ABSTRACT:
Psychological studies predominately find a positive relationship
between
violent video game
play and aggression. However, these studies cannot account for either
aggressive effects
of alternative activities video game playing substitutes for or the
possible
selection of
relatively violent people into playing violent video games. That is,
they lack
external validity.
We investigate the relationship between the prevalence of violent
video
games and violent
crimes. Our results are consistent with two opposing effects. First,
they
support the
behavioral effects as in the psychological studies. Second, they
suggest a larger
voluntary
incapacitation effect in which playing either violent or non-violent
games decrease
crimes. Overall,
violent video games lead to decreases in violent crime.
1. Introduction
From the sensational
crime stories of the 19th century (Comstock and Buckly 1883), to
the garish comic
books of the early 20 th century, (Hadju 2009), to today’s violent
video
games, Americans
have made efforts to reduce children’s access to violent media
because of
concerns over their
social costs. These concerns may not be unfounded as numerous studies
purport to find that
violent media of all sorts, including games, can cause increases in
measured aggression.
Aided in part by mounting evidence that violent video game play cause
aggression, states
have passed legislation criminalizing the distribution of violent
video games
to minors. 1
The research is not
clear on how large the increase in aggression caused by these
games. Craig
Anderson, a long-time researcher in the effect of violent media on
aggression
has contended that
"one possible contributing factor [to the Columbine High School
killings
was the shooters’
habits of playing] violent video games. [The shooters] enjoyed
playing the
bloody shoot-`em-up
video game Doom, a game licensed by the U.S. Army to train soldiers
to
effectively kill"
(quoted in Kutner and Olson 2009). 2
1 In 2010,
California passed a law making it a punishable offense for a
distributor to sell a banned violent video to a minor. The US Supreme
Court struck down this law in June, 2011.
2 In the opening
paragraph of his literature review, Anderson (2004) suggested violent
video games were
responsible for the
recent wave of school shootings since the late 1990s.
3
If violent video
games can be shown to cause violence, then laws aimed at reducing
access may benefit
society at large. Yet to date, though there is ample evidence that
violent
video games cause
aggression in a laboratory setting, laboratory stings cannot address
issues
of selection or
incapacitation. Ward (2010) shows that adolescents who are otherwise
predisposed to
violence tend to select into video game play. Dahl and Dellavegna
(2009)
suggest that violent
movies incapacitate violent crime offenders. Likewise, since the
hours it
takes to "beat
the game" substitute for some other activity, a complete
analysis of video game
effects must
consider the opportunity cost of this time. Violence may fall because
violent
people are attracted
to violent games and because gamers engaged in virtual violence are
not
simultaneously
engaged in actual violence.
To date, there is no
evidence that violent video games cause violence or crime. In fact,
two recently
published studies analyzed the effect of violent media (movies and
video game
stores) on crime,
and found increased exposure may have caused crime rates to decrease
(Dahl and Dellavegna
2009; Ward 2011). These studies, unlike the laboratory studies, were
conducted with
observational data, which poses unique scientific challenge to
establishing
causality. However,
since laboratory studies have never shown that video game violence
causes crime or
violence, despite researchers out-of-sample predictions (Anderson
2004),
observational
studies may be the only ethical and practical way to test for such a
causal effect.
To many in this
field, it is logical to assume that if exposure to violent media
causes
aggression in the
lab, it will therefore cause aggression when exposure occurs
non-randomly
outside the
laboratory. Psychologists have adapted the general aggression model,
or GAM, to
the video game
setting (Bushman and Anderson, 2002 and Anderson and Bushman, 2002).
GAM hypothesizes
that violent media, including violent video games, increases a
person’s
aggressive
tendencies through a process of social learning that occurs
simultaneous to the
exposure itself.
Violent media causes the person to mistakenly develop certain
scripts, or rules
4
of thumb, that are
used to interpret social situations both before they occur, as well
as
afterwards. GAM
posits, in other words, that violent video games cause aggression by
biasing
individuals towards
forming incorrect beliefs about relative danger that they are in.
Perception
biases towards
hostility, therefore, can in turn cause the person to respond in
either a “fight or
flight” fashion.
It may also permanently alter a person’s point of view, creating an
aggressive
personality as an
outcome (Bushman and Anderson 2002). A variant of the “rational
addiction” model
(Becker and Murphy 1988) may be a fair representation of GAM. The key
insight for GAM is
that consumption of a good in one period not only affects current
utility
directly but,
through a capital stock accumulation mechanism, it also affects
future utility
indirectly.
The opportunity cost
of playing a video game is not just pecuniary but also includes
lost time. In fact,
for many gamers, the value of the time spent playing a game may be
worth
much more than the
pecuniary cost of the game. This time spent gaming cannot be spent on
other activities,
legitimate or otherwise, if time use is rival in consumption. The
substitution
patterns from video
games may derive more from time use effects than from pecuniary costs
(Becker, 1965).
Evidence for video games having a time use component can be found in
Stinebrickner and
Stinebrickner (2008). The authors identified a causal effect of
studying on
academic performance
by utilizing the random assignment of college students to roommates
with a video game
console, relative to the counterfactual, which caused students to
study less
often, and in turn,
to perform worse in school
In this paper, we
argue that since laboratory experiments have not examined the time
use effects of video
games, which incapacitate violent activity by drawing individual
gamers
into extended
gameplay, laboratory studies may be poor predictors of the net
effects of violent
video games in
society. Consequently, they overstate the importance of video game
induced
aggression as a
social cost. We argue that since both aggression and time use are a
5
consequence of
playing violent video games, then the policy relevance of violent
video game
regulation depends
critically on the degree to which the one outweighs the other. If, as
we
find in our study,
the time use effect of violent video games reduces crime by more than
the
aggression effects
increase it, then the case for regulatory intervention becomes
weaker.
While some early
work has been done on the long-term effects of video game play,
nearly all
the laboratory
evidence that currently exists has only uncovered very short-term
effects,
which is when time
use effects could be the most important. 3
As with Dahl and
Dellavegna (2009) and Ward (2011), we use a proxy for individuals’
exposure to violent
video games – the volume of sales of violent video games in a week
among the top 50
best-selling video games from 2005-2008 – and relate it to a marker
for
violent behaviors –
weekly aggregate violent crime incidents from the National Incident
Based Reporting
System (NIBRS). Using time series modeling, as well as an
instrumental
variables approach,
we estimate the effect of an increased weekly volume of violent video
game sales on the
number of criminal incidents recorded to law enforcement over the
subsequent weeks and
find that increased violent video games are associated with decreases
in crime rates,
similar to Dahl and Dellavegna (2009) and Ward (2011).
