The Political Economy of Gun Control: An Analysis of Senatorial Votes on the 1993 Brady Bill
By Jody Lipford*
Economics and Business Administration,
Presbyterian College, Clinton, S.C.
Abstract
Although much research has addressed the effects of guns on violent crime and the efficacy of gun-control laws
in reducing violent crime, surprisingly little attention has been given to the political process through which
gun policies are determined. This paper contributes towards bridging this research gap by analyzing the important
factors that determined senatorial voting on the Brady Bill. Although the Democratic Party and pro-control ideology
enabled passage of the Brady Bill, senators were less likely to vote for the bill if they received pre-vote contributions
from the NRA, if their constituencies faced high rates of violent crime, or if their constituencies had a strong
interest in hunting.
I. Introduction
With approximately 212 million guns in private hands, 284,000 licensed gun dealers, and violent crime rates exceeding
those of most western democracies, it is hardly surprising that gun control has become a popular and controversial
political issue in the United States.[1] Arguments for and against gun control have become standard fare in political
races, on the editorial page, and in any debate over how to curb crime. In response to these debates, many researchers
have attempted to analyze the effects of gun-control laws on rates of violent crime.
The results of this research have not been conclusive. Some early research indicated that gun-control laws could
effectively reduce crime. However, later research challenged this conclusion. The literature is indeed voluminous.[2]
Surprisingly, the political process that yields gun-control laws has received scant attention. Langbein and Lotwis
(1990) provide a notable exception in their analysis of House votes on amendments to the Firearms Owners Protection
Act of 1986. In the spirit of their work, I analyze the political economy of the 1993 Brady Bill, the first federal
gun-control legislation to pass since the Gun Control Act of 1968. To carry out this analysis, I use data on senators’
votes on the Brady Bill and characteristics of their constituents, including the rates of violent crime these constituents
face, to infer constituent beliefs about the effects of gun-control laws. This methodology, predicated upon the
assumption that legislators’ votes reflect the preferences of their constituents, has strong theoretical and empirical
support in the legal and economic literature.[3] More directly, if a legislator’s constituents believe gun control
reduces violent crime, the legislator should reflect this belief by voting in favor of gun-control legislation.
On the other hand, if a legislator’s constituents believe gun control has a negligible impact on crime, or may
increase violent crime by disarming victims, or that gun control threatens other legitimate gun uses (e.g., hunting,
target shooting), the legislator should reflect this belief by voting against gun-control legislation.
Some might question whether constituents’ beliefs are accurate reflections of reality. Admittedly, the public may
not “know” the results of the empirical work cited in endnote 2; yet, this ignorance does not imply that members
of the public do not “know” the effects of public policies on their lives. With respect to the issue addressed
in this paper, surely individuals with an interest, particularly those confronted with the threat of violent crime
and the need for self-defense, should intuitively “know” the effects of a change in gun-control policy on their
safety, and express this knowledge through the political process, even if they cannot quantify these effects. The
contribution of this paper is to offer an alternative, yet complementary, means of testing the link between gun-control
laws and the prevalence of violent crime by examining how senators from states with vastly different rates of violent
crime voted on the Brady Bill.
Some of the important findings are these: (1) senators from states with high rates of violent crime were not differentially
likely to vote for the Brady Bill and, if anything, were more likely to vote against the Brady Bill; (2) senators
from states where hunters form a strong interest group were more likely to vote against the Brady Bill; (3) senators
receiving relatively large campaign contributions from the National Rifle Association (NRA) were more likely to
vote against the Brady Bill; and (4) democrats and politically “liberal” senators were more likely to vote for
the Brady Bill. These finding are important because they identify and measure the effectiveness of important political
interests that influence U.S. gun-control policy. Of particular significance, these findings corroborate the results
of other studies finding no link between gun-control laws and reductions in violent crime by their implication
that many citizens of highly violent states viewed the Brady Bill as either ineffective or as a potential impediment
to self-defense.
The paper is outlined as follows. The following section provides a brief review of the legislative history and
contents of the Brady Bill. Section three provides an analysis of the constituent characteristics that should have
influenced senatorial voting on the Brady Bill, paying special attention to the theories and evidence on the efficacy
of guns as means of self-defense and a deterrent to crime. The results of empirical tests of the significance and
impact of these constituent characteristics and other political variables on senatorial votes are presented and
discussed in section four. After briefly considering why the pro-gun lobby lost, the conclusion offers some final
thoughts on the effectiveness of the Brady Bill and the future of gun-control legislation.
II. The Brady Bill
On November 30, 1993, President Bill Clinton signed the Brady Bill (PL 103-159), ending a long and controversial
fight for the first piece of federal gun-control legislation in 25 years. The House approved the bill by a 238-189
margin on November 10, and the Senate followed suit 10 days later by a 63-36 vote. House Judiciary chairman, Jack
Brooks (D-Texas) facilitated passage by separating the Brady bill from the omnibus crime bill (HR 3131), which
he realized had far less chance of passage. (A Brady bill had died in 1992 as part of an omnibus crime package.)
