THE EFFICACY OF GUN CONTROLS
A Thesis
Presented to the faculty of the Department of Economics
California State University, Sacramento
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF ARTS
in
Economics
by
Wesley Joseph Bollinger
SPRING
2013
THE EFFICACY OF GUN CONTROLS
A Thesis
by
Wesley Joseph Bollinger
Approved by:
__________________________________, Committee Chair
Terri Sexton, Ph.D.
__________________________________, Second Reader
Suzanne O’Keefe, Ph.D.
____________________________
Date
ii
Student: Wesley Joseph Bollinger
I certify that this student has met the requirements for format contained in the University
format manual, and that this thesis is suitable for shelving in the Library and credit is to
be awarded for the thesis.
__________________________, Graduate Coordinator
Kristin Kiesel, Ph.D.
Department of Economics
iii
___________________
Date
Abstract
of
THE EFFICACY OF GUN CONTROLS
by
Wesley Joseph Bollinger
Guns are used in the commission of many violent crimes and gun controls are the
legislative attempt to reduce crimes by reducing access to guns. Crime has a significantly
negative effect on the economic productivity of high crime areas and diminishes human
capital. This study measures the efficacy of gun controls in reducing firearm homicide
rates for years 2007-2011 using state level data. An index created by the Brady
Campaign is used to reflect the totality of gun control legislation in each state. Evidence
is found that not only are gun controls ineffective in reducing the firearm homicide rate,
but may actually increase it. Due to possible endogeneity, future research should verify
these results with instrumental variables.
_______________________, Committee Chair
Terri Sexton, Ph.D.
_______________________
Date
iv
TABLE OF CONTENTS
Page
List of Tables .............................................................................................................. vi
List of Figures ............................................................................................................ vii
Chapter
1. INTRODUCTION……………………………………………………….. .............1
2. LITERATURE REVIEW ....................................................................................... 3
3. DATA EXPLORATION ........................................................................................ 8
4. EMPIRICAL ANALYSIS .................................................................................... 17
5. CONCLUSION ..................................................................................................... 23
References ................................................................................................................... 25
v
LIST OF TABLES
Tables
Page
1.
Summary Statistics of Dependent Variables…………........ .…………………10
2.
Summary Statistics of Independent Variables………………………………. . 15
3.
Correlation Matrix…………………… ... ………….…………………………16
4.
Model 1 Regression Results………………………………. …………………18
5.
Model 2 Regression Results………………………………. …………………20
6.
Model 3 Regression Results………………………………. …………………21
vi
LIST OF FIGURES
Figures
Page
1.
Firearm Homicide Trend, 2007-2011……….………………………………. 9
2.
Scatterplot of Gun Death vs. Index…………………………………………. 11
3.
Scatterplot of Gun Death vs. Spill-in…………….…………………………. 12
vii
1
Chapter 1
INTRODUCTION
Gun control advocates propose the hypothesis that gun controls can limit access to
guns and therefore reduce crime. This is accomplished by restricting access to certain
types of firearms or restricting certain types of people from legally acquiring firearms.
Many states have laws that prohibit the sale of assault rifles, “Saturday night specials”, or
high-capacity magazines to all citizens. Other states allow law abiding citizens to own
said weapons and accessories but prohibit or diminish the capability of criminals to
acquire these weapons. Another dimension of gun controls is to increase the safety of
firearms. This is accomplished through requiring manufacturers to incorporate safety
systems on the firearms, requiring child-proof locks for storage, and gun safety classes
before the acquisition of the firearm. Advocates of gun control believe states should
enforce stricter gun controls to reduce murder and violent crime rates.
Contrary to this belief, gun control opponents propose the hypothesis that gun
controls have no impact on crime. There is a minimal impact on crime because they see
no correlation between legal gun ownership and crime rates. Therefore, any restrictions
gun controls impose are enforced upon law-abiding citizens who typically do not commit
violent crimes. This hypothesis is supported by D’Alessio (2000) who finds only
illegally owned guns increase crime rates. In addition, some gun control opponents
believe guns can be viewed as a deterrent for criminals. This hypothesis is built on the
2
belief that a criminal is less likely to victimize someone who is armed and therefore an
armed citizenry could reduce violent crime rates.
