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Gender-Based Favoritism Among Criminal Prosecutors
Stephanie Holmes Didwania1
Abstract
Criminal prosecutors enjoy wide discretion in the decisions they make but are largely unstudied
by empirical scholars. This paper explores gender bias in prosecutorial decision-making. I find
that defendants are charged more leniently when they are the same gender as their prosecutor as
opposed to when the defendant and prosecutor are different genders. Such favoritism in charging
ultimately translates into significantly lower sentences for defendants who are paired with a same-
gender prosecutor. Gender-based leniency, however, is not uniform across all geographic areas,
all types of cases, and all defendant characteristics. It is more pronounced in states with less
prevalent sexism, cases in which gender is likely to be more salient, and on same-gender
prosecutorial teams. However, gender match in defendant-prosecutor pairs is not strongly
associated with differences in cooperation and bargaining. I conclude that prosecutors’ social
preferences are more likely to explain gender-based leniency than differences in how male and
female prosecutors work.
1 Assistant Professor of Law, Temple University Beasley School of Law. I am grateful to Emily Buss, Adam Chilton,
Travis Crum, Dhammika Dharmapala, Vikas Didwania, Nate Ela, Craig Garthwaite, J.B. Heaton, William Hubbard, Benjamin Jones, Cree Jones, Emma Kaufman, Amanda Kleintop, Genevieve Lakier, Dorothy Lund, Anup Malani, Jonathan Masur, Tom Miles, Margot Moinester, Rachel Montgomery, Meghan Morris, Jeffrey Omari, Manisha Padi, Asad Rahim, Weijia Rao, Kyle Rozema, CJ Ryan, Max Schanzenbach, Roseanna Sommers, Lior Strahilevitz, and participants at the Conference on Empirical Legal Studies, the Annual Meeting of the American Law and Economics Association, the Marquette University Law School Faculty Workshop; the Midwestern Law and Economics Association, the UC Berkeley Law, Economics, and Business Workshop; and the University of Chicago Law School Faculty Works-in-Progress Workshop for valuable comments and suggestions. Comments are welcome at didwania@temple.edu.
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Introduction
Observers have long commented on the immense power our criminal system bestows upon
its prosecutors. Nearly 80 years ago, then-U.S. Attorney General Robert H. Jackson famously
remarked, “The prosecutor has more control over life, liberty, and reputation, than any other person
in America. His discretion is tremendous”2 (Jackson 1940). Prosecutors alone have the power to
initiate criminal cases, and they exercise important decision-making authority throughout the
criminal process, for example by selecting which charges to bring against defendants and by
engaging in plea bargaining.
Although prosecutors exercise vast discretion in criminal cases, their behavior is largely
unstudied by quantitative empirical researchers.3 Like all people, however, prosecutors are
susceptible to bias in decision-making. In fact, because prosecutors enjoy expansive and largely
unreviewable discretion, one might expect the prosecutorial setting to be one in which bias is
especially likely to affect behavior.
This paper focuses on one important kind of bias: in-group favoritism. In-group favoritism
occurs when a decision-maker gives preferential treatment to those who share a salient trait with
the decision-maker, such as being a member of their gender, racial, ethnic, or religious group
(Everett, Faber, and Crockett 2015).4 Scholars have documented evidence of gender-based in-
group favoritism in a variety of laboratory and experimental settings, and a growing consensus
suggests that the majority of discrimination in the United States takes the form of in-group
favoritism rather than outright hostility towards people who are different (Greenwald and
Pettigrew 2014, Krieger 1998).
This paper explores in-group favoritism on the basis of gender.5 Researchers have shown
that in laboratory experiments, both men and women exhibit favoritism towards members of their
2 At the time of this statement, Robert H. Jackson was not yet a Supreme Court justice. 3 On the other hand, there is considerable theory on the promise and pitfalls of the way prosecutor offices self-
police, incentives that prosecutors face, and prosecutorial ethics (Bibas 2009, Leonetti 2012, Meares 1995, Ouziel 2017, Pfaff 2017, Sklansky 2017).
4 In this paper, I use the term in-group favoritism to represent a phenomenon: that decision-makers treat people relatively more favorably when the person is in their in-group It is important to emphasize that I do not take a view about the underlying causes of this differential favoritism. For example, I do not take a position on whether the effect that I document is conscious or subconscious, nor do I have a view about the extent to which it is driven by out-group disfavoritism versus in-group favoritism.
5 Because of the way the data is coded, this paper classifies people as having a binary gender that is either female or male. Unfortunately, neither of the data sources used in the paper include information about transgendered defendants or defendants who do not identify as having binary gender. Rosenblum (2000) provides a thoughtful discussion of the legal issues facing transgendered prisoners.
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own gender (Hoyt, Simon, and Reid 2009; Lindeman & Sundvik 1995), although some have found
that women demonstrate stronger in-group favoritism then men (Rudman and Goodwin 2004),
while others have found the opposite (Vial et al. 2017). Outside of lab settings, in-group favoritism
on the basis of gender has been documented in educational, professional, and financial settings.
For example, one recent working paper finds evidence of this phenomenon in attorney disciplinary
hearings: reporting that female attorneys are treated less harshly in such hearings when the panel
contains more female appellate judges (Kennedy, McDonnell, and Stephens 2017). Others have
documented in-group favoritism in the financial industry, finding, among other things, that male
analysts tend to under-forecast the earnings of firms led by female CEOs (Jannati et al. 2016).
This paper quantifies gender-based in-group favoritism among federal prosecutors with an
expansive data set that identifies—among other variables—the gender of the lead prosecutor
assigned to each case. Using data that covers nearly 150,000 federal criminal defendants sentenced
in fiscal years 2002 through 2016, I find that while male and female prosecutors exhibit small and
statistically insignificant differences in their treatment of defendants overall, they show relative
favoritism towards defendants of their own gender. The leniency associated with gender match in
defendant-prosecutor pairs ultimately translates into significant differences in sentence length for
defendants who match with their prosecutor on gender compare to those who do not.
To make sense of the findings, the paper also considers heterogeneity in the results.
Gender-based leniency is not uniform across all geographic areas, case types, and defendants. For
example, I show that the results are most pronounced in states with below-median levels of sexism.
The results are also stronger in cases in which gender is likely to be more salient, and among same-
gender teams of prosecutors (relative to prosecutors working alone and mixed-gender teams). On
the other hand, prosecutor-defendant gender match does not seem to influence the intensity of any
bargaining between defendants and prosecutors. Similarly, prosecutor-defendant gender match
does not appear to affect the extent to which the defendant cooperates with the prosecutor except
among those defendants for whom cooperation is likely to be incredibly valuable, where the
relationship is marginally significant. I conclude that this evidence suggests that the results are
more likely to be generated by preference-based, rather than process-based, channels.
The research setting presents a few important challenges. First, as is common in many
studies of in-group favoritism, the research design provides a relative—but not absolute—result.
Put another way, the research design cannot disentangle in-group favoritism (special treatment
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towards one’s own in-group) from out-group disfavoritism (hostile treatment towards one’s out-
group). As discussed in Section 7, one natural consequence of the research design is that the results
do not suggest an unambiguous policy solution.
A second challenge stems from the fact that prosecutors are not randomly assigned to cases,
which could bias the results. I discuss the identification challenges in Subsection 4.2 and present
several robustness checks in Section 6 that suggest that the non-random assignment of cases to
prosecutors is very unlikely to be generating the results of the paper.
Examining bias in prosecutorial decision-making is important for several reasons. First—
and perhaps most obviously—our criminal system is meant to shield defendants from bias. The
Equal Protection Clause of the Fourteenth Amendment to the U.S. Constitution, for example,
broadly dictates that the government should treat people equally,6 and gender is a category upon
which discrimination is thought to be especially invidious.7 Mitigating bias in the federal criminal
system is also a priority of the U.S. Department of Justice, which announced in 2016 that all federal
law enforcement officers and prosecutors would receive training in how to recognize and address
implicit bias.8
It is also important to study bias among prosecutors because prosecutorial decisions carry
meaningful consequences, implicating a suspect or defendant’s liberty and risking far-reaching
collateral effects on their life and livelihood. Scholars have documented the effects of contact with
the criminal justice system and criminal convictions on, for example, high school completion
(Aizer and Doyle 2015); recidivism (Di Tella and Schargrodsky 2013, Mueller-Smith 2015);
employment (Mueller-Smith 2015, Pager 2003); dependence on public assistance (Mueller-Smith
2015); wages and wage growth (Western 2002); and family formation and support (Geller,
Garfinkel, and Western 2011).
The paper proceeds as follows. Part 1 situates this paper in the prior literature. Part 2
examines the empirical setting, describing the key responsibilities of federal prosecutors and
explaining how their offices are organized. Part 3 describes the data. Part 4 presents the empirical
6 U.S. CONST, amend. XIV. 7 Craig v. Boren, 429 U.S. 190 (1976). Laws that explicitly classify people on the basis of gender are “inherently
suspect,” Frontiero v. Richardson, 411 U.S. 677, 682 (1973), and are analyzed with intermediate scrutiny, Craig, 429 U.S. at 197.
8 Department of Justice Announces New Department-Wide Implicit Bias Training for Personnel, U.S. DEP’T OF
JUSTICE (June 27, 2016), https://www.justice.gov/opa/pr/department-justice-announces-new-department-wide-implicit-bias-training-personnel. Ross (2016) provides a description of the Justice Department’s implicit-bias training.
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strategy and main results. Part 5 sheds light on the mechanisms responsible for the main findings
by examining heterogeneity in the results along several dimensions. Part 6 examines the robustness
of the results. Part 7 discusses the findings, considers policy interventions, and suggests directions
for further research.
1. Relevant Prior Literature
Limited quantitative work examines the behavior of criminal prosecutors, and the existing
research—while insightful—leaves many unanswered questions. Very few papers leverage
individualized data on line prosecutors. One important exception is a recent working paper by
CarlyWill Sloan, which documents an increased likelihood of conviction for property-crime
misdemeanors when the defendant and prosecutor are of different races, but not for other types of
misdemeanors (Sloan 2019).
This work contributes to the literature in several important ways. This paper is among the
first to examine bias in prosecutorial behavior using individualized data on line prosecutors. In
contrast to the majority of the literature on bias in the criminal system—which largely focuses on
race—this work considers gender. Gender disparities in federal criminal cases are large, highly
statistically significant, and difficult to explain. Assessing in-group favoritism by prosecutors
might help policymakers better understand and address gender disparity in the criminal system.
Earlier work, though largely lacking individualized data on line prosecutors, sheds
important light on the relationship between prosecutorial decision-making and career concerns by
analyzing charging and trial decisions.9 For example, Boylan and Long (2005) find that
prosecutors are more likely to take cases to trial in federal districts in which the local labor market
for attorneys has above-average salaries.10 Boylan (2005) examines the career paths of 570 United
States Attorneys—the attorneys who lead federal prosecutorial offices—over a thirty year period
and finds that the most important predictor of favorable career outcomes for U.S. Attorneys after
leaving their U.S. Attorney’s Office is the length of prison sentences imposed on defendants who
were prosecuted by the office. Boylan (2005) acknowledges, however, that the article does not use
9 One prior paper examines in-group favoritism among prosecutors on the basis of race, but only has racial
information aggregated to the federal district level (Ward, Farrell, and Rousseau 2009). The authors find, among other things, that districts with more black representation among prosecutors have smaller black-white disparities in sentencing outcomes.
10 The findings presented in Boylan and Long (2005), however, are also consistent with an effect that is driven by defense counsel facing the same kinds of incentives that the authors attribute to prosecutors. The decision to go to trial is not solely a prosecutorial decision. It is the defendant’s Sixth Amendment right to exercise, although prosecutors undoubtedly influence the decision during plea bargaining.
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data on the individual line prosecutors who carry out the work of the United States Attorney’s
Offices, who are the focus of this paper.
Other prior work has studied prosecutorial decision-making by analyzing charging
behavior. Bjerk (2005) found that prosecutors strategically charge mandatory minimums, for
example, by being more likely to reduce a charge to a misdemeanor when a felony charge would
trigger a mandatory minimum under a “three-strikes” law. Others have found that charging
decisions are a critical source of gender- and race-based sentencing disparity (Rehavi and Starr
2014, Starr 2015, Yang 2014). In particular, most race-based sentencing disparity begins at the
charging stage, in large part because prosecutors are more likely to charge crimes carrying
mandatory minimum sentences when the defendant is black (Rehavi and Starr 2014). This
prosecutorial tactic appears to respond to increased judicial discretion after the Supreme Court’s
2005 decision, United States v. Booker (Starr and Rehavi 2013, Yang 2014, Yang 2015).
Together the existing research illustrates that prosecutorial decision-making could be
influenced by bias.11 However, while this prior work suggests that prosecutorial offices treat
defendants in a biased way, it does not attempt to explain how bias enters the decision-making
process.12 This paper begins to fill this gap by examining behavior with individualized information
about line prosecutors—the people who enforce our criminal laws every day.
This paper is also related to two distinct bodies of literature, upon which the remainder of
this section focuses. First, prior work examines the behavior of other actors in the criminal system,
including judges, law enforcement officers, and jurors. Second, this paper is connected to prior
research on the effects of gender-based bias in organizations. The rest of this section discusses
these two areas of scholarship and describes the contributions of this paper.
11 There is mixed evidence that minority defendants also fare worse in plea bargaining. Kutateladze, Andiloro, and
Johnson (2014) and Kutateladze et al. (2014) find that black and Latino defendants are more likely to receive a custodial plea offer than white defendants, but Kutateladze et al. (2014) also finds that black and Latino defendants are more likely to have their cases dismissed. Both of these papers use data from New York City that is comprised primarily of misdemeanor defendants.
12 One important exception is a recent working paper in which the authors carried out a randomized, controlled experiment in which they surveyed prosecutors by presenting them with vignettes and asking them how they would charge in each situation. The vignettes were manipulated to change the race and class of the fictional perpetrator. The authors generally did not find evidence of bias in prosecutorial decision-making (Robertson, Baughman, and Wright 2019).
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1.1. Studies of Judge, Law Enforcement Officer, and Juror Behavior and Bias
Empirical scholars have long examined race- and gender-based bias by actors other than
prosecutors in the criminal system. Most of this scholarship has examined bias with respect to race
and ethnicity rather than gender, and the literature that investigates gender favoritism has produced
mixed results. One recent article finds no strong evidence that judges either favor or disfavor
defendants of the same gender (Lim, Silveira, and Snyder 2016). Earlier research reports that the
presence of more female judges on a district court reduces the court’s gender gap in sentence
length, but because this analysis is aggregated to the district court level, it does not constitute
evidence of in-group favoritism (Schanzenbach 2005). Other earlier work finds that female judges
show more gender disparity than male judges, but this study does not report whether this difference
are statistically significant (Steffensmeier and Hebert 1999).13
Considerably more work studies race-based favoritism in the criminal system, although
evidence related to in-group favoritism is mixed. For example, one recent article finds that judges
exhibit in-group disfavoritism14 towards juvenile defendants of the same race (Depew, Eren, and
Mocan 2017).15 Another recent paper, however, finds evidence that judges might slightly favor
adult defendants of their own race (Lim, Silveira, and Snyder 2016). Others similarly find that
African American judges exhibit smaller racial disparities in sentencing than their white
counterparts, which is consistent with race-based in-group favoritism (Abrams, Bertrand, and
Mullainathan 2012). In-group favoritism also presents—to some extent—along ethnic lines in
pretrial detention decisions: one study finds that Arab and Jewish judges in Israel are less likely to
detain defendants who share their ethnicity, but in-group favoritism does not affect the length of
detention ordered (Gazal-Ayal and Sulitzeanu-Kenan 2010).
