Low-Level, but High Speed?: Assessing Pretrial Detention Effects on the Timing and Content of Misdemeanor versus Felony Guilty Pleas

Abstract

While numerous studies have examined pretrial detention and felony case outcomes, little empirical attention has been devoted to misdemeanor pretrial detention. We theorize that misdemeanants detained for a longer proportion of time will plead guilty quicker because the costs of fighting their charges in jail often outweigh the sanctions they face. Utilizing data on 165,630 felony and misdemeanor cases from Miami-Dade County, Florida, during a 4-year period (2012–2015) we assess whether the effects of pretrial detention length on the timing and content of guilty pleas differ across lower-level and upper-level courts. Survival analyses and multinomial logistic regressions indicate that misdemeanor cases overall and those involving lengthier pretrial detention are resolved faster, with most resulting in non-carceral sanctions such as credit for time served (CTS). Given that misdemeanors make-up the bulk of U.S. criminal cases, these findings reveal important insights about how pretrial detention impacts case-processing dynamics in lower courts.

Keywords:

• Pretrial detention
• guilty plea
• low-level courts
• misdemeanor courts
• criminal case-processing

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

1 Under a credit for time served (CTS) plea, the time spent in pretrial detention is credited toward a defendant’s eventual sentence. In other words, a CTS sentence “means that the person is sentenced to the time that he or she already spent in custody before the plea,” which can range from several hours to days for months (Kohler-Hausmann, 2018, p. 70).

2 We spoke with a handful of officials about pretrial detention in Miami-Dade County and observed several bail hearings. While we did not systematically collect qualitative data, this information helped contextualize our study.

3 The fact that Miami-Dade County officials more frequently sentence low-level offenders to CTS rather than probation is consistent with research on lowercourts (Kohler-Hausmann, 2018), and is not surprising given high rates of probation failure (Beckett & Herbert, 2009; Hannah-Moffat & Maurutto, 2012; Kohler-Hausmann, 2018; Pelvin, 2017; Phelps, 2013, 2017; Van Cleve, 2016).

4 We present the incident rate of pleading guilty, rather than the mean, because doing so better describes survival variables like ours (Cleves et al., 2016).

5 Because the original data did not measure ethnicity, we classified a defendant as Latinx if 75% or more of the individuals with their surname self-identified as Hispanic in the Census Hispanic Surname List (Word, Coleman, Nunziata, & Kominski, 2008) or if the person was from a Spanish-speaking nation other than Spain. We exclude other racial-ethnic groups since there are too few (<1%) in the sample to warrant separate analyses.

6 Data were missing for roughly 6% of cases during this period. Given that these defendants represent such a smallproportion of the sample, we exclude them from our analysis as doing so is unlikely to bias model estimates (Allison, 2001).

7 Because trials occur in roughly 2% of cases, focusing on guilty pleas makes our results representative of the modal form of adjudication in criminal courts. In addition, our main substantive conclusions regarding the effects of pretrial detention and crime type are very similar when trials are included in our analysis, indicating that their exclusion is not driving any of the observed patterns.

8 Since the adjusted hazard rate captures both the likelihood and timing of guilty pleas (Cleves et al., 2016), it is similar to the inverse Mills ratio typically included in sentencing models (Bushway et al., 2007). As such, we include the adjusted hazard rate, rather than the inverse Mills ratio, because the former controls for selection effects (likelihood of pleading) as well as the timing of this selection process (speed of pleading guilty). The adjusted hazard rate was calculated in Stata using the “predict, hr” command after “stcox” syntax.

9 Inclusion of selection correction parameters like the hazard rate or inverse Mills ratio often results in multicollinearity, and thus exclusion restrictions help to reduce collinearity and facilitate model identification (Bushway et al., 2007). We use the arresting agency as our exclusion restriction since we suspect that police agency differences in the quality of evidence presented to prosecutors may influence conviction rates, but is likely unrelated to sentencing outcomes.

Additional information

Notes on contributors

Nick Petersen

Nick Petersen is an Assistant Professor of Sociology and Law at the University of Miami. His research focuses on racial stratification within criminal justice institutions, criminal case processing, and historical racial violence, among other topics.