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Old 12-18-2012, 10:36 AM   #204
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The Right To Keep And Bear Arms
The Chicago Study

Crime, Deterrence, and Right-to-Carry Concealed Handguns

John R. Lott, Jr.

School of Law

University of Chicago

Chicago, Illinois 60637


David B. Mustard

Department of Economics

University of Chicago

Chicago, Illinois 60637

July 26, 1996

* The authors would like to thank Gary Becker, Phil Cook, Clayton
Cramer, Gertrud Fremling, Ed Glaeser, Hide Ichimura, Don Kates, Gary
Kleck, David Kopel, William Landes, David McDowall, Derek Neal, Dan
Polsby, and Douglas Weil and the seminar participants at the
University of Chicago, American Law and Economics Association
Meetings, and the Western Economic Association Meetings for their
unusually helpful comments.

Crime, Deterrence, and Right-to-Carry Concealed Handguns


Using cross-sectional time-series data for U.S. counties from 1977 to
1992, we find that allowing citizens to carry concealed weapons deters
violent crimes and it appears to produce no increase in accidental
deaths. If those states which did not have right-to-carry concealed
gun provisions had adopted them in 1992, approximately 1,570 murders;
4,177 rapes; and over 60,000 aggravate assaults would have been
avoided yearly. On the other hand, consistent with the notion of
criminals responding to incentives, we find criminals substituting
into property crimes involving stealth and where the probabilities of
contact between the criminal and the victim are minimal. The largest
population counties where the deterrence effect on violent crimes is
greatest are where the substitution effect into property crimes is
highest. Concealed handguns also have their greatest deterrent effect
in the highest crime counties. Higher arrest and conviction rates
consistently and dramatically reduce the crime rate. Consistent with
other recent work (Lott, 1992b), the results imply that increasing the
arrest rate, independent of the probability of eventual conviction,
imposes a significant penalty on criminals. The estimated annual gain
from allowing concealed handguns is at least $6.214 billion.

I. Introduction

Will allowing concealed handguns make it likely that otherwise law
abiding citizens will harm each other? Or, will the threat of citizens
carrying weapons primarily deter criminals? To some, the logic is
fairly straightforward. Philip Cook argues that, "If you introduce a
gun into a violent encounter, it increases the chance that someone
will die."[1] A large number of murders may arise from unintentional
fits of rage that are quickly regretted, and simply keeping guns out
of people's reach would prevent deaths.[2] Using the National Crime
Victimization Survey (NCVS), Cook (1991, p. 56, fn. 4) further states
that each year there are "only" 80,000 to 82,000 defensive uses of
guns during assaults, robberies, and household burglaries.[3] By
contrast, other surveys imply that private firearms may be used in
self-defense up to two and a half million times each year, with
400,000 of these defenders believing that using the gun "almost
certainly" saved a life (Kleck and Gertz, 1995, pp. 153, 180, and
182-3).[4] With total firearm deaths from homicides and accidents
equaling 19,187 in 1991 (Statistical Abstract of the United States,
1995), the Kleck and Gertz numbers, even if wrong by a very large
factor, suggest that defensive gun use on net saved lives.

While cases like the 1992 incident where a Japanese student was shot
on his way to a Halloween party in Louisiana make international
headlines (Japan Economic Newswire, May 23, 1993 and Sharn, USA TODAY,
September 9, 1993), they are rare. In another highly publicized case,
a Dallas resident recently became the only Texas resident so far
charged with using a permitted concealed weapon in a fatal shooting
(Potok, March 22, 1996, p. 3A).[5] Yet, in neither case was the
shooting found to be unlawful.[6] The rarity of these incidents is
reflected in Florida statistics: 221,443 licenses were issued between
October 1, 1987 and April 30, 1994, but only 18 crimes involving
firearms were committed by those with licenses (Cramer and Kopel,
1995, p. 691).[7] While a statewide breakdown on the nature of those
crimes is not available, Dade county records indicate that four crimes
involving a permitted handgun took place there between September 1987
and August 1992 and none of those cases resulted in injury (pp.

The potential defensive nature of guns is indicated by the different
rates of so-called "hot burglaries," where residents are at home when
the criminals strike (e.g., Kopel, 1992, p. 155 and Lott, 1994).
Almost half the burglaries in Canada and Britain, which have tough gun
control laws, are "hot burglaries." By contrast, the U.S., with laxer
restrictions, has a "hot burglary" rate of only 13 percent. Consistent
with this, surveys of convicted felons in America reveals that they
are much more worried about armed victims than they are about running
into the police. This fear of potentially armed victims causes
American burglars to spend more time than their foreign counterparts
"casing" a house to ensure that nobody is home. Felons frequently
comment in these interviews that they avoid late-night burglaries
because "that's the way to get shot."[8]

The case for concealed handgun use is similar. The use of concealled
handguns by some law abiding citizens may create a positive
externality for others. By the very nature of these guns being
concealed, criminals are unable to tell whether the victim is armed
before they strike, thus raising criminals' expected costs for
committing many types of crimes.

