Tired Judges? An Examination of the Effect of Decision Fatigue in Bail Proceedings

Abstract

The current study begins to answer the recent call for scholars to reinvigorate the use of observational data to understand courtroom decisions. Drawing on the psychological effects of decision fatigue, the current study examines 284 bail hearing cases from two New Jersey jurisdictions to explore the role of decision fatigue on judges’ engagement, judicial deviations from prosecutors’ recommendations, and set bail amounts. The results suggest that judicial fatigue, measured as case order and session duration, limited the engagement for one judge, affected set bail amounts for both judges, and that proceeding modality may play some role in fatigue and engagement. Findings also suggest that observational data can work in tandem with administrative data to give better insight into the court process and decisions. Limitations and future research are discussed.

Keywords

decision fatigue judicial decision-making judicial engagement bail decisions

Introduction

Psychological literatures suggest that repeated acts of decision-making contribute to depleted quantities of available mental energy, or decision fatigue, which then affects subsequent decisions (Baumeister et al., 1998; Baumeister & Tierney, 2012; Muraven et al., 1998). For many outside of the psychology discipline, however, the phenomenon itself and its effects on decision-making sparked interest as a result of the dramatic findings of one Israeli study by Danziger and colleagues (2011a) examining judicial parole decisions, which finds that judges become increasingly punitive when having to make multitudes of legal decisions in subsequent order, an effect consequently referred to as the “irrational hungry judge effect” (Glöckner, 2016, p. 602).¹ Following Danziger et al.’s (2011a) study and subsequent critiques, no other study has reexamined the effects of decision fatigue on judicial decisions. In addition, no research has explored how, aside from the case decisions themselves, decision fatigue influences other actions and behaviors displayed by judges during case proceedings and before making case decisions.

Within the court literature specifically, researchers have yet to cultivate a more nuanced understanding of the decisions stemming from judicial processes and, perhaps even more so, the court process itself (Jones, 2013). Ethnographic and observational studies have explored the decision-making process within courts from a theory-building approach (see Clair, 2020; Feeley, 1979; Kohler-Hausmann, 2018; Van Cleve, 2016). However, influential theories (e.g., focal concerns) are often loosely and/or inaccurately tested by empirical studies that oversimplify the propositions set forth by those theories (Albonetti, 1991; Steffensmeier et al., 1998; Ulmer, 2019). For instance, most empirical research examining court decisions primarily utilizes large administrative datasets and adopts similar methodologies (Baumer, 2013; Ulmer, 2019). Specifically, and while using regression analysis, these studies typically employ factors readily available in the data—for example, legal (e.g., types of charges and the total number of charges) and nonlegal (e.g., age, race/ethnicity, and gender of defendants) relevant factors—to predict their effects on case decisions (e.g., bail amount & sentence length) and generally yield mixed results (Albonetti, 1991; Baumer, 2013; Steffensmeier et al., 1998; Ulmer, 2012).

Such inquiries, although informative in the way of identifying single or groups of factors that influence case decisions, largely ignore and “dehumaniz[e] complex social systems” by devoting minimal attention to surrounding factors, such as courtroom occurrences preceding case decisions, decision-makers, time of hearing throughout a judge’s caseload, mode of proceedings (in-person and video conference), and their individual and effects on case decisions (Lynch, 2019, p. 1165; Ulmer, 2019). As described by Ulmer (2019), courts may be viewed and examined through an “inhabited” institutional lens, or as organizations in which case decisions are products of a complicated interplay between court actors with agency, and who hold specialized roles and wield unequal amounts of power. And as a result, the decisions of individual court actors and those reached by the collective group are susceptible to forces beyond what is routinely available in administrative datasets. As a result of these data limitations, we argue that court decision-making research has been limited to superficial inquiries that obstruct the advancement of knowledge. Moreover, due to the secondary and aggregated nature of the data employed by most studies, research fails to provide the contextual details necessary to, at a minimum, allow for inferences regarding the potential causes of the disparities in decisions identified across studies. The exploration of these often-overlooked contextual factors may not only provide insight as to the causes of existing disparities identified by studies but may also lead the way to other unexplored avenues of research and to the collection of data that more accurately capture and reflect the complex inhabited nature of criminal courts. All in all, current practices help identify what factors, of those available, guide decision-making, but not how or why they matter.

Using observational data collected prebail reform in two bail hearing courts in the State of New Jersey and building on the Danziger et al. (2011a) study, the current study examines the importance of case order and session durations (a proxy for judicial fatigue) on judges’ displayed levels of engagement with defendants throughout proceedings, judges’ decision to deviate from prosecutors’ recommended bail amount, and set bail amounts, through the psychological lens of the decision fatigue phenomenon (Baumeister et al., 1998; Baumeister & Tierney, 2012; Muraven et al., 1998).² This current research contributes to numerous scholarships in unique and important ways, while answering court scholars’ recent calls for research to depart from the use of more conventional methods and theoretical frameworks (Lynch, 2019; Spohn, 2000; Ulmer, 2019; Zatz, 2000). First, the current article examines a phenomenon largely understudied in the context of court decision-making (decision fatigue), which may help further our understanding of case decisions beyond the likes of the focal concerns. Second, the current study uses a unique data source of court observations, allowing for an examination of the process (through judges’ engagement with defendants), to better understand how these factors may play a role in the decision-making process.

