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
   
   and
   
   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
   
   Abstract
   
   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.
   691-2).
   
   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
   Provisions
   
   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
   least-squares.
   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.
   
   While the number of arrests and offenses for each type of crime in
   every county from 1977 to 1992 were provided by the Uniform Crime
   Report, we also contacted the state department of corrections, State
   Attorney Generals, State Secretary of State, and State Police offices
   in every state to try to compile data on conviction rates, sentence
   lengths, and right-to-carry concealed weapons permits by county. The
   Bureau of Justice Statistics also released a list of contacts in every
   state that might have available state level criminal justice data.
   Unfortunately, county data on the total number of outstanding
   right-to-carry pistol permits were available for only Arizona,
   California, Florida, Oregon, Pennsylvania, and Washington, though time
   series county data before and after a change in the permitting law was
   only available for Arizona (1994 to 1996), Oregon (1990 to 1992) and
   Pennsylvania (1986 to 1992). Since the Oregon "shall issue" law passed
   in 1990, we attempted to get data on the number of permits in 1989 by
   calling up every county sheriff in Oregon, with 25 of the 36 counties
   providing us with this information. (The remaining counties claimed
   that records had not been kept.)[19] For Oregon, data on the county
   level conviction rate and prison sentence length was also available
   from 1977 to 1992.
   
   One difficulty with the sentence length data is that Oregon passed a
   sentencing reform act that went into effect in November 1989 causing
   criminals to serve 85 percent of their sentence, and thus judges may
   have correspondingly altered their rulings. Even then, this change was
   phased in over time because the law only applied to crimes that took
   place after it went into effect in 1989. In addition, the Oregon
   system did not keep complete records prior to 1987, and the
   completeness of these records decreased the further into the past one
   went. One solution to both of these problems is to interact the prison
   sentence length with year dummy variables. A similar problem exists
   for Arizona which adopted a truth-in-sentencing reform during the fall
   of 1994. Finally, Arizona is different from Oregon and Pennsylvania in
   that it already allowed handguns to be carried openly before passing
   its concealed handgun law, thus one might expect to find a somewhat
   smaller response to adopting a concealed handgun law.
   
   In addition to using county dummy variables, other data were collected
   from the Bureau of the Census to try controlling for other demographic
   characteristics that might determine the crime rate. These data
   included information on the population density per square mile, total
   county population, and detailed information on the racial and age
   breakdown of the county (percent of population by each racial group
   and by sex between 10 and 19 years of age, between 20 and 29, between
   30 and 39, between 40 and 49, between 50 and 64, and 65 and over).
   (See Table 2 for the list and summary statistics.) While a large
   literature discusses the likelihood of younger males engaging in crime
   (e.g., Wilson and Herrnstein, 1985, pp. 126-147), controlling for
   these other categories allows us to also attempt to measure the size
   of the groups considered most vulnerable (e.g., females in the case of
   rape).[20] Recent evidence by Glaeser and Sacerdote (1995) confirms
   the higher crime rates experienced in cities and examines to what
   extent this arises due to social and family influences as well as the
   changing pecuniary benefits from crime, though this is the first paper
   to explicitly control for population density. The data appendix
   provides a more complete discussion of the data.
   
   An additional set of income data was also used. These included real
   per capita personal income, real per capita unemployment insurance
   payments, real per capita income maintenance payments, and real per
   capita retirement payments per person over 65 years of age.[21]
   Including unemployment insurance and income maintenance payments from
   the Commerce Department's Regional Economic Information System (REIS)
   data set were attempts to provide annual county level measures of
   unemployment and the distribution of income.
   
   Finally, we recognize that other legal changes in penalties involving
   improper gun use might also have been changing simultaneously with
   changes in the permitting requirements for concealed handguns. In
   order to see whether this might confound our ability to infer what was
   responsible for any observed changes in crimes rates we read through
   various editions of the Bureau of Alcohol, Tobacco, and Firearms'
   State Laws and Published Ordinances - Firearms (1976, 1986, 1989, and
   1994). Excluding the laws regarding machine guns and sawed-off
   shotguns, there is no evidence that the laws involving the use of guns
   changed significantly when concealed permit rules were changed.[22]
   Another survey which addresses the somewhat boarder question of
   sentencing enhancement laws for felonies committed with deadly weapons
   (firearms, explosives, and knives) from 1970-1992 also confirms this
   general finding with all but four of the legal changes clustered from
   1970 to 1981 (Marvell and Moody, 1995, pp. 258-261). Yet, controlling
   for the dates supplied by Marvell and Moody still allows us to examine
   the deterrence effect of criminal penalties specifically targeted at
   the use of deadly weapons during this earlier period.[23]
   
   IV. The Empirical Evidence
   A. Using County Data for the United States
   
   The first group of regressions reported in Table 3 attempt to explain
   the natural log of the crime rate for nine different categories of
   crime. The regressions are run using weighted ordinary least squares.
   While we are primarily interested in a dummy variable to represent
   whether a state has a "shall issue" law, we also control for each type
   of crime's the arrest rate, demographic differences, and dummies for
   the fixed effects for years and counties. The results imply that
   "shall issue" laws coincide with fewer murders, rapes, aggravated
   assaults, and rapes.[24] On the other hand, auto theft and larceny
   rates rise. Both changes are consistent with our discussion on the
   direct and substitution effects produced by concealed weapons.[25]
   Rerunning these specifications with only the "shall issue" dummy, the
   arrest rates, and the fixed year and county effects produces even more
   significant effects for the "shall issue" dummy and the arrest rates.
   
   The results are large empirically. When state concealed handgun laws
   went into effect in a county, murders fell by 8.5 percent, and rapes
   and aggravated assaults fell by 5 and 7 percent. In 1992, there were
   18,469 murders; 79,272 rapes; 538,368 robberies; and 861,103
   aggravated assaults in counties without "shall issue" laws. The
   coefficients imply that if these counties had been subject to state
   concealed handgun laws, murders in the United States would have
   declined by 1,570. Given the concern that has been raised about
   increased accidental deaths from concealed weapons, it is interesting
   to note that the entire number of accidental gun deaths in the United
   States in 1992 was 1,409. Of this total, 546 accidental deaths were in
   states with concealed handgun laws and 863 were in those without these
   laws. The reduction in murders is as much as three times greater than
   the total number of accidental deaths in concealed handgun states.
   Thus, if our results are accurate, the net effect of allowing
   concealed handguns is clearly to save lives. Similarly, the results
   indicate that the number of rapes in states without "shall issue" laws
   would have declined by 4,177; aggravated assaults by 60,363; and
   robberies by 11,898.[26]
   
   On the other hand, property crime rates definitely increased after
   "shall issue" laws were implemented. The results are equally dramatic.
   If states without concealed handgun laws had passed such laws, there
   would have been 247,165 more property crimes in 1992 (a 2.7 percent
   increase). Thus, criminals respond substantially to the threat of
   being shot by instead substituting into less risky crimes.[27]
   
   A recent National Institute of Justice study (Miller, Cohen, and
   Wiersema, 1996) provides estimates the costs of different types of
   crime based upon lost productivity; out-of-pocket expenses such as
   medical bills and property losses; and losses for fear, pain,
   suffering, and lost quality of life. While there are questions about
   using jury awards to measure losses such as fear, pain, suffering, and
   lost quality of life, the estimates provide us one method of comparing
   the reduction in violent crimes with the increase in property crimes.
   Using the numbers from Table 3, the estimated gain from allowing
   concealed handguns is over $6.214 billion in 1992 dollars. The
   reduction in violent crimes represents a gain of $6.6 billion ($4.75
   billion from murder, $1.4 billion from aggravated assault, $374
   million from rape, and $98 million from robbery), while the increase
   in property crimes represents a loss of $417 million ($342 million
   from auto theft, $73 million from larceny, and $1.5 million from
   burglary). However, while $6.2 billion is substantial, to put it into
   perspective, it equals only about 1.33 percent of the total aggregate
   losses from these crime categories. These estimates are probably most
   sensitive to the value of life used (in the Miller et. al. study this
   was set at $1.84 million in 1992 dollars). Higher estimated values of
   life will increase the net gains from concealed handgun use, while
   lower values of life will reduce the gains.[28] To the extent that
   people are taking greater risks towards crime because of any increased
   safety produced by concealed handgun laws (again see Peltzman (1975)),
   these numbers will underestimate the total savings from concealed
   handguns.
   
