Lott's response is completely inadequate. As
shown elsewhere
permit holders are much less, not more, likely to encounter
violent criminals.
His argument here is not even internally consistent. In his
last paragraph he argues that if permit holders face the same risk of
being attacked as everyone else, only 0.65% of permit holders need to
thwart an aggravated assault to account for the observed drop in the
assault rate. But in the previous paragraph he stated that only 0.18of the population are victims of aggravated assault, so if permit
holders face the same risk as everyone else, only 0.18% of them will
even have a chance to thwart an aggravated assault. This is much less
than 0.65%, so if permit holders face the same risk, it is not
possible for 0.65% of them to thwart an aggravated assault.
The fact that permit holders are less likely to be crime victims makes
this comparison even worse for Lott.
Lott is correct that he controls for national crime cycles, but the
state time trends do not control for cycles at the state level.
Lott claims that the reductions in crime begin right when the carry
laws were passed and that it too much of a coincidence to expect a
crime cycle to have peaked exactly when the law was passed.
However, as
shown elsewhere
the fact that the declines in Lott's graphs begin at the time of
the law is an artifact of the way the graphs were created--it is not
easy for a decline to begin anywhere else in his graphs.
While it is true that Lott also included models where the effect
increased with time, the model given greatest prominence in his paper
is the one with an immediate and constant effect. The abstract of
that paper [31] states:
If those states without right-to-carry concealed gun provisions had
adopted them in 1992, county- and state-level data indicate that
approximately 1,500 murders would have been avoided
yearly. Similarly, we predict that rapes would have declined by over
4,000, robbery by over 11,000, and aggravated assaults by over
60,000.
These numbers are based on the constant-effect model, so it does not
seem unreasonable for critics to concentrate on that model.
While there do seem to be some problems in classifying when Maine and
Virginia passed their laws, showing that the results do not depend on
Maine or Virginia is a satisfactory response.
Lott does not seem to have even understood the criticism. Webster
wrote ``theft is the motive for only a small fraction of the violent
crimes for which Lott and Mustard find shall-issue effects''. Lott
responds that robberies made up 34% of violent crimes, missing
Webster's point that in his main analysis (table 3 of the paper and
the results reported in the abstract), Lott and Mustard did not find a
statistically significant effect on robbery.
Lott is correct in noting that it is the change in the number of
permits that matters. However, he has that data for only a few
states, which does not show consistent effects, possibly because there
is so little data.
Lott argues that he accounted for the fact the crime rate and the
arrest rate mutually effect each other with a two stage least squares
analysis (2SLS). He claims that the 2SLS estimates provide ``even
stronger evidence that concealed handguns deter crime''. This claim
is wrong. It is true that the crime reductions associated with the
laws are larger in the 2SLS analysis, but they are so much larger
(a 67% reduction in homicide and a 65% reduction in rape)
that they suggest that the model is incorrect.
Fortunately for Lott, his second argument is a better one--excluding
arrests from the analysis has little effect on the results.
Webster writes ``What is not obvious to the casual observer of the
graphs is that each data point represents an aggregate average for
states that liberalized their gun-carrying laws, but the states that
make up the average are not the same each year.''
Lott denies that the graphs are misleading, but the test for whether
the graphs are misleading is whether people have been misled.and it is
clear that Webster has been misled by Lott's graphs. The data points
on the graphs do not represent averages for states that liberalized
their laws. Rather, the points are not data points at all, but just
show where the curve fitted through the data points goes.
More about the problems with Lott's graphs is
here.
Lott's counterargument is that the observed changes in victim
characteristics is not statistically significant. However, if the
observed decrease in murder was caused by the laws, you would expect a
change in the characteristics of victims. Failure to observe
such a change is evidence that the change in the homicide rate was not
caused by the gun laws.
Lott argues that concealed carry laws could cause an increase in gun
ownership and hence deter crimes in homes and other private places.
However, Lott himself noted (page 28) that a Texas poll suggests that 97% of
first-time applicants for concealed-weapon permits already owned a
handgun, that is, concealed carry laws do not significantly increase
gun ownership.
