the
actual assistance they provide is unobserved. On all four charge- and conviction-severity
scales, women receive modestly but significantly larger charge reductions in plea-bargaining
than men do, and far more favorable findings of fact, suggesting that they may be offered
better factual stipulations. If women really are inherently more cooperative (or risk-averse),
one might think prosecutors could get away with offering them lesser discounts, and still
induce frequent guilty pleas. Yet the opposite appears to be true.
Whatever the merits of these indicators of cooperativeness, they seem to explain only
fairly modest portions of the gender gap. Adding a plea and elapsed-time indicator to the
reweighting reduces the unexplained disparity by about 8% (Table 5, Col. 21). Disparities in
departures for cooperation can explain up to 9% of the otherwise-unexplained gap in drug
cases, but no significant share in non-drug cases (Table 7). In addition, the “acceptance of
responsibility” reduction and the obstruction of justice enhancement do not explain any
substantial portion of the gender gap; in non-drug cases these offset one another, while in
drug cases neither is significant (Table 7). Unlike that of the family hardship departure, the
limited explanatory power of these adjustments and departures cannot be attributed to rarity
or tight legal constraints—all are very common. Formal mechanisms for recognizing
women’s purportedly greater cooperativeness are readily available, and yet they explain only
a modest share of the disparity in drug cases and none in non-drug cases.
3.5. Mental health, addiction, abuse, and other sympathetic life circumstances.
Another theory is that female defendants may have more troubled life circumstances,
such as poverty, mental illness, addiction, and abuse histories. If so, they may be perceived
as less morally culpable or as candidates for rehabilitation. Criminal defendants often come
from difficult backgrounds. This could well be disproportionately true for females; perhaps
because women more rarely commit crime, those who do are likelier to be in the upper tail of
the life-hardship distribution. Prisoner studies show more self-reported mental illness and
prior abuse among women. See James and Glaze (2006); Harlow (1999).
	
  

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  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

Socioeconomic status is not unobserved, however, and does not seem to explain the
gender gap. The main specification includes education, and the results are robust to adding
county-level socioeconomic controls and defense counsel type (a strong proxy for poverty).
But mental health, addiction, and abuse are not observable unless judges cite them as the
basis for a departure. The Guidelines permit departures for “unusual” mental and emotional
conditions (U.S.S.G. 5H1.3) and for “significantly reduced mental capacity” (U.S.S.G.
5K2.13). They prohibit departuers for “disadvantaged upbringing” (U.S.S.G. 5H1.12) and in
most cases for addiction (U.S.S.G. 5H1.4), although judges have more flexibility to disregard
these restrictions after Booker. Together, all such cited bases for departures explain only
between 1 and 2% of the otherwise-unexplained gap in sentence length; they are too rare too
explain more. If prosecutors or judges take such factors into account in informal ways (as
they seem to with family hardship, above), it would be unobservable.
3.6. Race-Gender Interactions.
Columns 11-12 of Table 5 show that the gender gap is substantially larger among
black than non-black defendants (74% versus 51%). The race-gender interaction adds to our
understanding of racial disparity: racial disparities among men significantly favor whites,29
but among women, the race gap in this sample is insignificant (and reversed in sign). The
interaction also offers another theory for the gender gap: it might partly reflect a “black male
effect”—a special harshness toward black men, who are by far the most incarcerated group in
the U.S. This possibility is not really an “explanation” for the gender gap, much less a reason
to worry less about it—but it might cause policymakers to understand it differently, as an
issue of intersectional race-gender disparity. This theory only goes so far, however—the
gender gap even among non-blacks is over 50%, far larger than the race gap among men.
3.7. Gender discrimination: preference-based and statistical.
Although several of the factors above appear to explain portions of the gender gap,
that gap is large enough that it is plausible that gender discrimination also contributes. If so,
several types of discrimination could be at play. The theoretical literature suggests
“chivalry” and “paternalism” (see, for example, Franklin and Fearn [2008]). Another theory
is selective sympathy: perhaps circumstances like family hardship or “bad influence” appear
more sympathetic when it is women who are in them. Psychology experiments have found
that attributions of blame and credit are often filtered through expectations that males are
“agentic” and active and women are “communal” and passive (see Eagly, Wood, and
Diekman [2000] for a review). If so, prosecutors or judges might more readily credit societal
or situational explanations for females’ crimes than for males.’
Statistical discrimination is also possible. Perhaps the likeliest such mechanism is
that prosecutors or judges might assume men are more dangerous than women. Studies
generally find that women have lower recidivism rates, though some of the difference may be
explained by characteristics that this study controls for (see Gendreau, Little, and Goggin
[1996] for a meta-analysis). I do not have recidivism data to test whether statistical
discrimination might be “rational” here. Note that if recidivism risk perceptions are based on
individual information about the offender (not based on gender), then it is perfectly
permissible to consider them. But punishment decisions based on statistical generalizations
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
29

	
  

Rehavi and Starr (2012) explore these more extensively, finding a 10% unexplained disparity.

