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1
PROFITING FROM TRAGEDY: AN EMPIRICAL INVESTIGATION ON MASS
SHOOTINGS AND GUN ACQUISITION
BY
Everett Brandon Price
Submitted to Princeton University
Department of Economics
In Partial Fulfillment of the Requirements for the A.B. Degree
April 13, 2016
2
Table of Contents
Introduction ………………………………………………………….. 3
Literature Review ……………………………………………………. 10
Data …………………………………………………………………... 33
Methodology …………………………………………………………..38
Results …………………………………………………………………49
Conclusion …………………………………………………………….56
Tables and Figures …………………………………………………….60
Bibliography………………………………………………………….. 69
3
I. Introduction
On the morning of December 2, 2015, Syed Rizwan Farook and his wife Tashfeen Malik
opened fire at a social services facility in San Bernardino, California, killing fourteen people and
injuring an additional twenty-one1. It was the deadliest mass shooting since the 2012 Sandy
Hook Elementary School shooting where Adam Lanza fatally shot twenty children and six
faculty members. Mass shootings like these are occurring more frequently in the United States
than ever before. Although there is no broadly accepted definition of a mass shooting, most
academic studies define mass shootings as events where an individual “kills four or more people
in a single incident (not including himself), typically in a single location”2. Using this definition,
a recent study by Harvard Research suggests that the rate of mass shootings has tripled since
20113. While there seems to be a consensus among scholars that mass shootings are on the rise,
there is anything but a consensus on how to prevent these tragedies from occurring in the future.
In the aftermath of these events, public discourse often focuses on the role of gun
legislation--or lack thereof--in the occurrence of mass shootings. While some argue that stricter
gun control will decrease the frequency of mass shootings, others contend that gun ownership
has a chilling effect on these incidents. In a nut shell, the United States is once again divided on
whether guns are seen as a problem or a solution. Although there is a wealth of research devoted
to the relationship between gun ownership and gun violence, scholars acknowledge that the
results of these studies vary widely and do not offer any conclusive evidence on the effectiveness
of gun control on crime and public safety4. Despite the fact that household gun ownership has
1 http://www.cnn.com/2015/12/06/us/san-bernardino-shooting-what-we-know/
2 http://www.motherjones.com/politics/2015/12/no-there-were-not-355-mass-shootings-this-year
3 http://www.hsph.harvard.edu/news/hsph-in-the-news/mass-shootings-becoming-more-frequent/
4 https://www.washingtonpost.com/news/wonk/wp/2014/11/14/more-guns-more-crime-new-research-debunks-
a-central-thesis-of-the-gun-rights-movement/
4
been steadily declining in recent decades, the number of guns in circulation has risen
dramatically since Obama took in office in 20085. The United States now has more guns than it
has people -- a feat no other country is even close to6. The Congressional Research Service
estimates that there are 40 million more guns than people in the US, not including the number of
illegal guns in the country.
Regardless of how you measure it, Americans are purchasing more guns than ever before.
The National Instant Criminal Background Check System (NICS) is a system “used by Federal
Firearms Licensees (FFLs) to instantly determine whether a prospective buyer is eligible to buy
firearms”7. Because there is not an officially reported statistic for guns sales, NICS background
checks are the most common proxy for gun sales. While the number of background checks has
been increasing dramatically for the last decade (see Figure 1), firearms manufacturers and
distributors have also been reporting record revenues and profits8(see Figure 2). Consequently,
gun stocks have continued to soar at a time when the rest of the economy has seemed to stall out.
As the number of guns in this country continues to increase, so does the rate of mass shootings.
While some deem this to be a mere coincidence, advocates of tighter gun regulation strongly
believe that the rise in mass shootings stems from the easy access to guns in this country9.
Although the relationship between gun ownership and gun-related deaths has been well-
documented over the years, there is much less research on the link between gun ownership and
mass shootings in particular.
5 http://www.norc.org/PDFs/GSS%20Reports/GSS_Trends%20in%20Gun%20Ownership_US_1972-2014.pdf
6 https://www.washingtonpost.com/news/wonk/wp/2015/10/05/guns-in-the-united-states-one-for-every-man-
woman-and-child-and-then-some/ 7 https://www.fbi.gov/about-us/cjis/nics
8 http://www.economist.com/blogs/graphicdetail/2015/08/graphics-americas-guns
9 http://www.nytimes.com/interactive/2015/10/07/us/gun-control-explained.html
5
In light of the conflicting views over the role that guns play in these tragic incidents, my
thesis challenges the common belief that mass shootings are the product of there being more
guns in the United States than in previous years. Using data on mass shootings, background
checks, and the revenues of gun companies, my study takes an empirical approach in examining
the unique relationship between gun sales and mass shootings. While this paper does not attempt
to identify the root cause of the recent uptick in mass shootings, the results of my analysis
support anecdotal evidence that mass shootings cause spikes in gun sales. More importantly, my
study extends on the research of Wallace (2015) by looking at the quarterly revenues of gun
companies in addition to monthly background checks10
.
The issue of gun control continues to be one of the most controversial and debated topics
in politics. President Obama has made numerous calls for more comprehensive background
checks and the banning of assault weapons and high-capacity magazines11
. After the San
Bernardino attack, he addressed the nation: “The one thing we do know is that we have a pattern
now of mass shootings in this country that has no parallel anywhere else in the world. And there
are some steps we could take not to eliminate every one of these mass shootings, but to improve
the odds that they don't happen as frequently: common-sense gun safety laws, stronger
background checks”12
.
Although many lauded Obama’s plan to curtail the circulation of firearms in the US,
there was strong opposition from the NRA and other gun rights advocates who saw this as a
violation of their Second Amendment rights. This clash between Obama and gun rights
advocates has become routine in the wake of high-profile mass shootings. On the one hand,
10
Lacey Wallace, Responding to violence with guns: Mass shootings and gun acquisition (Penn State 2015) 11
https://www.washingtonpost.com/politics/obama-launches-gun-violence-task-force/2012/12/19/90ff2d52-49f9-11e2-b6f0-e851e741d196_story.html 12
https://www.whitehouse.gov/blog/2015/12/02/president-obama-shooting-san-bernardino
6
defenders of the Second Amendment object that more guns do not necessarily lead to more mass
shootings. After all, most of us have heard the saying: “Guns don’t kill people, people do”. Some
gun-owning Americans claim that guns can prevent mass shootings or at least mitigate the
carnage. Even if it is unlikely that a mass shooter is stopped in the act by an armed civilian, there
are some who contend that the mere presence of guns dissuades people from committing crimes
in the first place. They argue that owning a firearm and being trained in how to use it can make a
household or community safer. The fact that 60% of Americans say they own a gun for personal
safety/protection purposes supports this belief13
. Considering that mass shootings have not
subsided, it is understandable why some Americans might view buying a gun as a step closer to
peace of mind.
Despite Obama’s multiple calls for tighter gun control, bills expanding background
checks were not able to make it through Congress. While there has not been any gun control
legislation passed at the federal level, certain states have taken measures into their own hands. In
a recent study on gun laws during the 2004–2014 period, Kristin Goss finds that 48 out of the 50
states introduced legislation on guns and mental health and “the majority (n = 26) both tightened
and loosened restrictions over the period (sometimes in the same bill)”14
. That is, there were bills
passed which contained provisions both tightening and relaxing certain aspects of gun control.
Her study suggests that nationally prominent shootings were directly responsible for the creation
of 39% of the firearms and mental health bills in her sample15
. While the introduction of stricter
gun legislation is expected after a mass shooting, it is surprising to see that there is also
legislation passed which expands the rights of gun owners. Goss’s findings challenge the
13
http://www.gallup.com/poll/1645/guns.aspx 14
Kristin Goss, Defying the odds on gun regulation: The passage of bipartisan mental health laws across the States (American Journal Of Orthopsychiatry 2015) 15
Ibid
7
conventional wisdom that mass shootings lead to tighter gun control and, instead, suggest that
they lead to the reformation of gun laws at the state level.
Goss’s research also suggests that policymakers have become more conscious of the role
that mental illness plays in gun violence, while this was not the case in years past. Following the
San Bernardino attack, speaker of the House Paul Ryan argued that guns are not the root of the
problem and explained that “people with mental illness are getting guns and committing these
mass shootings”16
. The NICS was put in place to prevent these mentally unstable individuals
from acquiring guns in the first place. Since being launched by the FBI in 1998, the NICS has
been responsible for denying roughly 2% of requests to purchase firearms17
. Although federally
licensed firearms dealers are required by law to conduct background checks on would-be gun
buyers, there are no federal laws governing the private sale of guns, which many refer to as the
“gun show loophole”. As a result, gun control laws vary widely from state to state. Because
private gun sales account for about 20% of total gun sales, there has been a massive push to
expand background checks to all firearms transactions, thus, closing the gun show loophole18
.
Like Obama, many Americans who support expanding background checks believe that it would,
at the very least, decrease the likelihood of mass shootings by making it more difficult for those
suffering from mental illness to obtain a gun.
There is still plenty of debate on how effective background checks are in practice. Some
scholars believe that background checks may successfully prevent guns from getting into the
wrong hands and often cite a Connecticut handgun licensing law as a prime example. In 1995
legislators in Connecticut passed a “permit to purchase” law which required private sellers to
16
http://www.nytimes.com/2015/12/16/opinion/dont-blame-mental-illness-for-gun-violence.html?_r=0 17
http://money.cnn.com/2015/12/06/news/fbi-gun-background-checks/ 18
Ibid
8
conduct background checks19
. Under the new handgun licensing law, prospective buyers were
required to apply for a permit in person with the local police, complete a mandatory class on
handgun safety, and they had to be at least 21 years old instead of the previous 1820
. Researchers
at Johns Hopkins found that the implementation of the new handgun licensing law “was
associated with a 40 percent reduction in the state’s firearm-related homicide rate,” while
accounting for all other variables21
. Study author Daniel Webster concluded that “licensing
handgun purchasers is a particularly effective way to achieve comprehensive background checks
and keep people from buying guns for people who are not legally allowed to own them”22
.
Studies like the one at Johns Hopkins seem convincing that background checks should be
expanded at the national level to prevent the mentally ill from obtaining dangerous weapons. It
should be noted, however, that the Johns Hopkins study focuses on Connecticut’s firearm-related
homicide rate from 1995-2005, which does not include the tragedy at Sandy Hook Elementary.
The shooter, Adam Lanza, had a history of mental health issues, but was able to steal two
semiautomatic handguns, an assault rifle, and a shotgun from his mother23
. While mental illness
plays an integral role in many mass shootings, it is evident that expanding background checks
will not fully get rid of mass shootings and other forms of gun violence altogether.
Scholars are quick to point out though that the majority of high-profile mass shootings
are committed with guns that were legally purchased by the shooter. In the last three decades,
82% of the weapons used in mass shootings were bought legally24
. Back in February, Uber
19
http://www.jhsph.edu/news/news-releases/2015/connecticut-handgun-licensing-law-associated-with-40-percent-drop-in-gun-homicides.html 20
Ibid 21
Ibid 22
http://www.jhsph.edu/news/news-releases/2015/connecticut-handgun-licensing-law-associated-with-40-percent-drop-in-gun-homicides.html 23
http://www.motherjones.com/mojo/2013/11/what-we-learned-sandy-hook-crime-report 24
http://www.msnbc.com/msnbc/most-guns-mass-shootings-obtained-legally
9
driver Jason Brian Dalton shot and killed six people in Kalamazoo, Michigan, with a gun that he
legally bought only hours before the shootings25
. Although neighbors reported that he was acting
paranoid prior to the initial shooting, Dalton did not have any history of mental illness. Unless
signs of mental illness are severe enough to be officially documented, background checks are
unlikely to reject every mentally ill individual who goes to purchase a gun. In some cases, mass
shooters were able to legally obtain a gun because signs of mental illness were unofficially
reported or not reported at all and, therefore, did not come up in the NICS background checks.
Although background checks can limit the number of would-be mass shooters from
buying guns, they unfortunately do not ensure that these deranged individuals will not be able to
gain access to firearms through other means. Many Americans believe that the sheer number of
guns in this country is the real problem. Having more guns in this country, they argue, makes it
easier for guns to get into the wrong hands. Considering that this increase in gun sales is only
unique to the US, those in favor of gun control often point out that guns must be the culprit
because mass shootings simply don’t happen as frequently in other developed countries.
