THE EFFECT OF CONCEALED WEAPONS LAWS: AN EXTREME BOUND ANALYSIS

I. INTRODUCTION In a recent paper, Lott and Mustard [1997] provide evidence on the relationship between concealed handgun laws (often called "right-to-carry" or "shall issue" laws) and crime. They examine 16 years (1977-1992) of county-level crime data from the FBI's Uniform Crime Reports (UCR) and compare crime rates before and after the introduction of right-to-carry laws, controlling for demographic factors and county-level arrest rates. They find that introducing right-to-carry laws resulted in significant reductions in the violent crimes of murder, rape, aggravated assault and robbery, and increases in some property crimes such as burglary, larceny and auto theft. The latter increase is attributed to a "substitution effect," suggesting that potential criminals will (at the margin) switch from violent crimes that have now become more risky to them, to property crimes where the probability of encountering an armed victim is much lower. Comparing these two effects (coupled with a slight increase in accidental gunshots from individuals owning concealed handguns), Lott and Mustard conclude that the social benefits of these laws exceed their costs. Economists who present one set of empirical results are always vulnerable to criticism that they could have selected another group of right hand side variables or modeled them in a different way. Lott and Mustard's analysis has been criticized by Black and Nagin [1998], who re-examine the data set choosing a different set of controls and a different model and reach different conclusions about the effects of the right-to-carry laws. Uncertainty about what to control for and how to do it has been called "model uncertainty" by Leamer [1983] who suggests estimating the size of model uncertainty by testing the sensitivity of the particular results to many different possible model specifications. This methodology is called "extreme bound analysis." In this paper, we report extreme bounds from nearly 20,000 estimated regressions that vary the modeling choices made by Lott and Mustard. We find support for the deterrence result from a broad set of specifications. In particular, we find strong support for the hypothesis that the right-to-carry laws are associated with a decrease in the trend in violent crime rates. We do not find much support for a substitution effect into property crimes. In what follows, we first describe some econometric issues related to the problem of estimating the effect of the right-to-carry laws on crime rates. We then report our extreme bound analysis and offer concluding remarks. II. EXTREME BOUND ANALYSIS Although behavioral theories can provide general guidance about the kinds of factors that should influence the crime rate, they do not specifically determine which variables should be included in a regression analysis. In addition, conflicting theories typically suggest alternate model specifications. In the context of crime rates, for example, we might expect higher crime rates in counties with higher unemployment and a larger percentage of young men age 18-30. Following convention, we call this a "supply" effect (Nagin [1978]). However, it is also possible that higher unemployment leads to the passage of right-to-carry laws, or that right-to-carry laws are less likely to be enacted in areas with high percentages of young men age 18-30. We call this a "demand" effect. Omitted or unobserved demand or supply variables may induce a spurious correlation between the adoption of the right-to-carry laws and crime rates that has nothing to do with the supply of crime. Leamer [1983] suggests a formal specification search, or "extreme bound" analysis, to estimate the size of such "model uncertainty" surrounding certain regression results. For example, by including different right hand side variables in a supply-of-crime equation for murder, Leamer estimates an extreme bound interval for the deterrent effect of capital punishment that ranges from +29 to -12 murders prevented for every execution. …