   The arrest rate produces the most consistent effect on crime. Higher
   arrest rates imply lower crime rates for all categories of crime. A
   one standard deviation change in the probability of arrest accounts
   for 3 to 17 percent of a one standard deviation change in the various
   crime rates. The crime most responsive to arrest rates is burglary (11
   percent), followed by property crimes (10 percent); aggravated assault
   and violent crimes more generally (9 percent); murder (7 percent);
   rape, robbery, and larceny (4 percent); and auto theft (both 3
   percent).
   
   For property crimes, a one standard deviation change in the percent of
   the population that is black, male, and between 10 and 19 years of age
   explains 22 percent of these crime rates. For violent crimes, the same
   number is 5 percent. Other patterns also show up in the data. For
   example, more black females between the ages of 20 and 39, more white
   females between the ages of 10 and 39 and those over 65, and other
   race females between 20 and 29 are positively and significantly
   associated with a greater number of rapes occurring. Population
   density appears to be most important in explaining robbery, burglary,
   and auto theft rates, with a one standard deviation change in
   population density being able to explain 36 percent of a one standard
   deviation change in auto theft. Perhaps most surprising is the
   relatively small, even if frequently significant, effect of income on
   crime rates. A one standard deviation change in real per capita income
   explains no more than 4 percent of a one standard deviation change in
   crime and in seven of the specifications it explains 2 percent or less
   of the change. If the race, sex, and age variables are replaced with
   variables showing the percent of the population that is black and the
   percent that is white, 50 percent of a standard deviation in the
   murder rate is explained by the percent of the population that is
   black. Given the high rates that blacks are arrested and incarcerated
   or are victims of crimes, this is not unexpected.
   
   Rerunning the regressions by adding a dummy variable to control for
   state laws that increase sentencing penalties when deadly weapon are
   used (Marvell and Moody, 1995, pp. 259-260) has no noticeable effect
   on the concealed handgun coefficients. The enhanced sentencing law
   dummy is negative and statistically significant only for aggravated
   assaults, with the coefficient implying that adopting this type of law
   reduces aggravate assaults by 4 percent. Otherwise these laws
   generally appear to have little effect on crime rates.
   
   Given the wide use of state level crime data by economists and the
   large within state heterogeneity shown in Table 1, Table 4 provides a
   comparison by reestimating the specifications reported in Table 3
   using state level rather than county level data. The only other
   difference in the specification is the replacement of county dummies
   with state dummies. While the results in these two tables are
   generally similar, two differences immediately manifest themselves: 1)
   all the specifications now imply a negative and almost always
   significant relationship between allowing concealed handguns and the
   level of crime and 2) concealed handgun laws explain much more of the
   variation in crime rates while arrest rates (with the exception of
   robbery) explain much less of the variation.[29] Despite the fact that
   concealed handgun laws appear to lower both violent and property crime
   rates, the results still imply that violent crimes are much more
   sensitive to the introduction of concealed handguns, with violent
   crimes falling three times more than property crimes. These results
   imply that if all states had adopted concealed handgun laws in 1992,
   1,777 fewer murders and 7,000 fewer rapes would have taken place.[30]
   Overall, Table 4 implies that the estimated gain from the lower crime
   produced by handguns was $10.3 billion in 1992 dollars (see Table 5).
   Yet, at least in the case of property crimes, the concealed handgun
   law coefficients' sensitivity to whether these regressions are run at
   the state or county level suggests caution in aggregating these data
   into such large units as states.
   
   Table 6 examines whether changes in concealed handgun laws and arrest
   rates have differential effects in high or low crime counties. To test
   this, the regressions shown in Table 3 were reestimated first using
   the sample above the median crime rate by type of crime and then
   separately using the sample below the median. High crime rates may
   also breed more crime because the stigma from arrest may be less when
   crime is rampant (Ramusen, 1996). If so, any change in apprehension
   rates should produce a greater reputational impact and thus greater
   deterrence in low crime than high crime counties.
   
   The results indicate that the concealed handgun law's coefficient
   signs are consistently the same for both low and high crime counties,
   though for two of the crime categories (rape and aggravate assault)
   concealed handgun laws have only statistically significant effects in
   the relatively high crime counties. For most violent crimes such as
   murder, rape, and aggravated assault concealed weapons laws have a
   much greater deterrent effect in high crime counties, while for
   robbery, property crimes, auto theft, burglary, and larceny the effect
   appears to be greatest in low crime counties. The table also shows
   that the deterrent effect of arrests is significantly different at
   least at the 5 percent level between high and low crime counties for
   eight of the nine crime categories (the one exception being violent
   crimes). The results do not support the claim that arrests produce a
   greater reputational penalty in low crime areas. While additional
   arrests in low and high crime counties produce virtually identical
   changes in violent crime rates, the arrest rate coefficient for high
   crime counties is almost four times bigger than it is for low crime
   counties.
   
   One relationship in these first three sets of regressions deserves a
   special comment. Despite the relatively small number of women using
   concealed handgun permits, the concealed handgun coefficient for
   explaining rapes is consistently comparable in size to the effect that
   this variable has on other violent crimes rates. In Washington and
   Oregon states in January 1996, women constituted 18.6 and 22.9 percent
   of those with concealed handgun permits for a total of 118,728 and
   51,859 permits respectively.[31] The time-series data which are
   available for Oregon during our sample period even indicates that only
   17.6 percent of permit holders were women in 1991. While it is
   possible that the set of women who are particularly likely to be raped
   might already carry concealed handguns at much higher rates than the
   general population of women, the results are at least suggestive that
   rapists are particularly susceptable to this form of deterrence.
   Possibly this arises since providing a woman with a gun has a much
   bigger affect on her ability to defend herself against a crime than
   providing a handgun to a man. Thus even if relatively few women carry
   handguns, the expected change in the cost of attacking women could
   still be nearly as great. To phrase this differently, the external
   benefits to other women from a women carrying a concealed handgun
   appear to be large relative to the gain produced by an additional man
   carrying a concealed handgun. If concealed handgun use were to be
   subsidized to capture these positive externalities, these results are
   consistent with efficiency requiring that women receive the largest
   subsidies.[32]
   
   As mentioned in Section II, an important concern with these data is
   that passing a concealed handgun law should not affect all counties
   equally. In particular, we expect that it was the most populous
   counties that most restricted people's ability to carry concealed
   weapons. To test this, Table 7 repeats all the regressions in Table 3
   but instead interacts the Shall Issue Law Adopted Dummy with county
   population. While all the other coefficients remain virtually
   unchanged, this new interaction retains the same signs as those for
   the original Shall Issue Dummy, and in all but one case the
   coefficients are more significant. The coefficients are consistent
   with the hypothesis that the new laws produced the greatest change in
   the largest counties. The larger counties have a much greater response
   in both directions to changes in the laws. Violent crimes fall more
   and property crimes rise more in the largest counties. The bottom of
   the table indicates how these effects vary for different size
   counties. For example, passing a concealed handgun law lowers the
   murder rate in cities two standard deviations above the mean
   population by 12 percent, 7.4 times more than a shall issue laws
   lowers murders for the mean population city. While the law enforcement
   officers we talked to continually mentioned population as being the
   key variable, we also reran these regressions using population density
   as the variable that we interacted with the shall issue dummy. The
   results remain very similar to those reported.
   