In the case of juror behavior, it appears that the diversity environment—and not just the
race of the individual actor—affects outcomes. Researchers have found that juries formed from
all-white jury pools convict black defendants more often than white defendants, and that this gap
is entirely eliminated when the jury pool includes at least one black member, even when the black
13 The study also does not include fixed effects for geography or time, nor are standard errors clustered so it is hard
to draw firm inferences from the results. 14 Depew, Eren, and Mocan (2017) refer to this phenomenon as “negative in-group favoritism” in their article. For
clarity, this paper uses the term “in-group disfavoritism” to mean the same thing: that a decision-maker treats in-group members worse than out-group members.
15Another recent contribution finds that judges treat more harshly defendants who share the same first initial of their name, which the author describes as an example of implicit egotism (Chen 2017).
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jury pool members are not seated (Anwar, Bayer, and Hjalmarsson 2012). Recent work has also
documented evidence of favoritism on the basis of race and gender in jurors’ decisions to convict
(Flanagan 2018). The effects of racial diversity among judges are more nuanced, but do not
constitute strong evidence of race-based in-group favoritism (Schanzenbach 2005, Schanzenbach
2015).
Work that examines in-group favoritism among law enforcement officers has largely
focused on racial bias,16 and—like the work on in-group favoritism among judges—has produced
mixed results. Recent work finds that black officers are more likely than white officers to shoot
unarmed white civilians (Fryer 2016), consistent with earlier work, aggregated to the police-force
level, that police departments with more minority officers are more likely to arrest white suspects,
with little impact on the arrests of non-white suspects (Donohue and Levitt 2001). Others similarly
find that officers are more likely to conduct a search of a driver of a different race, which they
attribute to preference-based discrimination after ruling out the possibility that officers are better
at searching members of their own racial group or that the results are driven by the non-random
assignment of officers to neighborhoods (Antonovics and Knight 2009). Others, on the other hand,
do not find race-based differences in officers’ propensities to arrest non-white suspects (Brown
and Frank 2006). An empirical study of traffic stops in Oakland, California offers similarly
muddled evidence: officers appear to exhibit race-based in-group favoritism in some
neighborhoods, and the opposite in other neighborhoods (Sanga 2014).
Outside the criminal system, there is also literature that seeks to estimate in-group
favoritism in civil and extralegal settings. A study of in-group favoritism in professional basketball
famously finds that NBA referees demonstrate race-based in-group favoritism towards players
(Price and Wolfers 2010). In the civil context, Israeli judges favor defendants that share their
ethnicity in small claims cases, and in-group favoritism increases at times when ethnicity is more
salient (operationalized as years in which there was a recent terrorist attack in the vicinity of the
court) (Shayo and Zussman 2011). Earlier work has similarly found that judges’ race and gender
affect decision-making in cases in which race and gender are salient, such as affirmative action,
16 One exception is an unpublished paper that examines ticketing behavior on the basis of gender. The author finds
that, compared to male police officers, female officers are less likely overall to ticket, but relatively more likely to ticket female drivers (Rowe 2009).
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discrimination, sexual harassment, and voting rights cases (Boyd, Epstein, and Martin 2010; Cox
and Miles 2008; Farhang and Wawro 2004; Peresie 2005).
1.2.Gender Differences in Organizations
A rich literature in social psychology investigates differences in the behavior and treatment
of men and women in organizations. This section highlights a few findings from this literature that
are most relevant to this work, with particular emphasis on studies involving the legal profession.
This section is not intended to be exhaustive, but rather, to help understand why one might or
might not expect to find gender-based differences in how prosecutors work.
Prevailing wisdom proposes that women face different expectations than men in the
workplace. Much scholarship has focused on expectations that women exhibit warmth (Fiske et
al. 2002, Rudman and Glick 1999). These findings could drive in-group favoritism if female
prosecutors are expected to show more mercy towards defendants in particularly sympathetic
circumstances, whom are often female.17 But recent research also suggests that female attorneys
face higher standards of ethical behavior, which suggests female prosecutors might exhibit less in-
group favoritism than male prosecutors (Kennedy, McDonnell, and Stephens 2017). Relevant to
this paper, Kennedy, McDonnell, and Stephens (2017) provides evidence of in-group favoritism:
finding that the harsher treatment of female attorneys diminishes as the share of female judges on
the appellate panel increases.18
Other work considers how female managers influence hiring and pay for male versus
female employees. In the context of legal employment, for example, one study examines hiring at
large U.S. law firms in the 1990s and finds evidence of in-group favoritism in hiring: when women
make entry-level hiring decisions they are more likely to fill vacancies with women than men
(Gorman 2005).
17 Relative to male defendants, female defendants are, for example: (a) less likely to commit violence; (b) more
likely to be custodial parents; and (c) more likely to be prior victims of abuse (Starr 2015). 18 On the other hand, some anecdotal accounts assert that federal prosecutors are unique among the legal profession
in that their offices do not exhibit gender discrimination. For example, in an article published nearly thirty years ago, Judge Reena Raggi described prosecutor offices as workplaces “where gender is irrelevant,” and declared that during the seven years she spent as a prosecutor at the United States Attorney’s Office for the Eastern District of New York, she “encountered virtually no gender discrimination, and that “women have succeeded in making gender an obsolete issue” in criminal prosecution (Raggi 1989).
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Although much prior literature considers in-group favoritism in the employment context,
it is not clear that these findings will translate to the criminal setting. Part 2 describes the research
setting studied here—the federal criminal system.
2. Research Setting
This paper studies individual prosecutors in the federal criminal system. This section
focuses on the institutional setting, describing the responsibilities of federal prosecutors and how
they carry out their work. Of course, as described in more detail below, U.S. Attorney’s Offices
(USAOs) vary on many dimensions, including the size and organization of the offices and the
types of cases that are prosecuted. This section does not attempt to provide comprehensive,
individualized information about each office. Instead, it describes the rules and procedures that are
common to all USAOs and highlights instances in which heterogeneity is particularly prominent.
2.1.The Responsibilities of Federal Prosecutors
There are 94 geographically distinct federal district courts that cover the United States.
Each federal district court is associated with precisely one USAO, with one exception.19 Figure 1
labels and depicts the boundaries of the federal district courts. The shaded districts indicate those
districts that are represented in the data used in this paper. Each USAO is led by a United States
Attorney. The lawyers who work in USAOs are called Assistant United States Attorneys (AUSAs).
USAOs represent the United States as a party in both civil and criminal federal cases in their
districts, and typically include separate criminal and civil divisions.20
In handling criminal cases, USAOs are broadly tasked with prosecuting violations of
federal criminal law in their jurisdictions. USAOs enjoy wide discretion in how they carry out this
work, and most prosecutorial decisions are unreviewable by courts except in limited
circumstances. 21 For example, a prosecutor’s decision to initiate a prosecution and, if so, which
19 The District of Guam and the District of the Northern Mariana Islands share a USAO. 20 I collected this information by hand by visiting the website of each USAO. The majority of USAO websites
indicated that they have separate civil and criminal sections. I did not find any instances in which a USAO website stated that the USAO’s civil and criminal sections were combined. There were a handful USAOs for which it was unclear whether the civil and criminal sections were combined or separate.
21 Some argue that the role of prosecutors raises separation-of-powers concerns by endowing prosecutors with what is essentially adjudicatory power (Barkow 2009).
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criminal violation(s) to charge are largely unreviewable by courts. Similarly, defendants typically
cannot challenge a prosecutor’s choices during plea bargaining.
Federal prosecutors also make recommendations to the court at various points during a
criminal prosecution. Most notably, AUSAs advise the court as to whether they believe the
defendant should be detained pending trial and recommend what they think is an appropriate
sentence. Prosecutors can also support or oppose reductions or enhancements to a defendant’s
sentence. For example, under Federal Rule of Criminal Procedure 35(b) and U.S. Sentencing
Guideline § 5K1.1, a prosecutor can ask the court to reduce a defendant’s sentence if the defendant
provides substantial assistance to the government.22
Despite limited oversight from the courts, however, a prosecutor’s decision-making
authority is not absolute. Individual AUSAs are subject to both formal and informal supervision
from several sources. First, like most employees, individual prosecutors are supervised within their
organization. For the time period covered by this paper, all AUSAs were governed by the United
States Attorneys’ Manual (the “Manual”) which laid out detailed guidelines for how USAOs
should be organized and how individual prosecutors should exercise discretion.23 For example, the
Manual dictated that “[t]o ensure consistency and accountability, charging and plea agreement
decisions must be reviewed by a supervisory attorney. All but the most routine indictments should
be accompanied by a prosecution memorandum that identifies the charging options supported by
the evidence and the law and explains the charging decisions therein.”24
Second, the Manual includes instructions for deciding which charges to file. During the
time period covered by this paper, the Manual expressed a policy that federal prosecutors should
usually charge “the most serious offense that is consistent with the nature of the defendant’s
conduct, and that will probably be sufficient to sustain a conviction.”25 The Manual, however, left
room for an AUSA to deviate from this policy by conducting an “individualized assessment” of
22 The Guidelines allows a sentencing judge to give the defendant a reduced sentence (including a sentence below
the mandatory minimum) “upon motion of the government stating that the defendant has provided substantial assistance in the investigation or prosecution of another person who has committed an offense.” U.S. SENTENCING
GUIDELINES MANUAL § 5K1.1 (2016). 23 As noted in footnote 9, the United States Attorney’s Manual was replaced by the Justice Manual in September
2018. Because AUSAs were governed by the United States Attorneys’ Manual for the time period covered by this paper, I cite this version of the Manual.
24 UNITED STATES ATTORNEYS’ MANUAL § 9-27.300. 25 UNITED STATES ATTORNEYS’ MANUAL § 9-27.300.
12
the defendant’s offense conduct and history.26 In theory, these directives ought to promote
uniformity in prosecutorial charging behavior and potentially mitigate bias.
Third, although courts are “hesitant to examine the decision to prosecute,”27 they exercise
oversight during plea bargaining and sentencing. For example, judges have the power to reject
certain kinds of plea agreements reached by the prosecutor and defendant.28 In federal court—the
setting studied here—defendants and prosecutors rarely bargain to a sentence, and judges enjoy
considerable discretion in choosing a defendant’s sentence, especially after the Supreme Court’s
decision in United States v. Booker.29 It is possible, then, that judges sentence in a way that
neutralizes any prosecutorial bias. In theory, a defendant can also challenge his prosecution on the
grounds that it was brought selectively—that is, based on a prohibited consideration such as the
defendant’s race or religion.30 In practice, however, selective prosecution challenges virtually
never succeed (McAdams 1998, Bibas 2009).
Finally, prosecutors are subject to outside pressures from the legislative branch, media,
advocacy organizations, and the general public. However, prosecutors tend to play an outsized role
in criminal justice policy-making (Barkow 2013). In the federal setting studied in this work, are
not subject to electoral pressures, and are in some ways insulated from political pressures.31 In
contrast, nearly all states elect the District Attorneys that lead their prosecutorial offices (Lantigua-
Williams 2016).
2.2.How U.S. Attorney’s Offices are Organized and Cases Assigned
USAOs largely work autonomously, although federal prosecutors occasionally collaborate
with other USAOs or federal agencies on cases. Individual AUSAs work under the control of their
26 UNITED STATES ATTORNEYS’ MANUAL § 9-27.300. 27 Wayte v. United States, 470 U.S. 598, 608 (1985). 28 Fed. R. Crim. P. 11(c)(3)(A), 11(c)(5). 29 543 U.S. 220 (2005). In Booker, the Supreme Court held that the U.S. Sentencing Guidelines are advisory; that
is, that Congress may not require district judges to sentence defendants within the Guidelines range. However, a district judge must always calculate a criminal defendant’s Guidelines range before sentencing and articulate any reasons for deviating from that advisory range. 543 U.S. at 259-60; see also Peugh v. United States, 569 U.S. 530, 530 (2013) (“District courts are required to being their sentencing analysis by looking at the ranges in the guidelines; a judge must have a good reason for deviating from those ranges”).
30 Oyler v. Boyles, 368 U.S. 448, 456 (1962). 31 Historically, U.S Attorneys have been insulated from political pressure, although some argue that the positions
have become increasingly politicized over the last decade. In 2006, President George W. Bush fired seven U.S. Attorneys, and in 2017, President Donald Trump dismissed all U.S. Attorneys that had been appointed by President Barack Obama.
13
district’s United States Attorney (through a system of supervisory oversight) and United States
Attorneys can only be removed by the President.32 As one might expect, USAOs substantially vary
in size and organizational structure. For example, the U.S. Attorney’s Office for the District of
Hawaii employs 27 AUSAs,33 and the criminal division of the office is divided into three sections:
Drug and Organized Crime, Fraud and Financial Crimes, and Special Crime. In contrast, the U.S.
Attorney’s Office for the Central District of California—based in Los Angeles—employs 264
AUSAs, and has a criminal division that is split into 10 sections: Asset Forfeiture, Criminal
Appeals, Cyber and Intellectual Property Crimes, Public Corruption and Civil Rights, Major
Frauds, Organized Crime Drug Enforcement Task Force (OCDETF), Public Integrity and
Environmental Crimes, and Violent and Organized Crime.34 On average, a USAO is divided into
roughly four sections.35
AUSAs are typically assigned cases by their supervisors. One USAO describes the process
as follows: “Typically, cases are brought for initial review to the Criminal Division Chief by
federal agents responsible for investigating crimes, then assigned to AUSAs within units to
work.”36 USAOs appear to vary in the extent to which AUSAs specialize in certain types of cases.
For example, the District of Wyoming explains that “[d]ue to the small size of the staff, criminal
[AUSAs] must each be prepared to handle a variety of matters. Specialization is encouraged in
order for AUSAs to develop deeper expertise in important areas of the law; however, every
criminal AUSA must be prepared to handle almost any criminal matter on short notice, in the event
other AUSAs are unavailable.37 Another USAO explains, “At any given time, any one of our
AUSAs has a caseload that includes violent crime, complex financial institution fraud, health care
fraud, financial crimes, computer fraud, environmental crime, public corruption, organized crime,
complex drug and money laundering activities, and cases involving multiple defendants and
international organizations.”38 While the majority of prosecutors in the data (74 percent) have
32 28 U.S.3. § 541(c). 33 Of these 27 AUSAs, 20 are assigned to the criminal division. 34 In addition to these sections, the office also includes a national security division and a tax division, each of which
handle both civil and criminal cases. 35 I collected this data by hand by visiting the website of each U.S. Attorney’s Office. It is on file with the author. 36 UNITED STATES ATTORNEY’S OFFICE FOR THE EASTERN DISTRICT OF TENNESSEE, “Criminal Division,” available
at: https://www.justice.gov/usao-edtn/criminal-division. 37 UNITED STATES ATTORNEY’S OFFICE FOR THE DISTRICT OF WYOMING, “Criminal Division,” available at:
https://www.justice.gov/usao-wy/criminal-division. 38 UNITED STATES ATTORNEY’S OFFICE FOR THE DISTRICT OF RHODE ISLAND, “About Us,” available at:
https://www.justice.gov/usao-ri/about-us.
14
worked on more than one offense type, around one-third (32 percent) have worked significantly
on multiple types of cases (defined as having prosecuted at least 20 cases of the offense type).
3. Data
This paper uses data from two distinct sources: (1) the United States Sentencing
Commission’s annual sentencing data files (the “Commission data”); and (2) federal case data
from the Legal Information Office Network System, which is published by the Executive Office
of United States Attorneys in response to Freedom of Information Act requests (the “LIONS
data”). This section describes the two data sources and explains how they were merged.39
3.1. United States Sentencing Commission Data
The United States Sentencing Commission (the “Commission”) is an independent agency
of the federal judiciary. One of the Commission’s primary responsibilities is to promulgate the
Federal Sentencing Guidelines—a manual that all federal judges use in sentencing criminal
defendants.40 The Commission also publishes annual data files containing detailed information
about defendants sentenced in federal district courts.41 The Commission creates these data files by
compiling information from sentencing documents submitted by the federal district courts. The
Commission extensively reviews the data it receives for completeness and quality before making
the data files available to the public on its website.42
The Commission’s individual-level data files include detailed case information, including:
demographic characteristics of the defendant; the defendant’s criminal history; the statutes under
which the defendant was convicted; the defendant’s recommended Guidelines range; the sentence
imposed; and any reasons for an out-of-range sentence.