One advantage of our
approach is that we can attempt to disentangle the separate
effects of both a
behavioral change toward more aggression and incapacitation due to
time
use. Our results
provide some support for the psychological finding that, absent
incapacitation,
violent video games lead to more aggression as measured by violent
crimes.
However, our results
also suggest that this is dominated by possible incapacitation and
selection effects
leading to a net reduction in violent crimes. This approach can help
guide
3 In Anderson
(2004), the author notes the glaring omission of longitudinal studies
of effects of violent video
games on aggression
in his conclusions on the state of the research, calling for more
studies aimed at
investigating the
long-term effects. If nothing else, though, this makes our point that
the abundance of evidence
that we know does
exist only speaks to short-term effects of violent video games on
aggression, which is the
purpose of this
study here.
6
investigators to
develop more holistic research designs, such as field experimentation
and
other
quasi-experimental methodologies, to determine whether the net social
costs of violent
games are
non-trivial. The main shortcoming of our approach is due to the
limitations of our
data on game sales.
Unfortunately, the industry does not report cross-sectional variation
in
game sales – only
the national weekly sales of the top 50 highest grossing games are
available. As a
result, our paper follows a methodology similar to Dahl and
Dellavegna
(2009), who
estimated the impact of violent movies, proxied by daily ticket
sales, on crime
using only time
series methods.
The paper is
structured as follows: the second section our data and methodology.
The
third presents and
discusses our results. We conclude with a brief discussion of the
implications for
public policy.
III. Data and
Methodology
Randomized
assignment of a treatment with comparison groups used to make
comparative
counterfactuals is widely considered the “gold standard” in the
social sciences
(Fisher 1935;
Campbell and Stanley 1963; Rosenbaum 2002). Yet, it is widely known
that
experimentalism may
fail to identify true causal effects for a variety of reasons (Berk
2005;
Deaton, 2010;
Heckman and Urzua, 2010; Imbens 2010). While others have noted the
failure
of researchers in
this literature to satisfy the rigorous conditions for establishing
causality
(Ferguson and
Kilburn 2008; Olson and Kuttner 2009) our study will focus on a
separate
statistical
challenge not mentioned in these earlier studies: the challenge of
internal versus
external validity.
Finding of a
positive effect of violent games on aggression does not therefore
mean
that violent video
games playe will cause crime if the incapacitation effects from time
use
7
swamp the marginal
increase in aggression in the person. By design, laboratory studies –
both
by ignoring
alternative time use and by treating both treatment and control
groups with this
separate effect –
cannot be used to guide researchers as to what expect outside the
lab. In this
sense, the studies
have internal validity, but may not have external validity on the
incidence
of socially costly
aggression from violent video game play (Campbell and Stanley 1963).
Quasi-experimental
methods, such as panel econometric methods, regression discontinuity
and instrumental
variables, as well as field experimentation (Harrison and List 2004;
Angrist
2006) may be more
suitable estimating the social costs of violent video games since
they
allow for the
estimation of all known and unknown theoretical mechanisms. In this
section,
we explain our
research design and the data used to overcome some of the limitations
of a
purely experimental
methodology.
A. Empirical
Methodology
The models of video
game violence suggest that the effect of violent video game play on
crime will depend on
whether a sizable stock of aggressive tendencies accumulates and on
the
games’ time use
intensities.
Since the
theoretical predictions are ambiguous and the policy relevance of the
laboratory studies
is unclear, empirical work outside of a laboratory context is
warranted.
However, without
experimental data, causal inference is problematic. Correlations
between
video game play and
crime may or may not reflect a causal relationship if the unobserved
determinants of
crime are correlated with the determinants of video game play. For
instance,
bad weather such as
rain or heavy snow which causes individuals to remain at home would
both increase the
likelihood of playing video games and decrease the returns to crime
through
higher chances of
finding a resident at home. Hence, negative correlations between
crime and
violent video game
play could purely be a consequence of omitted variable bias.
Similarly,
video game
publishers could strategically release violent video games during
periods of time
8
when gamers have a
lower value of time. But a low opportunity cost of time would affect
both
video game sales and
crime. For example, both video game sales and the crime rate increase
during summer when
most teenagers are out of school..
One solution to
omitted variable bias when there is time-variant heterogeneity is to
employ instrumental
variables (IVs). The researcher must have instruments that are
strongly
correlated with
individual game play but uncorrelated with the determinants of crime.
This
approach exploits
exogenous variation in video game play that is not due merely to
changes in
the determinants of
crime providing greater assurance that the estimated effect is
causal. We
use the ratings of
video games by a video games rating agency as IVs. Our IV strategy
exploits the
variation in game sales correlated only with the variation in
quality, and thus is
mostly free of
variation due to factors related to crime.
Zhu and Zhang (2010)
show that consumer reviews of video games are positively
related to game
sales. Ratings are valuable pieces of information for video games
because
games are complex
experience goods for which gamers cannot know their preferences
without playing. Our
data on professional ratings contain rich information that
communicates
the kinds of
information that gamers value in forecasting their beliefs about the
game, and as
beliefs and
anticipation are drivers of the game sales, we would expect these
rating
institutions to play
important roles in forming consumer prior beliefs about the game and
therefore their
purchases. But we also have some evidence from other industries that
would
suggest scores would
independently cause purchases to rise, independent of the unobserved
factors that cause
expert opinion and purchases to be highly correlated. Reinstein and
Snyder
(2005) used
exogenous variation in Siskel and Ebert movie ratings due to
disruptions in their
pair’s reviewing
to determine a causal effect on movie demand. More recently, Hilger
Rafert
and Villas-Boas
(2010) found that randomly assigned expert scores on bottles of wine
in a
retail grocery store
caused an increase in sales for the higher rated, but less expensive,
wines.
9
While these studies
do not confirm that there are exogenous forces in video game ratings
that
drive consumer
purchases, they are suggestive.
We begin by
estimating a standard multivariate regression model of the incidence
of
various crimes as
functions of, among other controls, the prevalence of non-violent and
violent video games.