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  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

about men and women are unconstitutional. The Supreme Court has repeatedly ruled that
reliance on gender stereotypes is impermissible even if those stereotypes are statistically well
founded (see J.E.B. v. Alabama ex rel T.B., 511 U.S. 127 [1994]).
Conclusion
This study finds dramatic unexplained gender gaps in federal criminal cases.
Conditional on arrest offense, criminal history, and other pre-charge observables, men
receive 63% longer sentences on average than women do. Women are also significantly
likelier to avoid charges and convictions, and twice as likely to avoid incarceration if
convicted. There are large unexplained gaps across the sentence distribution, and across a
wide variety of specifications, subsamples, and estimation strategies. The data cannot
disentangle all possible causes of these gaps, but they do suggest that certain factors (such as
childcare and offense roles) are partial but not complete explanations, even combined.
These estimates are much larger than those of prior studies, which have probably
substantially understated the sentence gap by filtering out the contribution of pre-sentencing
discretionary decisions. In particular, this study highlights the key role of sentencing factfinding, a prosecutor-dominated stage that existing disparity research ignores. Mandatory
minimums—prosecutors’ most powerful tools—are also important contributors to gender
gaps in drug sentencing. Understanding the relative roles of prosecutors and judges is
important. Gender disparities have been cited to support constraints on judicial discretion,
including when the Sentencing Guidelines were adopted. But such constraints typically
empower prosecutors, so if prosecutors drive disparities, they could backfire.
Policymakers might simply be untroubled by leniency toward women. They are a
small minority of defendants, and when disparities favor traditionally disempowered groups,
they might raise fewer concerns. But the gender disparity issue need not be framed in terms
of how women are treated. One could ask: why are men treated so harshly, if women are
(apparently) treated otherwise? It is hard to dismiss this question as trivial: over two million
American men are behind bars. While males generally are not a disadvantaged group, men
in the criminal justice system generally are; they are mostly poor and disproportionately
nonwhite. The especially high rate of incarceration of men of color is a serious social
concern, and gender disparity is one of its key dimensions.
From this perspective, one might think differently about some of the possible
explanations for the gender gap. Most defendants of both genders have suffered serious
hardship, have mental health or addiction issues, have minor children, and/or have
“followed” others onto a criminal path. Sentencing law provides very limited formal
mechanisms to account for such factors—which is probably why, with women, they appear
to mostly be considered sub rosa. If prosecutors, judges, and legislators are comfortable with
those factors playing a role in the sentencing of women, then perhaps it is worth explicitly
reconsidering their place in criminal sentencing more generally.

	
  

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  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

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  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

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  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

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Starr—Estimating	
  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

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  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

Table 1
SUMMARY STATISTICS

District court defendants sentenced for non-petty crimes:
Male
White
Black
Other Race
Age (Years)
U.S. Citizen
Non-Parent
Married Parent
Single Parent
Multi-Defendant Case
Education:
HS Dropout
HS Diploma
GED/Vocational
College
Criminal History:
Category 1 (low)
Category 2
Category 3
Category 4
Category 5
Category 6 (high)
Offense Category:
Property/Fraud
Regulatory
Drug
Violent
Sentenced to Prison
Prison Sentence Length (Months)
Prison Sentence Length (If Incarcerated)
All arrestees in filing-stage sample
Filed in District Court
All district-court defendants in conviction-stage sample
Convicted (Non-Petty)

	
  

(1)
Mean

(2)
Female
Mean

(3)
Male
Mean

(4)
Observations

0.808
0.646
0.310
0.044
34.1
73.7
0.368
0.300
0.333
0.473

0
0.652
0.295
0.053
34.5
82.6
0.374
0.244
0.383
0.472

1
0.645
0.313
0.042
34.0
71.6
0.366
0.313
0.321
0.473

231,694
231,694
231,694
231,694
231,694
231,694
187,651
187,651
187,651
231,694

0.418
0.213
0.130
0.239

0.342
0.236
0.123
0.300

0.436
0.208
0.132
0.224

231,694
231,694
231,694
231,694

0.565
0.106
0.127
0.066
0.038
0.097

0.737
0.093
0.091
0.034
0.018
0.028

0.524
0.109
13.6
0.074
0.043
0.114

231,694
231,694
231,694
231,694
231,694
231,694

0.282
0.055
0.590
0.073
0.818
56.9
69.5

0.468
0.054
0.446
0.032
0.639
25.2
39.5

0.237
0.055
0.625
0.083
0.861
64.4
74.8

231,694
231,694
231,694
231,694
231,617
231,617
161,032

0.919

0.905

0.922

386,205

0.928

0.913

0.932

286,709

22	
  

Starr—Estimating	
  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

Male
Black
Other
Age

Table 2
REGRESSION ESTIMATES OF MEAN GENDER DISPARITIES IN CASE PROCESSING*
(1)
(2)
(3)
(4)
Filing in District
Non-Petty Conviction
Incarceration
Log Prison Length
Court (Odds Ratios)
(Odds Ratios)
(Odds Ratios)
(If Incarcerated)
Coefficient
SE
Coefficient
SE
Coefficient
SE
Coefficient
SE
1.213*** .044
1.293*** .029
2.193*** .052
0.347*** .014
1.023 .045
0.919** .025
0.909*** .023
0.063*** .012
1.544**
1.009***

.201
.002

0.928
0.989***

.043
.001

0.929
1.001

.050
.001

0.0170
0.0063***

.029
.000

1.480**

.215

1.061

.035

0.674***

.027

-0.037*

.016

0.680***

.020

1.115***

.031

0.158***

.017

Ed. 2: HS Grad

0.864***

.020

-0.0205*

.008

Ed. 3: GED

0.902***

.026

0.0217**

.007

0.944*

.027

0.001

.008

Crim. His. Cat. 2

2.165***

.070

0.261***

.015

Crim. His. Cat. 3

3.525***

.124

0.364***

.015

Crim. His. Cat. 4

7.336***

.370

0.511***

.016

Crim. His. Cat. 5

11.573***

.820

0.650***

.017

U.S. citizen
Multi-defendant

Ed. 4: College

Crim. His. Cat. 6
N

379,148

282,938

19.424*** 1.238
231,613

0.944*** .014
189,498

NOTE. – Ed. Cat. = educational category; Crim His. Cat. = criminal history category. Odds ratios/coefficients
are from logistic and OLS regressions that also include arrest-offense and district fixed effects.
*Standard errors clustered on arrest-district, respectively. *p.<0.05; **p<0.01; ***p<0.001.

