The Shadow Carceral State and Racial Inequality in Turnout

Abstract

Scholars have studied the carceral state extensively. However, little is known about the ‘shadow’ carceral state, coercive institutions lacking even the limited safeguards of the carceral state. Pretrial incarceration is one such institution. It often lasts months and causes large resource losses. Yet it is imposed in rushed hearings, with wide discretion for bail judges. These circumstances facilitate quick, heuristic judgments relying on racial stereotypes of marginalized populations. We merge court records from Miami-Dade with voter records to estimate the effect of this ‘shadow’ institution on turnout. We find that quasi-randomly assigned harsher bail judges depress voting by Black and Hispanic defendants. Consistent with heuristic processing, these racial disparities result only from inexperienced judges. Unlike judge experience, judge race does not matter; minority judges are as likely to impose detention and reduce turnout. The ‘shadow’ carceral state undermines democratic participation, exacerbating racial inequality.

Full text

ARTICLE
The Shadow Carceral State and Racial Inequality in
Turnout
Ted Enamorado1, Anne McDonough2and Tali Mendelberg3
1
Department of Political Science, Washington University in St. Louis, St. Louis, MO, USA,
2
Law School, Yale University, New
Haven, CT, USA and
3
Politics Department, Princeton University, Princeton, NJ, USA
Corresponding author: Ted Enamorado; Email: ted@wustl.edu
(Received 15 March 2023; revised 23 January 2024; accepted 29 August 2024)
Abstract
Scholars have studied the carceral state extensively. However, little is known about the ‘shadow’carceral
state, coercive institutions lacking even the limited safeguards of the carceral state. Pretrial incarceration is
one such institution. It often lasts months and causes large resource losses. Yet it is imposed in rushed
hearings, with wide discretion for bail judges. These circumstances facilitate quick, heuristic judgments
relying on racial stereotypes of marginalized populations. We merge court records from Miami-Dade
with voter records to estimate the effect of this ‘shadow’institution on turnout. We find that quasi-ran-
domly assigned harsher bail judges depress voting by Black and Hispanic defendants. Consistent with
heuristic processing, these racial disparities result only from inexperienced judges. Unlike judge experi-
ence, judge race does not matter; minority judges are as likely to impose detention and reduce turnout.
The ‘shadow’carceral state undermines democratic participation, exacerbating racial inequality.
Keywords: shadow carceral state; pretrial incarceration; turnout; racial inequities
Introduction
The ‘carceral state’has become a well-documented feature of the American political system.
Record numbers of Americans –especially poor people of colour –regularly encounter harsh
treatment by police, and many have been imprisoned (Lerman and Weaver 2014; Soss and
Weaver 2017). This carceral contact has increasingly been featured as a possible cause of
depressed voting among disadvantaged groups in the US (Lerman and Weaver 2014; Morris
2021; White 2019).
Though voluminous, the literature on the carceral state has neglected the ‘shadow’carceral state
(Beckett and Murakawa 2012). The shadow carceral state consists of actors who draw on the gov-
ernment’s power of coercion unconstrained by the procedures of the carceral state
(Kohler-Hausmann 2018; Page, Piehowski, and Soss 2019). The carceral state must consider the
evidence and decide guilt or innocence before imposing punishment. The shadow carceral state
is not required to do so because it relies on administrative rather than judicial procedures. In the
shadow carceral state, citizens lose civil liberties with degraded due process and equal protection.
A key institution of the shadow carceral state is pretrial incarceration (PI). PI confines defen-
dants before the disposition of their case, to ensure they do not violate the law or fail to show up
for court. In local jails, which hold far more than state and federal prisons combined, approxi-
mately two-thirds of inmates are held pretrial (Digard and Swavola 2019, see also Sawyer and
Wagner 2022; U.S. Commission on Civil Rights 2022).
© The Author(s), 2024. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative
Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction,
provided the original article is properly cited.
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Typical of the shadow carceral state, PI lacks important elements of due process, with bail
hearings often lasting under four minutes (Gonzalez Van Cleve 2022, 135; Scott-Hayward and
Ottone 2018, 172–3).
1
Given this weak due process, it is not surprising that PI is punitive; five
months is not uncommon, even though most of these cases are nonviolent and even though a
plurality of cases are later dismissed or found not guilty (Rabuy and Kopf 2016; Sawyer and
Wagner 2022; Stevenson 2018). Like other shadow institutions, PI is also highly selective by
race and class (Arnold et al. 2018; Demuth and Steffensmeier 2004).
What are the consequences of this shadow carceral state for the individuals it punishes? Building
on carceral state literature, we argue that pretrial incarceration reduces turnout for poor people of
colour –even adjusting for prior vote propensity and case and defendant characteristics (Demuth
and Steffensmeier 2004;LermanandWeaver2014; White 2019). Crucially, this racial disparity is
associated with the distinctive features of the shadow carceral state and occurs regardless of the race
of the person who administers it (Arnold et al. 2018). Bail judges must make quick judgments lack-
ing relevant information –the type of decision-making most vulnerable to inaccurate stereotypes of
poor people of colour (Arnold et al. 2018; Rachlinkski and Wistrich 2017). Because PI is adminis-
tered with little time to weigh evidence, minimal accountability for incorrect decisions, and because
stereotypes of stigmatized poor defendants are prevalent even among minority judges, the racial
disparity in PI may be similar for minority judges. Thus, PI may be racially biased, with the
bias largely impervious even to descriptive representation on the bench. Finally, bail literature
shows that the cognitive challenges of the rushed decision situation are most severe for inexperi-
enced judges; thus, we expect them, in particular, to produce biased decisions. That is, the PI system
will disempower poor minority defendants facing inexperienced judges.
To test these propositions, we need a measure of pretrial incarceration, which has been missing
in the literature (McDonough, Enamorado, and Mendelberg 2022). In addition, we need to avoid
well-known pitfalls of studies of incarceration effects on turnout: omitted variable bias, inaccurate
self-reports, and an under-representation of incarcerated people in surveys (Burch 2011; Gerber
et al. 2017; White 2022). To that end, we use data from Miami-Dade County, where, on week-
ends, defendants are assigned to the bail judge on duty. Judges are assigned to shifts with approxi-
mately equal probability based on their last names, and judges vary in their propensity to set high
bail conditions. We leverage the quasi-random assignment of bail judges who vary in their ten-
dency to assign pretrial incarceration. This random assignment avoids the confounding effects of
case or defendant characteristics (McDonough, Enamorado, and Mendelberg 2022; White 2019).
We obtained full case records for the entire population of defendants in Miami-Dade County
(2008 −2016), which yielded a large sample of 42, 950 defendants. We merge these records
with voter files to estimate the effect of pretrial incarceration on turnout.
A final important advantage of this data is the presence of many more judges than in previous
carceral effects studies. Specifically, we have 156 judges (12 Black, 60 Hispanic, and 84 White), far
more than the 6–15 judges in recent studies of incarceration effects (McDonough, Enamorado,
and Mendelberg 2022; White 2019).
2
This large and racially varied sample of judges allows us
to test two alternative mechanisms: the race of the judge and judicial experience. While we
lack variation in due process safeguards, we leverage variation in judge experience to indirectly
assess the consequences of weaker safeguards. This data can show whether the effect diminishes
with same-race judges, who would not hold animus toward their racial group, or, alternatively,
diminishes with experienced judges, who would be less susceptible to erroneous heuristic judg-
ments triggered by the rushed decision situation.
We find that pretrial incarceration decreases voting by 7 percentage points. This is among the
larger effects in the literature on voting interventions (Gerber, Huber, and Hill 2013). The effect is
strong and precise only for Black and Hispanic defendants, and only for Black residents from
1
The jurisdictions include Cook County, Illinois, and Los Angeles and Orange Counties, California.
2
In White (2019), this is the number of courtrooms, the ‘treatment’unit.