Stories of individuals using guns to defend themselves has helped
motivate thirty-one states to adopt laws requiring authorities to
issue, without discretion, concealed-weapons permits to qualified
applicants.[9] This constitutes a dramatic increase from the nine
states that allowed concealed weapons in 1986.[10] While many studies
examine the effects of gun control (see Kleck, 1995 for a survey), and
a smaller number of papers specifically address the right-to-carry
concealed firearms (e.g., Cook, et al., 1995; Cramer and Kopel, 1995;
McDowall, et. al., 1995; and Kleck and Patterson, 1993), these papers
involve little more than either time-series or cross-sectional
evidence comparing mean crime rates, and none controls for variables
that normally concern economists (e.g., the probability of arrest and
conviction and the length of prison sentences or even variables like
personal income).[11] These papers fail to recognize that, since it is
frequently only the largest population counties that are very
restrictive when local authorities have been given discretion in
granting concealed handgun permits, "shall issue" concealed handgun
permit laws, which require permit requests be granted unless the
individual has a criminal record or a history of significant mental
illness (Cramer and Kopel, 1995, pp. 680-707), will not alter the
number of permits being issued in all counties.

Other papers suffer from additional weaknesses. The paper by McDowall,
et. al. (1995), which evaluates right-to-carry provisions, was widely
cited in the popular press. Yet, their study suffers from many major
methodological flaws: for instance, without explanation, they pick
only three cities in Florida and one city each in Mississippi and
Oregon (despite the provisions involving statewide laws); and they
neither use the same sample period nor the same method of picking
geographical areas for each of those cities.[12]

Our paper hopes to overcome these problems by using annual
cross-sectional time-series county level crime data for the entire
United States from 1977 to 1992 to investigate the impact of "shall
issue" right-to-carry firearm laws. It is also the first paper to
study the questions of deterrence using these data. While many recent
studies employ proxies for deterrence ---- such as police expenditures
or general levels of imprisonment (Levitt, 1996) ----, we are able to
use arrest rates by type of crime, and for a subset of our data also
conviction rates and sentence lengths by type of crime.[13] We also
attempt to analyze a question noted but not empirically addressed in
this literature: the concern over causality between increases in
handgun usage and crime rates. Is it higher crime that leads to
increased handgun ownership, or the reverse? The issue is more
complicated than simply whether carrying concealed firearms reduces
murders because there are questions over whether criminals might
substitute between different types of crimes as well as the extent to
which accidental handgun deaths might increase.

II. Problems Testing the Impact of "Shall Issue" Concealed Handgun

on Crime

Starting with Becker (1968), many economists have found evidence
broadly consistent with the deterrent effect of punishment (e.g.,
Ehrlich (1973), Block and Heineke (1975), Landes (1978), Lott (1987),
Andreoni (1995), Reynolds (1995), and Levitt (1996)). The notion is
that the expected penalty affects the prospective criminal's desire to
commit a crime. This penalty consists of the probabilities of arrest
and conviction and the length of the prison sentence. It is reasonable
to disentangle the probability of arrest from the probability of
conviction since accused individuals appear to suffer large
reputational penalties simply from being arrested (Lott, 1992b).
Likewise, conviction also imposes many different penalties (e.g., lost
licenses, lost voting rights, further reductions in earnings, etc.)
even if the criminal is never sentenced to prison (Lott, 1990b, 1992a
and b).

While this discussion is well understood, the net effect of "shall
issue" right-to-carry, concealed handguns is ambiguous and remains to
be tested when other factors influencing the returns to crime are
controlled for. The first difficulty involves the availability of
detailed county level data on a variety of crimes over 3054 counties
during the period from 1977 to 1992. Unfortunately, for the time
period we study, the FBI's Uniform Crime Report only includes arrest
rate data rather than conviction rates or prison sentences. While we
make use of the arrest rate information, we will also use county level
dummies, which admittedly constitute a rather imperfect way to control
for cross county differences such as differences in expected
penalties. Fortunately, however, alternative variables are available
to help us proxy for changes in legal regimes that affect the crime
rate. One such method is to use another crime category as an exogenous
variable that is correlated with the crimes that we are studying, but
at the same time is unrelated to the changes in right-to-carry firearm
laws. Finally, after telephoning law enforcement officials in all 50
states, we were able to collect time-series county level conviction
rates and mean prison sentence lengths for three states (Arizona,
Oregon, and Washington).