39 The data set used in this paper was previously used in work examining the consequences of pretrial detention on
case outcomes and is described in similar terms there (Didwania 2018). 40 In Booker, the Supreme Court held that the Guidelines are advisory; that is, that Congress may not require district
judges to sentence defendants within the Guidelines range. However, a district judge must always calculate a criminal defendant’s Guidelines range before sentencing and articulate any reasons for deviating from that advisory range. 543 U.S. at 264 (“The district courts, while not bound to apply the Guidelines, must consult those Guidelines and take them into account when sentencing.”).
41 The Commission reports data for defendants convicted of felonies and Class A misdemeanors. Notably, the Sentencing Commission data files do not include information about cases involving: juvenile offenders, defendants convicted of Class B and C misdemeanors, and death penalty cases (Reedt, Semisch, and Blackwell 2013).
42 The Commission cross-checks its data for completeness and quality with data from another source—the Administrative Office of U.S. Courts. It pays special attention to cases with out of range values, logical inconsistencies, and sentences outside the Guidelines range.
15
This paper uses Commission data for defendants sentenced in fiscal years 2002 through
2015. The primary outcome variable used in the analysis is the defendant’s base offense level,
which reflects the severity of the offense with which the defendant was charged. Other outcome
variables used include: whether the defendant faced a mandatory minimum sentence; whether the
defendant received a substantial assistance reduction; whether the defendant received less than the
mandatory minimum sentence (if facing one); the defendant’s ultimate sentence; and whether the
defendant received a sentence below the recommended Guidelines range. The paper also uses the
following defendant- and case-specific information, both as control variables and in matching
defendants between the two data sets: the defendant’s race, Hispanic ethnicity, gender, age, highest
level of education, and criminal history points; the months of incarceration and amount of
probation, fine, and supervised release imposed; the date sentenced; the lead statute of conviction;
and the types and quantities of controlled substances involved in the case, if any.
The Commission data is insufficient for purposes of this project, however, for two reasons.
Most critically, the Commission data does not contain any information about the prosecutor(s)
associated with each case. A second limitation is that the Commission data does not identify the
courthouse in which the defendant was sentenced; it simply identifies the federal district court.
Roughly 80 percent of federal district courts comprise more than one courthouse, to which
defendants, judges, and prosecutors are non-randomly assigned. It is therefore impossible for an
empirical researcher to fully account for intra-courthouse correlation using Commission data.
Another important feature of the Commission data is that it necessarily is a selected sample
because it only includes defendants who were ultimately sentenced. Issues related to sample
selection are discussed in more detail in Appendix B. The LIONS data—discussed in the next
subsection—fills these gaps.
3.2. LIONS National Caseload Statistical Data
The Executive Office for United States Attorneys (EOUSA) regularly publishes case data
from the 93 USAOs located throughout the United States. This data originates from the Legal
Information Office Network System (LIONS), which is the computer program that the EOUSA
uses to track cases. The breadth of the LIONS data is substantial—the data covers all cases in
which a USAO was involved, including those cases that are the primary responsibility of another
agency and those in which the USAO declines to prosecute. The LIONS data consists of many
16
discrete text files that can be linked using case and participant identification numbers that are
provided in the data.
The LIONS data includes abundant case-specific information, but little defendant
information. For example, the LIONS data includes: the lead charge, the type of offense,
sentencing information including the length of incarceration ordered and whether the defendant
received a term of supervised release, probation, community service, or a fine; the sentencing
judge; the sentencing date; and the drug type(s) and quantity involved in the case. On the other
hand, the LIONS data includes little demographic information about defendants, only identifying
a defendant’s gender and country of citizenship.
Crucially, the LIONS data also provides some information about the USAO staff members
who work on each case. LIONS anonymously identifies the staff members associated with each
case, and labels their role in the case as, for example, lead attorney, co-counsel, paralegal, or victim
witness coordinator. Roughly 84 percent of staff assignments in the data are coded as “lead
attorneys.” The LIONS data also includes a variable that indicates the staff member’s salutation—
such as “Mr.” or “Ms.”—which I use to deduce the staff member’s gender. A complete list of all
salutations and how each was coded is produced in Appendix A.
It is also important to note two shortcomings of the LIONS data. First, unlike the
Commission data, there is no evidence that the LIONS data is cross-checked for accuracy with
court documents. Second, the LIONS data contains many more missing values than the
Commission data.
3.3. Merged Sample
The data used in this paper comprises a sample that is merged between the Commission
and LIONS data sources of federal defendants sentenced in fiscal years 2002 through 2016. The
sample is restricted to U.S.-citizen defendants in district courts that do not border Mexico.43 In
43 I remove non-citizens and defendants in border districts for three reasons. First, non-citizens who are charged
with immigration offenses—as most non-citizen defendants are—are often eligible for reduced sentences as part of the Department of Justice’s Fast Track program (Cole 2012), so the extent to which prosecutors exercise charging discretion in these cases is very different from cases involving U.S. citizen-defendants. Second, the districts that border Mexico face unique caseload pressures compared to the other federal district courts. The five districts that border Mexico are: the District of Arizona, the Southern District of California, the District of New Mexico, the Southern District of Texas, and the Western District of Texas. Together, these five districts account for 34 percent of federal criminal defendants in the Commission data. It is plausible that prosecutors’ behavior towards all defendants in border districts is affected by their immigration-related workload, as in Huang (2011). Third, and consequently, because the border districts include so many immigration defendants receiving identical sentences, these defendants are
17
total, 73.6 percent of such defendants from the Commission data are identified in the LIONS data.
After merging, the sample is reduced in several ways. The most important restriction is removing
defendants whose lead prosecutor is of unknown gender—a little more than half of defendants.44
In addition to this restriction, I also remove defendants from four federal district courts whose
courthouse identification variables appear to be unreliable45 and those whose prosecutor
identification variable appears to be unreliable;46 defendants who are coded as different genders in
the LIONS and Commission data sets (1.6 percent of defendants); and defendants who are missing
entries in any of the following variables in the Commission data: gender, race, Hispanic ethnicity,
age, educational attainment, criminal history points, offense type, sentencing fiscal year, and base
offense level.
Because I control for the year in which a case was commenced and the Guidelines version
used at sentencing, I exclude defendants who cases commenced before 1999 and after 2014
because there are few defendants in these early and late years. Because I include courthouse-
section fixed effects in the main regression analysis, I exclude courthouse-section cells that have
fewer than 25 observations and courthouse-section cells in which less than five percent of
defendants are female. Finally, I exclude offenders charged with sex offenses or offenses involving
child exploitation, a decision that is discussed in subsection 4.1.
The sample used in the main analysis includes 143,457 defendants sentenced in 65 of the
94 federal district courts in the United States, comprising 182 unique courthouses and 503 unique
courthouse-sections. Table 1 presents summary statistics of the data. Roughly 72 percent of cases
have a male lead prosecutor. Eighty-three percent of defendants are male and 70 percent have
graduated from high school. Around 40 percent of defendants are black, 12 percent are Hispanic
ethnicity, and 43 percent are non-Hispanic white. The average defendant age in the sample is 36
years old.
intrinsically hard to merge between the Commission and LIONS data sets. The results, however, are largely robust to including citizen-defendants from the border districts (results on file with the author).
44 A lead prosecutor’s gender is unknown if the salutation field in the data is empty, or if the salutation entry does not convey gender information (for example, if it is “AUSA”). Appendix A contains a complete list of how salutations are coded. Missingness in the salutation field is largely driven by missingness between—rather than within—USAOs. Among those USAOs with any gender information, most have gender information for at least 75 percent of AUSAs.
45 Data from the Northern District of Alabama, the Northern District of Iowa, the District of Nebraska, and the District of South Carolina contain many more courthouses than actually exist in those districts.
46 This restriction removes 1.8 percent of defendants.
18
In terms of case outcomes, the vast majority of defendants—roughly 86 percent—receive
sentences of some incarceration, and the mean sentence among all defendants is 60 months.
Average sentence length is considerably lower than the mean Guidelines range, which is 84
months. This is because roughly half of defendants receive a sentence below their recommended
Guidelines range, while just two percent receive a sentence above their recommended Guidelines
range.
Appendix Table A.1 assesses the representativeness of the sample by comparing the
variable means of defendant and case characteristics presented in Table 1 to those: among all
Commission defendants in federal districts represented in the data (column (2)); among all
defendants in the merged sample (column (3)); and among the full universe of Commission
defendants (column (4)). Table A.1 allows readers to not only assess how well the data used in this
work represents the full universe (column (1) versus column (4)), but also to assess how well the
matching process worked (column (3) versus column (4)). In all columns, I restrict to U.S. citizen-
defendants and exclude districts that border Mexico so as to compare equivalent populations.
Table A.1 demonstrates that data used in this paper is extremely representative of the districts
represented in the data, the matched sample as a whole, and the universe of all Commission
defendants.
3.4. Graphical Description of the Results
The defendant’s base offense level is the primary dependent variable used in the analysis.
The base offense level is a natural number between 0 (least serious) and 43 (most serious).47 In
federal criminal cases, a defendant’s sentencing advisory Guidelines range is determined based on
two factors: their final offense level and their criminal history score. The final offense level is
computed by adjusting the base offense level to reflect the defendant’s offense conduct and their
behavior during the case. For example, under U.S. Sentencing Guideline § 3E1.1, a defendant can
earn up to a three-point reduction to their base offense level by quickly pleading guilty.48 This
paper uses the defendant’s base offense level because it reflects the severity of the charged offense
47 In the data, 22 out of the 143,454 defendants have base offense levels that are above 43 (ranging from 44-47). I
assume these are erroneous entries. 48 The three-point reduction is only available to defendants whose offense level is 16 or larger. Other defendants are
only eligible for a two-point reduction.
19
before any adjustments occur as the case proceeds. I therefore consider it to be the variable in the
data that most closely quantifies the prosecutorial charging decision.49
Increasing by one base offense level corresponds with a roughly ten percent increase in the
Guidelines-recommended sentencing range. Figure 2 presents a histogram of the base offense level
variable in the data. Due to the way the Guidelines assign base offense levels to offenses, base
offense levels that are even numbers are more prevalent than odd numbers, which Figure 2
illustrates.
Before turning to regression estimates, Figure 3 depicts the average base offense level for
the four possible gender pairings of prosecutor and defendant relative to the average across all
defendants within the same courthouse. In Figure 3, cases prosecuted by male prosecutors are
depicted with solid columns, and cases prosecuted by female prosecutors are depicted with striped
columns. Figure 3 constitutes suggestive evidence of gender-based in-group favoritism: female
defendants earn lower base offense levels (i.e. lesser charges) when assigned a female rather than
male prosecutor, while male defendants earn a lower base offense level when assigned a male
rather than female prosecutor. When all defendants are pooled, the overall difference between male
and female prosecutors are smaller than when one compares the averages within defendant gender.
4. Empirical Strategy and Main Results
4.1. Regression Design
While Figure 3 presents suggestive evidence of gender-based in-group favoritism in
charging decisions, it is important to control for defendant and case characteristics, changes over
time, and geographic differences. For the main analysis, I obtain estimates of gender-based
favoritism using the following empirical specification:
yipct = β0 + β1MaleDi + β2MalePip + β3MaleDi*MalePip + θXi + γfc + δt + εipfct (1)
where yipct is an outcome for defendant i, assigned prosecutor p, charged with an offense of type f,
in courthouse c, in year t; MaleDi is an indicator variable that equals one if defendant i is male;
MalePip is an indicator variable that equals one if defendant i's prosecutor p is male; Xi is a vector
49 More precisely, the base offense level will capture charging severity at sentencing—which will reflect any
bargaining between prosecutor and defendant. The results presented in the main analysis in section 4.3, however, are robust to alternative measures of charging severity. These results are presented in Appendix Table A.1.
20
of observable characteristics of defendant i and their case; and γfc and δt are courthouse-section
type and time fixed effects, respectively.
In this formulation, β1 captures average differences in outcomes for female and male
defendants when the prosecutor is female, β2 captures average differences in outcomes for female
and male prosecutors when the defendant is female, and β3 captures in-group favoritism: average
differences in how female and male prosecutors treat female defendants relative to male
defendants.50 In other words, Equation (1) is a difference-in-differences design51 in which, holding
all else equal, the predicted differences in base offense level for each defendant-prosecutor gender
combination relative to female-female pairs are:
Male Prosecutor Female Prosecutor Difference (M-F) Male Defendant β1+β2+β3 β1 β2+ β3 Female Defendant β2 - β2 Difference (M-F) β1+ β3 β1 β3
4.2. Estimation Challenges
A central challenge in estimating equation (1) is that whether a prosecutor and defendant
match on gender might be correlated with unobservable defendant or case characteristics that also
affect case outcomes, even after controlling for many observable defendant and case characteristics
and relevant fixed effects. In this case, a regression of equation (1) will produce biased coefficient
estimates.
There are several ways in which the assignment of cases to prosecutors might be non-
random in a way that biases the coefficient estimate of β3. Most obviously, the estimate of β3 will
be biased upward (downward) if supervisors are likely to assign cases with less serious (more
serious) defendants to prosecutors who share the defendant’s gender.
There are a few reasons why it is unlikely that cases are assigned in this way. For one thing,
while it might be possible for a supervisor to consider characteristics of the defendant in assigning
cases involving a solo defendant to prosecutors that share the defendant’s gender, this task
becomes unrealistically complicated when there are co-defendants of different genders. In the data,
however, more than half of all female defendants have at least one male co-defendant but all
defendants within a case are assigned the same lead prosecutor. In subsection 6.1, I show that the
50 The paper uses ordinary least squares regression throughout for interpretive clarity. All regressions in which the
outcome variable is binary are robust to using logit regression. 51 This empirical specification follows Shayo and Zussman (2011).
21
estimate of in-group favoritism is largely robust to restricting the sample to cases in which there
is at least one male and at least one female defendant, a restriction that removes more than 80
percent of the data.
For another thing, it is plausible that the factors that influence an AUSA’s charging
decision are not known by a supervisor at the time of case assignment. If supervisors do not have
a nuanced picture of a defendant’s social history and specific offense conduct at the time case
assignments are made, it would be difficult for such assignments to be made strategically. For this
reason, I rely on the first prosecutor assigned to a case under the assumption that the first attorney
assignment is made when a supervisor has the least information. Prosecutors who join a case later
might be more strategically assigned, but they are not part of the main analysis.52 In subsection
5.3, I define the prosecutor gender variable based on the gender composition of all lead prosecutors
assigned to a case, and find that the results are very similar to those in the main analysis.
A second concern is that female prosecutors might be disproportionately represented in
areas of federal criminal law in which women are treated more leniently. Courthouse-section fixed
effects in the regression equations should largely address this concern, which seems to materialize
in one setting: cases involving sexual offenses and child exploitation. These cases exhibit above-
average gender disparities, involve very few female defendants, and are more likely than other
types of cases to be staffed by female prosecutors. Therefore, I exclude this offense category from
the main analysis, although the results are robust to including these cases. I do not find evidence
of gender disproportionality in the share of prosecutors in any other offense type. Among the four
offense types remaining in the data after sex offenses are removed, the percentage of lead
prosecutors that are female ranges from 28 to 30 percent. In contrast, among sex offense cases,
roughly 44 percent of lead prosecutors are female.
I also look for quantitative evidence to shed light on whether prosecutors are assigned
systematically different defendants by gender. Table 2 presents results of regressing the
prosecutor’s gender on defendant and case characteristics that are fixed at the time of case
including: the defendant’s age, number of dependents, criminal history category, the prosecutor’s
52 In the data, 99.8 percent of cases have just one prosecutor assigned on the first day. For the few cases in which
more than one prosecutor is assigned to a case on the first day, I code the prosecutor as female if all prosecutors assigned on the first day are female, male if all prosecutors assigned on the first day are male, and remove the case from the data if both male and female prosecutors are assigned on the first day. In subsection 5.3 below, I examine the behavior of same-gender and mixed-gender teams of prosecutors.