	
  

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  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

Table 3
POSSIBLE EFFECTS OF SAMPLE SELECTION ON ESTIMATION OF DISPARITY IN NON-ZERO PRISON SENTENCES:
COMPARISON OF TRIMMED-SAMPLE ESTIMATES*
(1)
(2)
(3)
(4)
Coefficient
SE
Coefficient
SE
Coefficient
SE
Coefficient
SE
Male
0.347*** .014
0.669*** .020
0.629*** .018
0.497*** .014
Sample Trim
Untrimmed
Influence-Based
Shortest
Random Short
N
189,498
166,586
166,586
166,586
NOTE. – This table compares the "male" coefficient from Table 2, Column 4 to estimates for the same
regression in samples that have male observations dropped so that the gender ratio in the trimmed sample
matches the counterfactual ratio predicted by the Table 2, Column 3 regression if males were, conditional on
observables, incarcerated only at the rate of women. The samples in Columns 2-4 are trimmed based on
differing assumptions about which males are on the incarceration margin. Column 2 trims the male cases with
the most negative individual influence on the "male" coefficient, Column 3 trims those with the shortest
nonzero sentences, and Column 4 trims randomly from the male cases that have sentences no longer than 24
months.
*Standard errors are clustered on the offense-district. *p<0.05, **p<0.01, ***p<0.001

	
  

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  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

Table 4
AVERAGE GENDER DISPARITIES IN PRISON SENTENCE LENGTH (INCLUDING ZEROS): INVERSE PROPENSITYSCORE REWEIGHTING ESTIMATES*
Treatment
Average Treatment Effect
on
(Treated=Male)
Treatment on Treated (Men)
Women
(1)
(2)
(3)
(4)
(5)
(6)
(7)
23.23***
17.60***
17.29***
25.13***
18.67***
18.55***
15.34***
Male
(2.716)
(1.923)
(1.373)
(2.908)
(1.936)
(1.409)
(1.701)
36.58***
29.76***
27.85***
39.28***
31.57***
30.98***
25.20***
Constant
(3.393)
(2.986)
(2.254)
(3.505)
(2.985)
(2.183)
(2.472)
63.5
59.1
62.1
64.0
59.1
59.9
60.9
Percent
Sample
N

Full
231,582

PS Trim
190,535

Low CH
173,407

Full
231,582

PS Trim
190,535

Low CH
184,787

Full
231,582

NOTE. – Columns 1-3 show the average increase in sentence in months associated with changing all cases
from the female to the male treatment condition, estimated by inverse propensity-score reweighting. Covariates
used to estimate propensity scores are arrest offense, criminal history, education, age, race, U.S. citizenship, and
multi-defendant case flag. Column 1 shows full-sample results. The Column 2 sample is trimmed to eliminate
extreme propensity score values (p(m)>.93), and the Column 3 sample is limited to cases in criminal history
categories 1-3. For the same samples, Columns 4-6 shows the "average treatment effect on the treated" (men)
obtained by comparing the observed male average to the reweighted female average. Column 7 shows the
counterfactual "average treatment effect on the untreated" (women) obtained by comparing the observed female
average to the reweighted male average, for the full sample. The "constant" line is the average in the female
treatment condition and the "percent" line expresses the treatment effect as a percent of this female average.
*Standard errors are clustered on the strata within which propensity scores are balanced. *p<0.05,
**p<0.01, ***p<0.001.

	
  

25	
  

Starr—Estimating	
  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

Table 5
ALTERNATE SAMPLES AND SPECIFICATIONS: INVERSE PROPENSITY-SCORE REWEIGHTING ESTIMATES,
TREATMENT ON UNTREATED (WOMEN)*
(1)
(2)
(3)
(4)
(5)
(6)
Married
Sample
Main
Drug
Prop./ Reg.
Non-Parent
Single Parent
Parent
Male
15.34***
23.35***
5.975***
12.82***
13.40***
17.63***
(1.70)
25.20***
(2.472)
60.9
231,582

(1.115)
40.00***
(2.064)
58.4
136,730

(0.408)
11.01***
(0.893)
54.3
77,989

(1.717)
24.85***
(3.154)
51.6
68,890

(1.877)
22.60***
(2.531)
59.3
56,085

(2.608)
27.26***
(3.749)
67.3
62,419

(7)

(8)

(12)

Post-Booker

(10)
Sole
Defendant

(11)

Pre-Booker

(9)
MultiDefendant

Black

Non-Black

14.65***
(1.855)
23.81***
(2.554)
61.5
109,663

15.89***
(1.961)
26.46***
(3.067)
60.1
121,883

21.42***
(2.421)
32.43***
(2.90)
66.0
109,487

9.599***
(1.553)
18.73***
(2.99)
51.2
121,875

17.52***
(2.645)
23.68***
(3.80)
74.0
71,737

13.20***
(1.087)
25.83***
(1.947)
51.1
159,801

(13)

(14)

(15)

(16)

(17)

(18)

States w/ W.
Pris.
14.45***
(1.626)
25.79***
(2.78)
56.0
91,470

States w/o
W Pris.
15.59***
(2.149)
24.81***
(2.96)
62.8
139,932

Police Notes
Rec'd
15.75***
(1.665)
26.44***
(2.71)
59.6
134,613

Police Notes
Flags
15.57***
(1.606)
26.44***
(2.78)
58.9
134,613

Family Rec'd
15.07***
(1.842)
25.22***
(2.777)
59.8
187,553

Family
Added
15.19***
(1.407)
25.23***
(2.339)
60.2
187,549

(19)

(20)

(21)

(22)

(23)

(24)

Counsel
Rec'd

Counsel
Added

Plea/Time
Added

Drug Qty
Rec'd

Drug Qty
Ctrl.

