Discussion Paper Series
2021-01
Do video games negatively affect mental health under COVID-19?
Evidence from a natural experiment in Japan

Hiroyuki Egami, Takahisa Wakabayashi, and Tsuyoshi Yamamoto

September 29, 2021

Discussion Papers can be downloaded:
http://www1.tcue.ac.jp/home1/c-gakkai/dp/dp21-01

Do video games negatively affect mental health under COVID-19?
Evidence from a natural experiment in Japan
Hiroyuki Egamia*, Takahisa Wakabayashib, and Tsuyoshi YamamotocaCollege of Economics, Nihon University, Tokyo, Japan; bFaculty of Regional Policy, Takasaki

City University of Economics, Takasaki, Japan; cNational Graduate Institute for Policy Studies,Tokyo, Japan* Nihon University, 1-3-2 Misakicho, Chiyoda-ku, Tokyo 101-8360, Japan. E-mail address:
phd15410@grips.ac.jp ORCiD: https://orcid.org/0000-0002-5873-8994** This working paper is a preliminary version that only includes a part of the analysis of our
project.

1

Abstract
The COVID-19 pandemic and the lockdown policy have negatively affected mental health.
People have begun to spend more time on online communication, including via video games, asa part of their stress-coping behavior. Thus, video games have become an essential platform foronline communication. However, people are also concerned about whether playing video
games for a long time is really good for mental health. To this interest, the previous studies
show negative correlations between video games and mental health under the COVID-19
pandemic through cross-sectional analysis. In this paper, using a causal inference approach, we
examine whether video games negatively affect mental health under COVID-19. We exploit anatural experiment where people joined the lottery to purchase video games (Play Station 5)
and find no significant negative impact on mental health. We also employ the Inverse
Probability Weighting method to adjust for self-selection into joining a lottery, of which theestimates stay insignificant. These findings provide a better understanding of the relationshipbetween video games and mental health, thus helping policymakers to form more effective
policies against the psychological distress of the people under and post the COVID-19
pandemic.
Keywords: mental health, video game, lottery, COVID-19

2

1. Introduction
The Coronavirus 2019 (COVID-19) pandemic in 2020 has dramatically changed thelifestyle. People have been required to stay at home and forced to shift their communicationstyles from an offline basis to an online basis due to the lockdown policy implemented by thegovernment (Clark-Ginsberg and Petrun Sayers 2020; Gioia et al. 2021). The pandemic and thesudden change of lifestyle increased stress (Holmes et al. 2020; Yamamoto et al. 2020) andcaused psychological distress, which led to addictive behaviors (i.e., alcoholism) for somepeople (Bonny-Noach and Gold 2020).
As a part of their stress-coping behavior, people have begun to spend more time online,
including gaming. Video games have become an important platform for online communication
for the users, which ranks as one of the major leisure activities under the containment policy(Barr and Copeland-Stewart 2021; Washington Post 2021). Combined with phone calls, texts,
and chat tools, video games are giving people a way to share the fun with each other. However,people also concern whether spending time for playing video games longer time is really good—or at least not harmful—for mental health, though at first glance, it seems to be helpful forbattling isolation (ABC News 2020).
Do video games negatively affect mental health under the COVID-19 containment
policies? Numerous studies of the medical and psychological fields have examined the effect ofplaying video games on mental health and argue that video games are potentially harmful tomental health (Granic, Lobel, and Engels 2014). Some recent studies examining the effect ofvideo games on mental health under the COVID-19 containment policies conclude that playing
video games is associated with an increased risk of mental problems (Giardina et al. 2021; Kato,
Shinfuku, and Tateno 2020; Oka et al. 2021). The World Health Organization (WHO) and New
York Times have warned that during the COVID-19 pandemic, the increased screen time andgames-playing time may lead to internet and gaming addiction (The New York Times 2021). A
local government in Japan reported the negative impact of video games on mental health3

through their survey (Hyogo Prefecture 2020). Such information would consolidate the negative
image about video games on mental health, thus may lead to the policy based on this image.
The empirical evidence of associations between video games and mental health is,
however, not conclusive. Recent review papers highlight the methodological limitations of theprevious studies (Ferguson, 2015). Firstly, many studies are cross-sectional and the causal
inference is not carefully conducted. 1 Especially, whether playing video games induce stress or
people with higher stress prefer playing video games is less than clear. Secondly, studies basedon laboratory experiments heavily rely on college students as their target population rather thanthe child, adolescent, or elderly population. Thirdly, laboratory experiments examine short-term
emotional effects right after playing video games and do not measure mental health measures inreal situations. Considering those limitations, the review papers argue that policy implications
based on correlations between video games and mental health may be misleading and making
policies that discourage playing video games would be too simplistic (Ferguson 2015; Granic,
Lobel, and Engels 2014; Greitemeyer and Mügge 2014). Further, a review study (Granic, Lobel,
and Engels 2014) stresses that contrary to stereotypes, video gamers are not isolated because
recent video games have an aspect of an online communication tool. This aspect was not (orcould not have been) considered in the previous studies, though online communication is shown
to be beneficial for maintaining mental health (Morgan and Cotten 2003).
This study aims to fill the gap by exploiting a natural experiment that created a plausibly
exogenous random variation of ownership of a new video game. We focus on a lottery for
winning the right to purchase Play Station 5 (PS5), newly released in October 2020. Due to the
supply limitation, people in Japan are required to join the lottery to purchase PS5. 2 To the best of1

The most recent papers listed above (Giardina et al. 2021; Kato, Shinfuku, and Tateno 2020; Oka et al. 2021)
are not exceptions.
2
People can purchase PS5 in the secondhand market, but in our data, most of the people answered that they
got PS5 through lotteries.

