Thesis advisor: Arthur Lewbel / This dissertation consists of three related chapters. A unifying feature throughout all is a focus on the issues in the economics of crime, specifically in how different factors affect different types of index crimes. The first chapter, a collaboration with Abby Hong, examines the role of the stand-your-ground law in driving first-degree and second-degree murder rates. The second chapter, a collaboration with Benjamin Ferri, examines how the two ends of the income distribution impact emotional gain crime and financial gain crime. Both chapters one and two examine how different variables affect crime, and both have a theoretical part and an empirical part. The third chapter looks into measurement issues in crime. Specifically, it considers the impact of a change in data collection methods on the Uniform Crime Report (UCR). The first chapter, “Self-defense Regulations and Crime: Evidence from the Stand Your Ground Law,” provides a theoretical model of crime escalation when governments relax self-defense regulations. We then test the model with an empirical analysis of the “stand-your-ground” (SYG) laws’ impact on planned and unplanned murders. The game theoretical model shows that relaxing self-defense regulations can increase the arming of crime victims. It also increases the arming of offenders in crimes that lead to unplanned murders. If planned murder offenders are over-confident, then their level of arms increases as well. We then use a difference-in-differences (DiD) model to test these implications. We find that consistent with the model, SYG laws in the US increase the planned murder rate by 7.6% and the unplanned murder rate by 10.4%, on average. Also, the effect size increases over time, highlighting the persistence of the impact. The paper illustrates how interactions between victims and offenders result in unintended consequences of self-defense regulations. It also encourages policymakers to take into account criminal behavior when making policy decisions. The second chapter, “The Distinct Roles of Poverty and Higher Earnings in Motivating Crime,” develops a new model that articulates how Poverty (the lower tail of the earnings distribution) and Earnings (the upper tail) enter into equilibrium crime rates. In our model, individuals in Poverty have less to lose in the context of criminal punishment, so are less averse to committing crimes in general. The presence of high Earnings (therefore things worth stealing) heightens the expected gain to offenders per crime - but specifically in terms of financial gain, not emotional gain. We estimate our model on a comprehensive panel of U.S. Commuting Zones (1980-2016), deploying novel Shift-Share instruments to correct for reverse causality (of crime on the earnings distribution). Corroborating our hypothesis, we find that high Earnings plays a much larger role in driving crimes that yield financial gain to the offender (various forms of theft) than it does for crimes of emotional gain; while Poverty is a driving force equally across both types of crime. In each case, not accounting for reverse causality would underestimate both effects, often by more than double. The third and final chapter, “Crime Reporting Standards and Reported Crime,” This paper explores data discrepancies in the Uniform Crime Report (UCR) before and after the adoption and conversion of the National Incident-Based Reporting System (NIBRS). The FBI starts publishing the UCR in 1930 to understand crime trends in the United States. The UCR is published under the Summary Reporting System (SRS) until the 1990s, when the NIBRS is developed to collect more detailed data. The NIBRS is then converted to “synthetic SRS” and concatenated to historical SRS data when it enters the UCR. It uses a staggered event study design based on the year in which the agency switches from the SRS to the NIBRS. I find two factors that contribute to a large and statistically significant increase in reported crime for agencies that adopt the NIBRS compared with agencies that have not: the data conversion process and a change in reporting practices. When I convert the NIBRS to synthetic SRS based on published criteria, I observe a smaller and statistically insignificant increase in assault cases. However, this alternative conversion process does not improve the difference-in-differences (DiD) effects for total crime, murder, robbery, burglary, and theft, highlighting the fact that data from the NIBRS is more complete and more timely. / Thesis (PhD) — Boston College, 2023. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Economics.