Debbie Amewu

Pretrial detention accounts for a growing share of jail incarceration, yet while a large literature examines reforms to monetary bail, little is known about charge-based pretrial detention and its effects on the violent offenders it is specifically designed to detain. This paper studies the repeal of the Presumptive Denial of Bail statute in Virginia, which, prior to the repeal, made defendants charged with specified violent and serious offenses presumptively ineligible for bail. Using administrative court records from the Virginia court system and a difference-in-differences design comparing defendants with affected and unaffected charges before and after the reform, I estimate the effects of removing charge-based bail presumptions on case outcomes, pretrial misconduct, and recidivism. I find that the repeal increased pretrial release among treated defendants, and this translated into better case outcomes. Consistent with the broader literature on bail reform, I find that the improvement in case outcomes is driven by a decline in guilty pleas, although this decline did not translate into a statistically significant drop in conviction rates. On pretrial misconduct, I find that the repeal did not increase failure to appear in court or short-run recidivism, but I find that longer-run recidivism rose modestly. These results indicate that when release is expanded to violent offenders, it may not have significant negative consequences for pretrial misconduct and public safety.

How disruptive are leadership changes in local law enforcement? While a large body of literature studies the performance effects of electoral turnover among local officials, little is known about county sheriffs. Sheriff elections generate abrupt leadership changes in agencies with substantial authority, raising questions about whether turnover disrupts operations or whether institutional continuity mitigates such effects. This paper investigates whether electoral turnover in law enforcement impacts arrest rates. Using hand-collected data on sheriff elections merged with arrest records, I employ a difference-in-differences design to examine how arrest rates respond, first, to an incumbent sheriff's electoral defeat and, second, to the subsequent transition to new leadership. I find that election losses lead to modest declines in arrests for lower-level offenses, while no significant effects are observed for arrest rates of severe offenses. In contrast, the transition to a new sheriff shows little impact on arrest activity. Analysis of deputy employment data suggests that staffing declines may drive the decrease in arrests following election losses.

Owing to the explosion in incarceration rates, coupled with high crime and overcrowded prisons in America, much research has been conducted to analyse the causes of such phenomena. This paper contributes to such research by conducting a policy analysis of the effectiveness of the Justice Reinvestment Initiative, a reformative legislation instituted to reform the justice system and tackle the issue. Since its inception, no empirical analysis has been conducted in the literature to ascertain its impact on crime rates. This paper employs the Difference-inDifferences estimator to review the impact the policy has on crime rates. The results show that the policy's implementation led to significant increases in property and total crime rates, contrary to popular belief in its successes.

Pretrial risk assessment tools play an increasingly important role in informing judicial decisions about bail and pretrial release. Among the most widely adopted instruments is the Public Safety Assessment (PSA), which predicts the likelihood that a defendant will fail to appear in court or be rearrested while awaiting trial. Despite its widespread use, relatively little evidence exists on how the PSA's predictive performance compares with modern machine learning methods when applied to large administrative datasets. This study evaluates whether machine learning algorithms can improve upon the predictive accuracy of the PSA for forecasting failure to appear among pretrial defendants. Using a large dataset of court cases from the Virginia Pretrial Data Project, I will develop and compare several predictive models, including logistic regression, decision trees, and random forests. The performance of these models will be assessed using standard classification metrics and compared directly with the PSA's failure-to-appear score to determine whether more flexible algorithms provide meaningful improvements in predictive performance. By examining the strengths and limitations of both traditional statistical methods and machine learning approaches, this study aims to contribute to the growing literature on evidence-based pretrial decision-making and the use of predictive analytics in the criminal justice system.