2 Ted Enamorado et al.
https://doi.org/10.1017/S0007123424000358 Published online by Cambridge University Press

poor zip codes. The effect is not due merely to physical incapacitation while incarcerated. It is not
spuriously caused by a pre-existing vote propensity. Moreover, the race of the judge makes no
difference, either to the likelihood of PI or to its effect on turnout. Black and Hispanic judges
do not produce a smaller racial disparity, co-racial defendants are equally demobilized. By impli-
cation, then, harsh punishments and their disempowering effects are not explained by White
judges’racial animus. Instead, these consequences emerge from a system of weak due process:
quick, unchecked, stereotyped decisions about the risk posed by poor, stigmatized people of col-
our. We find that these decisions are most racially biased at the hands of inexperienced judges.
Racialized disempowerment in the shadow carceral state seems to arise from weak due process
administered by inexperienced judges. The pretrial incarceration system itself is demobilizing.
We use data from Miami-Dade because it meets the data requirements we noted above. These
findings from Miami-Dade plausibly generalize to other large US cities. Many large urban coun-
ties require bail judges to reach quick decisions with little opportunity for a defence and fre-
quently detain poor Black and Hispanic defendants for months (Arnold et al. 2018; Hood and
Schneider 2019; Olson and Taheri 2012). The racial disparity in PI holds in the 75 largest coun-
ties in the United States (Demuth and Steffensmeier 2004).
The effect of incarceration on voting has been subjected to numerous tests, but the literature
has focused on incarceration after a verdict (Burch 2011; Gerber et al. 2017; Lerman and Weaver
2014; White 2019). We advance the literature in three ways. First, we focus on incarceration
before a verdict by measuring PI and estimating its causal effects with quasi-random assignment
of judges to cases. Second, we test and reject the moderating role of judge race. Third, we test and
find a role for judge experience. This research allows us to conclude that the shadow carceral state
matters for turnout and that weak due process administered by inexperienced judges is an
important aspect of American punitive institutions. Through pretrial incarceration, the carceral
state permeates far deeper and in less formal ways than recognized to date in the literature on
the carceral state. This informality promotes a decision-making process that bypasses even
some of the limited protections of the official carceral state. These conclusions carry troubling
implications for the democratic health of the American political system, which rests on the prom-
ise of equal voice (Verba, Schlozman, and Brady 1995), and for the fairness of its carceral systems.
Pretrial Incarceration
The broad reach and disparate racial impact of the carceral state have been well documented (Soss
and Weaver 2017). Little explored, however, is the shadow carceral state. The shadow carceral
state uses ‘legally liminal authority, in which expansion of punitive power occurs through the
blending of civil, administrative, and criminal legal authority. In institutional terms, the shadow
carceral state includes institutional annexation of sites and actors beyond what is legally recog-
nized as part of the criminal justice system…These institutions…have nonetheless acquired the
capacity to impose punitive sanctions –including detention –even in the absence of criminal
conviction’(Beckett and Murakawa 2012, 222).
Pretrial incarceration is a significant element of the shadow carceral state. PI confines defen-
dants before their case is decided to ensure they do not violate the law or fail to show up for court.
PI has received little attention, but it is a major reason for the enormous size of the carceral state.
As we noted, local jails have more inmates than state and federal prisons put together, and nearly
two-thirds of jail inmates are being held pretrial (Sawyer and Wagner 2022). In addition, net jail
growth in recent years is almost entirely driven by PI (Sawyer and Wagner 2022).
3
PI is characterized by each element of the shadow carceral state. First, it contravenes notions of
justice that undergird safeguards in the formal legal system. Though PI is justified by the
3
In addition, over half of people with frequent police interactions also report very frequent contact with bail officials
(Garcia-Rios et al. 2023).
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imperative of public safety, in many large jurisdictions, most PI cases are nonviolent (Circuit
Court of Cook County, 2019; Scott-Hayward and Ottone 2018). For example, in one large
urban county, 60 per cent of cases with three or more PI days had nonviolent charges
(Stevenson 2018). PI is not a formal punishment commensurately fitting a specific crime, and
yet it often results in months in jail. For example, in Philadelphia County, 40 per cent of people
arrested were incarcerated for at least three days, and of those, the average detention period was
almost 5 months (Stevenson 2018). In large jurisdictions across the US, most jail inmates are held
pretrial for over a month on average (U.S. Commission on Civil Rights 2022, 28).
Second, PI is not subject to the usual protections of formal due process. It is often decided in a
pro forma hearing too brief to allow arguments from the defence. For example, in Los Angeles
and Orange Counties, defendants did not contest the decision in nearly 2/3 of the cases studied,
and when they did, judges ‘usually denied requests…without comment’(Scott-Hayward and
Ottone 2018, 173). In these and other large jurisdictions, the hearing typically lasts less than
2–4 minutes (Scott-Hayward and Ottone 2018; Stevenson 2018). In this rushed process, judges
have little opportunity to consider individual circumstances that may depart from heuristic,
stereotyped judgments.
Third, pretrial incarceration does not conform to standard notions of equal protection. It is
typically imposed on minority defendants who are too poor to post bail (Circuit Court of
Cook County, 2019; Scott-Hayward and Ottone 2018).
4
The defendant’s inability to pay is rarely
taken into account during the hearing (Scott-Hayward and Ottone 2018; Stevenson 2018). The
bail industry makes billions a year by extracting resources from poor communities (Page,
Piehowski, and Soss 2019; Rabuy and Kopf 2016). Bond companies impose additional fees
and seize homes and other property as collateral, even when the court later dismisses the charge,
leaving many ‘not-guilty’defendants thousands of dollars in debt (Page, Piehowski, and Soss
2019; U.S. Commission on Civil Rights, 2022). The vastly disproportionate burden placed on
poor defendants is typical of the shadow carceral state.
Thus, the PI process lacks some important defendant safeguards, and PI is imposed much
more often on racialized poor populations.
Bias, Heuristics, and Rushed Judgment
How would the PI process shape the PI decision? According to studies of street-level bureaucrats,
the high degree of discretion afforded by bail judges can lead to biased decisions (Lipsky 1980).
For example, in ‘welfare’cases, where caseworkers have discretion, they are more likely to apply
punitive sanctions against disadvantaged racial minorities (Keiser, Mueser, and Choi 2004). As
Einstein and Glick note, ‘in the absence of clear rules designed to preclude discrimination,
bureaucrats with discretion can act according to their own biases’(Einstein and Glick 2017, 101).
This pattern holds in the case of bail decisions. Bail judges are given discretion to decide how
likely the defendant is to pose a physical threat, and they tend to over-rely on race and under-
weight more proximal predictors (Arnold et al. 2018; Demuth and Steffensmeier 2004;
Gonzalez Van Cleve 2022; Kleinberg et al. 2018). Machine learning algorithms that lack this
human discretion, which excludes race, can generate more accurate predictions and reduce the
racial disparity in PI decisions compared to human judges (Kleinberg et al. 2018).
How might this racially biased process work? Is it due to racial animus; that is, general stereo-
types targeting all racial minorities? Or is it due instead to implicit cognitive heuristics applied to
particular, stigmatized subgroups of minority populations in a rushed judgment? (Bordalo et al.
2016) We argue that the PI process may produce the latter: quick heuristic decisions driven by
4
The typical local inmate is non-White and earns $16,000 a year (Gupta, Hansman, and Frenchman 2016; Rabuy and Kopf
2016). In a study of one large jurisdiction, most pretrial detainees could not afford even the $1,000 or less necessary to avoid
detention (Stevenson 2018, 512).
4 Ted Enamorado et al.
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associations between specific racialized poor populations and crime. As Rachlinkski and Wistrich
(2017) summarize, ‘judges, like most adults, rely too heavily on intuition while making important
decisions. This tendency leaves them vulnerable to using overly simplistic cognitive strategies to
decide cases, which creates predictable, systematic errors in judgment’(Rachlinkski and Wistrich
2017, 211).
