Methodology See the full study

The research examined changes in state-level child-access prevention (CAP) laws, stand-your-ground (SYG) laws, and right to carry (RTC) laws from 1970 to 2016, using data from publicly available sources.

We code a state as having a CAP law if the law specifies either civil or criminal penalties for storing a handgun in a manner that allowed access by a minor.

We code a state as having an SYG law if the state has a law that permits the use of lethal force for self-defense outside of the defender’s home or vehicle, even when a retreat from danger would have been possible. Without such laws, individuals who use deadly force in self-defense may face criminal or civil penalties if they could have avoided the threat by leaving the situation or using non-deadly means of defense.

We code a state as having an RTC law if concealed carry permits are issued whenever legally permissible without the discretion of law enforcement. Specifically, states that either prohibit concealed carry of firearms, or that “may issue” concealed carry permits are coded as not having an RTC law; states that either “shall issue” concealed carry permits to those who meet legal requirements or that allow concealed carry without any permit are coded as having an RTC law.

State laws were classified using the RAND State Firearm Law Database (Version 2.0, October 10, 2019).

We use Bayesian methods and a modeling approach that addresses several methodological limitations of prior gun policy evaluations.

Bayesian methods allow us to directly estimate the probability that a given law is associated with an increase or a decrease in firearms deaths. These probabilities directly correspond to the likely effects of the yes-or-no decisions facing policymakers who are considering such legislation, rather than tests of a null hypothesis.

Second, our simulations revealed that estimates of the effects of state gun policies generally lack sufficient statistical power to detect effects of the size likely to be found for common gun policies, even when these effects are of a magnitude that would interest policymakers. Conducting significance testing with such low statistical power results in a high probability of inconclusive or inaccurate results, even when there is useful information about the true effect within the available data. Using Bayesian inference generally avoids these problems in the same data when estimated with modestly informative priors.

Finally, we estimated the effects of the law by computing marginal effects in each year after implementation. This helped produce unbiased treatment estimates within an autoregressive model by estimating effects of the law in a specific year that take into account both the direct effect of the law on the outcome in that year and the indirect effect of the law in the prior year that is mediated through the autoregressive term into that year.

In addition to examining the three classes of laws individually, we also estimate their joint effects. Each of these three laws can be seen as either restricting firearm access and use or permitting firearm access and use. As such, we examine whether having a restrictive policy regime (a CAP law but no RTC or SYG laws) is associated with different firearm death rates than having a permissive policy regime (no CAP law but RTC and SYG laws).

Consistent with our findings about the effects of individual law, estimation of the joint effects of these laws indicates that a restrictive policy regime (with CAP laws, but without either RTC or SYG laws) is associated with a subsequent decrease in deaths relative to a permissive regime (with RTC and SYG laws but without CAP laws). We estimate that there is a 0.98 probability that the restrictive regime is associated with a subsequent decrease in firearm deaths by the sixth year after implementation.

Complete details about the methods and materials used to conduct this research can be found in the full study.