The primary provision of the bill is a five-day waiting period for the purchase of handguns. Advocates of the bill
argued the waiting period would help prevent “heat of the moment” shootings as well as allow police to conduct
background checks on buyers to prevent the sale of handguns to convicted felons. The five-day waiting period is
to be replaced within five years by a computerized system that would allow instant background checks of potential
buyers. Secondary provisions of the bill are an increase in the licensing fees of gun dealers and a requirement
that police be notified of multiple gun purchases.[4]
III. Constituent Interests and Gun Control
To elucidate the political pressures constituents may bring to bear on their legislators, I now turn to a discussion
of the utility of guns for self-defense, recreation, and cultural identification.[5]
A. Guns as instruments of violence or tools of self-defense
Individual opinions on gun-control policy are certain to vary, at least in part, depending upon an individual’s
assessment of the effects of such policies on violent crime. Three effects are possible: (1) the gun-control law
may effectively reduce crime, or (2) the gun-control law may have an insignificant impact on crime, or (3) the
gun-control law may effectively increase crime by reducing victims’ capacity for self-defense. If an individual
believes the net effect of crime reduction from gun control exceeds any increased threat of victimization, support
of gun control is rational. On the other hand, an individual who believes gun control impedes self-defense and
does not reduce violent crime will rationally oppose gun control. The beliefs of citizens, as expressed through
the voting of their legislators, is explored in the following section. However, insight into what constituent preferences
might be can be gained by examining theoretical, anecdotal, survey, and statistical evidence on the efficacy of
handguns as not only tools of self-defense, but also as effective deterrents to crime.
To begin, the theoretical positive link between gun availability and gun violence is suspect simply because correlation
need not imply causation. The high levels of gun ownership in the United States may be the result of crime-weary
citizens arming themselves against perceived and real dangers.[6] Of course, the causality may run both ways, but
an assumption of unilateral causality from guns to crime overlooks a hypothesis of equal validity. Indeed, some
researchers, examining game theory and the likelihood that the criminal tendencies of some segment of the population
may depend upon the effectiveness of deterrence, conclude that guns may be an important means of self-defense.[7]
Theoretical evidence, however, can only go so far towards determining the efficacy of guns as a deterrent to crime
or citizens’ beliefs about the effectiveness of gun control as a means of reducing crime or inhibiting defensive
capabilities. Fortunately, additional evidence is revealed in anecdotes and surveys.
For example, after a 1966-67 Orlando, Florida program trained 6,000 women in firearm safety, Orlando’s rape rate
dropped an astounding 88 percent the following year and did not rise to pre-program levels until 1972.[8] Similarly,
a 1982 ordinance requiring gun ownership in Kennesaw, Georgia reduced the burglary rate by 89 percent. Other programs
to effectively arm ordinary citizens have yielded similar results.[9]
At an individual level, the effectiveness of handguns to thwart a criminal attack is uncertain. Much conventional
wisdom, advice from criminal justice practitioners, and advocacy from pro-control supporters encourages potential
crime victims to comply with criminals’ demands. Nevertheless, Ziegenhagen and Brosnan (1985) conclude that “victim
compliance is no guarantee of safety from physical injury” (p. 687). Analyzing data from 3,679 robbery attempts,
they find that without resistance, most crime victims suffer loss of property, though not injury. However, when
potential victims do resist, they are less likely to suffer either property loss or injury. And potential victims
who resist by using or brandishing a weapon escape injury and property loss over 65 percent of time and suffer
injury or property loss only 28 percent of the time. Kates (1991) argues that resistance may be particularly valuable
to those threatened by repeated attacks.
Survey data from gun users corroborate these findings. Of the 20-25 percent of U.S. households owning handguns,
approximately 40 percent give self-defense as the primary reason.[10] And intent often translates into use. Citing
evidence from anti-gun organizations, Kates reports estimates of 645,000 defensive uses of handguns per year in
the United States.[11] Further, these uses are usually successful, since “(e)vidence suggests that handgun armed
defenders succeed in repelling criminals, however armed, in eighty-three to eighty-four percent of the cases” (p.
143). In a vast survey of the gun literature, Reynolds and Caruth (1992) cite evidence of approximately 1 million
defensive uses of handguns per year in the U.S. These defensive uses kill an estimated 2,000 to 3,000 criminals
and injure another 9,000 to 17,000, with few accidental shootings or occasions when criminals seize the gun and
turn it on the victim.
Kleck (1995) argues that the rising stock of handguns in the U.S. is a response to rising crime and that “(m)ost
handguns are owned for defensive reasons” (p. 13). Using data on total guns, Kleck estimates 2.5 million defensive
uses per year and that deterrence is a motive for ownership for approximately one third of gun owners.[12]
Further, surveys of criminals reveal that they perceive gun ownership as a valid threat against crime. Over half
of surveyed felons say they worry more about an armed victim than about the police and that an armed store owner
is less likely to be robbed.[13] Thirty-four percent of felons report worry about being shot at and an equal percentage
say they have been confronted by an armed victim with the result being either too much fear to carry out the crime,
or being fired upon, or injury or capture.[14]
Numerous studies analyze the statistical relationship between gun prevalence and crime. Kleck and Patterson (1993)
review these studies as well as their own study, and find that “(h)omocide (gun, nongun, and total), gun assault,
and rape rates all had significant positive coefficients in the gun prevalence equations,” supporting “the hypothesis
that some violence rates encourage the acquisition of firearms for self-defense” (p. 272). In sum, theoretical,
anecdotal, survey, and statistical evidence indicate that many constituents find guns an effective means of self-defense,
and therefore may lobby their legislators to vote against gun-control legislation.