Investigating the efficacy of gun controls in reducing violent crime and homicide
is an important topic of study, especially in the after-math of such tragedies as the
Aurora, Colorado theater shooting and the Newtown, Connecticut massacre where 20
children and 6 teachers lost their lives. Gun control has once again become a
contentious political issue being feverishly debated in the United States with many state
and national legislatures looking to take quick action and pass stricter gun controls in
response to such heinous crimes mentioned above.
Crime also has a devastating effect on economic activity. Crime usually
victimizes those who are lower income, minorities, and less able to receive adequate
protection from law enforcement (Glaeser and Sacerdote 1996). High levels of crime can
erode property values and create an exodus of productive peoples, usually from city
centers to the suburbs, further alienating those left behind in a crime ridden stagnant local
economy (Detotto et al 2010). Crime is also costly to society. It takes vast resources to
locate, prosecute, and incarcerate criminals. The crimes criminals commit cost society
through damaged property, medical expenses, correctional facilities, and loss of human
capital. Freeman (1996) estimates this yearly cost to be $250 billion or roughly 3.8% of
Gross Domestic Product. Gun controls are passed to reduce violent crime and therefore
measuring the efficacy of such laws is crucial in understanding how to properly fight
such a damaging menace to society.
3
Chapter 2
LITERATURE REVIEW
There is a vast body of literature dedicated to the study of gun control and its
effect on crime. Unfortunately, the earliest studies generally failed to use proper control
variables to adequately estimate the impact of gun control legislation. Newton and
Zimring (1970) deduce that gun controls reduce the number of violent crimes because
there is a zero-order correlation between gun ownership and gun related violence.
However, this study has been disregarded because of the inability to control for
socioeconomic conditions and other factors that could influence violent crime. Seitz
(1972) finds that there is a 0.98 correlation between homicides committed with a firearm
and total homicides. However, as Moorhouse and Wanner (2006) point out, this
relationship is to be expected because Jacobs (2002) finds that over 60% of all homicides
are committed using a firearm. Though there have been many studies conducted, the
impact of gun control remains ambiguous. Kleck and Patterson (1993) conduct a survey
of 29 contemporary studies on the effect gun controls have on crime, finding that four of
the surveyed studies conclude gun control lowers crime, eight have mixed results, and
seventeen find no effect. Of those surveyed that use state level data, two studies find gun
controls reduce crime, two are inconclusive, and nine find no evidence that gun controls
reduce crime.
There is a plethora of literature that uses state-level data and finds no correlation
between stricter gun controls and reduced crime. Murray (1975) uses state-level data and
4
a vector of socioeconomic factors to regress crime on gun controls and finds no evidence
that stricter gun controls lower the rates of violent crime. In addition, availability of guns
is also insignificant in determining violent crime when controlling for social factors.
Similar to the approach in Murray (1975), this study uses a gun-demand proxy variable to
control for gun availability and legal gun ownership within a state.
Magaddino and Medoff (1982) measure the impact of “cooling-off” laws on
violent crime. Cooling-off laws are those that enforce a waiting period between the time
a firearm purchaser buys the gun and when he receives the gun. It is thought that such
delays would reduce the incidence of “crimes of passion” where one may not be thinking
rationally at the time of purchase. No evidence is found that states which have
implemented this policy experienced a reduction in violent crime (Magadinno and
Medoff 1982). While this thesis does not directly measure the effectiveness of individual
laws such as waiting periods, individual laws are all encapsulated in a gun control index.
There are also several studies that find a correlation between gun controls and
crime. Kwon et al. (1997) use a multivariate regression with state-level data and a vector
of socioeconomic control variables. A weak connection between gun control laws and
reduced crime is found. However, the main conclusion is that gun control laws are quite
ineffective when socioeconomic variables are considered. Therefore, it would be more
efficient to reduce crime by working on problems such as high-school dropout rates and
alcohol abuse rather than passing more stringent gun controls.
Similar to this research, Kwon and Baack (2005) and Moorhouse and Wanner
(2006) investigate the effectiveness of gun control legislation using a comprehensive
5
approach. These researchers use an index system for determining the severity and
strictness of a state’s gun control laws. This allows multi-state gun control legislation
comparisons to be made while eliminating the dummy variable problem of using up too
many degrees of freedom for the sample size. The index was created by the Open
Society Institute’s Center on Crime which is a George Soros foundation whose goal is
stated as, “The Open Society Foundations work to build vibrant and tolerant democracies
whose governments are accountable to their citizens.”