Presumpt.
Sentence

Percent

15.33***
(1.531)
26.70***
(2.224)
57.4

14.83***
(1.351)
26.70***
(2.521)
55.5

14.06***
(1.607)
25.20***
(2.523)
55.8

19.28***
(1.943)
33.20***
(2.060)
58.1

17.83***
(1.720)
33.20***
(2.372)
53.7

5.661***
(0.748)
25.20***
(4.218)
22.5

N

135,471

135,470

231,582

37,074

37,074

231,617

Constant
Percent
N

Sample
Male
Constant
Percent
N

Sample
Male
Constant
Percent
N

Sample
Male
Constant

NOTE. – The constant reflects the observed female average sentence length in months for the designated
sample (including zeros) and the "male" coefficient is the average additional sentence length predicted if these
cases were treated as male, based on inverse propensity score reweighting of the observed male sentences using
the same covariates as in Table 4 except as noted. Standard errors are clustered on the strata within which
propensity scores are balanced. *p<0.05, **p<0.01, ***p<0.001.

	
  

26	
  

Starr—Estimating	
  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

Table 6
SERIAL DECOMPOSITION OF AVERAGE GENDER DISPARITY BY PROCEDURAL SOURCES: IPW ESTIMATES OF
TREATMENT ON UNTREATED (WOMEN)

Unexplained
Gap (Months)

[1] No
Controls
(Total Gap)
26.90***

Non-Drug Cases (Observed Female Mean: 13.27 Months)
[4] Add
[2] Pre[3] Add
Conviction
Charge
Charge Sev.
Sev.
[5] Add
Controls
Measures
Measures
Fact-finding
7.89***
7.18***
6.88***
2.13***

Remainder
(Attrib. to
Sentencing)
N/A

(0.37)

(0.31)

(0.30)

(0.29)

(0.27)

As % of Female
Mean

202

59.5

54.1

51.8

16.1

N/A

Share Explained
by This Stage

19.01***

0.71***

0.30***

4.75***

2.13***

N/A

(0.29)

(0.08)

(0.05)

(0.13)

(0.27)

N/A

70.7

2.6

1.1

17.7

7.9

N/A

N/A

9.0

3.8

60.2

27.0

This Stage As %
of Total Gap
This Stage as %
of Disparity in
Justice Process

Drug Cases (Observed Female Mean: 40.04 Months)
Remainder
(Attrib. to
Sentencing)

[1] No Controls
(Total Gap)
38.92***

[2] Pre-Charge
Controls
23.38***

[3] Add Conviction
Mand. Min.
15.57***

[4] Add
Fact-finding
8.67***

(0.42)

(0.38)

(0.35)

(0.29)

As % of Female
Mean

97.2

58.4

38.9

21.7

N/A

Share Explained
by This Stage

15.54***

7.81***

6.90***

8.67***

N/A

(0.30)

(0.22)

(0.20)

(0.29)

This Stage As %
of Total Gap

N/A

39.9

20.1

17.7

22.3

N/A

N/A

33.4

29.5

37.1

Unexplained
Gap (Months)

This Stage as %
of Disparity in
Justice Process

N/A

NOTE. – Column 1 shows the average observed male-female sentence gap in months, while Column 2 shows
the gap when males are reweighted on the inverse propensity score using the pre-charge covariates from Table
4. In the other columns, additional covariates have been added sequentially. In Panel A, Column 3 adds the
mandatory minimum, statutory maximum, and guidelines sentence associated with the initial charges; Column 4
further adds the same measures for the charges of conviction; and Column 5 further adds the final Guidelines
offense level. In Panel B, Column 3 adds the mandatory minimum at conviction, and Column 4 further adds the
final offense level. The "Share Explained by This Stage" row is based on the reduction of the unexplained
relative to the preceding step, and the rows below it express this share as a percentage of the total gap and the
gap unexplained by pre-charge covariates. The last column in each panel ("Share Remaining") expresses the
residual unexplained in the preceding column, which is attributed to the final sentencing decision, in percentage
terms, showing that the percentages the decomposition attributes to the procedural stages sum to 100%.
*Standard errors are bootstrapped (500 replications). *p<0.05, **p<0.01, ***p<0.001.

	
  

27	
  

Starr—Estimating	
  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

	
  
Table 7
SHARE OF MEAN GENDER GAP EXPLAINED BY SPECIFIC FINDINGS OF FACT AND DEPARTURES: IPW
DECOMPOSITION OF TREATMENT ON UNTREATED (WOMEN)*

Findings of Fact:
Aggravating/Mitigating Role
Acceptance of Responsibility
Obstruction of Justice
Loss Amount

Non-Drug Crimes (Gap Unexpl.
by Pre-Charge Controls: 7.9
Months)
Months
Share of Gap (%)

Drug Crimes (Gap Unexpl. By
Pre-Charge Controls: 23.4
Months)
Months
Share of Gap (%)

1.138***
(0.062)
-0.261***
(0.037)
0.228***
(0.042)
1.585***
(0.065)

4.578***
(0.128)
0.039
(0.094)
0.076
(0.070)

14.4
-3.3
2.9
20.1

Drug Quantity
N/A
Drug Safety Valves
(Guidelines/Mand Min Waiver)
Departures:
Family Ties
Substantial Assistance/
Cooperation
Mental Health/Abuse/Addiction

N/A
0.123***
(0.018)
0.069
(0.037)
0.136***
(0.024)

1.6
0.9
1.7

N/A
0.740***
(0.103)
2.074***
(0.111)
0.287***
(0.032)
2.141***
(0.108)
0.235***
(0.030)

19.6
0.2
0.3
N/A
3.2
8.9
1.2
9.2
1.0

NOTE. – Incremental reductions in unexplained disparity when particular findings of fact or departures are
added to the IPW reweightings in Table 6. Findings of fact are added to weights that already include all
variables through the conviction stage as noted in Table 6. Departures are added to weights that also included
the final Guidelines offense level.
*Standard errors are bootstrapped (500 replications). Because these figures are based on adding each of
these variables independently (rather than together or sequentially), their collective explanatory power may be
overstated if the variables are collinear with one another. *p.<0.05, **p<0.01, ***p<0.001.