22
workload, and indicators for the defendant being female, black, Hispanic, another race other than
white, a high school graduate, a college graduate, whether the case involved more than one
defendant, whether the case involved more than one prosecutor, and the type of offense.53 Columns
(1) through (3) present results in the whole sample, while column (4) restricts attention to female
defendants and column (5) restricts attention to male defendants.
In columns (1) and (2), prosecutor gender is regressed on defendant and case characteristics
without any geographic controls. In these regressions, male prosecutors are associated with an
increased likelihood of prosecuting male defendants and a decreased likelihood of prosecuting
defendants of color. Defendants’ educational attainment and criminal history are not statistically
different for male and female prosecutors, although male prosecutors are assigned defendants that
are statistically, but not meaningfully, younger than those prosecuted by female prosecutors when
no time controls are included. The coefficient estimates across columns (1) and (2) are also very
similar—suggesting that there are not large differences in caseload composition over time that are
correlated with prosecutor gender. Once courthouse-section fixed effects are included in column
(3), male and female prosecutors no longer appear to differ in the racial compositions of their
caseloads. This change demonstrates the crucial role of courthouse-section fixed effects—it
suggests that female prosecutors are more prevalent in geographic regions and offense types in
which non-white defendants are more prevalent.
Even after including courthouse-section fixed effects in column (3), the coefficient
estimate on defendant gender remains positive and highly significant. The point estimate—
0.022—suggests that a male defendant’s probability of being assigned a male prosecutor is about
2.2 percentage points larger than a female defendant’s probability of being assigned a male
prosecutor. Thus, it appears that supervisors might be influenced (consciously or not) by gender
in making case assignments, which is not surprising. Researchers studying the assignment of law
enforcement officers to neighborhoods similarly report that officers are more likely to be assigned
to areas in which the majority racial group is the officer’s racial group (Antonovics and Knight
2009, Donohue and Levitt 2001).
Given that defendants are slightly but statistically significantly more likely to be assigned
a prosecutor of the same gender, it is crucial to check whether prosecutor gender seems to be
53 The offense type categories are: drug offenses, fraud and other white-collar offenses, regulatory offenses, and
violent offenses.
23
correlated with any defendant and case characteristics that are fixed at the time of the case,
conditional on defendant gender. Table 2, column (4) reports results of regressing prosecutor
gender on all the covariates listed above among male defendants, while column (5) reports results
among female defendants. The point estimates presented in columns (4) and (5) are all close to
zero and all but one are statistically insignificant, suggesting that female and male prosecutors see
similar mixes of defendants within each gender group.
It is also important to remember that USAOs and sections within USAOs likely vary in
their assignment procedures. In many courthouse-sections, prosecutor gender is uncorrelated with
defendant gender. Subsection 6.2 restricts attention to courthouse-sections cells in which
prosecutor gender is not significantly related to any defendant characteristics that are fixed at the
time of the case—including gender—and finds that the results are quite similar (if not slightly
stronger) than those in the main analysis, offering more evidence that the results are not driven by
differences in prosecutors’ caseloads.
4.3. Initial Results
As described in the previous section, the base offense level is the primary outcome variable
used in the analysis. Table 3 presents results of estimating equation (1) with ordinary least squares
regression. Standard errors are clustered at the courthouse level to account for likely intra-
courthouse correlation in the error term (Bertrand, Duflo, and Mallainathan 2004).
The coefficient on the interaction term—in the top row of Table 3—represents gender-
based in-group favoritism. The regression with no controls—presented in column (1)—appears to
overstate gender-based favoritism. In the regression with demographic and case controls, year
fixed effects, and courthouse-section fixed effects presented in column (4), the coefficient estimate
of the interaction term is -0.542 and is statistically significant at the one-percent level. This result
can be interpreted as the differential effect of prosecutor gender on male defendants relative to
female defendants. Column (3) uses district-section fixed effects instead of courthouse-section
fixed effects and finds results that are extremely similar to those reported in column (4), suggesting
the additional geographic information contained in the courthouse variable is not critical. The
results in column (5) shows that using courthouse-section-year fixed effects produces a stronger
coefficient estimate on the interaction term, although this regression excludes roughly twenty
24
percent of the data from the full sample and is less precisely estimated.54 To maximize statistical
power, the remaining specifications include courthouse-section and year fixed effects separately,
as in column (4).
Consistent with in-group favoritism, the second row of Table 3 demonstrates that
prosecutor gender is a significant predictor of charging severity when the defendant is female
(columns (1)-(5)). However, in column (6), which does not include the interaction term, the
coefficient on prosecutor gender is quite small and is statistically insignificant—indicating that
prosecutor gender does not influence prosecutorial behavior on average.
The third row of results in Table 3 indicates that male defendants are subject to significantly
larger base offense levels than female defendants when the prosecutor is female, even after the
inclusion of many control variables and fixed effects.55 After controlling for demographic and case
characteristics, year fixed effects, and courthouse-section fixed effects, the coefficient estimates
are around a two-point increase in base offense level for male defendants in columns (3) through
(5). Column (6), which does not include a prosecutor-defendant interaction term (and therefore
represents average effects) estimates a 1.6-point gender gap in base offense level after controlling
for case and demographic characteristics.
Appendix Table A.2 reports similar results when equation (1) is estimated with alternative
measures of charging severity.56 The first such alternative is the defendant’s statutory minimum
across all counts of conviction. While the statutory minimum reflects charging severity untethered
from judicial interference, it is a coarse measure. Roughly two-thirds of defendants have no
statutory minimum, and some of these defendants are still sentenced to life in prison (suggesting
that they committed very serious offenses despite not facing a mandatory minimum). The second
alternative measure of charging severity is the leave-one-out average sentence for all defendants
54 As described in the notes to Table 3, the regression that included courthouse-section-year fixed effects removed
courthouse-section-year cells with fewer than 25 observations, reducing the sample size from 143,457 observations to 103,858 observations.
55 Because the regression equation includes an interaction term, the coefficients for the male defendant and male prosecutor variables must be interpreted as conditional effects rather than as main effects in columns (1) through (5) in Table 3.
56 Some might object that the base offense level is an imperfect measure of charging severity because it can include adjustments for facts that are found by the court. This concern is largely mitigated by the fact that the paper uses the value of the base offense level before Chapter Two adjustments for specific offense characteristics. The Commission data reports both the base offense level (inclusive of Chapter Two adjustments) and the Chapter Two adjustments themselves, so it is straightforward to measure the base offense level prior to Chapter Two adjustments, which is the outcome variable used in Table 3.
25
within the same courthouse who were sentenced under the same Guideline as the defendant,
excluding the defendant themselves. This is likely a noisy measure of charging severity given that
sentences are highly dependent on other factors (such as criminal history). Both of these alternative
measures of charging severity, however, produce estimates of gender-based favoritism that are
statistically significant.
4.4. Additional Outcome Variables
The prior analysis uses the defendant’s base offense level as the outcome of interest. While
the base offense level plausibly captures charging severity, it is not a case outcome—it is simply
an input into a defendant’s ultimate sentence. Thus, it is useful to think about additional outcome
variables. I organize additional outcome variables into three general categories. The first are
outcomes that are largely a function of prosecutorial discretion. As explained above, I believe the
defendant’s base offense level almost purely captures a prosecutorial decision.57 Other variables
of this nature include: whether the defendant was charged with a crime carrying a mandatory
minimum penalty and whether the government recommended a reduction to the defendant’s
offense level. The second type of outcome variable is one that is a function of both judicial and
prosecutorial discretion. The defendant’s ultimate sentence is an example of such an outcome: a
prosecutor can influence the sentence through the charging decision, the application of
enhancements and reductions, and their own recommendation, but, ultimately, the district judge
sentences the defendant. Finally, there are outcome variables over which the prosecutor has
relatively little control. For example, judges largely determine whether the defendant receives a
sentence below the recommended Guidelines range.58
57 To be more precise, the base offense level will also reflect any bargaining over the charges between the defendant
and the prosecutor. Section 5.4 further discusses the bargaining dynamics between prosecutors and defendants. The Justice Manual (formerly the U.S. Attorneys Manual), however, attempts to limit charge-bargaining by instructing prosecutors to “charge and pursue the most serious, readily provable offenses,” which are defined as “those that carry the most substantial guidelines sentence, including mandatory minimum sentences.” Justice Manual 9-27.300. The Manual does not completely foreclose charge bargaining, making clear that “there will be circumstances in which good judgment would lead a prosecutor to conclude that a strict application of the above charging policy is not warranted.” Id. In such a situation, the individual AUSA must get approval from a supervisor to bring a lesser charge. Id.
58 Of course, a prosecutor can request an out-of-range sentence, but such requests are rare in the data. Prosecutors request a below-Guidelines sentence for roughly four percent of defendants, while 46 percent of defendants ultimately receive sentences below the recommended Guidelines range. Upward departures requested by the prosecutor are not reported in the data, but only two percent of defendants receive sentences above their recommended Guidelines range, so it is likely that such requests by prosecutors are similarly rare.
26
Table 4 presents regression results estimating equation (1) with eight additional outcome
variables. Panel A considers early outcomes—variables capturing decisions that occur while a case
is going on and over which the prosecutor exercises large influence. These early outcomes include:
(1) the final offense level; (2) whether the defendant was charged with a mandatory minimum; (3)
the defendant’s final Guidelines Range (represented by its mean, transformed by the inverse
hyperbolic sine); and (4) whether the defendant received a substantial assistance reduction. Panel
B considers outcomes over which the prosecutor’s influence is more limited: (5) whether the
defendant was released pending trial; (6) the defendant’s sentence in months (transformed by its
inverse hyperbolic sine); (7) same as (6) but with the base offense level added as a control variable;
and (8) whether the defendant received a sentence below their recommended Guidelines range. If
gender-based favoritism shapes prosecutorial decision-making, one would expect coefficient
estimates of the interaction term to be significant for outcome variables that are under the
prosecutor’s control, but insignificant for outcome variables that are largely determined by the
defendant’s judge.
As predicted, the results in Panel A suggests that gender match—represented by the
coefficient on the interaction term—is associated with a reduction in the defendant’s final offense
level, a reduction in the probability that the prosecutor charges a mandatory minimum offense, and
a roughly six-percent reduction in the defendant’s Guidelines range. Defendant-prosecutor gender
match does not appear to affect the probability that a defendant receives a substantial assistance
reduction. This finding suggests that favoritism is concentrated at the beginning of the case,
perhaps because there is less involvement from other actors, like defense counsel, or because they
are simply because early decisions are made with less information about the defendant.59
Panel B, which considers outcomes in which a judge exercises decision-making authority,
finds that the prosecutor-defendant gender interaction match plays a smaller role. Sentence length
reported in column (5)—which is a function of both prosecutorial and judicial discretion—is the
only outcome that appears to be influenced by gender match. The coefficient estimate on the
interaction term, -0.102, suggests that female defendants can expect to receive a ten percent shorter
59 For example, Marianne Bertrand and Esther Duflo explain, “Implicit biases are more likely to drive behavior
under conditions of ambiguity, high time pressures and cognitive loads, or inattentiveness to the task.” (Bertrand and Duflo 2017).
27
sentence relative to male defendants when they are assigned a female prosecutor. These findings
suggest that judicial decision-making does not entirely mitigate in-group favoritism in charging.60
This sentencing reduction appears to enter through the charging channel: when the defendant’s
base offense level is included as a control variable in column (7), the point estimate is attenuated
and not statistically significant. Also as predicted, estimates of gender-based favoritism are
statistically insignificant predictors of whether the defendant received a sentence below the
recommended Guidelines range—an outcome that is largely within the judge’s purview. Nor is
gender match a statistically significant predictor of whether a defendant is released pending trial—
a decision that the prosecutor might influence, but that is ultimately decided by a judge.
Finally, it is worth also highlighting that being a male defendant is significantly predictive
of worse outcomes on every measure, consistent with the findings in Starr (2015) of large gender
disparities at all stages of a criminal case. Specifically, relative to female defendants, a male
defendant suffers: an increased probability of being charged with a mandatory minimum, a reduced
probability of receiving a substantial assistance reduction, a reduced probability of being released
pending trial, a longer sentence, an increased probability of receiving at least the mandatory
minimum when one is charged, and a reduced probability of being sentenced below the
recommended Guidelines range. Moreover, the estimated gender gap in sentence length reported
in Table 4 is extremely close in magnitude to the estimate in Starr (2015).61
4.5. Selection into the Sample
Because this paper uses data on defendants sentenced in federal district courts, it necessarily
excludes defendants who are not ultimately convicted. In this subsection, I present suggestive
evidence that it is unlikely that sample selection biases the results of the paper. All quantitative
work on the criminal system must grapple with the selected sample problem: researchers typically
only observe defendants who formally enter into the criminal system (or, in the case of many data
sources, are eventually sentenced). It is plausible that selection into the sample could bias the
60 This explanation is consistent with Joshua Fischman and Max Schanzenbach’s finding that judicial discretion
does not contribute to racial disparities and might mitigate racial disparity (Fischman and Schanzenbach 2012). 61 The coefficient on defendant gender in the regression using the inverse hyperbolic since of sentence length as a
dependent variable is 0.848, which is equivalent to a roughly 57 percent increase in sentence length for male defendants relative to female defendants. This point estimate is very close to the finding in Starr (2015) of a roughly 60 percent gender gap in sentence length in federal criminal cases.
28
results if there is in-group favoritism on the margin of selection.62 On the other hand, one might
not expect selection into the sample to pose a serious threat in the federal setting because nearly
all federal felony defendants—around 94 percent—are ultimately convicted. Only 0.4 percent are
acquitted after trial. The remaining 5.6 percent of cases that do not result in conviction are those
voluntarily dismissed by the government (Motivans 2017).
I am able to make progress on the sample selection issue because the LIONS data includes
defendants whose charges the government declines to prosecute, voluntarily dismisses, or of which
the defendant is acquitted. Therefore, I can investigate whether there appears to be gender-based
favoritism on the margin of whether a defendant is convicted. This subsection takes on this task
by using the LIONS data on its own, without matching it to the Commission data (which only
includes sentenced defendants).
Using the LIONS data alone has several drawbacks, which is why it is not the default for
the rest of this paper. First, the LIONS data does not include any of the outcome variables used in
this paper except the defendant’s sentence length. Second, the LIONS data lacks most of the
control variables used in the paper, such as the defendant’s race and ethnicity, level of education,
age, and criminal history. And although LIONS has a variable that indicates the defendant’s
gender, it contains many missing values. Third, the LIONS data does not appear to be quality-
checked for completeness and accuracy the way the Commission data has been. Despite these
shortcomings, I verify the LIONS-only sample’s reliability, as described below.
To create the LIONS-only subsample I apply the same restrictions to the data that I used
when constructing the sample used in the paper, as described in subsection 3.3. The LIONS-only
sample contains 89,117 defendants, of whom 83,790 are sentenced. This translates into a 6.0
percent charged-but-not-convicted rate, which is very similar to the Bureau of Justice Statistics
reporting that in 2014, 6.4 percent of federal felony defendants were not ultimately convicted
(Motivans 2017). Appendix Table A.3 presents the results similar to those in Table 2 by regressing
prosecutor gender on defendant and case characteristics. In the LIONS-only sample, however,
there are many fewer control variables than in Table 2. Even though the LIONS-only sample
contains many fewer observations and control variables, it produces results in Appendix Table A.3
62 In particular, if prosecutors display gender-based favoritism in the decision to decline or dismiss cases, the paper’s
estimates of gender favoritism would understate the phenomenon. On the other hand, if there is in-group disfavoritism in the decision to decline or dismiss cases, the paper’s estimates could overstate the magnitude.
29
that are very similar in magnitude and statistical significant to those produced in the full data.