This article first turns to the discussion of decision fatigue, its general theoretical importance in decision-making, and, while drawing primarily on Danziger et al.’s (2011a) study, how it may similarly impact judges during bail hearing proceedings. Analyses draw on bail hearing cases from two jurisdictions examining the effect of decision fatigue on judges’ engagement, judicial deviations from prosecutor recommendations, and set bail amounts, while controlling for other theoretically relevant factors. Due to modal differences across the two jurisdictions (video-conferencing and in-person proceedings), the current study does not compare findings across sites but rather independently presents and discusses the findings within the context of their respective settings. And although the current study differs from Danziger et al. (2011a) in several ways (e.g., context and outcomes of interest), we compare patterns identified in our work to those of Danziger et al. (2011a) to help promote and advance a discussion surrounding decision fatigues’ role in the court context. The article closes with a call for future research to replicate the current findings, with a larger and more diverse sample that might be able to incorporate both observational and administrative data.

Decision Fatigue

Decision fatigue stems from a more general phenomenon referred to in the psychology literature as “ego depletion” (Baumeister et al., 1998). Ego depletion suggests that humans have limited quantities of internal resources (i.e., mental energy) required to self-regulate behavior and, for example, make decisions at any given time. Once internal resources are scarce, this depleted state of resources is referred to as ego depletion (Baumeister et al., 1998). The state of ego depletion, specifically as a result of repeated decision-making, is referred to as the phenomenon of decision fatigue (Baumeister et al., 1998; Vohs et al., 2008). In other words, decision fatigue refers to the idea that repeated acts of decision-making impair one’s ability to make fully informed and rational subsequent decisions (Baumeister et al., 1998; Baumeister & Tierney, 2012; Muraven et al., 1998; Vohs et al., 2008). Decision-making requires internal resources, and once these resources are depleted as a result of previous acts of decision-making, decision-makers may suffer from and display symptoms of decision fatigue throughout subsequent acts of decision-making (Baumeister et al., 1998; Vohs et al., 2008). Research also finds that not all decisions require or exert equal quantities of internal resources. The more complex or difficult a decision, the more mental energy that is required and exerted during the decision-making process, and thus the more symptoms of decision fatigue, the decision-maker is expected to display during subsequent decision-making (Oto, 2012; Vohs et al., 2008).

There are a variety of decision fatigue symptoms, including an impaired ability to make trade-offs (i.e., difficulty assessing the costs and benefits of a potential decision), and irrational and impulsive decision-making (Hirshleifer et al., 2019; Tierney, 2011; Vohs et al., 2008). Decision fatigue may also be displayed through decision-makers’ amplified willingness to take a more passive or hands-off approach when making decisions. This hands-off response to decision fatigue has been widely examined and operationalized in research through the use of decision-makers “status-quo” (Palinkas et al., 2017; Riedel & Colao, 2014; Stewart et al., 2012; Tierney, 2011; Vohs et al., 2008). Although the status quo is largely dependent on context and thus varies across applications, it is generally defined and operationalized as the default, simplest, and most optimal decision available to the decision-maker if and when fatigued. Generally, researchers find that as decision-makers become increasingly fatigued due to repeated decision-making, they rely more heavily on the status quo as a means to simplifying the decision-making process, thus ultimately influencing their final decision (Danziger et al., 2011a; Hirshleifer et al., 2019; Levav et al., 2010; Stewart et al., 2012; Tierney, 2011; Vohs et al., 2008). In the context of judicial decision-making, fatigued judges may be more likely to, for example, rely on recommendations provided by surrounding court actors (e.g., prosecutor or defense counsel). Altogether, this decision-making shortcut aids the conservation of depleted resources of mental energy by allowing decision-makers to circumvent a more energy demanding and intensive decision-making process.

Prior Literature on Decision Fatigue and Judicial Decision-Making

Despite the evidence signaling to the effects of decision fatigue on decision-making, a few studies have examined its effects in the judicial decision-making context. One study by Danziger et al. (2011a), utilizing a sample of 1,112 parole decisions made by eight separate judges across a 10-month period in Israeli prisons, examines the effects of decision fatigue on parole decisions. Judges in the sample decided whether to grant or deny incarcerated persons’ parole release. On average, the judges made parole decisions on 23 individual cases per day (SD = 4.7, minimum = 14, maximum = 35), and each case lasted approximately 6 min (SD = 5.1). Danziger et al. (2011a) operationalized decision fatigue as a numerical indicator of case order, assigned in the chronological order, in which cases were heard within distinct decision sessions.³ In supplemental analyses, the authors also utilize the cumulative length of sessions (in minutes) as a measure of decision fatigue, followed by the inclusion of both case order and cumulative length of sessions as independent measures in the same models to examine their effects on parole decisions.

The authors hypothesized that in cases decided later in sessions, when judges are presumably fatigued, judges would be less likely to grant release, compared with cases decided earlier in judges’ sessions. In other words, as a result of decision fatigue, the authors hypothesized that in the latter parts of individual sessions, judges would be more likely to rely on decision-making shortcuts (i.e., the status quo) as a means of simplifying their decision-making process and conserving depleted quantities of internal resources. The authors posit that in the context of parole decisions, compared with the decision to grant prisoners early release from prison (i.e., granting parole), the decision to deny prisoners parole release requires less mental energy and is less risky, thus operationalizing the status quo as such.

Danziger et al. (2011a) find that decision fatigue plays a role in judges’ parole decisions. Using solely case order as a proxy for fatigue, the authors find that the likelihood of judges granting parole release declined from approximately 65% at the start of sessions to nearly 0% at the end of sessions. When the duration of sessions was used as a measure of fatigue, rather than case order, the authors find similar results—The longer the sessions, the less likely judges were to grant parole release. When examining the effects of both case order and session duration on judges’ decisions side by side, only case order remained statistically significant, suggesting that decision fatigue stems more so from the quantities of decisions rather than from the duration of sessions. Altogether, Danziger et al. (2011a) find that the order in which cases are heard, and less so the duration of decision-making sessions, plays a role in judges’ parole decisions. Overall, the study finds that when fatigued, judges simplify their decision-making process by relying more heavily on the status quo (i.e., deny parole release) to conserve their limited quantities of internal resources and evade employing a more energy-intensive decision-making process.