Our outcome variables of interest, C t , are the total number of
reported
criminal incidents
in week t as well as the number of such incidents that are classified
as
violent. While one
might interpret any criminal incident as reflecting some level of
aggression, we
interpret violent crimes as reflecting more aggression. While the
dataset we
use documents
criminal offenses on a daily basis, since the video game sales data
are
available only on a
weekly basis, we aggregate crimes into weekly measures to focus on
same-week exposure.
Accordingly, we employ a simple least squares estimator so as to more
easily instrument
for video game exposure. 6
Our main explanatory
variables are aggregated current and lagged values of weekly
sales volumes for
both non-violent and violent video games. Video games appear to
depreciate quickly
with use. This may be because new games are played intensively for a
few
weeks after purchase
and are not replaced with a new game until after some diminishing
returns have been
reached, or it may suggest that firms typically stagger the release
dates of
games. We measure
the cumulative effect of games with the sales volume of the current
week’s sales,
along with the various lags of previous weeks’ sales, so as to
capture the effect
of higher volume of
gameplay with an unknown time lag to trigger crime.
Our benchmark
specification is:
( ) ∑
[ (
)]
∑
[ (
)]
where L is the
lag operator of length . The number of crime incidents depends
on the
exposure to violent
video games
and non-violent
games . The sum over of can be
6 Our empirical
methodology is in large part based on Dellavegna and Dahl’s (2008)
study of the effect of movie
violence on crime.
10
interpreted as the
cumulative percentage increase over the weeks in criminal
incidents for
each percent
increase in violent video games sold in week t while the similar sum
for
can
be similarly
interpreted for non-violent video games. The trend and month dummies
attempt
to account for
secular increases and seasonality in video game purchases. The
identification of
the parameters is
based on the time-series variation in the style of violence in the
video
games. Again, we
instrument for both types of games using average quality ratings of
the
games on the market
that week.
The measured effect
from this specification can represent a confluence of multiple
effects. It is
possible for there to be a positive behavioral effect, as found in
the laboratory,
and a negative
voluntary incapacitation effect. This specification will typically
only measure
the net effect.
However, it may be possible to disentangle the behavioral effects
from the
incapacitation
effect from the estimated cumulative effects from non-violent and
violent
games. Both should
incorporate incapacitation effects but only the former will include a
behavioral effect
toward aggression. The difference between the two provides an
estimate of a
pure aggression
effect.
Besides the
benchmark specification we employ two additional specifications as
robustness checks.
These specifications identify specific segments of the population and
locations where we
expect a differential a gaming-to-violence link, e.g. crimes
committed by
teens and young
adults and those committed at high school and college campuses. For
each
crime incident,
NIRBS provides information on the age of the offender and on the
location of
the incident. In the
first robustness check, we select a sun-sample of offenders aged
between
15 and 30 years and
compare these results to the results obtained from a sub-sample of
offenders who are 35
to 50 years old. In our second check, we extend our estimation
procedure to compare
the effects on the number of incidents reported on school campuses to
the number committed
at other locations.
11
B. Video Game Sales
Data
Our treatment
variables for video game play are derived from the volume of video
game unit sales data
from VGChartz 7 . Beginning consistently in 2005, this site has
provided
unit sales volume
information for each of the top 50 selling video console based games
each
week. Among other
information, volumes are reported worldwide as well as for several
geographical areas
including USA, Japan, Europe, Middle East, Africa or Asia. In our
sample
period 2005 to 2008
the VGChartz dataset contains 1,091 different titles over the 208
weeks
for the US with some
of these titles being the same game for different gaming consoles. In
sum, the games are
provided from 47 different publishers and designed for nine different
gaming consoles.
While VGChartz includes the top 50 selling games each week, it only
covers a portion of
all sales in the US video game market. A game’s week of release is
almost
always its top
selling week. Figure 1 indicates that most games stay in the top 50
for only a
few weeks. Moreover,
as Figure 2 indicates, the top selling games sell much more than even
the lower ranked top
50 games. These features suggest that there is considerable week-to-
week variation in
the games, and the types of games, being played. According to the
Entertainment
Software Association (ESA) 8 VGChartz account for about one-quarter
of all
units in 2005 (ESA
Annual Report, 2010). The ESA also includes sales of non-console
based
games such as
computer and smartphone games. Still, this fraction rises to almost
one-half in
2008.
Our measure of
violent videogame content stems from the Entertainment Software
Rating Board (ESRB).
9 This non-profit body independently assigns a technical rating (E,
E10,
T, M, and A) which
defines the audience the game is appropriate for where E classifies
games
for everybody, E10
for everyone aged 10 and up, T for teens, M games for a mature
audience,
and A for adult
content. In addition, ESRB provides detailed description of the
content in each
7
http://www.vgchartz.com/
8
http://www.theesa.com – The reported numbers from ESA also include
games for personal computers which
amount to about 10
percent of the market each year and are intentionally not included in
VGChartz.
9
http://www.esrb.org
12
game on which the
rating was made, including the style of violence, e. g. language,
violence,
or adult themes. For
all of the 1,091 titles in our sample we collected the appropriate
ESRB-
rating and all
content descriptors. Based on this content information we identify
762 non-
violent and 329
violent games, of which 105 titles are described as intensely
violent. Almost
all violent games
are rated T or M. All intensely violent games are rated M. Since most
of the
policy concern stems
from these intensely violent games, these are the games we
concentrate
on. 10 Merging both
data sources together we can construct measures of the aggregate unit
sales of non-violent
and intensely violent video games for each week. The weekly sales are
pictured in Figure 3
for all games and for intensely violent games. Overall, the two
graphs
follow a similar
pattern with a peak around the Christmas gift purchasing period. In
the mid of
2008, however, the
intense violent games seem to account for almost all sales of the
violent
games.
As argued above the
prevalence of video games in a week is not randomly distributed
over the sample and
therefore may be endogenous. For instance, if changing economic
conditions that
caused a rise in unemployment, and in turn crime rates, may also have
caused
leisure activities
like video games to rise, then we might observe positive correlations
between video game
play and crime that is driven purely by these changing economic
factors
(Raphael and
Winter-Ebmer 2001; Gould, Weinberg and Mustard 2002). We address the
potential
endogeneity of video games with instrumental variables using expert
review of each
title as an
instrument for purchases.