Lott argues that while this objection was plausible when only a few states
had implemented the laws, now that there is data from many states, the
objection is now longer plausible.
The problem here is that while there is now data from many states,
there is not much data from those states that have only recently
changed their laws, and the results are dominated by the few states
that have had their laws for a long time. When more data becomes
Lott writes ``The theory is obvious: A would-be criminal is deterred
by the risk of being shot.'' However, as discussed
earlier, the change in that risk
caused by concealed-carry law is negligible.
Lott goes on to argue that he can link the laws to the crime
reductions because:
``Not only does the drop in crime begin when non-discretionary
laws are adopted.'' This is wrong, as shown earlier
``the extent of the decline is related to the number of permits
issued in the state.'' This is also wrong, as shown
earlier
``Non-discretionary laws reduce crime most in the areas with the
greatest increase in the number of permits.'' Lott has not shown
this at all. See the earlier
discussion.
``crimes that involve criminals and victims in direct contact
[consistently decrease the most]'' This is not true. See
the earlier
discussion.
``crimes occurring in places where the victim was previously
unable to carry a gun are the ones the consistently decrease the
most'' Nor is this true. See
the earlier
discussion.
The question at issue is the possibility that some other factor than
the gun law caused the reduction in crime.
Lott does have a good point when he argues that it would be better if
critics who argue that some other factor caused the reduction in
crime would specify what that factor was, however, as Lott concedes,
it remains possible that some unknown factor caused the crime
changes. Lott's own analysis that found crime trends before the laws
were passed demonstrates that there were factors operating that were
not explained in his model.
Lott goes on to argue that the
reductions were not caused by ``other factors'' because:
``States that were expected to issue the greatest number of new
permits ...observed the largest decreases in crime.'' Of more
relevance is the fact that states that in fact issued the smallest
number of new permits observed the largest decrease in crime. See
the earlier
discussion.
``the number of concealed-handgun permits in a state rises over
time, so we expect to see a greater reduction in crime after [...]
several years'' This is also consistent with normal up and down
trends in crime rates.
``where data on the actual number of permits at the county level
are available, we find that the number of murders declines as the
number of permits increases'' Since the number of permits just
increased with time, this is also consistent with a crime trend.
Lott devotes a whole page to criticism of
of Black and Nagin [5] conducting their analysis using
only counties with populations of more than 100,000, implying that
they were searching for a subset of the data to show that the laws
had no effect.
This is a very strange criticism, since Lott and Mustard did exactly
the same the thing:
Page 35 of [31]
We reran all the regressions in this section first by limiting the
sample to those counties over 10,000, 100,000, and then 200,000
people. Consistent with the evidence reported in Table 7, the more the
sample was limited to
larger population counties the stronger and more statistically
significant was the relationship between concealed handgun laws and
the previously reported effects on crime.
That is, according to Lott and Mustard's original paper, Black and
Nagin's analysis was biased towards finding a beneficial effect
for the gun laws.
Lott then writes:
Despite ignoring all these observations, it is only when they
also remove the data for Florida that they weaken my results
for murder and rape.
This statement is false. Black and Nagin report that removing
Florida makes the effects on murder and rape not statistically
significant whether or not the analysis is restricted to large
counties.
Lott doesn't really address the criticism in his answer. The
criticism is directed at something Lott writes at the end of the
chapter--that even his critics are correct and the models
misspecified, the gun laws have no effect.
As Zimring and Hawkins note, this is not correct. If the critics are
correct and the models wrong, you cannot draw any conclusion about the
effect of the gun laws on crime. It would be possible for the laws to
cause an increase, a decrease or to have no effect.
Lott's answer is about the state of the academic debate. This answer
is updated in section 9.16, so it will be dealt with there.
He also complains about ``the reluctance of gun-control advocates to
release their data'', mentioning Kellermann's study (data available
from the ICPSR, study
6898) and the Police Foundation study on gun ownership and use
(data available
from the ICPSR, study
6955). While Lott may have had trouble getting this data, it is
publicly available on the Internet.