Kwon and Baack (2005) use total firearm deaths, including suicides and
accidents, as the dependent variable. Several independent variables that may be
correlated to firearm deaths are added to the model, including; violent crime rates, race,
police force per capita, the unemployment rate, population density, and a gun control
index. A multivariate regression model is used to estimate the results. The study finds
that states with the highest level of violent crime also had the most comprehensive gun
control laws. Rural areas with lower population density had higher rates of gun crime,
the size of police force had a deterrent effect, and the proportion of African Americans
had a significant impact on gun fatalities. Most importantly, the employment rate and
comprehensive gun control laws have a negative effect on firearm deaths.
In contrast, Moorhouse and Wanner (2006) find very different results. Using nine
regressions, the endogenous variables are overall crime rate and eight other specific
crime categories including rape, assault, and burglary. The exogenous variables include;
the indexed gun control laws, population density, a spill-in effect from neighboring
states, the proportion of the population living in a metropolitan area, per capita income,
6
race, and several other variables. Since the gun control laws are indexed in 1998, the
regressions are tested using 1999 and 2001 data to capture the one year and three year
effect of 1998 laws. The regressions show that most explanatory variables have the
expected signs with the exception of spill-in which was positive yet statistically
insignificant, evidence against the hypothesis that a state with strict gun control laws gets
undermined by a neighboring state with lax gun control laws. The indexed gun control
variable is insignificant in all regressions which is evidence against the hypothesis that
gun control laws reduce crime.
Fleegler et al. (2013) use a cluster Poisson multivariate regression to analyze the
effect gun controls have on total firearm deaths which includes suicide and accidental
shootings. The gun controls are measured based on the Brady Campaign to Prevent Gun
Violence’s gun law state scorecard which measures the level of gun controls in each
state. Instead of using the scores as provided by the Brady Campaign, Fleegler et al.
(2013) alter the data by counting the number of gun control laws in each state and giving
1 point for each law. For example, Massachusetts had 28 laws and therefore received a
score of 28 points. The states are then divided into quartiles based upon their 1 law, 1
point index scores. After including socioeconomic control variables, they find that a
higher number of firearm laws are associated with a lower firearm fatalities rate.
However, this negative relationship between gun control laws and firearm deaths is only
found between states in the fourth quartile with the highest number of gun control laws
and states in the first quartile with no gun controls. Furthermore, using a count model
like the Poisson model and dividing the states into quartiles seems an inefficient method
7
to measure the impact that gun controls have on firearm fatalities when panel data models
can be used.
This study builds upon the existing literature by incorporating the use of the
Brady Campaign’s gun control index in a comprehensive approach to measure the
efficacy of gun controls in the United States using panel data models. In addition, a spillin variable and a gun-demand proxy variable are included in the models to test if they
have any significance.
8
Chapter 3
DATA EXPLORATION
The dependent variable is homicide by firearm (Gun Death) at the state level in
years 2007-2011.This data is available in the Unified Crime Reports (UCR) compiled by
the Federal Bureau of Investigation. While Gun Death represents all firearm homicides,
it is also broken down into firearm specific homicides including Handgun, Rifle, and
Shotgun. This allows the efficacy of gun controls in reducing firearm homicide to be
measured in overall terms as well as specific firearm categories. Alabama and Florida
are not included in this study because they failed to meet the guidelines of reporting
homicides. Gun Death, Handgun, Rifle, and Shotgun are adjusted from aggregated state
data to the homicide rate per 100,000 inhabitants of a state. Figure 1 below shows the
national firearm homicide trend for years 2007-2011 for the states included in this study.
9
Figure 1.
Firearm Homicide Trend, 2007-2011
Firearm Homicide
10000.00
9800.00
9600.00
9400.00
9200.00
9000.00
8800.00
8600.00
8400.00
2006
2007
2008
2009
2010
2011
2012
There is a clear downward trend in the number of firearm homicides. The year
2007 has the highest number of firearm homicides at 9,801 and they decline every year to
8,475 in 2011. Louisiana has the highest firearm homicide rate at 10.6 per 100,000
inhabitants in 2007 while North Dakota has the lowest rate having no firearm homicides
in 2008. Table 1 provides the summary statistics for the dependent variables.
10
Table 1.
Summary Statistics of Dependent Variables
Variable
Mean
Std. Dev.
Min.
Max.
