{"id":{"repo_id":"missouri","oai_identifier":"oai:mospace.umsystem.edu:10355/85779"},"canonical_url":"https://search.dev.ndltd.org/etd/missouri/oai:mospace.umsystem.edu:10355/85779","repository":{"repo_id":"missouri","name":"University of Missouri","base_url":"https://mospace.umsystem.edu/oai/request"},"display":{"title":"Three essays on crime policy and the Bayesian bootstrap","abstract":"This dissertation consists of three chapters. In the first chapter, I analyze credible intervals for quantiles constructed using Bayesian bootstrap techniques and show that credible intervals constructed using the \"continuity-corrected\" Bayesian bootstrap (Banks, 1988) have frequentist coverage probability error of only O(n⁻¹). In addition, I show that these \"continuity-corrected\" Bayesian bootstrap credible intervals achieve the same frequentist coverage probability as the frequentist confidence intervals of Goldman and Kaplan (2017), up to some error term of magnitude O(n⁻¹). Furthermore, I demonstrate that credible intervals constructed using the \"continuity-corrected\" Bayesian bootstrap have less frequentist coverage probability error than those constructed using the Bayesian bootstrap (Rubin, 1981). In the second chapter, I investigate three strikes laws, which mandate sharply increased sentences for criminals who commit a specific number of felonies. Specifically, I analyze the effect of these laws on violent crime rates using municipal-level data from the FBI. I compare violent crime rates of border municipalities in states with differing treatment statuses using a difference-in-differences specification with a sample matched on pre-treatment outcomes. I find no statistical evidence that three strikes laws reduce violent crime rates. I rule out reductions in violent crime rates greater than 1.3 % and reject the hypothesis that three strikes laws reduce violent crime rates at the 5 % significance level. Additional analyses and robustness checks support my main findings. In the third chapter, I examine medical marijuana laws (MMLs), which legalize the use, possession, and cultivation of marijuana by individuals with qualifying medical conditions. Namely, I employ municipal-level data from the FBI to analyze the effect of MMLs on violent crime rates. I compare municipalities in border regions with different treatments statuses using a difference-in-differences specification with a sample matched on pre-treatment outcomes. I find a lack of evidence for MMLs increasing violent crime rates, but I cannot eliminate the possibility of small-to-medium positive effects. However, I rule out increases in violent crime rates greater than 9.9 % and reject the hypothesis that MMLs increase violent crime at the 10 % significance level.","abstract_html":"This dissertation consists of three chapters. In the first chapter, I analyze credible intervals for quantiles constructed using Bayesian bootstrap techniques and show that credible intervals constructed using the "continuity-corrected" Bayesian bootstrap (Banks, 1988) have frequentist coverage probability error of only O(n⁻¹). In addition, I show that these "continuity-corrected" Bayesian bootstrap credible intervals achieve the same frequentist coverage probability as the frequentist confidence intervals of Goldman and Kaplan (2017), up to some error term of magnitude O(n⁻¹). Furthermore, I demonstrate that credible intervals constructed using the "continuity-corrected" Bayesian bootstrap have less frequentist coverage probability error than those constructed using the Bayesian bootstrap (Rubin, 1981). In the second chapter, I investigate three strikes laws, which mandate sharply increased sentences for criminals who commit a specific number of felonies. Specifically, I analyze the effect of these laws on violent crime rates using municipal-level data from the FBI. I compare violent crime rates of border municipalities in states with differing treatment statuses using a difference-in-differences specification with a sample matched on pre-treatment outcomes. I find no statistical evidence that three strikes laws reduce violent crime rates. I rule out reductions in violent crime rates greater than 1.3 % and reject the hypothesis that three strikes laws reduce violent crime rates at the 5 % significance level. Additional analyses and robustness checks support my main findings. In the third chapter, I examine medical marijuana laws (MMLs), which legalize the use, possession, and cultivation of marijuana by individuals with qualifying medical conditions. Namely, I employ municipal-level data from the FBI to analyze the effect of MMLs on violent crime rates. I compare municipalities in border regions with different treatments statuses using a difference-in-differences specification with a sample matched on pre-treatment outcomes. I find a lack of evidence for MMLs increasing violent crime rates, but I cannot eliminate the possibility of small-to-medium positive effects. However, I rule out increases in violent crime rates greater than 9.9 % and reject the hypothesis that MMLs increase violent crime at the 10 % significance level.","abstract_has_math":false,"creators":["Hofmann, Lonnie"],"institution":"University of Missouri--Columbia","degree_name":"Ph. D.","degree_level":"Doctoral","degree_discipline":"Economics (MU)","degree_department":null,"school":null,"contributors":[],"advisors":["Kaplan, David"],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021","date_published":"2021","updated_at":"2026-07-24T03:08:43Z","subjects":[],"languages":["eng","English"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.32469/10355/85779"],"render_values":[{"text":"https://doi.org/10.32469/10355/85779","href":"https://doi.org/10.32469/10355/85779","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10355/85779","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Kaplan, David"]},{"key":"dc:creator","label":"Author","values":["Hofmann, Lonnie"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2021-07-28T16:37:16Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2021-07-28T16:37:16Z"]},{"key":"dc:date.issued","label":"Date","values":["2021"]},{"key":"dc:publisher","label":"Institution","values":["University of Missouri--Columbia"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Economics (MU)"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph. 