B. Guns and recreation
A second motive for gun ownership is recreation. Wright (1984), citing evidence from a 1978 Decision Making Information
study for the NRA, reports that 54 percent of gun owners say hunting is the most important reason for ownership.
However, only 9 percent of handgun owners cite hunting as the most important reason. Target shooting and collection
are other important motives for gun ownership.
C. Guns and culture
Pro-control advocates are fond of criticizing a gun “subculture.” That this subculture exists is hardly questionable,
as many clearly identifiable traits indicate whether or not a given individual is likely to be a gun owner. Specifically,
an older male, with a high income and an interest in hunting, raised in the rural South with a Protestant background
is most likely to be a gun owner.[15] These “segments of the population . . . have the lowest rates of violent
behavior,”[16] and consequently are unlikely to view gun control as necessary to deter crime. If anything, gun
control is a threat to their cultural identity. The presence of a gun subculture provides indirect evidence that
the recent rise in gun ownership is a response to rising crime. Because members of the gun subculture have owned
guns since the country’s origin, the rise in gun ownership “since the mid-1960s” must be “attributable to concerns
about crime.”[17]
IV. An Empirical Analysis of Senatorial Votes on the Brady Bill
Standard arguments supporting the Brady Bill assert that waiting periods reduce violent crime, especially crimes
committed in the “heat of the moment.” If this assertion is correct, legislator’s constituents, especially those
subject to violent crime, should express their preferences in support of the Brady Bill. In turn, their legislators
can be expected to cast votes in favor of the Brady Bill. On the other hand, if constituents consider gun control
a threat to their self-defensive capabilities, recreational opportunities, or cultural identity, they will lobby
their legislators to vote against gun control.
A. The model
To test the effects of constituent interests on senators’ votes on the Brady Bill, I have estimated an econometric
model, based on the assumption that legislators do reflect their constituents’ interests when voting. The model
identifies significant constituent interests and measures their influence by estimating the effects these interests
had on the probability that a given senator voted for or against the Brady Bill.
The single-equation model is given below[18]:
BRADY = a0 + a1VCRIME + a2RURAL + a3HUNTREV + a4POLICE + a5NRA + a6HCI + a7PARTY + a8ADARESID + E. (1)
Variables are defined as follows:
(1) BRADY: A given senator’s vote on the Brady Bill, coded one if the senator voted in favor of the Brady Bill
and zero if the senator voted against the bill
(2) VCRIME: Violent crimes per 100,000 of population in a given senator’s state[19]
(3) RURAL: Rural population per 1,000 of total population in a given senator’s home state
(4) HUNTREV: Hunting license revenues per thousand of population in a given senator’s home state
(5) POLICE: State and local government full-time equivalent police employment per thousand of population in a given
senator’s home state
(6) NRA: NRA contributions received by a given senator, in real terms, from 1987 to 1992[20]
(7 ) HCI: Handgun Control Inc. contributions received by a given senator, in real terms, from 1987 to 1992
(8) PARTY: A given senator’s political party affiliation coded one if the senator is a Democrat and zero if the
senator is a Republican
(9) ADARESID: The residuals from a regression of each senator’s rating from the Americans for Democratic Action
against all independent variables in equation (1) and other socio-economic variables.[21]
All data are for 1992 or the year closest to 1992 for which data are available. Descriptive statistics for each
variable (and additional variables used later in the paper) are presented in Table 1[22], and an appendix lists
data sources.[23]
The equation provides an estimate of the probability that a given senator will vote for the Brady Bill, given all
constituent interests modeled. This equation is examined below.
The VCRIME variable measures the citizenry’s exposure to violent crime in a given senator’s state. If citizens
exposed to high rates of violent crime believed the Brady Bill would help to reduce that crime, then senators from
high crime state should be differentially likely to vote in favor of the Brady Bill, (i.e., a1 is predicted to
be positive). On the other hand, if citizens believed the Brady Bill would have no effect on violent crime or might
inhibit possibilities for self defense, senators from high crime states would not be differentially likely to vote
for the Brady Bill and would likely vote against it.
Other measures of constituent characteristics should also affect senators’ votes. RURAL may reflect the prevalence
of a “gun culture” in a given state. If so, a high share of state population that is rural should make a given
senator less likely to vote for the Brady Bill, all else equal, so a2 should be negative. HUNTREV proxies the economic
impact of hunting in a state. Because over half of gun owners and nine percent of handgun owners cite hunting as
the most important reason for gun ownership,[24] and because hunters may not perceive a link between gun ownership
and violent crime, hunters may be opposed to gun control of any kind. Therefore, senators from states where hunting
is an important business and hobby may be less likely to vote for the Brady Bill, and a3 is predicted to be negative.