Gun Death
2.62
1.70
0
10.60
Handgun
1.78
1.44
0
8.13
Rifle
0.13
0.13
0
0.72
Shotgun
0.13
0.11
0
0.55
The main explanatory variable in this study is the index of gun controls (Index) in
years 2007-2011 created by the Brady Campaign to Prevent Gun Violence. States are
given a score representing the totality of their gun control legislation strength with 0
being the lowest score and 100 being the maximum. Points are awarded based on five
broad categories; Curb Firearm Trafficking which makes up 35 out of the 100 points
possible, Strengthening Brady Background Checks for 40 points, Ban Assault Weapons
for 10 points, Child Safety for 7 points, and Guns in Public Places and Local Control for
8 points. The Strengthening Brady Background Checks category contains such laws as;
permit to purchase mandatory for all firearms (long guns and handguns), some safety
training and/or testing required, 'Permit to Purchase' also acts as a 'License to Possess',
'Permit to Purchase' also required for the purchase of ammunition, fingerprinting of
applicants required for identity verification, and permit process involves law
enforcement. States are awarded 3 points for each of these laws that they have
implemented. A complete description of how the index was constructed is available on
the Brady Campaign to Prevent Gun Violence’s website.
11
Several states in the data set, including Utah and Arizona, received a score of 0
for at least one year. California received the highest score in all years with a 79 in 2007,
increasing to 81 in 2011. The mean index score for all states during the timeframe is
roughly 17. Figure 2 shows a scatterplot between Index and Gun Death in which a linear
relationship does not seem to exist.
Figure 2.
Scatterplot of Gun Death vs. Index
6
4
2
0
Gun_Death
8
10
Gun Death vs. Index
0
20
40
Index
60
80
The hypothesized spill-in effect of strict gun control states being undermined by
neighboring states with lax controls is measured by the Spill-in variable. Borrowing from
Moorehouse and Wanner (2006), Spill-in is constructed by assigning each state the
12
lowest Brady Campaign Index score of a neighboring state. States that do not have a
neighboring state with a lower index score are instead given a score matching their own
Brady Index score. This variable is expected to be negative if the hypothesis is correct
that states with strict gun controls get undermined by neighboring states with lax controls.
States with strict gun controls get undermined because guns would flow from less
regulated areas to more regulated areas and thus increase the firearm homicide rate.
Figure 3 below is a scatterplot between Spill-in and Gun Death.
Figure 3.
Scatterplot of Gun Death vs. Spill-in
6
4
2
0
Gun_Death
8
10
Gun Death vs. Spill-in
0
10
20
30
Spillin
40
50
13
There seems to be no linear relationship between Spill-in and Gun Death. There is
almost a bi-model relationship with a cluster between a Spill-in value of 40-50 with the
majority being between zero and ten.
Other control variables are included to prevent omitted variable bias. A proxy
variable for gun demand in each state is used to test the hypothesis of Dugan (2001) who
finds that gun ownership rates are strongly linked to homicide rates. This study uses the
National Instant Criminal Background Check System (NICS) as a proxy for gun demand.
According to the FBI’s website, NICS was “Mandated by the Brady Handgun Violence
Prevention Act of 1993 and launched by the FBI on November 30, 1998, NICS is used by
Federal Firearms Licensees (FFLs) to instantly determine whether a prospective buyer is
eligible to buy firearms or explosives. Before ringing up the sale, cashiers call in a check
to the FBI or to other designated agencies to ensure that each customer does not have a
criminal record or isn’t otherwise ineligible to make a purchase. More than 100 million
such checks have been made in the last decade, leading to more than 700,000 denials”.
NICS is scaled to per capita terms for each state.
Socioeconomic factors may have a large impact on violence and homicide rates.
To control for economic factors, this study includes per capita income (IPC) and the
unemployment rate (Unemployment) for each state from 2007-2011. Per capita income
ranges from $29,497 for West Virginia in 2007 to $57,902 for Connecticut in 2011. The
five-year all-state mean is $39,168. This data is made available by the United States
Department of Commerce, Bureau of Economic Analysis. The expected coefficient for
per capita income is negative, meaning that states with a higher average income should
14
experience a lower firearm homicide rate. The unemployment rate, which is made
available through the Bureau of Labor Statistics, ranges from 2.6% for Utah in 2007 to
13.7% for Nevada in 2010. The average unemployment rate during this time period is
around 7%. Unemployment is expected to have a positive coefficient (Kwan and Baack
2005), meaning states that average higher rates of unemployment should experience
higher firearm homicide levels.
