Discussion Paper Series

2022-01"Video game play positively affects well-being: evidence froma natural experiment in Japan"

Hiroyuki Egami, Takahisa Wakabayashi, and Tsuyoshi Yamamoto,

October 19, 2022

Discussion Papers can be downloaded:
http://www1.tcue.ac.jp/home1/c-gakkai/dp/dp22-01

Video game play positively affects well-being: evidence from anatural experiment in Japan
Hiroyuki Egami1* , Takahisa Wakabayashi2 , Tsuyoshi Yamamoto3
1 College of Economics, Nihon University, Tokyo, Japan
2 Faculty of Regional Policy, Takasaki City University of Economics, Gunma, Japan
3 Department of Policy Studies, National Graduate Institute for Policy Studies, Tokyo,Japan* phd15410@grips.ac.jp

Abstract
The number of people who play video games has become close to three billion during
the COVID-19 pandemic. Yet, video games’ causal effect on well-being has been
understudied, and most studies have relied on correlation. We select k6 (mental health)and SWLS (life satisfaction) for measuring well-being and apply multiple causal
inference methods—regression, propensity score matching, anddifference-in-difference—to provide evidence of the game effect. We take advantage of anatural experiment where lotteries for purchasing video game consoles—the Nintendo
Switch and the PlayStation 5—created a random variation in access to video games.
Using online surveys with 100,000 responses from Japanese aged 10-69 in the COVID-19
pandemic, we find that playing video games impacts users’ well-being positively.
Winning the Nintendo Switch lottery positively affects mental health by 0.2 SD, while
the PS5 lottery’s positive effect is 0.1 SD. Additionally, winning the PS5 lottery has apositive impact of 0.2 SD on life satisfaction. The instrumental variable approachindicates that an additional hour per day of video game play—caused by purchasing a
new video game console—has a positive impact on mental health by 0.2 SD and life
satisfaction by 0.3 SD. A Machine learning method reveals a striking difference between
the heterogenous effects of the Nintendo Switch and the PS5. While the PS5 effect issmaller for teenagers, those married, non-gamers, and females, such heterogeneity is notfound for the Nintendo Switch effect.

Introduction

1

The COVID-19 pandemic has brought video games into the new spotlight as a leisureactivity compatible with social distance policies. The number of people who play games
has become historically high, close to three billion people worldwide, and the quantityof play increased [1]. Yet, the increase in video game engagement has also created astronger concern about the possibility that games may negatively affect health (forexample, [2]). In particular, policymakers, researchers, and public stakeholders have
paid special attention to game addiction and potential harm to well-being or mental
health.
Video games have appeared to the public with a negative image. Widely contested
health policy decisions, including a recent discussion on the gaming disorder by the
WHO (World Health Organization), have boosted a negative impression of gaming. The

October 4, 2022

1/29

2
3
4
5
6
7
8
9
10
11
12

inclusion of Gaming Disorder in the International Classification of Diseases (ICD-11)
has caused a significant stigma for millions of youths (and their caregivers) who playvideo games as a part of a normal life [3]. However, the current evidence base on theinfluence of game-play is inadequate.
There is a large amount of literature that studies how video games affect users (i.e.,aggression, addiction, well-being, cognitive functioning, and else). Aggression has
particularly attracted scholarly interest, but no conclusive evidence exists. In the last
decade, scholarly and policy makers’ interest has gradually shifted to the link between
video games and well-being (or mental health)[4].
The findings of the literature examining how video game play is associated withwell-being (or mental health) have been mixed. Negative associations are mostly
reported by observational studies[5, 6, 7, 8, 2], while a few laboratory experiments
studying the effect of violent games also report negative associations[9, 10]. Positive
associations are reported by both observational studies and laboratory experiments[11,12, 13, 14, 15, 16].
Importantly, the previous observational studies rely on correlation to draw policy
implications. Unfortunately, there is little empirical evidence on the causal links
between video games and well-being in the real world, not in the laboratory. For that,
one has to exploit a quasi-experimental situation that answers some essential questions(i.e., Do users play video games because they have stressful days, or does playing video
games induce stress?). However, finding a quasi-experimental situation satisfying
unconfoundedness is very difficult. This study fills the gap.

The present research

14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34

35

In this study, we utilize a natural experiment that creates a random variation in accessto video games. We use lotteries, in Japan, for video game consoles: Nintendo Switch
and PlayStation 5 (hereafter, PS5). One can purchase the video game console only if
one wins the lotteries (for Nintendo Switch or PS5, respectively). Therefore, whether or
not one owns Nintendo Switch and PS5 is determined nearly randomly.
To identify the causal effect, we employ multiple causal inference methods widelyused in economics, public health, or political science. In particular, we follow theempirical strategies used in a series of papers examining the effect of winning lotteries:
Imbens et al. (2001)[17] and Imbens (2015)[18]. Imbens uses two approaches: astandard regression analysis with control variables and a propensity score matching
method. Adding to those, we also show estimates of a difference-in-difference method
exploiting the panel structure.
We conducted multiple-round omnibus online surveys to the 10-65 aged population(n = approximately 100,000) of all 47 prefectures in Japan from December 2020 toMarch 2022 and created our data set. We collected the lottery-related information,
ownership of video game consoles, game play activities, and well-being measures (k6 andSWLS).
We collaborate with a gaming market research firm conducting a monthly survey ofvideo game players and non-players. As a result, we get access to the data of survey
respondent characteristics such as age, gender, household structure, job, and video
gaming preferences. This enables us to employ a machine learning technique to conduct
heterogeneity analysis and thereby explore the mechanism of the video game effect.
We find that playing video games impacts users’ well-being positively. Firstly,
winning the Nintendo Switch lottery positively affects mental health (measure: k6) by
0.2 SD, while winning the PS5 lottery positively affects mental health by 0.1 SD. As forhappiness (measure: SWLS), winning the PS5 lottery have a positive impact of 0.2 SD(SWLS of the Nintendo Switch was not collected due to the limited budget). Secondly,
winning a new video game console lottery increases the time spent playing games by 0.5

October 4, 2022

13

2/29

36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63

hours. Thirdly, playing video games for an additional hour per day responding topurchasing a new video game console positively affects mental health by 0.2 SD andhappiness by 0.3 SD.
We show the robustness of the above findings by multiple causal inference
methods—standard regression, propensity score matching, and difference-in-difference.
Moreover, the placebo tests recommended in Athey and Imbens (2017)[19] support theunconfoundedness.
The machine learning approach reveals the heterogeneous impact of playing video
games and contrasts the effect of the Nintendo Switch and PS5 on well-being. Firstly,
the estimated individual treatment effects suggest that video games negatively affect a
few people’s well-being. Secondly, we find a striking difference between the Nintendo
Switch and PS5 effects. The PS5’s positive effect is smaller for teenagers, non-gamers,
females, and caregivers (or households with children). In contrast, the Nintendo Switch
does not have a differential impact on gender and the household structure. Moreover,
the Nintendo Switch effect is larger for non-gamers.

Literature Review

65
66
67
68
69
70
71
72
73
74
75
76
77
78

79

This paper contributes to the scientific literature that explores how video games affectusers. Researchers and policymakers have been interested in the well-being (or mental
health) outcomes of video games. However, previous studies including observational
studies and laboratory experiments have three methodological challenges. First, lack ofevidence on the causal link between video games and well-being has been a problem[20,21]. Nearly all observational studies rely on correlation analysis using either
cross-sectional or longitudinal data. Even if one shows a negative correlation between
video game play and well-being, little can be known about the causal relationship andthereby policy implications. Second, laboratory experiments, which have provided mostof the causal evidence in this field, have not been tested the external validity andexposed to criticisms[22, 23, 16]. Typical studies that invite participants to play video
games for a short time are far from capturing the play as it occurs naturally.
Particularly, game play in recent days is associated with online communications (i.e.,voice chat, in-game text chat, Twitter, and Discord) and such an environment isobviously difficult to imitate in a laboratory. Third, most observational studies rely on
self-reports of game play behavior and well-being measures, which could be inaccurate
and potentially biased.
Two recent outstanding studies that examine the link between video games andwell-being (one is published[16] and the other is preprint[23]) attempt to overcome thechallenges, in particular, the third problem: self-report. The studies utilize game playadministrative data provided by video game companies and thereby capture accurate
video game play time, though well-being is reported through surveys. They show thatvideo game play time is positively correlated with well-being and—in terms of causal
relationship—does not negatively affect well-being. Yet, there are some drawbacks tousing video game administrative data. Firstly, the response rate of their survey is low atabout one percent, thus the implications are drawn from a limited sample. Secondly,
the data does not contain game players’ individual and socio-contextual variables (i.e.,age, gender, household structure, and geographic information of residence), possibly
because of obtaining very sensitive private information—accurate video game play time.
The drawbacks are not negligible because the literature has revealed that the video
game effect depends on such ’outside variables’[24].
Complementing their studies, we attempt to overcome the challenges, in particular,
the first and second problems: lack of causal evidence and external validity. Firstly, thisstudy exploits a natural experiment and applies multiple methods of causal inference