Our expert review
data comes from the GameSpot website. 11 GameSpot provides
news, reviews,
previews, downloads and other information for video games. Launched
in
May 1996 GameSpot’s
main page has links to the latest news, reviews, previews and portals
10 We have also
performed our analysis for the broader “violent” and “intensely
violent” definition of a violent
game. Qualitatively,
all of our general results described below hold however parameter
estimates are smaller (in
absolute value
terms) and are less precisely estimated.
11
http://www.gamespot.com
13
for all current
platforms. It also includes a list of the most popular games on the
site and a
search engine for
users to track down games of interest. The GameSpot staff reviewed
all but
a handful of the
games in our sample and rated the quality of the titles on a scale
from 1 to 10
with 10 being the
best possible rank. These so-called GameSpot-scores assigned to each
game
are intended to
provide an at-a-glance sense of the overall quality of the game. The
overall
rating is based on
evaluations of graphics, sound, gameplay, replay value and reviewer’s
tilt.
A possible issue
with this measure is that GameSpot changed the rating system in mid
of 2007
to employ guidelines
and a philosophy focusing more on a prospective customer rather than
a
hardcore-fan that
the reviewers had focused on before. Nevertheless, the five mentioned
aspects are
essential parts of a game that are still reviewed in detail by a
GameSpot reviewer
but will not get an
aspect-specific rating score anymore. We do not consider this change
in the
GameSpot focus to
noticeably affect the overall GameSpot-score.
We expect the
quality rating of the games to be positively correlated with their
sales as
better-rated games
usually are more highly demanded. It is possible that some games have
the
opposite
relationship if they are based on a popular tie-in from a movie, e.
g. Harry Potter, or
sequels, e. g. the
Final Fantasy series. Developers know that these games will sell well
due to
their popular tie-in
which may lower the returns to investment in game quality. However,
in
table 2 we show
that, a game title’s weekly sales are positively related to the
Game Spot score
for games of
different violence profiles.
C. Crime Data
For our measure of
weekly crime, we used the National Incident Based Reporting
System (NIBRS).
NIBRS is a federal data collection program begun by the Bureau of
Justice
Statistics in 1991
for gathering and distributing detailed information on criminal
incidents for
participating
jurisdictions and agencies. Participating agencies and states submit
detailed
information about
criminal incidents not contained in other data sets, such as the
Uniform
Crime Reports. For
instance, whereas the Uniform Crime Reports contain information on
all
14
arrests and cleared
offenses for the eight Index crimes, NIBRS consists of individual
incident
records for all
eight index crimes and the 38 other offenses (Part II offenses) at
the calendar
date and hourly
level (Rantala and Edwards 2001). A potential drawback of NIBRS is
that
many law enforcement
agencies do not participate. [Need a sentence or two here Scott] We
aggregate across
only the jurisdictions that participated during each of our sample
years.
Because of the
detailed information about the incident, including the precise time
and date of
the incident,
economists such as Dahl and Dellavegna (2009), Card and Dahl (2009),
Jacob
and Moretti (2003)
and Lefgren, Jacobs and Moretti (2007) have used it for event
studies. In
our case, we exploit
detailed information about the age of offenders and the crime’s
location –
on school campuses
or not – for our robustness checks.
Crimes follow a
seasonal pattern. Figure 4 indicates a consistent pattern of gradual
increases in both
violent and non-violent crimes from winter to summer. Our method was
developed to account
for seasonality in both of our main variables of interest crime and
games. Much of the
seasonality in crimes is believed to be due to weather while
seasonality in
games is likely due
to holiday gift giving (Lefgren, Jacobs and Moretti 2007). Failure to
address this will
likely lead to spurious correlations. As indicated above, we
accommodate
this in two ways.
First, month dummy variables should capture much of the seasonality.
Second, using Game
Spot scores as IVs should isolate the variation in game sales due to
game
quality.
Our final sample
includes 208 weekly observations on video games sales and crimes
from early 2005
through 2008. However, eight observations are excluded from final
regressions because
of the use of lagged video game sales. Table 3 reports basic
descriptive
statistics for our
sample.
IV. Results
A. Basic Results
15
Our basic regression
results are presented in Tables 4 and 5. Table 4 reports estimates
of specifications
for various lags of the effect video games sales on all crimes. Video
games
are separated
between those that the ESRB rated as “intensely violent” and
those that are not.
Recall that the
lesser rating of merely “violent” does not warrant an ESRB rating
of “M.” 12
Control variables
include month dummies to capture seasonality and a time trend to
capture
any secular trend.
The columns from left to right add more lags of video games to the
specification so as
to measure possible inter-temporal effects of game purchases in one
week
affecting crime in
subsequent weeks through continued play. Finally, each regression
employs
a 2SLS estimator
with the same set of current and eight lags of Game Spot scores
averaged
over intensely
violent games and over games that are not intensely violent. Since
the
specifications are
over-identified, we test for possible endogeneity of the instrument
set. As
expected, in all
cases, we fail to reject the exogeneity of Game Spot scores with
respect to the
level of crime. 13
The estimated effect
of video games sales in any single week is small. Most individual
coefficient
estimates are negative but few are significantly different from zero.
It appears that
lags of up to five
weeks of video game sales may be associated with current crime. It is
not
clear from this
table whether violent games have a different effect from those that
are not
violent. For ease of
comparison, we report the sum of the coefficients for various lags
for both
in the top panel of
Table 6 to calculate the cumulative effect of a change in video games
over
time. Here it
becomes clearer that video games are estimated to have an overall
negative
effect on crime for
specifications that include from two to six lags. That is, both
violent and
non-violent games
are associated with reductions in crimes. However, the effect is
small.
Since our
specification is double log, these estimates can be interpreted as
elasticities with
12 Unreported
regressions comparing games that are either “intensely violent”
or “violent” versus all other games
generally yield much
less precisely estimated parameters.
13 Estimates
assuming that game sales are exogenously determined typically
generated smaller (in absolute value
terms) and much less
precisely estimated coefficients.
16
values of up to
-0.025 for non-violent games and -0.010 for violent games. These
estimates
suggest that, over
all the mechanisms through which videogame play can affect crime, the
net
effect is to reduce
crime.