4

our knowledge, no study employs a natural experiment approach to estimate video games'
effects on mental health.
Our data of 18,189 samples, composed of people aged 10~69 from all the 47 prefectures inJapan, is collected through an internet survey in February 2021. Among them, 1,481 people
joined the PS5 lottery, and 254 people won the lottery. 3 Our major target population is thepeople who joined the lottery. We also attempt to conduct statistical inferences that adjust for
self-selection into the lottery by using the inverse probability weighting (IPW) approach.
In our main analysis, we conduct a regression analysis by assuming that the chance of
winning the PS5 lottery is quasi-random. As a pre-analysis, we first show that a cross-sectional
estimation based on our data indicates that playing video games (PS5) is negatively associatedwith mental health; this is consistent with the previous studies. In contrast, our main analysis
finds that the results obtained from a regression analysis exploiting the natural experimentindicate no significant negative impact of PS5 on mental health. This result differs from recent
related studies, which show negative impacts of video games on mental health under the
COVID-19 pandemic (Giardina et al. 2021; Kato, Shinfuku, and Tateno 2020; Oka et al. 2021).
A possible explanation is that the estimates of the previous studies relying on cross-sectional
analysis are contaminated with selection bias. At the same time, there are some other possible
explanations, including a loss of precision due to a smaller sample.
This study contributes to several strands of literature. Firstly, numerous medical fieldstudies examine the impact of playing video games on mental disorders such as depression(Granic, Lobel, and Engels 2014). 4 The majority of them find that high exposure to video games
is related to symptoms of depression. For example, Wang, Sheng, & Wang (2019) shows thatstudents with mobile game addiction tend to feel social anxiety, depression, and loneliness.

3

Some people have not received the results of the lottery yet. Those people are excluded from the sample ofthe lottery analysis.
4Some exceptions include Giardina et al. (2021) and Roy and Ferguson (2016). Those find a positive impactof video games on stress, though they have limitations within the rigorousness of their methodologies.

5However, the methods of most studies are cross-sectional, thus failing to eliminate the effects ofunobservable characteristics such as personality and family backgrounds. 5 Moreover, themedical researchers mainly focus on problematic gaming behavior ("gaming addiction"); studies
that examine the effect of video games on light players (or ordinary players) are scarce(Ferguson 2015). This study aims to fill these gaps by exploiting a natural experiment.
Secondly, this study is related to the literature of psychology that examines thepsychological effects of playing video games. The studies show that while playing violent video
games induces stress and aggressive behavior, playing non-violent video games induces positive
emotion and relieves stress (Bowman and Tamborini 2012; Rieger et al. 2014). However,evidence from these studies is likely to have limited external validity because most studies
employ laboratory experiments. Typically, university students are invited to play video games
for a few hours, and after that, immediately their emotional change is measured (e.g. Reinecke2009). Thus, there are unanswered questions regarding the consequences of playing video
games—and communicating online with friends—in the actual situation, especially for the
population other than university students (Granic, Lobel, and Engels 2014). In contrast, thisstudy utilizes the data collected through a large-scale online survey for a wide range of age
groups—between the ages of 10 and 69, joined from all 47 prefectures, ranging from light users(or non-users) to heavy game users.
Thirdly, this study contributes to the literature of the medical studies and leisure studies
that examines whether sedentary behavior (i.e., watching TV, using a computer, online
communications, and playing video games) affects mental health (Teychenne et al. 2019;
Teychenne, Ball, and Salmon 2010). Many cross-sectional studies find that sedentary behavior is5Some recent medical studies (Ferguson 2015; Stenseng, Hygen, and Wichstrøm 2020) argue that the existing
medical studies’ causal inferences are not likely to be free from endogeneity problems. Notably, Ferguson(2015) shows that the magnitude of the negative effect of video games is substantially reduced by including
control variables such as gender and family environment. Though the concern of endogeneity is mentioned inthese papers, there are no further studies—examining the video game effect—which conduct causal inference
rigorously, to our knowledge.

6

associated with a higher risk of depression (for example, Rezende, Rodrigues Lopes, ReyLópez, Matsudo, & Luiz, 2014). Meanwhile, several studies examining the effect of using acomputer find positive effects of online communication on mental health. For example, Shaw &Gant (2002) employ a laboratory experiment and show that internet chatting reduced depression
symptoms. Therefore, it is less than clear whether playing PS5, which is a sedentary behavior
but also facilitates online communication—online chatting, battle, and cooperative play—
negatively affects mental health or not. This study utilizes a natural experiment—neither crosssectional studies nor laboratory experiments—and examines the effect of video games which arecounted as a tool for online communication, on mental health.
The rest of the paper is organized as follows. Section 2 reviews study setting about how
PS5 represents the videogame by which people can enjoy online communication, and the reason
why there is a scarce supply of PS5 that leads to the lottery, followed both by the outline of thesurvey conducted by the internet survey company and by the explanation of K6 to capture
psychological distress. Section 2 also describes the identification strategy. Section 3 presents theempirical results to check whether video games have an impact on mental health. Finally,
section 4 starts with a conclusion, followed by the policy implication and the limitations of ourstudy.

2. Data and Study Design
2.1. Study setting
In October 2020, SONY corporation, a well-known electric company, released
PlayStation 5 or PS5, the newest console of the PlayStation series, which is a worldwide wellknown video game. In this study, we select PS5 as our primary target video game console
because of the increase in the number of people who communicate online through PS5 software
such as "Monster-hunter" and "Final Fantasy" under the lockdown policy COVID-19 pandemic.