   Admittedly, although arrest rates and county fixed effects are
   controlled for, these regressions have thus far controlled for
   expected penalties in a limited way. Table 8 reruns the regressions in
   Table 7 but includes either the burglary or robbery rates to proxy for
   other changes in the criminal justice system. Robbery and burglary are
   the violent and property crime categories that are the least related
   to changes in concealed handgun laws, but they are still positively
   correlated with all the other types of crimes. One additional minor
   change is made in two of the earlier specifications. In order to avoid
   any artificial collinearity either between violent crime and robbery
   or between property crimes and burglary, violent crimes net of robbery
   and property crimes net of burglary are used as the endogenous
   variables when robbery or burglary are controlled for.
   
   Some evidence that burglary or robbery rates will proxy for other
   changes in the criminal justice system can be seen in their
   correlations with other crime categories. The Pearson correlation
   coefficient between robbery and the other crime categories ranges
   between .49 and .80, and all are statistically significant at least at
   the .0001 level. For burglary the correlations range from .45 to .68,
   and they are also equally statistically significant. The two sets of
   specifications reported in Table 8 closely bound our earlier
   estimates, and the estimates continue to imply that the introduction
   of concealed handgun laws coincided with similarly large drops in
   violent crimes and increases in property crimes. The only difference
   with the preceding results is that they now imply that the affect on
   robberies is statistically significant. The estimates on the other
   control variables also essentially remain unchanged.
   
   We also reestimated the regressions in Table 3 using first differences
   on all the control variables (see Table 9). These regressions were run
   using a dummy variable for the presence of "shall issue" concealed
   handgun laws and differencing that variable, and the results
   consistently indicate a negative and statistically significant effect
   from the legal change for violent crimes, rape, and aggravated
   assault. Shall issue laws negatively affect murder rates in both
   specifications, but the effect is only statistically significant when
   the shall issue variable is also differenced. The property crime
   results are also consistent with those shown in the previous tables,
   showing a positive impact of shall issue laws on crime rates. Perhaps
   not surprisingly, the results imply that the gun laws immediately
   altered crime rates, but that an additional change was spread out over
   time, possibly because concealed handgun use did not instantly move to
   its new steady state level. The annual decrease in violent crimes
   averaged about 2 percent, while the annual increase in property crimes
   average about 5 percent.
   
   All the results in tables 3, 6, and 7 were reestimated to deal with
   the concerns raised in Section II over the "noise" in arrest rates
   arising from the timing of offenses and arrests and the possibility of
   multiple offenders. We reran all the regressions in this section first
   by limiting the sample to those counties over 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. The arrest
   rate results also tended to be stronger and more significant. We also
   tried rerunning all the regressions by redefining the arrest rate as
   the number of arrests over the last three years divided by the total
   number of offenses over the last three years. Despite the reduced
   sample size, the results remained similar to those already reported.
   
   Not only does this initial empirical work provide strong evidence that
   concealed handgun laws reduce violent crime and that higher arrest
   rates deter all types of crime, but the work also allows us to
   evaluate some of the broader empirical issues concerning criminal
   deterrence discussed in Section II. The results confirm some of our
   earlier discussion on potential aggregation problems with state level
   data. County level data implies that arrest rates explain about six
   times the variation in violent crime rates and eight times the
   variation in property crime rates that arrest rates explain when we
   use state level data. Breaking the data down by whether a county is a
   high or a low crime county indicates that arrest rates do not affect
   crime rates equally in all counties. The evidence also confirms the
   claims of law enforcement officials that "Shall Issue" laws
   represented more of a change in how the most populous counties
   permitted concealed handguns. One concern that was not borne out was
   over whether state level regressions could bias the coefficients on
   the concealed handgun laws towards zero. In fact, while state and
   county level regressions produce widely different coefficients for
   property crimes, seven of the nine crime categories imply that the
   effect of concealed handgun laws was much larger when state level data
   were used. However, one conclusion is clear: the very different
   results between state and county level data should make us very
   cautious in aggregating crime data and would imply that the data
   should remain as disaggregated as possible.
   
   B. The Endogeniety of Arrest Rates and the Passage of Concealed
   Handgun Laws
   
   The previous specifications have assumed that both the arrest rate and
   the passage of concealed handgun laws are exogenous. Following Ehrlich
   (1973, pp. 548-551), we allow for the arrest rate to be a function of:
   the lagged crime rates; per capita and per violent and property crimes
   measures of police employment and payroll at the state level (these
   three different measures of employment are also broken down by whether
   police officers have the power to make arrest); the measures of
   income, unemployment insurance payments, and the percentages of county
   population by age, sex, and race used in Table 3; and county and year
   dummies.[33] In an attempt to control for political influences, we
   also included the percent of a state's population that are members of
   the National Rifle Association and the percent of the vote received by
   the Republican presidential candidate at the state level. Because
   presidential candidates and issues vary between elections, the percent
   voting Republican is undoubtedly not directly comparable across years.
   To account for these difference across elections, we interacted the
   percent voting Republican with dummy variables for the years
   immediately next to the relevant elections. Thus, the percent of the
   vote obtained in 1980 is multiplied by a year dummy for the years from
   1979 to 1982, the percent of the vote obtained in 1984 is multiplied
   by a year dummy for the years from 1983 to 1986, and so on through the
   1992 election. A second set of regressions explaining the arrest rate
   also include the change in the natural log of the crime rates to proxy
   for the difficulty police forces face in adjusting to changing
   circumstances.[34] However, the time period studied in all these
   regressions is more limited than in our previous tables because state
   level data on police employment and payroll are only available from
   the U.S. Department of Justices' Expenditure and Employment data for
   the Criminal Justice System from 1982 to 1992.
   
   There is also the question of why some states adopted concealed
   handgun laws while others did not. As noted earlier, to the extent
   that states adopted the law because crime was either rising or was
   expected to increase, ordinary least squares estimates underpredict
   the drop in crime. Similarly, if these rules were adopted when crimes
   rates were falling, a bias is in the opposite direction. Thus, in
   order to predict whether a county would be in a state with concealed
   handgun laws we used both the natural logs of the violent and property
   crime rates and the first differences of those crime rates. To control
   for general political differences that might affect the chances of
   these laws being adopted, we also included the National Rifle
   Association membership as a percent of a state's population; the
   Republican presidential candidate's percent of the statewide vote; the
   percentage a state's population that is black and the percent white;
   the total population in the state; regional dummy variables for
   whether the state is in the South, Northeast, or Midwest; and year
   dummy variables.
   
   While the 2SLS estimates shown in the top half of Table 10 again use
   the same set of control variables employed in the preceding tables,
   the results differ from all our previous estimates in one important
   respect: concealed handgun laws are associated with large significant
   drops in the levels of all nine crime categories. For the estimates
   most similar to Ehrlich's study, five of the estimates imply that a
   one standard deviation change in the predicted value of the Shall
   Issue Law dummy variable explains at least 10 percent of a standard
   deviation change in the corresponding crime rates. In fact, concealed
   handgun laws explain a greater percentage of the change in murder
   rates than do arrest rates. With the exception of robbery, the set of
   estimates using the change in crime rates to explain arrest rates
   indicates a usually more statistically significant but economically
   smaller effect from concealed handgun laws. For example, concealed
   handgun laws now explains 3.9 percent of the variation in murder rates
   compared to 7.5 percent in the preceding results. While these results
   imply that even crimes with relatively little contact between victims
   and criminals experienced declines, the coefficients for violent
   crimes are still relatively more negative than the coefficients for
   property crimes.
   