This argument builds on studies of street-level bureaucrats finding that racial disparities are
caused not by outright racial hostility as much as workload and lack of accountability for biased
outcomes (Andersen and Guul 2019; Christensen, Szmer, and Stritch 2012; Lipsky 1980). The
situational factors present in the PI decision conform to the conditions identified in these studies.
As noted above, the detention hearing is too brief to allow the judge to take complex individual
circumstances into adequate account, or for a defendant to offer any defence. Decisions are made
under severe time pressure and with limited individuating information about the defendant. In
these circumstances, decisions tend to be based on ‘System 1’processing: a quick, less deliberate,
heuristic, and intuitive cognitive process (Rachlinkski and Wistrich 2017).
Judges’racially disparate decisions may be reinforced by a representativeness heuristic. This
heuristic takes a small grain of truth and exaggerates it. In this setting, while the average Black
and White defendants do not differ substantially in their risk of rearrest while released, Black
defendants are slightly more likely than White defendants to be among the very small percentage
of defendants with high risk (Arnold et al. 2018). This slight correlation of race with extreme and
rare behaviour may lead to a representativeness heuristic based on an illusory correlation. This
heuristic exaggerates the probability that the average member of a group will engage in a salient
action that most differentiates their group from another (Arnold et al. (2018). In other words, the
perceived probability of a behaviour by a group member becomes inflated when that behaviour
has a higher relative frequency at the extreme, even if the absolute frequency of that behaviour
is small.
Importantly, this process does not require generalized stereotypes against all Black or Hispanic
defendants, and no racial animus (Bordalo et al. 2016). In a System 1 process, bias comes not
from feelings of dislike for racial outgroups or from blanket stereotypes against an entire racial
group, but from quick, heuristic judgments about a particularly stigmatized, salient subgroup
of a minority population commonly associated with crime (Bordalo et al. 2016, footnote 9).
For example, in implicit bias experiments, participants who more strongly associate weapons
with Black people, during an implicit judgment task calling for a quick System 1 judgment,
also exhibit more severe racial bias in a ‘shooter’task, mimicking the quick decisions police offi-
cers must make in assessing a threat (Glaser and Knowles 2008). Importantly, these quick, puni-
tive reactions are triggered especially by Black defendants with a more racially stereotypical
presentation (Eberhardt et al. 2004; Kahn and Davies 2017). Furthermore, implicit associations
are not correlated with general negative assessments of all Black people (Judd, Blair, and
Chapleau 2004). That is, System 1 processing may promote implicit bias rather than categorical
bias, specifically against the most stereotyped subgroups of racial minority groups.
These studies of System 1 processing particularly highlight how time pressure and the absence
of deliberation allow such associations to dominate judgments. The rushed hearing, the high cog-
nitive load, the lack of accountability to the defendant and their legal counsel, the lack of oppor-
tunity to consider the facts of the case, and the circumstances of the individual –all these features
of the detention hearing are also well-known factors that exacerbate the biases from heuristics
(Rachlinkski and Wistrich 2017, 111–118). As a result of these mechanisms, judges may system-
atically assess poor minority defendants as posing too high a risk.
The (Null) Impact of Judge Race and the Effect of Judge Experience
This System 1 process has important implications for judge race: judge race should have a null
effect. The racial disparity in pretrial incarceration may have roots in heuristics that, with time
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pressure and lack of accountability, could produce racial disparity by judges of any race.
Consistent with this possibility, a study of bail decisions in Miami-Dade found that racially dis-
parate detention decisions were produced regardless of judge race (Arnold et al. 2018).
Furthermore, Rachlinski et al. (2009) found that about half the Black judges they studied exhib-
ited anti-Black bias on the Implicit Association Test (IAT), and these IAT bias scores predict
racial disparities in hypothetical court cases. These effects are obtained only with implicit racial
stimuli, consistent with a System 1 process (Rachlinkski and Wistrich 2017, 101). This finding is
buttressed by evidence that many minority judges hold negative views of lower-status subgroups
of their own racial group, as do many minority survey respondents (Jefferson 2023). These studies
suggest that the judgment process produces racially disparate outcomes because of a situation that
promotes reliance on heuristics about stigmatized subgroups of minority communities, not as a
result of generalized animus or blanket negative stereotypes held by White judges. Put differently,
the racial disparities may be built into the situation.
An additional reason why judges of any race might produce racial disparity is the requirements
of their role. Theories of judicial organization posit that judges are selected, socialized, or incen-
tivized to conform to the expectations of their organizational role as judges (Harris and Sen 2019;
Steffensmeier and Britt 2002). Judges’individual identities may be overridden by a judicial culture
or incentives that prioritize the avoidance of releasing defendants who may then commit harmful
crimes (for example, Steffensmeier and Britt 2002).
5
Judges issue much more punitive decisions
as their re-election date approaches, especially for violent crimes (Berdejó and Yuchtman 2013;
Huber and Gordon 2004). That is, judges may seek to avoid releasing defendants who go on to
commit further violent crimes, to avoid losing their seats. This institutional imperative may hold
for judges of any race (Harris and Sen 2019). These judges are chosen by elites or voters who
expect that judges will avoid releasing defendants who go on to commit violent crimes. As
Harris notes, Black judges may adopt the expectations of the White-dominated carceral system
(2024; see also Steffensmeier and Britt 2002).
For these reasons, we do not expect judge race to make much difference. As Harris put it,
‘non-White judges’racial identities, alone, do not appear to lead to a decrease in the
Black-White incarceration gap’(2024, 34). Our detailed review of the literature on judge race
effects supports this conclusion (see Appendix A). That is, there is no consistent evidence that
Black and Hispanic judges produce less racially disparate decisions.
Unlike judge race, we expect that the judge’s experience does matter. If System 1 processing
helps explain the racial disparity, then judges would make more inaccurate, racially disparate pre-
dictions if they are inexperienced. As Arnold et al. (2018) explain, defendants who violate the
conditions of their bail and are consequently taken into custody undergo a hearing before a
bail judge. These hearings allow bail judges to learn which factors often lead to unsafe releases.
The more that judges see first-hand that defendants do and do not re-offend, the more their
future decisions can become accurately informed.
This is in line with recent findings that people can learn to break mental habits that produce
bias. In recent randomized studies by Devine and colleagues, treated participants practised a set
of cognitive strategies, such as learning to rely on relevant information about an individual rather
than illusory correlations about a group (Devine et al. 2012). Treated participants continued to
apply these cognitive strategies up to two years later, suggesting that the right sort of experience
can establish new decision habits (Forscher et al. 2017). Another example comes from ‘shooter’
experiments. In these studies, untrained participants tend to make racially biased, inaccurate
shooting decisions while trained participants do not. Observational and experimental evidence
from police officers and other populations shows that training and similar learning opportunities
can greatly reduce this racial bias (Singh et al. 2020). As Singh et al. conclude, ‘through practice,
5
For example, judges in districts with competitive partisan elections issue more punitive sentences than judges in districts
that use non-competitive retention elections (Gordon and Huber 2007).
6 Ted Enamorado et al.
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police officers and trained participants learn to more effectively identify and use information
other than race’(Singh et al. 2020, 566).
The cognitive psychology literature finds that the more a decision task is repeated, the less cog-
nitive effort it requires; by performing a decision task over and over again, and learning from their
mistakes, practised decision-makers have available greater cognitive resources for taking into
account detailed individuating information in any individual case (Tobin and Grondin 2015).
Conversely, decision-makers unused to the challenge may be too cognitively depleted to apply
the required mental resources. As Devine et al. put it, ‘overcoming prejudice is a protracted pro-
cess that requires considerable effort’(2012, 1,268). These studies support the notion that, as
judges gain experience, they may make more individuating, accurate, and unbiased decisions.
Practice and experience are likely to matter especially when the decision task is difficult.