7After its first debut in 1994, the PlayStation series has been widespread. As of June 2021, thesales of PS4 reached 114 million in the world, and more than 100 million people actively play
Play Station Network, a digital entertainment service associated with PlayStation for online
communication.
However, due to the limited supply of semiconductors for PS5, people can rarely
purchase PS5 by a standard method such as online shopping or shopping on-site; hence the main
way for people to get PS5 is to join the lottery provided by dealers. Otherwise, they need to pay
extra costs to get PS5. For example, if someone wants to purchase PS5 online at Amazon Japan,
he/she needs to pay more than 1,000 dollars as of July 2021 (the regular price of PS5 is about
400 dollars).
If people want to join the lottery, they usually need to register their information such asname and email address at the dealer's website. After entering the lottery, they waited for several
weeks for the result of dealers that randomly decide the winner. After the announcement of theresult, people purchased it if they win the lottery. These conditions for the natural experiment
differentiate this paper from previous studies.

2.2. Data
For this study, we work with the Gameage Research and Institution (GRI) in Japan to
conduct a survey. GRI hired an Internet survey company called Cross Marketing, Inc. andemployed random sampling from about 55,000 people across the nation who had pre-registeredas potential survey participants. The survey was implemented on February 2, 2021, under a stateof emergency in Japan. GRI sent out our survey to pre-registered 55,000 people in the actual
implementation, and 18,189 people responded. Among them, 1,481 people joined the lottery andgot the result, and 254 people won the lottery.
Next, we use the Japanese version of K6 as a scale of psychological distress to measuremental health. K6 is composed of 6-item questions that measure the scale of nonspecific8psychological distress in the past 30 days and score the degree of the psychological distress from0 (the lowest) to 24(the highest) following the methodology used in the previous
paper(Yamamoto et al. 2020). In this measure, each question is rated on a scale of 0 (none of thetime) to 4 (all of the time). Given its brevity and high accuracy, the K6 is an ideal scale forscreening for mental disorders in population-based health surveys in Japan(Furukawa et al.
2003).
We included a question asking about the degree of psychological distress scored on thescale from 0 to 24 measured by K6 and asking if people join the lottery. We specifically focusedon the psychological distress of the people who joined the lottery to purchase PS5.
Table 1 shows the descriptive statistics of our sample, including the following variables:
age, gender, the degree of the preferences of the games, marital status, job status. This tableshows that our sample consists of wide ranges of age and various socio-economic groups. In
addition, our sample consists of various characteristics. About half of the respondents weremale, married, or have children regarding the distribution of samples. For example, the age
structure ranges from children, adolescents 6 to adults, while the sample in the previous
experimental studies is typically university students. Besides, the mean of our target variable,
K6, is 5.91. Appendix Table A-1 describes the distribution of K6, which shows that 53.2% ofscores are below 5, a similar result to the previous study (Yamamoto et al. 2020).
In addition, we include the degree of people's preference on games measured by fivescales, ranging from 1(the most frequent user/ heavy user) to 5(non-user), defining it as"Gaming preference.". GRI annually captured this variable through the different questionnaires
asking about the frequency of playing the video games and uses it as a significant indicator of6

In our survey, parents or guardians sometimes respond on behalf of their children between 15 and 19 years
old.

9GRI. The mean of "Gaming Preference" was distributed relatively equally in each preference (1:22%, 2:24%, 3:24%, 4:23%, and 5:7%). 7

7This measure is created and calculated by Gameage Research and Institute. The gaming preference iscalculated based on some factors such as frequency of playing the video games, collected by surveys.

10

Table 1. Descriptive Statistics of the people in our survey
Variables

Mean

SD

Outcome variables
Kessler Psychological Distress Scale (K6): 0-24

5.91

6.41

Basic characteristics
Age
Gender(Male=1)
Married (=1)
Have child(ren) (=1)
Divorced (=1)
Student (=1)
Self-employed (=1)
Full-time employee (=1)
Part-time employee (=1)
No job (=1)
Gaming preference (Heavy player=1 ~ No gamer=5) = 1
Gaming preference (Heavy player=1 ~ No gamer=5) = 2
Gaming preference (Heavy player=1 ~ No gamer=5) = 3
Gaming preference (Heavy player=1 ~ No gamer=5) = 4
Gaming preference (Heavy player=1 ~ No gamer=5) = 5

36.53
0.50
0.55
0.47
0.06
0.04
0.08
0.50
0.15
0.10
0.22
0.24
0.24
0.23
0.07

15.61
0.50
0.50
0.50
0.23
0.20
0.27
0.50
0.35
0.30
0.42
0.43
0.43
0.42
0.25

No. of observation

18,189Notes: K6 is a scale of psychological distress, where there are 6-item questions in the questionnaire that measures
the scale of nonspecific psychological stress in the past 30 days and scores the degree of the psychological distressfrom 0 (the lowest) to 24(the highest). Gaming Preference is created and calculated by Gameage Research instituteand calculated based on some factors such as frequency of playing the video games, collected by surveys. The
survey was conducted in February 2021.

2.3. Identification Strategy
In this research, we use the following regression equations to examine the impact ofvideo games on mental health. First, we examine the relationship between video games andmental health by cross-sectional analysis, following the previous studies examining whether
video games negatively affect mental health. We use equation (1) to see the correlation between
video games and mental health (n = 18,189).