October 4, 2022

64

3/29

80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113

and thereby shows robust causal evidence, while the two studies show cross-sectionalcorrelation or structural model-based estimates. Secondly, collaborating with a video
game research firm, we use a large survey data covering a population of wide-ranged
socio-contextual backgrounds. Moreover, our survey data captures the video game play
as it occurs naturally; those benefits would increase the external validity of our study.
The motivation of our study is also close to that of Cunningham, Engelstatter, andWard (2016) [22]. The study examines the effect of violent games on committing violent
crimes in the real world and does not find a significant effect. Though someexperimental studies have found evidence suggesting that violent games increase
aggression, Cunningham et al. (2016)’s quasi-experimental approach reveals that such a
causal relationship may not exist in the real world. Our approach is similar in the sensethat we attempt to identify the causal relationship between video game play andwell-being in the real world.

115
116
117
118
119
120
121
122
123
124
125
126

Materials and methods

127

Study setting

128

After the first debut in 2017 of the Nintendo Switch and 2020 of the PS5, both video
game consoles have been globally well-known. The global sales reached 100 million for
the Nintendo Switch and 10 million for PS5 in 2022. The number of players increased
during the COVID-19 pandemic, and the game consoles have been used as online
communication devices with friends, or family members [25].
Due to the limited supply of semiconductors for the Nintendo Switch and PS5,
people in Japan have been rarely able to purchase the game consoles by a standard
method such as online shopping or shopping on-site. Hence the major way to buy thegame consoles has been to join lotteries managed by retailers, otherwise one has to seek
the game consoles in second-hand markets. Yet, one needs to pay a large amount of
extra costs. While the supply-side problem of the PS5 is still ongoing, the problem of
the Switch was resolved by the beginning of 2021. See Appendix E for the price history
of PS5.
To join the lotteries, people first register their information such as name and email
address at retailers’ websites (or on-site registrations are sometimes open). After a few
weeks, if one wins a lottery, one obtains the right to purchase the game console. Someretailers require being a member of their member clubs to join the game console
lotteries, yet the membership is open to anyone.

Data

129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146

147

Our data is collected through 5-round online surveys obtaining answers from around
15,000-20,000 respondents for each. We contacted 25,000-50,000 people each time, thus
the response rate is approximately 60 percent. We designed surveys measuring
respondents’ mental health (Kessler 6 scale, k6) and happiness (Satisfaction with Life
Scale, SWLS), used in previous studies [6, 26]. We also collected self-reported play timeand information about lotteries of the Switch and PS5. The survey schedule is shown in
Fig 1. Due to a limited budget, we could not measure all the variables listed above inevery round. In particular, as for the Switch, we could not collect SWLS andself-reported play time. See Appendix F for more details on the outcome variables
collected in each round.
We conducted omnibus surveys with a gaming market research comapny: thegameage R&I (GRI). The GRI regularly conducts monthly surveys—collecting
information related to video games—to pre-registered people via the Cross Marketing (aOctober 4, 2022

114

4/29

148
149
150
151
152
153
154
155
156
157
158
159
160

Fig 1. Survey schedule and video game consoles’ lotteries.
Red shadow indicates the period of the State of Emergency in Japan due to the COVID-19 pandemic. Blue shadow indicates
the Quasi State of Emergency. Source: https://www.worldometers.info/coronavirus/country/japan/

survey agency); we added some questions to five rounds of their surveys (December
2020, February, May, November 2021, and March 2022). Thus, the number of samples isdetermined by the GRI, not by us. As a result, the number of samples differs for eachsurvey; more details can be found in Appendix J. In addition, the respondents’characteristics such as age, gender, marital status, job status, and gaming preference areprovided by the research companies. Gaming preference is a measure created andcalculated by the GRI. The GRI measures the gaming preference of respondents in
November every year. Time spent playing games and other factors indicating gameengagement are used for calculation. See Appendix D for more details about the gaming
preference.
Our survey, contacting randomly selected samples from the pool of approximately
150,000 potential respondents (see more details of the sampling procedure in AppendixB), is close to a repeated cross-sectional survey with some important differences. Firstly,
we have the identifiers of samples, thus we can construct a panel structure for the people
who joined our survey more than once. Secondly, once a year—usually from October toDecember—the research companies refresh their pool of samples, though they do not
replace all the samples. Such a limited panel structure is used for robustness checks.

162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177

Measures (See Appendix A for more details)

178

Well-being measure 1: mental health179

We assessed well-being from two aspects. Firstly, we measured mental health with ascale of psychological distress, k6 [27]. K6 is composed of 6-item questions that measure
the scale of nonspecific psychological distress in the past 30 days and score the degree ofthe psychological distress from 0 (the lowest) to 24(the highest).

October 4, 2022

161

5/29

180
181
182
183

Well-being measure 2: life satisfaction

184

Secondly, we measured well-being with a scale of life satisfaction, SWLS [28]. The
SWLS consists of five statements about judgement of respondents’ life, scored on a scaleranging from 1 (completely disagree) to 7 (completely agree). The sum scores thedegree of life satisfaction from 5 (the lowest) to 35 (the highest).
Video game engagement measure 1: Nintendo Switch

186
187
188

189

Respondents reported whether their household had a Nintendo Switch and whether
he/she played it during the last 30 days.

190
191

Video game engagement measure 2: PS5

192

Respondents reported whether their household had a PS5 and whether he/she played it
during the last 30 days.
Video game engagement measure 3: Self-reported play time

193
194

195

Respondents also reported how much total time they spent playing video games on theweekdays (and in another question, the weekends) over the past 30 days. We separately
asked i) the time spent playing smartphone games and ii) video games on TV/computer.
This is not limited to playing the Nintendo Switch or PS5. Additionally, we asked about
the time spent for iii) web browsing, SNS, and video apps such as Youtube as far as it isrelated to video games.

196
197
198
199
200
201

Empirical strategy

202

Reduced form analysis

203

Using a dummy indicating whether people have PS5 or not as a variable of interest
causes endogeneity. Instead, we exploit the quasi-experimental situation occurred due tothe supply-side problem of the Nintendo Switch and PS5. We assume unconfoundednessof the video game consoles’ lotteries and thereby use a dummy variable indicating
whether one won lotteries or not as a treatment variable and conduct regular regression
analysis. We employ equation (1) as our baseline specification to measure the causal
impact of winning game console lotteries, following Imbens et al. (2001)[17]. It is worth
noting that the target population of our analysis is the people who joined the video
game consoles’ lottery (n = 1773 for the Nintendo Switch, 6419 for the PS5).

Yip = βlotteryip + ψXip + ϕjoinip + αp + ϵip

204
205
206
207
208
209
210
211
212

(1)

where Yip is an outcome of interest such as well-being measures for an individual i inprefecture p. lottery is a dummy taking 1 if an individual wins the lotteries and 0
otherwise. The coefficient β is the parameter of interest. Xip is a set of control variablesconsisting of individual characteristics, including age, gender, marital status, job status,
whether having children or not. We also include gaming preference as control variables:
dummies of each scale (heavy gamer: 1, middle gamer: 2, light gamer: 3, super lightgamer: 4, non-gamer: 5). joinip is the number of times of joining the lotteries for thehousehold of individual i (see more details about questions regarding lotteries inAppendix C). αip are prefecture dummies.