While male prosecutors are slightly (around two percentage points) but statistically significantly
more likely to be assigned to cases with male defendants, the case characteristics that are available
in the LIONS data—whether the case involved multiple defendants, whether the case involved
multiple prosecutors, and the offense type—are not statistically different for male relative to
female prosecutors.
Table 5 looks for in-group favoritism on the margin of whether a person is ultimately
sentenced (i.e., enters the data used in the paper). The outcome variable is a binary variable that
equals one if a defendant’s charges were acquitted, declined or dismissed and zero if the defendant
was convicted and sentenced. The coefficient estimates on the interaction term are statistically
insignificant in all specifications. The point estimates on the interaction term, although statistically
insignificant, are all greater than zero, which suggests that if anything, in-group favoritism might
exist on the margin of selection into the sample, which would likely attenuate (rather than bolster)
the findings of in-group favoritism later in the case that are documented in this section.
5. Heterogeneity and the Sources of Gender-Based Favoritism
The previous section demonstrates that prosecutors appear to charge defendants more
leniently when the defendant is the same gender as the prosecutor. In theory, there are many
underlying mechanisms that could generate this finding. This section broadly considers two. First,
gender-based leniency might derive from social preferences, or, taste-based discrimination
(Becker 1957). I call this the preference-based explanation for the findings. Second, the
prosecutorial process might generate gender-based leniency. Prosecutors might be able to more
(or less) effectively prosecute defendants with whom they have more in common. The paper refers
to mechanisms in this category as process-based explanations for the findings. For example, if
prosecutors are better able to evaluate the risk of recidivism among defendants of their gender than
opposite-gender defendants, differential treatment could constitute statistical discrimination, along
the lines of Cornell and Welch (1996). Conversely, defendant response to their prosecutor’s gender
could drive the results if, for example, defendants are more likely to trust and therefore cooperate
with an own-gender prosecutor.
This section presents evidence of heterogeneity in the results. Heterogeneity is not only
descriptively interesting in its own right, it also has the potential to shed light on the mechanisms
30
responsible for in-group favoritism, which is the focus of this section. In particular, this section
evaluates whether preference- or process-based mechanisms are likely to be responsible for
gender-based favoritism by examining how, if at all, the results are heterogeneous across many
dimensions, including geography and sexism (subsection 5.1); offense type (subsection 5.2); and
prosecutorial team composition (subsection 5.3). These subsample comparisons help discern
possible explanations for the gender-based favoritism documented in Section 4. Subsection 5.4
further probes the process-based explanation by examining whether favoritism affects the
defendant’s level of cooperation, operationalized in several different ways.
Broadly speaking, I find substantially more support for the preference-based explanation
of gender-based favoritism. As described in more detail below, the preference-based explanation
is bolstered by findings that in-group favoritism is stronger in states with below-median measures
of sexist attitudes (compared to states with above-median sexism), and in cases in which gender is
more salient. I also find that favoritism is stronger in same-gender prosecutorial teams (compared
to mixed-gender teams or solo prosecutors), but I argue that depending on the nature of group
decision-making, this result could be consistent with either preference- or process-based
explanations (or both). I fail, however, to find any concrete evidence to support the process-based
explanation: defendant-prosecutor gender match is not a significant predictor of whether the
defendant receives a substantial assistance reduction, a safety valve reduction, an acceptance of
responsibility reduction, or takes their case to trial.
5.1. Geography, Sexism, and Gender-Based Gender Favoritism
Gender-based favoritism might vary across geographic regions. In the United States, there
is significant regional variation in the prevalence of sexist attitudes. It is plausible that a locality’s
level of background sexism could affect the extent to which prosecutors demonstrate gender-based
leniency, particularly if such favoritism is preference-based. On the other hand, because criminal
processes are similar across geographic regions within the federal criminal system, one might not
expect gender-based favoritism to vary across geographic regions if such favoritism is purely
process-based.
A new working paper by Kerwin Kofi Charles, Jonathan Guryan, and Jessica Pan quantifies
regional variation in sexism across the United States, and I exploit this state-level variation to
explore the possibility that gender-based leniency in charging is related to the intensity of sexist
31
attitudes in the locality.63 As a preliminary matter, Figure 5 depicts district-level estimates of in-
group favoritism by estimating equation (1) separately for each federal district court in the sample.
Darker shading indicates that the federal district court demonstrates stronger gender-based
favoritism. Figure 5 presents evidence that gender-based favoritism is geographically
heterogeneous, but demonstrates no clear geographic pattern.
I next examine whether the role of prosecutor-defendant gender match is moderated by the
background level of sexism in the state in which the district is situated. It is worth emphasizing
that sexism can manifest in many different ways. For example, sexism can be either conscious or
implicit. No matter its form, however, one threshold prediction—which I verify below—is that
female defendants will be treated more harshly relative to male defendants in states with higher
levels of sexism than in states with less prevalent sexism.
In Table 6, I divide states in the sample by whether they exhibit either above- or below-
median sexism according to Charles, Guryan, and Pan (2018).64 Table 6 examines how the
interactions of defendant gender, prosecutor gender, and local sexism relate to charging decisions.
As above, the defendant’s base offense level is the outcome variable. As a preliminary matter,
women appear to be charged more harshly relative to men in states with above-median sexism
(row 5 in column (8)), providing validation that the sexism variable captures negative attitudes
toward women.
The results in Table 6 also present several important clues about the results presented in
Section 4. First, as columns (1) and (2) demonstrate, gender-based favoritism in the data is largely
driven by differential treatment of female defendants. For female defendants, the increased
charging severity associated with having a male prosecutor (0.398 in the base offense level) is
nearly eight times larger than the decreased charging severity for male defendants assigned a male
prosecutor (-0.053). In other words, female defendants earn a more important benefit from being
assigned a gender-matching prosecutor than male defendants do.
This finding could be explained by the fact that gender is likely to be more salient for female
than male defendants. Around 83 percent of criminal defendants are men, so a defendant’s gender
is likely to be more noteworthy—and thus more salient—when the defendant is female. Typically,
63 Charles, Guryan, and Pan (2018) are interested in the labor market and familial status effects of sexism. They find
that background sexism affects women’s wages, labor force participation, marriage age, and childbearing. 64 Six states and territories—Washington DC, Guam, Hawaii, Idaho, Nevada, and Puerto Rico—are removed from
the sample because Charles, Guryan, and Pan (2019) do not report sexism scores for these states and territories.
32
in-group preferences are more pronounced for in-groups that have higher salience. (Everett, Faber,
and Crockett 2015; McLeish and Oxoby 2011).
Second, although matching with one’s prosecutor on gender is important for female
defendants, it is significantly more important in states with below-median sexism scores than in
states with more prevalent sexism (columns (3)-(8)). This suggests that in-group favoritism might
work against, rather than in tandem with, sexism. Overall, the results seem to reject the idea that
sexism cultivates in-group favoritism. Instead, the results are consistent with gender favoritism
that is generated by positive feelings toward women.
It is plausible that inter-group empathy bias is responsible for the findings. As others have
shown, people are more likely to feel empathy towards in-group members (Cikara et. al 2011).
And, perhaps most relevant to the prosecutorial setting,65 in experimental settings, people are more
likely to help in-group than out-group members (Everett, Faber, and Crockett 2015), and to feel
pain and empathy when observing the pain of an in-group member compared to an out-group
member (Xu et al. 2009, Gutsell and Inzlicht 2012).66 Others have found that people with higher
implicit bias tend to have reduced empathy in response to out-group pain (Avenanti, Sirigu, &
Algiuoti 2010). If sexism reduces in-group favoritism, it might break these empathies.
5.2. Heterogeneity by Offense Type
This subsection considers heterogeneity by offense type. Cases in the data are categorized
into four discrete offense types: drug offenses, regulatory and other offenses, violent offenses, and
white collar offenses. A little over half of offenders are prosecuted for drug-related offenses. If
gender-based favoritism is preference-based, I predict that it will be the most intense among
offense types for which gender is most salient.
Table 7 presents results of estimating equation (1) by offense type. While the estimates of
in-group favoritism are statistically significant at the ten-percent level for all offense types, they
are the strongest among violent offenders and, to a lesser extent, drug offenders, and weaker for
regulatory and white collar offenders. These findings are consistent with in-group favoritism being
65 Prosecutors are both witnesses to and agents of the pain felt and costs imposed on criminal defendants when they
are prosecuted, convicted, and ultimately sentenced. 66 While people also experience the opposite—pleasure in response to an out-group member’s adversity (a
phenomenon called schadenfreude)—a review of the literature concluded that “positive social preferences of in-group love may play a stronger role than negative social preferences for outgroup derogation.” (Everett, Faber, and Crockett 2015).
33
strongest in offense types in which gender is more salient. Among violent offenders—for which
in-group favoritism is the strongest—only six percent of offenders are women. In contrast, among
the roughly equally-sized population of white collar offenders—for which in-group favoritism is
the weakest—roughly one-third of defendants are female. In fact, gender might be especially
salient for women in violent crime cases not only because female defendants are rare in such
prosecutions, but also due to prevailing social norms that women do not engage in violent behavior
(Russell 2014). These findings are consistent with the findings above that salience appears to
impact the intensity of gender-based favoritism exhibited by prosecutors.
The results in Table 7 are also notable because they suggest that gender favoritism is
heightened among more serious offenders, presenting a conflict with prior work that examines the
labor market concerns of federal prosecutors. As described in section 2, Boylan and Long (2005)
and Boylan (2005) find that better career outcomes in the private legal market are associated with
more aggressive tactics by federal prosecutors—longer prison sentences and increased
probabilities of taking a case to trial. Much labor literature suggests that male prosecutors are more
likely to benefit from these future, private-sector rewards than female prosecutors (Goldin 2014,
Wiswall and Zafar 2018). Yet, male prosecutors appear to show leniency in cases involving male
defendants—who are, on average, significantly more serious offenders. This unexpected finding
is consistent with a preference-based explanation for leniency by male prosecutors toward male
defendants because such leniency seems to work against what one would predict would be in
prosecutors’ self-interest.
I also consider two alternative potential explanations for these findings. First, if the
relationship between gender match and charging severity is concave rather than linear, one would
expect stronger results in offenses with higher average base offense levels, which might explain
why the levels of in-group favoritism are larger for violent and drug crimes. This explanation does
not entirely explain the results. Logging the base offense level variable still produces estimates of
in-group favoritism that are the larger among violent offenders than other offense types. Moreover,
drug offenders are charged with offenses carrying the highest average base offense levels, but
violent offenders generate the largest estimates of in-group favoritism.
A second possible explanation is that in-group favoritism could be more intense for
defendants of color, and these defendants tend to be over-represented in violent and drug cases
relative to regulatory and white collar cases. It is possible, of course, that the extent to which
34
prosecutors behave with gender-based favoritism could vary depending on the defendant’s race
and ethnicity. For example, Starr (2015) finds that gender disparity in federal criminal cases is
significantly larger among black than white defendants.
To examine this possibility more directly, Appendix Table A.4 presents results of
estimating equation (1) by defendant race and ethnicity. The coefficient estimates of in-group
favoritism are statistically significant at conventional levels in all racial groups, although the point
estimates are more extreme in the subsamples including minority defendants. However, three-way
interactions between prosecutor gender, defendant gender, and race or ethnicity are not statistically
significant. I therefore cannot rule out the possibility of equal gender-based favoritism across racial
groups. Appendix Tables A.5 and A.6 present results by race and ethnicity further subdivided by
offense type (Appendix Table A.5) and for all additional outcomes (Appendix Table A.6). Table
A.5 provides more support for the hypothesis that gender-based favoritism is strongest in cases in
which gender is most salient and that this pattern persists across black, Hispanic, and white
defendants.
5.3. Prosecution in Teams
So far the analysis has only considered the interaction between one defendant and one
prosecutor—the first lead prosecutor assigned to each case. However, around one-third of the cases
in the data involve more than one lead prosecutor. This subsection compares levels of gender-
based favoritism between cases prosecuted by solo prosecutors, mixed-gender prosecutorial teams,
and same-gender prosecutorial teams. In this subsection, I first test whether prosecutors exhibit
these kinds of peer effects and, after finding that they do, attempt to untangle what this finding
suggests about the mechanisms responsible for gender-based leniency.
To carry out this analysis, I compare estimates of in-group favoritism in three kinds of
cases: (a) those prosecuted by solo prosecutors (62 percent of observations); (b) those prosecuted
by same-gender teams of prosecutors (22 percent of observations); and (c) those prosecuted by
mixed-gender teams of prosecutors (16 percent of observations).67 Although I use the word team
to refer to the situation in which a case in the data includes more than one lead prosecutor, it is
likely that at least some of these cases involve instances of reassignment, such as if an AUSA
67 While 68 percent of cases have a solo prosecutor, only 62 percent of observations in the sample have a solo
prosecutor because some cases include multiple defendants who appear in the data as separate observations.
35
resigns from their job or takes temporary leave from work. In other words, the extent to which
these arrangements reflect group decision-making likely varies and, if anything, the results
probably understate peer effects.
When more than one prosecutor is ultimately assigned to a case, the prosecutor gender
variable can be defined in several different ways. The preceding analysis defined the prosecutor
gender variable for each case as a binary variable indicating whether the first prosecutor assigned
to the case was male. A second way to define the prosecutorial gender of a mixed-gender team is
to use the fraction of male prosecutors assigned to the case.68 A third way to define the
prosecutorial gender of a team is to define it as a binary variable that indicates whether there is at
least one female prosecutor on the team. This subsection explores in-group favoritism in
prosecutorial teams using all three formulations.
Table 8 presents results of estimating equation (1) among defendants prosecuted by solo
prosecutors, those prosecuted by a same-gender team of prosecutors, and those prosecuted by a
mixed-gender team of prosecutors. For reference, column (1) reports results from the full sample—
it is identical to column (4) in Table 3. The results in Table 8 suggest that peer effects influence
charging behavior. The estimates among same-gender teams of prosecutors (reported in column
(3)) represent the effect of switching from an all-female team of prosecutors to an all-male team.
The estimate of in-group favoritism (-1.136 in the base offense level) is statistically significant,
and nearly three times the magnitude of the estimate among solo prosecutors in column (2)
(-0.442). Column (4) represents the effect of switching from an any-female team to an all-male
team, and suggests that the presence of just one female prosecutor in the group matters (the point
estimate is -0.743 and statistically significant), but does not generate as much favoritism as moving
to an all-female team.
In contrast, the estimates of in-group favoritism among mixed-gender teams of prosecutors
(reported in columns (5) and (6)) are not statistically significant when defining the team’s gender
composition as the gender of the first prosecutor (column (5)) or the fraction of male prosecutors
(column (6)). In both columns (5) and (6), the point estimates are negative and the subsample
68 Appendix Table A.7 presents results in which prosecutor gender is defined as the fraction of prosecutors that are
male for cases in which there is a mixed-gender team. As in Table 4, gender match signficantly predicts all early outcome variables except the substantial assistance reduction. Among sentencing outcomes, only the estimate of in-group favoritism on sentence length is statistically significant when the regression does not control for charging severeity.
36
includes many fewer observations than the full sample, suggesting that even these mixed-gender
teams might not completely mitigate gender-based favoritism. Overall, however, the results are
consistent with the peer effects hypothesis that mixed-gender teams moderate the effects of in-
group favoritism, while same-gender teams heighten it.
Without more information about the group dynamics of prosecutorial teams, peer effects
are consistent with both the preference- and process-based explanations. For example, if
prosecutorial teams reach decisions by simply aggregating the preferences of the teams’ individual
members one would expect to find evidence of peer effects in gender-based favoritism. If gender-
based leniency is preference-based, one might also expect same-gender teams to exhibit enhanced
leniency towards own-gender defendants, akin to the now well-established finding that
ideologically homogeneous panels of federal appellate judges exhibit stronger ideological
preferences in group decision-making than heterogeneous panels (Kim 2009; Sunstein, Schkade
and Ellman 2004).69
But peer effects could also be consistent with process-based explanations if one
conceptualizes group decision-making by prosecutors as highly deliberative. If prosecutors are
more skilled at prosecuting defendants with whom they have more in common, other prosecutors
in the group might defer to the prosecutor who has the most commonality with the defendant,
which would also generate results as in Table 8.
