The Great Gun Fight. - book review

Reason, August, 2001

A REASON ONLINE debate

More guns mean less crime. That's the essential thesis of John R. Lott Jr.'s path-breaking book, appropriately titled More Guns, Less Crime: Understanding Crime and Gun Control Laws (University of Chicago Press, 2000), which looked at the relationship between liberalized gun laws and criminal activity. In both the original 1998 and revised 2000 editions, Lott, a senior research scholar at Yale Law School, used national gun and crime data to perform an unprecedentedly thorough study of the issue. On the face of it, his claim makes sense: If criminals assume that potential victims may be armed, they'll be less likely to act. (See "Cold Comfort," January 2000.)

Not so fast, says George Mason University physicist Robert Ehrlich. In his new book, Nine Crazy Ideas in Science (A Few May Even Be True) (Princeton University Press), Ehrlich argues that the data are in fact inconclusive and that Lott is massaging the results to fit his theory. Ehrlich, a gun owner himself, concludes that liberalized gun laws have had no appreciable effect one way or another.

So which is it? We invited Ehrlich and Lott to debate the issue on REASON ONLINE from May 21-24. Each was allowed to make two contributions and, after the initial salvo, each had to respond within hours of the other's posting. Readers interested in more information can visit reason.com/hod/debatel.html, which includes links to many of the sources mentioned below, including both Ehrlich's and Lott's books.

Robert Ehrlich

More Guns Mean More Guns

Why John Lott is wrong

John Lott's book, More Guns, Less Crime contains many points with which I agree. For example, I believe that many criminals are leery of approaching potential victims who may be armed--an idea at the core of his deterrence theory that guns help to prevent crime. I also believe that violent criminals are not typical citizens, and that the possession of a gun by a law-abiding citizen is unlikely to turn him into a crazed killer. Additionally, Lott has a point when he speaks of the media's overreporting of gun violence by and against kids and the corresponding underreporting of the defensive use of guns to prevent crime.

As a gun owner myself, I was quite prepared to accept Lott's thesis that the positive deterrent effect of guns exceeds their harmful effects on society, but as a scientist I have to be guided by what the data actually show, and Lott simply hasn't made his case. Here's why:

Lott misrepresents the data. His main argument that guns reduce crime is based on the impact on various violent crime rates of "concealed carry laws," which allow legal gun owners to carry concealed weapons. Since these laws were passed at different dates in different states, he looks at how the crime rates change at t=0, the date of the law's passage in each state. Lott's book displays a series of very impressive-looking graphs that show dramatic and in some cases immediate drops in every category of violent crime at time t=0. The impact on robberies is particularly impressive, where a steeply rising robbery rate suddenly turns into a steeply falling rate right at t=0--almost like the two sides of a church steeple. As they say, when something looks too good to be true, it probably is. Lott neglects to tell the reader that all his plots are not the actual FBI data (downloadable from their Web site), but merely his fits to the data.

The actual data are much more irregular with lots of ups and downs, and they show nothing special happening at time t=0. Lott has used the data from 10 states in his book. When we look at changes in the robbery rate state by state, only two of the states (West Virginia and Georgia) show decreases at t=0, while the other eight show increases. 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. In fact, Lott's method of doing his fits is virtually guaranteed to produce an "interesting" result at time t=0. What he 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.

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. Only if the data just happened to lie on a single parabola on both sides of t=0 would the fits show nothing special at that time. Since random data would show a drop or a rise equally often at t=0, we have a 50 percent chance of finding a drop--not a very good argument for the drop being real. The fact that all categories of violent crime (murder, rape, assault, robbery) show drops is also not particularly surprising, since the causes of violent crime (whatever they are) probably affect the rates in all the separate categories. Similarly, it is no more mysterious that when the overall stock market rises or falls dramatically the individual sectors (industrials, utilities, etc.) are more likely than not to move in the same direction.