October 4, 2022

185

6/29

213
214
215
216
217
218
219
220
221

Propensity Score Matching (PSM)

222

Following Imbens (2015)[18]—Imbens revisits the ”lottery data” used in Imbens et al.
(2001)[17] as a good example to apply the propensity score matching method—we use a
PSM approach to estimate the ATT effect in terms of winning the lotteries. We follow
the recommendation of Imbens (2015) to select variables for matching and construct a
subsample of the original data set. While the linear regression approach relies on
assumptions of linearity and extrapolations beyond observed variable combinations tocorrect for differences between treatment group and control group, the PSM approach
does not rely on such parametric assumptions. More detailed specification is found in
Appendix M.
Fixed effect difference-in-difference

225
226
227
228
229
230
231

233
234

(2)

where γi are individual fixed effects and µt are time (round) fixed effects. The rest of
the variables are as defined above.

236

Instrumental variable regressions

237

We can also treat the lottery data as instruments and employ standard IV methods tocontrol for the endogeneity of Nintendo Switch/PS5 users. As excluded instruments, we
use the dummy indicating whether one wins a lottery for the Nintendo Switch and PS5,
respectively.
Assumption checks

235

238
239
240
241

242

To conduct a valid causal inference, we have to assume that there is no self-selection
into winning Switch/PS5 lotteries. The biggest threat to our identification strategy is
the number of times that one participated in Switch/PS5 lotteries because the moretimes that one participates in the lotteries, the more likely one is to win. To mitigate
the problem, following the literature [17], we control for the number of times
participating in lotteries in Eq 1. By checking whether controlling for such covariates
affects the estimate or not, one can see how far the self-selection problem is serious.
Moreover, we conduct PSM and compare a sub-sample whose characteristics are similar,
following Imbens (2015) [18].
Table 1 presents the comparison of summary statistics for the covariates for lottery
winners and non-winners of the PS5 lotteries. Following the literature [18], we show thenormalized difference (or standardized difference) between the two groups. The
differences are small, with only one out of 30 normalized differences larger than 0.30 inabsolute value. That is the number of times joining lotteries, which is expected to belarger for lottery winners. Considering that the threshold of normalized differences forchecking the balance after using PSM is 0.1[29], one can find the characteristics are
well-balanced, even before using PSM. A similar comparison table for the Nintendo
Switch lotteries is shown in Appendix G, and the characteristics are also well-balanced.
Heterogeneity analysis

243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260

261

We employ a machine learning algorithm called causal forest developed by Wager andAthey (2018)[30], predicting treatment effects on each individual based on theirOctober 4, 2022

224

232

We use a difference-in-difference approach with individual fixed effects to test
robustness. We use the following specification:
Yipt = βlotteryipt + ϕjoinipt + γi + µt + ϵipt

223

7/29

262
263

Table 1. Balance table for PS5 lottery winners and non-winners.
Variable# of times joined lottery

N
5022

Age

5022

Gender (Male = 1)

5022

Respondent/household head is married (=1)

5022

Respondent/household head is divorced (=1)

5022

Respondent/household head has child(ren) (=1)

5022Student (=1)

5022

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

5022

Full-time employee (=1)

5022Part-time employee (=1)

5022

Self employed / other types worker (=1)

5022Unemployed / currently not study (=1)

5022

Gaming preference: Heavy gamer=1

5022

Gaming preference: Middle gamer=1

5022

Gaming preference: Light gamer=1

5022

Gaming preference: Super light gamer=1

5022

Gaming preference: Non-gamer=15022

Job: Civil engineering, Construction, Real estate, Housing and building services

5022

Job: Daily necessaries, Textile and apparels, Cosmetics, Food and Beverages=15022

Job: Manufacturing=15022

Job: Trading companies, Publishing, Printing, Mass media=15022

Job: Distributors, Retailers=15022

Job: Carriers, Warehousing, Logistics=1

5022

Job: Public works=1

5022

Job: Software and Information services=1

5022

Job: Banks and Financial services=1

5022

Job: Food services, Hairdressing, Cosmetology, Other Services=1

5022

Job: Medical care, Welfare=1

5022

Job: Education=1

5022

Job: Other industries and types of business=1

5022

(1)
Did not win PS5 lottery
Mean/SD
3.958[4.569]36.988[15.724]
0.619[0.486]0.593[0.491]
0.051[0.220]0.511[0.500]0.212[0.409]0.069[0.253]0.499[0.500]0.088[0.283]0.063[0.243]0.070[0.255]0.432[0.495]0.265[0.441]0.165[0.371]
0.097[0.296]0.041[0.199]0.065[0.246]0.070[0.255]0.134[0.340]0.037[0.188]0.046[0.210]0.045[0.206]0.054[0.226]0.067[0.250]0.036[0.187]0.104[0.305]0.063[0.243]0.035[0.183]0.067[0.249]

N
1397
1397
1397
1397
1397
1397
1397
1397
1397
1397
1397
1397
1397
1397
1397
1397
1397
1397
1397
1397
1397
1397
1397
1397
1397
1397
1397
1397
1397
1397

(2)
Won PS5 lottery
Mean/SD
5.555[5.735]37.425[15.454]0.598[0.490]0.592[0.492]0.052[0.223]0.516[0.500]0.199[0.399]0.081[0.273]0.458[0.498]0.112[0.315]0.069[0.254]0.081[0.273]0.411[0.492]0.258[0.438]0.173[0.379]0.105[0.306]0.054[0.225]0.077[0.267]0.060[0.238]0.155[0.362]0.030[0.171]
0.038[0.191]
0.041[0.198]0.062[0.240]0.059[0.235]0.026[0.161]
0.096[0.295]0.059[0.236]0.033[0.179]0.066[0.248]

Normalized
difference(1)-(2)-0.326-0.0280.042
0.003-0.005-0.0100.032-0.0470.082-0.083-0.027-0.0420.042
0.016-0.022-0.024-0.060-0.0490.039-0.062
0.037
0.040
0.019
-0.034
0.034
0.055
0.027
0.015
0.009
0.003

Notes. Respondents or their household heads’ characteristics are used.

October 4, 2022

8/29

characteristics. This method, used in recent studies (i.e.,[31, 32]), allows flexible,
high-dimensional combinations of covariates to identify the video game effect on eachindividual. For this prediction, we focus on estimating conditional ATEs
(E(Y1 − Y0 |X = x)) capturing differences in lottery winners and non-winners.

264
265
266
267

Results and Discussion

268

Preliminary analysis

269

Before beginning our main analysis, as a preliminary analysis, we first check thecorrelation between playing video games and well-being. Many previous papers haveshown that video game engagement has a negative correlation with well-being measures.
We use multiple measures of game engagement: i) whether respondent households have
Nintendo Switch, ii) whether respondents play Nintendo Switch this month, iii) whether
respondent households have PS5, iv) whether respondents play PS5 this month, v)
video game play time (including any video game consoles and computer games). We use
the following specification:
Yip = βGameip + ζXip + αp + ϵip

270
271
272
273
274
275
276
277

(3)

where Gameip is a measure of video game engagement for individual i in prefecture p.
The rest of the variables are as defined above.
In Table 2, for each outcome variables, we show estimates of three specifications:
without control variables, with control variables except for gaming preference dummies,
and with a full set of control variables. By comparing the multiple estimates, we can see
how much the correlation is affected by the selection of control variables.
As expected, we find that specifications having less number of control variables tend
to show negative correlations between the video engagement measures and well-being
measures. One can see that that the correlation is strongly affected by control variables.
In particular, video gaming preference dummies strongly affect the estimates and insome cases reverse the sign. This sensitivity suggests that drawing policy implications
on video games based on correlation studies should be avoided.

278
279
280
281
282
283
284
285
286
287
288
289

Table 2. Correlation between game engagement and psychological distressscale.
(1)

(2)

(3)

.125
(.102).389***(.104)-.153(.105)

(4)

(5)

(6)

.452***(.128).522***(.129)-.16(.132)

(7)

(8)
k6

(9)

.46**
(.202).71***(.207).229(.195)

VARIABLES
1 if have Nintendo Switch
1 if play Switch this month
1 if have PS5
1 if play PS5 this month

(10)

(11)

(12)

.308
(.231).395(.245)-.276(.238)

Video Game play time (Hours/Day)

Observations
Controls
Gaming Preference
Prefecture FE
Mean

18,912
No
No
Yes
5.528

18,912
Yes
No
Yes
5.528

18,912
Yes
Yes
Yes
5.528

18,912
No
No
Yes
5.528

18,912
Yes
No
Yes
5.528

18,912
Yes
Yes
Yes
5.528

78,690
No
No
Yes
5.492

78,690
Yes
No
Yes
5.492

78,690
Yes
Yes
Yes
5.492

78,690
No
No
Yes
5.492

78,690
Yes
No
Yes
5.492

78,690
Yes
Yes
Yes
5.492

(13)

(14)

(15)

.218***(.0238).153***(.0213).0565**(.0245)42,022
No
No
Yes
5.205

42,022
Yes
No
Yes
5.205

42,022
Yes
Yes
Yes
5.205

Notes. Standard errors are clustered by prefectures. ∗ ∗ ∗p < 0.01, ∗ ∗ p < 0.05, ∗p < 0.1.
We also find that SWLS shows a consistent positive correlation with video gameengagement measures (shown in Appendix H). This is possible considering that some

October 4, 2022

9/29

290
291

previous studies have reported positive correlations between video game engagement
and well-being. Yet, whether this is causal or not has to be examined through causal
inference.