Criminal justice expert Adam Lankford investigates whether the uptick in mass shootings is
present in other countries as well as the US. As it turns out, the US has the highest rate of gun
ownership and leads the world in mass shootings. Although the US has less than 5% of the
world’s population, it “accounted for 31% of global mass shooters during the period from 1966
to 2012, more than any other country”26
. Lankford also finds that countries with higher rates of
gun ownership recorded more mass shooters per capita27
. He believes that his findings have
25
http://www.cnn.com/2016/02/22/us/kalamazoo-michigan-what-we-know-and-dont-know/ 26
http://www.wsj.com/articles/u-s-leads-world-in-mass-shootings-1443905359 27
Ibid
10
important policy implications: “If we could simply limit the number of weapons—one firearm,
instead of multiple—even that pretty minimal progress would save lives”28
.
Lankford is right about there being a relationship between gun ownership and mass
shootings, but there is a more striking and prominent relationship between mass shootings and
gun sales. Since 2009, gun makers have more than doubled their yearly output and the rate of
mass shootings has increased to an average of one per month during this same time frame29
.
Regardless of which statistics you look at, the relationship between gun acquisition and mass
shootings is startling. Many people read statistics like the one above and point fingers at the gun
companies for “fueling” these tragic events without bearing any liability. Some propose that
holding gun companies liable for crimes that were committed with their products would
incentivize them to take additional measures to prevent guns from getting into the wrong hands.
While studies like Webster’s suggest that expanding background checks can effectively limit the
number of mass shootings, Lankford’s research points to gun ownership in general being the
problem. In any event, the remainder of this paper uses trends in household gun ownership and
background checks to explore the relationship between mass shootings and gun sales.
II. Lit Review
There are numerous articles out there claiming that mass shootings have become more
common and deadlier in recent years. Because there is not a widely-accepted definition of a mass
shooting, studies have come to different conclusions about the frequency of mass shootings in
the United States. For example, the New York Times uses a very liberal definition and finds that
28
http://www.wsj.com/articles/u-s-leads-world-in-mass-shootings-1443905359 29
https://www.washingtonpost.com/news/wonk/wp/2015/10/05/guns-in-the-united-states-one-for-every-man-woman-and-child-and-then-some/
11
mass shootings occur more than once a day on average30
. Their analysis does not use the FBI’s
definition of a mass shooting, so it includes incidents involving gang shootings and domestic
violence, as well as events where people were shot at but not necessarily killed. Since most
Americans wouldn’t classify a gang shootout or a household homicide as a mass shooting, the
conclusion that mass shootings happen every day can be misleading.
There have been several notable studies that use a stricter definition to examine the
frequency of mass shootings in the US. Researchers from the Harvard School of Public Health
measure the frequency of mass shootings by calculating the days in between each mass shooting
in their sample31
. While other studies have counted the number of mass shootings per year to
make comparisons, the Harvard researchers believe that their method “is more effective than
counting the annual number of incidents because it is more sensitive to detecting changes in
frequency when the number of events per year is small, as is the case with public mass
shootings”32
. They use the Mother Jones database as I have done in my analysis, which limits
their sample to “public attacks in which the shooter and victims were generally unknown to each
other and four or more people were killed”33
. Using this definition, they find that there were
fourteen mass shootings between 2011 and 2014 and the average distance between each one was
sixty-four days. For the twenty-nine years prior to 2011, mass shootings only occurred every 200
days on average34
. Their findings imply that mass shootings occur more than three times as
frequently in recent years as they did in previous decades, but offer no explanation as to why this
is the case.
30
http://www.nytimes.com/2015/12/03/us/how-often-do-mass-shootings-occur-on-average-every-day-records-show.html 31
http://www.hsph.harvard.edu/news/hsph-in-the-news/mass-shootings-becoming-more-frequent/ 32
http://www.motherjones.com/politics/2014/10/mass-shootings-increasing-harvard-research 33
http://www.hsph.harvard.edu/news/hsph-in-the-news/mass-shootings-becoming-more-frequent/ 34
Ibid
12
What makes the accelerating rate of mass shootings even more peculiar is the fact that
murder, violent crime, property crime, rape, and aggravated assault have all significantly
decreased since the 1990’s35
. In fact, between 1990 and 2012 the crime rate declined by almost
45%36
. There are many factors that are said to contribute to this overall drop in the crime rate,
but one has to wonder why mass shootings have increased during this same time. The trends
above suggest that mass shootings are unique from or independent of other forms of violent
crime. That being said, there is no reason to think that mass shootings have anything to do with
more common crimes. To put things in perspective, in 2014 there was a violent crime committed
every 26.3 seconds, a property crime every 3.8 seconds, and a murder every 36.9 minutes,
compared to a total of four mass shootings in the year 201537
. Considering that there is a massive
disparity in the frequencies of mass shootings and murders, it would not make sense to assume
that there must be a relationship between the two. This has important implications because
policies used to combat crime in general should have little to no influence on the rate of mass
shootings if the two are not related to each other.
We can see that mass shootings are becoming more frequent at a time when most other
types of violent crimes are at all-time lows. If mass shootings are connected in any way to the
number of guns, shouldn’t all firearm-related deaths be increasing? It turns out that random
shootings in general have been on the rise too. Although accidental firearms-related deaths have
been slowly decreasing, active shootings, which are one step below mass shootings, have been
35
http://www.hamiltonproject.org/papers/ten_economic_facts_about_crime_and_incarceration_in_the_united_states 36
Ibid 37
https://www.fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2014/crime-in-the-u.s.-2014/offenses-known-to-law-enforcement/browse-by/national-data
13
increasing over the last decade38
. An active shooter is defined by the United States Department
of Homeland Security as “an individual actively engaged in killing or attempting to kill people in
a confined and populated area; in most cases, active shooters use firearms(s) and there is no
pattern or method to their selection of victims”39
. In September of 2014, the FBI released a
“Study on Active Shooter Incidents” which examines the 160 active shooter incidents that
transpired in the US between 2000 and 2013. The FBI notes that shootings related to drug or
gang violence are excluded from the list of active shooters. Similar to the Harvard study, the FBI
finds that “the first seven years of the study show an average of 6.4 incidents annually, while the
last seven years show 16.4 incidents annually”40
. Another interesting finding from the study lies
in the fact that 21 out of the 160 incidents ended after unarmed citizens were able to successfully
restrain the shooter41
. It is worth pointing out that this number is already greater than the amount
of times a mass shooting was stopped by a citizen with a gun.
The FBI’s “Study on Active Shooter Incidents” supports the claim that random shootings
in general have become more popular in the US, but we are still left with the question of why
these tragic events are occurring more frequently. There are a wide variety of theories on why
mass shootings are on the rise. While mental illness is often cited as a potential reason,
contributors at Mother Jones believe “it is unlikely that this recent shift is the result of social and
cultural factors that have remained relatively constant over the past decade—such as the
prevalence of mental illness”42
. Mental illness is most likely one of the root causes in every mass
shooting, however, this would not explain the increase in mass shootings unless there was a
38
http://fivethirtyeight.com/features/mass-shootings-have-become-more-common-in-the-u-s/ 39
https://www.dhs.gov/xlibrary/assets/active_shooter_booklet.pdf 40
https://www.fbi.gov/about-us/office-of-partner-engagement/active-shooter-incidents/a-study-of-active-shooter-incidents-in-the-u.s.-2000-2013 41
Ibid 42
http://www.motherjones.com/politics/2014/10/mass-shootings-increasing-harvard-research
14
nationwide increase in mental illness. Considering that mental illness is taken more seriously
now than it was a decade ago, it is difficult to imagine that the number of people in the US with a
mental illness in recent years is significantly greater than the number of people with a mental
illness before mass shootings became more common. If anything, mental illness is diagnosed and
treated more frequently now than it was in years past which should at least work to decrease the
number of untreated cases. Moreover, the fact that mental illness is diagnosed and treated more
frequently nowadays could make people less likely to commit mass shootings and other
premediated crimes.
The recent uptick in mass shootings has prompted more research on the relationship
between guns and firearm-related deaths. While this has been a particularly new subject of
interest, there have been multiple older studies examining how gun ownership affects crime. One
way to examine if gun ownership is responsible for the increase in mass shootings is to examine
historical changes in gun control affected gun-related deaths. If it is true that more guns lead to
more mass shootings, then gun control would seem to be the only practical solution. Economist
John Lott conducted a statistical analysis on how concealed-carry laws affect crime rates from
1977 to 2005 at the city, county, and state level43
. Lott’s findings lead him to conclude that
“concealed handguns are the most cost-effective method of reducing crime thus far analyzed by
economists”44
. Although the bulk of his research pertained to guns and crime in the United
States, Lott noted that countries like Great Britain have seen murder rates rise after banning
guns.
Some critics today like Daniel Webster “completely discredit” Lott’s study and assert that
Connecticut’s firearm-related homicide rate from 1995-2005 is evidence against Lott’s
43
John Lott, Concealed Carry Permit Holders Across the United States (Social Science Electronic Publishing 2015) 44
Ibid
15
conclusion45
. In light of Lott’s research, the Nation Research Council published a lengthy and
comprehensive report which “concluded that existing research was insufficient to say much of
anything about a causal connection between crime and right-to-carry laws”46
. To complicate
matters further, a 2012 paper by researchers at Stanford and Johns Hopkins investigated the same
topic but came to a different conclusion: “Overall, the most consistent, albeit not uniform,
finding to emerge from both the state and county panel data models conducted over the entire
1977-2006 period with and without state trends and using three different specifications is that
aggravated assault rises when [right to carry] laws are adopted”47
. Their results imply that gun
control does play a significant role in reducing certain forms of violence. While there are a
number of studies like the ones above that examine the link between guns and crime, the results
lead to conflicting conclusions about the relationship between these two variables.
Furthermore, if guns are indeed the sole cause of the rise in mass shootings, then limiting
or getting rid of the country’s firearms should in theory solve the problem and, in the very least,
decrease the frequency of mass shootings and other gun-related homicides. Considering how
many guns are already in the US, it might be the case that restricting future gun purchases would
not be sufficient enough to prevent guns from getting into the wrong hands. If restricting future
gun purchases would not be enough to significantly decrease the country’s supply of firearms,
then the next option would be directly taking guns away from the people. While some Americans
would view any attempt by the federal government to disarm the public as extreme and
unconstitutional, this is what actually happened in Great Britain and more notably Australia.
45
https://www.washingtonpost.com/news/wonk/wp/2015/12/10/huge-numbers-of-people-are-suddenly-interested-in-carrying-concealed-guns/ 46
Ibid 47
http://news.stanford.edu/news/2014/november/donohue-guns-study-111414.html
16
In December of 2015, the New York Times published an article titled “How a
Conservative-Led Australia Ended Mass Killings” which discusses the measures that Australian
officials took to decrease the number of mass shootings48
. Australia was rocked by a devastating
mass shooting in 1996 when a lone gunman shot and killed 35 people in the Tasmanian town of
Port Arthur49
. Following the shooting, the Australian public demanded that the government take
immediate action to considerably tighten gun laws and prevent these kinds of incidents from ever
happening again. As a result, conservative Prime Minister John Howard passed a series of gun
control laws including a national gun buyback program which effectively removed more than
20% of firearms from public circulation50
. These laws also “tightened licensing rules, established
a 28-day waiting period for gun purchases, created a national gun registry and instituted a
temporary buyback program,” and placed an outright ban on auto/semiautomatic assault rifles
and pump shotguns51
.
Australia has yet to have a mass shooting since the creation of the National Firearms
Programme Implementation Act 1996 (NFA)52
. To be fair, mass shootings were rare in Australia
before 1996, however, there is something to be said in light of the fact that the rate of gun-related
suicides and homicides per 100,000 residents in Australia also sharply declined in the years after
199653
. Andrew Leigh and Christine Neil’s paper “Do Gun Buybacks Save Lives? Evidence
from Panel Data” investigates whether or not it was a coincidence that gun-related deaths fell in
Australia after the implementation of their national gun buyback program54
. Their study was
motivated by prior research on the effect of the NFA which presented “time series evidence
48
http://www.nytimes.com/2015/12/05/world/australia/australia-gun-ban-shooting.html 49
Ibid 50
Ibid 51
Ibid 52
Ibid 53
Ibid 54
Andrew Leigh and Christine Neil, Do Gun Buybacks Save Lives? Evidence from Panel Data (IZA 2010)
17
against the notion that stricter gun laws have led to increases in total homicides” (Leigh and Neil
p. 16).
