Spurious Correlations

As well as finding significant correlations with the carry law Lott found many other correlations. Some of these make sense, for example, the an increase in the percentage of the population that is black, male, and aged 10-19 is correlated with an increase in the property crime rate. Others, however do not, for example, an increase of 1 percentage point in the percentage of the population that is black, female, and 40-49 is associated with a 30% decrease in rape, and a 24% increase in homicide in the average county.1These two correlations (and many others) are spurious.

In response to a similar critique by Alschuler [1] Lott argues that these correlations make sense because black females aged 40-49 could be more likely to be crime victims (page 144). It is true that if they were about 24 times more likely to be homicide victims than the general population, then the correlation would make sense. However, the FBI's Uniform Crime Reports [36] shows that black females aged 40-49 made up just 1.3% of murder victims. This is more than the 0.4% of the population that is black female aged 40-49, but clearly not 24 times more. And the association with a 30% decrease in rape makes even less sense. Even if no women in this group were ever rape victims, this would only account for an association with a 1% decrease.

Even more troublesome are the results of the two-stage least squares (2SLS) regressions. The correlations discussed so far were computed assuming that crime rates do not affect arrest rates, which does not seem a reasonable assumption. Table 11 of [31] reports the results of rerunning the regressions using two-stage least squares, which allows arrest rates and crime rates to affect each other. The size of the effect associated with the carry law are spectacularly different from those in table 3 of [31]. For example, the effect on property crime changes from a 3% increase to a 67% decrease and the effect on violent crime changes from 5% decrease to a 72% decrease. The 2SLS regressions are clearly spurious and indicate severe problems with the model.

Dezhbakhsh [9] offers further evidence that Lott's 2SLS model is incorrect:

[Lott] obtains mostly negative numbers for arrests. For example, more than 19,000 of approximately 33,000 county-level auto theft arrests are "negative"; the number of negative arrest rates for aggravated assault and property crimes are, respectively, 9,900 and 13,500. What does a negative arrest rate mean? Obviously, the number of individuals arrested for crimes can only be zero or positive.

Black and Nagin [5] report further evidence that Lott's model is not correct. They applied Heckman-Hotz tests [16] which indicated the presence of systematic factors, not modeled by Lott, which significantly affected the crime rate.

Tim Lambert