	
  
	
  
	
  
	
  
	
  
	
  
	
  

	
  

28	
  

Starr—Estimating	
  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

FIGURES
Figure 1a. – Distribution of Gender Propensity Scores for Full Sample

Figure 1b. - Distribution of Gender Propensity Scores for Low Criminal History Sample

	
  

29	
  

Starr—Estimating	
  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

Figure 2. - Gender Disparities in the Sentencing Distribution: Females vs. Reweighted Males
0.4

Share of Distribution

0.35
Observed Male

0.3

Reweighted Male

0.25

Observed Female

0.2
0.15
0.1
0.05
0
NP

<12

12 to 36 36 to 60 60 to 84 84 to 150 >=150
Sentence (Months)

	
  

30	
  

Starr—Estimating	
  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

Figure 3a. - Sequential Reweighting of the Sentencing Distribution: Non-Drug Cases
0.6

Observed Male
Pre-Chg Controls

Share of Distribution

0.5

+ Chg Controls
0.4

+ Conv Ctrls

+ Factfinding

0.3
0.2
0.1
0
NP

<12

12 to 24 24 to 36 36 to 60 60 to 120

>120

Sentence (Months)

Figure 3b. - Decomposition of Gender Gaps in the Sentencing Distribution by Procedural

Percentage Point Gap in Prob. of
Exceeding Threshold (Females vs.
Reweighted Males)

Source: Non-Drug Cases

0.14

Sentencing

0.12

Factfinding

0.1
0.08

Charge-Bargaining
Charging

0.06
0.04
0.02
0
Any Pris
12
24
36
60
Sentence Length Threshold (Months)

	
  

120

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Starr—Estimating	
  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

Figure 3c. - Sequential Reweighting of the Sentencing Distribution: Drug Cases

Share of Distribution

0.3

Observed Male

0.25

Pre-Chg Controls
+ Mand Min

0.2

+Factfinding
0.15

Observed Female

0.1
0.05
0
NP

<24

24 to 48

48 to 72 72 to 120 120 to 180

>=180

Sentence (Months)

Figure 3d. – Decomposition of Gender Gaps in the Sentencing Distribution by Procedural

Percentage Point Gap in Prob.
of Exceeding Threshold
(Females v. Reweighted Males)

Source: Drug Cases
0.2
0.18
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0

Sentencing
Factfinding
Mand Min

Any Pris

24

48

72

120

180

Sentence Length Threshold (Months)

	
  

32	
  

Starr—Estimating	
  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

DATA APPENDIX
1. The Linked Dataset
This project is based on a linked, multi-agency dataset from four federal agencies: the
U.S. Marshals’ Service (USMS), the Executive Office for the U.S. Attorneys (EOUSA), the
Administrative Office of the U.S. Courts (AOUSC) and the U.S. Sentencing Commission
(USSC).30 These datasets are collected by the Bureau of Justice Statistics (BJS) and,
pursuant to security conditions, made available to researchers via the National Archive of
Criminal Justice Data along with linking files that allow records to be linked at an individual
level across the agencies.31 Together, these files trace cases from arrest through sentencing.
USMS collects information upon booking of arrestees in federal custody, based on
arrest-stage information drawn from law enforcement. Their data include arrest offense,
race, age, gender, marital status, a written offense description based on information from law
enforcement, U.S. citizenship status, arrest date, the federal judicial district, and the arresting
agency. EOUSA collects investigation- and case-related data for prosecutors; its fields were
used to determine whether arrestees were charged before a district judge and for information
on the type and quantity of drugs seized in arrests. Data on the initial and final charges in the
case (and their disposition) as well as the number of co-defendants, defense counsel type, and
the county of the offense came from the AOUSC, which compiles district court records. The
USSC provides information recorded by judges on the sentence, the mandatory minimum
applicable at sentencing, the defendant’s criminal history, education level, number of
dependents, and Hispanic ethnicity, as well as rich detail on the specific findings of
“sentencing facts” entered by judges as well as the reasons given for departure from the
Sentencing Guidelines range.
The linking algorithm is dyadic and includes both inter- and intra-agency links,
because EOUSA and AOUSC each have multiple kinds of files. There are two possible
linking pathways that incorporate all the relevant fields. The first runs from USMS to the
EOUSA suspect investigation file to the EOUSA “cases terminated” file to the AOUSC
“cases terminated” file to the USSC. The second runs from USMS to the EOUSC suspect
investigation file to the EOUSA “cases filed” file to the AOUSC “cases filed” file to the
AOUSC “cases terminated” file to the USSC. The sample for the sentencing analysis is
limited to cases that linked all the way through one of these two pathways. Link-through
rates were 81% (USMS to EOUSA investigation files),32 93% (EOUSA to AOUSC, among

	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
30

The underlying linked dataset is the same as that used in a related paper on racial disparity by Rehavi and
Starr (2012). However, the samples are constructed differently, in part because of different common-support
concerns; this study uses more years of data, includes different case types, and includes all federal districts.
This study also includes a number of additional covariates.
31
Descriptions of the files are at http://www.icpsr.umich.edu/icpsrweb/content/NACJD/guides/fjsp.html.
32
The lower link rate at this stage is probably because there are substantive reasons cases might not link
through, in addition to failures of the linking algorithm. Cases would not link through if they were immediately
either declined or transferred to some other authority (before opening a suspect investigation file).

