Criminal Justice Reform Act of 2018 (The First Step Act)

Impetus for the Law

Catherine Toney was the first woman released from prison under the First Step Act (more formally known as the Criminal Justice Reform Act of 2018). President Trump signed this act into law on December 20, 2018, and Ms. Toney was released in early 2019, after having served 16 years of a 20-year sentence for a drug offense. She had attempted and failed to receive an early release from prison multiple times prior to the enactment of the First Step Act. She was freed under the act’s retroactive crack cocaine sentence reduction provision (Creed (2019) and Gornoski (2019)).

Tanesha Bannister was also released early because of the First Step Act. She was originally sentenced to life in prison for drug trafficking but was released in 2019 after serving 16 years in prison (Monk, 2019).

Various researchers have suggested that a conscientious approach to early release can be greatly beneficial. For example, in a study involving a large sample of federal offenders, Rhodes et al (2018) stated that prisoners’ average length of stay could be reduced by 7.5 months with little impact on recidivism. Wakefield (2018) provides a critique to the Rhodes et al study.

The First Step Act addresses an unease over the last few decades that the nation’s war on drugs had led to incarceration of too many nonviolent offenders; also, when they were released, many of these offenders were not adequately prepared to reenter society. In addition to the retroactive extension of the Fair Sentencing Act of 2010 (Fair Sentencing Act of 2010, 2010), the First Step Act ends the “three-strikes” provision (except for those convicted of a “prior serious felony”) of the Clinton Crime Bill which resulted in a mandatory life sentence for those convicted of three drug crimes (Ho, 2018).

Blumstein (2011) noted that a primary reason for the growth in the prison population from the late 1970s to 2010 was a tenfold increase in the incarceration of drug criminals. Hence, addressing the issue of drug crimes was of great importance in reducing the prison population.

The retroactive extension of the Fair Sentencing Act of 2010, which addresses the inequities of sentencing for possession of powder cocaine vs. crack cocaine among other things, resulted in 2,387 Federal inmates receiving reductions in sentences averaging 71 months, or 26% of their sentences (The First Step Act of 2018, 2019, and Department of Justice, 2019a). The projection is that the First Step Act will result in a reduction of the federal prison population by about 53,000 prisoners over the next 10 years (Drusch, 2018b).

Provisions of the Law

For 2021, the First Step Act provides a budget of $409 million to the Department of Justice’s Bureau of Prisons, an increase of $319 million over the 2020 budget. This increase of $319 million is to be used to (1) fund recidivism-reducing programs for inmates who are given access to pre-release custody in the community ($244 million),

  • (2) expand drug treatment programs for inmates ($37 million), and
  • (3) increase the availability of such recidivism reduction efforts like classes in vocational training and life skills and mental health treatment ($23 million). In addition, the act provides $90 million in 2020 to support its implementation (The First Step Act of 2018, 2019).

Criticisms of the Law

Two of the criticisms of the First Step Act are that (1) it affects only federal prisoners and (2) it focuses on “back-end reforms”—i.e., on federal prisoners who have already served the bulk of their sentences. About 87% of prisoners are held in state, as opposed to federal, prisons (Haynes, 2018). Of course, these criticisms might also be called “missed opportunities”, to be corrected in the future. In addition, given the annual costs associated with crime in the United States, one might also argue that the provisions of the act could have been funded at a much higher level, resulting in even larger benefits. In comparison, funding provided for the Clinton Crime Bill was at a much higher level than that for the First Step Act.

Even though the First Step Act affects only federal prisoners, it is based on similar reforms done on the state level in states such as Kentucky, Georgia, South Carolina, and especially Texas. For example, in Texas, reforms similar to those instituted in the First Step Act were implemented and resulted in savings of more than $4 billion between 2006 and 2016, after an initial investment of $241 million in rehabilitation programs (Drusch, 2018a).

The Prisoner Assessment Tool Targeting Estimated Risks and Needs (PATTERN)

Another important aspect of the First Step Act (FSA) is a new approach for classifying prisoners according to forecasts of (1) their tendencies toward violent behavior and (2) their respective likelihoods of recidivism. (Caulkins et al (1996) among others have also developed approaches for predicting recidivism.) This approach, termed the FSA Risk and Needs Assessment System, has been named using an acronym, PATTERN (Prisoner Assessment Tool Targeting Estimated Risk and Needs). The forerunners of PATTERN, which were used by its developers as a base from which to start the development of PATTERN were the Bureau of Prison’s BRAVO (Bureau Risk Assessment Verification and Observation) system and BRAVO-R which added the methodological feature of dynamics and the application feature of recidivism to the BRAVO tool.