The effect of POLICE is ambiguous. If constituents consider police protection effective, senators from states with
high levels of police protection may face little pressure to vote for or against the Brady Bill, regardless of
constituent views of the effectiveness of gun control. On the other hand, in states with relatively little police
protection, citizens who believe gun control works will lobby their senators to vote for the Brady Bill, while
those who believe gun control is ineffective or an impediment to self-defense will lobby against the bill. However,
consideration of individual citizens alone ignores the lobbying efforts of police. Public statements given by many
chiefs of police, police organizations, and police unions indicate that police forces take active positions in
the fight for gun control.[25] For example, Washington, D.C. Metropolitan Police Department chief, Fred Thomas,
and New York City’s police commissioner, Raymond Kelly, strongly supported the Brady Bill, with Kelly saying that
“(g)un control laws, the stricter the better, are critical [to reduce violent crime].”[26] Further, both the Fraternal
Order of Police and the National Association of Police Organizations favored the Brady Bill.[27] Nevertheless,
Ayoob calls these statements and positions into question by arguing that unlike police chiefs and commissioners,
whose public statements may reflect political appointments and realities, the majority of “street cops” believe
gun control does nothing to reduce crime and that guns are an effective defense against crime. The sign on a4 is
uncertain.
The importance of campaign contributions to political outcomes is well recognized, so NRA and HCI are included
in the model, with the sign of a5 expected to be negative and the sign of a6 expected to be positive [28] With
over 3 million members and over $2.5 million spent on congressional races in 1992,[29] the NRA has long been recognized
as a potent political force.[30] Its rival organization, HCI, is smaller, with only 360,000 members in 1993, but
still an important political force, whose president, Richard Aborn, considered the Brady Bill “a national referendum
on public support for a more comprehensive gun control debate.”[31]
Finally, political affiliation and ideology are considered. Since the Democratic Party is known to generally favor
gun control, PARTY is included in the model, and a7 is expected to be positive, especially if party affiliation
reflects a constituency’s preferences not fully captured by the state average statistics. PARTY also proxies for
the effects of party control, loyalty, and discipline, which may have been especially important, given a Democratic
president who firmly supported the Brady Bill. The variable ADARESID is designed to capture any ideological preference
not reflected in constituent characteristics. If a senator’s ADA rating is greater than predicted by PARTY and
other variables reflecting constituent interests, that senator is more “liberal” than his constituents and is predicted
to be more likely to vote for the Brady Bill (i.e., a8 is expected to be positive).[32]
B. The results
The results of the empirical estimate are shown in Table 2. Before examining these results, three notes are in
order. First, the empirical model is estimated using logit regression because the dependent variable is qualitative.
Second, the results are presented for two equations, one with the POLICE variable and one with the POLICE variable
omitted. The second equation is presented because of multicollinearity between POLICE and VCRIME, though the estimates
of the two equations are fundamentally the same.[33] Finally, because the coefficient is not equivalent to the
derivative in logit regression, the derivative of each variable (noted as the partial effect) is presented in an
adjacent column.[34]
The predictive power of the model is high as evidenced by the significance of the likelihood ratio test, the R-square
value, and the fraction of senatorial votes forecasted correctly.[35] The model clearly identifies many of the
factors that influenced senatorial votes on the Brady Bill and provides reasonable measures of their effects.
Turning to the variable of primary interest, VCRIME, we find that senators from states with high rates of violent
crime were not more likely to vote for the Brady Bill. Though the coefficient is significant at only the relatively
weak 10 percent level for a one-tail test, the negative sign indicates that senators from states with high rates
of violent crime were less likely to vote for the Brady Bill. And when the POLICE variable is omitted, the coefficient
becomes significant at the 10 percent level for a two-tail test. The partial effects suggest that an increase in
the violent crime rate of 100 violent crimes per 100,000 of population reduced the probability a senator voted
for the Brady Bill by about 0.05.
The importance of hunters as an interest group is evident, with the coefficient on HUNTREV being negative and significant
in both regressions. An additional $1,000 per capita in hunting license revenues reduced the likelihood a senator
would vote for the Brady Bill by almost 0.05.
Campaign contributions, at least those given by the NRA, are clearly important determinants of senatorial votes.
The coefficient on NRA contributions is negative and significant in both regressions, and the partial effect indicates
that an additional $1,000 contribution to a senator’s campaign yielded the NRA an increased likelihood of a vote
for its position (against the Brady Bill) of at least 0.035. Senators clearly do respond to NRA contributions.
The partial effect of HCI contributions appears even larger than that of NRA contributions, indicating an additional
$1,000 contribution from HCI yielded this pro-control lobby an increased likelihood of a vote for the Brady Bill
of approximately 0.07. This relatively high effect indicates that HCI contributions are more effective than NRA
contributions, and perhaps that HCI allocates its funds more efficiently; however, the efficacy of HCI contributions
is called into question by the insignificance of the coefficients.