Reduced form analysis: Video game play time

P laytimeip = βlotteryip + ψXip + ϕjoinip + αp + ϵip# of times joined lottery.568***(.106).532***(.102).0375***(.0072).213*(.109).16(.107).0368***(.00677).128*(.0638).0937(.0662).0302***(.00792)

Observations
3,491
3,491
3,491
3,491
3,491
3,491
Controls
No
Yes
No
Yes
No
Yes
Prefecture FE
Yes
Yes
Yes
Yes
Yes
Yes
Mean
1.518
1.518
1.314
1.314
1.395
1.395
Notes. Standard errors are clustered by prefectures. ∗ ∗ ∗p < 0.01, ∗ ∗ p < 0.05, ∗p < 0.1.

296
297
298
299
300

301
302
303
304
305
306
307
308
309
310

(7)
(8)
Time for video game play
and web browsing.781***(.203).692***(.197).0744***(.0133)

3,491
No
Yes
2.831

3,491
Yes
Yes
2.831

Reduced form analysis: well-being measures

311

The estimates of the effects of winning the lotteries from our baseline specification(Eq 1) are shown in Table 4. We show the estimates taking k6 as an outcome variable incolumns (1)-(4) while we use the Nintendo Switch lotteries in columns (1)-(2) and thePS5 lotteries in columns (3)-(4), respectively. In columns (5)-(6), the estimates taking
SWLS as an outcome variable are shown. In contrast to the correlation estimates inTable 2, the estimated coefficients of interest in Table 4 only slightly change by
including control variables. The estimates for k6 in columns (1)-(4) consistently indicatethat winning the Nintendo Switch / PS5 lotteries improves mental health (orwell-being). Also, the estimates for SWLS in columns (5)-(6) indicate that winning thePS5 lotteries increases satisfaction with life.

October 4, 2022

294

(4)

where P laytimeip is video game play time for individual i in prefecture p. As theoutcome variables, we also test other variables such as online browsing time related tovideo games and smartphone game play time. lottery is a dummy taking 1 if an
individual wins the lotteries and 0 otherwise. The coefficient β is the parameter ofinterest. We control for a set of control variables explained before.
We show the estimated results in Table 3. As expected, winning the lottery increases
video game play time and online browsing time (though web browsing time ismarginally significant). In contrast, smartphone game play time is not affected. The
outcome variable for Columns 7 and 8, shown for reference, is a sum of that for columns
1-2 and 3-4. In a nutshell, the results support our argument.

Win the lottery

293

295

Purchasing new video game consoles (i.e., Nintendo Switch or PS5) would increase thetime spent playing video game. It may also affect the time spent online browsing
related to video games (i.e., Youtube and SNS). We first confirm those hypotheses andthereby show that the video game lottery effect is due to the purchased new video game
consoles. We use the following specification:

Table 3. Winning game console lottery increases video game play time.
(1)
(2)
(3)
(4)
(5)
(6)
Web browsing time Smartphone game
VARIABLES
Video game play time
related to gameplaytime

292

10/29

312
313
314
315
316
317
318
319
320
321

Table 4. Winning game console lottery improves well-being.
(1)
(2)
(3)
(4)
VARIABLES
k6: psychological distress scale
Win the lottery-1.43***(.246)

1.06***(.175)

1.01***(.166).0393**(.017)

Observations
1,773
1,773
6,419
6,419
6,419
Controls
No
Yes
No
Yes
No
Prefecture FE
Yes
Yes
Yes
Yes
Yes
Mean
8.249
8.249
7.202
7.202
16.867
Lottery
SwitchSwitchPS5
PS5
PS5
Notes. Standard errors are clustered by prefectures. ∗ ∗ ∗p < 0.01, ∗ ∗ p < 0.05, ∗p < 0.1.

6,419
No
Yes
16.867PS5# of times joined lottery-1.29***(.189).0961*(.0565)-.408*(.209)-.517**(.197).0502**(.0187)(5)(6)
SWLS: life satisfaction

PSM

322

We next use the PSM approach to estimate coefficients of the key variables estimated inTable 3 and Table 4. Both the estimates from the linear regressions and the PSM
approach are shown in Fig 2 and Fig 3. Note that the estimates are standardized by thestandard deviations of the samples for estimation. One can see that all the estimatesfrom the linear regressions and the PSM approach are close to each other.

Fig 2. Winning game console lotteries increases game play time.
Notes. PSM : Propensity Score Matching. Approximate standard errors are shown for PSM. Equation (1) is used for the
regression estimates. Regression standard errors are clustered by prefectures.

October 4, 2022

11/29

323
324
325
326
327

Fig 3. Nintendo Switch and PS5 positively affect well-being.
Notes. PSM : Propensity Score Matching. Approximate standard errors are shown for PSM. Equation (1) is used for the
regression estimates. Regression standard errors are clustered by prefectures.

Robustness checks

328

Placebo tests

329

We next do placebo analyses to assess the plausibility of unconfoundedness for the video
game consoles’ lotteries. As for the Nintendo Switch lotteries, we cannot conduct aplacebo test because we do not have pre-lottery outcomes. Thereby we do placebo
analyses for the PS5 lotteries.
We treat pre-lottery (lagged) outcomes for k6 and SWLS as pseudo-outcomes. We
use k6 and SWLS of round 2—the first round that we collected information on the PS5lotteries—as a pseudo outcome and run regressions using observations of rounds 3-5.
See Appendix F for the list of outcome variables collected in each round.
The estimated average causal effect on pre-lottery outcomes is shown in Table 5.
The mean estimates are small in magnitude and statistically insignificant, suggestingthat nonconfoundedness holds.
From the placebo analyses samples, we removed people who won PS5 lotteries inround 2. Otherwise, outcomes of people who were treated are included as
pseudo-outcomes. Notice that at the time of rounds 1 and 2 survey, the PS5 lotteries
were already open and people won PS5 lotteries (see Fig 1).
Individual panel regression

331
332
333
334
335
336
337
338
339
340
341
342
343
344

345

As a robustness check, we use a subsample of the data set and run regressions withindividual fixed effects—individual panel estimation—to estimate the PS5 lottery
winning effect (Eq 2). By construction, we restrict our sample to those who responded
more than two times and thus the number of observations become less than half. Recall
that our survey partly refreshes the sample every year. Further, the survey agency haveapproximately 150,000 potential respondents thereby the same respondents do not

October 4, 2022

330

12/29

346
347
348
349
350
351

Table 5. Assessing unconfoundedness for the lottery data: pseudo outcomes.
Outcome variables

k6 R2

SWLS R2(1)
Regression

(2)
PSM

(3)
Regression

(4)
PSM

Win the lottery.266(.528)-0.077(0.626)-.0211(.625)-0.236(0.5676)

Observations
Controls
Prefecture FE
Mean
Lottery

1,621
Yes
Yes
7.321
PS5

1,579
7.34
PS5

1,621
Yes
Yes
16.405
PS5

1,579
16.37
PS5

Notes. Regression standard errors are clustered by prefectures. Approximate standard
errors are shown for PSM. ∗ ∗ ∗p < 0.01, ∗ ∗ p < 0.05, ∗p < 0.1.

always receive survey offers. Therefore, what we can obtain from the longitudinal
regression is the ATE of a subsample with larger standard errors.
The results shown in Table 6 largely support the robustness of our findings. The
PS5 lottery winning increases the time spent playing video game and browsing theinternet and consequently improves the life satisfaction (SWLS). Yet, the estimates for
k6 is not statistically significant.