Leigh and Neil use a panel technique to compare the number of firearms withdrawn and
corresponding firearm homicide and suicide rates between Australian states. Although their
sample of gun-related deaths includes both homicides and suicides, they find that the NFA had a
significant impact on each of them individually55
. Using a differencing approach, they find “that
the largest falls in firearm deaths occurred in states where more firearms were bought back” (p.
33). Controlling for various differences across states, firearm homicide rates fell by anywhere
from 35% to 50% after 1997 (p. 35). Leigh and Neil also estimate that “the economic value of
the gun buyback was A$500 million per year, or more than A$800,000 per firearm bought back”
which puts the impact of the national gun buyback program into perspective (p. 34). Their
empirical results are said to be more robust than the time series evidence mentioned above,
however, their findings nevertheless support the belief that tighter gun control does not lead to
more firearm-related deaths.
We can see that Leigh and Neil’s study strongly suggests that restricting the supply of
firearms does not increase the number of shootings and, if anything, it reduces the number of
shootings. If forms of gun control such as national buyback programs are effective at decreasing
the number of shootings, one has to wonder if this would also be the case in the United States.
Although their analysis examines the effects of the NFA across states, extrapolating their results
to countries other than Australia can be problematic for a variety of reasons. The factors that
influence firearm homicides in one country might be more or less present in other countries. For
55
Ibid
18
example, some countries like Mexico exhibit an unusual amount of firearm homicides because of
drugs and the presence of the cartels rather than from the presence of guns.
The interesting thing about Australia’s buyback program is that it directly affected the
supply of guns making the change in the number of guns in circulation fairly easy to estimate. It
would be much more difficult to distinguish the effects of other forms of gun control such as
expanding background checks or mandating safety training sessions. Because other forms of gun
control mostly affect future firearms purchases rather than guns already in the hands of the
public, programs like gun buybacks can be said to have immediate effects on gun ownership.
That being said, Leigh and Neil’s study seems to support the theory that there is a relationship
between gun ownership and shootings. Although the introduction of a national gun buyback
program is highly unlikely in the US, it is quite possible that the creation of such a program
would result in a decrease in the number of mass shootings, active shooters, or both.
Gun ownership might play a major role in the frequency of shootings in Australia but this
relationship doesn’t seem to explain the recent trend of mass shootings in the United States.
While states with higher rates of gun ownership exhibit more gun deaths per 100,000 people, the
same has not been proven for mass shootings in particular56
. Even if someone wanted to
empirically tests this hypothesis, conducting a panel study on firearm ownership and mass
shootings from state to state would be problematic because mass shootings, as defined by the
FBI, are simply too rare. There are several states that haven’t had a mass shooting in more than
35 years which would make it difficult to draw comparisons between states57
. While studies like
Jeremy Lankford’s suggest that countries with higher gun ownership experience more mass
shootings per person, the US has always had relatively higher rates of gun ownership than other 56
http://dailycaller.com/2015/10/12/remember-the-2007-harvard-study-showing-more-guns-led-to-less-crime/ 57
Mark Follman, Gavin Aronsen, and Deanna Pan, US Mass Shootings, 1982-2016: Data From Mother Jones' Investigation (Mother Jones 2016)
19
countries58
. Having a consistently high rate of gun ownership compared to other countries might
make the US more susceptible to mass shootings in general, however, the fact that the US has
always had the most guns in the world does not sufficiently explain why mass shootings have
only begun to increase in recent years. It would be one thing if gun ownership had been recently
increasing but this is not the case.
In fact, historically speaking, gun ownership and mass shootings in the US have trended
in opposite directions. According to statistics from the General Social Survey, household gun
ownership as a percentage has been steadily declining in the US since the late 1970’s59
. This
should not be taken to mean that household gun ownership is not related at all to the occurrence
of mass shootings. There is something to be said for the fact that countries with higher rates of
gun ownership display higher rates of mass shootings; however, mass shootings are occurring
more frequently in the US at a time when household gun ownership is as low as it has ever been.
If it is true that there is a positive relationship between household gun ownership and the number
of mass shootings, then there must be something else going on that is missing from the picture.
Interestingly enough, although gun ownership has been falling for years now, gun sales have
been soaring like never before. Because there is no official statistic for the amount of guns sold,
most studies use NICS background checks as a proxy for gun sales. Last year was a record-
setting year for background checks. According to FBI data, there were a total of 23,141,970
background checks in the year 2015 which surpassed the previous record of 21,093,273 set in
201360
.
58
https://www.washingtonpost.com/news/wonk/wp/2015/10/05/guns-in-the-united-states-one-for-every-man-woman-and-child-and-then-some/ 59
http://www.norc.org/PDFs/GSS%20Reports/GSS_Trends%20in%20Gun%20Ownership_US_1972-2014.pdf 60
https://www.fbi.gov/about-us/cjis/nics/reports/
20
There is still something to be said for the fact that background checks have increased by a
factor of 2.6 in the last decade61
. Considering that gun makers have also more than doubled their
yearly output since 2009, it is evident that gun acquisition is surging in the US62
. We are still left
with the question of how gun sales can be skyrocketing at a time when household gun ownership
is around all-time lows. If more guns are being purchased yet household gun ownership is
decreasing, this must mean that a small number of citizens are buying a disproportionate amount
of guns. While there is a handful of anecdotal evidence out there on gun enthusiasts who own
thousands of guns, this hypothesis is also supported by the fact that 8% of gun owners own a
stockpile of 10 or more weapons63
.
From the above, we can see that more guns are being sold now than ever before while the
percentage of American households owning guns has continued to fall. Is it possible that gun
sales are responsible for the recent uptick in mass shootings instead of household gun
ownership? In other words, has the recent boom in gun sales made it easier for would-be mass
shooters to acquire a gun? Considering that the recent wave of gun acquisition has significantly
boosted the number of guns in circulation, criminals should have an easier time obtaining guns
through illegal means. The same logic applies for the illegal drug market. For example, if there is
a greater amount of marijuana in the US than in previous years, it makes sense that citizens
would be able to acquire marijuana more easily than before as well as acquire more of it.
Regardless of if less people are using it, an increase in the supply of any illegal good or service
would more often than not make it easier to acquire.
61
https://www.fbi.gov/about-us/cjis/nics/reports/ 62
https://www.washingtonpost.com/news/wonk/wp/2015/10/05/guns-in-the-united-states-one-for-every-man-woman-and-child-and-then-some/ 63
http://www.thetrace.org/2015/12/gun-violence-stats-2015/
21
There is no denying the fact that this recent surge in gun sales has significantly increased
America’s stockpile of firearms. While this likely has some influence on the availability of guns
to criminals, there is little to no empirical evidence suggesting that the elevated level of gun sales
has had any effect on the frequency of mass shootings. To clarify, there is a good chance that
always having a massive number of guns has made the US more vulnerable to the occurrence of
mass shootings and other forms of gun violence compared to other countries; however, the recent
trend of gun sales in the US has not been responsible for the increase in the number of mass
shootings. Even if guns in general are primarily responsible for mass shootings in the US, there
is no reason to conclude that a change in present-day gun sales would significantly affect the
number of mass shootings in the near future.
Believe it or not, the recent surge in gun sales is actually a response to the abnormal
amount of mass shootings experienced in the US64
. On the surface, it sounds ironic that the
demand for guns would increase after an event like a mass shooting, but data on the number of
NICS background checks suggests that there has been an unrivaled surge in gun acquisition in
the months after high-profile mass shootings. In March of this year, there was an article from the
New York Times with a graph of monthly background checks from 2000 to 201665
. After
adjusting for seasonal differences, we can see that the largest spikes in NICS background checks
occurred after the September 11th
attacks, the month of Obama’s first election, in the months
after the mass shootings at Aurora and Newtown in late 2012, and most recently in the months
following the San Bernardino shooting. This same pattern is also evident in the quarterly
revenues of gun companies. Both Sturm, Ruger & Co. and Smith & Wesson saw their sales and
profits soar after each of the four events listed above. Furthermore, this spike in gun acquisition
64
Wallace 65
http://www.nytimes.com/interactive/2015/12/10/us/gun-sales-terrorism-obama-restrictions.html
22
was also present after various other mass shootings during this same time period which forms the
impetus for my paper.
Mass shootings have had a history of causing gun sales to spike but this relationship was
not well-documented until fairly recently. If you google ‘mass shootings and gun sales’, the
earliest relevant result is a Bloomberg article from January 12, 2011, titled “Arizona Shootings
Trigger Surge in Glock Sales Amid Fear of Ban.” Author Michael Riley describes how handgun
sales in Arizona soared in wake of the Tucson shooting that killed six and injured fourteen66
. He
interviewed owner of two gun shops in Arizona, Greg Wolff, who saw his sales double in the
two days after the mass shooting. Riley explains that “a national debate over weaknesses in state
and federal gun laws stirred by the shooting has stoked fears among gun buyers that stiffer
restrictions may be coming from Congress”67
. The fear of impending gun legislation causes
citizens to go out and legally purchase guns while they still can, thus, boosting background
checks in the months after mass shootings. In an economic sense, mass shootings tend to cause
gun enthusiasts to switch their consumption preferences from the future to the present.
While there is a handful of anecdotal evidence suggesting that mass shootings lead to an
increase in gun sales, empirical research on the subject is severely limited. To the best of my
knowledge, there is only one paper in addition to mine that uses an empirical approach to
determine whether or not mass shootings cause gun sales to increase. Dr. Lacey Wallace
investigates the relationship between mass shootings and background checks in her paper
“Responding to violence with guns: Mass shootings and gun acquisition”68
. Wallace (2015) uses
panel-data linear models estimated using generalized least squares to measure how the monthly
66
http://www.bloomberg.com/news/articles/2011-01-11/glock-pistol-sales-surge-in-aftermath-of-shooting-of-arizona-s-giffords 67
Ibid 68
Wallace
23
number of NICS background checks was affected by mass shootings in the US from 2000 to
2010.
After gathering data on mass shootings from Mother Jones, she cut her sample down to
six events to have a more practical size for the empirical analysis. All six of the events in her
analysis are included in my sample of mass shootings. In order to correct for a skewed
distribution, Wallace (2015) uses the log of the number of monthly NICS background checks as
the dependent variable which is a common strategy in many econometric studies69
. The predictor
variable in her tests is mass shootings which a dummy indicator that records “0s for all time
periods before the shooting and 1s thereafter” (p. 160). Because the study examines the effect of
mass shootings on a time series, she performs unit root tests “to verify that the outcome series
was difference-stationary within state [and] the series pass this test” (p. 160). This important
because it ensures that the effects observed are the result of mass shootings instead of being part
of a longer time trend.
Wallace (2015) tests for both nationwide and regional effects and uses models that
account for “autocorrelation within states and cross-sectional correlation and heteroscedasticity
across states” (p. 160). The models in her analysis are very well controlled which speaks to the
robustness of her results. Drawing from the Uniform Crime Reports, Wallace includes a variable
that controls for the monthly number of crimes which “helps to distinguish the effects of mass
shootings from effects of changes in crime rates overall” (p. 160). As mentioned before, changes
in gun legislation within states can impact gun acquisition thereby convoluting the effect of mass
shootings. Since 2005, twenty states have passed Castle Doctrine laws which ultimately expand
the rights of existing gun owners. To account for these changes in gun laws, Wallace’s analysis
69
Wallace
24
“includes a dummy variable indicating when Castle Doctrine legislation passed for each state in
the years 2000–2010” (p. 160).
Wallace (2015) is primarily interested in “whether gun acquisition is accounted for by the
mass shootings themselves or instead by fear of increased gun restrictions,” so she includes two
other control variables that serve as proxies for the concern with gun control and restriction (p.
160). The first is referred to as the “Obama Effect” which is a dummy variable that distinguishes
the months before and after Barrack Obama’s election in 2008. The “Obama Effect” variable is
used to measure his individual effect on gun acquisition which is important to the latter half of
her analysis and mine. Using data from Google Trends that begins in 2004, Wallace creates a
second variable which measures the “nationwide rate of Google searches for gun law, gun
control, or gun restrictions by month (p. 160). The Google searches and “Obama Effect” variable
are imperative for distinguishing the effects of mass shootings from the political climate
surrounding gun control.
