Essays on the Effects of Correctional Policies on Prison Misconduct

This dissertation analyzes the effects of two correctional policies on prison misconduct. Chapter 1 briefly frames prison as a policy built environment and provides an overview of mass incarceration in the United States. Chapters 2 and 3 provide causal estimates of the effects of two correctional policies on prison misconduct.Chapter 2 estimates the relationship between prison visits and self-reported inmate misconduct using the 2004 Survey of Inmates in State Correctional Facilities (SISCF). This paper contributes to the extant literature by broadening the scope of the conversation about the determinants of inmate behavior to include influences from outside of the prison, namely prison visits, as opposed to limiting the discussion to individual or prison-specific influences. By employing an instrumental variables approach to estimating the relationship between prison visits and inmate misconduct the paper is the first to address the threats to internal validity posed by direct estimation of the effect of visitation on prison misconduct. The intuition behind my identification strategy is that distance between an inmate's home and place of incarceration isolates quasi-random variation in prison visitation, in effect assigning prison visits to inmates in a given state at random. The results suggest receiving visits reduces certain types of misconduct and the findings suggest the potential to reduce prison misconduct without resorting to increased isolation. Chapter 3 estimates the relationship between facility security level and prison misconduct using an administrative data set from the California Department of Corrections and Rehabilitation (CDCR). The different levels of prison facility are designed to recognize heterogeneity in the inmate population and to appropriately house inmates during their incarceration to minimize risk of misconduct and escape. Prison facility security levels vary in physical characteristics, average levels of violence and other misconduct and staff perceptions of safety. An increase in facility security level could result in a suppression effect on misconduct and/or a peer effect which could positively or negatively effect misconduct. In this chapter, I employ a regression discontinuity (RD) design that exploits cutoffs in the security classification score to characterize the relationship between security classification and prison misconduct. The results of the paper suggest that inmates placed in a Level III facility are 8 percentage points less likely to incur a RVR than inmates placed in Level II, and that this result is driven almost entirely by a lower likelihood of write ups for Division E or F violations, which are the lowest level of violations eligible for write up as RVRs. I hypothesize that this result may be a result in differences in the priorities of custody staff as opposed to lower numbers of these types of violations at Level III prisons. In contrast to the findings between Levels II/III, I do not find an effect of facility security classification on the incidence of serious RVRs at the Level III/IV cutoff.Overall, the goal of the dissertation is to contribute to the extant knowledge about the effects of correctional policies on inmate outcomes by describing how certain correctional policies shape the in-prison behavior of both inmates and custody staff. Since the effects of incarceration most likely reverberate to those who interact with inmates during their incarceration and persist after an inmate is released, understanding the effects of correctional policies on in prison behavior contributes to our understanding of how incarceration affects individuals, their families and their communities post-release and, in doing so, contribute, in some part, to a better understanding of what it means to use incarceration so extensively in the United States

The Effects of Providing Postsecondary Educational Opportunities to Inmates: A Pilot Randomized Trial

A pilot randomized trial to assess the effects of providing postsecondary educational opportunities to inmate

No ground truth? No problem: Improving administrative data linking using active learning and a little bit of guile.

While linking records across large administrative datasets ["big data"] has the potential to revolutionize empirical social science research, many administrative data files do not have common identifiers and are thus not designed to be linked to others. To address this problem, researchers have developed probabilistic record linkage algorithms which use statistical patterns in identifying characteristics to perform linking tasks. Naturally, the accuracy of a candidate linking algorithm can be substantially improved when an algorithm has access to "ground-truth" examples-matches which can be validated using institutional knowledge or auxiliary data. Unfortunately, the cost of obtaining these examples is typically high, often requiring a researcher to manually review pairs of records in order to make an informed judgement about whether they are a match. When a pool of ground-truth information is unavailable, researchers can use "active learning" algorithms for linking, which ask the user to provide ground-truth information for select candidate pairs. In this paper, we investigate the value of providing ground-truth examples via active learning for linking performance. We confirm popular intuition that data linking can be dramatically improved with the availability of ground truth examples. But critically, in many real-world applications, only a relatively small number of tactically-selected ground-truth examples are needed to obtain most of the achievable gains. With a modest investment in ground truth, researchers can approximate the performance of a supervised learning algorithm that has access to a large database of ground truth examples using a readily available off-the-shelf tool