More Data, More Guns, More Crime

In a critique of econometric studies such as Lott's[15] Ted Goertzel makes an important point--for any study that involves a multiple regression that finds a significant association, it seems that there is another study that applies a different model to the same data and gets a different answer. There are several examples of this happening with Lott's study below.

Goertzel argues convincingly that

When presented with an econometric model, consumers should insist on evidence that it can predict trends in data other than the data used to create it. Models that fail this test are junk science, no matter how complex the analysis.
In the case of Lott's model we are in the fortunate position of being able to test its predictive power. Lott's original data set ended in 1992. Between 1992 and 1996, 14 more jurisdictions (13 states and Philadelphia) adopted carry laws. We can test the predictive power of Lott's model by seeing if it finds less crime in those jurisdictions. Ayres and Donahue [2] have done this test. They found that, using Lott's model, in those jurisdictions carry laws were associated with more crime in all crime categories . Lott's model fails the predictive test.

Ayres and Donahue go on to examine all the states adopting carry laws using data up to 1997 and found that carry laws were associated with crime increases in more states than they were associated with decreases. They rather pointedly observe that

Those who were swayed by the statistical evidence previously offered by Lott and Mustard to believe the more guns, less crime hypothesis should now be more strongly inclined to accept the even stronger statistical evidence suggesting the crime- inducing effect of shall issue laws.

Tim Lambert