Political party affiliation and ideology are apparently very important determinants of senatorial votes on gun
control. The power of the Democratic Party’s position in favor of the Brady Bill is evidenced by the partial effect
showing that, all else equal, a Democratic senator was more likely to support the Brady Bill by a factor of at
least 0.36. Similarly, senators with a more liberal ideology than their constituents were more likely to vote for
the bill.[36]
The negative coefficients on RURAL are consistent with the presence of a “gun culture” in less densely populated
areas, but the variable is only marginally significant in the first estimate and insignificant in the second. The
POLICE variable is also insignificant, perhaps reflecting the conflicting views and interests captured in this
variable.[37]
To test the robustness of these results, I re-estimated the equation, replacing the rate of violent crime with
the murder rate and the rate of murders by handguns.[38] Because these results are nearly identical to those reported
in Table 2, they are not fully reported.[39] However, the coefficients on the crime measures reveal that an increase
in the murder rate of one per 100,000 of population reduced the likelihood a senator voted for the Brady Bill by
at least 0.03, and an increase in the rate of murder by handgun by one per 100,000 reduced the likelihood of voting
for the Brady Bill by approximately 0.05 to 0.06. These results offer no support to the hypothesis that senators
from states with high rates of violent crime are differentially likely to support a national waiting period for
purchases of handguns. To the contrary, the evidence presented indicates that these senators were less likely to
support a national waiting period, reflecting the preferences of constituents who perceived the Brady Bill as at
best ineffective and at worst an impediment to crime deterrence and self-defense.[40]
V. A Closer Look at NRA Campaign Contributions
The effects of campaign contributions on any political outcome, including gun control, is the subject of much debate
and controversy. Rather than enter that debate, I present a positive analysis of how the NRA determines contributions
to (and against) senatorial candidates by estimating the following model:
pBRADY = B0 + B1VCRIME + B2RURAL + B3HUNTREV + B4POLICE + B5HCI + B6PARTY + B7ADARESID + E. (2)
pNRA = d0 + d1pBRADY + d2pBRADYSQ + d3MARGIN + E. (3)
In equation (2), predicted values of the probability a senator will vote for the Brady Bill (pBRADY) are estimated
using all the variables in equation (1) except NRA contributions.[41] Then in equation (3), predicted NRA contributions
are modeled as a function of the probability a senator will vote for the Brady Bill, the squared probability a
senator will vote for the Brady Bill (pBRADYSQ), and the senator’s margin of victory in the last election (MARGIN).[42]
This model tests hypotheses about how the NRA allocates contributions. One argument is that the NRA should first
determine a senator’s likely vote before determining what contribution, if any, to make to that senator’s campaign.[43]
Contribution dollars should be most effective when given to candidates who are vacillating in their voting decision
(i.e., candidates with pBRADY values of approximately 0.5). Dollars contributed to candidates known to staunchly
oppose gun control (candidates with pBRADY values approaching zero) and candidates known to staunchly favor gun
control (candidates with pBRADY values approaching one) are unlikely to affect voting behavior. Hence, NRA contributions,
if wisely allocated, should be highest for undecided candidates and low or zero for those candidates with known
and firm positions. (Inclusion of the pBRADYSQ variable allows determination of whether or not the NRA follows
this strategy.)
Nevertheless, Langbein (1993) argues just the opposite on grounds that the NRA is a “membership group” that must
respond to constituents’ preferences, especially on highly visible issues, to reward legislators who vote the NRA’s
position and to withhold contributions from those who do not. If Langbein’s hypothesis is correct, NRA contributions
should be a monotonically increasing function of pBRADY. In an analysis of the Firearms Owners Protection Act,
Langbein finds that although the NRA did allocate some funds to pro-control House representatives, the vast majority
of NRA contributions went to representatives securely in the NRA camp. If d1 is positive and significant and d2
is insignificantly different from zero, Langbein’s hypothesis is supported. On the other hand, if d1 is positive
and significant and d2 is negative and significant, the first hypothesis is supported.
In addition, contributions should be greater, all else equal, for candidates in close races, where additional funds
may have a significant impact on the outcome of the race.[44]
Ordinary Least Squares and Tobit estimates of equation (3) are shown in Table 3, where VCRIME is used as the crime
variable to estimate a senator’s probability of voting for the Brady Bill.[45] The estimates provide strong support
for the first hypothesis presented. The positive and significant estimate of d1, and the negative and significant
estimate of d2, indicate that when mapped against the probability of voting for the Brady Bill, NRA contributions
follow and inverted-U pattern. Solving for the contribution-maximizing value of pBRADY yields a value of 0.35 for
the OLS estimate and 0.37 for the Tobit estimate. Though these estimates are not exactly 0.5, they are close to
the center of the political spectrum and may reflect the NRA’s efforts to concentrate on candidates moderately
opposed to gun control. The predictive power of equation (2) and the significance of the estimate of d2 suggest
the finding is not spurious. Perhaps the NRA changed strategies for the Brady Bill vote relative to the Firearms
Owners Protection Act votes of seven years earlier. At a minimum, this result indicates that additional research
into the allocation of funds by the NRA is needed.
Finally, every 10 percentage point difference in the victor’s margin over his opponent reduced contributions by
approximately $640 to $1,369, depending upon the estimate. The NRA clearly distinguishes close races, where contributions
matter most, from races that are settled or races that could only be affected by enormous contributions.[46] As
a whole, these results provide evidence that the NRA is a rational and efficient allocator of campaign funds.