5.4. In-Group Favoritism, Bargaining, and Cooperation
Evidence contained in Table 4 and described in subsection 4.4 suggests that gender-based
leniency is concentrated at charging, which itself challenges the process-based hypothesis. This
subsection explores the process-based hypothesis more closely by examining whether prosecutor-
defendant gender match is related to defendant cooperation or other aspects of the bargaining
process.
Table 9 presents results of estimating equation (1) for seven outcome variables that are
meant to capture the extent to which the defendant places trust in and cooperates with their
prosecutor. Column (1) finds that gender match is not related to the probability that the case
involves a superseding indictment or information—a variable that plausibly captures the intensity
69 Judges also appear to be influenced by their panel colleagues’ genders in deciding cases in which gender is salient.
(Boyd, Epstein, and Martin 2010; Farhang and Wawro 2004; Peresie 2005).
37
of charge-bargaining. The coefficient on the interaction term is near-zero (0.008) and not
statistically significant.
Columns (2) and (3) reconsider the substantial assistance reduction. As described in section
4.4, a prosecutor can request a sentencing reduction for a defendant who provides substantial
assistance to the government.70 Column (2) (reproduced from column (4) in Table 4) shows that
across the whole defendant population, gender match between prosecutor and defendant is not a
statistically or economically significant predictor of whether the defendant receives a substantial
assistance reduction.
The substantial assistance reduction, however, is not equally important for all defendants.
Column (3) restricts attention to defendants for whom the substantial assistance reduction is
especially valuable—defendants facing a mandatory minimum who are ineligible for safety-valve
relief.71 For these defendants, a substantial assistance is especially beneficial because it is the only
way that they can receive a sentence below their mandatory minimum. Defendants in this group
comprise roughly 23 percent of the sample, and gender match is a marginally significant predictor
of whether the defendant receives a substantial assistance reduction among these defendants
(p=0.095).
This result is consistent with both preference-based and process-based explanations. If the
likelihood of receiving a substantial assistance reduction is driven by the defendant’s willingness
to cooperate with the government, significance on the match term suggests that either defendants
feel more comfortable cooperating with an own-gender prosecutor or prosecutors are better at
eliciting cooperation from like-gender defendants (or both). These are process-based explanations
for the results. On the other hand, if the substantial assistance is driven by prosecutorial discretion,
which a prosecutor may use at their will to benefit some defendants but not others, the results
would suggest a preference-based explanation.
Column (4) estimates whether gender match is related to whether the defendant receives a
safety valve reduction. Critically, to earn a safety valve reduction, a defendant must make a truthful
proffer to the government of “all information and evidence the defendant has concerning the
offense[s]”72—an element that requires the defendant to place significant trust in their prosecutor.
70 See note 26. 71 I define a defendant as likely to be ineligible for a safety-valve reduction if their case does not involve drugs, they
are charged with a weapon, or they have more than one criminal history point. 72 U.S. SENTENCING GUIDELINES MANUAL § 5C1.2 (2016).
38
Columns (5), (6), and (7) use other dependent variables that seek to capture the extent to which
the defendant cooperated with the prosecution by pleading guilty rather than going to trial (column
(5)) and by receiving an acceptance of responsibility reduction (columns (6) and (7)).
If gender match between prosecutor and defendant leads to increased cooperation, one
would expect the coefficient estimates on the interaction terms to be greater than zero and
statistically significant in all columns of Table 9. This is not borne out for most defendants—with
one exception, none of the coefficient estimates are statistically significant and the 95% confidence
intervals suggest little influence of gender match on any of the outcome variables. The one
exception in which a coefficient estimate is marginally significant is the estimate on the interaction
term when the outcome variable is the substantial assistance reduction and the sample is limited to
those defendants for whom the reduction is likely to be very valuable. This evidence thus provides
more support for the idea that in-group favoritism is driven by automatic or implicit preferences,
rather than process-based explanations, with the caveat that cooperation might play a role for
defendants for whom it is extremely valuable.
6. Robustness
This section presents three robustness checks of the results. As reported in Table 2, a
defendant is—on average—around 2.2 percentage-points more likely to be assigned a prosecutor
of their own gender than a prosecutor of the opposite gender, which suggests that the assignment
of cases could be influenced, either consciously or subconsciously, by defendant and prosecutor
characteristics. As described in subsection 4.2, researchers observe similar assignment patterns in
the policing context, where officers typically are not randomly assigned to the neighborhoods in
which they work. The first two checks—described in subsections 6.1 and 6.2—are designed to test
whether differential assignment practices could be generating the results. The third robustness
check, described in subsection 6.3, demonstrates that the results are robust to including individual
prosecutor fixed effects.
6.1. Mixed-Gender Cases
Because prosecutors are assigned at the case-level (rather than the defendant-level) in the
data, I check whether the results hold up in the subset of cases with at least one male and at least
39
one female co-defendant. As one might imagine, this restriction significantly reduces the sample—
from 143,457 defendants in the full data to just 26,099 defendants in these mixed-gender cases.
Appendix Table A.8 presents regression results based on the defendant composition of the
case. For reference, column (1) reproduces results in the full sample and is identical to column (4)
in Table 3. The remaining results in Table A.8 include estimates among cases with solo defendants
(column (2)); cases with multiple defendants of different genders (column (3)); and cases with
multiple defendants of the same gender (column (4)). Most observations in the data are cases with
just one defendant (column (2)) and the estimate of gender-based favoritism there is similar to the
estimate in the full data.73 The results are robust, however, to restricting to defendants who have
an opposite-gender co-defendant (column (3)). The point estimate on the interaction term is similar
to that in the full sample and is statistically significant at the five-percent level. The results among
cases in which there are multiple defendants of the same gender are reported for completeness but
are not particularly insightful because virtually all of these cases involve male defendants—it is
extremely rare for a multi-defendant case to include only female defendants.
6.2. Prosecutor and Defendant Characteristics Uncorrelated
The extent to which prosecutor gender is correlated with defendant gender (and other
covariates) is likely to be heterogeneous across USAOs and USAO sections. In particular, some
USAOs or USAO sections might assign cases using random assignment, or in ways that more
closely approximate random assignment, than others. In this subsection, I examine the results in
courthouse-sections in which prosecutor gender is uncorrelated with defendant gender and other
defendant characteristics that are fixed at the time of case assignment.
To carry out this robustness check, I regress prosecutor gender on each of five defendant
characteristics: age, and indicators for being black, Hispanic ethnicity, male, and a high school
graduate. I do a separate regression for each characteristic in each courthouse-section. All
regressions include year fixed effects. I then store the p-values associated with the coefficient
estimate of the relationship between each defendant characteristic and prosecutor gender for each
courthouse-section.
73 Although the coefficient estimate on prosecutor gender is significant in cases with a solo defendant, it is important
to remember that this represents a conditional effect. The average effect of prosecutor gender—obtained from a regression without the interaction term—is 0.004 and highly insignificant (p=0.964) (a precise zero).
40
Appendix Table A.9 presents regression results when the data is restricted to courthouse-
sections in which prosecutor gender is not related to any of the defendant characteristics at the
one-, five-, and ten-percent levels. I find that the main regression results are highly robust to and
virtually unchanged by these restrictions, even in the specification that removes courthouse-
sections in which the coefficient estimate of prosecutor gender is significant at the ten-percent
level for any of the five defendant characteristics (column (7)), a restriction that meaningfully
reduces the size of the sample. These results suggest that the findings presented in the main
analysis are not driven by non-random assignment of cases to prosecutors. The even-numbered
columns report results without interaction terms and show that the coefficient estimate on the
prosecutor gender variable (representing average gender differences between male and female
prosecutors) is small and statistically insignificant in all specifications.
6.3. Prosecutor Fixed Effects
The data includes an anonymous prosecutor id variable. As a result, the analysis can be
performed with prosecutor-specific fixed effects. In the data, there are roughly 1,500 unique
prosecutors who appear in at least 25 cases. Appendix Table A.10 presents these results. Prosecutor
fixed effects control for unobserved differences between prosecutors that might be correlated with
the prosecutor’s gender.74
In Table A.10, I restrict the sample to prosecutors with at least 25 cases in the data—this
restriction reduces the sample by about ten percent, from 143,457 to 127,296. Column (1) in Table
A.6 presents the results in the full data with courthouse-section fixed effects—it is analogous to
column (4) in Table 3. In column (2), I include both courthouse-section fixed effects and individual
prosecutor fixed effects. In column (3), I perform an even more restrictive regression that includes
prosecutor-courthouse-section fixed effects. I then restrict to prosecutor-courthouse-section cells
with at least 25 observations, which further reduces the sample by roughly twenty percent. The
estimates of in-group favoritism in columns (2) and (3), however, are still statistically significant
at the one-percent level and are of a similar magnitude to the other estimates.
74 The ability to include individual-specific fixed effects will depend on the extent to which variation in the data is
within rather than across observations. For example, Price & Wolfers (2010) and Depew, Eren, & Mocan (2017) both include individual fixed effects in their main specifications. For Price & Wolfers (2010), these are individual player and referee fixed effects; for Depew, Eren, & Mocan (2017), these are judge (but not defendant) fixed effects. On the other hand, Antonovics & Knight 2009) investigate the possibility of using officer-specific fixed effects but conclude that including these fixed effects would not be well identified, and would drop many observations from the data.
41
7. Discussion
This paper uses novel federal criminal case data covering nearly 150,000 criminal
defendants sentenced between 2002 and 2015 in 65 of the 94 federal district courts in the United
States. By exploiting the salutation field of the LIONS data, I obtain a variable that captures
prosecutor gender. The paper finds that defendants receive more lenient charges when they match
with their prosecutor on gender relative to when they are assigned a prosecutor of the opposite
gender. Through this charging channel, prosecutor-defendant pairs that match on gender produce
lower sentences relative to prosecutor-defendant pairs that mismatch. On the other hand, being
assigned a prosecutor of the same gender does not affect the probability that a defendant will
receive a sentence below the recommended Guidelines range—a decision that largely falls to the
defendant’s sentencing judge.
By examining estimates in subsamples of the data, I suggest that the findings are more
consistent with preference-based favoritism than process-based mechanisms. Specifically, gender-
based favoritism is stronger in states with less prevalent sexist attitudes, in cases in which gender
is likely to be more salient, and among same-gender prosecutorial teams. I argue that these findings
suggest that gender-based leniency derives from prosecutor preferences, and, in particular, could
be generated by female prosecutors feeling stronger empathic ties to female defendants than male
prosecutors do. I find little evidence to support a process-based explanation for the results: across
many alternative measures, defendants do not appear more likely to cooperate or place trust in a
prosecutor with whom they match on gender except among defendants for whom cooperation is
likely to be extremely valuable.
This paper thus documents an important and perhaps troubling phenomenon. The U.S.
Supreme Court has repeatedly emphasized that a central goal in federal sentencing law is to
promote the uniformity of sentences across defendants.75 The extent to which defendants are
treated differently based on something they cannot control—whether they happen to be assigned
a prosecutor of the same gender—conflicts with this essential idea that the punishment imposed
upon similarly situated defendants must be similar.
75 See, e.g., Booker, 543 U.S. at 255 (“Congress enacted the sentencing statutes in major part to achieve greater
uniformity in sentencing”); Kimbrough v. United States, 552 U.S. 85, 107 (2007) (“[I]t is unquestioned that uniformity remains an important goal of sentencing”).
42
Critically here, where there is no clear benchmark as to a defendant’s optimal sentence,
one can not know whether the decisions produced by defendant-prosecutor gender matches are
superior or inferior to decisions produced by mismatches. There are, however, a few reasons to
believe the outcomes produced by gender match might produce better results.
First, some might think that leniency is an inherently desirable outcome. There is a
widespread—but not universal—view that the federal criminal system is too punitive. As evidence
of this point, for example, nearly half of federal felony defendants are sentenced below their
recommended Guidelines range while only two percent are sentenced above.
Second, if in-group leniency derives from empathy, as I suggest in subsection 5.1, gender-
favoritism might be desirable because empathy is considered “prosocial,” or, beneficial for society
(Cikara et. al 2011). Moreover, the fact that gender-based favoritism is more intense in states with
less pervasive sexism also suggests that such favoritism could create desirable outcomes.
Third, it is plausible that prosecutors are incentivized to seek the highest charges possible—
both because they are directed to do so by the Justice Manual76 and because this might be a metric
that is used for advancement. If so, leniency could be seen as a careful, thoughtful choice that
should be encouraged.
Fourth, in other contexts that examine in-group favoritism where there is an objective
measure of outcome quality, prior work has found that in-group matches produce better results.
For example, in the financial context, prior work has found that hedge fund managers are able to
better predict the performance of firms led by CEOs of the manager’s gender (Jannati et al. 2016),
and that loans perform better when the lender and borrower are culturally proximate (Fisman,
Paravisini, and Vig 2017). Both of these papers suggest that gender match produces objectively
better results. Of course, it is not clear that these results generalize to the criminal system.
Unfortunately, the lack of available data hinders understanding these important dynamics
in the criminal setting. While scholars have made strides in understanding judicial decision-making
thanks to rich and publicly-available data about judges,77 there is little publicly-available data for
researchers to use in analyzing decision-making by individual prosecutors. With more detailed
data about prosecutors, researchers would be able to further explore other kinds of individual bias
76 Justice Manual § 9-27-300. 77 For example, researchers can easily download a database with detailed biographical information about all past
and current federal judges from the Federal Judicial Center website: BIOGRAPHICAL DIRECTORY OF FEDERAL JUDGES: EXPORT, https://www.fjc.gov/history/judges/biographical-directory-article-iii-federal-judges-export.
43
in prosecutorial decision-making. With this suggestion in mind, the remainder of this paper
discusses two potential policy changes that prosecutorial offices might implement.
Because gender-matching is associated with leniency, some might be tempted to embrace
in-group favoritism and propose policies that promote explicit gender-matching in prosecutorial
offices. Implementing an express policy of matching defendants to prosecutors on the basis of
gender would undoubtedly raise both constitutional and practical concerns. Moreover, even if one
wanted to match defendants to proximate prosecutors in an attempt to promote leniency, without
having examined in-group favoritism on many other dimensions—like race or religion—it is not
clear that gender is the best variable on which to match.
Another class of solutions might attempt to address outgroup disfavoritism and therefore
generate more equal treatment across defendants. Implicit bias training could be promising in this
respect. In 2016, the U.S. Department of Justice announced that it would begin providing all
federal law enforcement officers and prosecutors with implicit bias training. If in-group favoritism
stems from preference-based discrimination, this directive could address the problem but might,
perversely, make things worse. There is little evidence about the extent to which such training
programs can reduce bias in the criminal system and over the long-term, although such programs
have some success in simulated settings in the short-term (Plant and Peruche 2005). In the
employment setting more generally, however, many scholars have found that antibias and diversity
training are often ineffective, and can even lead to decreased support for diversity among white
participants (Plaut et al. 2011).
Prosecutors’ offices might also experiment with prosecution by teams. As described in
subsection 5.3, it appears that the presence of an opposite-gender peer mitigates the kind of bias
that generates in-group favoritism. There are, however, reasons to be cautious of this approach.
First, while it is possible that prosecution by teams could reduce in-group favoritism if team
members have a moderating influence on each other, one could imagine the group dynamics
playing out in other ways, too. For example, one might imagine that group decision-making in
prosecutor offices could lead to harsher treatment of defendants if team members always agreed
to the path proposed by the most punitive team member.
Second, and more broadly, it is important to remember that interventions in the criminal
and employment settings that policymakers expect will reduce disparity can often have perverse
effects. The prosecutorial response to Booker—in which prosecutors responded to a decrease in
44
their ability to affect sentencing outcomes (via mandatory Guidelines) by charging mandatory
minimums more frequently—is just one example. Particularly when interventions alter the balance
of information and power between actors in the criminal system, caution is warranted.