To control for demographics that may influence the firearm homicide rate, two
racial variables are included in the model. The percentage of Hispanics and African
Americans in each state is used to proxy for other complex socioeconomic or cultural
problems that may contribute to homicide rates. The percentage of African Americans in
a state ranges from less than 1% to 38%, and similarly, the percentage Hispanic ranges
from 1% to 48%. Table 2 below provides the summary statistics for the independent
variables.
15
Table 2.
Summary Statistics of Independent Variables
Variable
Mean
Std. Dev.
Min.
Max.
Index
17.35
19.62
0
81
Spill-in
6.83
10.79
0
50
NICS
0.058
0.070
0.004
0.550
Unemployment
6.94
2.46
2.6
13.7
IPC
39.17
5.80
29.497
57.902
African American
0.099
0.093
0.004
0.376
Hispanic
0.102
0.099
0.011
0.477
In addition, a correlation matrix is provided to show the correlation coefficients
between the variables. Most notably, there is a positive correlation between Index and
the dependent variables Gun Death and Handgun. However, there is a negative
correlation between Index and the dependent variables Rifle and Shotgun. Also, the
correlation between Gun Death and Handgun is very high at 0.944. This is because the
majority of firearm homicides are committed using a handgun
16
Table 3.
Correlation Matrix
Variables
Gun
Death
Handgun
Rifle
Shotgun
Index
Spill-in
NICS
Unemp.
IPC
African
American
Hispanic
Gun Death
1.000
-
-
-
-
-
-
-
-
-
-
Handgun
0.944
1.000
-
-
-
-
-
-
-
-
-
Rifle
0.501
0.398
1.000
-
-
-
-
-
-
-
-
Shotgun
0.486
0.411
0.374
1.000
-
-
-
-
-
-
-
Index
0.019
0.060
-0.310
-0.275
1.000
-
-
-
-
-
-
Spill-in
-0.189
-0.180
-0.241
-0.250
0.525
1.000
-
-
-
-
-
NICS
-0.078
-0.069
0.063
0.166
-0.324
-0.215
1.000
-
-
-
-
Unemp.
0.164
0.111
-0.024
0.026
0.139
-0.014
0.067
1.000
-
-
-
IPC
-0.169
-0.120
-0.355
-0.377
0.677
0.395
-0.269
-0.127
1.000
-
-
African
American
0.754
0.743
0.201
0.379
0.093
-0.037
-0.137
0.236
-0.075
1.000
-
Hispanic
0.132
0.132
0.013
-0.051
0.279
-0.022
-0.170
0.174
0.105
-0.134
1.000
17
Chapter 4
EMPIRICAL ANALYSIS
The goal of this thesis is to measure the impact of state level gun control
legislation on reducing the rate of homicide by firearms. Since the Brady Campaign’s
gun control index spans five years, there are time and cross-sectional aspects to the data.
Therefore, a panel data model is an efficient analytical method. The panel is balanced
because there are the same number of time observations for every state.
The time and entity fixed effects model is a method of controlling for omitted
variables that vary over time and entities. The intercepts are treated as unknowns and are
estimated for each state. In addition, the slope coefficients are forced to be the same.
Hausman (1978) created a test to determine whether fixed effects or random effects is
more appropriate. Under the Hausman test, the null hypothesis is that the random effects
model is consistent. However, after performing this test for all three models, we failed to
reject the null hypothesis. The P > .05 and therefore the random effects model is more
appropriate than the fixed effects model.
In the random effects model the constants are random parameters. The model
estimates a single intercept term but allows for variation in errors across entities. The
intercept is seen as a random variable in the form of:
𝑎𝑖 = 𝑎 + 𝑣𝑖
Where 𝑣𝑖 is a zero mean standard random variable. The random effects model takes the
following form:
18
𝑌𝑖𝑡 = (𝑎 + 𝑣𝑖 ) + 𝛽1 𝑋1𝑖𝑡 + 𝛽2 𝑋2𝑖𝑡 + ∙∙∙ + 𝛽𝑘 𝑋𝑘𝑖𝑡 + 𝑢𝑖𝑡
𝑌𝑖𝑡 = 𝑎 + 𝛽1 𝑋1𝑖𝑡 + 𝛽2 𝑋2𝑖𝑡 + ∙∙∙ + 𝛽𝑘 𝑋𝑘𝑖𝑡 + (𝑣𝑖 + 𝑢𝑖𝑡 )
𝑌𝑖𝑡 represents the dependent variable for entity i at time t, 𝑎 the intercept, β as
coefficients, X as independent variables for entity i at time t, and ( 𝑣𝑖 + 𝑢𝑖𝑡 ) as an error
term. Since a random effects model is being used, a time variable (T) is added to control
for time trends that occur from 2007-2011. In 2007, T is set to = 1, in 2008 T = 2, and so
on. Robust standard errors are used to control for heteroskedasticity among the 48 state
clusters.