The FBI crime reports include seven categories of crime: murder, rape,
aggravated assault, robbery, auto theft, burglary, and larceny.[14]
Two additional summary categories were included: violent crimes
(including murder, rape, aggravated assault, and robbery) and property
crimes (including auto theft, burglary, and larceny). Despite being
widely reported measures in the press, these broader categories are
somewhat problematic in that all crimes are given the same weight
(e.g., one murder equals one aggravated assault). Even the narrower
categories are somewhat broad for our purposes. For example, robbery
includes not only street robberies which seem the most likely to be
affected by "shall issue" laws, but also bank robberies where the
additional return to having armed citizens would appear to be
small.[15] Likewise, larceny involves crimes of "stealth," but these
range from pick pockets, where "shall issue" laws could be important,
to coin machine theft.[16]

This aggregation of crime categories makes it difficult to separate
out which crimes might be deterred from increased handgun ownership,
and which crimes might be increasing as a result of a substitution
effect. Generally, we expect that the crimes most likely to be
deterred by concealed handgun laws are those involving direct contact
between the victim and the criminal, especially those occurring in a
place where victims otherwise would not be allowed to carry firearms.
For example, aggravated assault, murder, robbery, and rape seem most
likely to fit both conditions, though obviously some of all these
crimes can occur in places like residences where the victims could
already possess firearms to protect themselves.

By contrast, crimes like auto theft seem unlikely to be deterred by
gun ownership. While larceny is more debatable, in general ---- to the
extent that these crimes actually involve "stealth" ---- the
probability that victims will notice the crime being committed seems
low and thus the opportunities to use a gun are relatively rare. The
effect on burglary is ambiguous from a theoretical standpoint. It is
true that if "shall issue" laws cause more people to own a gun, the
chance of a burglar breaking into a house with an armed resident goes
up. However, if some of those who already owned guns now obtain
right-to-carry permits, the relative cost of crimes like armed street
robbery and certain other types of robberies (where an armed patron
may be present) should rise relative to that for burglary.

Previous concealed handgun studies that rely on state level data
suffer from an important potential problem: they ignore the
heterogeneity within states (e.g., Linsky, et. al., 1988 and Cramer
and Kopel, 1995). Our telephone conversations with many law
enforcement officials have made it very clear that there was a large
variation across counties within a state in terms of how freely gun
permits were granted to residents prior to the adoption of "shall
issue" right-to-carry laws.[17] All those we talked to strongly
indicated that the most populous counties had previously adopted by
far the most restrictive practices on issuing permits. The implication
for existing studies is that simply using state level data rather than
county data will bias the results against finding any impact from
passing right-to-carry provisions. Those counties that were unaffected
by the law must be separated out from those counties where the change
could be quite dramatic. Even cross-sectional city data (e.g., Kleck
and Patterson, 1993) will not solve this problem, because without time
series data it is impossible to know what impact a change in the law
had for a particular city.

There are two ways of handling this problem. First, for the national
sample, we can see whether the passage of "shall issue" right-to-carry
laws produces systematically different effects between the high and
low population counties. Second, for three states, Arizona, Oregon,
and Pennsylvania, we have acquired time series data on the number of
right-to-carry permits for each county. The normal difficulty with
using data on the number of permits involves the question of
causality: do more permits make crimes more costly or do higher crimes
lead to more permits? The change in the number of permits before and
after the change in the state laws allows us to rank the counties on
the basis of how restrictive they had actually been in issuing permits
prior to the change in the law. Of course there is still the question
of why the state concealed handgun law changed, but since we are
dealing with county level rather than state level data we benefit from
the fact that those counties which had the most restrictive permitting
policies were also the most likely to have the new laws exogenously
imposed upon them by the rest of their state.

Using county level data also has another important advantage in that
both crime and arrest rates vary widely within states. In fact, as
Table 1 indicates, the standard deviation of both crime and arrest
rates across states is almost always smaller than the average within
state standard deviation across counties. With the exception of
robbery, the standard deviation across states for crime rates ranges
from between 61 and 83 percent of the average of the standard
deviation within states. (The difference between these two columns
with respect to violent crimes arises because robberies make up such a
large fraction of the total crimes in this category.) For arrest
rates, the numbers are much more dramatic, with the standard deviation
across states as small as 15 percent of the average of the standard
deviation within states. These results imply that it is no more
accurate to view all the counties in the typical state as a homogenous
unit than it is to view all the states in the United States as one
homogenous unit. For example, when a state's arrest rate rises, it may
make a big difference whether that increase is taking place in the
most or least crime prone counties. Depending upon which types of
counties the changes in arrest rates are occurring in and depending on
how sensitive the crime rates are to changes in those particular
counties could produce widely differring estimates of how increasing a
state's average arrest rate will deter crime. Aggregating these data
may thus make it more difficult to discern the true relationship that
exists between deterrence and crime.