Subsequent critiques of the Danziger et al.’s (2011a) study provide alternative explanations for the decline in decisions favorable to defendants across sessions attributed to decision fatigue. Weinshall-Margel and Shapard (2011) argue that cases are ordered systematically based on the presence of counsel during the parole hearing and the quality of the representation. More specifically, they find that cases of prisoners who were not represented by counsel were typically heard last, and that attorneys representing numerous prisoners during the individual session may have argued their best cases first, thus resulting in the decline in favorable decisions across sessions. Alternatively, it may be that other unmeasured factors—such as prisoner behavior—influence the ordering of cases. In response to Weinshall-Margel and Shapard’s (2011) critique, subsequent work by Danziger et al. (2011b) finds that case order remained a significant and robust predictor of case outcome and that it was mathematically impossible that the representation of multiple prisoners by a single counsel influenced their original findings. Finally, Danziger et al. (2011b) argue that because prisoner behavior does not influence the ordering of cases, it cannot explain their initial findings, though they do not explicitly test such relationship.

A different critique partially attributes Danziger et al.’s (2011a) findings to the selective dropout of “favorable cases” by judges due to rational time management and “statistical artifacts” (Glöckner, 2016, p. 603). Glöckner (2016) finds that the supposed decision fatigue effect could be partially explained by the systematic ordering of cases by judges based on the complexity of the case. Furthermore, the author finds that Danzigers et al.’s (2011a) findings were also influenced by how the data were censored and autocorrelation in the time series (Glöckner, 2016). Despite finding some evidence for an alternative explanation to Danziger et al.’s (2011a) findings, Glöckner (2016) concludes that “extraneous factors such as causal effects of serial case ordering and mental depletion might have played a role” in the parole decisions made by judges (p. 608).

Current Study

Drawing from Danziger and colleagues’ (2011a) work and the raised critiques in mind, the current study examines the effect of the interaction between temporal case ordering and session duration as a proxy measure for decision fatigue on judicial engagement, the decision to deviate from bail amount recommendations provided by prosecutors and final bail amount imposed by two judges deciding on bail hearing cases.⁴ This operationalization follows along the lines of the decision fatigue literature—The number of decisions being made as well as the length of time making decisions ultimately impact the amount of fatigue expected. We assess the effects of decision fatigue and judges’ reliance on the status quo by relying on three hypotheses tested independently by judge. More clearly put, we individually examine our three hypotheses by judge, and do not, because of modal differences across the two courtrooms, make comparisons across judges. The first hypothesis pertains to judges’ engagement with defendants (process), whereas the latter two are related to case decisions (judges’ deviation from prosecutors’ recommendation and final bail amount). First, as a result of decision fatigue, judges may begin to reduce their quantities of interactions with defendants by taking a more hands-off approach during the processing of cases, which may be displayed through a decline in quantities of engagement with defendants. Therefore, we hypothesize the following:

Hypothesis 1: Judges will be less engaging with defendants in cases heard later in sessions, particularly when the session duration is longer than average.

Decision fatigue is also expected to affect case outcomes through judges’ amplified reliance on the status quo, operationalized using the bail recommendations provided by prosecutors and higher bail amounts. During bail hearing cases, prosecutors generally provide to the court bail amount recommendations based on the characteristics of cases. These recommendations provide judges with an optimal decision or avenue through which they can simplify their decision-making process when fatigued, which ultimately aids in the preservation of depleted levels of internal resources. Thus, second, utilizing prosecutors’ recommendations as the status quo, we hypothesize the following:

Hypothesis 2: Judges will be less likely to deviate from the recommendations provided by prosecutors in cases heard later in longer sessions.

Finally, judges are expected to rely more heavily on higher bail amounts that decrease the likelihood that a defendant can secure release, allowing judges to absolve themselves of any potential drawbacks associated with the release of a defendant (e.g., the defendant commits new offenses while on pretrial release), thus making this the simplest and less risky decision. The rationale behind this hypothesis is like the one provided by Danziger et al. (2011a), where it was hypothesized that judges would make the less risky decision via the denial of parole release during cases later in sessions. Therefore, our third and last hypothesis is as follows:

Hypothesis 3: Judges will set higher bail amounts in later cases heard in longer sessions.

Methods

Data Collection and the Bail Hearing Process

To test the effect of decision fatigue in judges, the study relies on a subset of bail hearing cases that were observationally collected prebail reform by seven trained fieldworkers using a paper-and-pencil checklist in two New Jersey courtrooms from November 2015 to November 2016.⁵ During these proceedings, judges ultimately decided on whether to release defendants on their own recognizance (hereafter ROR), and if ROR is not appropriate, the bail amount required for defendants to pay to secure pretrial release. The current study limits its examination to the single main presiding judges from each site who presided over most of the jurisdictions’ cases and to those cases whose final case decision involved a monetary bail amount.⁶ ROR cases were also excluded. Not only were there few of these cases in our sample, but research also finds that bail decisions are “qualitatively different” (Goldkamp, 1979, p. 242) from ROR decisions, and are thus influenced by different factors (e.g., Albonetti, 1989; Nagel, 1983). For example, ROR decisions are primarily driven by legal factors, whereas bail decisions allow for judges to more heavily weigh extralegal factors in their decisions to set higher or lower bail amounts. In total, the analytic sample is composed of 284 cases—157 decided by Judge Karen across 18 sessions in one jurisdiction and 127 by Judge Judy across 16 sessions in the other.⁷ Both judges in our sample were White, female, and in their 50s.