   For the first stage regressions explaining which states adopt
   concealed handgun laws (shown in the bottom half of Table 10), both
   the least square and logit estimates imply that the states adopting
   these laws are relatively Republican with large National Rifle
   Association memberships and low but rising violent and property crime
   rates. The other set of regressions used to explain the arrest rate
   shows that arrest rates are lower in high income, sparsely populated,
   Republican areas where crime rates are increasing.
   
   We also reestimated the state level data using similar two-stage least
   squares specifications. The coefficients on both the arrest rates and
   concealed handgun law variables remained consistently negative and
   statistically significant, with the state level data again implying a
   much stronger effect from concealed handguns and a much weaker effect
   from higher arrest rates. Finally, in order to use the longer data
   series available for the nonpolice employment and payroll variables,
   we reran the regressions without those variables and produced similar
   results.
   
   C. Concealed Handgun Laws, the Method of Murder, and the Choice of
   Murder 
   
   Victims
   
   Do concealed handgun laws cause a substitution in the methods of
   committing murders? For example, it is possible that the number of gun
   murders rises after these laws are passed even though the total number
   of murders falls. While concealed handgun laws raise the cost of
   committing murders, murderers may also find it relatively more
   dangerous to kill people using nongun methods once people start
   carrying concealed handguns and substitute into guns to put themselves
   on a more even basis with their potential prey. Using data on the
   method of murder from the Mortality Detail Records provided by the
   United States Department of Health and Human Services, we reran the
   murder rate regression from Table 3 on counties over 100,000 during
   the period from 1982 to 1991. We then separated out murders caused by
   guns from all other murders. Table 11 shows that carrying concealed
   handguns appears to have been associated with approximately equal
   drops in both categories of murders. Carrying concealed handguns
   appears to make all types of murders realtively less attractive.
   
   There is also the question of what effect does conceal handgun laws
   have on determining which types of people are more likely to be
   murdered? Using the Uniform Crime Reports Supplementary Homicide
   Reports we were able to obtain annual state level data from 1977 to
   1992 on the percent of victims by sex and race as well as information
   on the whether the victim and the offender knew each other (whether
   they were members of the same family, knew each other but were not
   members of the same family, strangers, or the relationship is
   unknown).[35] Table 12 implies no statistically significant
   relationship between the concealed handgun dummy and the victim's sex,
   race, or relationships with offenders. However, while they are not
   quite statistically significant at the .10 level for a two-tailed
   t-test, two of the point estimates appear economically important and
   imply that in states with concealed handgun laws victims know their
   nonfamily offenders 2.6 percentage points more frequently and that the
   percent of victims where it was not possible to determine whether a
   relationship existed declined by 2.9 percentage points. This raises
   the question of whether concealed handguns cause criminals to
   substitute into crimes against those whom they know and presumably are
   also more likely to know whether they carry concealed handguns.
   
   The arrest rate for murder variable produces more interesting results.
   The percent of white victims and the percent of victims killed by
   family members both declined when states passed concealed handgun
   laws, while the percent of black victims and the percent that killed
   by nonfamily members that they know both increased. The results imply
   that higher arrest rates have a much greater deterrence effect on
   murders involving whites and family members. One explanation is that
   whites with higher incomes face a greater increase in expected
   penalties for any given increase in the probability of arrest.
   
   D. Arizona, Pennsylvania, and Oregon County Data
   
   One problem with the preceding results was the use of county
   population as a proxy for how restrictive counties were in allowing
   concealed handgun permits before the passage of "shall issue" laws.
   Since we are still going to control county specific levels of crime
   with county dummies, a better measure would have been to use the
   actual change in a gun permits before and after the adoption of a
   concealed handgun law. Fortunately, we were able to get that
   information for three states: Arizona, Oregon, and Pennsylvania.
   Arizona and Oregon also provided additional information on the
   conviction rate and the mean prison sentence length. However, for
   Oregon, because the sentence length variable is not directly
   comparable over time, it is interacted with all the year dummies so
   that we can still retain any cross-sectional information in the data.
   One difficulty with the Arizona prison sentence and conviction data is
   that they are available only from 1990 to 1995 and that since the
   shall issue handgun law did not take effect until July 1994, it is not
   possible for us to control for all the other variables that we control
   for in the other regressions. Unlike Oregon and Pennsylvania, Arizona
   did not allow private citizens to carry concealed handguns prior to
   July 1994, so the value of concealed handgun permits equals zero for
   this earlier period. Unfortunately, however, because Arizona's change
   in the law is so recent, we are unable to control for all the
   variables that we can control for in the other regressions.
   
   The results in Table 14 for Pennsylvania and Table 15 for Oregon
   provide a couple of consistent patterns. The most economically and
   statistically important relationship involves the arrest rate: higher
   arrest rates consistently imply lower crime rates, and in 12 of the 16
   regressions the effect is statistically significant. Five cases for
   Pennsylvania (violent crime, murder, aggravated assault, robbery, and
   burglary) show that arrest rates explain more than 20 percent of a
   standard deviation change in crime rates. Automobile theft is the only
   crime for which the arrest rate is insignificant in both tables.
   
   For Pennsylvania, rape is the one crime where a one standard deviation
   change in per capita concealed handgun permits explains a greater
   percentage of a standard deviation in crime rates than it does for the
   arrest rate. However, increased concealed handguns usage explains more
   than 10 percent of a standard deviation change in murder, rape,
   aggravated assualt, and burglary rates. For six of the nine
   regressions, the concealed handgun variable for Pennsylvania exhibits
   the same coefficient signs that were shown for the national data.
   Violent crimes, with the exception of robbery, show that higher
   concealed handgun use significantly lowers crime rates, while property
   crimes exhibit the opposite tendency. However, concealed handgun use
   only explains about half the variation for property crimes that it
   explains for violent ones.[36] The regressions for Oregon weakly imply
   a similar relationship between concealed handgun use and crime, but
   the effect is only statistically significant in one case: larceny,
   which is also the only crime category where the negative concealed
   handgun coefficient differs from our previous findings.
   
   The Oregon data also show that higher conviction rates consistently
   result in significantly lower crime rates. A one standard deviation
   change in conviction rates explains 4 to 20 percent of a one standard
   deviation change in the corresponding crime rates. However, increases
   in conviction rates appear to produce a smaller deterrent effect than
   increases in arrest rates for five of the seven crime categories.[37]
   The biggest differences between the deterrence effects of arrest and
   conviction rates produce an interesting pattern. For rape, increasing
   the arrest rate by one percentage point produces more than ten times
   the deterrent effect of increasing the conviction rate conditional on
   arrest by one percent. The reverse is true for auto theft where a one
   percentage point increase in reduces crime by about ten times more
   than the same increase in convictions. These results are consistent
   with arrests producing large shaming or reputational penalties (e.g.,
   see Kahan 1996). In fact, the existing evidence shows that the
   reputational penalties from arrest and conviction can dwarf the other
   legally imposed penalties (Lott, 1992a and b). However, while the
   literature has not separated out whether these drops are occurring due
   to arrest or conviction, these results are consistent with the
   reputational penalties for arrests alone being significant for at
   least some crimes.
   
