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Sentenced to Pretrial Detention: A Study of Bail Decisions and Outcomes

Abstract

Previous research on bail practices has shown that both legal factors, such as offense severity and prior criminal record, and demographic factors such as race and age, exert a strong influence on bail decisions and outcomes. Using a novel application of Knowledge Discovery statistical methods, Bayesian probability analytics, this study utilized a sample of (n = 975) cases collected by New Jersey’s Criminal Disposition Commission, followed from arrest through disposition, to examine bail decisions made by judges and subsequent bail outcomes, i.e., whether defendant was able to meet financial bail requirements to secure release from jail. We found the following: Black and Hispanic defendants are more likely than their white counterparts to have to pay a financial bail requirement; modest differences between races with regards to bail amount set by the court; and that minority defendants, and especially Hispanic defendants, are at a disadvantage when it comes to being able to post bail and they are therefore much more likely than their white counterparts to be held in pretrial detention.

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Notes

  1. Release on recognizance is a non-financial bail condition under which a defendant pledges his/her word to return to the court for all future appearances.

  2. The size of our sample is not a problem for Bayesian statistics. In fact, it is more than a suitable size to overcome the possibility of statistical artifacts in the data; however, one concern would be the distribution over the state. Since our pool is the entire state one could argue that this study is a pilot that gives a baseline to use for comparison for future studies. Ideally there would be a greater number of observations from each jurisdiction under study. In other words, our sample is mathematically adequate (it does not put us in “math jail”); however, since it represents each and every county and there is great variation in each county we would expect that there would be a greater number of observations per county. In a perfect world we would have a random sample from each county that whose count, per county, proportional to the number of criminals processed in that county’s courts (relative to the whole) and is demographically proportionally representative as well.

  3. Although bail amount set is a continuous variable we recode as a categorical variable, as Bayesian analysis requires categorical variables for probability calculations.

  4. New Jersey allows criminal defendants to post property as bail or to enlist a bail bondsman to post bail for the court; however, our data does not contain any information about how defendants satisfied their bail requirements.

  5. The data provided by the CDC originally contained a six-category variable for offense type. We condensed this to a four-category variable by placing the lowest-level, non-violent and regulatory crimes into one category as there were a limited number of these cases in our sample.

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Correspondence to Meghan Sacks.

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Sacks, M., Sainato, V.A. & Ackerman, A.R. Sentenced to Pretrial Detention: A Study of Bail Decisions and Outcomes. Am J Crim Just 40, 661–681 (2015). https://doi.org/10.1007/s12103-014-9268-0

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  • DOI: https://doi.org/10.1007/s12103-014-9268-0

Keywords

  • Pretrial detention
  • Bail
  • Offense severity
  • Case processing
  • Judicial decision makin