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In addition, I show that these \"continuity-corrected\" Bayesian bootstrap credible intervals achieve the same frequentist coverage probability as the frequentist confidence intervals of Goldman and Kaplan (2017), up to some error term of magnitude O(n⁻¹). Furthermore, I demonstrate that credible intervals constructed using the \"continuity-corrected\" Bayesian bootstrap have less frequentist coverage probability error than those constructed using the Bayesian bootstrap (Rubin, 1981). In the second chapter, I investigate three strikes laws, which mandate sharply increased sentences for criminals who commit a specific number of felonies. Specifically, I analyze the effect of these laws on violent crime rates using municipal-level data from the FBI. I compare violent crime rates of border municipalities in states with differing treatment statuses using a difference-in-differences specification with a sample matched on pre-treatment outcomes. I find no statistical evidence that three strikes laws reduce violent crime rates. I rule out reductions in violent crime rates greater than 1.3 % and reject the hypothesis that three strikes laws reduce violent crime rates at the 5 % significance level. Additional analyses and robustness checks support my main findings. In the third chapter, I examine medical marijuana laws (MMLs), which legalize the use, possession, and cultivation of marijuana by individuals with qualifying medical conditions. Namely, I employ municipal-level data from the FBI to analyze the effect of MMLs on violent crime rates. I compare municipalities in border regions with different treatments statuses using a difference-in-differences specification with a sample matched on pre-treatment outcomes. I find a lack of evidence for MMLs increasing violent crime rates, but I cannot eliminate the possibility of small-to-medium positive effects. However, I rule out increases in violent crime rates greater than 9.9 % and reject the hypothesis that MMLs increase violent crime at the 10 % significance level."]},{"key":"dc:title","label":"Title","values":["Three essays on crime policy and the Bayesian bootstrap"]}]}],"canonical_facts":{"dc:contributor.advisor":["Kaplan, David"],"dc:creator":["Hofmann, Lonnie"],"dc:date.accessioned":["2021-07-28T16:37:16Z"],"dc:date.available":["2021-07-28T16:37:16Z"],"dc:date.issued":["2021"],"dc:description.abstract":["This dissertation consists of three chapters. In the first chapter, I analyze credible intervals for quantiles constructed using Bayesian bootstrap techniques and show that credible intervals constructed using the \"continuity-corrected\" Bayesian bootstrap (Banks, 1988) have frequentist coverage probability error of only O(n⁻¹). In addition, I show that these \"continuity-corrected\" Bayesian bootstrap credible intervals achieve the same frequentist coverage probability as the frequentist confidence intervals of Goldman and Kaplan (2017), up to some error term of magnitude O(n⁻¹). Furthermore, I demonstrate that credible intervals constructed using the \"continuity-corrected\" Bayesian bootstrap have less frequentist coverage probability error than those constructed using the Bayesian bootstrap (Rubin, 1981). In the second chapter, I investigate three strikes laws, which mandate sharply increased sentences for criminals who commit a specific number of felonies. Specifically, I analyze the effect of these laws on violent crime rates using municipal-level data from the FBI. I compare violent crime rates of border municipalities in states with differing treatment statuses using a difference-in-differences specification with a sample matched on pre-treatment outcomes. I find no statistical evidence that three strikes laws reduce violent crime rates. I rule out reductions in violent crime rates greater than 1.3 % and reject the hypothesis that three strikes laws reduce violent crime rates at the 5 % significance level. Additional analyses and robustness checks support my main findings. In the third chapter, I examine medical marijuana laws (MMLs), which legalize the use, possession, and cultivation of marijuana by individuals with qualifying medical conditions. Namely, I employ municipal-level data from the FBI to analyze the effect of MMLs on violent crime rates. I compare municipalities in border regions with different treatments statuses using a difference-in-differences specification with a sample matched on pre-treatment outcomes. I find a lack of evidence for MMLs increasing violent crime rates, but I cannot eliminate the possibility of small-to-medium positive effects. However, I rule out increases in violent crime rates greater than 9.9 % and reject the hypothesis that MMLs increase violent crime at the 10 % significance level."],"dc:identifier.doi":["https://doi.org/10.32469/10355/85779"],"dc:identifier.uri":["https://hdl.handle.net/10355/85779"],"dc:language":["English"],"dc:language.iso":["eng"],"dc:publisher":["University of Missouri--Columbia"],"dc:title":["Three essays on crime policy and the Bayesian bootstrap"],"dc:type":["Thesis"],"thesis:degree_discipline":["Economics (MU)"],"thesis:degree_level":["Doctoral"],"thesis:degree_name":["Ph. D."]},"updated_at":"2026-07-24T03:08:43Z"}