After using Stata to run a series of tests, Wallace (2015) finds that the coefficients of the
mass shootings variable, the “Obama Effect” variable, and the Google searches variable are all
positive and significant. Her findings confirm the initial hypothesis that both Obama’s election in
2008 and the six mass shootings contributed to an increase in the number of monthly background
checks. Although the coefficient of the “Obama Effect” variable is considerably larger than that
of each of the mass shootings variables, this was somewhat expected because his influence on
gun acquisition had both immediate and permanent effects. In contrast, the results from her
models testing for immediate and permanent effects for the six mass shootings suggest that “the
effect of mass shootings is delayed by several months and temporary” (p. 164). Lastly, in light of
25
the statistics on household gun ownership, Wallace’s results seem to suggest that “a sizeable
number of gun sales are to those who already have a gun in the household” (p. 164).
Dr. Lacey Wallace is a pioneer when it comes to empirically measuring the effect of mass
shootings on gun acquisition. We can see from her results that the six mass shootings in her
sample have a noticeable influence on background checks even after controlling for concerns
over gun control and differences in crime, gun legislation, and demographics across states. The
fact that these mass shootings still had a significant effect on gun acquisition after controlling for
fears of impending gun legislation suggests that Americans were shook by these tragic events
and consequently bought guns for the purpose of protection. This is important to the debate of
mass shootings and gun sales because it challenges the belief that fears of impending gun
legislation are the sole driver for boosting background checks. It follows from this that gun sales
might continue to soar if mass shootings persist in the future regardless of if people are expecting
gun control laws to be implemented.
In light of the results of Wallace (2015), there are a number of important economic
implications that have been neglected by scholars for the most part. If background checks
increase in the months after mass shootings, then it would appear that gun companies are
benefitting from the occurrence of these incidents. Unless background checks are in fact a poor
proxy for gun sales, it should be true that the revenues or sales of gun companies
correspondingly spike in the months after high-profile mass shootings. This hypothesis forms the
core of my empirical analysis which examines whether or not this applies to the two publicly-
trades gun companies Sturm, Ruger & Co. and Smith & Wesson. Considering that changes in
any company’s fundamentals such as revenue should affect the value of that company, one must
wonder if mass shootings have any influence on the stock prices of firearms manufacturers.
26
Like Wallace (2015), Gopal and Greenwood (2015) are quite possibly the first ones to
investigate the financial market’s response to mass shootings. Their paper “Traders, Guns, and
Money” focuses on how mass shootings affect the stock prices of firearms manufacturers rather
than the fundamentals of these companies70
. They draw data on mass shootings from Mayors
Against Illegal Guns press releases in order to create a sample of 93 mass shootings that took
place in the US between January 2009 and September 2013. Gopal and Greenwood (2015)
provide a snapshot of their Mass Shootings Dataset towards the end of their paper. This snapshot
contains some of the events in their sample and includes a detailed account of the attack as well
as other information about the shooter, the gun he used, and whether or not he was eligible to
purchase a gun. This additional data on the mass shootings is used in extensions of their
empirical analysis. Because they collect data on mass shootings from Mayors Against Illegal
Guns press releases rather than Mother Jones, the events in their sample are not restricted to the
strict criteria of a mass shooting that the FBI uses. As a result, there are some events in their
sample which are shootouts between gangs or domestic homicides and, therefore, are not
included in my sample of mass shootings.
Gopal and Greenwood (2015) explain that mass shootings can have both positive and
negative effects on firearm manufacturers’ stock prices. I like to compare this to the substitution
effect and the income effect in economics which can both be present at once. On the one hand, if
traders and investors anticipate higher-than-expected sales, then mass shootings may cause gun
stocks to rally, thus generating positive abnormal returns. Gopal and Greenwood (2015) mention
how “anecdotal data shows a sharp spike in firearm purchasing immediately after [mass
shootings]” which is precisely what Wallace empirically confirms in her study (Gopal and
70
Brad Greenwood and Anand Gopal, Traders, Guns, and Money, (2015)
27
Greenwood p. 2). With respect to her results, it is worth pointing out that this increase in short-
term demand could also come from Americans that buy more guns after mass shootings as a
form of protection.
On the other hand, mass shootings may also adversely affect the stock prices of gun
companies if people anticipate the implementation of gun control laws what would hamper
future gun sales. Ironically, these calls for regulation of the industry could have both positive and
negative effects on the perceived value of gun companies. Although “mass shootings may result
in calls for regulation of the industry, rendering the firm’s business model untenable in the long
run”, these calls for regulation could also create a higher demand for guns as they might be more
difficult to obtain in the future (p. 1). Using an event study technique, Gopal and Greenwood
(2015) resolve this tension by running a series of regressions that test for abnormal stock returns
in the wake of mass shootings. Like Wallace (2015) and I, Gopal and Greenwood (2015) make
the fundamental assumption that mass shootings are exogenous events.
In their equations, the dependent variable is the percentage change in the stock prices of
Sturm, Ruger & Co. and Smith & Wesson while the independent variable is presence of a mass
shooting. The mass shooting variable is the treatment in their models and “is dichotomous and
applied on the first full day of trading after the event occurs to prevent partial information
dissemination” (p. 10). The effect of mass shootings on the dependent variable is estimated over
many different time windows because “a short window does not allow the model to sufficiently
capture how the market and the return to the stock price of firm ‘i’ correlate, and too long a
window allows for other events to contaminate the analysis, thereby creating identification
problems” (p. 11). The other right-hand side variable measures the percent change in the broad
market which is the SP500 in this case. Using the SP500 variable as a predictor enables them
28
observe only abnormal changes in the stock prices of Sturm, Ruger & Co. and Smith & Wesson.
This is important to their model because it “represents a differences-in-differences approach
wherein the stock price variation of firearm manufacturers is compared to the average movement
of the stock prices of the S&P 500 Index before and after the event” (p. 11). Because mass
shootings are assumed to be exogenous and unpredictable events, Gopal and Greenwood (2015)
note that they do not need to include covariates or any other control variables in their models.
The results from their empirical analysis suggest that mass shootings cause the stock
prices of firearms manufacturers to significantly decline over a 2, 5, 10, and 20 day observation
window. This translates into “a penalty of between 22.4 and 49.5 basis points, per day” (Gopal,
p. 3). Moreover, they find that “the number of victims and use of a handgun significantly
increases the adverse reaction from the market” (p. 20). If the stock prices of Sturm, Ruger &
Co. and Smith & Wesson have historically reacted negatively to mass shootings, then it could be
the case that financial markets are pessimistic or at least uncertain about the “long-term viability
of the firms’ business models” (p. 20). While it might be true that financial markets still do
anticipate an increase in short-term demand for firearms due to the reasons discussed before,
Gopal and Greenwood’s findings imply that traders and investors are fearful that any regulation
on gun sales will do more harm than good. In other words, the short-term boost in sales does not
outweigh the long-term drag on sales from future regulation. This leads Gopal and Greenwood
(2015) to conclude that mass shootings cause investors to reduce their valuations of firearms
manufacturers and argue that their empirical results “are reflective of the systematic violations of
the social contract existing between firms and society in the US as a result of gun violence” (p.
20).
29
While one would think that a boost in gun sales would help gun stocks, the empirical
findings presented above indicate that mass shootings actually caused investors to reduce their
valuations of firearms manufacturers from 2009 to 2013. This is at least true for the 93 mass
shootings in Gopal and Greenwood (2015) sample. It is likely that the effects on gun stocks
varied widely between each mass shooting depending on the nature of the event such as how
many people were killed. Nonetheless, the theory that mass shootings adversely affect gun stocks
was strongly supported by what happened towards the end of 2012 which happened to be the
deadliest year for mass shootings. Five out of the thirty-two mass shootings in my sample
occurred in 2012, including the Aurora Shooting during the midnight screening of the Dark
Knight Rises and the unspeakable Newtown Shooting at Sandy Hook Elementary.
Javier David discusses this phenomenon in his CNBC article “The Sandy Hook Effect:
Gun Sales Rise as Stocks Fall”71
David writes that the Newtown Shooting “sparked a national
conversation about imposing stricter legislation on the nearly $32 billion firearms industry”
which prompted gun enthusiasts to stock up on new guns before any legislation could be
passed72
. Despite the surge in gun and ammunition sales, David notes that “gun maker stocks
have taken it on the chin since the news on Sandy Hook broke”73
. In the two weeks following the
Newtown Shooting, shares of Sturm, Ruger & Co. and Smith & Wesson fell by roughly 15% and
24% respectively74
. Some of the gun companies’ losses during this period could be attributed to
private equity firm Cerberus Capital Management announcing that they would immediately sell
their stake in gun maker Freedom Group. This understandably startled investors as they were
71
http://www.cnbc.com/id/100325110 72
Ibid 73
Ibid 74
Ibid
30
already “reacting to the growing possibility that new gun laws will have a negative impact on
their profitability even as gun sales soar”75
.
David’s article fits hand in hand with Gopal and Greenwood’s conclusion that mass
shootings cause gun stocks to decline in the near future. Considering how hard gun stocks got hit
in the month after the mass shooting in Newtown, it would be difficult to argue that investors
weren’t reducing their valuations of firearms manufacturers at this particular point in time. In
December, the P/E ratios of Sturm, Ruger & Co. and Smith & Wesson fell to historical lows of
13 and 9.5 respectively76
. Although investors and analysts were most likely expecting higher
sales from these companies for the fourth quarter of 2012, the reaction of their stock prices
suggested that the market did not believe that any acceleration in sales was likely to be
sustainable in the long run or simply not worth paying for. After all, Sturm, Ruger & Co. and
Smith & Wesson’s revenues increased by, respectively, 52% and 38% year-over-year (YOY) for
the third quarter of 2012. It‘s quite possible that investors and analysts thought this trend in gun
sales was unlikely to continue for whatever reason. Did investors and analysts believe that
Congress was going to pass legislation at the federal level making it more difficult to purchase
firearms, thus, compromising the long-term business model of gun companies? Anyhow, the
market must have had a good reason for punishing gun stocks in December despite posting eight
straight quarters of exceptional YOY revenue growth.
At this time, not much was known about the relationship between mass shootings and
gun sales. While there was scant anecdotal evidence of this phenomenon in 2012, empirical
research on mass shootings and gun acquisition was nonexistent until fairly recently. Following
the Aurora movie theatre shooting in late July of 2012, NICS background checks set records in
75
http://www.cnbc.com/id/100325110 76
Ibid
31
each of the next four months77
. Then, in that December the American public was brought to tears
after news broke out about the Newtown shooting where twenty children and six faculty
members were shot and killed. As the country tried to cope with the tragedies in Aurora and
Newtown, Barrack Obama was being reelected for a second term. This combination of events led
to largest YOY increase in background checks for the month of December since NICS data
began in 199878
. To put things in perspective, background checks jumped from 1,862,327 in
December of 2011 to a whopping 2,309,684 a year later. The second largest December increase
occurred after the San Bernardino shooting in 2015.
Even after Obama’s election, gun acquisition has continued to surge. Until this day, the
market is attempting to digest this strange concept of background checks soaring after mass
shootings. We know from Wallace’s (2015) study that gun acquisition increases after mass
shootings even in the absence of fears over future gun control legislation. It follows from this
that gun sales should continue to increase if the frequency of mass shootings persists into the
future. Although we haven’t seen events as deadly as the shootings in Aurora and Newtown in
the last couple years, mass shootings have certainly not subsided in the United States. Since
December of 2012, background checks have been consistently increasing as these tragic events
continue to traumatize the American public79
. Consequently, gun sales have continued to reach
all-time highs and gun companies have been reporting record revenues and profits.
The theory that mass shootings cause investors to reduce their valuations of firearms
manufacturers no longer seems to apply. If mass shootings are detrimental to the stock prices of
gun companies, then we should have seen these stocks fall lower and lower as mass shootings
continued to spring up across the country. Despite getting pummeled in December of 2012, both 77
http://www.nytimes.com/interactive/2015/12/10/us/gun-sales-terrorism-obama-restrictions.html 78
https://www.fbi.gov/about-us/cjis/nics 79
Ibid
32
Sturm, Ruger & Co. and Smith & Wesson recovered their losses and then some. In the year 2013
alone, Sturm, Ruger & Co. and Smith & Wesson saw their shares rally 66% and 61%
respectively. Considering that the S&P 500 was only up 27% during that same time period,
investors were increasing their valuations of firearms manufacturers. As the fundamentals of
Sturm, Ruger & Co. and Smith & Wesson continued to improve with the surge in gun sales, so
did the stock prices of these companies.