Bail hearing proceedings in both jurisdictions took place daily (Monday through Friday) and involved similar groups of actors: judges, prosecutors, and defense counsel (public and private). In cases processed by Judge Judy, defendants were physically brought into the courtroom. In contrast, cases processed by Judge Karen were done so using video-conferencing technology. Specifically, defendants appeared in court virtually while detained at the local jail. The virtual meeting was broadcasted to court actors and the general public physically present in the courtroom via a large television.

In general, both courts follow a similar format when processing bail hearing cases. In both courts, prosecutors typically provide a bail amount recommendation and there are discussions among judges, prosecutors, and defense counsels as to the appropriateness of the recommended bail amount. For example, a defense counsel may oppose the bail amount recommended by the prosecutor and provide the court with relevant factors justifying their objection. However, in some instances, the judge may not allow the prosecutor to provide a recommendation. Following discussions, the judge assesses the provided arguments and makes her bail amount decision. During the entirety of these proceedings, judges have the ability and discretion to engage directly with defendants if they deem appropriate and necessary.

Dependent Variables

Engagement

An additive measure of judicial engagement is created using five individual items capturing different forms of verbal engagement displayed by judges, consistent with prior work (see Rengifo et al., 2021, for a similar measure of judicial engagement).⁸ Nonverbal forms of engagement (e.g., eye contact) were excluded from the engagement measure, as such instances are difficult to capture observationally, particularly during video-conferencing sessions. The individual forms of engagement capture whether or not the judge (1 = yes, 0 = no): (a) engaged with the defendant off of their “regular” script,⁹ (b) asked the defendant if they understood the process, (c) asked the defendant if they understood the decision, (d) explained the penalty for noncompliance with pretrial services to the defendant, and (e) showed interest in the defendant’s success.¹⁰

On average, Judge Judy displayed 2.08 counts of engagement per case (SD = 1.10), while Judge Karen displayed 0.68 counts of engagement (SD = 0.99). Although the specific form of engagement is not of main interest in this article, the drastic differences in the form in which judges in the sample engaged defendants are worth noting.¹¹ Specifically, Judge Judy displayed higher counts of engagement across all individual forms of engagement, except for engagement with defendants off of her regular script. Judge Karen engaged with defendants off of her regular script in 37% of cases, whereas Judge Judy did so in 24% of cases. See Table 1 for the descriptive statistics.

Table 1:

Descriptive Statistics

	Judge Judy (n = 127)				Judge Karen (n = 157)
Variables	M	SD	Minimum value	Maximum value	M	SD	Minimum value	Maximum value
Case order	4.72	2.86	1.00	12.00	7.95	5.98	1.00	28.00
Engagement	2.08	1.10	0.00	5.00	0.68	0.99	0.00	3.00
Deviation	0.15	0.36	0.00	1.00	0.36	0.48	0.00	1.00
Bail amount (US$)	113,488.20	239,166.30	500.00	1,500,000.00	44,466.56	64,926.28	350.00	500,000.00
Duration of individual hearing	5.52	3.21	1.00	30.00	3.64	2.50	1.00	17.00
Duration of session	47.47	17.71	7.00	83.00	46.38	21.47	10.00	78.00
Black	0.65	—	0.00	1.00	0.49	—	0.00	1.00
Hispanic	0.21	—	0.00	1.00	0.43	—	0.00	1.00
White/Other (reference)	0.15	—	0.00	1.00	0.08	—	0.00	1.00
Male	0.93	—	0.00	1.00	0.90	—	0.00	1.00
Age
16–25	0.23	—	0.00	1.00	0.28	—	0.00	1.00
26–35	0.44	—	0.00	1.00	0.45	—	0.00	1.00
36–45	0.16	—	0.00	1.00	0.16	—	0.00	1.00
≥46 (reference)	0.17	—	0.00	1.00	0.10	—	0.00	1.00
Prior record	0.46	—	0.00	1.00	0.58	—	0.00	1.00
Total number of charges	2.42	0.76	1.00	3.00	2.23	0.80	1.00	3.00
Pending cases	0.28	—	0.00	1.00	0.13	—	0.00	1.00
On supervision	15.0	—	0.00	1.00	0.29	—	0.00	1.00
Nature of crime
Against person	0.33	—	0.00	1.00	0.40	—	0.00	1.00
Drug related	0.31	—	0.00	1.00	0.21	—	0.00	1.00
Weapon related	0.11	—	0.00	1.00	0.10	—	0.00	1.00
Property/other (reference)	0.25	—	0.00	1.00	0.29	—	0.00	1.00
Public defender	0.97	—	0.00	1.00	0.94	—	0.00	1.00
Court interpreter	0.08	—	0.00	1.00	0.10	—	0.00	1.00

Deviation From the Prosecutor’s Recommendation

Deviation is a dichotomous variable (1 = Judge deviated from prosecutor’s recommendation; 0 = Judge did not deviate from prosecutor’s recommendation) capturing whether or not the judge’s final bail amount decision differed from the recommendation provided by the prosecutor. Although cases in which prosecutors did not provide a bail recommendation were excluded from this analysis, it is important to note that recommendations by prosecutors were not provided in 42 of the total 284 cases in both courtrooms combined. Specifically, recommendations were not provided in 33 cases decided by Judge Karen and in nine cases decided by Judge Judy.

The number of times in which judges relied on the prosecutor’s recommendation varied across judges. The magnitude of the amount (in U.S. dollars) of the deviations also varied across judges. Judge Judy deviated from the recommendation a total of 18 times out of 118 total cases (15%), with an absolute average difference of US$18,309.30 (SD = US$165,654.80). Judge Karen deviated from the recommendation a total of 44 times out of 122 cases (36%), with an absolute average difference of US$3,032.80 (SD = US$5,521.50). Altogether, Judge Karen deviated in more than twice as many of the observed cases as Judge Judy. However, the magnitude of the deviation by Judge Karen was much smaller than that of Judge Judy.