11

(1) 𝑌𝑝𝑝 = 𝛽𝛽𝛽𝛽𝛽𝛽𝛽5 + 𝜉𝑋𝑝𝑝 + 𝛼𝑝 + 𝜀𝑝𝑝
where 𝑌𝑝𝑝 is the psychological distress measure (K6) for individual i in prefecture p. Haveps5 is
a dummy taking 1 if an individual has PS5 and 0 otherwise. X is a set of control variablesconsisting of individual characteristics, including age, gender, marital status, job status, whether
having children or not. We also include the gaming preference as control variables: dummies ofeach scale (1~5). We also include prefecture fixed effects to control for the correlation between
the psychological distress measure (K6), whether having PS5, and geographical characteristics.
Note that the method above only leads to correlation, not a causal relationship, while ituses all samples (n=18,189). In other words, the method above has a problem of endogeneity.
For example, people who have PS5 would have various characteristics that make people decideto play PS5, which could create a selection bias.
Second, based on this result, we employ a natural experiment based on the lottery topurchase PS5. Here, the target population of our analysis is the people who joined and got theresult of the PS5 lottery (n = 1,334). 8 9 If a lottery is conducted randomly, comparing thepsychological distress measure of people who win and lose the lottery would reduce theselection bias caused by the difference between people who have PS5 and people who do not.
Hence, we view whether people win the lottery or not as the treatment in our paper and employ
the following equation to measure the impact of video games:(2) 𝑌𝑝𝑝 = 𝛽𝑙𝑙𝑙𝑙𝑙𝑙𝑦𝑦𝑦𝑦𝑝𝑝 + 𝜙𝑗𝑗𝑗𝑗𝑗𝑗𝑗𝑗𝑙𝑝𝑝 + 𝜓𝜓𝑝𝑝 + 𝛼𝑝 + 𝜀𝑝𝑝
8

The target population of this analysis is the people who had the will to play PS5, not all the people whojoined our survey. The leisure literature stresses the importance of autonomy of choosing leisure (Caldwell2005) by oneself considering one’s personality differences or one’s mood (“self-determination hypotheses”).
If one does not choose the leisure by oneself, the stress-coping effect of the leisure is expected to be reduced.
The psychology literature also argue that this point should be considered in the future research (Granic, Lobel,
and Engels 2014). In that sense, limiting our target population to the people who joined the PS5 could bereasonable.
9
Among 1,481 people who joined the lottery, 147 people had not received the result yet. We use the rest of theparticipants (1,334) as the target population.

12

where dependent variable Y pi is k6 for individual i in prefecture p, lotterywin is a dummy taking
1 if an individual wins the lottery and 0 if an individual loses the lottery, and 𝑗𝑗𝑗𝑗𝑗𝑗𝑗𝑗𝑗 is thetotal number of times that people joined the lottery for individual i in prefecture p,. 𝑋𝑝𝑝 is a setof control variables that are the same as those used in equation (1); 𝛼𝑝 are prefecture dummies.

Our main interest lies in the coefficients β, which could capture the causal effect of video games.
As mentioned earlier, using a dummy indicating whether people have PS5 or not(HavePS5)—as a variable of interest—causes endogeneity. So instead, as our main analysis, we
adopt the Intention to Treat (ITT) approach, using the PS5 lottery result (lotterywin) as avariable of interest to mitigate endogeneity problems. In the later part, we also examine thecausal effect of HavePS5 on mental health with the instrumental variable method.
Note that we need to deal with two challenges even in ITT approach: selection about
joining the lottery and randomness of the lottery. To begin, one may concern the non-random
selection of joining the PS5 lottery. Indeed, the characteristics of the people who joined the PS5lottery are different from those who did not join the lottery. In Appendix Table A-2, we display
the comparison of the observable characteristics. If one wants to apply our estimates to thepeople who do not join the PS5 lottery, the estimates of our main analysis based on regression(2) could be biased. To adjust for selection into the lottery, we adopt the inverse probability
weighting (IPW) method and conduct additional analysis to conjecture what happens if allparticipants are assumed to join the lottery. In our IPW regressions, the data is weighted by the
inverse of the probability of joining the lottery. 10 Probit model estimates are shown in Appendix
Table A-3. We predict the probability for each person to join the lottery through the probit
model.
1

The data of the people who joined the lottery—used for estimating equation (2)—is weighted by where 𝜋𝜋denotes the probability of joining the lottery. This is analogous to the way the IPW method is used for dealing
with attrition problems (Wooldridge 2011). In our case, for the people who did not join the lottery, whether
they won the lottery or not and the subsequent outcomes such as k6 (if they joined the lottery) are notavailable—attrited.

10

13

Next, one would also concern whether the lottery is conducted randomly. Table 2 showsa balance table that is used for verifying the validity of the natural experiment. As explained in
the Data and Study setting section, our underlying assumption for the internal validity is that
there is no systematic difference in predetermined characteristics between the control group(people who did not win the lottery) and the treatment group (people who win the lottery).
Column (1) and Column (2) show the mean of various characteristics for the control group andthe treatment group, respectively. Column (3) shows the results of the t-tests and the joint F-test.
One can find that two variables (full-time employment dummy and gaming preference= 5 dummy) show significant correlations with the result of the lottery in columns (2). It ispuzzling that the statistically positive estimate of the coefficient of the dummy of gaming
preference = 5 (a non-gamer) because this result implies that a non-gamer is more likely to win
PS5 than a heavy gamer. Unfortunately, we do not have a clear explanation of this at this stage.
Though the t-stats for some variables indicate statistically significant relationships, theresult of the joint F-test (1.557) is not statistically significant. The result of the balance test is not
perfect, but at least, it does not strongly reject the validity of the natural experiment. In our
regressions, we control for the observed characteristics to address the potential selection bias.
Nevertheless, we still need to admit that other factors may contaminate the randomnessof the PS5 lotteries—our estimation is not free from endogeneity problems, including omitted
variable bias. For example, one can join the PS5 lotteries multiple times to increase theprobability of winning the lottery. We show the comparison of the total number of joining thelottery in the lower part of Table 2. As expected, it is significantly different between people who
win the lottery and not (-0.893), which explains that the more times the people join the lottery,the more likely they win the lottery. Moreover, the PS5 lotteries are implemented by stores, andthe probability of winning is likely to be different between stores. One might find stores where
one can win PS5 at a higher likelihood, though such information is not available online to ourknowledge14

To mitigate the potential selection bias in estimating the coefficient of interest (𝛽), we
additionally control for the number of times of joining the lottery. However, the variable itself ispotentially endogenous. To address the problem, we run the regression of equation (2) with andwithout the number of times of joining the lottery. If the estimated relationship changes with theinclusion of this variable, it would indicate that such variables are potentially driving part of theobserved relationship between being a PS5-lottery winner and the psychological distress scale.
As it turns out, the data do not indicate that to be the case.