Table 4
Model 1 Regression Results
Variables
Index
Observations
Gun Death
0.011
(0.008)
-0.021**
(0.009)
0.919
(0.575)
-0.094***
(0.036)
-0.035
(0.024)
14.17***
(2.32)
3.50***
(1.25)
-0.039
(0.044)
240
Handgun
0.015*
(0.008)
-0.020**
(0.009)
0.839
(0.528)
-0.096***
(0.033)
-0.034*
(0.020)
11.77***
(1.92)
3.26***
(1.09)
-0.012
(0.050)
240
Rifle
-0.001
(0.001)
-0.001
(0.001)
0.079
(0.097)
-0.004
(0.006)
-0.005*
(0.003)
0.344**
(0.174)
0.180**
(0.089)
-0.008
(0.009)
240
Shotgun
-0.001
(0.001)
-0.001
(0.001)
0.229***
(0.073)
-0.002
(0.005)
-0.003
(0.003)
0.520***
(0.080)
0.117*
(0.067)
-0.009
(0.008)
240
R²
.66
.66
.20
.32
Spill-in
NICS
Unemployment
IPC
African
American
Hispanic
T
*** means significant at 1%, ** means significant at 5%, * means significant at 10% level
19
The key variable of interest, Index, is only significant in explaining the variation
in the handgun homicide rate. However, the coefficient is positive meaning an increase
in Index corresponds to an increase in the handgun homicide rate by 0.015 persons per
100,000. Spill-in is significant and negative for Gun Death and Handgun, evidence in
support of the hypothesis that states do get undermined by neighboring states with lax
controls. NICS is insignificant in three out of the four regressions, yet positive and
significant at the 1% level for Shotgun. Unemployment has an unexpected negative sign,
meaning higher levels of unemployment correspond to lower firearm homicide rates.
IPC is negative and significant at the 10% level for Handgun and Rifle. Both race
variables, African American and Hispanic, are positive and significant for all four
dependent variables. It should be noted that the R² value for Rifle is quite low, meaning
the regression does a poor job of explaining the variation in the rifle homicide rates. This
may arise from the fact that rifle homicides account for only about 4% of the firearm
homicides because the vast majority of firearm homicides involve the use of handguns.
In model 2, the regressions include the lag of Index and Spill-in, taking the form:
𝑌𝑖𝑡 = 𝑎 + 𝛽1 𝐼𝑛𝑑𝑒𝑥1𝑖𝑡 + 𝛽2 𝐼𝑛𝑑𝑒𝑥2𝑖(𝑡−1) + ∙∙∙ + 𝛽𝑘 𝑋𝑘𝑖𝑡 + (𝑣𝑖 + 𝑢𝑖𝑡 )
𝑌𝑖𝑡 represents the dependent variable for entity i at time t, 𝐼𝑛𝑑𝑒𝑥2𝑖(𝑡−1) the lagged Index,
𝑎 the intercept, β as coefficients, X as independent variables for entity i at time t, and
( 𝑣𝑖 + 𝑢𝑖𝑡 ) as an error term. Including the lag of Index allows the ability to measure how
firearm homicide rates in year T are affected by the level of gun controls in year T-1.
The lagged Index is meant to test the hypothesis put forward by The Open Society which
speculates that gun control laws may not have an immediate effect and could take time to
20
change homicide rates. Because this study only spans five years, only one lag is
included. The regression results are provided below.
Table 5.