Adding time pressure and other forms of ‘cognitive load’–that is, making the decision situation
more difficult –exacerbates heuristic thinking and decreases accuracy (Glaser and Knowles 2008;
Govorun and Payne 2006; Kleider, Parrott, and King 2010). A primary mechanism for this cog-
nitive depletion effect is a weakened ability to control one’s thought process (Govorun and Payne
2006). Although judges have more time than police officers to confront possible danger on the
street, they too must make decisions about whether the person before them poses a safety risk,
and do so in a highly compressed time window. The time pressure and lack of ready information
would be much more demanding for judges who lack experience in making decisions accurately.
Thus, one way to examine whether the heuristic-inducing situation matters is to compare
experienced and inexperienced judges. Following this literature, we use judge experience to test
whether the decision process creates cognitive distortions, as these would especially affect judges
with little experience (Arnold et al. 2018).
Having developed an experience-based explanation of racial disparity in PI decisions, we turn
to the effect of PI on voting. We ask whether the shadow carceral state may inhibit the democratic
practice of voting.
The Demobilizing Effect of Pretrial Incarceration
Does PI reduce voting? The answer is not obvious. The literature on the formal carceral state finds
mixed effects from post-conviction incarceration. Some studies find no decrease in participation
(Burch 2011; Gerber et al. 2017; Walker 2020), while others find a large negative effect (Lerman
and Weaver 2014), especially for Black defendants (White 2019). We hypothesize that the shadow
carceral state reduces voting among Black and, perhaps, Hispanic defendants.
There are several reasons why PI would reduce voting.
6
We do not aim for definitive tests of
these mechanisms. They are the reasons why we expect PI to reduce voting by poor minority
defendants. Our main focus will be on the role of judges in mitigating this effect.
First, PI reduces concrete resources and imposes substantial costs (Dobbie, Goldin, and Yang
2018; Gupta, Hansman, and Frenchman 2016; Heaton, Mayson, and Stevenson 2017; Stevenson
2018). It increases unemployment by 9 percentage points for up to four years, causing substantial
income loss (Dobbie, Goldin, and Yang 2018, 204). The political science literature on political
participation has established that these resources are a significant antecedent of voter turnout
(Verba, Schlozman, and Brady 1995). Moreover, even short stints of financial hardship reduce
turnout for people already living in distressed economic circumstances (Schaub 2021). Thus,
PI may reduce voting by decreasing the material resources needed to cast a vote (Schlozman,
Verba, and Brady 2012).
6
Some studies find that carceral contact increases political participation (Burch 2013; Walker 2020). For example,
Garcia-Rios et al. (2023) find that people of colour who report personal racial discrimination and have high linked fate report
more political participation if they have contact with authoritarian institutions. However, Garcia-Rios et al. do not find this
mobilizing effect on voting (2023, Appendix Table A4).
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In addition, PI may work through a symbolic mechanism. The process violates common
notions of fairness and basic dignity. A system that appears to ignore the principles of liberty
enshrined in the Constitution may come to be regarded as hopelessly undemocratic (Lerman
and Weaver 2014). For example, those detained pretrial are more likely to then plead guilty,
often because they realize that, as their detention drags on, they may lose their job and incur
other negative consequences (Dobbie, Goldin, and Yang 2018; Heaton, Mayson, and Stevenson
2017). Taking a plea is, in turn, associated with negative perceptions of the criminal justice system
because detained defendants often plead guilty despite the lack of evidence against them (Lerman,
Green, and Dominguez 2022). This abrogation of justice may alienate them from the government
and symbolize its lack of accountability and responsiveness to its citizens (Lerman and Weaver
2014). Importantly, this process may enhance distrust of government in all its forms and func-
tions (Weaver, Prowse, and Piston 2020). As Gimpel, Lay, and Schuknecht (2003) put it, ‘evalua-
tions of a variety of institutional authorities –teachers, police, judges –are positively associated’
(145). In the political science literature on voting, trust in government responsiveness is a major
symbolic antecedent of voting (Verba, Schlozman, and Brady 1995). PI may reduce voting by
reducing that trust.
These resource and symbolic mechanisms would especially apply to Black and, perhaps,
Hispanic defendants. Regarding the resources mechanism, Black and Hispanic defendants have
fewer assets (even before arrest) (Page, Piehowski, and Soss 2019). They tend to reside in under-
served areas with more entrenched poverty (Lerman and Weaver 2014). Encounters with the car-
ceral state reduce Black defendants’earnings and pose a higher barrier to employment for them
than they do for White defendants (Apel and Powell 2019; Harris and Harding 2019). These
racial disparities in resources may mean that PI reduces turnout more for Black and Hispanic
defendants. Consistent with this hypothesis, some studies of the impact of formal conviction
find that incarceration reduces turnout among Black, not White, defendants (White 2019).
The symbolic mechanism would also hold especially for Black and, to some extent, Hispanic
defendants. As we noted, Black defendants are much more likely to be detained pretrial even
when accounting for their prior record and the nature of the charges. Consequently, in many
large jurisdictions, those detained would be surrounded primarily by Black and Hispanic defen-
dants (Page, Piehowski, and Soss 2019). It would be apparent to those detained –in a literal, vis-
ual sense –that the system is racially disparate. Black and Hispanic defendants would thus be
especially likely to conclude that it is unjust.
These beliefs are in line with studies of procedural justice. The fairness of the process can be
more consequential for people’s attitudes about the criminal justice system than the favourability
of the outcome (Tyler 2001). Black individuals are far more likely than White individuals to per-
ceive the criminal justice system as unfair (Hurwitz and Peffley 2005). Furthermore, Black indi-
viduals are also more likely to apply that perception to assessments of specific events involving
misdeeds by police (Hurwitz and Peffley 2005). These symbolic mechanisms may reduce voting,
especially by Black and Hispanic detainees, who are more likely to bear the brunt of the system’s
injustice –and to generalize it to the government’s view of their lack of worth as citizens (Lerman
and Weaver 2014). Several recent studies find that perceptions of racial discrimination are asso-
ciated with lower turnout for young Black individuals (Cohen 2010; Gimpel, Lay, and Schuknecht
2003). Black detainees would be especially likely to be affected by this symbolic mechanism.
7
What is the role of judge race in this symbolic process of demobilization? On one hand,
descriptive representatives may elicit greater trust (Bobo and Gilliam 1990). For example, a hypo-
thetical news article reporting that the Black percentage of judges reflects the Black percentage of
the population increases Black respondents’institutional trust (Scherer and Curry 2010). Black
judges, then, may not trigger political alienation by Black defendants, or at least less so than
7
We also test, and reject, the possibility that Black detainees are more strongly affected because of their higher prior voting
propensity.
8 Ted Enamorado et al.
https://doi.org/10.1017/S0007123424000358 Published online by Cambridge University Press

White judges do. And, likewise, for Hispanic defendants facing Hispanic judges. In that case, the
symbolic mechanism for reduced turnout by Black or Hispanic defendants may not hold when PI
is assigned by a same-race judge.
However, as we noted, there is reason to expect that judge race will not substantially affect the
racial disparity in PI. Defendants may not perceive minority judges as fairer. Consequently, judge
race may not mute the effect of PI on turnout. That is, conditional on being detained, Black or
Hispanic defendants would respond similarly to decisions by Black, Hispanic, and White judges.
That would be consistent with studies finding a null impact of judge race on perceptions of judi-
cial fairness. For example, the number of Black judges in the county has no effect on Black defen-
dants’perception of the fairness of judges in Mississippi (Overby et al. 2005). This null effect is
also found in the literature on officer race and community trust in the police (Brunson and Gau
2015), and is consistent with studies finding a null effect of a representative’s race on political
trust (Fowler, Merolla, and Sellers 2014). Thus, same-race judges may not mute the effect of
PI. That would be consistent with the notion that the institution itself matters beyond the effect
of individual judges (Harris 2024).
In sum, we expect a negative effect of PI on voting by Black and, perhaps, Hispanic defendants,
especially among those living in poverty.
8
If the decision situation matters as we hypothesized, we
would see the effect among judges of any race, but not among experienced judges.