Bail Amount

Bail amount captures the final bail amount (continuous variable in U.S. dollars) set by the judge. The average bail amount set by Judge Judy was US$113,488.20 (SD = US$239,166.30), and US$44,466.56 (SD = US$64,926.28) by Judge Karen. Due to skewness, we use the natural log of the bail amount to fit a more normal distribution.

Independent Variables

The current study operationalizes a proxy of decision fatigue using an interaction term of the chronological order in which individual cases were heard during sessions (case order) and the duration of sessions (duration of session).

Case Order

Case order is a numeric value assigned to each case based on the order in which it was heard within the individual session.¹² For example, if a single session was comprised of a total of 10 cases, the first case heard received a value of “1,” whereas the last received a value of “10.” On average, Judge Judy decided on 4.72 cases per session (SD = 2.86), whereas Judge Karen decided on an average of 7.95 cases per session (SD = 5.98).

Duration of Session

Duration of session captures the total duration (in minutes) of individual sessions (i.e., the amount of time the judge sat on the bench during the entirety of individual sessions).¹³ On average, the sessions presided over by Judge Judy lasted 47.47 min (SD = 17.71), whereas Judge Karen’s sessions lasted 46.38 min (SD = 21.47).

Control Variables

We control for legal and nonlegal factors consistent with prior court research. These factors include defendant characteristics such as gender (1 = male, 0 = female), race/ethnicity (1 = yes, 0 = no): Black, Hispanic, and White/Other (reference category),¹⁴ and dichotomous variables capturing age groupings (16–25, 26–35, 36–45, and 46 and older as the reference group). In addition, we account for the following legally relevant case factors (1 = yes, 0 = no): prior criminal record, pending criminal cases, or if on supervision at the time of the hearing (e.g., probation, parole, pretrial release). The total number of charges is coded as a categorical variable (1, 2, 3+). The nature of the highest charge (most serious) is captured by four individual dichotomous variables (1 = yes, 0 = no): crimes involving harm to another person, drug charges, weapon charges not involving other persons, and property/other related (used as the reference category). This study also controls for the length/duration (in minutes) of each case. Finally, we control for court interpreters and public defenders (1 = yes, 0 = no).

Analytic Approach

To examine the relationship between decision fatigue and judicial engagement, deviation from prosecutor’s recommendation, and bail amount, the study estimates three types of multivariate regression models: Poisson, logistic, and ordinary least squares (OLS) regression. Poisson is used to test the relationship between case order and the dependent variable, engagement. This type of model is most appropriate to test this relationship because engagement is measured as a count variable. The binary outcome of deviation from the prosecutor’s recommendation (deviation) necessitates a logistic regression strategy to predict the odds of a judge deviating from recommendations. Finally, OLS regression is used to examine the relationship between case order and logged bail amount. To deal with the nested nature of the data within judges, all three models used robust standard errors (RSEs).¹⁵ Due to differences in mode of proceedings (in-person and video-conferencing), we test each hypothesis and present our multivariate results separately by judge (see the appendix for bivariate results).

Results

Judge Judy (In-Person Proceeding)

Model 1 in Table 2 (see below) displays the results for judicial engagement for Judge Judy (in-person). Of most salience to the current study is the effect of case order and session duration (judge fatigue) on engagement.¹⁶ Fatigue does not significantly predict counts of engagement for Judge Judy, providing no support for the first hypothesis.

Table 2:

Multivariate Results Predicting Engagement, Deviation, and Bail Amount

Variables	Study 1: Judge Judy (in-person)						Study 2: Judge Karen (video-conferencing)
	Model 1: Engagement		Model 2: Deviation		Model 3: Bail amount (logged)		Model 4: Engagement		Model 5: Deviation		Model 6: Bail amount (logged)
	IRR	RSE	OR	RSE	Coefficient	RSE	IRR	RSE	OR	RSE	Coefficient	RSE
Case order	1.03	0.06	1.55	0.51	–0.47	0.15**	1.34	0.12**	0.81	0.13	0.19	0.06**
Duration of session	1.00	0.00	1.03	0.03	–0.04	0.01**	1.02	0.01*	0.99	0.02	0.01	0.01
Case Order × Duration of Session	0.99	0.00	0.99	0.01	0.01	0.00***	0.99	0.00***	1.00	0.00	–0.00	0.00*
Duration of individual hearing	1.03	0.01*	1.06	0.14	–0.07	0.04	1.11	0.04**	0.84	0.11	0.17	0.04***
Black	0.68	0.10**	3.90	6.11	0.46	0.43	0.99	0.45	0.22	0.20	–0.31	0.39
Hispanic	1.05	0.15	2.98	5.46	0.37	0.50	0.75	0.31	0.34	0.32	–0.49	0.34
Male	0.98	0.17	—	—	0.88	0.71	0.96	0.42	1.40	1.22	0.18	0.46
Age
16–25	1.06	0.16	16.25	24.40	0.05	0.41	1.53	1.11	1.88	1.83	–0.31	0.41
26–35	0.99	0.12	5.53	6.26	–0.11	0.35	1.26	0.82	2.11	1.85	–0.46	0.37
36–45	0.85	0.13	4.57	7.06	–0.64	0.43	1.43	0.94	7.11	7.08*	–0.45	0.40
Prior record	1.00	0.09	2.61	2.48	–0.14	0.30	1.05	0.30	0.58	0.33	0.20	0.24
Total number of charges	0.99	0.06	1.88	0.95	0.59	0.21**	0.95	0.21	0.82	0.29	0.42	0.14**
Pending cases	0.76	0.08*	1.45	0.80	0.35	0.28	0.68	0.24	1.22	0.76	–0.60	0.31
On supervision	1.05	0.16	0.59	0.63	–0.11	0.49	0.95	0.27	1.02	0.56	–0.15	0.21
Nature of crime
Against person	0.86	0.11	0.40	0.35	1.17	0.47*	1.53	0.52	0.55	0.38	0.79	0.28**
Drug related	1.04	0.13	0.51	0.39	0.29	0.40	1.19	0.47	1.50	1.19	0.71	0.31*
Weapon related	1.38	0.19*	0.76	0.72	0.84	0.41*	2.00	0.80	2.10	1.87	1.23	0.42**
Public defender	1.08	0.46	0.11	0.18	–1.21	0.44**	0.37	0.11**	0.24	0.35	–0.08	0.53
Court interpreter	1.05	0.18	1.55	2.31	–0.19	0.60	0.11	0.11*	1.54	1.81	–0.49	0.41
	n = 126		n = 108		n = 126		n = 118		n = 101		n = 115

