Description of original award (Fiscal Year 2015, $564,773)
St. Louis faces a significant firearm violence problem as well as concerns regarding race disparities in criminal justice treatment. Yet policymakers cannot determine whether the criminal justice response is either just or enhances public safety. That is because the end-to-end integrated database needed to evaluate the system response does not exist. The proposed project implements such a system and uses it to examine possible disparities in the criminal justice response to firearm-related crimes committed in neighborhoods that differ in racial composition and socioeconomic status.
The proposed project grew directly from the St. Louis Public Safety Partnership, an NIJ-supported collaboration among the Mayor's Office, police department, and the University of Missouri-St. Louis to enhance public safety through research. This application extends the Partnership to other agencies and reflects the success of the planning and design process the Partnership afforded.
The project will implement the database by automatically extracting relevant data elements from police, prosecutor, and court records and merge them into a single integrated data platform. A pre-test involving manual data extraction confirms the policy research potential of the integrated database. A cost-benefit analysis of the use of the automated database for policy research will be carried out.
A study that addresses significant gaps in sentencing research will be conducted on approximately 3,000 firearm-related cases compiled in the database. The study contributes to the sentencing disparity literature by investigating whether African-Americans arrested for gun-related crimes in predominantly white or affluent neighborhoods receive more severe treatment (e.g. higher bond, extended pretrial confinement, longer sentences) than those arrested for similar crimes in predominantly black or disadvantaged neighborhoods a complaint commonly heard in St. Louis minority communities.
The key analytical challenge facing this study is the possible selection bias associated with non-experimental data. The richly detailed data afforded by the integrated system will facilitate the use of propensity score methods tomatch or weight cases on relevant characteristics. Multi-level modeling will be used to analyze the data.
Three papers reporting the results of the project will be submitted for publication to refereed scholarly journals. Specialized reports will be produced and shared with local media and policymakers. Project results will also be disseminated at the American Society of Criminology conference. The planning, design, and implementation processes leading to the integrated database will be described in a well-publicized webinar for criminal justice practitioners and researchers. The data will be archived at project conclusion.
Note: This project contains a research and/or development component, as defined in applicable law.
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