Perhaps the relatively small across-state variation as compared to
within-state variations is not so surprising given that states tend to
average out differences as they encompass both rural and urban areas.
Yet, when coupled with the preceding discussion on how concealed
handgun provisions affected different counties in the same state
differently, these numbers strongly imply that it risky to assume that
states are homogenous units with respect to either how crimes are
punished or how the laws which affect gun usage are changed.
Unfortunately, this focus of state level data is pervasive in the
entire crime literature, which focuses on state or city level data and
fails to recognize the differences between rural and urban counties.

However, using county level data has some drawbacks. Frequently,
because of the low crime rates in many low population counties, it is
quite common to find huge variations in the arrest and conviction
rates between years. In addition, our sample indicates that annual
conviction rates for some counties are as high as 13 times the offense
rate. This anomaly arises for a couple reasons. First, the year in
which the offense occurs frequently differs from the year in which the
arrests and/or convictions occur. Second, an offense may involve more
than one offender. Unfortunately, the FBI data set allows us neither
to link the years in which offenses and arrests occurred nor to link
offenders with a particular crime. When dealing with counties where
only a couple murders occur annually, arrests or convictions can be
multiples higher than the number of offenses in a year. This data
problem appears especially noticeable for murder and rape.

One partial solution is to limit the sample to only counties with
large populations. For counties with a large numbers of crimes, these
waves have a significantly smoother flow of arrests and convictions
relative to offenses. An alternative solution is to take a moving
average of the arrest or conviction rates over several years, though
this reduces the length of the usable sample period, depending upon
how many years are used to compute this average. Furthermore, the
moving average solution does nothing to alleviate the effect of
multiple suspects being arrested for a single crime.

Another concern is that otherwise law abiding citizens may have
carried concealed handguns even before it was legal to do so. If shall
issue laws do not alter the total number of concealed handguns carried
by otherwise law abiding citizens but merely legalizes their previous
actions, passing these laws seems unlikely to affect crime rates. The
only real effect from making concealed handguns legal could arise from
people being more willing to use handguns to defend themselves, though
this might also imply that they more likely to make mistakes using
these handguns.

It is also possible that concealed firearm laws both make individuals
safer and increase crime rates at the same time. As Peltzman (1975)
has pointed out in the context of automobile safety regulations,
increasing safety can result in drivers offsetting these gains by
taking more risks in how they drive. The same thing is possible with
regard to crime. For example, allowing citizens to carry concealed
firearms may encourage people to risk entering more dangerous
neighborhoods or to begin traveling during times they previously
avoided. Thus, since the decision to engage in these riskier
activities is a voluntary one, it is possible that society still could
be better off even if crime rates were to rise as a result of
concealed handgun laws.

Finally, there are also the issues of why certain states adopted
concealed handgun laws and whether higher offense rates result in
lower arrest rates. To the extent that states adopted the law because
crime were rising, ordinary least squares estimates would underpredict
the drop in crime. Likewise, if the rules were adopted when crimes
rates were falling, the bias would be in the opposite direction. None
of the previous studies deal with this last type of potential bias. At
least since Ehrlich (1973, pp. 548-553), economists have also realized
that potential biases exist from having the offense rate as both the
endogenous variable and as the denominator in determining the arrest
rate and because increasing crime rates may lower the arrest if the
same resources are being asked to do more work. Fortunately, both
these sets of potential biases can be dealt with using two-stage
III. The Data

Between 1977 and 1992, 10 states (Florida (1987), Georgia (1989),
Idaho (1990), Maine (1985), Mississippi (1990), Montana (1991), Oregon
(1990), Pennsylvania (1989), Virginia (1988), and West Virginia
(1989)) adopted "shall issue" right-to-carry firearm laws. However,
Pennsylvania is a special case because Philadelphia was exempted from
the state law during our sample period. Nine other states (Alabama,
Connecticut, Indiana, Maine, New Hampshire, North Dakota, South
Dakota, Vermont, and Washington) effectively had these laws on the
books prior to the period being studied.[18] Since the data are at the
county level, a dummy variable is set equal to one for each county
operating under "shall issue" right-to-carry laws. A Nexis search was
conducted to determine the exact date on which these laws took effect.
For the states that adopted the law during the year, the dummy
variable for that year is scaled to equal that portion of the year for
which the law was in effect.

more here...I'm sure we'll have some TLDR's but it's here anyways.
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