Note. IRR = incidence rate ratio; RSE = robust standard error; OR = odds ratio.

p < .05. **p < .01. ***p < .001.

Several other predictors were significantly related to the judge’s counts of engagement. For Judge Judy, cases involving Black defendants received 32% fewer counts of engagement (p = .006) relative to White defendants, while Hispanic defendants did not experience significant differences in engagement. Results further suggest that defendants who had other pending cases at the time of the hearing were associated with 24% fewer counts of engagement (p = .011), while cases involving a weapon-related charge were associated with a 38% increase in counts of engagement (p = .020). A 1-min increase in the duration of the individual case hearing was associated with a 3% increase in counts of engagement in cases involving Judge Judy (p = .014).

Models 2 and 3 (Table 2) explore the relationships between fatigue and the odds a judge deviates from the prosecutor’s recommendation and the logged final bail amount for Judge Judy. Fatigue did not significantly affect Judge Judy’s odds of deviating from the prosecutor’s recommendations; however, it did have a significant influence on the bail amount. For Judge Judy, the main effects of case order and session duration are associated with significantly lower bail amounts (coefficient = –0.47, p = .002 and coefficient = –0.04, p = .004, respectively), while the interaction effect significantly, yet marginally, increases bail amounts (coefficient = 0.01, p < .001).

Consistent with prior court decision-making literature, bail amount decisions were largely based on the legally relevant factors, including total number and nature (harm to person and weapon-related offenses) of charges. For every additional charge, the logged bail amount increases by 80% (p = .006). For crimes committed against persons and weapon-related, logged bail amount increased by 222% (p = .015) and 131% (p = .044), respectively. In addition, defendants who were represented by a public defender received lower bail amounts—The use of a public defender decreases logged bail amount by 70% (p = .007).

In summary, the results for Judge Judy do not find support for the first two hypotheses. Decision fatigue did not influence the amount of engagement or the decisions to deviate from the prosecutors’ recommendations of the bail amount. We do find support for the third hypothesis—As predicted, case order and session duration increased the final bail amount imposed by Judge Judy, therefore suggesting that fatigue impacted her decision-making through increased reliance on the less risky decision (i.e., higher bail amounts).

Judge Karen (Video-Conference Proceeding)

Model 4 in Table 2 provides the results for engagement for Judge Karen (video-conferencing). The main effects of case order and session duration on counts of engagement suggest that both are significantly associated with higher counts of engagement. However, the interaction term of case order and session duration finds that cases that are heard later in sessions, when the duration of the session is longer, had a significantly diminishing effect on the influence of case order and session duration on counts of engagement (p < .001). This finding suggests that Judge Karen suffered from decision fatigue, with its effects displayed through the impact on counts of engagement with defendants across cases within sessions.

For Judge Karen, defendant race/ethnicity did not significantly predict counts of engagement. However, cases in which defendants were represented by a public defender and relied on the services of a court interpreter were associated with significantly lower counts of engagement. Specifically, cases involving a public defender and court interpreter were, respectively, associated with 63% (p = .001) and 89% (p = .034) fewer counts of engagement. As expected, the duration of the individual hearings significantly increased the counts of engagement for Judge Karen; a 1-min increase in hearing duration was associated with an 11% increase in counts of engagement (p = .005).

Models 5 and 6 in Table 2 present the results related to Judge Karen’s willingness to deviate from prosecutors’ recommendations and the set logged bail amounts, respectively. Only one of the predictors included in Model 5 (deviation) was significant at the conventional level—Defendants between the ages of 36 and 45 had a significantly higher likelihood of deviation from prosecutors’ recommendations, relative to older defendants (46+ years old; p = .049).

Judge fatigue significantly impacted bail amount decisions for Judge Karen. In the main effects, only case order was significantly associated with increased bail amounts. However, counter to our proposed hypotheses, cases heard later in the session, during longer sessions, were associated with a significant, though marginal, decrease in logged bail amount (p = .019). Furthermore, an increase in the total number of charges was associated with a 52% increase in bail amount (p = .004). Cases involving charges related to harm to other persons, those that involve weapons, and those involving drugs were associated with significantly higher bail amounts relative to property/other crime cases, 120% (p = .007), 242% (p = .004), and a 103% (p = .025), respectively. A 1-min increase in a hearing’s duration was also associated with a 19% increase in bail amount (p < .001). Finally, having other pending cases was associated with a 45% decrease in bail amount (p = .060), though this finding failed to reach the p < .05 threshold.

In summary, consistent with our hypothesis, Judge Karen’s counts of engagement significantly declined throughout individual sessions and during longer sessions, suggesting that engagement with defendants was limited when suffering from decision fatigue to conserve depleted levels of internal resources. However, and counter to our hypotheses, decision fatigue had an inverse effect on bail amounts (decreased) set by Judge Karen and no effect on deviations.