Model 2 Regression Results
Variables
Index
Observations
Gun Death
0.014
(0.010)
-0.004
(0.011)
-0.018
(0.020)
-0.003
(0.021)
1.07**
(0.489)
-0.081***
(0.030)
-0.039
(0.025)
13.59***
(2.10)
3.06***
(1.18)
-0.053
(0.042)
192
Handgun
0.012
(0.014)
0.002
(0.016)
-0.005
(0.023)
-0.016
(0.023)
0.964
(0.657)
-0.073**
(0.030)
-0.028
(0.021)
11.43***
(1.80)
2.92***
(1.02)
-0.034
(0.048)
192
Rifle
0.001
(0.002)
-0.001
(0.002)
-0.001
(0.005)
0.001
(0.005)
0.033
(0.070)
-0.001
(0.005)
-0.005*
(0.003)
0.309**
(0.150)
0.071
(0.078)
-0.002
(0.007)
192
Shotgun
-0.004*
(0.002)
0.004
(0.003)
-0.004
(0.003)
0.003
(0.004)
0.206***
(0.082)
-0.009
(0.006)
-0.007
(0.004)
0.491***
(0.089)
0.118*
(0.072)
-0.007
(0.007)
192
R²
.66
.67
.20
.33
Lagged Index
Spill-in
Lagged Spill-in
NICS
Unemployment
IPC
African
American
Hispanic
T
*** means significant at 1%, ** means significant at 5%, * means significant at 10% level
Index is significant and negative for the shotgun homicide rate while the Index
lagged is insignificant for all four dependent variables. This means the level of gun
controls in year T-1 had no effect on firearm homicide rates in year T. Both the Spill-in
and lagged Spill-in are insignificant, evidence against the hypothesis of guns (and
21
therefore crime) flowing from states with lower Index scores into states with high Index
scores. NICS, Unemployment, IPC, African American, and Hispanic maintain similar
results to model 1 after the inclusion of a lag on Index and Spill-in.
In model 3, the dependent variables, Index, and Spill-in are differenced in the
regressions. The differenced variables measure how the change in Index and Spill-in
from year T-1 to year T affect the change in firearm homicide rates from year T-1 to T.
Table 6.
Model 3 Regression Results
Variables
Differenced
Index
Differenced
Spill-in
NICS
Observations
Gun Death
0.030***
(0.001)
-0.018
(0.029)
-0.344*
(0.201)
-0.014
(0.019)
-0.001
(0.006)
-0.651
(0.485)
-0.881***
(0.176)
0.086**
(0.040)
192
Handgun
0.030***
(0.011)
-0.005
(0.024)
-0.411**
(0.170)
-0.015
(0.014)
-0.001
(0.006)
-0.250
(0.373)
-0.817***
(0.182)
0.107***
(0.037)
192
Rifle
0.002
(0.003)
-0.006
(0.007)
0.002
(0.069)
0.006
(0.004)
-0.001
(0.001)
-0.073
(0.085)
-0.158**
(0.067)
0.007
(0.007)
192
Shotgun
-0.003
(0.003)
-0.005
(0.006)
-0.085
(0.056)
-0.005
(0.004)
-0.001
(0.001)
-0.019
(0.055)
0.026
(0.025)
-0.001
(0.008)
192
R²
.05
.07
.02
.02
Unemployment
IPC
African
American
Hispanic
T
*** means significant at 1%, ** means significant at 5%, * means significant at 10% level
In model 3, the R² values of the regressions are dramatically lower than in models
1 and 2. This means the independent variables do a poor job in explaining the variation
22
in the changes in firearm homicide rates. However, further evidence is found against the
efficacy of gun controls because the differenced Index is positive and significant for Gun
Death and Handgun. The differenced Spill-in is insignificant for all regressions. NICS is
negative and significant for Gun Death and Handgun yet insignificant for Rifle and
Shotgun. African American becomes insignificant while Hispanic becomes negative and
significant in three out the four regressions.
23
Chapter 5
CONCLUSION
The use of a state-level index for gun controls allows for a comprehensive
approach in measuring the efficacy of gun controls in reducing violent crime and firearm
homicide in the United States. This thesis extends the use of an index from a simple
Ordinary Least Squares regression into panel data models which use cross-sectional and
time series data. The main conclusion drawn from this study is that no evidence is found
that increasing gun controls lowers firearm homicide rates. In model 1, Index is found to
be insignificant in three out of the four regressions. In model 2, Index is again
insignificant in three of the four regressions. In addition, the lagged Index is insignificant
in all regressions. In model 3, Index is significant and positive for Gun Death and
Handgun. However, the low R² values of the regressions call into question the validity of
these results. Because positive results for Index were found in some regressions, possible
endogeneity concerns between firearm homicides and gun controls could be addressed by
instrumenting the gun control index in a Two Stage Least Squares model.