	
  

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  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

cases filed in district court only), and 90% (AOUSC to USSC, among cases with convictions
of non-petty offenses only), and did not significantly differ by gender.33
2. Sample Restrictions
2.1. Timing
The main sentencing sample consisted of cases sentenced between October 1, 2000
through September 30, 2009. The analyses of filing and conviction rates required case
initiation (arrest or opening of the EOUSA investigation file, whichever is later) or
disposition, respectively, during the same period.
2.2. Case Type
Immigration cases were excluded because their stakes typically center on deportation
rather than sentencing and because they often are handled via a very different fast-track
process.34 In order to achieve better overlap between the male and female samples, I also
excluded several case categories in which the arrestees were over 95% male: sex and
pornography-related offenses (except for prostitution), weapons offenses, conservation
offenses (mainly illegal hunting and fishing), and family offenses (mainly failure to pay child
or spousal support). The remaining case types were property and fraud offenses, regulatory
offenses (excluding those mentioned above), non-sexual violent crimes, and drug offenses.
All case type exclusions were based on the USMS arrest code. Defining the sample
based on the arrest stage data alone avoided potentially serious sample selection issues that
could have emerged had the exclusions been based on the prosecutor's discretionary
decisions. The USMS codes are based on the principal arrest offense and may exclude some
secondary criminal conduct (although in most cases, because concurrent sentencing is the
default rule, secondary conduct will not affect the sentence). While virtually nobody in the
sample was convicted of any immigration, sex/pornography, family, or conservation
offenses, overlap between weapons cases and other cases is more common: about 6% of the
sample was convicted of a weapons charge. The presence of weapons in violent crimes is
often captured by the arrest code, and their presence in any kind of case is often captured by
the police-notes-based description field that I use in robustness checks.
Cases with arrest codes indicating a reason for detention other than a criminal offense
(material witness warrants and violations of the conditions of parole or probation) were also
excluded from the sample.
3. Construction of Key Independent Variables
3.1. Demographics
Gender, race, U.S. citizenship, marital status, and age are recorded by USMS. Race is
coded as white, black, Asian, Native, and Other/Unknown; the last three groups together
constitute only 4% of the sample, and I combined them. USSC provides number of
dependents, education level, and Hispanic ethnicity (ethnic Hispanics are overwhelmingly
coded as white in the race data). Marital status, number of dependents, and Hispanic
ethnicity are sometimes missing and are included only in robustness checks. Also as a
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
33

Rates of filing in district court and conviction of non-petty offenses are outcomes assessed in the paper; cases
that drop out of the sample due to non-filing, dismissal, or acquittal do not reflect linking failures.
34
Citizenship was included as a covariate, and non-citizens were excluded in robustness checks, because
deportation is also an important concern when non-citizens are charged even in non-immigration cases.

	
  

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  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

robustness check, the county fields in AOUSC were linked to county level unemployment,
poverty, and wage data from the Census Bureau and to crime data from the FBI’s Uniform
Crime Reporting Program.
3.2. Arrest Offense
There are 430 unique arrest offenses listed in the USMS data. The original arrest
offense codes included many very similar offense descriptions, including some that were
slightly more detailed versions of others (for instance, “vehicle theft” and “vehicle theft by
bailee”). Often the more detailed ones were rarely used. Therefore, the smallest categories
were combined with others that could describe the same legal offense.35 In addition, I
subdivided some of the drug arrest offense codes based on the drug type information in the
EOUSA investigation-stage file. This is because many drug cases are simply given the arrest
code “dangerous drugs,” and because the cocaine arrest codes combine crack and powder,
which have different sentencing schemes. There were 123 resulting arrest-code groups. The
results are robust to the use of the original offense codes.
Note that the drug offense codes do not specify quantity. The drug quantity at arrest
(in addition to type) is usually identified by the EOUSA investigation-stage file; however, the
quantity field is unreliable starting in FY 2004.36 Therefore, the main analyses do not
include quantity in the controls, but robustness checks confined to FY 2001-03 do. To
enable quantity comparisons across drug types, quantities were translated into “marijuana
equivalents” according to the conversion tables in the Sentencing Guidelines.
3.3. Criminal History
Criminal history data are only available in the USSC data and are accordingly only
available for those sentenced for guideline offenses. The variable used was the defendant's
criminal history category, which ranges from 1 to 6 and forms the basis of the Guidelines
sentencing grid. In 0.2% of the sentencing sample, this field was originally missing but
could be calculated based on another Sentencing Commission field called "criminal history
points," according to the rules laid out in the Guidelines.
3.4. Charge Severity Measures
The raw charge and conviction data are the statutory provisions under which the
defendant was charged and convicted. These provisions had to be translated into measures of
charge severity in order to assess the contribution of charging and conviction severity to
sentence disparities.37 On the basis of legal research, I identified the statutory maximum and
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
35

No single number defined what categories were small enough to be combined, because the combination
depended on the legal assessment that the crimes were sufficiently similar.
36
There are drastic changes in the apparent quantity distribution in this field from 2003 to 2004 as well as large
inconsistencies in quantity between this field and the sentencing-stage quantities recorded by USSC beginning
in 2004. EOUSA adopted a new data entry system in 2004, and it seems apparent that the problem is with this
system; unfortunately the inconsistencies appeared neither to be uniformly applicable nor confined to particular
drug types or districts, so there is no way to identify which cases are problematic.
37
While the AOUSC data include a “severity” field, which is ostensibly based on the statutory maximum, it is
not very useful because appears to automatically be based on the very highest maximum contained anywhere in
the statute cited, even when that maximum is only triggered by an exceptional circumstance that rarely applies.
For instance, charges under 18 U.S.C. § 1347 (health care fraud) are coded by AOUSC as having a statutory
maximum of life, even though that maximum only applies when the fraud leads to a death.