There are still many important questions about prosecutorial behavior that remain
unanswered. For example, examining in-group favoritism on the basis of race is critical,
particularly in light of recent evidence of race-based in-group disfavoritism among judges in
criminal cases. Further work examining the scope of prosecutorial discretion and quantifying
disparities stemming from the assignment of prosecutors to defendants would also be valuable in
defining the contours of prosecutorial discretion in the criminal system.
45
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Appendix A: Gender Coding
This Appendix lists all of the salutation codes in the LIONS data and indicates how they were coded in the paper. F indicates female; M indicates male; and U indicates unknown.
Salutation Frequency (pct) Coding Continued… AUS 0.33 U SAUSA 0.01 U
AUSA 1.98 U UFAP 0.04 U Al 0.01 M USA 0.00 U
Amy 0.01 F Vaughn 0.02 M Ausa 0.00 U a 0.02 U
CAPT 0.00 U aa 0.00 U Capt 0.01 U ch 0.00 U Capt. 0.02 U d 0.00 U
Captain 0.02 U db 0.04 U Captian 0.00 U ep 0.00 U Caption 0.00 U gh 0.00 U
Cpt 0.01 U js 0.00 U Cpt. 0.01 U kp 0.00 U
Diana 0.00 F mR. 0.05 M Edie 0.00 U mS. 0.00 F
FAUSA 0.01 U mr 0.16 M Gentry 0.02 U mr. 0.01 M Hello 0.04 U ms. 0.10 F Hon. 0.04 U s 0.05 U Janet 0.01 F ss 0.04 U Jeff 0.02 M wn 0.08 U Jim 0.01 M Joe 0.00 M
John 0.02 M Lt. 0.00 U M 0.02 U
MISS 0.00 F MR 1.34 M MR. 2.77 M MRS 0.04 F MRS. 0.05 F MS 0.89 F MS. 1.45 F Miss 0.07 F Mr 4.65 M Mr, 0.00 M Mr. 59.10 M Mr.s 0.00 F Mrs 0.13 F Mrs. 2.52 F Ms 2.10 F Ms, 0.00 F Ms. 21.71 F Ns. 0.00 U
Richard 0.00 M
53
Figure 1. Federal Judicial Districts Appearing in the Data
Notes: Shaded districts are those that are represented in the paper.
54
Figure 2. Histogram of Base Offense Level
Notes: This figure plots the distribution of base offense levels in the data (N=143,457).
55
Figure 3. Base Offense Level Averages by Defendant and Prosecutor Gender
Notes: Average base offense level de-meaned by courthouse.
16
17
18
19
20
21
Male D Female D All
Male P Female P
56
Figure 4. Share of Cases with a Male Lead Prosecutor, Over Time
Notes: This figure plots the mean share of male lead prosecutors by year of case initiation. Annual means are computed with one observation per defendant. Horizontal line at the mean among all years: 0.716.
57
Figure 5. District-Level Estimates of In-Group Favoritism
Notes: Shades categorize the quartile in which each federal judicial district’s point estimate of in-group favoritism falls.
Top 25%
25-50%
50-75%
Bottom 25%
58
Table 1. Summary Statistics
Female Defendants
Male Defendants
All Defendants Min Max
Defendant Characteristics Male - - 0.829 0 1 Black 0.317 0.419 0.402 0 1 Hispanic 0.103 0.127 0.123 0 1 White 0.518 0.407 0.426 0 1 Another Race 0.062 0.047 0.050 0 1 High School Graduate Only 0.682 0.610 0.622 0 1 College Graduate 0.081 0.071 0.072 0 1 Age (years) 37.4 36.2 36.4 16 92 Number of Dependents 1.322 1.482 1.455 0 28 Criminal History Points78 2.20 5.54 4.97 0 65 Case Characteristics Lead Male Prosecutor 0.706 0.718 0.716 0 1 Multiple Defendants 0.444 0.377 0.389 0 1 Multiple Prosecutors 0.354 0.387 0.381 0 1 Base Offense Level 15.4 20.4 19.5 0 44 Final Offense Level 16.7 21.3 20.5 1 43 Case Start Year 2006.8 2006.7 2006.7 1998 2014 Sentencing FY 2008.5 2008.4 2008.4 2002 2016 Intermediate Outcomes Facing Mandatory Minimum 0.223 0.367 0.342 0 1 Substantial Assistance Reduction 0.255 0.217 0.223 0 1 Mean Guidelines Range 47.1 91.9 84.3 0 470 Case Outcomes Any Incarceration 0.684 0.892 0.856 0 1 Sentence (months)79 26.2 67.2 60.2 0 470 Sentence / Mean Guidelines Range 0.455 0.702 0.660 0 32 Below-Guidelines (0/1) 0.603 0.481 0.502 0 1 Above-Guidelines (0/1) 0.011 0.024 0.022 0 1 In-Range (0/1) 0.385 0.495 0.476 0 1 Observations 24,514 118,943 143,457 143,457 143,457
78 Total criminal history points are unadjusted. 79 Not including alternative confinement, such as house arrest. Sentences are capped at 470 months—the
Commission’s value for life sentences.
59
Table 2. Regressing Male Prosecutor on Defendant Characteristics All
Defendants All Defendants All Defendants
Female Defendants
Male Defendants
(1) (2) (3) (4) (5) Male 0.015**
(0.006) 0.015** (0.006)
0.022*** (0.005)
- -
Black -0.032*** (0.012)
-0.031*** (0.012)
-0.001 (0.007)
-0.012 (0.011)
-0.0001 (0.007)
Hispanic -0.065** (0.027)
-0.062** (0.027)
-0.0009 (0.007)
-0.004 (0.012)
-0.00007 (0.008)
Another Race -0.044* (0.026)
-0.043 (0.026)
-0.004 (0.013)
-0.003 (0.024)
-0.004 (0.013)
Age -0.0004** (0.0002)
-0.0003 (0.0002)
-0.0003** (0.0001)
0.00006 (0.0004)
-0.0003** (0.0001)
Dependents 0.00003 (0.001)
0.00003 (0.001)
0.0003 (0.0008)
-0.0006 (0.0004)
0.0006 (0.0008)
HS Only 0.002 (0.004)
0.004 (0.004)
-0.0002 (0.003)
-0.001 (0.006)
-0.0003 (0.003)
College Grad 0.008 (0.009)
0.012 (0.009)
0.009 (0.007)
0.012 (0.014)
0.005 (0.008)
Multiple Defendants 0.004 (0.013)
0.007 (0.013)
-0.001 (0.010)
-0.003 (0.011)
-0.002 (0.011)
Multiple Prosecutors -0.025 (0.015)
-0.024 (0.016)
-0.009 (0.011)
-0.003 (0.015)
-0.011 (0.011)
Prosecutor Workload 0.0003 (0.001)
0.0002 (0.001)
-0.0003 (0.0009)
0.0005 (0.0009)
-0.0004 (0.0009)
F-Stat: Criminal History
0.40 (p=0.81)
0.43 (p=0.78)
1.28 (p=0.28)
1.07 (p=0.37)
0.88 (p=0.48)
F-Stat: Offense Type 1.60 (p=0.19)
1.73 (p=0.16)
0.26 (p=0.85)
0.38 (p=0.77)
0.71 (p=0.55)
F-Stat: Race/Ethnicity 4.49*** (0.005)
4.24*** (p=0.006)
0.03 (p=0.99)
0.43 (p=0.73)
0.03 (p=0.99)
Month and Year FEs X X X X Courthouse-Section FEs X
X X
Observations 143,457 143,457 143,457 21,512 118,318 OLS regressions of the gender of the defendant’s first lead prosecutor (1=male; 0=female) on defendant and case characteristics. ***: p<0.01; **: p<0.05; *: p<0.10. Standard errors are clustered at the courthouse level. Prosecutor workload variable is the total number of cases for which that prosecutor was the lead prosecutor in that year. Month and year fixed effects include sentencing fiscal year, Guidelines version used, and month and year of case initiation. Observations in columns (4) and (5) do not add up to 143,457 because they exclude courthouse-sections with fewer than 25 observations.
60
Table 3. Main Results (Base Offense Level) (1) (2) (3) (4) (5) (6) Interaction -0.727**
(0.294)
-0.533*** (0.147)
-0.529*** (0.148)
-0.542*** (0.149)
-0.661*** (0.173)
-
Male Defendant 5.432*** (0.249)
1.946*** (0.155)
1.927*** (0.160)
1.938*** (0.161)
2.014*** (0.199)
1.554*** (0.095)
Male Prosecutor 0.761
(0.495) 0.461** (0.191)
0.426** (0.182)
0.421** (0.185)
0.436** (0.219)
-0.027 (0.099)
Demographic Controls X X X X X Month and Year FEs X X X X District-Section FEs X Courthouse-Section FEs X X Courthouse-Section-Year FEs
X
Observations 143,457 143,457 143,457 143,457 103,858 143,457
OLS regressions of the defendant’s base offense level. ***: p<0.01; **: p<0.05; *: p<0.10. Standard errors are clustered at the courthouse level. Demographic controls include race, age at sentencing, number of dependents, criminal history quintile, prosecutor caseload, and indicators for the defendant being a high school graduate, a college graduate, the case having multiple defendants, and the case having multiple prosecutors. Month and year fixed effects include sentencing fiscal year, Guidelines version used, and month and year of case initiation. There are fewer observations in column (5) because courthouse-section-year cells with fewer than 25 observations are excluded.
61
Table 4. Additional Outcome Variables Panel A: Prosecutor-Based Outcomes
Final Offense Level
(1)
Facing Mandatory Minimum (0/1)
(2)
Final Guidelines Range (IHS)
(3)
Substantial Assistance (0/1)
(4) Interaction -0.347**
(0.157)
-0.017*** (0.006)
-0.057** (0.024)
-0.0003 (0.008)
Male Defendant
2.452*** (0.168)
0.078*** (0.006)
0.370*** (0.024)
-0.041*** (0.008)
Male Prosecutor 0.276 (0.188)
0.009 (0.008)
0.055* (0.029)
0.012 (0.008)
Observations 143,457 143,307 143,456 143,246
Panel B: Judge- and Prosecutor-Based Outcomes Released Pretrial
(0/1) (5)
Sentence Length (IHS)
(6)
Sentence (IHS) (control for BOL)
(7)
Below-Guidelines Sentence (0/1)
(8) Interaction 0.013
(0.009) -0.102** (0.043)
-0.042 (0.034)
0.005 (0.008)
Male Defendant
-0.127*** (0.009)
0.848*** (0.041)
0.632*** (0.034)
-0.087*** (0.010)
Male Prosecutor -0.001 (0.008)
0.081* (0.048)
0.034 (0.035)
0.004 (0.007)
Observations 141,046 143,457 143,457 143,456 OLS regressions. ***: p<0.01; **: p<0.05; *: p<0.10. Standard errors are clustered at the courthouse level. All regressions include demographic controls listed in Table 3, column (4), month and year fixed effects, and courthouse-section fixed effects.
62
Table 5. In-group Favoritism and Selection into the Sample (1) (2) (3) (4) Dependent Variable: Acquittal/Dismissal/Declination Interaction 0.003
(0.006) 0.004
(0.006) 0.002
(0.006) -
Male Defendant -0.014**
(0.006)
-0.015** (0.006)
-0.015** (0.006)
-0.013*** (0.003)
Male Prosecutor -0.002 (0.008)
-0.003 (0.008)
-0.006 (0.007)
-0.004 (0.003)
Demographic Controls X X X X Month and Year FEs X X X Courthouse-Section FEs X X Observations 89,117 89,117 87,183 87,183
OLS regressions of whether a defendant’s case was acquitted, declined, or dismissed (0/1). ***: p<0.01; **: p<0.05; *: p<0.10. Standard errors are clustered at the courthouse level. Month and year fixed effects include sentencing fiscal year and month and year of case initiation. Demographic controls are indicators for whether the defendant is male, whether the case involved multiple defendants, whether the case had multiple prosecutors, and offense type.
63
Table 6. In-Group Favoritism and Sexism
Male Defendants
Female Defendants
Male Defendants
Female Defendants
Low Sexism States
High Sexism States
All Defendants All Defendants
(1) (2) (3) (4) (5) (6) (7) (8)
Dependent Variable: Base Offense Level
Male Defendant - - - - 2.233*** (0.166)
1.582*** (0.233)
1.682*** (0.104)
2.175*** (0.176)
Male Prosecutor -0.053 (0.116)
0.398** (0.180)
-0.021 (0.168)
0.709*** (0.251)
0.791*** (0.259)
0.151 (0.280)
-0.040 (0.104)
0.785*** (0.268)
High Sexism State (0/1) -0.057 (0.216)
-0.163 (0.239)
-0.007 (0.231)
0.270 (0.308)
- - -0.439 (0.272)
0.480 (0.353)
Male D * Male P - - - - -0.834*** (0.199)
-0.240 (0.204)
- -0.824*** (0.204)
Male D * High Sexism - - - - - - -0.336* (0.183)
-0.512* (0.288)
Male P * High Sexism - - -0.068 (0.227)
-0.606* (0.344)
- - - -0.635* (0.377)
Male D * Male P * High Sexism
- - - - - - - 0.577** (0.286)
Observations 112,943 23,366 112,943 23,366 67,595 68,714 136,309 136,309
OLS regressions of the the defendant’s base offense level. ***: p<0.01; **: p<0.05; *: p<0.10. Standard errors are clustered at the courthouse level. All regressions include demographic controls listed in Table 3, month and year fixed effects.
64
Table 7. In-Group Favoritism by Offense Type Drug
Offenses Regulatory Offenses
Violent Offenses
White Collar Offenses
Dependent Variable: Base Offense Level Interaction -0.706***
(0.250) -0.481* (0.252)
-0.996** (0.432)
-0.223* (0.125)
Male Defendant 1.836***
(0.262) 2.604*** (0.260)
2.342*** (0.375)
1.021*** (0.100)
Male Prosecutor 0.434
(0.334) 0.303
(0.269) 0.915** (0.451)
0.369*** (0.100)
Male Defendants (share) 0.86 0.78 0.94 0.67 Male Prosecutors (share) 0.72 0.70 0.70 0.72 Observations 69,449 17,376 23,450 22,741
OLS regressions of the the defendant’s base offense level. ***: p<0.01; **: p<0.05; *: p<0.10. Standard errors are clustered at the courthouse level. All regressions include demographic controls listed in Table 3, month and year fixed effects, and courthouse-section fixed effects. Observations do not add up to 143,457 because courthouse-sections types with fewer than 25 observations are excluded.
65
Table 8. In-Group Favoritism in Prosecutorial Teams All Cases Solo Prosecutors Multiple Ps of
Same Gender Multiple Ps: No
Female Ps of Mixed
Genders: First Ps of Mixed
Genders: Fraction (1) (2) (3) (4) (5) (6) Dependent Variable: Base Offense Level
Interaction -0.542*** (0.149)
-0.442*** (0.163)
-1.136*** (0.353)
-0.743*** (0.211)
-0.465 (0.300)
-0.877 (1.230)
Male Defendant 1.938*** (0.161)
1.696*** (0.177)
2.453*** (0.328)
2.128*** (0.172)
2.393*** (0.276)
2.586*** (0.655)
Male Prosecutor 0.421** (0.185)
0.355* (0.199)
0.820** (0.338)
0.639*** (0.228)
0.300 (0.394)
0.671 (1.435)
Observations 143,457 87,649 29,207 52,439 20,345 20,345 OLS regressions of the the defendant’s base offense level. ***: p<0.01; **: p<0.05; *: p<0.10. Standard errors are clustered at the courthouse level. All regressions include demographic controls listed in Table 3, month and year fixed effects, and courthouse-section fixed effects. All regressions restricted to courthouse-section cells with at least 25 observations. Columns (3)-(6) also control for the number of prosecutors assigned to the case.