Discussion

While building on the Danziger et al. (2011a) study, the current study finds that our proxy of decision fatigue has an impact on some of the measured outcomes. Danziger et al. (2011a) find that case order is ultimately more important than session duration—The inclusion of both measures in the models yields a nonsignificant finding for session duration while case order remains significant.¹⁷ However, the current study finds that case order and duration of session are better measured in tandem (as an interaction) rather than included independently in the models. Combined, both studies find that decision fatigue leads to less favorable decisions for defendants in some circumstances. For Judge Karen, the findings are mixed. While Danziger et al.’s (2011a) study finds that higher levels of fatigue led to a lower likelihood of defendants being granted parole release, the current study finds that fatigue is associated with lower levels of engagement while also being a significant predictor of lower bail amounts set by Judge Karen. For Judge Judy, higher decision fatigue does not appear to have any significant impact on levels of engagement—though, comparable to Danziger et al. (2011a), less favorable outcomes for defendants of higher bail amounts are imposed when judges were fatigued. We speculate that the contrasting effects of decision fatigue on bail amount are likely due to the mode of hearing and judicial differences.

The current study further offers a unique behind-the-door view of courtroom inner workings within two jurisdictions. This offers a starting point to, as Zatz (2000, p. 529) suggests, “better develop and assess theoretical paradigms capable of reflecting the complexities of people’s lives and the multiple factors that influence criminal justice decision-making” through the use of court ethnographies, a sentiment echoed by Spohn (2000). Based on the results of the current study, it appears that some of the factors related to “inhabited institutions,” as Lynch (2019) and Ulmer (2019) have suggested, work in conjunction with some of the factors already well established to predict pretrial detention decisions.

One such measure that is not traditionally available for examination is judicial engagement. The results suggest several factors influence judicial engagement and that they differ between judges. While the results do not provide a clear distinction as to whether the effect of decision fatigue on levels of engagement, the decision to deviate, or bail amount is due to differences in judges or mode of proceeding, they do provide evidence of the importance of understanding how court proceedings are being conducted, a factor that is relatively ignored by most courtroom studies, and one of growing importance during the current COVID-19 pandemic. In the wake of the pandemic, courts across the country shifted to “virtual environments” (Baldwin et al., 2020, p. 746), with relatively little knowledge about the potential effects this may have on judicial decisions. The results here suggest that lower average levels of engagement, and its decline throughout individual decision-making sessions, occurred within the processing of cases via video-conferencing. Prior research in the area of video-conferencing and courts finds that the use of this technology during judicial hearings plays some role in case decisions (Diamond et al., 2010; Eagly, 2015), thus signaling the importance of examining the context of video-conferencing in the courtroom. While the current study does not provide a representative sample to adequately test these differences, and while we can only speculate to what the findings suggest, it provides some evidence to the fact that differences may exist and court actors should take notice of the potential impact video-conferencing may have on their engagement with defendants.

The second measure that is more salient to the theoretical progression of the courtroom decision-making literature beyond administrative data is judicial fatigue. The fact that decision fatigue can lead to an impaired ability to make informed or adequate trade-off decisions, irrational and impulsive decisions, should raise concern regarding its relative neglect in courtroom studies and questions of equity. When fatigue plays a role in judicial engagement and decisions, defendants toward the latter parts of the sessions are likely to face less favorable outcomes and be treated more harshly. Future work might assess how alternative scheduling of sessions may affect the levels of judicial fatigue during the workday. Furthermore, research has shown that while holding charge type constant, early case decisions such as pretrial detention and bail amounts substantially affect future decisions (Ulmer, 2012). Specifically, higher bail amounts and longer stints of pretrial detention increase the odds that a defendant will be sentenced to prison, and thus should be at the forefront of questions of equal justice (Campbell et al., 2020).

Although the purpose of this study was not to make cross-site comparisons due to differences in mode of proceedings, we provide additional insight and details of both jurisdictions which may better contextualize and explain our findings, particularly Judge Karen’s decline in engagement, in hopes of informing future research in this area of study. For example, compared to Judge Judy, Judge Karen on average decided on three more cases per session, made more arguably complex, “higher stake,” and energy exerting decisions related to actual case outcomes—She did not allow for prosecutors to provide recommendations in 21% of cases (vs. 7% in cases decided by Judge Judy), and when Judge Karen did allow for a recommendation to be provided, she deviated from the recommendation in 36% of cases (vs. 15% by Judge Judy). Consistent with prior literature, higher quantities of decisions and more complex decisions ultimately lead to higher levels of decision fatigue (Oto, 2012). In other words, we speculate that Judge Karen, compared with Judge Judy, more likely assessed the facts of cases and reached her final bail amount decision without the guidance of recommendations provided by prosecutors, and thus was more heavily affected by decision fatigue, and its effects were displayed through a decline in engagement.

Limitations and Future Research

The current study is not without limitations, but nevertheless offers several important potential avenues for future research. One such limitation is the reliance on a small sample. The results suggest that the mode of court proceedings may influence judicial engagement and judicial decisions, though the current sample does not allow for in-depth analysis across judges, jurisdictions, or modes of proceedings. The limitation of difference in video versus in-person, and jurisdiction or judge, may call into question the generalizability or validity of the current findings, but it is important to note that this is quite exploratory. Future research should employ a similar ethnographic approach with a larger, more diverse sample that might better control for some of these differences.

Another potential limitation may be how decision fatigue is operationalized and measured. The use of case order and duration session as a proxy for decision fatigue provides a rough estimate of decision fatigue. In addition, decision fatigue and the magnitude of the effect of decision fatigue on decision-makers are expected to vary across decision-makers. Decision-makers may perceive and weigh the importance and complexity of similar decisions differently. Therefore, future research should devote some attention to a more personalized measurement of decision fatigue to provide a more accurate examination of its effects. Future research should also consider, if at all possible, the quantities of decisions made by decision-makers before the observed and collected occurrences. Put differently, in the current study, we examine decisions made by two judges within single judicial decision-making sessions and are unable to consider other potentially important and energy exerting decisions that may have been made by them before taking the bench. Combined, these two factors may help better quantify decision fatigue to more accurately track the debilitating effects of the phenomenon throughout decision-making sessions.