2013 can be thought of as a turning point for investor sentiment regarding the economic
effect of mass shootings. Prior to then, mass shootings had a tendency to adversely affect gun
stocks according to Gopal and Greenwood (2015). While it could be true that gun stocks still
exhibit slightly negative abnormal returns shortly after mass shootings, it is difficult to contend
that this effect is present any farther beyond the 20 day observation window that Gopal and
Greenwood (2015) use in their analysis. As more people have caught on to the pattern of gun
sales rising after mass shootings, mass shootings have actually benefitted gun stocks in the long
haul and particularly for Smith & Wesson. Considering that mass shootings are still happening
and there hasn’t been any significant gun control legislation passed at the federal level, gun sales
have had no reason to slow down in 2015. With higher sales and profits, gun stocks have
continued to look very attractive from a fundamental point of view. As a result, Sturm, Ruger &
Co. and Smith & Wesson are up a whopping 74% and 262% respectively since their initial
plunge in December of 2012.
For whatever reason, there are very few studies other than those of Wallace (2015) and
Gopal and Greenwood (2015) that choose to investigate the economic impact of mass shootings.
Due to the disturbing nature of these events, most of the existing literature on mass shootings
tends to focus on the psychology and sociology behind them or the implications for policy
33
change. That said, the lack of economic research on the relationship between mass shootings and
gun sales/gun stocks is concerning. Although there has been plenty of anecdotal evidence
supporting this relationship, it is rare to come across anything empirical. It is worth pointing out
that the phenomenon of gun sales rising after mass shootings has permeated into the
entertainment world as well. One of the earlier pieces to reference this peculiar relationship
appeared in satirical news outlet The Onion. Several years ago they released an article titled
“Gorilla Sales Skyrocket After Latest Gorilla Attack”80
. The article does a great job of
highlighting the irony in the otherwise gloomy topic of mass shootings and gun sales: “Reports
confirmed that gorilla sales have historically risen sharply in the immediate aftermath of a major
gorilla attack, most notably after the 2010 tragedy in the small town of Logan, NM… The latest
attack marked the fifth of its kind in the United States within the last six months and has
reignited the explosive national debate over gorilla control”81
.
III. Data
There are a limited number of reliable databases that collect information on historical
mass shootings in the United States. Because there is no broadly accepted definition of a mass
shooting, the number of mass shootings in a given year varies widely from source to source. The
most common and reputable online databases used to study mass shootings are Mayors Against
Illegal Guns, shootingtracker.com, Gun Violence Archive, Stanford Mass Shootings, and Mother
Jones. While there are unique advantages to using each of the databases above, Mother Jones has
the strictest criteria for an event to be classified as a mass shooting. For example, in the year
2015, Mother Jones identified a total of only four mass shootings in their database, while
80
http://www.theonion.com/article/gorilla-sales-skyrocket-after-latest-gorilla-attac-30860 81
Ibid
34
shootingtracker.com, Gun Violence Archive, and Stanford Mass Shootings recorded 353, 300,
and 61 mass shootings respectively82
.
Data on each of the mass shootings in my sample is collected from Mother Jones’
Investigation on US mass shootings from 1982-2016. The Mother Jones database echoes the FBI
and defines a mass shooting as “a single incident typically in a single location” where four or
more people are killed not including the perpetrator83
. Unlike the sources shootingtracker.com
and the Gun Violence Archive, Mother Jones excludes gang violence, armed robbery, and
domestic violence from its database. By using the Mother Jones database, I can distinguish these
events from high-profile mass shootings and prevent them from contaminating my analysis.
Because there is a strict series of criteria for an event to be considered a mass shooting,
each mass shooting in the Mother Jones database can be seen as causing a stir in the American
public. While the majority of Americans were not directly affected by the shootings in my
sample, the incessant media coverage and ensuing gun control debate was difficult to avoid. The
notoriety of each mass shooting plays a crucial role in my empirical analysis. In order to measure
how mass shootings affect background checks and gun sales, I wanted to ensure that each mass
shooting in my sample was significant enough to permeate through the media and influence
Americans’ decisions to purchase guns. While gang violence, armed robbery, and domestic
violence are all tragedies that continue to plague our country, they do not have the same salient
effect that mass shootings have on people. Moreover, they do not play as significant of a role in
the current gun control debate as mass shootings do.
The Mother Jones mass shooting database is incredibly comprehensive and provides a
plethora of information on each mass shooting that is useful for several extensions of my
82
Ibid 83
http://www.motherjones.com/politics/2012/12/mass-shootings-mother-jones-full-data
35
empirical analysis. For example, the database includes the obvious details regarding the mass
shooting itself such as where it happened, why it happened, and the number of deaths and
injuries that resulted from it. The Mother Jones database also records who the shooter was, his
race and mental health history, what weapon or weapons were used, where he obtained them, and
if the guns used in the shooting were obtained through legal means or not. It is worth noting that
only two out of the 75 mass shootings from 1982-2016 involved a lone female shooter84
.
Furthermore, a majority (60%) of the mass shooters were white.
Drawing from the Mother Jones database, I create a unique dataset of mass shootings in
the US from 1999 to the beginning of 2016. There are several reasons why I chose to begin my
analysis in 1999. Because mass shootings were not as prevalent in the early 2000’s, I wanted to
see if the effect of mass shootings changed over time as they started occurring more frequently.
Moreover, since my analysis covers a relatively long time span of more than 17 years, it includes
the dot-com bubble and the Great Recession of 2008-2009. Although this could be seen as
creating more noise in my analysis, it is important to make sure the effects of mass shootings are
present throughout the analysis and are not convoluted with other events. That being said,
another advantage of beginning in 1999 is the fact that there are three US presidents over the
course of the analysis. Because Obama was elected in 2008 and 2012, I can directly measure the
impact that his election had on gun acquisition like Wallace (2015) does in her paper. If my
analysis only included one president aside from Obama, then I would not be able to estimate the
effect that his presidency had on gun acquisition.
There are a total of 49 events in my sample of mass shootings with the first event being
the notorious Columbine shooting which occurred on April 20, 1999. The Columbine High
84
http://www.motherjones.com/politics/2012/12/mass-shootings-mother-jones-full-data
36
School massacre is one of the more notable mass shootings in my sample. The last event is the
Excel Industries shooting which occurred on February 25, 2016, in Hesston, Kansas. Although
the Excel Industries shooting only killed three people, it is still included in the Mother Jones’
Investigation on US mass shootings from 1982-2016. Mother Jones does not specify why an
event that killed less than four people is included in their list of mass shootings; however, this
could be due to the fact that Congress redefined the term in 2013 to include murders of 3 or more
people85
. In any event, the Excel Industries shooting also wounded fourteen people which could
be seen as causing a stir in the American public and reigniting the already heated gun debate.
The number of victims in each of the 49 mass shootings in my sample varies widely with
the minimum being five and the maximum being 56. As a result, it is expected that the change in
gun acquisition is related to the severity of the mass shooting. That is, events with more deaths or
total victims can be seen as having a larger impact on background checks and gun sales in the
following months. The mass shootings in my sample occur in schools, military bases, religious
buildings, workplaces, and other public venues which can also affect the American public’s
response to a mass shooting. My sample happens to include both of the two cases where the
shooting was carried out by a lone female shooter although this does not play a role in my
analysis. It is also worth pointing out that there are four mass shootings in particular that are not
included in my sample because they occurred before 1999. These events include Luby's
massacre, the GMAC massacre, the United States Postal Service shooting, and the San Ysidro
McDonald's massacre86
. All three of these events claimed the lives of 10 or more people.
Aside from data on NICS background checks, most of the databases that were used in the
quantitative part of my analysis were accessed through the Wharton Research Data Services.
85
http://www.motherjones.com/politics/2015/12/no-there-were-not-355-mass-shootings-this-year 86
http://www.motherjones.com/politics/2012/12/mass-shootings-mother-jones-full-data
37
Like Wallace (2015), my study is concerned with how mass shootings affect gun acquisition.
While most studies like hers use NICS background checks as a proxy for gun sales, I include this
but also use the quarterly revenues of the gun companies Sturm, Ruger & Co. and Smith &
Wesson to represent gun sales. Because Sturm, Ruger & Co. and Smith & Wesson are both
public companies, they are required by law to report quarterly earnings to the public. These
quarterly earnings reports are known as 10-Q’s and contain information regarding a company’s
revenues, expenses, profits, and other important financials for the specified quarter.
In order to measure the effect that mass shootings have on the sales of firearms
producers, I gathered historical data on the quarterly revenues of Sturm, Ruger and Co. and
Smith & Wesson from the Compustat North America Database. Compustat is renowned for its
vast collection of financial statements such as cash flows and balance sheets. The fundamentals
are listed by quarter and include, but are not limited to, revenue, earnings per share (EPS), and
net income. All data points on Sturm, Ruger and Co. and Smith & Wesson’s fundamentals are
measured millions of US dollars, so a quick conversion is needed to calculate the logged values
of their total quarterly revenues. There were also multiple points in my study where I had to
calculate the return of Ruger and Smith & Wesson’s stocks. The CRSP database has a very
comprehensive collection of historical stock data which traces all the way back to 1925. Using
the CRSP database, I was able to collect daily stock data for Ruger and Smith & Wesson as well
as the S&P 500 index. While Sturm, Ruger and Co. has been publicly traded since 1969, Smith
& Wesson did not become publicly traded until May of 2001.
The second part of my empirical analysis examines how mass shootings affect the
number of background checks for firearms permits. Using the FBI’s official website, I was able
to collect data on the number of NICS background checks per month in the United States. After
38
being mandated by the Brady Handgun Violence Prevention Act of 1993, the FBI launched its
NICS background check system on November 30, 199887
. The FBI’s database provides records
of background checks that go back to 1998 and includes data for the first couple months of 2016.
Although there are several drawbacks to using background checks as a proxy for gun sales,
“NICS checks are a good indicator of overall trends” and have been used in previous studies on
gun acquisition (Wallace p. 159).
IV. Methodology:
Although there is anecdotal evidence suggesting that mass shootings cause gun sales to
spike, there is almost no empirical research on this phenomenon. The primary goal of this paper
is to estimate how gun acquisition in the United States changes in the wake of mass shootings. I
am particularly interested in how mass shootings affect the number of monthly NICS
background checks and the quarterly revenues of the gun companies Sturm, Ruger & Co. and
Smith & Wesson. While studies and articles on gun acquisition including Wallace (2015) use
NICS background checks as a proxy for gun sales, this paper represents the first attempt to use
the quarterly revenues of firearms manufacturers to represent gun sales. This paper tests for the
immediate and delayed effects of mass shootings on gun acquisition and also looks at how
political events such as the 2008 and 2012 elections influenced gun acquisition.
Gun acquisition is difficult to measure because there are no official records kept for the
number of guns sold. As a result, statistics on gun sales are often approximated using NICS
background checks. Generally speaking, NICS background checks are a well-accepted
representation of the legal demand for firearms, but there are several caveats that are very
important to understand for the purpose of this study. First, the number of background checks
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39
does not take into account guns that were bought off of the books. A large percentage of firearms
in the United States are bought, sold, and owned illegally, so there is no record of these guns and
gun owners. According to federal data, there were roughly 200,000 legally purchased guns that
were reported lost or stolen during the course of last year which speaks to the lack of
transparency on gun ownership in the US88
.
Due to the gun show loophole, approximately 20% of total gun sales are done through
private vendors and, therefore, are not obligated to conduct NICS background checks89
.
Although these guns are not necessarily sold or owned illegally, private firearms transactions are
not legally documented, so they are not represented by the number of background checks.
However, the other 80% of gun sales are done through federally licensed firearms dealers who
are required to conduct background checks. For the purpose of my study, each background check
can be said to represent a legal firearms transaction. The FBI makes it clear that there is not a
perfect one-to-one correlation between background checks and gun sales90
.
The discrepancies between the number of background checks and the number of guns
also stem from the fact that background check laws differ from state to state91
. For example,
some states allow individuals to purchase multiple guns with one background check, which
implies that the number of background checks would underestimate the number of guns sold.