Interestingly, while gun controls seem to be either inefficient or counterproductive in reducing firearm homicides within a state, this thesis finds evidence in
model 1 that there could be a spill-in effect from neighboring states. The Spill-in variable
is negative and significant for Gun Death and Handgun meaning guns and violent crime
may indeed flow from states with lax gun controls into states with more stringent gun
controls. However, the results on the spill-in effect are far from conclusive. The lagged
24
Spill-in and differenced Spill-in were insignificant for all four dependent variables.
Furthermore, the way in which the spill-in is measured is quite crude and over-simplifies
a complex issue. For example, it does not control for states that share a border with
Mexico and it also does not allow for guns to travel further than one state away. In
addition, the predicted flow of guns from states with less gun controls into states with
more controls does not seem to match the data provided by the Bureau of Alcohol,
Tobacco, Firearms and Explosives (ATF). The ATF conducts a gun trace study in which
firearms found within a state are traced to their state of origin. Based on this data, it
seems firearms travel much farther than just between neighboring states.
Another variable of interest is NICS, the proxy for gun demand in each state. In
models 1 and 2, NICS is positive and significant for at least one dependent variable
which lends evidence towards the hypothesis that higher gun ownership levels in a state
correspond to higher firearm homicide rates. This result is actually quite intuitive. More
guns lead to more gun homicides. What it does not answer is if more guns lead to higher
overall homicide rates. For example, a state with strict gun controls may indeed have a
lower firearm homicide rate yet have a higher knife homicide rate. This is the
substitution theory that criminals will use whatever weapons are available to them to
increase their advantage over a victim.
Though no evidence is found that gun controls reduce firearm homicides, the
scope of this study is limited by the short timeframe that the Brady Campaign’s index
spans. The construction of a gun control index that spans more than five years would
allow for more conclusive results in determining the efficacy of gun controls.
25
REFERENCES
Detotto, C., & Otranto, E. (2010). Does Crime Affect Economic Growth?. Kyklos, 63(3),
330-345. doi:10.1111/j.1467-6435.2010.00477.x
Fleegler, E., Lee L., Monuteaux, M., Hemenway, D., Mannix R., (2013) Firearm
Legislation and Firearm-Related Fatalities in the United States. JAMA Intern
Med.2013;():1-9. doi:10.1001/jamainternmed.2013.1286.
Freeman, R. (1996). Why Do So Many Young American Men Commit Crimes and What
Might We Do About It? Journal of Economic Perspectives 10(1) 25-42.
Glaeser, E., Sacerdote, B. (1996) Why is There More Crime in Cities? Journal of
Political Economy, 107(6) 225-258.
Jacobs, J. B. (2002). Can Gun Control Work? New York: Oxford University Press.
Kleck, G., and Petterson, E. B. (1993) “The Impact of Gun Control and Gun Ownership
Levels on Violence Rates.” Journal of Quantitative Criminology 9: 249-287.
Kwon, I. G., & Baack, D. W. (2005). The Effectiveness of Legislation Controlling Gun
Usage: A Holistic Measure of Gun Control Legislation. American Journal Of
Economics
And Sociology, 64(2), 533-547
Kwon, I. G.; Scott, B.; Safranksi, S. R.; and Bae, M. (1997). “The Effectiveness of Gun
Control Laws: A Multivariate Statistical Analysis.” American Journal of
Economics and Sociology 56: 41-50.
26
Magaddino, J. P., and Medoff, M. H. (1982). “Homicides, Robberies, and State ‘Coolingoff’ Schemes.” In D. B. Kates Jr. (ed.) Why Handgun Bans Can’t Work.
Bellevue, Wash.: Second Amendment Foundation.
Moorhouse, J. C., & Wanner, B. (2006). Does Gun Control Reduce Crime or Does Crime
Increase Gun Control?. Cato Journal, 26(1), 103-124.
Murray, D. R., (1975). “Handguns, Gun Control Laws, and Firearm Violence.” Social
Problems 23: 81-92.
Newton, G. D., and Zimring, F.E. (1970). Firearms and Violence in American Life,
Washington: Govt. Printing Office.
Open Society Institute. (2000, April). “Gun Control in the United States: A Comparative
Survey of State Firearm Laws.” Available at www.soros.org/crime/gunreport.htm
Seitz, S. T., (1972). “Firearms, Homicides, and Gun Control Effectiveness.” Law and
Society Review 6: 595-614.