15

Table 2 Randomness check of lottery
(1)

(2)

Did not win PS5lottery

Won PS5
lottery

VariableN

Mean/SEN

Mean/SE

Age

1080

254

Gender (Male=1)

1080

Married (=1)

1080

Have child(ren) (=1)

1080

Age

1080

Gender (Male=1)

1080

Stay-at-home wife/husband (=1)

1080

Full-time employee (=1)

1080

Self-employed (=1)

1080Part-time employee (=1)

1080

No job (=1)

1080

35.017[0.299]0.619[0.016]0.544[0.014]0.470[0.016]0.050[0.007]0.044[0.006]0.076[0.007]0.655[0.016]0.073[0.009]0.089[0.010]0.064[0.007]35.618[1.187]0.571[0.032]0.551[0.041]
0.472[0.039]0.047[0.015]0.067[0.016]0.091[0.016]0.547[0.030]0.102[0.020]0.098[0.016]0.094[0.020]Gaming preference (Heavy player=1 ~ No gamer=5)
=1

1080

0.491

254
254
254
254
254
254
254
254
254
254
254

[0.015]Gaming preference (Heavy player=1 ~ No gamer=5)
=2

1080

0.252

1080

0.144

254

1080

0.100

254

1080

0.013

254

-0.0020.003-0.023-0.0150.107***-0.029-0.010-0.031
0.003

0.240

0.012

0.122

0.022

0.106

-0.006[0.014]254[0.003]0.043-0.030**[0.010]F-test of joint significance (F-stat)F-test, number of observations# of times joined lottery-0.008[0.030][0.010]Gaming preference (Heavy player=1 ~ No gamer=5)
=5

0.049

[0.025][0.011]Gaming preference (Heavy player=1 ~ No gamer=5)=4-0.601[0.034][0.014]Gaming preference (Heavy player=1 ~ No gamer=5)
=3

0.488

(3)
t-testDifference(1)-(2)

1.557
1334
1080

3.276
[0.110]254

4.169[0.312]-0.893***

Notes: Gaming preference is created and calculated by Gameage Research institute and calculated based on somefactors such as frequency of playing the video games, collected by surveys. The number of times of joining lotteries
is censored at 99 percentiles to deal with extreme values. Regressions include prefecture fixed effects. Standard
errors are clustered by prefectures. ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1.

16

Finally, to estimate the causal effect of having PS5 on mental health, we use the PS5lottery result (win or not) as an instrument, focusing on the situation where the lottery result
influences mental health only through having PS5. Thus, the endogenous variable used in the IV
regression is a dummy of having PS5. This setting allows us to compare the estimates of the
OLS and the IV regressions. With the IV regression, the estimation of local average treatment
effects (LATE; Lousdal 2018)) is of interest to evaluate the impact of video games on mental
health.

3. Empirical Results
3.1. Estimates by cross-sectional analysis
First, we examine the relationship between PS5 ownership and psychological distressmeasure (K6) by equation (1). In our data, 319 samples have PS5, and the result in each Column
in Table 3 indicates a positive correlation between haveps5 and psychological distress (k6),which aligns with the findings of the previous studies. Column (1) shows the results ofregression (1) without control variables, which describes that the people are feeling higher stressby 1.59 in K6 scale when they have PS5. Column (2) displays the results of regression (1) with
control variables except for gaming preference with the anticipation that gaming preference has
a strong correlation with stress based on the previous studies. The point estimate of thecoefficient of the having PS5 dummy is 1.84, meaning people is still under higher stress when
they have PS5. Finally, Table 3 Column (3) shows regression (1) results with control variables,
including gaming preference, which reports people feeling higher stress by 1.17 in the K6 scale
when they have ps5. Indeed, as argued by Ferguson (2015), including control variables reduces
the magnitude of the negative impact. Particularly, we find that the magnitude of the impact
drops when we include gaming preference. In sum, the results of our cross-sectional analysis
successfully traced the results of the previous studies.

17

Table 3. The relationship between Psychological Distress (K6) and Having PS5

VARIABLES
1 if having PS5(1)
Kessler PsychologicalDistress Scale: 0-24

(2)Kessler PsychologicalDistress Scale: 0-24

(3)Kessler PsychologicalDistress Scale: 0-24

1.59***(.539)

1.84***(.515)

1.17**(.495)

Observations
18,18918,18918,189R-squared.00607.0595.077
ControlsNo
Yes
Yes
Gaming preference dummies
No
No
Yes
Prefecture FE
Yes
Yes
Yes
Notes: Results show the correlation between the psychological distress measure (K6) and the having PS5 dummy
from equation (1). Control variables include age, gender, marriage dummy, dummy indicating having child(ren),dummy indicating divorced, dummy indicating student, dummy indicating stay-at-home wife/husband, dummy
indicating self-employed, dummy indicating part-time employed, and dummy indicating jobless. Standard errors
are clustered by prefectures. ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1.