Fortunately, this is partially offset by the fact that some states like Kentucky subject their permit
holders to monthly background checks which tends to overestimate gun sales92
. Furthermore,
some background checks also don’t result in the purchase of a gun, which could also help offset
the effect of individuals buying multiple guns with only one background check.
88
https://www.fbi.gov/about-us/cjis/nics/reports/ 89 http://money.cnn.com/2015/12/06/news/fbi-gun-background-checks/ 90
https://www.fbi.gov/about-us/cjis/nics/reports/ 91
IbiD 92
Ibid
40
Although there have not been any significant gun reform laws passed at the federal level,
Kristin Goss’s study proves that the majority of states have been both tightening and loosening
their gun laws over the years. Many of these state reforms affect whether or not background
checks are needed for certain gun transactions. As a result, trends in the number of background
checks over time can be associated with changes in legislation and classification rather than
changes in purchasing behavior. For example, states like Connecticut that passed bills requiring
private gun sellers to conduct background checks might see an increase in the number of
background checks but experience a decrease in aggregate gun sales during the same period.
Since data on NICS background checks does not account for changes in legislation over the
years, it is imperative to control for these changes when testing for the effects on gun acquisition
on the state or regional level. Because my analysis focuses on gun acquisition at the national
level, I do not compare the number of background checks between states or regions.
Although there are certainly some flaws in using NICS background checks to measure
gun sales, looking at the trends and growth rates of background checks can still provide
reasonable insight into changes in national gun acquisition. With that being said, we can safely
assume that significant jumps in the number of background checks represent an increase in the
demand for guns and, therefore, lead to more guns being purchased nationwide. Instead of
relying on a government system to measure gun acquisition, we can look at the sales of the
largest gun companies to estimate gun sales. There are dozens of companies that make up the
roughly $15 billion firearms industry, but the top 10 manufacturers account for almost 90% of
the industry’s annual revenue in the US93
. In addition to being the only publicly-traded gun
93
http://www.nytimes.com/interactive/2015/12/10/us/gun-sales-terrorism-obama-restrictions.html
41
companies, Sturm, Ruger & Co. and Smith & Wesson are the largest firearms manufacturers in
the US.
Considering the numerous caveats associated with using NICS background checks as a
proxy for gun sales, I am confident that using the quarterly revenues of firearms manufacturers
can correct for some of these setbacks. Because nearly 100% of the revenues from Ruger and
Smith & Wesson come from the sale of firearms and ammunition, their quarterly revenues
should be a good representation of gun sales after controlling for seasonal factors. It is worth
noting that Ruger and Smith & Wesson do not sell their products to private vendors. As a result,
guns manufactured by Ruger and Smith & Wesson are only sold through independent federally
licensed retailers that are required to conduct background checks. This suggests that there should
be a fairly strong relationship between background checks and the sales of these firearms
companies.
While NICS background checks give us a good idea of the number of new people looking
to buy guns, the quarterly revenues of Ruger and Smith & Wesson provide more information
about the aggregate number of guns sold. Because multiple guns can be purchased with one
background check, the number of background checks will not be equal to the number of guns
sold, thereby giving an incomplete representation of gun acquisition. The decline in household
gun ownership implies that an even smaller fraction of the US population owns at least one gun
compared to in previous years. The Americans that have been buying guns must be buying more
than ever before if background checks are continuing to rise sharply.
Unlike background checks which represent the number of new gun-buyers, the quarterly
revenues of Ruger and Smith & Wesson better reflect the total number of guns purchased during
a particular period of time. Consequently, changes in YOY revenue should provide a more
42
accurate estimate of any significant changes in gun acquisition. The increase in the quarterly
revenues of firearms manufacturers could come from more people buying guns or a similar
number of people buying more guns. Although there is no way to perfectly distinguish how
many guns the average person is buying, we can compare the growth rates of quarterly revenues
to the growth rate of background checks to get a better idea of the elasticity of the demand for
firearms.
The three models below attempt to estimate the effect that mass shootings have on gun
acquisition. Like any regression, it is integral to control for factors other than mass shootings that
could affect the demand for guns. Because we are looking at revenues and background checks in
a time series, it is important to make sure that the effects we observe are not coming from lurking
variables. Although mass shootings are random events by definition, there can be other trends in
revenues and background checks that wouldn’t be apparent at first glance. It is commonplace in
econometrics to control for seasonal effects when measuring variables like sales that tend to have
cyclical patterns.
My regressions include variables such as t, MONTH, QUARTER, SP500, and
ELECTIONYEAR, which control for factors other than mass shootings that could influence the
number of background checks and/or quarterly revenues. For example, t starts at 1 with the first
observation and counts up by one to the last observation which provides the regression with a
linear time trend. Linear time trends are helpful in distinguishing the effects of independent
variables from long-run trends in the dependent variable. It is true that the number of background
checks and the revenues of gun companies gradually increase from the beginning of my analysis
in 1999 to the end in 2016 and the independent variable t ensures that the coefficients of other
independent variables such as mass shootings are not influenced by this linear time trend.
43
The variables MONTH and QUARTER control for seasonality, but they are never used in
the same regression because of issues with multi-collinearity. MONTH is used in regressions
where the monthly number of background checks is the variable of interest. It takes on the values
1-12 depending on which month the observation lies in. Likewise, QUARTER is only used in
regressions where quarterly revenue is the variable of interest and it takes on the values 1-4
depending on which fiscal quarter the observation lies in. Because more guns are purchased in
November and December as opposed to other months, the variable MONTH accounts for this to
ensure that seasonal changes in consumer behavior do not affect the coefficients of the
independent variables of interest. The same is true for the variable QUARTER. If a
disproportionate number of mass shootings in my sample occurred in November or December
for whatever reason, we would observe a higher number of background checks during this same
time. Without the presence of control variables like MONTH and QUARTER, we could end up
drawing incorrect conclusions about how mass shootings affect background checks or quarterly
gun sales.
The variable SP500 simply tracks the value of the S&P 500 Composite Index for the
duration of my analysis. Many econometric studies include one of the broad market indices as a
control variable in their regressions. Because the S&P 500, NASDAQ, and DOW are used to
gauge the overall health of the US stock market and economy as a whole, using them as control
variables can prevent macro factors from contaminating an analysis. My analysis spans from
1999-2016 which includes the tech bubble of 2000 and the Great Recession of ‘08-’09. For
example, the Great Recession of ‘08-’09 caused household incomes to shrink across the country
thereby affecting their spending preferences. By including a right-hand side variable like SP500
44
in my regressions, I can be confident that macro events like recessions will not affect the
coefficient of the mass shootings variables.
A. Model for Quarterly Revenues
The first six out of the eight tests in my empirical analysis examine how the mass
shootings in my sample affect the revenues of gun companies like Ruger and Smith & Wesson in
the current and/or following fiscal quarters. Using Wallace (2015) as a guide, I run a series of
regressions on Stata in order to test the hypothesis that mass shootings boost the quarterly
revenues of firearms manufacturers as a result of the elevated demand for guns. This paper
examines changes in revenue over time, so the ordinary least squares (OLS) method is used to
estimate the effect that mass shootings have on these changes. Because mass shootings are
random events by definition, we don’t have to worry about any underlying trends of mass
shootings within the time series. This is evident from a scatterplot of the mass shootings in my
sample versus time.
On the other hand, the quarterly revenues are not exogenous variables, so unit root tests
are necessary to “verify that the outcome series was difference-stationary within state” (Wallace
p. 160). Using Stata, I ran a Dicky-Fuller test to ensure that the data was stationary and the series
passed the test, so we can proceed to estimate the effect of mass shootings on quarterly revenues.
Regressions testing the hypothesis above are based on the following equation:
(1) R(i,t) = B0 + B1 X(s,t) + B2 P(t) + B3 M(t) + B4 Q(t) + B5(t) + ε
logREV = B0 + B1qrtlyMS + B2ELECTIONyear + B3SP500 + B4QUARTER + t + ε
logREV = B0 + B1qrtlyMS + B2OBAMAeffect + B3SP500 + B4QUARTER + ε
45
where the first term R(i,t) is the dependent variable, which represents the logged value of the
quarterly revenue of firm ‘i’ at time ‘t’. The term B0 is the intercept in the equation and can be
thought of as the average revenue for Ruger or Smith & Wesson from 1999-2016, although it is
not necessary to include this term in the equation. X(s,t) is the total number of mass shootings ‘s’
during the current quarter ‘t’. B1 is the primary independent variable of interest as it estimates
the average effect that the total number of mass shootings during the quarter has on that quarter’s
revenue. The next term B2P(t) controls for political factors that vary over time and can affect the
demand for guns. In this case, P(t) is a dummy variable coded 1 for election years 2000, 2004,
2008, 2012, and 2016 and is coded 0 otherwise. I also run separate regressions that include the
“Obama Effect” variable that Wallace (2015) employs in her analysis. The next two terms
B3M(t) and B4Q(t)control for the broader market (SP500) and seasonality (by quarter)
respectively. Finally, B5(t) serves as a linear time trend and ε is the error term, which captures
the effects of other variables that are omitted from the model above.
After looking at how different numbers of mass shootings during a quarter (1 mass
shooting versus 3) affect revenue, I decided to test if the severity or notoriety of a mass shooting
has an effect on revenue. The number of deaths per mass shooting varies widely in my sample
with some having the minimum of 4 and others like the VA Tech Massacre having as many as
33. The variable ‘Morethan10’ is a dummy variable that identifies events where 10 or more
people were killed, which represents 9 out of the 49 mass shootings in my sample. Table 3 shows
the results of regressing the logged quarterly revenue on the ‘Morethan10’ variable.
Using the log of quarterly revenue rather than the value itself corrects for its skewed
distribution and is beneficial for interpretational purposes. Because there are only two firms in
my analysis, ‘i’ identifies either Ruger or Smith & Wesson. Moreover, since I am looking at
46
quarterly revenue instead of monthly background checks, ‘t’ identifies the quarter and
corresponding year for revenue recorded during this time. That is, Y(i,t) is the quarterly revenue
for the current quarter rather than representing a later time period when the revenue is actually
reported. It follows from this that Y(i,t+1) would identify next quarter’s revenue for the and Y(i,t-
1) would identify the previous quarter’s revenue.
If the quarterly revenues of gun companies are in fact a good proxy for gun sales, then we
should see similar results to those in Wallace’s (2015) study. Similar results would also suggest
that there is some sort of correlation between background checks and the revenues of gun
companies. Considering that analysts already use NICS data to estimate these revenues, it would
not be surprising to find a very strong relationship between these two variables. Because Wallace
(2015) already found empirical evidence supporting the idea that mass shootings lead to more
gun purchases, I expect the coefficient of B1 in each regressions with quarterly revenue to be
positive and significant. A positive coefficient for B1 would imply that mass shootings increase
quarterly revenue after controlling for other factors, whereas a negative coefficient would imply
that mass shootings have a detrimental effect on the quarterly revenue of gun companies.
Although I am also looking at how mass shootings affect gun acquisition, this section of
my empirical analysis takes a different approach than Wallace (2015). Most importantly, the
response variable in my model is revenue instead of background checks. Additionally, the
explanatory variable in my model is the total number of shootings during the quarter, so the
revenue recorded during this period of time is ultimately being regressed against the effect of
however many mass shootings occurred over that three month span. Since Wallace (2015) uses
an explanatory variable that is measured monthly, the results in her analysis provide more insight
into the immediate effects of mass shootings. Nevertheless, I think it is beneficial in some
47
aspects to use a longer event window to estimate the effects of mass shootings in a time series.
From 1999-2016, there are only a couple of months that have more than one mass shooting. As a
result, it is difficult to distinguish the effects between having one mass shooting during a month
versus two. On the other hand, the number of mass shootings during a fiscal quarter takes on the
values 0, 1, 2, or 3 in my analysis. By using a longer event window, I can estimate how the
presence of more than one mass shooting affects quarterly revenues.
While I still have to control for seasonal differences between quarters, using an
independent variable that is measured quarterly can help to smooth out differences from one
month to the next. Lastly, from a practical point of view, measuring variables over quarters
instead of months helps to mitigate the problem that occurs when mass shootings occur towards
the end of a month. If a mass shooting occurs in the last couple days of the month, then it would
be unlikely to see background checks spike during that month unless there was another mass
shooting in previous months. From 1999-2016, there have only been 4 instances where there was
more than one shooting within the same calendar month of that year. Due to lagged effects of
mass shootings, shootings that occur towards the end of the month could cause people to buy
guns during the following month, thus, making it difficult to distinguish the effects on each
month. Although this same problem could occur if mass shootings in my sample occur towards
the end of a quarter, it is by definition less likely to happen using larger event windows.