352
353
354
355
356
357

Table 6. Individual panel estimates of a subsample of the data set.
(1)

(2)

(3)

.192
(.363).166(.368).0274(.0267).582*(.295)

2,570
No
Yes
7.236

2,570
Yes
Yes
7.236

2,570
No
Yes
16.674

VARIABLES
Win the lottery

k6# of times joined lottery

Observations
Controls
Individual FE
Mean

(4)
SWLS

(5)
(6)
Video game play time

(7)(8)
web browsing time for game

(9)
(10)
Smartphone game playtime.547*(.302).0373(.0385)

1.3*
(.67)

1.18*
(.662).0467*(.0262)1.31*(.705)

1.25*(.683).0257(.0216)-.0695(.386)-.195(.372).0523*(.0278)

2,570
Yes
Yes
16.674

502
No
Yes
1.872

502
Yes
Yes
1.872

502
No
Yes
1.367

502
Yes
Yes
1.367

502
No
Yes
1.417

502
Yes
Yes
1.417

Notes. Standard errors are clustered by prefectures. ∗ ∗ ∗p < 0.01, ∗ ∗ p < 0.05, ∗p < 0.1.

Instrumental variable approach358

We estimate the causal impact of the video game engagement on well-being by the IV
approach exploiting lottery winning as an instrument. The estimated coefficients of
selected endogenous variables—video engagement measures—on k6 and SWLS are
shown in Fig 4 (A table with weak IV tests is found in Appendix K. Additional
outcome variables are tested in Appendix L). For example, the estimated coefficients of”Time for video game play and web browsing for game (Hour/Day)” indicate that one
hour per day of video game play and related web-use improves both the satisfying withlife and k6 by approximately 0.2 SD.
The estimates should be understood as upper-bounds, respectively, because
exclusion restriction does not seem viable. For example, after winning a lottery, one
increases time spent playing video game. At the same time, one would spend timebrowsing the internet and using Twitter in terms of topics related to video games. One

October 4, 2022

13/29

359
360
361
362
363
364
365
366
367
368
369
370

may also increase time spent using voice/video chat. All of those may contribute to theimprovement of well-being. If so, i) whether one has the video game console (of thelottery), ii) whether one plays the video game console, or iii) increased time spent forvideo game play, would probably not satisfy exclusiveness. We believe the sum of video
game play time and web browsing time (including Youtube, Twitter and otherSNS)—related to video games—is most comprehensive. Thereby, the estimates on thevariable is closer to a valid IV estimate than those on the rest of the variables.

371
372
373
374
375
376
377

Fig 4. The video game engagement improves well-being.
Notes. Standard errors are clustered by prefectures.

Heterogeneity analysis

378

We use the machine learning technique to analyze the heterogeneity of the effect ofplaying video games through examining the effect of lottery winning. We separately
examine the heterogeneity of the Nintendo Switch effect and the PS5 effect. We mainly
argue the heterogeneous effect on k6. We also examine the heterogeneity in terms of
SWLS, but for brevity figures are not shown; it is similar to that of k6.
We first show the distribution of the conditional ATEs estimated by causal forest in
Fig 5. The results indicate that video games positively affect most of the people’s
well-being, while a few people receive a small negative effect.

October 4, 2022

14/29

379
380
381
382
383
384
385
386

(a) Nintendo Switch(b) PS5

Fig 5. The video games positively affect most users’ well-being.
Notes. The conditional ATEs are estimated by causal forest.
We next show the heterogeneity in the video game effect due to some importantcharacteristics: age, gaming preference, gender, and household structure. In addition,
we argue the combination of those characteristics.
Age(b) PS5

Fig 6. Contrasting heterogeneous effect with age.
Notes. The conditional ATEs are estimated by causal forest.

October 4, 2022

388
389

390

The heterogeneity with age is shown in Fig 6. We find a striking difference between theNintendo Switch effect and the PS5 effect. While the PS5 is less beneficial forwell-being of teenagers, such a tendency is not found for the Nintendo Switch. One canfind the distribution of the conditional ATEs among students in 13.

(a) Nintendo Switch

387

15/29

391
392
393
394

Gaming preference395

The heterogeneity with gaming preference is shown in Fig 7. We find another striking
difference; while the Nintendo Switch is more beneficial for well-being of non-gamers,
the PS5 is more beneficial for heavy gamers.

(a) Nintendo Switch

396
397
398

(b) PS5

Fig 7. Contrasting heterogeneous effect with gaming preference.
Notes. The conditional ATEs are estimated by causal forest.
Gender399

The heterogeneity with gender is shown in Fig 8. We also find a difference; while theNintendo Switch is beneficial for both females and males, the PS5 is more beneficial formales.

(a) Nintendo Switch(b) PS5

Fig 8. Contrasting heterogeneous effect with gender.
Notes. The conditional ATEs are estimated by causal forest.

October 4, 2022

16/29

400
401
402

Household structure403

The heterogeneity with whether households have children is shown in Fig 9. While thePS5 is more beneficial for households without children, such a tendency is not found for
the Nintendo Switch effect.

(a) Nintendo Switch

404
405
406

(b) PS5

Fig 9. Contrasting heterogeneous effect with household structures.
Notes. The conditional ATEs are estimated by causal forest.
Correlation between the Nintendo Switch effect and the PS5 effect407

The heterogeneity analysis so far reveals that there is a striking difference between thepeople who receive a larger benefit—in improving well-being—from the Nintendo Switch
and those from the PS5. As a result, we find no correlation between the Nintendo
Switch effect and the PS5 effect (Fig 10). People who joined both the Nintendo Switch
lottery and the PS5 lottery is used for calculation (n=678).

October 4, 2022

17/29

408
409
410
411
412

Fig 10. No correlation between the Nintendo Switch effect and the PS5
effect.
Notes. The conditional ATEs are estimated by causal forest.

(a) k6 (psychological distress scale)(b) SWLS (Life satisfaction)

Fig 11. Correlation between the effects on game playtime and well-being.
Notes. The conditional ATEs are estimated by causal forest.

Conclusion

413

The present evidence base of video game effect on well-being has lacked causal evidence
in the real world. Using original survey data, we studied the impact of video game playwith two major video game consoles: the Nintendo Switch and PS5. The measures ofwell-being are k6 and SWLS. Exploiting a random variation created by video game

October 4, 2022

18/29

414
415
416
417

consoles’ lotteries, we found video games’ positive effect on well-being.
The heterogeneity of video game play effects has been understudied because causal
inference studies in the real world were scarce. We conducted causal inference andfound heterogeneity with age, gender, gaming preference, and household structures.
Moreover, we found a striking difference between the heterogeneity of the Nintendo
Switch effect and the PS5 effect.
Our findings contribute to a more comprehensive understanding of the mechanism of
Gaming Disorder. We find that video games affect a few people’s mental healthnegatively. This is consistent with the fact that there are a few people with Gaming
Disorder.
The heterogeneous video game impact with age and household structures implies
that the environment at home affects the video game effect. The positive PS5 effect ismore prominent for people living alone. This suggests that the environment in which one
can play the PS5 without being disturbed by others enhances the game effects the most.
This implies the importance of examining the video game play effect in the real world.
How game genre affects the video game effect has been examined only in laboratory
experiments, not in the field. In future work, using the data used in this paper, weintend to study how the type of games affects the effect on well-being in the real world.
This is difficult because the game purchase has self-selection bias (i.e., a specific kind of
person likes to play a particular game). Thereby, for example, an observational study
with a regression using a variable capturing game play time of a particular type of game
(i,e., action game) gives a biased estimate. We intend to overcome this challenge byusing respondents’ history of game purchases in our natural experimental setting.
Appendix A.

Measures.