However, this method to measure the impact of video games on mental health has one
challenge: selection bias. People who have PS5 are likely to be different from those who do nothave PS5 in terms of individual characteristics, family background (as shown in Appendix Table
A-2), or other ways that are unobservable. Indeed, Table 3 shows that the coefficient of β inequation (1) varies depending on the control variable, especially on the existence of gaming
preference. Therefore, we need to find a way to differentiate the causal impact of video games
on mental health, even though there are positive correlations between K6 and Haveps5.

3.2. Estimates based on natural experiment
In response to the challenge, we examine the impact of video games (PS5) on themental health measured by K6 in equation (2), exploiting the natural experiment. The results inTable 4 indicate statistically insignificant relationships between the result of the lottery andmental distress, which is different from the previous studies and the results in Table 3, while weadmit that the decrease of the sample size would reduce the precision of the estimation. Firstly,
Column (1) describes the results of equation (2) without control variables, which wasstatistically insignificant. Secondly, in Column (2), we run the regression with control variables

18except jointotal to check how it affects the results because of potential endogeneity between the
result of the lottery and the number of joining the lottery, as seen in section 2.3. Thirdly, Column(3) describes the results of the regression, including jointotal.
Notably, the results in Table 4 Columns (2) and (3) show a statistically insignificant
relationship between winning PS5 and psychological distress. We do not see a huge difference inthe estimates of coefficient β between with (0.462) and without (0.474) the number of joining
the lottery. Thus, the estimates of the coefficient of the variable of interest (1 if won PS5 lottery)
do not change significantly despite the inclusion of the potentially endogenous variable,
jointotal.

Table 4. The impact of video games on mental health estimated by exploiting lottery(1)
VARIABLES
1 if won PS5 lottery

(2)

(3)

Kessler Psychological Distress Scale (k6): 0~24.546(.448)# of times joined lottery.474(.421).462(.437).0145(.0623)Observations
1,334
1,334
1,334
R-squared
.0455
.103
.103
Controls
No
Yes
Yes
Prefecture FE
Yes
Yes
Yes
Mean
5.86
5.86
5.86
Notes: Results show the ITT estimates from equation (2). Control variables include age, gender, marriage dummy,
dummy indicating having child(ren), dummy indicating divorced, dummy indicating student, dummy indicating
stay-at-home wife/husband, dummy indicating self-employed, dummy indicating part-time employed, dummy
indicating jobless, and gaming preference dummies of scale 1~5 for each. Standard errors are clustered by
prefectures. ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1.

There are some possible explanations for the insignificant estimates. Firstly, theprevious studies have overestimated the impact of video games due to bias such as selection
bias. Secondly, an important motivation for people to purchase PS5 is to play games online tospend time with friends under the COVID-19 lockdown policy. Such a social connection
generated through PS5 might mitigate the negative impact of video games on psychologicaldistress (Shaw and Gant 2002). Thirdly, the number of samples for this analysis (n = 1,334) is19

smaller than that of the samples for our OLS (n = 18,189). Smaller sample size would increase
standard errors, thus may cause insignificant results.
We also admit that our study design targets a limited population. The main target
population is those who joined and got the result of the lottery (n = 1,334). It does not include
those who did not join the survey (n = 16,708). Indeed, there are significantly different baselinecharacteristics between people who joined the lottery and people who had never joined thelottery in Appendix Table A-2. For example, we see a significant difference in some variables
such as K6 (-2.068) and gaming preference.
To adjust for the selection into the PS5 lottery, we employ the IPW method. Table 5shows the results of our IPW regressions. Columns (1), (2), and (3) indicate no significant
impact of video games on mental health, consistent with the results in Table 4. The structure of
Table 5 is analogous to that of Table 4. First, Column (1) describes the results without control
variables, which were statistically insignificant. Then, in columns (2) and (3), we run the
regression with control variables. The difference between Columns (2) and (3) is whether the
results include variable jointotal. The results in Table 5 Columns (2) and (3) show a statisticallyinsignificant relationship between the result of the lottery and stress again.

20

Table 5. The impact of video games on stress through the lottery adjusted by IPW
(1)
(2)
(3)
Kessler Psychological Distress Scale (k6): 0~24

VARIABLES
1 if won PS5 lottery

.594
(.58)

.433
(.48)

# of times joined lottery.431(.499).00348(.0783)Observations
1,334
1,334
1,334
R-squared
.0572
.139
.139
Controls
No
Yes
Yes
Prefecture FE
Yes
Yes
Yes
Mean
5.86
5.86
5.86
Notes: Results show the ITT estimates adjusted by inverse probability weighting. Control variables include age,gender, marriage dummy, dummy indicating having child(ren), dummy indicating divorced, dummy indicating
student, dummy indicating stay-at home wife/husband, dummy indicating self-employed, dummy indicating parttime employed, dummy indicating jobless, and gaming preference dummies of scale 1~5 for each. Standard errors
are clustered by prefectures. ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1.

To estimate the causal effect of having PS5 on mental health, we run the instrumental
variable (IV) regression, using the dummy indicating winning the lottery as an instrumental
variable. In this setting, our main explanatory variable is whether people have PS5 (havePS5)—
same as equation (1). Table 6 shows the results of the IV analysis. Specifically, we run the
regression without the IPW method in Columns (1) and (2) while the results based on the IPW
method are shown in Columns (3) and (4). Columns (1) and (3) describes the results without
control variables, while Columns (2) and (4) show the results with control variables.
Columns (1) through (4) of Table 6 indicate no significant impact of video games onmental health, still consistent with the results in Tables 4 and 5. The magnitude of the point
estimates is smaller than those from OLS shown in Table 3. Note that the IV method provides
less efficient estimates than OLS, which might affect our results. Still, we find the coherency of
the results through the reduced form, the IPW, and the IV methodology.
The first stage regression results are shown in Appendix A-4. The F-stats for the weakIV test, which is shown in Table 6, is high enough. Thus, the weak IV problem is not a big
concern for our IV analysis.