B. Model for NICS Background Checks
The second set of tests in my empirical analysis estimates how the number of background
checks is affected by the presence of mass shootings, which is precisely what Wallace (2015)
examines in her study. While her study ends in 2010 and contains six mass shootings, my study
spans from 1999 to the present and includes 49 mass shootings. Considering the differences in
48
our time periods and sample sizes, I was interested in how our results would compare to each
other. Because 2012 was the deadliest year for mass shootings in the US, I expected these events
to have a larger impact on background checks than mass shootings in previous years. With that
being said, my second hypothesis proposes that there should be a strong, positive relationship
between the presence of mass shootings and the number of NICS background checks. In addition
to testing for delayed effects using a distributed lag model, I measured the effect of mass
shootings on background checks over different sized time intervals like I did in the previous
section with quarterly revenues. Moreover, I anticipated that the magnitude of this effect was
close to, but slightly larger than, what Wallace (2015) came up with. The equation used to test
the effect of mass shootings on background checks is given by:
(2) Y(t) = B0 + B1Z(s,t) + B2P(t) + B3M(t) + B4L(t) + B5 (t) + ε
logNICS= B0 +B1monthlyMS +B2ELECTIONyear +B3SP500 + B4QUARTER + t + ε
logNICS = B0 + B1monthlyMS + B2OBAMAeffect + B3SP500 + B4month + ε
Like Wallace (2015), the dependent variable Y(t) is the logged number of monthly NICS
background checks. Aside from this, the only differences between Equation (1) and Equation (2)
are the control variables used and the size of the event windows. The mass shootings variable
Z(s,t) measures the number of shootings that occurred during that month instead of during the
quarter. Likewise, I use L(t) to control for seasonality in this equation, which measures the
difference in the average number of background checks between months rather than quarters.
Depending on the regression, the mass shootings variables either represent the number of
shootings during the current month or the total number of shootings during the previous three
months. It is important to point out that the mass shootings variable that counts the total number
of shootings during the previous three months is different from the mass shootings variable in the
49
first model. While the mass shootings variable X(s,t) in Model 1 counts the number of mass
shootings during that fiscal quarter, the mass shootings variable in this model counts the total
number of mass shootings from the current month, the previous month, and the month two
periods before. The equation for the model with quarterly shootings and background checks is
given by:
(3) K(t) = B0 + W(s,t)B1 + P(t)B2 + M(t)B3 + Q(t)B4 + (t)B5 + ε
logNICS = B0 + B1MSprev3m + B2Politics + B3SP500 + B4month + t + ε
logNICS = B0 + B1MSprev3m + B2OBAMAeffect + B3SP500 + B4month + ε
where the dependent variable K(t) is the log of the total number of monthly NICS background
checks in the last three months. This can also be thought of as the sum of Y(t) and its first two
lags. To maintain consistency, the mass shootings variable W(s,t) measures the number of
shootings that occurred during this same three month period. Notice that I use Q(t) instead of
L(t) because we are dealing with chunks of three months instead one like in model 2.
V. Results
A. Quarterly Revenues of Firearms Manufacturers
Table 1 displays the results from my first series of regressions. The logged quarterly
revenue is the dependent variable in each of the three models. Model 1 tests for the effect of
mass shootings but does not include a variable controlling for politics. As expected, there was a
strong and positive correlation between the number of mass shootings during the quarter and
logged quarterly revenues of Sturm, Ruger & Co. and Smith & Wesson. We can see from the
coefficient of ‘qrtlyMS’ (0.0928) that the presence of each additional mass shooting increases the
revenues of these gun companies during that quarter by 9.3% on average. In order to test if this
effect is present after controlling for politics, I ran separate regressions with the election year and
50
“Obama Effect” variables. We can see from Model 2 that the coefficient of ‘qrtlyMS’ (0.0889) is
positive and significant at the 1% level; however, it is slightly smaller than the coefficient in
Model 1. The election year variable most likely absorbed some of this effect considering that its
coefficient is also positive and significant. The coefficient of ‘qrtlyMS’ is also positive in Model
3 although it is only significant at the 10% level, which suggests that mass shootings have an
influence on gun sales even in the absence of political pressures. We can also see that the
coefficient of the “Obama Effect” variable is enormous and significant at the 1% level, which is
consistent with the results of Wallace (2015).
Although my results were significant and exactly what I expected, I decided to double
check that there was not an issue with my time series. After creating a variable that measured the
number of mass shootings one period into the future, I regressed the logged quarterly revenues
on this variable to ensure that there was no significance between the two. Because mass
shootings are random events that cannot be predicted, there should not be a significant
relationship between future mass shootings and present revenue. Luckily, after running the
regression of future mass shootings on present revenue, I could see that there was no significant
relationship. It is important to note that this test alone seems to contradict the belief that current
gun sales are leading to mass shootings. If the value of present revenue is high, then it is most
likely the case that more guns are being sold. The fact that there is no statistical relationship
between present revenue and future mass shootings suggests that gun sales in the present do not
have an effect on the future occurrence of mass shootings.
Table 2 is unique because it measures the effect that different numbers of mass shootings
during the quarter have on revenues. As expected, the presence of three mass shootings during
the quarter had the largest effect on revenues as it was more than three times the size of the effect
51
of one mass shooting in Model 1 (0.315 vs 0.0937). The coefficients of all three variables were
significant at the 5% level suggesting that mass shootings do not have diminishing marginal
returns when it comes to affecting gun sales. Although the presence of one mass shooting is not
significant in Model 2, the coefficient on the variable that estimates the effect of three mass
shootings is significant at the 1%. Considering that the third mass shooting variable is large and
very significant in both models, I think it is safe to conclude that the presence of three mass
shootings during a quarter has an overwhelmingly positive impact on the revenues of Ruger and
Smith & Wesson.
Table 3 shows the effect of the highest-profile shootings on revenues. The variable
‘MOREthan10’ is like ‘qrtlyMS’ except it only includes the mass shootings in my sample that
claimed 10 lives or more. We can see that the coefficients of ‘MOREthan10’ in Model 1 and
Model 2 are significant at the 1% level. Furthermore, these coefficients are much larger than the
coefficients of ‘qrtlyMS’ in Table 1, which suggests that deadlier mass shootings have a stronger
effect on revenues and gun acquisition altogether. The values of the coefficients of ‘qrtlyMS’ in
Table 3 are very similar to those in Table 2. It is also worth pointing out that the coefficients of
the “Obama Effect” and election year variables do not vary much across the tests.
All of the previously mentioned regressions are detrended by either the “Obama Effect”
variable or the variable ‘t’. The “Obama Effect” variable somewhat behaves like a time trend,
because it is only coded 0 for the first half of my analysis (1999-2007) and 1 for the latter half
(2008-2016). To avoid issues with multicollinearity, the “Obama Effect” variable is never used
in the same regression as variable ‘t’. Instead, each of my regressions with the election year
variable also includes variable ‘t’ which tracks time in a linear fashion over the course of my
analysis. The coefficient for the linear time trend variable ‘t’ was small and positive (0.02021)
52
suggesting that there is a presence of a natural growth rate in revenue. As mentioned before,
including a linear time trend in a time series can help avoid the problem of spurious regression.
Some scholars argue that including a linear time trend in a regression is not sufficient
enough for detrending a time series. In order to double check that the estimated effects from
Tables 1-3 were not confounded by time, I ran an additional regression using a differencing filter
technique which is shown in Table 4. In this regression, the dependent variable ‘REVgrowth’
represents the change in quarterly revenue as a percentage from the previous quarter (t-1) to the
current quarter (t) and the independent variable is still the number of mass shootings during the
quarter. We can see that the coefficient of ‘MSgrowth’ is positive (0.0632) and significant at the
5% level in Model 1. This suggests that the quarterly revenues of gun companies increase by
6.3% on average when the number of mass shootings increases by one from t-1 to t. As expected,
the coefficient of variable ‘MOREthan10growth’ is larger than that of ‘MSgrowth’. Surprisingly,
the coefficient of variable ‘MOREthan10growth’ is only significant at the 5% which was not
expected. In any event, the coefficients of ‘MSgrowth’ and ‘MOREthan10growth’ are positive
and significant in Models 1 and 2 respectively, which further supports the idea that the outcome
series from Tables 1-3 was “difference-stationary within state” (Wallace, p. 160).
Table 5 and Table 6 show extensions of Equation (1) in the form of distributed lag
models. These regressions are useful because they capture the delayed effects of mass shootings
which none of the previous regressions do. In each of these models, the dependent variable is
still the logged quarterly revenue like it is in Tables 1-3, but there are two mass shooting
variables instead of one. In Table 5, ‘qrtlyMS’ is the number of mass shootings during the
current quarter and ‘qrtlyMSlag1’ is its value in the prior quarter. It follows from this that
‘qrtlyMSlag1’ estimates the effect of there being a mass shooting in the last quarters on the
53
current quarter’s revenue. After running a regression with ‘qrtlyMS’ and its values 1, 2, 3, and 4
quarters ago, I found that only the coefficients of ‘qrtlyMS’ and its first lag were significant so I
dropped the other three lag variables from the regression.
Considering that Wallace (2015) found mass shootings to have temporary and delayed
effects on gun acquisition, I was expecting at least one of its lags to positive and significant.
Table 5 shows the results from regressing the logged quarterly revenue on ‘qrtlyMS’ and
qrtlyMSlag1. We can see that ‘qrtlyMS’ is positive and significant like it is in the previous
regressions; however, qrtlyMSlag1 is only positive and significant in Model 1 and is actually
negative and significant in Model 2. While the results from Model 1 suggest that there is a
presence of delayed effects, Model 2 suggests that there is a sharp drop in revenue after the
initial spike from a mass shooting. With that being said, I am skeptical that the mass shootings in
my sample have delayed effects on gun acquisition.
The models in Table 6 are just like those in Table 5 except the ‘Morethan10’ variable is
used instead of ‘qrtlyMS’. Although the aggregate of mass shootings in my sample might not
have significant delayed effects, I am more confident that high-profile mass shootings will at
least have longer lasting effects on revenue. Like before, I run an initial regression with
‘Morethan10’ and its values 1, 2, 3, and 4 quarters ago and find that only the first lag is
significant. Table 6 shows the results from regressing logged revenue on ‘Morethan10’ and the
‘Morethan10lag1’. We can see that both coefficients are positive and significant in Model 1, but
once again the lagged variable is not significant in Model 2. Although it is not negative and
significant like it is in Model 1, the results lead me to conclude that there is not enough empirical
evidence to confirm that even the deadliest mass shootings in my sample have effects on gun
acquisition that persist after three months or one quarter.
54
B. NICS Background Checks
This last section of the empirical analysis looks at how mass shootings affect the number
of NICS background checks. Contrary to the previous section, the results in this section speak
more to the number of people legally requesting to purchase firearms. Table 7 shows the results
from regressing the logged number of monthly NICS background checks on the number of mass
shootings during that month. The same control variables that were used in previous regressions
are used in Models 1 and 2 with the exception of variable ‘MONTH’, which measures
seasonality instead of ‘QUARTER’. As we can see, the coefficient of the “Obama Effect”
variable is significant at the 1% level like it is in previous tests. This makes it even stranger that
the election year variable is not significant in Model 2. Considering that it has been significant in
every regression with revenue, it is surprising to see that it is not significant with background
checks. Additionally, we can see that ‘monthlyMS’ is positive in the first two models but only
significant at the 10% level. Moreover, the coefficient of ‘monthlyMS’ loses its significance
after controlling for the “Obama Effect.” Although the coefficient of ‘monthlyMS’ in Model 3 is
very close to being significant, this was not expected in light of Wallace’s (2015) findings.
Table 8 displays the results from my last set of tests which examine how the number of
mass shootings during the last three months affects the total number background checks during
this period of time. This variable is rolling from month to month, so it is not broken down into
fiscal quarters like ‘logREV’. By using a variable that measures the aggregate value of the
current month and the two previous months, the disparity between one and two mass shootings
per month is smoothed out and the effect of present and/or past mass shooting is captured with
one variable. It is not necessary for each of these regressions to include variables of the same
time length, but it does make it easier for interpretation purposes.