419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440

441

k6 We use the Japanese translation of K6, following the literature such as
Yamamoto et al (2020) [33]. Given its brevity and high accuracy, the K6 is used forscreening for mental disorders in population-based health surveys in Japan [34].
SWLS We use the Japanese translation of the Satisfaction with Life Scale(SWLS), following the literature such as Sumino (1994) [35]. The SWLS is useful forevaluating the overall life satisfaction and valid measurement in Japan[36].
Work productivity The respondents reported self-assessment of their work
productivity, though we do not elaborate on it in the current draft. We use 9-item
questions. We find neither positive nor negative significant impact of playing video
game on work productivity.
Appendix B. Sampling process. Our data is not collected through a simple
random sampling procedure. The GRI uses sampling strata composed of gender, age,and gaming preference. Thus, our data is guaranteed to have a certain number ofsamples for each strata. Yet, the number of samples for each strata is not the same. As
the GRI’s purpose is collecting information about video games, GRI tries to obtain
more samples for people who like video games. As a result, our data tends to havelarger samples for younger people and smaller samples for non-gamers.
Appendix C. Lottery questionnaire. We collected the information on Nintendo
Switch/PS5’s lotteries by two types of questions. Firstly, we asked respondents: thetime that you, your household members, or relatives joined the lotteries to purchaseyour household’s video game consoles, respectively. We then sum the number for each

October 4, 2022

418

19/29

442
443
444

445
446
447

448
449
450
451

452
453
454
455
456
457
458

459
460
461
462

and use it as the number of times the respondent joined the lotteries. Secondly, we also
asked about the retailers (i.e., Amazon, Yodobashi Camera, and Sony Store) where anyof the household members joined the lotteries and the number of times for each. We
then also sum the number for each and use it as the total number of joining lotteries.
As expected, the figures for the total number of joining lotteries calculated by the twoquestions are similar. Therefore, through two types of questions, we attempted to makethe respondents recall the number of times of participating in the lotteries as accurately
as possible. We use the first one in our analysis while using the other does not affect theanalysis results.
Some respondents answered that they did not receive the lottery result yet. We
include them as people that did ”not win lottery=lose lottery.”Appendix D. Video gaming preference. The GRI uses cluster analysis for the
calculation, though we are not allowed to show the details. The algorithm considers not
only video games but also smartphone and computer games. Factors such as frequencyof game-play or game purchase are used. Also, one can see that game play time isclosely related to the video gaming preference in the table below.
In our analysis, the video gaming preference measured at the beginning of November
2020 is used where available (if not available, the one measured in November 2021 isused instead). This is for using the characteristics measured before the interventions(where available) and avoiding ”bad controls.” We are considering obtaining November
2019 data (where available) from the GRI and using it for the Nintendo Switch analysis
in future updates.
Table 7. Video gaming preference and video game play time.
Variablegame play time (Hour/Day)N6975

web browsing time for game (Hour/Day)

6975

smartphone game playtime (Hour/Day)

6975

(1)
Heavy gamer
Mean/SD
1.626[2.281]
1.305[1.968]1.372[2.066]

N
9604
9604
9604

(2)
Middle gamer
Mean/SD
1.113[1.852]0.987[1.727]1.269[1.959]

N
11067
11067
11067

(3)
Light gamer
Mean/SD
0.615[1.355]0.689[1.413]1.059[1.646]

N
9333
9333
9333

(4)
Super light gamer
Mean/SD
0.319[0.963]0.411[1.131]0.671[1.347]

N
5043
5043
5043

(5)
Non-gamer
Mean/SD
0.144[0.743]0.201[0.887]0.223[0.929]Notes: Observations of round 4 and 5 are used.

October 4, 2022

20/29

463
464
465
466
467
468
469
470
471
472
473

474
475
476
477
478
479
480
481
482
483
484

Appendix E.

Price history of PS5.

Fig 12. Price history of second-hand PS5 (Yen).
Source: https://kakaku.com//

Appendix F. Outcome variables collected in each survey round.
Table 8. Outcome variables collected in each round.

Outcome variables
K6
SWLS
Video game play timeWork productivity
Have Nintendo Switch
Play Nintendo Switch this month
Have PS5
Play PS5 this month1
Switch
✓

2
PS5
✓
✓

Round
3
PS5
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓

4
PS5
✓
✓
✓
✓
✓
✓
✓

5
PS5
✓
✓
✓
✓
✓
✓
✓
✓

Notes. Round one survey collected the Nintendo Switch lotteries’ information. From round two, we collected the PS5 lotteries’
information. K6: Kessler psychological distress scale, SWLS: The Satisfaction with Life Scale.

October 4, 2022

21/29

Appendix G.
Table 9. Balance table for Nintendo Switch lottery winners and non-winners.
Variable# of times joined lottery

N
847

Age

847

Gender (Male = 1)

847

Respondent/household head is married (=1)

847

Respondent/household head is divorced (=1)

847

Respondent/household head has child(ren) (=1)

847Student (=1)

847

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

847

Full-time employee (=1)

847Part-time employee (=1)

847

Self employed / other types worker (=1)

847Unemployed / currently not study (=1)

847

Gaming preference: Heavy gamer=1

847

Gaming preference: Middle gamer=1

847

Gaming preference: Light gamer=1

847

Gaming preference: Super light gamer=1

847

Gaming preference: Non-gamer=1

847

Job: Civil engineering, Construction, Real estate, Housing and building services

847

Job: Daily necessaries, Textile and apparels, Cosmetics, Food and Beverages=1

847

Job: Manufacturing=1847

Job: Trading companies, Publishing, Printing, Mass media=1847

Job: Distributors, Retailers=1847

Job: Carriers, Warehousing, Logistics=1

847

Job: Public works=1

847

Job: Software and Information services=1

847

Job: Banks and Financial services=1

847

Job: Food services, Hairdressing, Cosmetology, Other Services=1

847

Job: Medical care, Welfare=1847

Job: Education=1

847

Job: Other industries and types of business=1

847

October 4, 2022

(1)
Did not win PS5 lottery
Mean/SD
3.268[3.663]35.967[16.575]
0.564[0.496]0.643[0.479]0.052[0.222]0.580[0.494]0.241[0.428]0.084[0.277]0.442[0.497]
0.096[0.294]0.074[0.263]0.064[0.244]0.436[0.496]0.194[0.395]0.185[0.389]0.118[0.323]0.067[0.251]
0.071[0.257]0.051[0.220]0.129[0.335]0.037[0.188]0.045[0.207]0.052[0.222]0.059[0.236]0.055[0.229]0.034[0.182]0.105[0.307]0.071[0.257]0.032[0.176]0.079[0.270]

N
926
926
926
926
926
926
926
926
926
926
926
926
926
926
926
926
926
926
926
926
926
926
926
926
926
926
926
926
926
926

(2)
Won PS5 lottery
Mean/SD
4.305[4.467]36.254[15.919]0.504[0.500]0.692[0.462]0.039[0.193]0.609[0.488]0.233[0.423]0.102[0.302]0.455[0.498]0.098[0.298]0.058[0.234]0.054[0.226]0.402[0.491]
0.242[0.428]0.190[0.393]0.121[0.326]0.045[0.208]0.070[0.256]0.066[0.248]0.103[0.304]0.037[0.188]0.044[0.206]0.032[0.177]0.051[0.220]0.070[0.256]0.036[0.185]0.103[0.304]0.070[0.256]0.025[0.156]0.068[0.252]

Normalized
difference(1)-(2)-0.251-0.0180.120-0.1040.063-0.0600.018-0.061-0.026-0.009
0.065
0.042
0.069
-0.117-0.012-0.0090.096
0.003-0.0640.082-0.001
0.003
0.098
0.036
-0.060-0.008
0.008
0.003
0.043
0.042

22/29

Appendix H.
Table 10. Correlation between game engagement and life satisfaction.
(1)

(2)

(3)

.908***(.0541).237***(.0508).339***(.057)

(4)

(5)

(6)

(7)
(8)
(9)
SWLS (life satisfaction).852***(.0617).406***(.0558).535***(.0561)VARIABLES
1 if have Nintendo Switch
1 if play Nintendo Switch this month
1 if having PS5.977***(.218).795***(.193)

(10)

(11)

(12)

1.41***(.304)

1.49***(.281)

1.67***(.283)

Video game play time

78,690.00765
No
No
Yes
17.1

78,690
.0862
Yes
No
Yes
17.1

78,690
.0872
Yes
Yes
Yes
17.1

78,690.00619
No
No
Yes
17.1

78,690
.0865
Yes
No
Yes
17.1

78,690
.0876
Yes
Yes
Yes
17.1

78,690.00414
No
No
Yes
17.1

Appendix I.
Table 11. Alternative outcome variables: PD and SPD.
(1)
(2)
(3)
PD: Psychological distress;
VARIABLES
k6>=5 (=1)
Win the lottery-.0849***(.0186).047**(.0175).0679***(.019)

78,690
.0872
Yes
Yes
Yes
17.1

78,690.00422
No
No
Yes
17.1

78,690
.0866
Yes
No
Yes
17.1

78,690
.0876
Yes
Yes
Yes
17.1

42,022.00462
NoNo
Yes
17.11542,022
.0925
Yes
No
Yes
17.11542,022
.0928
Yes
Yes
Yes
17.115