Data
We obtained records from Miami-Dade County for all individuals arrested and charged with
criminal offences. We analyzed those who were arrested and had a first appearance bail hearing
during the period between the November 2008 and November 2016 general election days. The
records provide detailed information about the defendant (name, date of birth, gender, race)
and their case, including the charges at the time of arrest, the timing of arrest and release
from custody, and the judge who set the conditions of pretrial release.
9
As discussed below,
our identification strategy relies on defendants who had a first appearance bail hearing that
occurred on a weekend.
10
Thus, we omit all other cases from our sample.
11
We merge each defendant with the official state voter files from Florida after the 2008, 2012,
and 2016 elections, by first name, last name, and date of birth, using probabilistic record linkage
(Enamorado, Fifield, and Imai 2019).
12
To account for the possibility that defendants were resi-
dents of other states during or after their case, we repeat the merges with all other states’voter
files, using probabilistic merge on the same fields.
13
Appendix C has further details.
8
Hispanic defendants may be less likely than Black defendants but more likely than White defendants to experience the
resource losses and hear the symbolic message (Page, Piehowski, and Soss 2019; Walker 2020).
9
The court records include a person identifier. However, we found a non-negligible percentage of exact name and date of
birth combinations associated with different IDs (9 per cent). We thus generated a new person identifier using probabilistic
record linkage and clerical review (Appendix B.2). Results are almost identical to the courts’identifier (Appendix Table A8).
10
Weekend and weekday cases are similar. The largest covariate difference between them is only 2 percentage points (see
Appendix Table A4).
11
We omit cases in which the defendant secured release before the first appearance. We also omit a small number of cases
from judges with sparse data (replacement judges); cases involving serious charges that rarely result in pretrial release regard-
less of the assigned judge; defendants younger than 18 at the time of treatment; defendants released to U.S. immigration
enforcement (non-citizens); and cases that suffer from other data limitations (see Appendix B.3).
12
The official voter files from FL were provided by L2, Inc., a national non-partisan firm that collects and prepares voting
records. The official Florida files we used were not altered by the company. We use the first snapshot of the voter file L2
collected following the general election. The turnout counts from the files we use match official counts closely, within
0.03–0.62 per cent.
13
For these merges, we use the ‘uniform’voter files prepared by L2, for which L2 incorporates additional data sources, like
National Change of Address (NCOA), and standardizes formats across states.
British Journal of Political Science 9
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Following Dobbie, Goldin, and Yang (2018) and Stevenson (2018), we measure PI as being
detained for more than three days after the bail hearing. This threshold has been used by advo-
cacy groups and researchers based on evidence that a) the judge in the first bail hearing (our
instrument) has the most influence over the defendant’s pretrial incarceration status within the
first three days before defendants can petition for and secure modified pretrial conditions,
and b) the more severe collateral effects of pretrial incarceration typically begin after three
days (Dobbie, Goldin, and Yang 2018; Stevenson 2018).
14
As we will show, the results are robust
to alternative measures of PI.
After constructing the instrument, we make several additional modifications to the sample.
First, we omit cases whose pretrial release decisions are outliers in relation to other decisions
made by the same judge in the same year and violent charge level (N= 3, 288).
15
Second, we
drop cases in which the defendant is likely already disenfranchised due to a prior felony convic-
tion (N= 6, 710; Appendix B provides details). Third, because our outcome of interest (general
election turnout) is at the defendant-level and observed only once per general cycle, if a defendant
had multiple weekend cases, we select only the defendant’s last weekend case for each election
cycle (2008 −2012 and 2012 −2016).
16,17,18
Our final sample includes 45, 107 cases, involving 42,950 unique defendants and only one case
per defendant in each four-year election cycle (their last weekend case).
19
As shown in Appendix
Table A3, 23 per cent of the defendants were detained pretrial. The average pretrial incarceration
lasted twenty-one days and the average bail was approximately $9,500. Compared to those
released, defendants incarcerated pretrial were more likely to be male, Black, reside in zip
codes with incomes below the median, committed a drug-related offence, and have a prior case.
Methods
Quasi-random Assignment of Bail Judges
PI decisions may be endogenous to many defendant or case characteristics associated with turn-
out. And so, OLS regression could lead to biased estimates of the average treatment effect of PI on
turnout for the population of defendants (White 2019).
20
We address this issue by taking advan-
tage of the quasi-random assignment of bail judges to weekend cases (following Arnold et al.
2018; Dobbie, Goldin, and Yang 2018).
Specifically, in Miami-Dade, weekend bail cases are assigned to judges who spend weekdays as
trial court judges. On weekends, they take turns serving as judges in felony and misdemeanour
bail hearings. Within a few hours of arrest, the court system automatically assigns the defendant
to the bail judge on duty. As a result, on weekends, defendants cannot select their bail judge. They
14
See Appendix B.4.
15
The inclusion of outliers does not affect our substantive findings (Appendix Table A8).
16
If we had included all cases from a defendant in an election cycle, we would add variation in treatment assignment with-
out the possibility for variation in the outcome, which occurs only every four years. Therefore, we focus on each defendant’s
last case before the election while controlling for prior cases. Selecting the first case in each election cycle without accounting
for multiple later treatments would not be possible because controlling for post-treatment variables would introduce post-
treatment bias. Selecting the last weekend case of a defendant does not result in a larger sample of individuals detained
on election day.
17
As we will explain below, less than 2 per cent of defendants in our sample are still detained on election day. The exclusion
of these observations does not alter our conclusions.
18
Omitted cases are included in the instrument construction and improve its precision.
19
Some defendants (≈2.5 per cent) appear twice in the sample because they had a weekend case in both election periods
and our data spans two election cycles (2008–2016). If a defendant was arrested on multiple cases or if their case was later
consolidated or transferred to a new case number, we combine all such cases in one observation.
20
Appendix Table A5 reports OLS estimates of the average treatment effect (ATE) of PI on turnout. While OLS estimates
may be biased, as discussed below, Appendix Table A17 presents results for an unbiased estimate of the ATE of PI on turnout
following Aronow and Carnegie (2013).
10 Ted Enamorado et al.
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are assigned by the court system to whichever judge happens to be assigned to that day.
Importantly, judges are allocated to weekends in a quasi-random fashion –in alphabetical
order by last name (see Appendix B.1 for details). Furthermore, there is significant variation
in judges’tendencies to set incarceration-inducing bail amounts: as we will show, some judges
are consistently more likely to set higher bail amounts that result in pretrial incarceration com-
pared to judges deciding observably similar cases.
We use this quasi-random assignment of bail judges to construct an instrument to address
endogeneity concerns between PI and turnout (see McDonough, Enamorado, and Mendelberg
2022). Using Two Stage Least Squares (2SLS), we identify the local average treatment effect
(LATE) for defendants on the margin of incarceration and release. In other words, we identify
the effect of PI on turnout for a defendant who would be released by a lenient judge but may
have been detained pretrial had they been assigned to a harsher judge.
21
Instrument
For the instrument, we construct a measure of judge punitiveness net of the focal defendant. We
allow punitiveness to vary by time and case severity. Specifically, the instrument leaves out the
defendant’s case(s) and uses all other cases assigned to that judge in that year with that severity
type (see Appendix B.4.2). To measure severity, we use an indicator variable for violent charge
(see Appendix B.3.3). The instrument represents the proportion of other cases with the same vio-
lent charge indicator decided by that judge that year which resulted in PI (Aizer and Doyle 2015;
Stevenson 2018).
There are 156 bail judges in our analysis sample, with a median number of 107 cases per
judge-year-violent charge.
22
The average leave-out-case PI rate is 0.24 (s.d. = 0.12). As we go
from the least to the most punitive judge, the likelihood of PI increases by 44 percentage points
for defendants with non-violent charges, and 53 points for those with a violent charge.
