Statistics informing decisions for our communities on gun and crime policy

David Banks Chair
Duke University
Aleksandra Slavkovic Organizer
Pennsylvania State University
Sunday, Aug 6: 4:00 PM - 5:50 PM
Invited Paper Session 
Metro Toronto Convention Centre 
Room: CC-203C,CC-203D 



Main Sponsor

Statistics and Public Policy


Changes in Crime Rates during the COVID-19 Pandemic Open Access

We estimate changes in the rates of five FBI Part 1 crimes during the 2020 spring COVID-19 pandemic lockdown period and the period after the killing of George Floyd through December 2020. We use weekly crime rate data from 28 of the 70 largest cities in the United States from January 2018 to December 2020. Homicide rates were higher throughout 2020, including during early 2020 prior to March lockdowns. Auto thefts increased significantly during the summer and remainder of 2020. In contrast, robbery and larceny significantly declined during all three post-pandemic periods. Point estimates of burglary rates pointed to a decline for all four periods of 2020, but only the pre-pandemic period was statistically significant. We construct a city-level openness index to examine whether the degree of openness just prior to and during the lockdowns was associated with changing crime rates. Larceny and robbery rates both had a positive and significant association with the openness index implying lockdown restrictions reduced offense rates whereas the other three crime types had no detectable association. While opportunity theory is a tempting post hoc explanation of some of these findings, no  


Mikaela Meyer, Carnegie Mellon University

Optimal Matching for Observational Studies That Integrate Quantitative and Qualitative Research

A quantitative study of treatment effects may form many matched pairs of a treated subject and an untreated control who look similar in terms of covariates measured prior to treatment. When treatments are not randomly assigned, one inevitable concern is that individuals who look similar in measured covariates may be dissimilar in unmeasured covariates. Another concern is that quantitative measures may be misinterpreted by investigators in the absence of context that is not recorded in quantitative data. When text information is automatically coded to form quantitative measures, examination of the narrative context can reveal the limitations of initial coding efforts. An existing proposal entails a narrative description of a subset of matched pairs, hoping in a subset of pairs to observe quite a bit more of what was not quantitatively measured or automatically encoded. A subset of pairs cannot rule out subtle biases that materially affect analyses of many pairs, but perhaps a subset of pairs can inform discussion of such biases, perhaps leading to a reinterpretation of quantitative data, or perhaps raising new considerations and perspectives. The large literature on qualitative rese 


Ruoqi Yu, University of California, Davis

Statisticians Engage in Gun Violence Research

Government reports document more than 14,000 homicides and more than 195,000 aggravated assaults with firearms in 2017. There were 346 mass shootings, with 4 or more victims, including over 2000 people shot. And these statistics do not include suicides (two-thirds of gun deaths) or accidents (5% of gun deaths). This article describes statistical issues discussed at a national forum to stimulate collaboration between statisticians and criminologists. Topics include: (i) available data sources and their shortcomings and efforts to improve the quality, and alternative new data registers of shootings; (ii) gun violence patterns and trends, with statistical models and clustering effects in urban areas; (iii) research for understanding effective strategies for gun violence prevention and the role of the police in solving gun homicides; (iv) the role of reliable forensic science in solving cases involving shootings; and (v) the topic of police shootings, where they are more prevalent and the characteristics of the officers involved. The final section calls the statistical community to engage in collaborations with social scientists to provide the most effective methodological tools. 


James Rosenberger, Penn State University and NISS