55
The independent variable in Models 1-3 is ‘MSprev3m’ instead of ‘monthlyMS’. Similar
to ‘monthlyMS’ in Table 7, the coefficient for ‘MSprev3m’ is positive and significant at the 5%
level, but it loses significance in Model 3. Furthermore, the election year variable is not
significant in Model 2 but the “Obama Effect” variable is still significant at the 1% level. Lastly,
it’s worth pointing out that the coefficient for ‘monthlyMS’ is slightly larger than the coefficient
for ‘qrtlyMS’ in each of the three models in Tables 7 and 8. This could result from the fact that
some states allow individuals to buy multiple guns with one background check. Alternatively,
there is also a possibility that, contrary to prior beliefs, using quarterly intervals captures less of
the effect of a mass shooting compared to monthly intervals.
From 1999-2016, there have only been 4 instances where there was more than one
shooting within the same calendar month of that year. As a result, ‘monthlyMS’ is not sensitive
to shootings that can occur days apart but in different months. In order to smooth out the effect
of each shooting, I created a variable that tracks the number of mass shootings in the last three
months. ‘MSprev3m’ is the sum of ‘monthlyMS’ and, its first and second lags. Like the
independent variable, the dependent variable is also measured across a period of three months. In
each regression ‘qrtlyNICS’ represents the log of the total number of background checks in the
last 3 months. This variable is rolling from month to month, so it is not broken down into fiscal
quarters like ‘logREV’. By using a variable that measures the aggregate value of the current
month and the 2 previous months, the disparity between 1 and 2 mass shootings per month is
smoothed out and the effect of present and/or past mass shooting is captured with one variable. It
is not necessary for each of these regressions to include variables of the same time length, but it
does make it easier for interpretation purposes.
56
VI. Conclusion
While many still believe that gun acquisition is leading to more mass shootings, few are
aware that mass shootings are actually leading to more gun acquisition. The relatively high level
of gun ownership in the US most likely plays a role in this debate; however, the recent surge in
gun sales is not responsible for the recent uptick in mass shootings. Instead of attempting to
identify what’s causing mass shootings to increase, this paper attempts to shed light on a
phenomenon that is rarely talked about in an economic sense. With that being said, the primary
goal of this paper is to better understand the nature of this unique relationship between mass
shootings and gun acquisition. Although there is ample anecdotal evidence suggesting that mass
shootings cause gun sales to increase, empirical research on this subject is scant at best. To the
best of my knowledge, this is only the second empirical study to examine the relationship
between mass shootings and gun acquisition. Moreover, this paper represents the first and only
empirical analysis that uses quarterly revenues of gun companies as a proxy for gun sales in the
context of mass shootings.
It wasn’t until fairly recently that scholars and those in the investment community started
talking about the economic impact of mass shootings. While analysts and investors have just
begun to accept that gun sales spike after mass shootings, this pattern has existed for more than a
decade. Mass shootings have historically been huge catalysts for gun acquisition, but nobody
thought to empirically test this hypothesis until Wallace (2015). Her study examines mass
shootings in the US from 2000-2010 and confirms the previous notions that NICS background
checks increase in the months after mass shootings. There are many important implications that
stem from this finding. If gun acquisition tends to rise in the wake of mass shootings, then gun
companies should be making a killing off of these tragedies as they continue to plague the
57
American public. After reviewing the recent financial history of the largest US firearms
manufactures Sturm, Ruger & Co. and Smith & Wesson, it is clear that these companies are
profiting from mass shootings and the ensuing gun debates.
My study examines a fairly large sample of mass shootings in the US from 1999-2016.
Although my findings on the relationship between mass shootings and background checks are
somewhat inconclusive, the results for the revenue portion of my analysis make up for it. I took a
leap of faith in using quarterly revenues as a proxy for gun sales, but I am confident that it paid
off. Nearly every mass shooting variable in my analysis is positive and significant with most of
them being significant at the 1% level. Based on the results from my regressions, I would
estimate that the presence of a mass shooting increases the revenues of Sturm, Ruger & Co. and
Smith & Wesson for that quarter by an average of 6-7%.
Furthermore, the value and significance of the ‘Morethan10’ variable across multiple
regressions suggests that this effect would be two to three times stronger if the mass shooting
happened to kill ten or more people. This finding supports my initial hypothesis that deadlier
mass shootings will have a more pronounced effect on that quarter’s revenue. Another important
aspect of my study was the role that politics play in gun acquisition. For the most part, the
election year variable was positive and significant which could suggest that people might be
more fearful of impending gun control even in the absence of mass shootings. Furthermore, the
“Obama Effect” was the largest and most significant variable in my regressions by a longshot.
Even though these political variables had a strong presence in my regressions, the mass
shootings variables were rarely compromised. If mass shootings had a pronounced effect on gun
sales after controlling for Obama’s presidency or election years in general, it follows from this
58
that Americans have been buying guns out of fear, which is consistent with the results and
conclusions of Wallace (2015).
According to my results, the revenues of gun companies might be a better representation
of gun sales than the number of background checks. If it is true that a small percentage of the
country is buying an enormous amount of guns, it follows from this that the revenues of gun
companies should better reflect this heightened demand for guns as long as they are being bought
legally. Since one background check can buy several guns, I argue that at this point in time the
revenues of gun companies are the best representation of gun sales. Considering that I have
already examined the relationship between mass shootings and gun acquisition, it would be
interesting to see more research on what the true correlation is between background checks and
the revenues of gun companies. If Wallace (2015) used background checks to approximate gun
acquisition and came to the same conclusion that I did, then there’s a good chance that there is a
very strong relationship between background checks and the revenues of gun companies.
I was actually able to catch up with Dr. Lacey Wallace to learn more about her research
in the field. We talked about what the future holds in store for gun acquisition and whether or not
the same factors such as politics will continue to influence gun sales into the future. One thing
that she brought up was the important role that the media plays in gun acquisition. Wallace
pointed out that Obama had a particularly large impact on the gun debate because of his frequent
speeches on the subject and his campaign’s focus on gun control. I asked her if we should expect
the same from a Democratic candidate if one is elected to office to which she responded: “I do
not think we would necessarily see the same sort of impact on gun purchasing for other
Democratic candidates unless those candidates placed a similar emphasis on the issue [of gun
control].”
59
It should be interesting to see how investors and analysts that cover Ruger and Smith &
Wesson react to the presence of a mass shooting in the future. In light of my results, buying these
stocks after a mass shooting would appear to be the strategic move. In the very least, revenue and
earnings estimates should be revised which would inherently affect the stock prices as well. If
history repeats itself which it tends to do, the stock prices of these companies should continue to
hit new highs. Regardless of who is elected, there is one thing that we do know for sure. If mass
shootings continue to occur as frequently as they have been, Americans will continue to buy
guns, background checks will continue to set records, and gun companies will continue to profit
from the chaos.
60
VII. Tables and Figures
Table 1:
(1) (2) (3)
VARIABLES Model 1 Model 2 Model 3
qrtlyMS 0.0928*** 0.0889*** 0.0788*
(0.0213) (0.0187) (0.0420)
ELECTIONyear 0.0504***
(0.0180)
SP500 0.000420 0.000434 0.000760***
(0.000375) (0.000364) (5.02e-05)
QUARTER -0.0456*** -0.0455*** -0.0315***
(0.00690) (0.00690) (0.00516)
T 0.0213* 0.0212*
(0.0120) (0.0119)
OBAMAeffect 0.754***
(0.184)
Constant 16.77*** 16.75*** 16.60***
(0.207) (0.195) (0.0263)
Observations 127 127 127
Number of stock 2 2 2
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
61
Table 2:
(1) (2)
VARIABLES Model 1 Model 2
1.qrtlyMS 0.0937** 0.0684
(0.0464) (0.0814)
2.qrtlyMS 0.169*** 0.148*
(0.0516) (0.0874)
3.qrtlyMS 0.315*** 0.315***
(0.103) (0.0154)
ELECTIONyear 0.0467
(0.0285)
SP500 0.000435 0.000764***
(0.000374) (5.76e-05)
QUARTER -0.0457*** -0.0321***
(0.00595) (0.00437)
t 0.0212*
(0.0120)
OBAMAeffect 0.753***
(0.183)
Constant 16.75*** 16.60***
(0.191) (0.0289)
Observations 127 127
Number of stock 2 2
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
62
Table 3:
(1) (2)
VARIABLES Model 1 Model 2
MOREthan10 0.178*** 0.159***
(0.00230) (0.0238)
ELECTIONyear 0.0689**
(0.0282)
SP500 0.000451 0.000778***
(0.000362) (3.48e-05)
QUARTER -0.0500*** -0.0353***
(0.00976) (0.00800)
t 0.0216*
(0.0123)
OBAMAeffect 0.764***
(0.197)
Constant 16.75*** 16.61***
(0.199) (0.0227)
Observations 127 127
Number of stock 2 2
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
63
Table 4:
(1) (2)
VARIABLES Model 1 Model 2
MSgrowth 0.0632**
(0.0320)
T 0.00197** 0.00195**
(0.000840) (0.000837)
MOREthan10growth 0.0972*
(0.0562)
Constant -0.0558** -0.0548**
(0.0259) (0.0260)
Observations 126 126
Number of stock 2 2
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
64
Table 5:
(1) (2)
VARIABLES Model 1 Model 2
qrtlyMS 0.0893*** 0.0821**
(0.0185) (0.0399)
qrtlyMSlag1 0.0360*** -0.00635***
(0.000522) (0.000553)
ELECTIONyear 0.0418***
(0.00888)
SP500 0.000393 0.000757***
(0.000356) (4.79e-05)
QUARTER -0.0369*** -0.0277***
(0.0107) (0.00803)
t 0.0219*
(0.0116)
OBAMAeffect 0.763***
(0.180)
Constant 16.73*** 16.59***
(0.211) (0.0172)
Observations 126 126
Number of stock 2 2
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
65
Table 6:
(1) (2)
VARIABLES Model 1 Model 2
MOREthan10 0.180*** 0.162***
(0.00513) (0.0213)
MOREthan10lag1 0.0918* 0.0606
(0.0490) (0.0488)
ELECTIONyear 0.0813***
(0.0182)
SP500 0.000426 0.000773***
(0.000358) (3.24e-05)
QUARTER -0.0439*** -0.0318***
(0.0125) (0.0102)
t 0.0223*
(0.0121)
OBAMAeffect 0.768***
(0.197)
Constant 16.73*** 16.59***
(0.217) (0.0120)
Observations 126 126
Number of stock 2 2
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
66
Table 7:
(1) (2) (3)
VARIABLES Model 1 Model 2 Model 3
monthlyMS 0.0515* 0.0512* 0.0486
(0.0308) (0.0308) (0.0340)
ELECTIONyear 0.00874
(0.0331)
SP500 0.000157*** 0.000157*** 0.000420***
(5.25e-05) (5.24e-05) (4.70e-05)
Month 0.0172*** 0.0172*** 0.0212***
(0.00431) (0.00432) (0.00435)
T 0.00540*** 0.00541***
(0.000315) (0.000321)
Obamaeffect 0.560***
(0.0298)
Constant 12.94*** 12.94*** 12.86***
(0.0647) (0.0653) (0.0624)
Observations 206 206 206
R-squared 0.766 0.766 0.762
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
67
Table 8:
(1) (2) (3)
VARIABLES Model 1 Model 2 Model 3
MSprev3m 0.0395** 0.0393** 0.0268
(0.0174) (0.0176) (0.0167)
ELECTIONyear 0.00549
(0.0311)
SP500 0.000149*** 0.000149*** 0.000419***
(4.42e-05) (4.42e-05) (3.91e-05)
month -0.00549 -0.00549 -0.00260
(0.00407) (0.00408) (0.00415)
t 0.00533*** 0.00534***
(0.000301) (0.000306)
Obamaeffect 0.546***
(0.0277)
Constant 14.19*** 14.19*** 14.11***
(0.0554) (0.0562) (0.0542)
Observations 204 204 204
R-squared 0.789 0.790 0.781
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
68
Figure 1:
Figure 2:
0
500000
1000000
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69
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72
Pledge:
This paper represents my own work in accordance with University regulations.
Everett Brandon Price