Testing The Demobilizing Effects of Pretrial Incarceration
To estimate the effect of PI on voting, we rely on two-stage least squares (2SLS). The first stage is:
Pdtjh =
a
0+
a
1Zdtjh +X`
dt V+
e
dtjh (1)
and the second stage is:
Td,e=
b
0+
b
1

Pdtjh +X`
dt G+1dtjh (2)
where e∈{2012, 2016} indicates an election, dis for defendant, jis for judge, tis for year of bail,
and h∈{violent, non-violent} is the offense violent charge level. T
d,e
is an indicator for voting in
election e,P
dtjh
is a binary variable measuring PI (¿3 days),
ˆ
Pdtjh represents the predicted values
for PI from the first stage, Z
dtjh
is our instrument, and X
dt
is a set of defendant- and case-level
covariates and fixed effects. Defendant-level covariates are: age, age squared, gender, race,
voting-age-ineligible, pretreatment turnout (previous election), and pretreatment registration.
Case-level covariates are indicators for firearm, robbery, drug-related crime, and prior arrest.
Fixed effects are hearing day, month, year, and violent charge. While the quasi-random assign-
ment of judges allows us to omit many major predictors of turnout, we include variables poten-
tially associated both with PI and with turnout (such as resources), in case judge assignment is
21
Our results are not representative of defendants who would always be detained pretrial regardless of the type of bail judge
(always-takers) or of defendants who would never be detained pretrial (never-takers).
22
The judge-year median number of cases is 163.
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not fully random. This approach also increases statistical precision. In doing so, we follow other
studies of quasi-randomly assigned judges (Dobbie, Goldin, and Yang 2018; McDonough,
Enamorado, and Mendelberg 2022; Stevenson 2018). Because bail judge rotations are set by
year, we follow Dobbie, Goldin, and Yang (2018) and Cameron and Miller (2015) and report
bootstrap standard errors (based on 500 samples) clustered at the judge-by-year level.
Additionally, Appendix Table A8 presents almost identical findings when using heteroskedastic-
consistent standard errors and when clustering at the judge level.
Following McDonough, Enamorado, and Mendelberg (2022), to preview the connection
between judge punitiveness and the outcomes of interest, Appendix Figure A1 displays the non-
parametric fit between the residualized instrument and residualized pretrial incarceration (left
panel), as well as residualized turnout (right panel). Residualizing involves removing the variation
attributed to fixed effects. As expected, the figure reveals a positive (reduced form) correlation
between the instrument and pretrial incarceration, along with a negative correlation between
the instrument and voting. Additionally, the figure depicts the residualized distribution of the
instrument, confirming that the extremes of the distribution are not driving these relationships.
Our analysis indicates ample variation in the instrument, allowing us to predict both the
endogenous variable (pretrial incarceration) and the outcome (turnout).
Instrument Validity
For valid inference, the instrument must meet the exclusion restriction, be sufficiently correlated
with the endogenous variable (PI), and exhibit monotonicity. In this section, we evaluate each
requirement.
First, as we explained above, judges are assigned quasi-randomly to cases, making our instru-
ment exogenous. Furthermore, if the instrument is exogenous, pre-existing defendant and case
covariates should be uncorrelated with the decision tendencies of the assigned judge.
Following Dobbie, Goldin, and Yang (2018), Stevenson (2018), and McDonough, Enamorado,
and Mendelberg (2022), in Appendix Table A6 we regress the instrument ( punitiveness) on
the covariates and the fixed effects described above. Though we detect statistically significant cor-
relations between a few individual covariates and our instrument, the magnitude of the correla-
tions is exceedingly small. In addition, to rule out the possibility that those small correlations may
introduce bias, we construct a measure of predicted turnout such that all variation in it is coming
from defendant- and case-level covariates. As shown in Appendix Figure A2, this measure and
the residualized instrument are not correlated (r= 0.002).
The exclusion restriction states that bail judges cannot influence turnout through means other
than the bail hearing itself. The fact that defendants cannot choose their bail judge, that bail hear-
ings are brief, and that there are no further interactions between the judge and defendant after the
hearing suggests that judge punitiveness affects turnout only through PI. While there is no direct
test for the exclusion restriction, these reasons make the assumption plausible (Dobbie, Goldin,
and Yang 2018; Stevenson 2018).
Second, we assess instrument strength. Appendix Table A7 presents the first stage estimates.
The instrument is a significant predictor of pretrial incarceration. A one-unit increase in judge
punitiveness is associated with a 0.74 to an 0.80 increase in the likelihood of being detained pre-
trial. These results make weak instrument bias unlikely.
Third, we assess monotonicity. In our setting, monotonicity requires that punitive judges are
more punitive than judges in all cases of violent charge hin year t. In other words, assignment to
a more punitive judge increases the likelihood of pretrial incarceration. In settings such as ours,
Frandsen, Lefgren, and Leslie (2019) suggest testing for average monotonicity. Appendix
Table A7 presents the first stage estimates across a variety of subsets, including race, gender,
prior contact with the justice system, and charge types. Across subsets, assignment to harsher
judges consistently increases the likelihood of pretrial incarceration.
12 Ted Enamorado et al.
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Results
Main Effect of Pretrial Incarceration on Turnout
Figure 1 presents the 2SLS estimates of the effect of pretrial incarceration on voting. The top esti-
mate includes only fixed effects (day, month, year, and violent charge); the middle estimate
includes those and defendant covariates (gender, age, age squared, race, voting-age-ineligible, pre-
treatment turnout and registration); the bottom estimate includes all those and case covariates
(binary measures of prior case, firearm, drug, and property offense). Pretrial incarceration causes
a7–9 percentage point decrease in voting in the subsequent election.
This effect survives a large set of robustness checks and placebo tests. As we show in Appendix
Table A8, we obtain similar results if we include outliers on judge punitiveness; use different cut
points to define PI (7 and 14 days); use a continuous measure (the logarithm of the number of
pretrial detention days);
23
code PI using three categories; use a residualized version of the instru-
ment;
24
use a deterministic instead of probabilistic merge; use bivariate probit instead of two-
stage least squares; or include additional controls for a felony charge and any prior conviction.
Furthermore, PI does not predict turnout in the election prior to the case (Appendix
Table A8) or beyond the first general election post-treatment (see Figure A5 and Table A18 in
Appendix F). These tests confirm we are not measuring a spurious correlation with defendants
who are less likely to vote. In addition, the effect is located almost entirely among prior voters
(Panel A of Appendix Table A9), further evidence that the effect is not spuriously caused by a
lower propensity to vote. Finally, because these are LATE effects for compliers, we use weighted
2SLS (with complier weights) to recover an estimate of the ATE from the LATE (Aronow and
Carnegie 2013). The ATE is similar to the LATE (see Appendix Table A17).
25
Figure 1. The Demobilizing Effect of PI. Marginal effects from 2SLS estimates with 95 per cent CI.
23
See also Appendix Figure A4.
24
Following Dobbie, Goldin, and Yang 2018, we obtain residuals from regressing pretrial incarceration on bail hearing
year, month, and violent charge, and then calculate judge punitiveness in year tand violent charge has the mean of the
residuals.
25
Table A16 in Appendix E shows that compliers and the average defendant are similar (less than 3 percentage points
apart) on all demographics and electoral covariates and on having a prior case and the use of a firearm. Compliers are
British Journal of Political Science 13
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Before proceeding, we consider and reject a mechanical explanation: if defendants are incar-
cerated on election day, they may find it difficult or impossible to vote. That is, PI may demobilize
by increasing the chances of post-conviction incarceration (when defendants are not allowed to
vote), or because of the difficulty of voting while detained pretrial. However, less than 2 per cent
of defendants are still detained on election day, and fewer than one-fifth of cases in the sample
result in any form of incarceration post-conviction. Furthermore, as detailed in Appendix B.4.5
and Appendix Table A10, the effect is unchanged or larger if we remove cases more likely to be
incarcerated on election day because of proximity to election day (cases that began within 2, 4,
and 6 months before election day); if we examine cases with an offence that rarely results in a
post-conviction incarceration sentence if convicted; and if we exclude cases likely incapacitated
on election day due to the case’s actual observed dates of PI and length of post-conviction
sentence, regardless of when the case began.