VI. Why Did the Pro-Gun Lobby Lose?
The central task of this paper has been to determine and measure the factors that influenced senatorial votes on
the Brady Bill. The Brady Bill vote is special, not only because it marked the most important gun-control vote
since 1986, but also because the pro-gun forces (NRA) lost. Unfortunately, the analysis reveals little about the
forces leading to passage of the Brady Bill, though it does yield valuable insight into the factors that worked
(unsuccessfully) against its passage. Clearly, Democratic party affiliation and “liberal” ideology played pivotal
roles in passing the Brady Bill, with Democratic party affiliation alone raising the probability of a vote for
the Brady Bill by over 0.36. (To contrast, a $1,000 contribution from the NRA reduced the probability of a vote
for the Brady Bill by less than 0.04.) The Democratic party variable may capture the influence of politically active,
pro-gun interests that are not identified in state average statistics. And the positive and significant coefficient
on ADARESID may suggest that some senators voted in favor of the Brady Bill to impose their views of how to fight
crime or how to form a “better society,” even if their views differed from those of a majority of their constituents.
Future political battles over gun control are virtually assured and will provide other examples to determine the
important interests that drive political outcomes on this important and controversial issue.
VII. Politics and the Future of Gun Control
Predicting the future of the gun-control movement in the United States is hazardous. Early indications are that
the Brady Bill is of dubious effectiveness. As reported in Business Week, the impending passage of the Brady Bill
spurred countless Americans to buy guns. Legislation to ban some types of assault weapons produced an identical
effect,[47] leading to the ironic result that legislation designed to reduce gun purchases may, in the short run,
increase them. In addition, claims by President Clinton during the 1996 campaign that the Brady Bill had prevented
60,000 to 100,000 “felons, fugitives and stalkers” from obtaining handguns are clearly false.[48]
Indeed, the climate may be shifting against control. Fear of crime is spurring many states to pass laws permitting
citizens to carry concealed weapons. A crime-weary public, led in part by women, are supporting this legislation
in the name of crime deterrence and self-defense. And, evidence from Florida and academic researchers indicates
that concealed-carry laws do not increase gun violence.[49]
Consistent with the ideas expressed in this paper, public opinion, reflected through elected legislators, will
determine the ultimate outcome of gun-control legislation in the United States. So long as crime rates soar and
ordinary citizens believe guns are an effective means of protection, the constitutional rights of gun owners will
be, in large part, preserved.
References
Ayoob, Massad F. (1981). The Experts Speak Out: The Police View of Gun Control. Second Amendment Foundation: 1-21.
Bender, Bruce, and Lott, John R. Jr. (1996). Legislator Voting and Shirking: A Critical Review of the Literature.
Public Choice 87: 67-100.
Benson, Bruce L. (1984). Guns for Protection and other Private Sector Responses to the Fear of Rising Crime. In
Firearms and Violence: Issues of Public Policy. Edited by Don B. Kates, Jr. Cambridge, Massachusetts: Ballinger
Publishing Company, 225-258.
Blackman, Paul H. (1990). Law Enforcement Lobbying and Policymaking on Gun Control. Journal of Firearms and Public
Policy 3 (Summer): 29-56.
Blackman, Paul H. and Gardiner, Richard E. (1986). The N.R.A. and Criminal Justice Policy: The Effectiveness of
the National Rifle Association as a Public Interest Group. Institute for Legislative Action. National Rifle Association:
1-22.
Bovard, James. (1996). Clinton's Gun Hoax. Wall Street Journal (September 17): A18(1).
Carson, R.T. and Oppenheimer, J.A. (1984). A Method of Estimating the Personal Ideology of Political Representatives.
American Political Science Review 78 (March): 163-178.
DeFronzo, James. (1979). Fear of Crime and Handgun Ownership. Criminology 17 (November): 331-339.
Eskridge, Chris W. (1986). Zero-Order Inverse Correlations between Crimes of Violence and Hunting Licenses in the
United States. Sociology and Social Research 71 (October): 55-57.
Geisel, Martin S., Roll, Richard, and Wettick, R. Stanton Jr. (1969). The Effectiveness of State and Local Regulation
of Handguns: A Statistical Analysis. Duke Law Journal 4: 242-272.
Goff, Brian L., and Grier, Kevin B. (1993). On the (Mis)measurement of Legislator Ideology and Shirking. Public
Choice. 76: 5-20.
Green, Gary S. (1987). Citizen Gun Ownership and Criminal Deterrence: Theory, Research, and Policy. Criminology
25: 63-81.
Grier, Kevin B., and Munger, Michael C. (1993). Comparing Interest Group PAC Contributions to House and Senate
Incumbents, 1980-86. Journal of Politics. 55 (August): 615-643.
Home on the Range. (1994). The Economist (March 26): 23-24, 28.
Idelson, Holly. (1993). Gun Rights and Restrictions: The Territory Reconfigured. Congressional Quarterly (April
24): 1021-1026.
Kates, Don B. Jr. (1991). The Value of Civilian Handgun Possession as a Deterrent to Crime or a Defense Against
Crime. American Journal of Criminal Law 18: 113-167.
Kelly, Raymond. (1993). Toward a New Intolerance: Gun Control and Community Policing. Vital Speeches 59 (March
15): 332(3).
Kime, Roy Caldwell. (1993). IACP's Deep Involvement in the Legislative Process. The Police Chief 60 (October):
14(1).
Kleck, Gary. (1995). Guns and Violence: An Interpretive Review of the Field. Social Pathology 1 (January): 12-47.
Kleck, Gary, and Patterson, E. Britt. (1993). The Impact of Gun Control and Gun Ownership Levels on Violence Rates.