As mentioned above,
these estimates may also allow us to make some inferences that
distinguish between
potential mechanisms. While both violent and non-violent games are
hypothesized to have
incapacitation effects, only violent games are hypothesized to alter
behaviors. Indeed,
the top panel of Table 6 indicates that the difference in effects
between
violent and
non-violent games is for violent games to reduce crime by a smaller
amount and
that this difference
is statistically significant for specifications that include between
one and
five lags. Moreover,
it is possible that the incapacitation effect for violent games is
greater
than for non-violent
games, though we cannot test this hypothesis. If so, the difference
of
these estimates may
represent a downwardly biased estimate of a behavioral effect. This
provides some
support for the laboratory findings of a reinforcing behavioral
effect that
partially
counterbalances the incapacitation effect.
Table 5 repeats
these specifications where the dependent variable is now the log of
violent crimes. By
doing so, we focus on criminal acts that clearly entail an element of
aggression. Again,
we include various lags for the effects of video games and, again,
more
individual estimates
are negative than positive but few are significantly different from
zero.
The bottom panel of
Table 6 reports the aggregation of the lagged video game coefficients
to
calculate the
cumulative effects. From this panel we usually find an overall
negative effect of
video games on the
number of violent criminal incidents. These estimates are quite
similar to
those for all crimes
in upper panel of this table. If anything, these parameter estimates
are
slightly larger (in
absolute value terms) and aggregations with more specifications yield
results
significantly different from zero. These estimates indicate that both
violent and non-
17
violent video game
play is generally associated with reductions in the number of violent
crimes.
The test for a
difference in the effects for violent and non-violent games may be
more
informative. There
are no known previously hypothesized mechanisms through which non-
violent games would
affect violent crimes. We propose that the appropriate test for
violent
video games
affecting violent behavior is the difference in these effects by game
type. In this
case, the marginal
effect of violent video games, relative to non-violent games, is to
increase
violent crimes.
Decomposing the two effects suggests that a one hundred percent
increase in
violent video game
sales implies an incapacitation effect reducing violent crime by as
much
as 2.6% and an
aggression effect increasing violent crimes by as much as 1.5%.
B. Age of Offender
Results
A potential
robustness check is to examine the effects of video games on criminal
offenders by age of
offender. While the age profile of video game players is increasing,
video
games are still
primarily played by children, teens and younger adults. For most
offenses, the
NIBRS data records
information on the age of the offenders for an incident. We
separately
examine the effects
of video game sales on offenders aged 15-30, the prime video game
playing population,
versus those 35-50, a population for which video game play is not as
popular. If our
basic results were spurious and did not reflect any direct link
between video
game play and
criminal acts, we would have no reason to expect a differential
effect by age
group. In contrast,
under our hypotheses, we would expect larger effects for the younger
group.
Table 7 reports
cumulative estimates for both these younger and older groups. The
specifications are
otherwise identical to those reported in Table 4. However, rather
than report
the individual
estimates as in Tables 4 and 5, we report the estimated sums over all
lags as in
18
Table 6. As before,
specifications with lags from between two and five achieve some level
of
statistical
significance for both the young and the old. The estimated effects of
both violent
and non-violent
video games are both negative, as before. And, as before, violent
video games
decrease crime by
less than do non-violent video games. That is, there are few, if any,
qualitative
differences across the two groups.
Table 8 reports
cumulative estimates where the dependent variable is violent crimes,
for both these
younger and older groups. The specifications are otherwise identical
to those
reported in Table 5
and again we report the estimated sum of effects over all lags as in
Table
6. Now, there are
noticeable differences across the two groups. None of the estimates
for the
older group approach
traditional levels of statistical significance. In contrast, the
estimates for
the younger group
are generally larger (in absolute value) and many are statistically
significant. In
addition, the differences in estimates between violent and nonviolent
games are
often statistically
significant. We again find that, for the younger group, non-violent
games, as
well as violent
games, reduce the number of violent crimes. In these specifications,
the
measured by the
difference between the coefficients in the two rows which is about
0.06.
Thus, this is
evidence that the behavioral effect of violent video games on violent
behavior is
found only within
the younger population that tends to play video games more
intensively.
C. On Campus Results
Another potential
robustness check is to distinguish between crimes committed at
schools and colleges
and those committed elsewhere. Schools and colleges tend to aggregate
people who are of
video game playing age. The NIBRS data record the location of each
incident as a
categorical variable where one possible choice out of eleven is
“school or college
campus.” One
advantage of this variable over the age of offender variable is that
it is recorded
for all incidents
while the age of offender can be missing if no one witnessed the
incident in
progress. One
disadvantage is that crimes committed at schools and colleges need
not be
19
committed by a
member of the younger video game playing demographic, though most
are.
Perhaps a bigger
problem is that many of the younger video game playing population
commit
crimes away from
schools. Finally, since such a small number of crimes are committed
on
campus, we may lose
statistical power for that sub-sample while the off-campus sub-sample
will be quite
similar to the overall sample.
Table 9 reports
cumulative estimates for both crimes committed on campuses and
those committed
off-campus. The specifications are otherwise identical to those
reported in
Table 4 but we
report the estimated cumulative effect over all lags as in Table 6.
As before,
specifications with
lags from between two and five achieve some level of statistical
significance for
both the young and the old. The pattern of estimated effects for both
violent
and non-violent
video games is similar to before except that they are much larger for
the on-
campus sample than
off-campus sample. In the lower panel, the estimates are
qualitatively
similar to the base
results in Table 6. However, the upper panel estimates are about five
times
larger. Other than
the difference in magnitudes, the pattern of effects on-campus is
unchanged. There is
still a negative effect for non-violent video games in columns 2-5
that we
interpret as an
incapacitation effect. The estimated effect for violent video games
is
statistically
significantly smaller (in absolute value) and we interpret the
difference as a
possible estimate of
a behavioral effect of violent video games on crime for this
sub-sample.
Table 10 reports
cumulative estimates where the dependent variable is the number of
violent crimes, for
both crimes on and off campus. The specifications are otherwise
identical
to those reported in
Table 2 and again we report the estimated sum of effects over all
lags as
in Table 6. In this
case, fewer effects are estimated to be significantly different from
zero.
However, the pattern
is similar to those for all crimes in table 9. The magnitudes are
about
five times larger
for the on-campus sub-sample relative to the off-campus sub-sample.
As
20
expected, the
off-campus results are more similar our basic results reported in the
bottom
panel of Table 6.
V. Conclusion
Content regulation
of the video game industry is usually predicated on the notion that
the industry has
large and negative social costs through games’ effect on
aggression. Many
researchers have
argued that these games may also have caused extreme violence, such
as
school shootings,
because laboratory evidence has found an abundance of evidence
linking
gameplay to
aggression. Yet few studies before this one had examined the impact
of these
games on crime, with
the exception of Ward (2011) and Dahl and Dellavegna (2009).