21

Table 6. IV results(1)

VARIABLES

1 if having PS5

(2)

(3)

(4)

Kessler Psychological Distress Scale (k6): 0-24

.91(.764).777(.749)

1.02(1.02).721(.848)

Observations
1,334
1,334
1,334
1,334
Controls
No
Yes
No
Yes
Prefecture FE
Yes
Yes
Yes
Yes
IPW
No
No
Yes
Yes
Mean
7.776
7.776
7.776
7.776
Kleibergen-Paap rk Wald F statistic
524
498.3
199.6
303
Notes: Results show the IV estimates with and without inverse probability weighting. Control variables include age,gender, marriage dummy, dummy indicating having child(ren), dummy indicating divorced, dummy indicating
student, dummy indicating stay-at-home wife/husband, dummy indicating self-employed, dummy indicating parttime employed, dummy indicating jobless, and gaming preference dummies of scale 1~5 for each. Standard errors
are clustered by prefectures. ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1.

4. Conclusion
The objectives of the present study were to investigate whether video games negatively
affect mental health under COVID-19 containment policies. The present study focuses on alottery to purchase PS5—a well-known console for online communication through its
software—and uses it as a natural experiment to measure the impact of video games on mental
health. Based on previous findings from observational studies within the existing literature(Giardina et al. 2021; Kato, Shinfuku, and Tateno 2020; Oka et al. 2021), we hypothesized that
our sample would show a negative impact of video games on mental health.
Contrary to our hypothesis, the results of our study indicated that video games have nosignificant negative effects on people's mental health. One of the possible explanations is thatthe previous studies have overestimated the impact due to bias such as selection bias. We also
adjust for the selection into the lottery by applying the IPW method; the results are consistent
with our primary results. Our IV estimation for robustness checks also supports these results.

22

The effectiveness of our finding would probably not be limited under the COVID-19
pandemic situation where people are forced to communicate online under the lockdown policies.
Some of the changes in people's lifestyle would not return to the past. If so, finding stressors
under the new lifestyle would stay a field of academic or public interest. Thus, our results could
potentially be the key to understanding the relationship between video games and mental healthfor future studies.

4.1. Policy Implication
As concisely discussed in section 1, how to deal with stress has been one of the coreinterests among policymakers, especially after the COVID-19 pandemic, because the magnitudeof psychological distress (k6) increases under the lockdown policy. For example, thegovernment provides hotline services for people who suffered from mental health. For
policymakers to form proper measures to tackle this issue, it is essential to understand themechanism that induces stress among people. If policymakers misunderstand the mechanism,
they will spend valueless money to improve the problem.
This paper contributes to policymakers having a proper understanding of therelationship between video games and mental health. Without this paper, policymakers would
imagine that video games increase stress based on previous studies' findings. Thismisunderstanding would encourage them to regulate the activity regarding video games. For
example, Kagawa prefecture in Japan places an obligation on parents to make rules at home andmake efforts to have their children comply with the guideline that those under 18 should use
video games for no more than 60 minutes a day (90 minutes on school holidays) and stop using
smartphones by 9 p.m. for junior high school students and younger and by 10 p.m. for all others
to avoid the addiction on video games (IT media news 2020). Our findings would assist the
government to make wiser policy decisions on regulating people's activities of gaming.

23

In addition, video games play a significant role as a tool for online communication.
Indeed, some local governments in Japan and the U.S. employ video games in education such ase-Sports, where students play video games as a part of sports (Impress Watch 2020; New YorkState 2019). Therefore, it is essential to have a proper view of video games on mental healthwhen the authorities account for the value of the programs to stakeholders such as parents. Thus,
the results of the present study help policymakers formulate good educational policies based ona less biased idea about the impact of video games on mental health.

4.2. Limitations
Despite the results that we find in this study, we need to acknowledge some limitations
in our research. First, due to a limitation of the survey method, we could not capture the durationof the playing game. Instead, we focus on the condition that people have PS5. As discussed insome of the previous research, addiction to video games occurs depending on the duration ofplay. We can solve this issue by including the question asking about the duration of the play inthe future survey so that the result shows the impact of the duration of playing video games onmental health.
Second, this study focuses on the impact of PS5 on mental health, whereas many
consoles such as Nintendo Switch also provide a platform for online communication. Whilefocusing on PS5 because people need to join the lottery to purchase PS5, we need to find a way
to estimate other consoles' impact.
Third, this study uses cross-sectional data and cannot conclude the long-term effects ofvideo games on mental health. Therefore, we cannot infer how playing games over time could
contribute to a change of stress. Repetition of the survey over time is a solution to this challenge.
Fourth, our approach of exploiting the natural experiment can only utilize a smaller
number of samples compared to an approach using the full sample. This leads to less precise

24estimates by its design. This can be a reason for obtaining insignificant estimates. At least, we
attempt to adjust the selection into a smaller number of samples by using the IPW method.
Finally, the randomness of the natural experiment is far from perfect. One can join thelottery multiple times, and many different stores conduct the lottery. This mechanism of thelottery would create room for finding a better opportunity to win the lottery. A field experimentof exogenously creating variation in playing video games in a sample population would generate
more reliable data.