Journal of Quantitative Criminology 9: 249-287.
Kopel, David B. (1993). Peril or Protection? The Risks and Benefits of Handgun Prohibition. Saint Louis University
Public Law Review 12: 285-359.
Langbein, Laura. (1993). PACs, Lobbies, and Political Conflict: The Case of Gun Control. Public Choice. 77: 551-572.
Langbein, Laura, and Lotwis, Mark A. (1990). The Political Efficacy of Lobbying and Money: Gun Control and the
U.S. House, 1986. Legislative Studies Quarterly. 15 (August): 413-440.
Lott, John R., and Mustard, David B. (1997). Crime, Deterrence, and Right-to-Carry Concealed Handguns. Journal
of Legal Studies. 26: 1-68.
Magaddino, Joseph P. and Medoff, Marshall H. (1984). An Empirical Analysis of Federal and State Firearm Control
Laws. In Firearms and Violence: Issues of Public Policy. Edited by Don B. Kates, Jr. Cambridge, Massachusetts:
Ballinger Publishing Company, 225-258.
Martin, Justin. (1994). Johnny Rushes to Get His Gun. Fortune 129 (January 10): 16(1).
McAneny, Leslie. (1993). Americans Tell Congress: Pass Brady Bill, Other Tough Gun Laws. The Gallup Poll Monthly
(March): 2(4).
Polsby, Daniel D. (1986). Reflections on Violence, Guns, and the Defensive Use of Lethal Force. Law and Contemporary
Problems 49: 89-111.
President Signs 'Brady' Gun Control Law. (1993). 1993 Congressional Quarterly Almanac: 300-303.
Reynolds, Morgan O. and Caruth, W.W. III. (1992). Myths about Gun Control. National Center for Policy Analysis.
NCPA Policy Report No. 176: 1-34.
Shiflett, Dave. (1995). Have Gun, Will Eat Out. Wall Street Journal (February 28): A20(1).
Smart, Tim, Yang, Catherine, and Seemuth, Mike. (1993). Ready, Aim . . . . Business Week (December 27): 34-35.
Witkin, Gordon. (1994). New Support for Concealed Weapons: Fear of Crime Inspires Liberalized Laws. U.S. News &
World Report 117 (November 28): 56(3).
Wright, James D. (1984). The Ownership of Firearms for Reasons of Self-Defense. In Firearms and Violence: Issues
of Public Policy. Edited by Don B. Kates, Jr. Cambridge, Massachusetts: Ballinger Publishing Company, 301-327.
Ziegenhagen, Eduard A. and Brosnan, Dolores. (1985). Victim Response to Robbery and Crime Control Policy. Criminology
23: 675-695.
Appendix: Data Sources
Votes on Brady Bill: 1993 Congressional Quarterly Almanac, p. 51-S.
Political Party: 1993 Congressional Quarterly Almanac, p. 51-S.
ADA Ratings: Almanac of American Politics, various issues.
Electoral Margins: Almanac of American Politics, various issues.
Consumer Price Index: 1996 Economic Report of the President, Table B-56, p. 343.
Violent Crime Rate: Crime State Rankings 1994: Crime in the 50 United States, Kathleen O'Leary Morgan, Scott Morgan,
and Neal Quitno, editors. Morgan Quitno Corp., 1994, p. 283.
Murder Rate: Crime State Rankings 1994: Crime in the 50 United States, Kathleen O'Leary Morgan, Scott Morgan, and
Neal Quitno, editors. Morgan Quitno Corp., 1994, p. 289.
Murder with Handgun Rate: Crime State Rankings 1994: Crime in the 50 United States, Kathleen O'Leary Morgan, Scott
Morgan, and Neal Quitno, editors. Morgan Quitno Corp., 1994, p. 295.
Rural Population: Crime State Rankings 1994: Crime in the 50 United States, Kathleen O'Leary Morgan, Scott Morgan,
and Neal Quitno, editors. Morgan Quitno Corp., 1994, p. A5.
State Population: Crime State Rankings 1994: Crime in the 50 United States, Kathleen O'Leary Morgan, Scott Morgan,
and Neal Quitno, editors. Morgan Quitno Corp., 1994, p. A1 for 1992 figures, p. A2 for 1990 figures.
Hunting License Revenues: Gale State Rankings Reporter, Table 87, p. 49.
State and Local Government Full-Time Equivalent Police Employment: Sourcebook of Criminal Justice Statistics 1994,
Table 1.27, pp. 34-38.