	
  

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  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

minimum sentence, and the Guidelines-recommended sentence associated with each
combination of charges and convictions.
Because the cited statutory provisions sometimes contain varied sentencing schemes
depending on the facts of the case, I researched the most common ways in which these
statutes are charged in order to be able to make realistic assumptions in the face of such
ambiguities. When possible, ambiguities were resolved by reference to the other charges in
the case, when the legal elements of those charges revealed additional facts that the
prosecutor must have been alleging. For instance, suppose Charge 1 is a burglary offense
that usually has a maximum sentence of 10 years, but has a 20-year maximum if someone is
seriously injured in the course of the burglary. Charge 2 is an aggravated assault charge,
with a 15-year maximum, in which aggravated assault is defined to require that serious injury
be proven. Because Charge 2’s presence indicates that the prosecutor was alleging serious
injury, the maximum sentence for Charge 1 is raised to 20 years.
Implementing this approach required constructing a number of flags for every federal
criminal statute, a complicated statutory interpretation task. The flags indicated whether
certain facts were elements of the crime: death, injury, serious injury, drug crime, sex crime,
fraud, official victim, minor victim, terrorist motive, an assault, use of a weapon, use of a gun
specifically, a “crime of violence,” obstruction of justice, taking a person for ransom, and
whether the crime was a predicate offense for the crime of felony murder. Statutes also had
to be coded to reflect adjustments to the statutory or guidelines sentences that would be
triggered by the presence of particular facts as identified by the flags for the other charges in
the case. Remaining ambiguities were resolved according to default assumptions that varied
between the severity measures.38
Constructing a measure of the Guidelines sentence involved additional challenges.
First, the statutory provisions cited by AOUSC had to be matched to corresponding
Sentencing Guidelines. The actual Guidelines range calculated by the judge is not solely
determined by the charges; rather, it is heavily driven by sentencing fact-finding. However,
the point of the charge-severity measures is to distinguish the effect of charging and
conviction severity itself from that of sentencing fact-finding. Thus, the Guidelines-based
measures of charge and conviction severity represent base offense levels determined solely
by what the prosecutor charged (or what the defendant was convicted of), that is, the
elements of the crime. It is based on applying the Guidelines assuming the elements of all
charges brought were proven, but no additional findings of fact were made at sentencing.
The Guidelines define the “offense level”—a severity scale running from 1 to 43—
associated with each offense. In order for the units of this measure to be comparable to the
other metrics, this offense level had to be converted into an implied sentence length in
months. Under the Guidelines, offense levels translate mechanically into sentence ranges
based on a grid, with criminal history as the other axis. The same column (Column 6) was
used for the translation in all cases, such that the charging and conviction measures are blind
to the defendant’s actual criminal history—they reflect charge severity alone, and criminal
history is a separate covariate. The number of months used was the low end of the range in
the applicable grid cell.
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
38

	
  

A detailed spreadsheet showing these flags and other details on coding choices is available on request.

iv	
  

Starr—Estimating	
  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

Once the severity of the individual charges were coded, they were combined into total
severity measures for all charges. In general, the severity of federal cases is determined by
the most serious charge alone, because concurrent sentencing is the default rule. Thus,
secondary charges affected the charge severity measures only when one of the charging
statutes was an offense specifying that consecutive sentencing was required. As described
above, however, information drawn from secondary charges could be used to adjust the
coding of the primary charge. This approach to combining charges follows the method
specified in the Sentencing Guidelines (see U.S.S.G. § 5G1.2).
Two final adjustments were then made. First, the statutory minimum and the sum of
the individual-charge maximums were imposed as lower and upper constraints, respectively,
on the Guidelines sentence, which also tracks sentencing law (see U.S.S.G. § 5G1.2).
Second, zeros on the statutory maximum, guidelines, and mean sentence scales were replaced
with half a month—half of the lowest nonzero values otherwise calculated—to reflect the
fact that no criminal charge truly has zero severity, even if no incarceration is imposed. This
adjustment affected only 0.05% of cases for the statutory maximum measure, 0.2% of cases
for the guidelines measure, and 0.5% for the mean sentence measure.
The mandatory minimum measure was turned into an indicator variable for whether
there was any nonzero mandatory minimum and (for alternate specifications) a categorical
variable designating whether the mandatory minimum was 0, less than 10 years, and 10 years
or more. Similar variables were constructed based on the actual mandatory minimum of
conviction recorded at the sentencing stage in the USSC data.
3.5. Conviction and Sentence Outcomes
A dummy variable for whether the defendant was convicted of a non-petty offense
was constructed based on AOUSC records. Non-petty offenses are those carrying more than
six months as a statutory maximum, so the classification of offenses is based on the statutory
maximum measure described above. Conviction of a non-petty offense is a prerequisite for
inclusion in the Sentencing Commission data.
Sentence data were drawn from the Sentencing Commission and are therefore only
available for those convicted of offenses covered by the sentencing guidelines. Sentence
lengths were truncated at 540 months, and life sentences were given that value. This length
is longer than the highest non-life statutory maximum found in federal law (480 months), and
corresponds approximately to the remaining life expectancy of an American of the sampleaverage age. Only 0.7% of sentenced cases were affected by this truncation.