(5)
(6)
(7)
(8)
SPD: Serious psychological distress;k6>=13 (=1)-.0983***(.0167).0022(.0028)-.0405***(.0145)-.047***(.0122).00303**(.00137)-.0581***(.0179)-.0571***(.0173).00851**(.00337)-.0113(.0123)-.0174(.0124).00291**(.0013)

1,773
No
Yes
.6
Switch

1,773
Yes
Yes
.6
Switch

6,419
No
Yes
.548
PS5

6,419
Yes
Yes
.548
PS5

1,773
No
Yes
.291
Switch

1,773
Yes
Yes
.291
Switch

6,419
No
Yes
.228
PS5

6,419
Yes
Yes
.228
PS5

Appendix J.
Table 12. Number of observations for each round.
# of people that were
# of people
Roundsent survey offersthat answered survey
1
34,615
18,912
2
27,18618,1893
29,462
18,4794
47,183
26,996
5
26,14315,026

October 4, 2022

(4)

(15)

-.109***(.0219)# of times joined lottery

Observations
Controls
Prefecture FE
Mean
Lottery

78,690
.0863
Yes
No
Yes
17.1

(14)

.922***(.192)

1 if playing PS5 this month

Observations
R-squared
Controls
Gaming preference dummies
Prefecture FE
Mean

(13)

# of people
who joined lotteries
1,773
1,481
1,447
2,127
1,364

# of people
who won lotteries
926
254
332
499
312

Switch lotteries
or PS5 lotteries
Switch
PS5
PS5
PS5
PS5

23/29

Appendix K.
Table 13. Average treatment effects estimated by the instrumental variableapproach.
(1)
VARIABLES
1 if have Nintendo Switch

(2)
(3)
(4)
(5)
k6: psychological distress scale

(7)

(8)
(9)
(10)
SWLS: life satisfaction-4.25***(.721)

1 if play Nintendo Switch this month-5.73***(.949)

1 if having PS5-.797**(.299)

1 if playing PS5 this month

1.55***(.253)-1.39**(.518)

Video game play time

2.71***(.456)-1.41**(.621)

Time for video game play andweb browsing for game (Hour/Day)

Observations
Controls
Prefecture FE
Mean
Lottery
Kleibergen-Paap rk Wald F statistic

(6)

1,773
Yes
Yes
8.249
Switch
150.1

1,773
Yes
Yes
8.249
Switch
83.24

6,419
Yes
Yes
7.202
PS5
1614

6,419
Yes
Yes
7.202
PS5
422.5

1.72**(.68)-1.11*

1.35**(.573)(.646)

3,491
Yes
Yes
6.6
PS5
29.04

3,491
Yes
Yes
6.6
PS5
12.61

6,419
Yes
Yes
16.867
PS5
1614

6,419
Yes
Yes
16.867
PS5
422.5

3,491
Yes
Yes
17.269
PS5
29.04

3,491
Yes
Yes
17.269PS5
12.61

Notes. Standard errors are clustered by prefectures. ∗ ∗ ∗p < 0.01, ∗ ∗ p < 0.05, ∗p < 0.1.
Appendix L.
Table 14. ATEs on PD and SPD estimated by the IV approach.
(1)
(2)
(3)
(4)
PD: Psychological distress;
VARIABLES
k6>=5 (=1)
1 if having PS5-.0725***(.0186)

1 if playing PS5 this month

(5)
(6)
(7)
(8)
SPD: Serious psychological distress;k6>=13 (=1)-.0269(.0188)-.126***(.0321)

Video game play time-.0469(.0327)-.129***(.04)

Time for video game play andweb browsing for game (Hour/Day)-.0459(.0348)-.102**-.036(.0399)(.0296)

Observations
6,419
6,419
3,491
3,491
6,419
Controls
Yes
Yes
Yes
Yes
Yes
Prefecture FE
Yes
Yes
Yes
Yes
Yes
Mean
.548
.548
.524
.524
.228
Lottery
PS5
PS5
PS5
PS5
PS5
Kleibergen-Paap rk Wald F statistic
1614
422.5
29.04
12.61
1614
Notes. Standard errors are clustered by prefectures. ∗ ∗ ∗p < 0.01, ∗ ∗ p < 0.05, ∗p < 0.1.

Appendix M.

October 4, 2022

6,419
Yes
Yes
.228
PS5
422.5

3,491
Yes
Yes
.19
PS5
29.04

3,491
Yes
Yes
.19
PS5
12.61

Below is details of our PSM specification.

485• One to one matching without replacement.

486• Logit model.

487

24/29

• Descending method.

488• psmatch2 in STATA.

489

Appendix N.

(a) Nintendo Switch(b) PS5

Fig 13. Histogram of CATE of students.
Notes. The conditional ATEs are estimated by causal forest.

October 4, 2022

25/29

Acknowledgments

490

We thank Dr. Toshifumi Kuroda, Dr. Tomoya Matsumoto, Dr. Kazushi Takahashi, Dr.
Yoko Kijima, Dr. Keiko Ono for insightful comments and feedback; Chihiro Egami andYu Yoshinari for technical and graphical support; Hiroaki Kanemitsu and Takashi
Kinoshita for precious feedback on our survey questionnaire, and the gameage R&I for
conducting omnibus surveys together with us and sharing invaluable data.

Funding

492
493
494
495

496

We gratefully acknowledge fundings from the following organizations: JSPS KAKENHI(Grant Numbers: JP19K13804), the Takasaki City University of Economics
Grant-in-Aid for Encouragement of Social Scientists, and the Nihon University Collegeof Economics Grant-in-Aid for Encouragement of Social Scientists.

Data availability

497
498
499
500

501

The data set supporting our findings is not publicly available due to access restrictions
based on the agreement with the gameage R&I. The data set is available on reasonable
request.

Conflicts of interest

502
503
504

505

The authors declare no conflict of interest. The funders had no role in the design of thestudy; in the collection, analyses, or interpretation of data; in the writing of themanuscript, or in the decision to publish the results.

References

October 4, 2022

491

1.

Vuorre M, Zendle D, Petrovskaya E, Ballou N, and Przybylski AK. A Large-Scale
Study of Changes to the Quantity , Quality , and Distribution of Video Game PlayDuring a Global Health Pandemic. Technology, Mind, and Behavior 2021 :1–8

2.

Ueno C and Yamamoto S. The relationship between behavioral problems andscreen time in children during COVID-19 school closures in Japan. Scandinavian
Journal of Child and Adolescent Psychiatry and Psychology 2022; 10:1–8. doi:
10.21307/sjcapp-2022-001

3.

Aarseth E, Bean AM, Boonen H, Carras MC, Coulson M, Das D, Deleuze J,
Dunkels E, Edman J, Ferguson CJ, Haagsma MC, Bergmark KH, Hussain Z,
Jansz J, Kardefelt-Winther D, Kutner L, Markey P, Nielsen RKL, Prause N,
Przybylski A, Quandt T, Schimmenti A, Starcevic V, Stutman G, Van Looy J, and
Van Rooij AJ. Scholars’ open debate paper on the world health organization
ICD-11 gaming disorder proposal. Journal of Behavioral Addictions 2017;
6:267–70. doi: 10.1556/2006.5.2016.088

4.

Turner NE, Paglia-boak A, Ballon B, Cheung JTW, Adlaf EM, Henderson J,
Chan V, Rehm J, Hamilton H, and Mann RE. Prevalence of Problematic Video
Gaming among Ontario Adolescents. International Journal of Mental Health
Addiction 2012; 10:877–89. doi: 10.1007/s11469-012-9382-5

5.

Wenzel HG, Bakken IJ, Johansson A, Götestam KG, and Øren A. Excessive
Computer Game Playing among Norwegian Adults: Self-Reported Consequences ofPlaying and Association with Mental Health Problems. Psychological Reports
2009 Dec; 105:1237–47. doi: 10.2466/PR0.105.F.1237-1247. Available from:https://doi.org/10.2466/PR0.105.F.1237-1247

26/29

506
507
508

October 4, 2022

6.

Mentzoni R, Brunborg GS, Myrseth H, and Bergen BH. Problematic Video Game
Use : Estimated Prevalence and Associations with Mental and Physical Health.
Cyberpsychology, Behavior, and Social Networking 2011. doi:
10.1089/cyber.2010.0260

7.