NRA Contributions: Federal Election Commission, Committee Index of Candidates Supported/Opposed (D)
HCI Contributions: Federal Election Commission, Committee Index of Candidates Supported/Opposed (D)
Table 1. Descriptive Statistics
| Name | N | Mean | Std Dev. | Minimum | Maximum |
| BRADY | 98 | .633 | 0.485 | 0.000 | 1.000 |
| VCRIME | 98 | 565.35 | 288.82 | 85.30 | 1,207.2 |
| MURDER | 98 | 7.038 | 3.856 | 0.600 | 17.400 |
| MUHGUN | 96 | 3.282 | 2.408 | 0.000 | 10.380 |
| RURAL | 98 | 315.92 | 146.50 | 73.56 | 678.51 |
| HUNTREV | 98 | $3,604 | $4,935 | $92 | $27,893 |
| POLICE | 98 | 2.646 | 0.453 | 1.667 | 3.968 |
| NRA | 98 | $3,725 | $7,427 | -$28,718 | $51,136 |
| HCI | 98 | $491 | $1,289 | -$56 | $6,886 |
| PARTY | 98 | 0.551 | 0.500 | 0.00 | 1.00 |
| ADA | 98 | 52.01 | 33.92 | 2.50 | 99.00 |
| MARGIN | 98 | 22.94 | 20.60 | 0.00 | 100.00 |
| Variable Name | Coefficient / (t-statistic) | Partial Effect | Coefficient / (t-statistic) | Partial Effect |
| VCRIME | -0.00222 (-1.420) |
-0.00045 | -0.00280 (-1.974)* |
-0.00055 |
| RURAL | -0.00348 (-1.319) |
-0.00070 | 0.00275 (-1.090) |
-0.00054 |
| HUNTREV | -0.000245 (-2.356)** |
-0.000049 | -0.000247 (-2.473)** |
-0.000049 |
| POLICE | -0.880 (-0.824) |
-0.177 | ||
| NRA | -0.000188 (-2.259)** |
-0.000038 | -0.000183 (-2.251)*** |
-0.000036 |
| HCI | 0.000359 (0.784) |
0.000072 | 0.000341 (0.743) |
0.000067 |
| PARTY | 1.854 (2.649)** |
.373 | 1.850 (2.644)** |
0.364 |
| ADARESID | .847 (2.612)** |
0.170 | 0.840 (2.632)** |
0.165 |
| CONSTANT | 6.0219 (1.935)* |
3.833 (2.499)** |
||
| L.R. TEST | 58.616*** | 57.940*** | ||
| R-SQUARE | 0.455 | 0.450 | ||
| PERCENT CORRECT | 85.7 | 86.7 | ||
| N | 98 | 98 |
| Variable Name | Coefficient / (t-statistic) |
| pBRADY | 17,460 (1.931)* |
| PBRADYSQ | -24,940 (-3,.050)*** |
| MARGIN | -64.05 (-1.941)* |
| CONSTANT | 6,773 (3.345)*** |
| OLS | Tobit | |
| Variable | Coefficient / (t-statistic) | Regression Coefficient / (asymptotic normal statistic) |
| pBRADY | 17,460 (1.931) |
35,138 (2.988)*** |
| pBRADYSQ | -24,940 (-3.050)*** |
-48,110 (-4.282)*** |
| MARGIN | -64.05 (-1.941)* |
-136.94 (-2.770)*** |
| Constant | 6,773 (3.345)*** |
5,713 (2.242)** |
| Variable Name | Coefficient / (t-statistic) | Partial Effect | Coefficient / (t-statistic) | Partial Effect |
| MURDER | -0.149 (-1.576) |
- 0.03006 |
-0.180 (-1.993)* |
-0.03569 |
| RURAL | -0.00257 (-0.981) |
- 0.00052 |
-0.00113 (-0.505) |
-0.00022 |
| HUNTREV | -0.000251 (-2.357)** |
- 0.00005 1 |
-0.000245 (-2469)** |
-0.000049 |
| POLICE | -1.062 (-1.058) |
-.214 | ||
| NRA | -0.000179 (2.108)** |
- 0.00003 6 |
-0.000177 (-2.144)** |
-0.000035 |
| HCI | 0.000352 (0.771) |
0.00007 1 |
0.000320 (0.703) |
0.000063 |
| PARTY | 1.846 (2.674)*** |
0.372 | 1.808 (2.634)** |
0.358 |
| CONSTANT | 5.994 (1.918)* |
3.001 (2.439)** |
||
| L.R. Test | 59.072 | 57.949 | ||
| R-square | 0.458 | 0.450 | ||
| Percent Correct | 85.7 | 84.7 | ||
| N | 98 | 98 |
Table 5. Regression Results with Murder Rate by Handgun as Independent Variable
| Variable name | Coefficient / (t-statistic) | Partial Effect | Coefficient / (t-statistic) | Partial Effect |
| MUHGUN | -0.223 (-1.410) |
-0.0469 | -0.284 (-1.849)* |
-0.0582 |
| RURAL | -0.00369 (-1.361) |
-0.00077 | -0.00181 (-0.799) |
-0.00037 |
| HUNTREV | -0.000254 (-2.258)** |
-0.000053 | -0.000243 (-2.389)** |
-0.000050 |
| POLICE | -1.305 (-1.296) |
-0.274 | ||
| NRA | -0.000178 (-2.111)*** |
-0.000037 | -0.000176 (-2.148)** |
-0.000036 |
| HCI | 0.000340 (0.736) |
0.000071 | 0.000293 (0.652) |
0.000060 |
| PARTY | 1.954 (2.737)*** |
0.410 | 1.904 (2.686)*** |
0.390 |
| ADARESID | 0.762 (2.359)** |
0.160 | 0.734 (2.353)** |
0.150 |
| CONSTANT | 6.515 (2.038)** |
2.372 (2.373)** |
||
| L.R. Test | 58.413 | 56.717 | ||
| 0.460 | 0.447 | |||
| Precent Correct | 83.3 | 85.4 | ||
| N | 96 | 96 |