   The results for the prison sentences are not shown, but the
   t-statistics are frequently near zero and the coefficients indicate no
   clear pattern. One possible explanation for this result is that all
   the changes in sentencing rules produced a great deal of noise in this
   variable not only over time but also across counties. For example,
   after 1989 whether a crime was prosecuted under the pre or post 1989
   rules depended upon when the crime took place. If the average time
   between when the offense occurred and when the prosecution took place
   differs across counties, the recorded prison sentence length could
   vary even if the actual time served was the same.
   
   Finally, the much more limited data set for Arizona used in Table 16
   produces no significant relationship between the change in concealed
   handgun permits and the various measures of crime rates. In fact, the
   coefficient signs themselves indicate no consistent pattern with the
   fourteen coefficients being equally divided between negative and
   positive signs, though six of the specfications imply that a one
   standard deviation change in the concealed handgun permits explains at
   least 8 percent of a one standard deviation change in the
   corresponding crime rates. The results involving either the mean
   prison sentence length for those sentenced in a particular year or the
   actual time served for those ending their sentences also imply no
   consistent relationship between prison and crime rates. While the
   coefficients are negative in 11 of the 14 specifications, they provide
   weak evidence of the deterrent effect of longer prison terms: only two
   coefficients are negative and statistically significant.
   
   Overall, the Pennsylvania results provide more evidence that concealed
   handgun ownership reduces violent crime, murder, rape, aggravated
   assault, and burglary; and in the case of Oregon larceny decreases as
   well. While the Oregon data implies that the change in handgun permits
   is statistically significant at .11 percent level for a one-tailed
   t-test, the point estimate is extremely large economically: implying
   that a doubling of permits reduces murder rates by 37 percent. The
   other coefficients for Pennsylvania and Oregon imply no significant
   relationship between the change in concealed handgun ownership and
   crime rates. The evidence from the small sample for Arizona implies no
   relationship between crime and concealed handgun ownership. All the
   results also support the claim that higher arrest and conviction rates
   deter crime, though, possibly in part due to the relatively poor
   quality of the data, no systematic effect appears to occur from longer
   prison sentences.
   
   V. Accidental Deaths from Handguns
   
   Even if "shall issue" hand gun permits lower murder rates, the
   question of what happens to accidental deaths still remains. Possibly,
   with more people carrying handguns, accidents may be more likely to
   happen. Earlier we saw that the number of murders prevented exceeded
   the entire number of accidental deaths. As Table 2 showed, while only
   a small portion of either accidental deaths are attributable to
   handgun laws, there is still the question whether concealed handgun
   laws affected the total number of deaths through their effect on
   accidental deaths.
   
   To get a more precise answer to this question, Table 17 uses county
   level data from 1982 to 1991 to test whether allowing concealed
   handguns increased accidental deaths. Data are available from the
   Mortality Detail Records (provided by the United States Department of
   Health and Human Services) for all counties from 1982 to 1988 and for
   counties over 100,000 population from 1989 to 1991. The specifications
   are identical to those shown in all the previous tables with the
   exceptions that we no longer include variables related to arrest or
   conviction rates and that the endogenous variables are replaced with
   either a measure of the number of accidental deaths from handguns or
   accidental deaths from all other nonhandgun sources.
   
   While there is some evidence that the racial composition of the
   population and the level of income maintenance payments affect
   accident rates, the coefficient of the shall issue dummy is both quite
   small economically and insignificant. The point estimates for the
   first specification implies that accidental handgun deaths rose by
   about .5 percent when concealed handgun laws were passed. With only
   156 accidental handgun deaths occurring in counties over 100,000
   population (27 accidental handgun deaths occurred in states with
   "shall issue" laws), this point estimate implies that implementing a
   concealed handgun law in those states which currently do not have it
   would produce less than one more death (.645 deaths).
   
   Given the very small number of accidental handgun deaths in the United
   States, the vast majority of counties have an accidental handgun death
   rate of zero and thus using ordinary least squares is not the
   appropriate method of estimating these relationships. To deal with
   this, the last two columns in Table 17 reestimate these specifications
   using Tobit procedures. However, because of limitations in statistical
   packages we were no longer able to control for all the county dummies
   and opted to rerun these regressions with only state dummy variables.
   While the coefficients for the concealed handgun law dummy variable is
   not statistically significant, with 186 million people living in
   states without these laws in 1992,[38] the third specification implies
   that implementing the law across those remaining states would have
   resulted in about 9 more accidental handgun deaths. Combining this
   finding with the earlier estimates from Tables 3 and 4, if the rest of
   the country had adopted concealed handgun laws in 1992, the net
   reduction in total deaths would have been approximately 1,561 to
   1,767.
   
   VI. Conclusion
   
   Allowing citizens without criminal records or histories of significant
   mental illness to carry concealed handguns deters violent crimes and
   appears to produce an extremely small and statistically insignificant
   change in accidental deaths. If the rest country had adopted
   right-to-carry concealed handgun provisions in 1992, at least 1,570
   murders and over 4,177 rapes would have been avoided. On the other
   hand, consistent with the notion that criminals respond to incentives,
   county level data provides evidence that concealed handgun laws are
   associated with increases in property crimes involving stealth and
   where the probability of contact between the criminal and the victim
   are minimal. The largest population counties where the deterrence
   effect on violent crimes is the greatest is also where the
   substitution effect into these property crimes is the highest. The
   estimated annual gain in 1992 from allowing concealed handguns was
   over $6.21 billion.
   
   The data also supply dramatic evidence supporting the economic notion
   of deterrence. Higher arrest and conviction rates consistently and
   dramatically reduce the crime rate. Consistent with other recent work
   (Kahan, 1996 and Lott, 1992b), the results imply that increasing the
   arrest rate, independent of the probability of eventual conviction,
   imposes a significant penalty on criminals. Perhaps the most
   surprising result is that the deterrence effect of a one percentage
   point increase in arrest rates is much larger than the same increase
   in the probability of conviction. Also surprising was that while
   longer prison lengths usually implied lower crime rates, the results
   were normally not statistically significant.
   
   This study incorporates a number of improvements over previous studies
   on deterrence, and it represents a very large change in how gun
   studies have been done. This is the first study to use cross-sectional
   time-series evidence for counties at both the national level and for
   individual states. Instead of simply using cross-sectional state or
   city level data, our study has made use of the much bigger variations
   in arrest rates and crime rates between rural and urban areas, and it
   has been possible to control for whether the lower crime rates
   resulted from the gun laws themselves or other differences in these
   areas (e.g., low crime rates) which lead to the adoption of these
   laws. Equally importantly, our study has allowed us to examine what
   effect concealed handgun laws have on different counties even within
   the same state. The evidence indicates that the effect varies both
   with a county's level of crime and its population.
   
   Bibliography
   
   Andreoni, James, "Criminal Deterrence in the Reduce Form: A New
   Perspective on Ehrlich's Seminal Study," Economic Inquiry, Vol. 33,
   no. 3 (July 1995): 476-483.
   
   Annest, J.L.; J.A. Mercy; D.R. Gibson; and G.W. Ryan, "National
   Estimates of NonFatal Firearem-related Injuries, Beyond the Tip of the
   Iceberg," Journal of the American Medical Association (June 14, 1995):
   1749-54.
   
   Barhnhart, Bob, "Concealed Handgun Licensing in Multnomah County,"
   mimeo from the Intelligence/Concealed Handgun Unit: Multnomah County
   (October 1994).
   
   Block, Michael K. and John Heineke, "A Labor Theoretical Analysis of
   Criminal Choice," American Economic Review, Vol. 65 (June 1975):
   314-325.
   