The small sample size limited the ability to explore defendant variation in race or ethnicity. Despite this limitation, the fact that Black defendants received significantly lower levels of engagement than their White or Hispanic counterparts during in-person proceedings is a finding that raises some serious questions regarding judge behavior toward Black defendants that is not typically measured in the traditional administrative data or final judicial decisions. The type of observational data may help to better highlight some of these racialized issues in the criminal justice system. Future observational research with a larger, more diverse sample, with the ability to merge observational and administrative data, might be better able to grapple with the single finding regarding race across models.

Despite these limitations, the current study offers some important implications for the study of courtroom decision-making and processes. As proposed by Ulmer (2019), the results suggest that scholars should take into consideration the environment of courtrooms rather than focusing exclusively on administrative data and case outcomes. The results also show that data derived from court observations can be combined with factors related to the focal concerns perspective to better understand case decisions. In addition, it could be possible to speculate from the findings that court environments, along with declines in judicial engagement and higher levels of fatigue, may help foster decisions based on the “status quo,” or prosecutor recommendations, to avoid difficult and energy-consuming decisions. Future research will benefit from taking a similar methodological approach.

Footnotes

Appendix

Bivariate Results Predicting Engagement, Deviation, and Bail Amount

Variables	Study 1: Judge Judy (in-person)						Study 2: Judge Karen (video-conferencing)
	Model 1: Engagement		Model 2: Deviation		Model 3: Bail amount (logged)		Model 4: Engagement		Model 5: Deviation		Model 6: Bail amount (logged)
	IRR	RSE	OR	RSE	Coefficient	RSE	IRR	RSE	OR	RSE	Coefficient	RSE
Case order	1.02	0.02	0.92	0.09	–0.03	0.05	0.96	0.02**	0.98	0.03	0.06	0.02***
Duration of session	1.00	0.00	1.00	0.01	–0.00	0.01	0.99	0.00	0.99	0.01	0.01	0.01
Duration of individual hearing	1.04	0.01***	1.01	0.06	–0.00	0.03	1.07	0.03*	0.96	0.08	0.21	0.04***
Black	0.66	0.06***	2.09	1.27	0.44	0.30	1.15	0.27	0.97	0.37	0.30	0.22
Hispanic	1.33	0.12***	0.70	0.48	0.01	0.34	0.89	0.21	0.97	0.38	–0.36	0.23
White/Other (reference)	1.33	0.17*	0.31	0.33	–0.80	0.38*	0.72	0.28	0.88	0.65	–0.02	0.44
Male	0.98	0.21	1.00	—	1.34	0.60*	0.99	0.35	1.57	1.11	0.73	0.40
Age
16–25	0.98	0.12	1.86	1.04	0.66	0.35	0.93	0.25	1.12	0.46	0.33	0.26
26–35	0.95	0.09	1.30	0.67	–0.10	0.30	1.29	0.28	0.72	0.28	–0.22	0.23
36–45	0.97	0.12	0.65	0.52	–0.42	0.35	0.92	0.28	2.39	1.25	–0.25	0.25
>46 (reference)	1.14	0.15	0.25	0.26	–0.25	–0.25	0.76	0.34	0.39	0.32	0.27	0.39
Prior record	0.92	0.09	1.17	0.60	–0.40	0.29	0.83	0.19	0.72	0.28	0.44	0.23
Total number of charges	0.97	0.06	2.08	0.92	0.66	0.19***	0.96	0.12	0.87	0.22	0.67	0.15***
Pending cases	0.81	0.10	1.72	0.92	0.19	0.31	0.78	0.26	1.29	0.69	–0.32	0.31
On supervision	0.96	0.14	0.61	0.49	–0.31	0.48	0.74	0.19	0.79	0.32	0.11	0.24
Nature of crime
Against person	0.93	0.09	0.93	0.51	1.21	0.31***	1.02	0.25	0.48	0.20	0.42	0.23
Drug related	0.91	0.09	0.86	0.49	–0.39	0.27	1.13	0.33	1.47	0.66	0.03	0.24
Weapon related	1.23	0.13*	2.02	1.47	0.42	0.31	1.58	0.46	1.51	0.90	0.86	0.30**
Property/other (reference)	1.07	0.12	0.86	0.53	–1.20	0.32***	0.68	0.19	1.30	0.56	–0.90	0.25***
Public defender	1.68	0.88	0.53	0.63	–1.39	0.60*	0.44	0.16*	0.71	0.56	–0.12	0.40
Court interpreter	1.50	0.19***	0.68	0.74	0.17	0.48	0.27	0.14*	1.20	0.81	–0.16	0.33

Note. IRR = incidence rate ratio; RSE = robust standard error; OR = odds ratio.

p < .05. **p < .01. ***p < .001.

Authors’ Note:

We would like to thank Andres F. Rengifo, Lee A. Slocum, Beth B. Huebner, Marisa Omori, and Kristina Thompson for their assistance in the completion of this manuscript.

ORCID iDs

Luis C. Torres

Joshua H. Williams

Notes

Luis C. Torres is a PhD candidate in the Department of Criminology and Criminal Justice at the University of Missouri–St. Louis. He earned his master’s from Rutgers University–Newark. His current research interests include pretrial detention and decision-making.

Joshua H. Williams is a visiting assistant professor in the Department of Sociology and Criminology at the University of North Carolina Wilmington. He earned his PhD from the University of Missouri–St. Louis in 2018. His current research interests include pretrial detention, bail, sentencing, and politics and punishment.

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