26
Racially Disparate Effect
As noted, we expect pretrial incarceration to especially affect defendants of colour. To test this
hypothesis, we interact race with the instrument ( judge punitiveness) and PI. The model includes
all covariates and fixed effects. As shown in Fig. 2, pretrial incarceration reduces turnout by 8
percentage points for Black and Hispanic defendants. These effects are strong and precise. By
contrast, the effect for non-Hispanic White defendants is nearly zero and statistically indistin-
guishable from null.
27
In the remainder of the paper, we focus on explaining the effect on
Black and Hispanic defendants.
28
PI is not only more likely to affect defendants of colour, it is more likely to affect poor defen-
dants of colour. To measure poverty, we divide defendants into three groups according to zip
code income: below the sample median per calendar year (between 28 and 32 thousand dollars
Figure 2. The Demobilizing Effect of PI by Defendant Race. 2SLS marginal effects and 95 per cent CI from a specification
including defendant- and case-level covariates, fixed effects, and interactions between PI and race.
less likely to have a drug-related offence on file and to have been accused of a property offence, and more likely to have a
violent charge.
26
We caution that excluding observations based on post-treatment outcomes (detention length and sentence) may intro-
duce bias. Nevertheless, our main finding is largely unchanged.
27
Appendix Table A17 shows the same pattern of racially disparate effects with ATE.
28
We cannot distinguish the effect on White defendants from the effect on Black and Hispanic defendants due to the very
large confidence interval for White defendants. The difference in the effect of PI is indistinguishable from zero for Black vs.
White (p-value: 0.18) and Hispanic vs. White defendants ( p-value: 0.25).
14 Ted Enamorado et al.
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depending on the year), at or above the median, and unobserved income (see Appendix B.4 for
more details). We interact these income groups with race to create race-class indicators and inter-
act these indicators with punitiveness (in the first stage) and with predicted PI (second stage).
As Fig. 3 shows, poverty indeed matters, for Black defendants. The effect is large and statis-
tically different from zero only for Black defendants below median zip code income. By contrast,
the effect for White defendants below the median is 0 (with a wide confidence interval). Likewise,
the effect for Black defendants above the median is small and highly imprecise.
29
In sum, the pat-
tern is consistent with a race-class disparity. Black defendants living in poor circumstances are the
population most clearly affected.
30
Judge race
So far, we have shown that the shadow carceral state has a racially disparate effect on turnout, and
that this process disempowers poor Black defendants. What is it about the shadow carceral state
that leads to this racial disparity? It is impossible to randomize people to be ‘treated’by shadow
institutions versus non-shadow institutions. But we can exploit variation in judges within this
shadow institution. As we noted, theories of descriptive representation suggest Black judges
may be less likely than White judges to impose harsh measures on Black defendants. Perhaps,
then, the dearth of Black judges explains the racial disparity. On the other hand, there are reasons
to expect that judging race does not matter. The institution’s pressures and expectations may
prevent even Black judges from making fair decisions.
Figure 3. The Demobilizing Effect of PI by Zip Code Income and Defendant Race. 2SLS marginal effects and 95 per cent CI
from a specification including defendant- and case-level covariates, fixed effects, and interactions between PI, race, and zip
code income.
29
We cannot statistically distinguish between any effects in the figure because all but one of them is highly imprecise, but
the only precise large effect is for poor Black defendants.
30
We considered another explanation for racial disparities. In White’s(2019) study, racial disparities are partly accounted
for by Black defendants’higher vote propensity. In our study, White prior voters are not much affected, however. The nega-
tive effect of PI is located almost entirely among Black and Hispanic prior voters (see Panel B of Appendix Table A9). Thus,
the hypothesis that racial disparities in the PI effect are due to racial differences in prior voting is not supported, as White
defendants are mostly unaffected even when they are prior voters.
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Miami-Dade offers sufficient numbers of judges for analysis: 12 Black, 60 Hispanic, and 84
White judges.
31
Each group has sufficient cases for analysis: 3, 604 cases assigned to a Black
judge, 15, 956 cases to a Hispanic judge, and 25, 547 cases to a White judge. In addition, we
find that judges are seeing the same types of cases and defendants. For example, case- and
defendant-level covariates are balanced across judge race and the judge-defendant’s race.
32
We now investigate whether PI decisions vary by judge race. First, we find that the raw PI rates
do not vary by judge race, consistent with a null effect of judge race.
33
Second, we find that puni-
tive variance is the same –the distribution of residualized judge punitiveness is almost identical
across the combinations of defendant and judge race (Appendix, Figure A3). Thus, we find that
Black and Hispanic judges are as punitive as White judges, both with co-racial defendants and
with any defendant.
To test the moderating effect of judge race on turnout, we created a binary variable that takes
the value of one if the defendant’s race matches the race of the judge and zero otherwise.
34
To
keep the 2SLS model identified, we interact this race match indicator with the instrument (the
judge’s punitiveness) and with PI. The results are in Fig. 4. PI reduces turnout for Black defen-
dants regardless of judge race. Black judges do not mute the PI effect for Black defendants.
Hispanic defendants likewise do not benefit from Hispanic judges.
35
In addition, as shown in
the appendix, judge race does not affect turnout even as a standalone predictor (not interacted),
either for all defendants, Black defendants, or Hispanic defendants (Appendix Table A12).
No matter how we estimate the effect, judge race does not matter. The effect of PI is not due to
White judges acting out of racial animus, and it is not alleviated by minority judges. In line with
Figure 4. The Demobilizing Effect of PI by Judge and Defendant Race. 2SLS marginal effects and 95 per cent CI from a
specification including defendant- and case-level covariates, fixed effects, and interactions between PI, defendant race,
and judge race.
31
We obtained this data from Arnold et al. (2018) for judges up to 2014 and hand-coded the remaining judges using simi-
lar methods (see Appendix B.4.4).
32
See Appendix Table A11.
33
The PI rate is 0.22, 0.23, and 0.24 for Black, Hispanic, and White judges respectively.
34
We use this race match indicator due to the smaller number of Black judges. Appendix Table A13 presents similar results
using interactions between the defendant and judge race.
35
The larger and more precise effect of Hispanic judges on Hispanic defendants is not because Hispanic judges are more
punitive; Appendix Figure A3 already ruled that out. Possibly, the demobilizing process itself is more precise for Hispanic
defendants facing Hispanic judges.
16 Ted Enamorado et al.
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much of the existing literature on judge race, our evidence points away from the racial identity of
the judge and toward systemic explanations.
Judge Experience
What, then, explains the racial disparity? As noted, we hypothesize that features of the punitive
institution may explain it. Rushed bail hearings lack the time necessary for a full consideration of
the facts. This setting may foster an over-reliance on widespread associations between stereotyp-
ical defendants and threats. Racial bias may be further exacerbated by the institutional role these
judges must inhabit. Judges are incentivized to avoid scandalous crimes committed by defendants
released pretrial. These judges are accountable to crime-sensitive electorates. Together with the
prevalence of racial stereotypes about poverty and crime, this role, and the time-pressured deci-
sion situation, may lead to biased cognitive processing and produce a racial disparity.
Crucially, this process would especially affect judges with little experience of bail decisions. As
we theorized, these judges would be most vulnerable to the distortions of the situation. System 1
processing would produce biased decisions, especially for judges unused to overriding stereotypes
and unpracticed in reaching accurate judgments under time pressure. As judges gain experience
on the job, they would become less overwhelmed by the cognitive load and thus less likely to rely
on stereotypes.
To measure judge experience, we take the difference (in years) between the bail hearing and
the first time that judge appears in the court records. Judges above the median of 12 years are
coded as experienced. We then verify that covariates are balanced across judge experience.