66
Table 9. Alternative Measures of Cooperation Dependent Variable: Supserseding
Info/Indictment (1)
Substantial Assistance
(2)
Sub Assist: MM & Safety Inelig.
(3)
Safety Valve
(4)
Pled Guilty (No Trial)
(5)
Any Acceptance of Responsibility
(6)
Max Acceptance of Responsibility
(7) Interaction 0.008
(0.006)
-0.0003 (0.008)
0.038* (0.022)
-0.012 (0.009)
0.003 (0.003)
0.002 (0.004)
0.001 (0.005)
Male Defendant 0.004 (0.005)
-0.041*** (0.008)
-0.153*** (0.021)
-0.055*** (0.008)
-0.018*** (0.003)
-0.014*** (0.004)
-0.022*** (0.004)
Male Prosecutor -0.010 (0.008)
0.012 (0.008)
-0.017 (0.024)
0.014 (0.009)
-0.002 (0.003)
-0.002 (0.004)
-0.0001 (0.005)
Observations 143,457 143,457 33,630 68,303 143,457 143,457 143,457 OLS regressions. ***: p<0.01; **: p<0.05; *: p<0.10. Standard errors are clustered at the courthouse level. All regressions include demographic controls listed in Table 3, month and year fixed effects, and courthouse-section fixed effects. Column (2) includes defendants who are facing a mandatory minimum greater than zero and are ineligible for a safety-valve reduction, either because their cases does not involve a drug offense, they are charged with a weapon, or they have more than one criminal history point.
67
Table A.1. Representativeness of the Sample Data in this
paper (1)
Districts in this paper
(2)
Matched Defendants
(3)
Commission Defendants
(4) Defendant Characteristics Male 0.83 0.84 0.83 0.84 Black 0.40 0.39 0.38 0.39 Hispanic 0.12 0.13 0.13 0.13 White 0.43 0.43 0.44 0.43 Another Race 0.05 0.05 0.05 0.05 High School Graduate Only 0.62 0.62 0.62 0.62 College Graduate 0.07 0.08 0.08 0.08 Age (years) 36.4 36.7 36.9 36.6 Number of Dependents 1.5 1.4 1.4 1.4 Criminal History Points80 5.0 4.7 4.7 4.7 Case Characteristics Lead Male Prosecutor 0.72 - - - Multiple Defendants 0.39 - - - Multiple Prosecutors 0.38 - - - Base Offense Level 19.5 19.3 19.1 19.3 Final Offense Level 20.5 20.6 20.5 20.6 Case Start Year 2007 - - - Sentencing FY 2008 2009 2009 2008 Intermediate Outcomes Facing Mandatory Minimum 0.34 0.34 0.33 0.35 Substantial Assistance Reduction 0.22 0.21 0.20 0.20 Mean Guidelines Range 84.3 85.5 82.1 86.0 Case Outcomes Any Incarceration 0.86 0.83 0.84 0.83 Sentence (months)81 60.2 62.4 59.3 63.1 Sentence / Mean Guidelines Range 0.66 0.65 0.66 0.65 Below-Guidelines (0/1) 0.50 0.50 0.48 0.47 Above-Guidelines (0/1) 0.02 0.02 0.02 0.02 In-Range (0/1) 0.48 0.48 0.50 0.51 Observations 143,457 381,503 374,529 501,537
Districts in the sample are depicted in Figure 1.
80 Total criminal history points are unadjusted. 81 Not including alternative confinement, such as house arrest. Sentences were capped at 470 months—the
Commission’s value for life sentences.
68
Table A.2. Alternative Measures of Charging Severity Base Offense Level Stat Min
(IHS) Average Sentence
(IHS) (1) (2) (3) Interaction -0.542***
(0.149)
-0.094*** (0.033)
-0.037** (0.018)
Male Defendant 1.938*** (0.161)
0.424*** (0.035)
0.143*** (0.018)
Male Prosecutor 0.421** (0.185)
0.054 (0.044)
0.033** (0.020)
Observations 143,457 143,307 132,585
OLS regressions. ***: p<0.01; **: p<0.05; *: p<0.10. Standard errors are clustered at the courthouse level. All regressions include demographic controls listed in Table 3, month and year fixed effects, and courthouse-section fixed effects. The statutory minimum is the total statutory minimum prison term for all counts of conviction transformed by its inverse hyperbolic sine.82 The average sentence is the leave-one-out-mean sentence for defendants convicted under the same Chapter Two Guideline in the defendant’s courthouse transformed by its inverse hyperbolic sine.
82 The statutory minimum is capped at 470 months; 410 defendants had statutory minimums above this value.
69
Table A.3. Prosecutor Gender and Defendant Characteristics in LIONS Sample Data Used in
the Paper (All Controls)
Data Used in the Paper
(LIONS Controls)
Full LIONS (LIONS Controls)
Male 0.022*** (0.005)
0.020*** (0.005)
0.022*** (0.007)
Multiple Defendants -0.001
(0.010) -0.001 (0.010)
0.002 (0.012)
Multiple Prosecutors -0.009
(0.011) -0.009 (0.011)
-0.012 (0.016)
F-Stat: Offense Type 0.26
(p=0.85) 0.42
(p=0.74)
0.93 (p=0.43)
Month and Year FEs X X X Courthouse-Section FEs X X X Observations 143,457 143,457 82,021
OLS regressions of the gender of the defendant’s first lead prosecutor on defendant and case characteristics. ***: p<0.01; **: p<0.05; *: p<0.10. Standard errors are clustered at the courthouse level. Month and year fixed effects include sentencing fiscal year and month and year of case initiation. All Controls are demographic controls listed in Table 3. LIONS Controls are indicators for whether the defendant is male, whether the case involved multiple defendants, whether the case had multiple prosecutors, and offense type. All regressions restricted to courthouse-sections with at least 25 observations.
70
Table A.4. Results by Defendant Race/Ethnicity All Cases Black
Defendants Non-Black Defendants
Hispanic Defendants
Non-Hispanic Defendants
White Defendants
Non-White Defendants
(1) (2) (3) (4) (5) (6) (7) Dependent Variable: Base Offense Level Male D * Male P -0.494**
(0.198) -0.560***
(0.213) -0.466***
(0.164) -0.924***
(0.289) -0.440***
(0.161) -0.357** (0.180)
-0.569*** (0.185)
Male Defendant 2.677*** (0.203)
2.441*** (0.222)
1.554*** (0.187)
2.659*** (0.280)
1.735*** (0.175)
1.231*** (0.180)
2.439*** (0.184)
Male Prosecutor 0.396** (0.192)
0.472** (0.228)
0.330 (0.204)
0.846** (0.363)
0.297 (0.206)
0.236 (0.238)
0.492** (0.202)
Male D * Male P * Hispanic -0.252 (0.307)
- - - - - -
Male D * Male P * White 0.179 (0.245)
- - - - - -
Number of Courthouses 180 133 158 85 174 151 153 Number of CH-Sections
494 304 438 137 481 414 359
Observations 136,203 55,467 77,610 15,174 118,277 59,598 73,468 OLS regressions of the the defendant’s base offense level. ***: p<0.01; **: p<0.05; *: p<0.10. Standard errors are clustered at the courthouse level. All regressions include demographic controls listed in Table 3, month and year fixed effects, and courthouse-section fixed effects. All regressions restricted to courthouse-sections cells with at least 25 observations. Regression coefficients for race, race*prosecutor gender and race*defendant gender are unreported but included in the regression reported in column (1).
71
Table A.5. Results by Defendant Race/Ethnicity and Offense Type Drug Offenses Regulatory Offenses Violent Offenses White Collar Offenses Non-White White Non-White White Non-White White Non-White White Dependent Variable: Base Offense Level Interaction -0.748**
(0.318) -0.305 (0.299)
-0.351 (0.270)
-0.620 (0.381)
-0.721 (0.675)
-0.816* (0.480)
-0.069 (0.163)
-0.203 (0.194)
Male Defendant 2.982*** (0.284)
0.610** (0.294)
3.191*** (0.243)
2.782*** (0.385)
2.513*** (0.552)
1.707*** (0.398)
0.715*** (0.146)
1.275*** (0.162)
Male Prosecutor 0.664* (0.367)
0.059 (0.456)
0.356 (0.246)
0.179 (0.382)
0.636 (0.691)
0.866* (0.509)
0.177 (0.127)
0.304** (0.137)
Male Defendants (share) 0.905 0.791 0.780 0.788 0.950 0.932 0.635 0.694 Male Prosecutors (share)
0.716 0.742 0.672 0.715 0.681 0.743 0.689 0.738
Observations 42,548 26,518 8,201 10,358 14,249 10,253 10,285 13,931 OLS regressions of the the defendant’s base offense level. ***: p<0.01; **: p<0.05; *: p<0.10. Standard errors are clustered at the courthouse level. All regressions include demographic controls listed in Table 3, month and year fixed effects, and courthouse-section fixed effects. All regressions restricted to courthouse-section with at least 25 observations.
72
Table A.6. Additional Outcome Variables—By Race/Ethnicity Panel A: Early Outcomes
Final Offense Level (1)
Facing Mandatory Min (0/1) (2)
Final Guidelines Range (IHS) (3)
Subst. Assistance (0/1) (4)
Non-White White Non-White White Non-White White Non-White White Interaction -0.412**
(0.208) -0.185 (0.185)
-0.019* (0.011)
-0.006 (0.008)
-0.070** (0.029)
-0.031 (0.028)
0.00006 (0.010)
0.0002 (0.010)
Male Defendant
2.810*** (0.212)
1.830*** (0.195)
0.104*** (0.011)
0.044*** (0.008)
0.430*** (0.030)
0.280*** (0.030)
-0.057*** (0.013)
-0.019** (0.009)
Male Prosecutor 0.348 (0.229)
0.115 (0.232)
0.006 (0.011)
0.004 (0.012)
0.068** (0.034)
0.033 (0.035)
0.009 (0.010)
0.015 (0.011)
Observations 73,468 59,598 73,381 59,551 73,467 59,598 73,468 59,598
Panel B: Sentencing Outcomes Pretrial Release (0/1)
(5)
Sentence (IHS)
(6)
Sentence Length (IHS) (control for BOL)
(7)
Below-Guidelines (0/1)
(8) Non-White White Non-White White Non-White White Non-White White Interaction -0.002
(0.011) 0.021
(0.013) -0.117** (0.054)
-0.065 (0.051)
-0.057 (0.048)
-0.023 (0.042)
-0.003 (0.011)
0.007 (0.010)
Male Defendant
-0.170*** (0.013)
-0.087*** (0.011)
1.015*** (0.057)
0.659*** (0.044)
0.761*** (0.051)
0.515*** (0.035)
-0.105*** (0.014)
-0.066*** (0.008)
Male Prosecutor 0.014 (0.011)
-0.010 (0.012)
0.108* (0.057)
0.034 (0.058)
0.057 (0.048)
0.007 (0.041)
0.009 (0.010)
0.005 (0.008)
Observations 72,594 58,348 73,468 59,598 73,468 59,598 73,469 59,598 OLS regressions. ***: p<0.01; **: p<0.05; *: p<0.10. Standard errors are clustered at the courthouse level. All regressions include demographic controls listed in Table 3, column (4), month and year fixed effects, and courthouse-section fixed effects.
73
Table A.7. All Outcome Variables with Gender Share Panel A: Early Outcomes
Base Offense Level
(1)
Final Offense Level
(2)
Facing Mandatory
Minimum (0/1) (3)
Final Guidelines Range (IHS)
(4)
Substantial Assistance (0/1)
(5)
Interaction -0.610*** (0.165)
-0.471*** (0.172)
-0.019** (0.007)
-0.068*** (0.026)
0.003 (0.008)
Male Def (0/1)
1.982*** (0.169)
2.538*** (0.177)
0.079*** (0.007)
0.378*** (0.025)
-0.043*** (0.008)
Male Pros (fraction)
0.454** (0.194)
0.284 (0.192)
0.011 (0.009)
0.053* (0.030)
0.007 (0.009)
Observations 143,457 143,457 143,307 143,457 143,457
Panel B: Sentencing Outcomes Pretrial Release (0/1)
(6)
Sentence Length (IHS)
(7)
Sentence (IHS) (control for BOL)
(8)
Below-Guidelines Sentence (0/1)
(9) Interaction 0.023**
(0.009) -0.122***
(0.043)
-0.054 (0.034)
0.007 (0.009)
Male Def (0/1)
-0.134*** (0.010)
0.861*** (0.042)
0.640*** (0.033)
-0.089*** (0.011)
Male Pros (fraction)
-0.007 (0.009)
0.082* (0.047)
0.031 (0.033)
0.003 (0.009)
Observations 141,046 143,457 143,457 143,456 OLS regressions. ***: p<0.01; **: p<0.05; *: p<0.10. Standard errors are clustered at the courthouse level. All regressions include demographic controls listed in Table 3, month and year fixed effects, and courthouse-section fixed effects. All regressions restricted to courthouse-sections with at least 25 observations.
74
Table A.8. Mixed-Gender Cases
All Cases Solo Defendants
Multiple Ds Different Gender
Multiple Ds Same Gender
(1) (2) (3) (4) Dependent Variable: Base Offense Level Interaction -0.542***
(0.149)
-0.641*** (0.175)
-0.448** (0.226)
0.366 (0.570)
Male Defendant 1.938*** (0.161)
2.068*** (0.142)
1.751*** (0.222)
1.540*** (0.571)
Male Prosecutor 0.421** (0.185)
0.517*** (0.169)
0.284 (0.276)
-0.459 (0.594)
Observations 143,457 87,046 26,099 24,474
OLS regressions of the the defendants base offense level. ***: p<0.01; **: p<0.05; *: p<0.10. Standard errors are clustered at the courthouse level. All regressions include demographic controls listed in Table 3, month and year fixed effects, and courthouse-section type fixed effects. All regressions restricted to courthouse-sections with at least 25 observations. Columns (3)-(4) also control for the number of defendants in each case.
75
Table A.9. Courthouse-Sections in which Prosecutor Gender Uncorrelated with Five Characteristics Full Sample Full Sample p>0.01 p>0.01 p>0.05 p>0.05 p>0.10 p>0.10 (1) (2) (3) (4) (5) (6) (7) (8) Dependent Variable: Base Offense Level Interaction -0.542***
(0.149)
- -0.600*** (0.162)
- -0.585*** (0.191)
- -0.546*** (0.200)
-
Male Defendant 1.938*** (0.161)
1.554*** (0.095)
1.951*** (0.132)
1.530*** (0.088)
2.026*** (0.178)
1.610*** (0.118)
1.948*** (0.196)
1.551*** (0.140)
Male Prosecutor 0.421** (0.185)
-0.027 (0.099)
0.515*** (0.187)
0.020 (0.103)
0.480** (0.214)
-0.008 (0.118)
0.352* (0.200)
-0.101 (0.120)
Observations 143,457 143,457 84,614 84,614 54,823 54,823 39,229 39,229 OLS regressions of the defendant’s base offense level. ***: p<0.01; **: p<0.05; *: p<0.10. Standard errors are clustered at the courthouse level. All regressions include demographic controls listed in Table 3, month and year fixed effects, and courthouse-section fixed effects. All regressions restricted to courthouse-sections with at least 25 observations.
76
Table A.10. Individual Prosecutor Fixed Effects (1) (2) (3) Dependent Variable: Base Offense Level Interaction -0.596***
(0.167)
-0.458*** (0.153)
-0.513*** (0.176)
Male Defendant 1.940*** (0.182)
1.766*** (0.161)
1.681*** (0.184)
Male Prosecutor 0.449** (0.215)
- -
Prosecutor FEs X Prosecutor-Offense FEs X Observations 127,296 127,296 102,717 OLS regressions of the the defendants base offense level. ***: p<0.01; **: p<0.05; *: p<0.10. Standard errors are clustered at the courthouse level. All regressions include demographic controls listed in Table 3, month and year fixed effects, and courthouse-section fixed effects. All regressions restricted to courthouse-section, prosecutor, or prosecutor-section cells with at least 25 observations.
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