PATTERN works by initially assigning incoming prisoners to specific categories of risk with respect to both general and violent recidivism and violent/serious misbehavior at the point of prison intake. The risk levels are defined as being minimum, low, medium, and high. Periodically, based upon the prisoner’s current level of risk as well as other dynamic and static factors, the prisoner is assigned to specific amounts and types of programming (classes for rehabilitation) and provided with incentives and rewards. In addition, on a periodic basis, the determination of when a prisoner is to be transferred into prerelease custody or supervised release is made. The process just repeats over time for each prisoner (page 5, Department of Justice, 2019b). As mentioned, the initial assessment of risk categories and of programming takes place upon prisoner intake, while subsequent assignments occur seven months after intake and then yearly after that.

In one sense, PATTERN can be viewed as representing a stochastic process in which the prisoner moves from one state (representing the risks of recidivism and violent/serious misbehavior) to another, on a periodic basis. The movement from one state to another is a function of both static and dynamic factors (discussed in greater detail below), as well as the programming/rehabilitation classes that the prisoner willingly undergoes. The states associated with the process can be defined not only by risk categories, but also by location—e.g., in prison, in prelease custody, or supervised release. If one wants to represent the action of recidivism, PATTERN could be used to represent the transition from the supervised release back to prison.

As noted in the document describing PATTERN, eligible prisoners earn 10 days of credit for every 30 days of successful engagement in programming and other effective activities (page 6, Department of Justice, 2019b).

PATTERN was developed by Drs. Zachary Hamilton and Grant Duwe; they used a data set consisting of information about 278,940 inmates released from the Bureau of Prison facilities between 2009 and 2015. Static factors (or predictors) used by PATTERN include things such as gender, age at the time of first conviction, age at the time of assessment, an indicator of whether or not the crime of conviction was violent in nature, and an indicator of whether or not the prisoner was a sex offender. Dynamic factors used by PATTERN include the following: the inmate’s participation and performance in the programs to which he or she was assigned, number of infraction convictions during the current incarceration, number of serious infraction convictions during the current incarceration, number of programs (educational, vocational, drug treatment, etc.) completed as measured according to an ordinal category, an indicator (yes or no) as to whether the inmate participated in federal industry employment during current incarceration, drug treatment programs completed according to an ordinal scale, the inmate’s willingness to use income earned during incarceration to reimburse victim(s) of their crimes (page 45, Department of Justice, 2019b).

An important project that could be accomplished as a follow-on to the development of the PATTERN system would be the development of a simulation model. This model would predict, over time, for a federal prison, the numbers of prisoners contained within the various categories of risk; the numbers of prisoners granted early release, pre-release custody, and supervised release; and the number of prisoners arrested or returned to federal prison within three years of release (a measure of recidivism).

Viewed as a process-oriented simulation, prisoners would be represented as entities being routed from one state to another. Input to the simulation would include probabilities associated with the various levels of risk for violent misbehavior, general recidivism, and violent recidivism. For example, a minimum risk of violent recidivism might correspond to a probability of .1, while a high risk of violent recidivism might correspond to a probability of .7. Attributes (or descriptors) for the entities (prisoners) would be the values associated with the static factors and dynamic factors for each prisoner.

Output from the simulation would be the numbers of prisoners associated with various categories of risk (for violent misbehavior, and general/violent recidivism), and the number of released prisoners who commit a crime (thereby providing statistics on recidivism) over simulated time, say a ten-year period. The output would be probabilistic in nature, thereby requiring appropriate experimentation with the model to reach valid conclusions. This type of output, especially the output which gives the numbers of prisoners contained in the various risk-level categories, would be useful to the prison for planning purposes. One of the directives associated with the First Step Act is that prisoners in similar risk-level categories be housed together, so the output providing information on the numbers of prisoners at the various risk levels over time would be useful in capacity planning for the prison.

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