The Great Gun Fight

James V. DeLong from the August/September 2001 issue

(Page 2 of 5)

But that's not right. He needs to look at the probability of a change in the crime rate for years t= -3, -2, -1, 0, 1, 2, 3, etc. Only if the probability is very much less for year zero than the other years can he consider his results meaningful. It seems very likely, however, that Lott would find similarly low probabilities for all these other years, because only if the violent crime rate were static over time would there be no significant variation on either side of year t=0, or any other given year. In fact, John Donahue, a law professor at Stanford, analyzed Lott's data and found that the most significant turning point for the robbery rates occurs before t=0.

Lott has correctly observed that, by passing concealed carry laws in various states in various years, the U.S. has been in effect conducting an extremely interesting social experiment. That experiment, in principle, can give us an empirical answer to the relationship between easing restrictions on gun-carrying permits and crime. However, his one-sided analysis of the data inspires little confidence that we can count on him to tell us the true results of this experiment. From all indications it seems that the concealed carry laws probably have had almost no effect, one way or the other.

John R. Lott Jr.

Taking Stock
Less gun control means less violent crime

Robert Ehrlich's review of the first edition of my book, More Guns, Less Crime, is well-written, and it is interesting to know that he owns a gun despite his concerns about research on the benefits of doing so. Unfortunately, however, his discussion is incomplete and simply inaccurate. Below are responses to the more important claims he makes.

"Lott neglects to tell the reader that all his plots are not the actual FBI data...but merely his fits to the data." There are several places in my book that discuss how the diagrams show how crime rates change before and after right-to-carry laws are adopted once other factors have been taken into account. It is important to distinguish not just whether there was a decline in crime rates, but whether there was a decline relative to other states that did not adopt the right-to-carry laws. The second edition of More Guns, Less Crime, which was published in 2000, was also clear on this point, and its graphs showed the changes in crime relative to other states that did not change their laws and were in the same region of the country.

"Lott has used the data from 10 states in his book." I used data from the entire United States. The first edition used state-level data from all the states and the District of Columbia, as well as county-level data for the entire country from 1977 through 1992 (and, in some estimates, up to 1994). The second edition of the book not only updated the county and state data through 1996, but also used city-level data for the largest 2,000 cities. Possibly what Ehrlich means here is that only 10 states (with a total of 718 counties) adopted right-to-carry laws during the 1977-1992 period. The point of examining all counties in all the states was to make a year-by-year comparison of how the crime rates had changed in the counties with the right-to-carry laws relative to the counties in states without the laws. In the second edition of my book, a total of 20 states, representing 1,432 counties, adopted right-to-carry laws between 1977 and 1996.

"The actual data are much more irregular with lots of ups and downs, and they show nothing special happening at time t=0." My book reports the year-to-year changes in crime rates, and these results are consistent with the before-and-after trends. One of the benefits of examining the change in trends is that there are straightforward statistical tests to see if the change is statistically significant.

"Overall, averaging the 10 states, there is a small but not statistically significant increase in the robbery rate at t=0, certainly not the dramatic decrease Lott's fits show." Ehrlich has examined state-level robbery rates for the 10 states that had adopted right-to-carry laws between 1977 and 1992, using data extended up until 1995 for the four years on either side of adoption. He finds that there is no statistically significant change in before-and-after trends. He claims to use data up until 1997, but that is not possible since he limited the sample to only four years after adoption and the first full year these states had the law in effect was 1992. I have tried to replicate his results, but have been unable to do so: Robbery rates are declining after adoption relative to how they were changing prior to adoption.

Yet even if his data analysis had been correct, his approach has a lot of problems. The main difficulty is that there is no comparison of what is going on in the states that do not adopt right-to-carry laws. When such a comparison is made, the drop in crime is about twice as large in right-to-carry states and twice as statistically significant. Accounting for other factors (e.g., the arrest rate for robbery) also increases the statistical significance of the drop. Many aspects of what he did are unclear, such as whether he weighted each state equally or weighted them by population (as is normally done). But neither approach altered the final result.

"What [Lott] does is to fit a smooth curve (actually a parabola) to the data earlier than t=0, and a separate curve to the data later than t=0." This is only one of several different approaches reported in my book. The first edition also presented actual data on the number of permits issued per county over time for several states where the data were available. The second edition further examined whether differences in right-to-carry laws can affect the number of people who get permits (e.g., the permitting fees, the length of the training requirement, and how many years the law has been in effect), and whether this in turn can explain the changes in crime rates.

"Given a completely random set of data, Lott's fitting procedure is virtually guaranteed to yield either a drop or a rise near time t=0." This is not literally true. Besides a flat line, other possibilities very obviously include the crime rate first rising and then falling after adoption -- or falling and then rising. The question is also not whether there is a change in trends, but also whether those changes are statistically significant.

"Similarly, Lott shows the rate of multiple public shootings declining dramatically (by 100 percent) only two years after t=0. But using follow-up data in a more recent paper, Lott shows multiple shootings rising precipitously the year before t=0 and then declining right at t=0." There are no inconsistencies. This paper, co-authored with William M. Landes, examined whether the results were sensitive to removing observations from the year of adoption, as well as the two years prior to adoption. We found that the results remained essentially unchanged.

"It's difficult enough understanding why the impact of the laws should be so much greater on multiple shootings by crazed killers than ordinary murders (which drop only 10 percent), but figuring out how the laws could work in reverse time on the thinking of these psychos is a real challenge." It is all too easy to dismiss mass murderers as totally irrational. But individuals who go on shooting sprees are often motivated by goals such as fame. Making it difficult to obtain those goals may discourage some from engaging in their attacks. There is also the issue of stopping attacks that do still occur. Suppose that a right-to-carry law deters crime primarily by raising the probability that a perpetrator will encounter a potential victim who is armed. In a single-victim crime, this probability is likely to be very low. Hence the deterrent effect of the law -- though negative -- might be relatively small.

Now consider a shooting spree in a public place. In a crowd, the likelihood that one or more potential victims or bystanders are armed would be very large even though the probability that any particular individual is armed is very low. This suggests a testable hypothesis: A right-to-carry law will have a bigger deterrent effect on shooting sprees in public places than on more conventional crimes.