Date of Award
2025
Degree Type
Open Access Dissertation
Degree Name
Economics, PhD
Program
School of Social Science, Politics, and Evaluation
Advisor/Supervisor/Committee Chair
Gregory DeAngelo
Dissertation or Thesis Committee Member
Scott Cunningham
Dissertation or Thesis Committee Member
Minjae Yun
Terms of Use & License Information
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Rights Information
© 2025 Victor Olu Kilanko
Keywords
Augmented Synthetic Control Method, Generalized Synthetic Control Method, Gun liability insurance, Partially-pooled synthetic control method, Psychedelics, Zero-cash bail
Subject Categories
Criminology | Economics
Abstract
This dissertation investigates the intersection of public health and criminal justice by empirically evaluating the effects of three distinct policy reforms: psychedelic decriminalization, firearm liability mandates, and zero cash bail, on crime outcomes in California. It aims to inform evidence-based policymaking by leveraging quasi-experimental methods to identify the causal impact of each intervention. A growing number of U.S. jurisdictions have decriminalized psychedelic substances, and there are attendant theoretical expectations that such reforms could promote desistance from crime through the rehabilitative potential of psychedelics. This study examines the causal impact of psychedelic decriminalization on violent and property crime rates using monthly city-level panel data from 2017–2023 for five California cities that enacted such reforms. We apply a partially pooled synthetic control method (PPSCM) to estimate the reforms’ effects. The analysis includes pre-treatment fit metrics (pooled and unit-specific root mean squared error, RMSE), estimates of the average treatment effect on the treated (ATT), placebo tests for significance, and a robustness check excluding one treated city (Arcata). Results indicate that the estimated effects on both violent and property crime are small in magnitude and statistically uncertain. For violent crime, the estimated 6.2% reduction corresponds to a decline from about 54.6 to 51.2 incidents per 100,000 people, while for property crime, the estimated 3.1% reduction corresponds to a shift from roughly 434 to 420 incidents per 100,000 people. These changes are statistically indistinguishable from zero, a finding that holds across all models including the Arcata-exclusion robustness check. The findings suggest no measurable impact of psychedelic decriminalization reforms on crime rates in the post-reform period. The second chapter evaluates the impact of San Jose’s firearm liability insurance mandate, implemented in January 2023, on firearm-related assaults using the Augmented Synthetic Control Method (ASCM). San Jose is the first U.S. jurisdiction to mandate liability insurance for gun owners. Using city-level monthly panel data from the Uniform Crime Reporting (UCR) database and the California Department of Justice (CA DOJ), we estimate the causal effect of this policy. We employ various ASCM specifications to improve pre-treatment balance and minimize bias, including the standard SCM, Ridge-ASCM, Ridge-ASCM with covariates (number of police officers and median household income), Residualized ASCM, and Demeaned ASCM. Results show that the estimated average treatment effect on the treated (ATT) is generally negative but not statistically significant across most models. The standard SCM reports an ATT of -0.0885 (p = 0.90), while Ridge-ASCM yields -0.0895 (p = 0.89). Including covariates produces a slightly stronger estimate of -0.12 (p = 0.62), and residualized and demeaned models give estimates of -0.223 (p = 0.62) and -0.204 (p = 0.72), respectively. A consistent short-term decline is observed in May 2023, with the ATT dropping to -0.578 (p-values range from 0.025 to 0.033). These findings suggest that the policy’s overall effect on firearm assaults is limited, though a temporary reduction occurred shortly after implementation. Further research with extended post-intervention data may better assess the policy’s long-term impact. The third chapter examines the causal impact of California’s zero-cash bail policy on city-level crime outcomes using the Generalized Synthetic Control Method (GSCM). The analysis leverages panel data from 2017 to 2023 across multiple treated and untreated cities, allowing for flexible adjustment to unobserved time-varying confounders through interactive fixed effects. The results indicate a statistically significant 7.7% increase in property crime rates following the re-enactment of zero-cash bail, equivalent to approximately 12 to 13 additional crimes per 100,000 residents. Property crime clearance rates rose modestly by about 7%, although this change is not statistically significant. In contrast, violent crime and aggravated assault rates show no measurable effects, suggesting that the policy’s influence was concentrated among non-violent property offenses rather than serious or violent crimes. The number of latent factors selected by cross-validation ranged between zero and one across outcome models, implying that a limited set of unobserved common shocks such as changes in policing capacity, court operations, or pandemic-related effects explains most of the residual variation. The results highlight both the benefits and trade-offs of pretrial reform: the policy advances decarceration goals but may impose small costs in the form of higher property crime.
ISBN
9798273323780
Recommended Citation
Kilanko, Victor Olu. (2025). The Economics of Crime: Exploring the Impact of Psychedelics, Gun Liability Insurance, and Zero-Bail Policies on Crime. CGU Theses & Dissertations, 1052. https://scholarship.claremont.edu/cgu_etd/1052.