Maras D, Flament MF, Murray M, Buchholz A, Henderson KA, Obeid N, andGoldfield GS. Screen time is associated with depression and anxiety in Canadian
youth. eng. Preventive medicine 2015 Apr; 73:133–8. doi:
10.1016/j.ypmed.2015.01.029

8.

Von Der Heiden JM, Braun B, Müller KW, and Egloff B. The association between
video gaming and psychological functioning. Frontiers in Psychology 2019;
10:1–11. doi: 10.3389/fpsyg.2019.01731

9.

Hasan Y, Laurent B, and Bushman BJ. Violent Video Games Stress People Out
and Make Them More Aggressive Violent Video Games Stress People Out andMake Them. Aggressive Behavior 2013. doi: 10.1002/ab.21454

10.

Ferguson CJ, Trigani B, Pilato S, Miller S, Foley K, and Barr H. Violent Video
Games Don’t Increase Hostility in Teens, but They Do Stress Girls Out. eng. The
Psychiatric quarterly 2016 Mar; 87:49–56. doi: 10.1007/s11126-015-9361-7

11.

Roy A and Ferguson CJ. Competitively versus cooperatively? An analysis of theeffect of game play on levels of stress. Computers in Human Behavior 2016;
56:14–20. doi: 10.1016/j.chb.2015.11.020

12.

Reinecke L. Games and Recovery: The Use of Video and Computer Games to
Recuperate from Stress and Strain. Journal of Media Psychology 2009; 21:126–42.
doi: 10.1027/1864-1105.21.3.126

13.

Russoniello C. The effectiveness of casual video games in improving mood anddecreasing stress. Journal of Cyber Therapy and Rehabilitation 200914.

Kovess-masfety V, Keyes K, Hamilton A, Hanson G, Bitfoi A, Golitz D, Koc C,
Pez O, and Keyes K. Is time spent playing video games associated with mental
health , cognitive and social skills in young children ? 2016 :349–57. doi:
10.1007/s00127-016-1179-6

15.

Hazel J, Kim HM, and Every-Palmer S. Exploring the possible mental health andwellbeing benefits of video games for adult players: A cross-sectional study. eng.
Australasian psychiatry : bulletin of Royal Australian and New Zealand College of
Psychiatrists 2022 May :10398562221103081. doi: 10.1177/10398562221103081

16.

Johannes N, Vuorre M, and Przybylski AK. Video game play is positively
correlated with well-being. Royal Society Open Science 2021; 8. doi:
10.1098/rsos.202049

17.

Imbens GW, Rubin DB, and Sacerdote B. Estimating the Effect of Unearned
Income on Labor Earnings, Savings, and Consumption: Evidence from a Survey ofLottery Players. American Economic Review 2001; 91

18.

Imbens GW. Matching Methods in Practice : Three Examples Matching Methods
in Practice Three Examples. Journal of Human Resources 2015; 50. doi:
10.3368/jhr.50.2.373

19.

Athey S and Imbens GW. The State of Applied Econometrics: Causality and
Policy Evaluation. Journal of Economic Perspectives 2017; 31:3–32

20.

Király O, Tóth D, Urbán R, Demetrovics Z, and Maraz A. Intense video gaming is
not essentially problematic. Psychology of Addictive Behaviors 2017; 31:807–17.
doi: 10.1037/adb0000316

27/29

October 4, 2022

21.

Van Rooij AJ, Ferguson CJ, Carras MC, Kardefelt-Winther D, Shi J, Aarseth E,
Bean AM, Bergmark KH, Brus A, Coulson M, Deleuze J, Dullur P, Dunkels E,
Edman J, Elson M, Etchells PJ, Fiskaali A, Granic I, Jansz J, Karlsen F,
Kaye LK, Kirsh B, Lieberoth A, Markey P, Mills KL, Nielsen RKL, Orben A,
Poulsen A, Prause N, Prax P, Quandt T, Schimmenti A, Starcevic V, Stutman G,
Turner NE, Van Looy J, and Przybylski AK. A weak scientific basis for gaming
disorder: Let us err on the side of caution. Journal of Behavioral Addictions 2018;
7:1–9. doi: 10.1556/2006.7.2018.19

22.

Cunningham S, Engelstatter B, and Ward MR. Violent video games and violent
crime. Southern Economic Journal 2016; 82:1247–65. doi: 10.1002/soej.12139

23.

Vuorre M, Johannes N, Magnusson K, and Przybylski AK. Time spent playing
video games is unlikely to impact well-being. 2021. Available from:https://psyarxiv.com/8cxyh/24.

Halbrook YJ, O’Donnell AT, and Msetfi RM. When and How Video Games Can
Be Good: A Review of the Positive Effects of Video Games on Well-Being.
Perspectives on Psychological Science 2019; 14:1096–104. doi:
10.1177/1745691619863807

25.

Barr M and Copeland-Stewart A. Playing Video Games During the COVID-19
Pandemic and Effects on Players’ Well-Being. Games and Culture 2021 :1–18.
doi: 10.1177/15554120211017036

26.

Ojio Y, Matsunaga A, Kawamura S, Horiguchi M, Yoshitani G, Hatakeyama K,
Amemiya R, Kanie A, and Fujii C. Anxiety and Depressive Symptoms in the New
Life With COVID-19: A Comparative Cross-Sectional Study in Japan Rugby Top
League Players. International Journal of Public Health 2022; 66:1–7. doi:
10.3389/ijph.2021.1604380

27.

Kessler RC, Barker PR, Colpe LJ, Epstein JF, Gfroerer JC, Hiripi E, Howes MJ,
Normand SLT, Manderscheid RW, Walters EE, and Zaslavsky AM. Screening forserious mental illness in the general population. eng. Archives of general psychiatry
2003 Feb; 60:184–9. doi: 10.1001/archpsyc.60.2.184

28.

Diener E, Emmons RA, Larsen RJ, and Griffin S. The Satisfaction With Life Scale.
eng. Journal of personality assessment 1985 Feb; 49:71–5. doi:
10.1207/s15327752jpa4901_13

29.

Stuart EA, Lee BK, and Leacy FP. Prognostic score-based balance measures canbe a useful diagnostic for propensity score methods in comparative effectivenessresearch. Journal of Clinical Epidemiology 2013; 66:S84–S90.e1. doi:
10.1016/j.jclinepi.2013.01.013. Available from:http://dx.doi.org/10.1016/j.jclinepi.2013.01.013

30.

Wager S and Athey S. Estimation and Inference of Heterogeneous Treatment
Effects using Random Forests. Journal of the American Statistical Association
2018; 113:1228–42. doi: 10.1080/01621459.2017.1319839. arXiv: 1510.04342

31.

Hoffman I and Mast E. Heterogeneity in the effect of federal spending on local
crime: Evidence from causal forests. Regional Science and Urban Economics 2019;78:103463. doi: 10.1016/j.regsciurbeco.2019.103463. Available from:https://doi.org/10.1016/j.regsciurbeco.2019.103463

32.

Davis JM and Heller SB. Rethinking the benefits of youth employment programs:
The heterogeneous effects of summer jobs. Review of Economics and Statistics
2020; 102:664–77. doi: 10.1162/rest_a_00850

28/29

October 4, 2022

33.

Yamamoto T, Uchiumi C, Suzuki N, Yoshimoto J, and Murillo-Rodriguez E. The
psychological impact of ‘mild lockdown’ in Japan during the COVID-19 pandemic:
A nationwide survey under a declared state of emergency. International Journal ofEnvironmental Research and Public Health 2020; 17:1–19. doi:
10.3390/ijerph17249382

34.

Furukawa TA, Kessler RC, Slade T, and Andrews G. The performance of the K6
and K10 screening scales for psychological distress in the Australian National
Survey of Mental Health and Well-Being. Psychological Medicine 2003; 33:357–62.
doi: 10.1017/S0033291702006700

35.

Sumino Z. Jinsei ni taisuru manzoku syakudo (the Satisfaction With Life Scale[SWLS]) nihonban sakusei no kokoromi [Development of the Japanese version of
the Satisfaction With Life Scale]. Proceedings of the 36th Annual Meeting of theJapanese Association of Educational Psychology 1994; 36:192

36.

Whisman MA and Judd CM. A cross-national analysis of measurement invarianceof the Satisfaction With Life Scale. Psychological assessment 2016; 28:239–44. doi:
10.1037/pas0000181

29/29

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/dp22-01