Consistent with
these studies, we find that the social costs of violent video games
may be
considerably lower,
or even non-existent, once one incorporates the time use effect into
analysis.
These analyses are
suggestive of the hypothesis that violent video games, like all video
games, paradoxically
may reduce violence while increasing the aggressiveness of
individuals
by simply shifting
these individuals out of alternative activities where crime is more
likely to
occur. Insofar as
our findings suggest that the operating mechanism by which violent
gameplay causes
crime to fall is the gameplay itself, and not the violence, then
regulations
should be carefully
designed so as to avoid inadvertently reducing the time intensity, or
the
appeal, of video
games.
Our findings also
suggest unique challenges to game regulations. Because GAM
proposes that the
individual playing violent video games is developing, accidentally, a
biased
hermeneutic towards
people wherein they believe they are in danger, then the decrease in
violent outcomes
that we observe in our study – the incapacitation effect from time
use – may
21
be masking the
long-run harm to society if these violent behaviors are developing
within
gamers. This
suggests that regulation aimed at reducing violent imagery and
content in games
could in the
long-run reduce the aggression capital stock among gamers, but
potentially also
cause crime to
increase in the short-run if the marginal player is being drawn out
of violent
activities. This may
be too costly a tradeoff, and may not pass any cost-benefit test. But
another possibility
is that individuals who play games could be regularly taught to
recognize
these errors in
their framing of situations, which theoretically would reduce the
aggressive
capital and thus
reduce any negative outcome that is determined by the amount of
aggression
the person has built
up, without losing the short-run gains from crime reduction.
22
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24
Figure 1
25
Figure 2
26
Figure 3
27
Figure 4
28
Table 1
Unit Sales of Video
Games (millions) from VGChartz and ESA
Year VGChartz ESA
Pct
2005 56.7 240.7
23.6%
2006 76.2 267.8
28.5%
2007 107.0 298.2
35.9%
2008 141.3 273.5
51.7%
VGChartz from
authors’ calculations and ESA from
http://www.theesa.com/facts/pdfs/VideoGames21stCentury_2010.pdf.
29
Table 2
The Effect of Game
Quality (Game Spot Score) on Log Sales
All Intensely
Violent Not Intensely Violent
Games Games Games
GameSpot
Score
0.0803** 0.1221**
0.0769**
(0.0060) (0.0181)
(0.0065)
Week of
Release
-0.0039** -0.0081**
-0.0036**
(0.0002) (0.0008)
(0.0003)
Trend 0.0058**
0.0040** 0.0060**
(0.0001) (0.0003)
(0.0001)
February -0.0902*
-0.2169* -0.0663+
(0.0361) (0.1020)
(0.0385)
March -0.0212
-0.0576 -0.0081
(0.0348) (0.0967)
(0.0371)
April -0.1770**
-0.3466** -0.1361**
(0.0344) (0.0945)
(0.0369)
May -0.2838**
-0.4069** -0.2485**
(0.0355) (0.1004)
(0.0378)
June -0.1663**
-0.3593** -0.1217**
(0.0363) (0.1036)
(0.0386)
July -0.2251**
-0.5266** -0.1732**
(0.0358) (0.1059)
(0.0378)
August -0.3607**
-0.6881** -0.3126**
(0.0364) (0.1151)
(0.0381)
September -0.2700**
-0.4117** -0.2422**
(0.0358) (0.1200)
(0.0374)
October -0.1326**
0.0065 -0.1333**
(0.0365) (0.1159)
(0.0383)
November 0.6122**
0.6812** 0.6051**
(0.0361) (0.1052)
(0.0382)
December 1.2038**
1.1363** 1.2153**
(0.0349) (0.1073)
(0.0367)
Constant -4.8503**
-0.5994 -5.3472**
(0.2957) (0.8309)
(0.3189)
Observations 10,648
1,345 9,303
R-squared 0.38
0.40 0.38
Standard errors in
parentheses
+ significant at
10%; * significant at 5%; ** significant at 1%
30
Table 3
Summary Statistics
Variable Mean Std.
Dev.
Ln All Video Game
Sales 0.407 0.632
Ln Intensely Violent
Video Game Sales -1.900 1.037
Ln Not Intensely
Violent Video Game Sales 0.781 0.340
Average GameSpot
Score 7.634 0.435
Average Intensely
Violent GameSpot Score 8.546 0.646
Average Not
Intensely Violent GameSpot Score 7.506 0.468
Ln All Crimes
10.889 0.085
Ln Violent Crimes
9.967 0.083
Ln All Crimes on
Campuses 7.463 0.421
Ln Violent Crimes on
Campuses 6.663 0.506
Ln All Crimes Not on
Campuses 10.852 0.091
Ln Violent Crimes
Not on Campuses 9.925 0.091
Ln All Crimes
Offender Aged 15-30 9.854 0.068
Ln Violent All
Crimes Offender Aged 15-30 9.360 0.084
Ln All Crimes
Offender Aged 35-50 9.040 0.082
Ln Violent All
Crimes Offender Aged 35-50 8.603 0.095
Descriptive
statistics of the 200 observations used in later tables.