36
Next, we find that the raw PI rates of inexperienced judges vary substantially by defendant
race: 0.21 for White and 0.29 for Black defendants. As expected, experienced judges’rates do
not vary by defendant race, which is consistent with our hypothesis.
37
Figure 5. Judge Experience and Racial Disparities. The difference in the marginal effects and 95 per cent CI, from a 2SLS
specification including defendant- and case-level covariates, fixed effects, and interactions between PI and judge
experience.
36
Appendix Table A14 shows no large and significant difference in defendant and case characteristics by judge experience,
for Black or all defendants.
37
The raw PI rate for experienced judges is approximately 0.20 across groups. In addition, on average, instrumented puni-
tiveness does not vary across defendant race, for either experienced or inexperienced judges (0.21 and 0.27, respectively),
lending credibility to the quasi-random assignment of a judge to the defendant as captured by the instrument.
British Journal of Political Science 17
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To test the hypothesis about judge experience, we follow Arnold et al. (2018). As demonstrated
in that study, if there is a racial difference in the outcome for defendants at the margin of PI, that
difference is the result of racial bias. In other words, it means that judges are providing Black and
Hispanic defendants at the margin of PI with a punishment they withhold from White defen-
dants at the margin of PI. If the judges are unbiased, no such differences should exist. The
2SLS estimation strategy allows us to test this mechanism because it identifies the impact of PI
on turnout for defendants at the margin of PI.
Figure 5 presents the results. Inexperienced judges indeed produce disparities in turnout
between White and Black or Hispanic defendants at the margin of PI.
38
By contrast, experienced
judges do not.
39
In sum, experience explains the racial disparity.
This result is consistent with our theory that institutional features of the shadow carceral state
give rise to racially biased cognitive shortcuts. In such a system, where street-level bureaucrats face
intense time pressure and are incentivized to prevent re-offences, prevalent heuristics about mar-
ginalized subgroups may come into play, regardless of judge race. Also consistent with our theory,
as judges are exposed to more accurate information about who actually violates the terms of their
release, and gain practice in applying it, they are better able to override these heuristics and reduce
racial disparities.
Conclusion
A Growing body of work documents the importance of the carceral state in American politics.
Little noticed, however, is the importance of the ‘shadow’carceral state. This article underscores
the significance of this shadow carceral state by documenting the effects of one of its key institu-
tions: pretrial incarceration. Pretrial incarceration is widespread. In fact, it accounts for many of
the people jailed in the United States, the world leader in incarceration. Yet studies are only
beginning to examine its impact.
To do so, we merged the population of defendants in a large, diverse county with voter
records. We leveraged the quasi-random assignment to harsher bail judges to estimate the causal
effect of pretrial incarceration on turnout for those on the margin of pretrial release. We found
that pretrial incarceration reduces voting by Black and Hispanic defendants, especially by poor
Black defendants. The effect passes a large set of robustness checks and placebo tests and
holds only among defendants who had voted before, meaning we are not simply detecting the
spurious impact of individual characteristics that predict voting. Moreover, this effect occurs
regardless of the race of the judge, and holds only among judges with less experience, those
most prone to inaccurate decisions resting on stereotypes. These findings are consistent with
the distinguishing features of the shadow carceral state: weaker procedures for due process and
equal protection.
How well do these findings generalize to other places? There is reason to expect these effects in
large metropolitan areas, places with substantial numbers of people in poverty of whom a dispro-
portionate percentage are Black or Hispanic, who are targeted for punitive interactions with the
government (Hood and Schneider 2019). The findings may generalize to other places where
many poor people of colour reside.
How well do these effects on voting generalize to other forms of political action? Studies are
increasingly documenting the positive relationship between carceral contact and non-voting
forms of participation in politics (Garcia-Rios et al. 2023; Owens and Walker 2018; Walker
2020; Weaver, Prowse, and Piston 2020). This poses a puzzle for future work to address. One pos-
sibility is that voting is different, perhaps because it is much more susceptible to a shock to con-
crete antecedents. Incarceration substantially reduces employment, income, and housing stability.
38
Appendix Table A15 presents the results from the 2SLS regressions used to construct Fig. 5.
39
As shown, Hispanic and Black defendants at the margin of PI are indistinguishable.
18 Ted Enamorado et al.
https://doi.org/10.1017/S0007123424000358 Published online by Cambridge University Press

These may be resources that particularly interfere with voting and may not interfere much with
protesting, contacting a representative, wearing a campaign button, and other actions that do not
require bureaucratic navigation and housing stability.
The study also has implications for policy reforms. Our results suggest that a rushed hearing
with little opportunity for a defence and little accountability is part of the problem. In addition,
leaving discretion in the hands of poorly trained judicial actors may be problematic. For example,
programmes aiming to reduce PI often fail when they allow discretion by prosecutors or judges,
while programmes without discretion succeed. As Albright (2021) writes in a study of a successful
Kentucky release program, ‘the…program…is distinctive in its avoidance of judicial discretion.
Bail reform, like many policy reforms, is often at the mercy of the discretion of criminal justice
actors, meaning effects are often weaker than expected’(5). This conclusion echoes studies of
street-level bureaucrats, which emphasize that discretion, lack of accountability, and workload
can explain racial and class disparities. To be sure, simply eliminating discretion may not be
effective. The key may be training and accountability.
This study carries troubling implications for the American criminal justice system. The injus-
tices of the shadow carceral system are perhaps even more insidious than those of the formal sys-
tem. While these practices appear to many who are caught up in the system as violations of basic
rights, they have not been so declared by official authorities.
The justice system is not only a backbone of law and order in society, but it also has down-
stream consequences for democracy, and, in particular, the ability of groups living at a structural
disadvantage to exercise equal voice (Lerman and Weaver 2014; Soss and Weaver 2017). Pretrial
incarceration is part of a powerful system that pervades the lives of marginalized groups. This
system makes it more difficult for these groups to participate in politics and obtain fair
representation.
We focused on pretrial incarceration, but the shadow carceral state includes other institutions too.
Those include legal financial obligations from fees, debt owed to private actors, and many others
(Beckett and Murakawa 2012; Harris 2024; Meredith and Morse 2017; Page, Piehowski, and Soss
2019). The shadow carceral state has been proliferating even as felony incarceration is decreasing.
The full reach of these institutions into the political lives of Americans requires further study.
Supplementary material. The supplementary material for this article can be found at https://doi.org/10.1017/
S0007123424000358.
Data availability statement. Replication data for this article can be found in Harvard Dataverse (Enamorado, McDonough,
and Mendelberg 2024)at:https://doi.org/10.7910/DVN/ECV72Y.
Acknowledgements. We would like to extend our gratitude to the team at Cambridge and the BJPS editors, especially Jayne
Daldry and René Lindstädt for their efforts in guiding this paper through the editorial process. The authors appreciate
Nicholas Short and the audiences at the Junior Faculty Workshop, the Center for Race, Ethnicity, & Equity (CRE2) at
Washington University in St. Louis, and the Statistical Science Department at Duke University for their valuable feedback.
Ted Enamorado acknowledges the generous support from the National Science Foundation and CRE2 at Washington
University in St. Louis. Tali Mendelberg thanks the Radcliffe Institute for Advanced Study at Harvard University and the
John Simon Guggenheim Memorial Foundation for fellowships, and research funding from Princeton University’s Center
for the Study of Democratic Politics, the Data-Driven Social Science Initiative, and the University Center for Human Values.
Financial support. Enamorado’s work was funded by the National Science Foundation (grant number SES-2417609).
Mendelberg’s work was supported by Princeton University’s Center for the Study of Democratic Politics, the Data-Driven
Social Science Initiative, and the University Center for Human Values.
Competing interests. None.
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Cite this article: Enamorado T, McDonough A, Mendelberg T (2024). The Shadow Carceral State and Racial Inequality in
Turnout. British Journal of Political Science 1–22. https://doi.org/10.1017/S0007123424000358
22 Ted Enamorado et al.
https://doi.org/10.1017/S0007123424000358 Published online by Cambridge University Press