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In January of 2012, Philadelphia Mayor Michael Nutter outlined the crime fighting measures that his administration would pursue during his second term as mayor. Included was a plan to introduce a multi-agency crime reduction program, which Philadelphia Police Commissioner Charles Ramsey and District Attorney Seth Williams would co-chair, called GunStat. GunStat was described as a collaborative effort to reduce gun violence through (1) identifying locations with a high incidence of violent crime, (2) pinpointing violent offenders responsible for these crimes, (3) focusing on arresting and prosecuting these offenders for crimes committed at these places, and (4) enhanced monitoring of offenders on probation and parole who are living and/or offending within these locations. In effect, GunStat was designed to target the right people (prolific, violent known offenders) at the right places (hot spots of violent crime). This dissertation is an in-depth, ex post facto evaluation of Philadelphia’s GunStat model as implemented over two phases and two years. It involved both a quasi-experimental research design which employed propensity score matching methods to generate comparisons, and a process-evaluation where several themes, including program implementation, were explored. The results here suggest that GunStat did not reduce crime relative to comparison locations. However, the qualitative data highlighted the importance of informal inter-agency networks that were developed during the course of the intervention, and suggested that GunStat put future collaborations on a solid footing. The implications for criminal justice policy, theory and evaluation design are discussed. / Criminal Justice

This thesis assesses the impact of the Jepara Furniture Value Chain (FVC) project, which was conducted by the Center for International Forestry Research (CIFOR) to address challenges faced by small-scale furniture producers in Jepara, Indonesia. This assessment focuses on the effect of membership in the APKJ, a producer association started as part of the project. The propensity score for association membership was estimated using unchanging firm and owner characteristics, as well as information recalled about firm operations in 2009 (before the association was formed). Propensity score matching was used to compare outcome variables of association members and non-members. Results suggest that membership in the APKJ does not have a significant effect on profit levels. Using differenced current and recalled marketing and production behaviors as outcome variables with propensity score matching indicates that members have improved their bargaining position and marketing behaviors more than non-members since 2009. Additionally, APKJ members are more likely to have obtained certificates of timber legality / Master of Science

Although originally conceived as an educational intervention for at-risk students, modern career academies have expanded their scope to programs designed to promote critical thinking, problem solving, and analytical skills to be successful in an advanced career path. Through the integration of career and technical education courses and a rigorous, college preparatory academic curriculum, career academies serve as a key piece of a larger strategy for developing a well- prepared STEM workforce. This study focuses on the Virginia Governor's STEM Academies, a state-wide initiative containing programs designed to expand options for the general student population to acquire STEM literacy and other critical skills, knowledge and credentials that will prepare them for high-demand, high-wage, and high-skill careers. Currently, 22 Academies exist serving students across 36 Virginia School Divisions. Using educational administrative data housed within the Virginia Longitudinal Data System, I examined the Virginia Governor's STEM Academies regarding characteristics of student participation and the relationship between Academy participation and high school and postsecondary outcomes. Using multi-level regression modeling, I found that male students, Asian and Hispanic students, and non-economically disadvantage students have a higher rate of Academy participation. After matching students with propensity score matching on demographic and early academic characteristics, I find that Academy participants are more likely to take Algebra II at an earlier grade, enroll in more Career and Technical Education and dual enrollment courses, and declare a STEM major after enrolling at a postsecondary institution. This research provides a valuable new contribution to the study of career academies after such educational programs have undergone a paradigm shift to preparing students for high-demand, high-wage, and high-skill careers. By incorporating propensity score matching and multi-level regression model, I employ a statistically rigorous approach that can serve as important benchmarking of the enrollment and academic outcomes of the Virginia Governor's STEM Academies. / Doctor of Philosophy / Although originally conceived as an educational intervention for at-risk students, modern career academies have expanded their scope to programs designed to promote critical thinking, problem solving, and analytical skills to be successful in an advanced career path. Through the integration of career and technical education courses and a rigorous, college preparatory academic curriculum, career academies serve as a key piece of a larger strategy for developing a well- prepared STEM workforce. This study focuses on the Virginia Governor's STEM Academies, a state-wide initiative containing programs designed to expand options for the general student population to acquire STEM literacy and other critical skills, knowledge and credentials that will prepare them for high-demand, high-wage, and high-skill careers. Currently, 22 Academies exist serving students across 36 Virginia School Divisions. Using educational administrative data housed within the Virginia Longitudinal Data System, I examined the Virginia Governor's STEM Academies regarding characteristics of student participation and the relationship between Academy participation and high school and postsecondary outcomes. Using multi-level regression modeling, I found that male students, Asian and Hispanic students, and non-economically disadvantage students have a higher rate of Academy participation. After matching students with propensity score matching on demographic and early academic characteristics, I find that Academy participants are more likely to take Algebra II at an earlier grade, enroll in more Career and Technical Education and dual enrollment courses, and declare a STEM major after enrolling at a postsecondary institution. This research provides a valuable new contribution to the study of career academies after such educational programs have undergone a paradigm shift to preparing students for high-demand, high-wage, and high-skill careers. By incorporating propensity score matching and multi-level regression model, I employ a statistically rigorous approach that can serve as important benchmarking of the enrollment and academic outcomes of the Virginia Governor's STEM Academies.