31
Table 4
The Effects of Video
Game Sales on the Log of both Violent and Non-Violent Crime
(1) (2) (3) (4)
(5) (6) (7)
Ln Video Game Sales
Not Intensely
Violent
-0.028 0.029 0.030
0.041 0.042 0.032 0.044
(0.60) (0.50)
(0.44) (0.54) (0.52) (0.40) (0.55)
Ln VG Sales Not
Intensely Violent
lag 1
-0.130+ -0.110
-0.090 -0.089 -0.099 -0.088
(1.92) (1.35)
(1.03) (1.03) (1.15) (1.02)
Ln VG Sales Not
Intensely Violent
lag 2
-0.131+ -0.098
-0.095 -0.044 -0.040
(1.71) (1.16)
(1.13) (0.50) (0.46)
Ln VG Sales Not
Intensely Violent
lag 3
-0.068 -0.067
-0.064 -0.075
(0.91) (0.88)
(0.86) (0.90)
Ln VG Sales Not
Intensely Violent
lag 4
0.010 0.042 0.029
(0.12) (0.53)
(0.35)
Ln VG Sales Not
Intensely Violent
lag 5
-0.125+ -0.126+
(1.73) (1.72)
Ln VG Sales Not
Intensely Violent
lag 6
0.026
(0.30)
Ln Intensely Violent
Video Game Sales
-0.009 0.014 0.019
0.030 0.031 0.023 0.026
(0.44) (0.56)
(0.64) (0.94) (0.94) (0.71) (0.81)
Ln Intensely Violent
VG Sales lag 1
-0.055+ -0.043
-0.029 -0.029 -0.034 -0.027
(1.77) (1.11)
(0.70) (0.69) (0.83) (0.65)
Ln Intensely Violent
VG Sales lag 2
-0.063+ -0.044
-0.042 -0.021 -0.017
(1.72) (1.06)
(1.02) (0.49) (0.39)
Ln Intensely Violent
VG Sales lag 3
-0.048 -0.047
-0.047 -0.051
(1.41) (1.27)
(1.29) (1.22)
Ln Intensely Violent
VG Sales lag 4
0.001 0.011 0.006
(0.04) (0.29)
(0.15)
Ln Intensely Violent
VG Sales lag 5
-0.036 -0.032
(1.10) (0.91)
Ln Intensely Violent
VG Sales lag 6
-0.000
(0.01)
Sample includes 200
weekly observations from 2004-2008. Month dummy variables and a time
trend were also
included but are not
reported. Average GameSpot scores for intensely violent and not and
for the current period
and eight lags are
used as IVs. The Sargon statistic for over-identification always
fails to reject the exogeneity of
the instrument set.
Absolute value of z-statistics in parentheses. + significant at 10%;
* significant at 5%; **
significant at 1%.
32
Table 5
The Effects of Video
Game Sales on the Log of Violent Crime
(1) (2) (3) (4)
(5) (6) (7)
Ln Video Game Sales
Not Intensely
Violent
-0.061 0.006 0.012
-0.001 0.001 -0.015 -0.006
(1.21) (0.09)
(0.16) (0.02) (0.01) (0.17) (0.07)
Ln VG Sales Not
Intensely Violent
lag 1
-0.154* -0.138
-0.109 -0.107 -0.114 -0.108
(2.04) (1.49)
(1.14) (1.12) (1.22) (1.16)
Ln VG Sales Not
Intensely Violent
lag 2
-0.147+ -0.138
-0.134 -0.099 -0.093
(1.70) (1.50)
(1.46) (1.04) (0.98)
Ln VG Sales Not
Intensely Violent
lag 3
0.001 0.003 0.009
-0.009
(0.01) (0.04)
(0.11) (0.10)
Ln VG Sales Not
Intensely Violent
lag 4
0.009 0.031 0.029
(0.11) (0.35)
(0.31)
Ln VG Sales Not
Intensely Violent
lag 5
-0.072 -0.078
(0.93) (0.99)
Ln VG Sales Not
Intensely Violent
lag 6
0.041
(0.44)
Ln Intensely Violent
Video Game Sales
-0.024 0.001 0.003
0.007 0.009 0.000 0.003
(1.15) (0.04)
(0.10) (0.20) (0.24) (0.01) (0.08)
Ln Intensely Violent
VG Sales lag 1
-0.060+ -0.050
-0.031 -0.030 -0.036 -0.033
(1.73) (1.14)
(0.68) (0.66) (0.80) (0.72)
Ln Intensely Violent
VG Sales lag 2
-0.066 -0.062
-0.060 -0.044 -0.040
(1.61) (1.37)
(1.32) (0.97) (0.87)
Ln Intensely Violent
VG Sales lag 3
-0.020 -0.017
-0.016 -0.025
(0.54) (0.42)
(0.41) (0.54)
Ln Intensely Violent
VG Sales lag 4
-0.001 0.000 0.000
(0.02) (0.00)
(0.00)
Ln Intensely Violent
VG Sales lag 5
0.011 -0.012
(0.30) (0.30)
Ln Intensely Violent
VG Sales lag 6
0.011
(0.24)
Sample includes 200
weekly observations from 2004-2008. Month dummy variables and a time
trend were also
included but are not
reported. Average GameSpot scores for intensely violent and not and
for the current period
and eight lags are
used as IVs. The Sargon statistic for over-identification always
fails to reject the exogeneity of
the instrument set.
Absolute value of z-statistics in parentheses. + significant at 10%;
* significant at 5%; **
significant at 1%.
33
Table 6
The Cumulative
Effect of Video Games on Crimes
Aggregate Effect on
All Crimes (from Table 4)
Number of Lags
Included
0 1 2 3 4 5 6
Not Intensely
Violent Coefs.
-0.028 -0.102
-0.213* -0.216* -0.203+ -0.257* -0.230+
(0.046) (0.062)
(0.096) (0.105) (0.122) (0.124) (0.139)
Intensely
Violent Coefs.
-0.009 -0.041
-0.088* -0.093* -0.088+ -0.104* -0.096+
(0.020) (0.027)
(0.041) (0.044) (0.050) (0.050) (0.056)
Chi-Sq test of
difference
0.42 2.70+ 4.75*
3.83* 2.40 4.07* 2.50
Aggregate Effect on
Violent Crimes (from Table 5)
Violent/
All Crimes
Number of Lags
Included
0 1 2 3 4 5 6
Not Intensely
Violent Coefs.
-0.061 -0.148*
-0.272* -0.247* -0.227+ -0.260* -0.224
(0.050) (0.069)
(0.109) (0.116) (0.134) (0.135) (0.148)
Intensely
Violent Coefs.