   Cook, P.J., "The Role of Firearms in Violent Crime," In Wolfgang, M.E.
   and N.A. Werner (eds.), Criminal Violence, Beverly Hills: Sage
   Publishers (1982): 236-291.
   
   ________, "The Technology of Personal Violence," Crime and Justice:
   Annual Review of Research, Vol. 14 (1991): 57-87.
   
   ________, Stephanie Molliconi, and Thomas B. Cole, "Regulating Gun
   Markets," Journal of Criminal Law and Criminology, Vol. 86, no. 1
   (Fall 1995): 59-92.
   
   Cramer, Clayton E. and David B. Kopel, "`Shall Issue': The New Wave of
   Concealed Handgun Permit Laws," Tennessee Law Review, Vol. 62 (Spring
   1995): 679-758, and expanded version of this paper dated 1994 is also
   available from the Independence Institute, Golden, Colorado.
   
   Ehrlich, Isaac, "Participation in Illegitimate Activities: A
   Theoretical and Empirical Investigation," Journal of Political
   Economy, Vol. 81, no. 3 (1973): 521-565.
   
   Federal Bureau of Investigation, Crime in the United States, Federal
   Bureau of Investigation: Washington, D.C. (editions for 1977 to 1992).
   
   Fort Worth Star-Telegram, "Few Probelms Reported After Allowing
   Concealed Handguns, Officers Say," Fort Worth Star-Telegram (July 16,
   1996).
   
   Glaeser, Edward L. and Bruce Sacerdote, "Why is There More Crime in
   Cities?" Presented at Symposium in Honor of Gary Becker's 65th
   Birthday, Harvard University working paper (November 14, 1995).
   
   Greenwald, Bruce C. "A General Analysis of the Bias in the Estimated
   Standard Errors of Least Squares Coefficients," Journal of
   Econometrics, Vol. 22 (August 1983): 323-338.
   
   Grossman, Michael, Frank J. Chaloupka, and Charles C. Brown, "The
   Demand for Cocaine by Young Adults: A Rational Addiction Approach,"
   NBER Working Paper (July 1996).
   
   Japan Economic Newswire, "U.S. jury clears man who shot Japanese
   student," Kyodo News Service (May 24, 1993).
   
   Kahan, Dan M., "What Do Alternative Sanctions Mean?," University of
   Chicago Law Review, Vol. 63, no. 1 (1996): 591-653.
   
   Kleck, Gary, "Guns and Violence: An Interpretive Review of the Field,"
   Social Pathology, Vol. 1, no. 1 (January 1995): 12-47.
   
   ________ and E. Britt Patterson, "The Impact of Gun Control and Gun
   Ownership Levels on Violence Rates," Journal of Quantitative
   Criminology, Vol. 9 (1993): 249-287.
   
   ________ and Marc Gertz, "Armed Resistance to Crime: The Prevalence
   and Nature of Self-Defense with a Gun," Journal of Criminal Law and
   Criminology, Vol. 86, no. 1 (Fall 1995): 150-187.
   
   Kopel, David B., The Samuri, the Mountie, and the Cowboy, Prometheus
   Books: Buffalo, New York (1992).
   
   ________, Guns: Who Should Have Them?, Prometheus Books: Buffalo, New
   York (1995).
   
   Landes, William M., "An Economic Study of U.S. Aircraft Hijacking,
   1961-1976," Journal of Law and Economics, Vol. 21, no. 1 (April 1978):
   1-31.
   
   Levitt, Steven, "The Effect of Prison Population Size on Crime Rates:
   Evidence from Prison Overcrowding Litigation," Quarterly Journal of
   Economics (1996).
   
   Lipton, Eric, "Virginians Get Ready to Conceal Arms; State's New
   Weapon Law Brings a Flood of Inquiries," The Washington Post (June 28,
   1995): A1.
   
   Lott, John R., Jr., "Juvenile Delinquency and Education: A Comparison
   of Public and Private Provision," International Review of Law and
   Economics, Vol.7, no. 2 (December 1987): 163-175.
   
   ________, "A Transaction-Costs Explanation for Why the Poor are More
   Likely to Commit Crime," Journal of Legal Studies, Vol. 19, no. 1
   (January 1990a): 243-245.
   
   ________, "The Effect of Conviction on the Legitimate Income of
   Criminals," Economics Letters, Vol. 34, no. 12 (December 1990b):
   381-385.
   
   ________, "An Attempt at Measuring the Total Monetary Penalty from
   Drug Convictions: The Importance of an Individual's Reputation,"
   Journal of Legal Studies, Vol. 21, no. 1 (January 1992a): 159-187.
   
   ________, "Do We Punish High Income Criminals too Heavily?" Economic
   Inquiry, Vol. 30, no. 4 (October 1992b): 583-608.
   
   ________, "Now That The Brady Law is Law, You Are Not Any Safer Than
   Before," Philadelphia Inquirer, Tuesday, February 1, 1994, p. A9.
   
   Marvell, Thomas B. and Carlisle E. Moody, "The Impact of Enhanced
   Prison Terms for Felonies Committed with Guns," Criminology, Vol. 33,
   no. 2 (May 1995): 247-282.
   
   McCormick, Robert E. and Robert Tollison, "Crime on the Court,"
   Journal of Political Economy, Vol. 92, no. 2 (April 1984): 223-235.
   
   McDowall, David; Colin Loftin; and Brian Wiersema, "Easing Conealed
   Firearm Laws: Effects on Homicide in Three States," Journal of
   Criminal Law and Criminology, Vol. 86, no. 1 (Fall 1995): 193-206.
   
   Miller, Ted R.; Mark A. Cohen; and Brian Wiersema, Victim Costs and
   Consequences: A New Look, National Institute of Justice: Washington,
   D.C. (February 1996).
   
   Moulton, Brent R., "An Illusration of a Pitfall in Estimating the
   Effects of Aggregate Variables on Micro Units," Review of Economics
   and Statistics, Vol. 72 (1990): 334-338.
   
   Peltzman, Sam, "The Effects of Automobile Safety Regulation," Journal
   of Political Economy Vol. 883, no. 4 (August 1975): 677-725.
   
   Polsby, Daniel D., "Firearms Costs, Firearms Benefits and the Limits
   of Knowledge," Journal of Criminal Law and Criminology, Vol. 86, no. 1
   (Fall 1995): 207-220.
   
   Potok, Mark, "Texan says gun law saved his life'I did what I thought I
   had to do'," USA TODAY (March 22, 1996): 3A.
   
   Rasmusen, Eric, "Stigma and Self-Fulfilling Expectations of
   Criminality," Journal of Law and Economics, forthcoming October 1996.
   
   Reynolds, Morgan O., "Crime and Punishment in America," National
   Center for Policy Analysis, Policy Report 193 (June 1995).
   
   Sharn, Lori, "Violence shoots holes in USA's tourist image," USA TODAY
   (September 9, 1993): 2A.
   
   Southwick, Lawrence, Jr., "Self-defense with Guns: The Consequences,"
   SUNY Buffalo working paper (1996).
   
   Uviller, H. Richard, Virtual Justice, Yale University Press: New Haven
   (1996).
   
   Will, George F., "Are We `a Nation of Cowards'?" Newsweek (November
   15, 1993): 93-94.
   
   Zimring, Franklin, "Is Gun Control Likely to Reduce Violent
   Killings?," University of Chicago Law Review, Vol. 35 (1968).
   
   ________, "The Medium is the Message: Firearm Caliber as a Determinant
   of Death from Assult" Journal of Legal Studies, Vol. 1 (1972): 97-123.
   