	
  

v	
  

Starr—Estimating	
  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

Data Sources
U.S. Census Bureau, 2000. “Census of Population and Housing, Summary File 3.”
http://www.census.gov/census2000/sumfile3.html (last updated October 13, 2011).
U.S. Department of Justice. Office of Justice Programs. Bureau of Justice Statistics. 2009.
Federal Justice Statistics Program: Paired-Agency Linked Files, 2009. Ann Arbor, MI:
Inter-university Consortium for Political and Social Research. ICPSR Study Number
30701-v3 (2011-11-11).
U.S. Department of Justice. Office of Justice Programs. Bureau of Justice Statistics. Federal
Justice Statistics Program: Arrests and Bookings for Federal Offenses, 2000-2009.
Ann Arbor, MI: Inter-university Consortium for Political and Social Research. ICPSR
Study Numbers 24126-v2 (2011-03-08), 24145-v2 (2011-03-08), 24164.v2 (2011-0308), 24181.v2 (2011-03-08), 24216.v2 (2011-03-08), 24199.v2 (2011-03-08), 24211.v2
(2011-03-08), 24226.v2 (2011-03-08), 24231.v2 (2011-03-08), 29428.v2 (2011-03-08),
30794-v1 (2011-07-22). Original Data Source: U.S. Marshals’ Service (“USMS”).
U.S. Department of Justice. Bureau of Justice Statistics. Federal Justice Statistics Program:
Suspects in Federal Criminal Matters Concluded, 2000-2009 [United States]. Ann
Arbor, MI: Inter-university Consortium for Political and Social Research. ICPSR Study
Numbers: 24120-v2 (2011-03-08), 24139.v2 (2011-03-08), 24158.v2 (2011-03-08),
24175.v2 (2011-03-08), 24193.v2 (2011-03-08), 24210.v2 (2011-03-08), 24225.v2
(2011-03-08), 29424.v2 (2011-03-08), 30790.v1 (2011-06-03). Original Data Source:
Executive Office of U.S. Attorneys (“EOUSA Matters Out”).
U.S. Department of Justice. Bureau of Justice Statistics. Federal Justice Statistics Program:
Defendants Charged in Criminal Cases Filed in District Court, 2000-2009 [United
States]. Ann Arbor, MI: Inter-university Consortium for Political and Social Research.
ICPSR Study Numbers: 24121-v2 (2011-03-08), 24140.v2 (2011-03-08), 24159.v2
(2011-03-08), 24176.v2 (2011-03-08), 24194.v2 (2011-03-08), 24211.v2 (2011-03-08),
24226.v2 (2011-03-08), 29426.v2 (2011-03-08), 30791.v1 (2011-06-03). Original Data
Source: Executive Office of U.S. Attorneys (“EOUSA Cases In”).
U.S. Department of Justice. Bureau of Justice Statistics. Federal Justice Statistics Program:
Defendants in Federal Criminal Cases -- Terminated, 2000-2009 [United States]. Ann
Arbor, MI: Inter-university Consortium for Political and Social Research. ICPSR Study
Numbers: 24122-v2 (2011-03-08), 24141.v2 (2011-03-08), 24160.v2 (2011-03-08),
24177.v2 (2011-03-08), 24195.v2 (2011-03-08), 24212.v2 (2011-03-08), 24227.v2
(2011-03-08), 29433.v2 (2011-03-08), 30792.v1 (2011-06-03). Original Data Source:
Executive Office of U.S. Attorneys (“EOUSA Cases Out”).
U.S. Department of Justice. Bureau of Justice Statistics. Federal Justice Statistics Program:
Defendants in Federal Criminal Cases Filed in District Court, 2000-2009 [United
States]. Ann Arbor, MI: Inter-university Consortium for Political and Social Research.
ICPSR Study Numbers: 24114-v2 (2011-03-08), 24133.v2 (2011-03-08), 24152.v2
(2011-03-08), 24169.v2 (2011-03-08), 24186.v2 (2011-03-08), 24204.v2 (2011-03-08),
24221.v2 (2011-03-08), 29402.v2 (2011-03-08), 30781.v1 (2011-06-03). Original Data
Source: Administrative Office of the U.S. Courts (“AOUSC Cases In”).

	
  

vi	
  

Starr—Estimating	
  Gender	
  Disparities	
  in	
  Federal	
  Criminal	
  Cases	
  

U.S. Department of Justice. Bureau of Justice Statistics. Federal Justice Statistics Program:
Defendants in Federal Criminal Cases in District Court -- Terminated, 2000-2009
[United States]. Ann Arbor, MI: Inter-university Consortium for Political and Social
Research. ICPSR Study Numbers: 24115-v2 (2011-03-08), 24134.v2 (2011-03-08),
24153.v2 (2011-03-08), 24170.v2 (2011-03-08), 24187.v2 (2011-03-08), 24205.v2
(2011-03-08), 24222.v2 (2011-03-08), 29242.v2 (2011-03-08), 30784.v1 (2011-0603). Original Data Source: Administrative Office of the U.S. Courts (“AOUSC Cases
Out”).
U.S. Department of Justice. Bureau of Justice Statistics. Federal Justice Statistics Program:
Defendants Sentenced Under the Sentencing Reform Act, 2001-2009 [United States].
Ann Arbor, MI: Inter-university Consortium for Political and Social Research. ICPSR
Study Numbers: 24127-v2 (2011-03-08), 24146.v2 (2011-03-08), 24165.v2 (2011-0308), 24182.v3 (2011-03-08), 24200.v3 (2011-03-08), 24217.v3 (2011-03-08), 24232.v2
(2011-03-08), 29381.v2 (2011-03-08), 30795.v1 (2011-06-06). Original Data Source:
U.S. Sentencing Commission (“USSC”).
U.S. Department of Justice. Office of Justice Programs. Federal Bureau of Investigation.
Uniform Crime Reporting Program Data: County-Level Detailed Arrest and Offense
Data, 2007-2009. Ann Arbor, MI: Inter-university Consortium for Political and Social
Research. ICPSR Study Numbers: 30763-v1 (2012-01-25), 27644-v1 (2011-04-21),
25114-v1 (2009-07-31).

	
  

vii