-0.024 -0.061*
-0.113* -0.107* -0.099+ -0.108* -0.095
(0.021) (0.030)
(0.046) (0.048) (0.054) (0.055) (0.056)
Chi-Sq test of
difference
1.36 4.44* 5.99*
4.10* 2.45 3.40* 1.95
For both the top and
bottom panels, each column represents results from a separate
instrumental variables
regression. Each row
reports the sum of coefficients for a variable for different possible
lag lengths. Not
reported are
coefficients of month dummies and a time trend. Absolute value of
z-statistics in parentheses. +
significant at 10%;
* significant at 5%; ** significant at 1%
34
Table 7
The Effect of Video
Games on All Crimes by Offender Age
Aged 15-30 Number
of Lags Included
0 1 2 3 4 5 6
Not Intensely
Violent Games
-0.028 -0.098
-0.182* -0.178+ -0.167 -0.218+ -0.214
(0.046) (0.061)
(0.092) (0.100) (0.115) (0.117) (0.134)
Intensely
Violent Games
-0.012 -0.043
-0.079* -0.081+ -0.077+ -0.093* -0.093+
(0.019) (0.026)
(0.039) (0.042) (0.046) (0.047) (0.054)
Chi-Sq test of
difference
0.32 2.31 3.56+
2.62 1.66 3.04+ 2.18
Aged 35-50 Number
of Lags Included
0 1 2 3 4 5 6
Not Intensely
Violent Games
-0.020 -0.089
-0.236* -0.210+ -0.214 -0.243+ -0.235
(0.049) (0.068)
(0.112) (0.117) (0.136) (0.138) (0.157)
Intensely
Violent Games
-0.014 -0.042
-0.103* -0.096* -0.098+ -0.105+ -0.102
(0.021) (0.029)
(0.047) (0.049) (0.055) (0.056) (0.062)
Chi-Sq test of
difference
0.05 1.37 4.01*
2.63 1.99 2.69 1.91
For both the top and
bottom panels, each column represents results from a separate
instrumental variables
regression. Each row
reports the sum of coefficients for a variable for different possible
lag lengths. Not reported
are coefficients of
month dummies and a time trend. Absolute value of z-statistics in
parentheses. + significant at
10%; * significant
at 5%; ** significant at 1%
35
Table 8
The Effect of Video
Games on Violent Crimes by Offenders Age
Aged 15-30 Number
of Lags Included
0 1 2 3 4 5 6
Not Intensely
Violent Games
-0.034+ -0.057*
-0.090* -0.087* -0.100* -0.087+ -0.059
(0.019) (0.025)
(0.038) (0.040) (0.048) (0.051) (0.057)
Intensely
Violent Games
-0.010 -0.018+
-0.031+ -0.029+ -0.034+ -0.028 -0.017
(0.008) (0.011)
(0.016) (0.017) (0.019) (0.021) (0.022)
Chi-Sq test of
difference
4.32* 6.48**
6.84** 5.61* 5.11* 3.54+ 1.40
Aged 35-50 Number
of Lags Included
0 1 2 3 4 5 6
Not Intensely
Violent Games
-0.023 -0.021
-0.028 -0.011 0.005 0.020 0.046
(0.016) (0.020)
(0.028) (0.033) (0.040) (0.043) (0.048)
Intensely
Violent Games
-0.008 -0.006
-0.009 -0.002 0.003 0.009 0.019
(0.007) (0.009)
(0.012) (0.014) (0.016) (0.017) (0.019)
Chi-Sq test of
difference
1.94 1.42 1.24
0.20 0.00 0.17 0.84
For both the top and
bottom panels, each column represents results from a separate
instrumental variables
regression. Each row
reports the sum of coefficients for a variable for different possible
lag lengths. Not reported
are coefficients of
month dummies and a time trend. Absolute value of z-statistics in
parentheses. + significant at
10%; * significant
at 5%; ** significant at 1%
36
Table 9
The Aggregate Effect
of Video Games on All Crimes by Campus Location
Crimes on
Campus
Number of Lags
Included
0 1 2 3 4 5 6
Not Intensely
Violent Games
-0.041 -0.434
-0.837* -1.126* -1.099+ -1.381* -0.976
(0.266) (0.308)
(0.417) (0.486) (0.567) (0.600) (0.724)
Intensely
Violent Games
0.018 -0.174
-0.340* -0.465* -0.458* -0.557* -0.399
(0.112) (0.133)
(0.176) (0.203) (0.231) (0.242) (0.289)
Chi-Sq test of
difference
0.13 1.97 4.00*
5.14* 3.40+ 5.05* 1.70
Crimes off
Campus
Number of Lags
Included
0 1 2 3 4 5 6
Not Intensely
Violent Games
-0.024 -0.088+
-0.192* -0.187+ -0.175 -0.222+ -0.208
(0.045) (0.061)
(0.095) (0.103) (0.120) (0.121) (0.137)
Intensely
Violent Games
-0.008 -0.035
-0.080* -0.081+ -0.076 -0.091+ -0.087
(0.019) (0.026)
(0.040) (0.043) (0.049) (0.049) (0.055)
Chi-Sq test of
difference
0.30 2.04 3.95*
2.93+ 1.80 3.15+ 2.09
For both the top and
bottom panels, each column represents results from a separate
instrumental variables
regression. Each row
reports the sum of coefficients for a variable for different possible
lag lengths. Not
reported are
coefficients of month dummies and a time trend. Absolute value of
z-statistics in parentheses. +
significant at 10%;
* significant at 5%; ** significant at 1%
37
Table 10
The Effect of Video
Games on Violent Crimes by Campus Location
Crimes on
Campus Number of
Lags Included
0 1 2 3 4 5 6
Not Intensely
Violent Games
0.048 -0.376
-0.758 -1.052+ -0.953 -1.265+ -0.810
(0.302) (0.353)
(0.456) (0.550) (0.649) (0.688) (0.832)
Intensely
Violent Games
0.039 -0.171
-0.327 -0.455+ -0.422 -0.529+ -0.349
(0.128) (0.152)
(0.201) (0.230) (0.263) (0.278) (0.332)
Chi-Sq test of
difference
0.00 0.93 2.31
3.27+ 1.81 3.06+ 0.82
Crimes off
Campus Number of
Lags Included
0 1 2 3 4 5 6
Not Intensely
Violent Games
-0.061 -0.136*
-0.255* -0.219+ -0.200 -0.224 -0.202
(0.050) (0.069)
(0.109) (0.116) (0.134) (0.137) (0.153)
Intensely
Violent Games
-0.025 -0.056+
-0.106* -0.095+ -0.088 -0.093+ -0.086
(0.021) (0.030)
(0.046) (0.048) (0.055) (0.055) (0.061)
Chi-Sq test of
difference
1.32 3.74+ 5.02*
3.14+ 1.86 2.46 1.53
For both the top and
bottom panels, each column represents results from a separate
instrumental variables
regression. Each row
reports the sum of coefficients for a variable for different possible
lag lengths. Not
reported are
coefficients of month dummies and a time trend. Absolute value of
z-statistics in parentheses. +
significant at 10%;
* significant at 5%; ** significant at 1%