   ________, "Firearms and Federal Law: The Gun Control Act of 1968"
   Journal of Legal Studies, Vol. 4 (1975): 133-198.
   
   Data Appendix
   
   The number of arrests and offenses for each crime in every county from
   1977-1992 were provided by the Uniform Crime Report. The UCR Program
   is a nationwide, cooperative statistical effort of over 16,000 city,
   county and state law enforcement agencies to compile data on crimes
   that are reported to them. During 1993, law enforcement agencies
   active in the UCR Program represented over 245 million U.S.
   inhabitants, or 95% of the total population. The coverage amounted to
   97% of the U.S. population living in Metropolitan Statistical Areas
   (MSAs) and 86% of the population in non-MSA cities and in rural
   counties.[39] The Uniform Crime Reports Supplementary Homicide Reports
   supplied the data on the victim's sex and race and whatever
   relationship might have existed between the victim andthe
   offender.[40]
   
   The regressions report results from a subset of the UCR data set,
   though we also ran the regressions with the entire data set. The main
   differences were that the effect of concealed handgun laws on murder
   were greater than what is shown in this paper and the effects on rape
   and aggravated assult were smaller. Observations were eliminated
   because of changes in reporting practices or definitions of crimes
   (see Crime in the United States (1977 to 1992)). For example, from
   1985 to 1994 Illinois adopted a unique "gender-neutral" definition of
   sex offenses. Another example involves Cook county, Illinois from 1981
   to 1984 where there was a large jump in reported crime because there
   was a change in the way officers were trained to report crime. The
   additional observations droped from the data set include: Florida
   (1988 to 1992); Georgia (1980); Kentucky (1988); Hawaii (1982); Iowa
   (1991); Oakland, Ca. (1991 to 1992). The counties with the following
   cities were also eliminated: aggravated assult for Steubenville, OH.
   (1977 to 1990); aggravated assult for Youngstown, OH (1977 to 1988);
   aggravated assult and burglary for Mobile, Al. (1977 to 1985);
   aggravated assult for Milwaukee, WI (1977 to 1985); Glendale, AZ (1977
   to 1984); aggravated assult for Jackson, MS (1982 and 1983);
   aggravated assult for Aurora, CO (1982 and 1983); aggravated assult
   for Beaumont, TX (1982 and 1983); aggravated assult for Corpus Cristi,
   TX (1982 and 1983); rape for Macon, GA (1977 to 1981); robbery and
   larceny for Cleveland, OH (1977 to 1981); aggravated assult for Omaha,
   NE (1977 to 1981); Little Rock, Ark. (1977 to 1979); burglary and
   larceny for Eau Claire, WI (1977 to 1978); Green Bay, WI. (1977); and
   Fort Worth, TX (1977). For all of the different crime rates, if the
   true rate equals zero, we added .1 before we took the natural log of
   those values. For the accident rates, if the true rate equals zero, we
   added .01 before we took the natural log of those values.[41]
   
   The number of police in a state, which of those police have the power
   to make arrests, and police payrolls for a state by type of police
   officer are available for 1982 to 1992 from the U.S. Department of
   Justice's Expenditure and Employment Data for the Criminal Justice
   System.
   
   The data on age, sex and racial distributions estimate the population
   in each county on July 1 of the respective years. The population is
   divided into five year segments and race is categorized as white,
   black and neither white nor black. The population data, with the
   exception of 1990 and 1992, were obtained from the Bureau of the
   Census.[42] The estimates use modified census data as anchor points
   and then employ an iterative proportional fitting technique to
   estimate intercensal populations. The process ensures that the county
   level estimates are consistent with estimates of July 1 national and
   state populations by age, sex, and race. The age distributions of
   large military installations, colleges, and institutions were
   estimated by a separate procedure. The counties for which special
   adjustments were made are listed in the report.[43] The 1990 and 1992
   estimates have not yet been completed by the Bureau of the Census and
   made available for distribution. We estimated the 1990 data by taking
   an average of the 1989 and 1991 data. We estimated the 1992 data by
   multiplying the 1991 populations by the 1990-1991 growth rate of each
   county's populations.
   
   Data on income, unemployment, income maintenance and retirement were
   obtained by the Regional Economic Information System (REIS). Income
   maintenance includes Supplemental Security Insurance (SSI), Aid to
   Families with Dependent Children (AFDC), and food stamps. Unemployment
   benefits include state unemployment insurance compensation,
   Unemployment for Federal Employees, unemployment for railroad
   employees, and unemployment for veterans. Retirement payments include
   old age survivor and disability payments, federal civil employee
   retirement payments, military retirement payments, state and local
   government employee retirement payments, and workers compensation
   payments (both federal and state). Nominal values were converted to
   real values by using the consumer price index.[44] The index uses the
   average consumer price index for July 1983 as the base period.
   
   Data concerning the number of concealed weapons permits for each
   county were obtained from a variety of sources. The Pennsylvania data
   were obtained from Alan Krug. Mike Woodward of the Oregon Law
   Enforcement and Data System provided the Oregon data for 1991 and
   after. The number of permits available for Oregon by county in 1989
   was provided by the sheriffs departments of the individual counties.
   Cari Gerchick, Deputy County Attorney for Maricopa County in Arizona,
   provided us with the Arizona county level conviction rates, prison
   sentence lengths, and concealed handgun permits from 1990 to 1995. The
   National Rifle Association provided data on NRA membership by state
   from 1977 to 1992. Information on the dates at which states enacted
   enhanced sentencing provisions for crimes committed with deadly
   weapons was obtained from Marvell and Moody (1995, pp. 259-260). The
   first year where the dummy variable comes on is weighted by the
   portion of that first year that the law was in effect.
   
   The Bureau of the Census provided data on the latitude, longitude and
   area in square kilometers for each county. The number of total and
   firearm unintentional injury deaths was obtained from annual issues of
   Accident Facts and The Vital Statistics of the United States. The
   classification of types of weapons is in International Statistical
   Classification of Diseases and Related Health Problems, Tenth Edition,
   Volume 1. The handgun category includes guns for single hand use,
   pistols and revolvers. The total includes all other types of firearms.
   
Table 1:  Comparing the

Deviation in Crime Rates

Between States and By

Counties Within States From

1977 to 1992:  Does it make

sense to View States as

Relatively Homogenous Units?



                               Standard Deviation             Mean of Within St
ate
                               of State Means                 Standard Deviatio
ns
Crime Rates Per 100,000

Population



Violent Crime Rate             284.77                         255.57

Murder Rate                    6.12                           8.18

   Murder Rate for Guns        3.9211                         6.4756

    (from 1982 to 1991)



Variable |     Obs

Mean   Std. Dev.       Min

Max

---------+-------------------

-----------------------------

-----

   RATMG |   23278

3.921104   6.475649

.0199036   142.6038

  RATMNG |   21908

1.566327   8.675772

-120.2111   502.6832

 LRATMUR |   19534

.1921255   2.139152

-2.3       6.22



Rape Rate                      16.33                          23.55

Aggravate Assault Rate         143.35                         172.66

Robbery Rate                   153.62                         92.74



Property Crime Rate            1404.15                        2120.28

Auto Theft Rate                162.02                         219.74

Burglary Rate                  527.70                         760.22

Larceny Rate                   819.08                         1332.52



Arrest Rates Defined as the

Number of Arrests

Divided By the Number of

Offenses45



Arrest Rate for Violent        23.89                          112.97

Crimes

Arrest Rate for Murder         18.58                          88.41

Arrest Rate for Rape           19.83                          113.86

Arrest Rate for Robbery        21.97                          104.40

Arrest Rate for Aggravated     25.30                          78.53

Assault



Arrest Rate for Property       7.907                          44.49

Crimes

Arrest Rate for Burglary       5.87                           25.20

Arrest Rate for Larceny        11.11                          71.73

Arrest Rate for Auto Theft     17.37                          118.94



Truncating Arrest Rates to

be no greater than one



Arrest Rate for Violent        11.11                          25.40

Crimes

Arrest Rate for Murder         10.78                          36.40

Arrest Rate for Rape           10.60                          31.59

Arrest Rate for Robbery        8.06                           32.67

Arrest Rate for Aggravated     11.14                          27.08

Assault



Arrest Rate for Property       5.115                          11.99

Crimes

Arrest Rate for Burglary       4.63                           14.17

Arrest Rate for Larceny        5.91                           12.97

Arrest Rate for Auto Theft     8.36                           26.66


Table 2:

National Sample

Means and

Standard

Deviations



Variable           Obs.               Mean               Standard Dev.