25

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29

Appendix
Table A-1. Distribution of K6 in our sample
K6
score
0

# of
Percent Cumulative(%)
Observation
5641
31.01
31.01

K6
score
13

# of Observation Percent

Cumulative(%)

419

2.3

86.01

1

1201

6.6

37.62

14

366

2.01

88.02

2

1120

6.16

43.77

15

328

1.8

89.82

3

877

4.82

48.6

16

301

1.65

91.48

4

844

4.64

53.24

17

257

1.41

92.89

5

777

4.27

57.51

18

330

1.81

94.71

6

942

5.18

62.69

19

201

1.11

95.81

7

630

3.46

66.15

20

173

0.95

96.76

8

604

3.32

69.47

21

112

0.62

97.38

9

548

3.01

72.48

22

95

0.52

97.9

10

506

2.78

75.27

23

75

0.41

98.31

11

501

2.75

78.02

24

307

1.69

100

12

1034

5.68

83.7

Note: K6 is a scale of psychological distress, where there are 6-item questions in the questionnaire that measures thescale of nonspecific psychological stress in the past 30 days and scores the degree of the stress from 0 (the lowest)to24(thehighest).
Detailed
informationisprovidedbyNational
Commodity
Survey(https://www.hcp.med.harvard.edu/ncs/k6_scales.php).

30

Table A-2. Characteristics of people who join the lottery and people who do not join.

Variable
Outcome variables
Kessler Psychological Distress Scale (k6): 024
Measure of happiness: 5-35
Basic characteristics
Age

N

(1)
Not joined
lottery
Mean/SE

16708
16708

16708

Gender (Male = 1)

16708

Married (=1)

16708

Have child(ren) (=1)

16708

Divorced (=1)

16708

Student (=1)

16708

Stay-at-home wife/husband (=1)

16708

Full-time employee (=1)

16708

Self-employed (=1)

16708

Part-time employee (=1)

16708

No job (=1)

16708

Gaming preference (Heavy player=1 ~ Nogamer=5) = 1

16708

DifferenceN(2)
Joinedlottery
Mean/SE

5.707

1334

7.776

-2.068***[0.048]17.046[0.050]1334[0.197]
16.468[0.183]0.578***

36.669[0.122]0.493[0.004]0.546[0.004]0.472[0.004]0.059[0.002]0.039[0.001]0.133[0.003]0.493[0.004]0.078[0.002]0.151[0.003]0.105[0.002]

1334

0.197

1334

1334
1334
1334
1334
1334
1334
1334
1334
1334
1334

[0.003]Gaming preference (Heavy player=1 ~ Nogamer=5) = 2

16708

0.241

16708

0.246

1334

16708

0.245

1334

16708

0.071

1334

0.001
0.001
0.009
-0.0090.054***-0.141***-0.001
0.061***
0.036***-0.294***0.250-0.0080.140

0.106***

0.101

0.143***[0.008]1334[0.002]
F-test of joint significance (F-stat)F-test, number of observations***, **, and * indicate significance at the 1, 5, and 10 percent critical level.

31-0.117***[0.010][0.003]Gaming preference (Heavy player=1 ~ Nogamer=5) = 5

1.538***[0.012][0.003]Gaming preference (Heavy player=1 ~ Nogamer=5) = 4

0.490(1)-(2)[0.014][0.003]Gaming preference (Heavy player=1 ~ Nogamer=5) = 3

35.131[0.392]0.610[0.013]0.545[0.014]0.471[0.014]0.049[0.006]0.048[0.006]0.079[0.007]0.634[0.013]0.079[0.007]0.091[0.008]0.070[0.007]t-test0.019

0.053***[0.004]60.602***
18042

Table A-3. Estimation results for the non-lottery-joining probit model.
(1)
VARIABLES
Age-.0016(.000998).166***(.0315).000136(.0726)-.247***(.0536)-.121**(.0555)-.315***(.049)-.316***(.0549)-.445***(.0371)-.732***(.0417)-.878***(.0451)-1.06***(.0864)-.907***(.0469)Gender (Male=1)
Student (=1)
Stay-at-home wife/husband (=1)
Self-employed (=1)
Part-time employee (=1)
No job (=1)
Gaming preference (Heavy player=1 ~ No gamer=5) = 2
Gaming preference (Heavy player=1 ~ No gamer=5) = 3
Gaming preference (Heavy player=1 ~ No gamer=5) = 4
Gaming preference (Heavy player=1 ~ No gamer=5) = 5
Constant

Observations
18,042
Notes: Standard errors are in parentheses. ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1.

Table A-4. First stage regression results.

VARIABLES
1 if won PS5 lottery(1)
1 if having
PS5(2)
1 if having
PS5(3)
1 if having
PS5(4)
1 if having
PS5.599***(.0262).594***(.0266).585***(.0414).598***(.0344)

Observations
1,334
1,334
1,334
1,334
Controls
No
Yes
No
Yes
Prefecture FE
Yes
Yes
Yes
Yes
IPW
No
No
Yes
Yes
Mean
.16
.16
.16
.16
Notes: First stage regression results for the IV analysis with and without inverse probability weighting are shown.
Control variables include age, gender, marriage dummy, dummy indicating having child(ren), dummy indicating
divorced, dummy indicating student, dummy indicating stay-at-home wife/husband, dummy indicating selfemployed, dummy indicating part-time employed, dummy indicating jobless, and gaming preference dummies ofscale 1~5 for each. Standard errors are clustered by prefectures. ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1.

32

The Society of Regional Policy, Takasaki City University of Economics
1300, Kaminamie-machi, Takasaki-city, Gunma 370-0801 Japan+81-27-344-6244
c-gakkai@tcue.ac.jp
http://www1.tcue.ac.jp/home1/c-gakkai/dp/dp21-01