Marginal Structural Models to Estimate Causal Effects of Right-to-Carry Laws on Crime

Abstract

Right-to-carry (RTC) laws allow the legal carrying of concealed firearms for defense, in certain states in the United States. I used modern causal inference methodology from epidemiology to examine the effect of RTC laws on crime over a period from 1959 up to 2016. I fitted marginal structural models (MSMs), using inverse probability weighting (IPW) to correct for criminological, economic, political and demographic confounders. Results indicate that RTC laws significantly increase violent crime by 7.5% and property crime by 6.1%. RTC laws significantly increase murder and manslaughter, robbery, aggravated assault, burglary, larceny theft and motor vehicle theft rates. Applying this method to this topic for the first time addresses methodological shortcomings in previous studies such as conditioning away the effect, overfit and the inappropriate use of county level measurements. Data and analysis code for this article are available online.

Keywords:

• Causal modeling
• Concealed carry
• Firearms
• Gun laws
• Inverse probability weighting
• Longitudinal data
• Marginal structural model
• Multiple imputation
• Random effects

Supplementary Materials

rtcdat.rda:Dataset used in this study. (R datafile)

rtcdat_metadata.txt:Metadata for rtcdat. (text file)

rtc_analysis.R:R code to replicate the main results. (R script)

rtc_supplementary_material.pdf:Supplementary material including longitudinal plots of crime rates and predictors as described in Section 3.1, positivity plots as described in Sections 3.1 and 4.3, boxplots and descriptive statistics for the weights as described in Section 4.3. (PDF file)