Gun Ownership

Information:



                   Shall Issue Dummy  50056              0.164704           0.3
68089


Arrests Rates

are the ratio of

arrests to

offenses for a

particular crime

category:



                   Arrest Rate for    45108              27.43394           126
.7298
                   Index Crimes

                   Arrest Rate for    43479              71.30733           327
.2456
                   Violent Crimes

                   Arrest Rate for    45978              24.02564           120
.8654
                   Property Crimes

                   Arrest Rate for    26472              98.04648           109
.7777
                   Murder

                   Arrest for Rape    33887              57.8318            132
.8028
                   Arrest for         43472              71.36647           187
.354
                   Aggravated

                   Assault

                   Arrest Rate for    34966              61.62276           189
.5007
                   Robbery

                   Arrest Rate for    45801              21.51446           47.
28603
                   Burglary

                   Arrest Rate for    45776              25.57141           263
.706
                   Larceny

                   Arrest Rate for    43616              44.8199            307
.5356
                   Auto Theft



Crime Rates are

Defined per

100,000 People:



                   Crime Rate for     46999              2984.99            336
8.85
                   Index Crimes

                   Crime Rate for     47001              249.0774           388
.7211
                   Violent Crimes

                   Crime Rate for     46999              2736.59            317
8.41
                   Property Crimes

                   Crime Rate for     47001              5.651217           10.
63025
                   Murder

                   Murder Rate for    12759              3.9211             6.4
756
                   Guns



(from 1982 to

1991 in



counties over

100,000)

                   Crime Rate for     47001              18.7845            32.
39292
                   Rape

                   Crime Rate for     47001              44.6861            149
.2124
                   Robbery

                   Crime Rate for     47001              180.0518           243
.2615
                   Aggravated

                   Assault

                   Crime Rate for     47001              811.8642           119
0.23
                   Burglary

                   Crime Rate for     47000              1764.37            203
6.03
                   Larceny

                   Crime Rate for     47000              160.4165           284
.5969
                   Auto Theft



Causes of

Accidental

Deaths and

Murders per

100,000 People:



                   Rate of            23278              0.151278           1.2
16175
                   Accidental

                   Deaths from Guns

                   Rate of            23278              1.165152           4.3
42401
                   Accidental

                   Deaths from

                          Sources

                   Other than Guns

                   Rate of Total      23278              51.95058           32.
13482
                   Accidental Deaths

                   Rate of Murders    23278              0.444301           1.9
30975
                   Using Handgun

                   Rate of Murders    23278              3.477088           6.1
15275
                   Using Other Guns



Income Data (All

$ Values in Real

1983 dollars):



                   Real Per Capita    50011              10554.21           249
8.07
                   Personal Income

                   Real Per Capita    50011              67.57505           53.
10043
                   Unemployment

                   Insurance

                   Real Per Capita    50011              157.2265           97.
61466
                   Income

                   Maintenance

                   Real Per Capita    49998              12328.5            439
7.49
                   Retirement Per

                   Over 65




Population

Characteristics:



                   County Population  50023              75772.78           250
350.4
                   County             50023              214.3291           142
1.25
                   Population per

                   Square Mile

                   State Population   50056              6199949            534
2068
                   State NRA          50056              1098.11            516
.0701
                   membership per

                   100,000

                           State

                   Population

                   % of votes         50056              52.89235           8.4
10228
                   Republican in

                   Pres. Election

                   % of Pop. Black    50023              0.920866           1.5
56054
                   Male Between

                   10-19

                   % of Pop. Black    50023              0.892649           1.5
45335
                   Female Between

                   10-19

                   % of Pop. White    50023              7.262491           1.7
47557
                   Male Between

                   10-19

                   % of Pop. White    50023              6.820146           1.6
73272
                   Female Between

                   10-19

                   % of Pop. Other    50023              0.228785           0.7
69633
                   Male Between

                   10-19

                   % of Pop. Other    50023              0.218348           0.7
42927
                   Female Between

                   10-19

                   % of Pop. Black    50023              0.751636           1.2
14317
                   Male Between

                   20-29

                   % of Pop. Black    50023              0.762416           1.2
783
                   Female Between

                   20-29

                   % of Pop. White    50023              6.792357           1.9
91303
                   Male Between

                   20-29

                   % of Pop. White    50023              6.577894           1.7
96134
                   Female Between

                   20-29

                   % of Pop. Other    50023              0.185308           0.5
57494
                   Male Between

                   20-29

                   % of Pop. Other    50023              0.186327           0.5
59599
                   Female Between

                   20-29

                   % of Pop. Black    50023              0.539637           0.8
79286
                   Male Between

                   30-39

                   % of Pop. Black    50023              0.584164           0.9
86009
                   Female Between

                   30-39

                   % of Pop. White    50023              6.397395           1.4
60204
                   Male Between

                   30-39

                   % of Pop. White    50023              6.318641           1.4
22831
                   Female Between

                   30-39

                   % of Pop. Other    50023              0.151869           0.4
56388
                   Male Between

                   30-39

                   % of Pop. Other    50023              0.167945           0.4
54721
                   Female Between

                   30-39

                   % of Pop. Black    50023              0.358191           0.5
71475
                   Male Between

                   40-49

                   % of Pop. Black    50023              0.415372           0.6
90749
                   Female Between

                   40-49

                   % of Pop. White    50023              4.932917           1.0
86635
                   Male Between

                   40-49

                   % of Pop. White    50023              4.947299           1.0
38738
                   Female Between

                   40-49

                   % of Pop. Other    50023              0.105475           0.3
02059
                   Male Between

                   40-49

                   % of Pop. Other    50023              0.115959           0.3
04423
                   Female Between

                   4049

                   % of Pop. Black    50023              0.43193            0.7
08241
                   Male Between

                   50-64

                   % of Pop. Black    50023              0.54293            0.9
21819
                   Female Between

                   50-64

                   % of Pop. White    50023              6.459038           1.4
10181
                   Male Between

                   50-64

                   % of Pop. White    50023              6.911502           1.5
4784
                   Female Between

                   50-64

                   % of Pop. Other    50023              0.101593           0.3
67467
                   Male Between

                   50-64

                   % of Pop. Other    50023              0.11485            0.3
74837
                   Female Between

                   50-64

                   % of Pop. Black    50023              0.384049           0.6
71189
                   Male Over 65

                   % of Pop. Black    50023              0.552889           0.9
80266
                   Female O65

                   % of Pop. White    50023              5.443062           2.0
82804
                   Male Over 65

                   % of Pop. White    50023              7.490128           2.6
9476
                   Female Over 65

                   % of Pop. Other    50023              0.065265           0.2
86597
                   Male Over 65

                   % of Pop. Other    50023              0.077395           0.2
64319
                   Female Over 65




Back To The Top.

Back To The Political Page.

Back To The RKBA Page.


Mail to:

drupal statistics