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Equivalent gambling warning labels are perceived
differently
Philip W. S. Newall
1,2
, Lukasz Walasek
3
&ElliotA.Ludvig
3
Experimental Gambling Research Laboratory, School of Health, Medical and Applied Sciences, Central Queensland University, Melbourne, VIC, Australia,
1
Applied
Psychology, Warwick Manufacturing Group, University of Warwick, Coventry, UK
2
and Department of Psychology, University of Warwick, Coventry, UK
3
ABSTRACT
Background and Aims The same information may be perceived differently, depending on how it is described. The risk
information given on many gambling warning labels tends to accentuate what a gambler might expect to win, e.g. ‘This
game has an average percentage payout of 90%’(return-to-player), rather than what a gambler might expect to lose, e.g.
‘This game keeps 10% of all money bet on average’(house-edge). We compared gamblers’perceived chances of winning
and levels of warning label understanding under factually equivalent return-to-player and house-edge formats.
Design Online surveys: experiment 1 was designed to test how gamblers’perceived chances of winning would vary un-
der equivalent warning labels, and experiment 2 explored how often equivalent warning labels were correctly understood
by gamblers. Setting United Kingdom. Participants UK nationals, aged 18 years and over and with experience of vir-
tual on-line gambling games, such as on-line roulette, were recruited from an on-line crowd-sourcing panel (experiment
1, n= 399; experiment 2, n=407).Measurements The main dependent variables were a gambler’s perceived chances
of winning on a seven-point Likert scale (experiment 1) and a multiple-choice measure of warning label understanding
(experiment 2). Findings The house-edge label led to lower perceived chances of winning in experiment 1, F
(1,
388)
= 19.03, P<0.001. In experiment 2, the house-edge warning label was understood by more gamblers [66.5, 95%
confidence interval (CI) = 60.0%, 73.0%] than the return-to-player warning label (45.6%, 95% CI = 38.8%, 52.4%,
z=4.22,P<0.001). Conclusions House-edge warning labels on electronic gambling machines and on-line casino
games, which explain what a gambler might expect to lose, could help gamblers to pay greater attention to product risk
and would be better understood by gamblers than equivalent return-to-player labels.
Keywords Behavioural science, electronic gambling machines, framing effect, house-edge, return-to-player, risk
communication.
Correspondence to: Philip Newall,Experimental Gambling Research Laboratory, School of Health, Medicaland Applied Sciences, Central Queensland University,
120 Spencer Street, Melbourne, VIC 3000, Australia. E-mail: p.newall@cqu.edu.au
Submitted 25 June 2019; initial review completed 30 September 2019; final version accepted 30 December 2019
INTRODUCTION
Firms use their marketing to present their products in the
best light possible. For example, food packaging will often
state that an item is, for instance, ‘90%-fat-free’,which
sounds more attractive than the equivalent description of
‘10%-fat’. Although these descriptions are factually equiva-
lent, food products are evaluated more positively with the
90%-fat-free description than with the 10%-fat description
[1,2]. This is an example of a ‘framing’effect, where judge-
ments are influenced by how information is described [3].
Here we explore a potential framing effect relevant to gam-
bling warning labels. The United Kingdom’sgamblingreg-
ulator, the Gambling Commission, states that remote
virtual gambling games, such as on-line roulette, must
provide ‘information that may reasonably be expected to
enable the customer to make an informed decision about
his or her chances of winning’([4] p. 12). Among the op-
tions allowed by the Gambling Commission are two equiv-
alent frames for the gambler’s chances of winning: the
‘return-to-player’and ‘house-edge’percentages.
Despite this regulatory flexibility, only the return-to-
player format seems to be in current widespread use, e.g.
‘This game has an average percentage payout of 90%’.A
return-to-player of 90% means that for every £100 wa-
gered, the gambler will receive an average of £90 back.
The same information communicated as a house-edge
would instead state that the game keeps an average of
£10 per £100 wagered. Therefore, return-to-player and
house-edge are factually equivalent frames [5]. Both are
© 2020 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction Addiction
SHORT REPORT doi:10.1111/add.14954
This is an openaccess article underthe terms of the Creative CommonsAttribution-NonCommercialLicense, whichpermits use, distribution andreproductio n
in any medium, provided the original work is properly cited and is not used for commercial purposes.
allowed by the Gambling Commission for virtual on-line
gambling [4] and yet, in practice, only return-to-player
framing appears to be in use.
There is some evidence that gamblers struggle to un-
derstand return-to-player information. A survey of 25 UK
electronic gambling machine (EGM) gamblers found that
only 24% correctly answered a four-alternative multiple-
choice question on return-to-player information correctly
[6]. This failure is worrying, given that the return-to-player
is also displayed on UK EGMs [7]. A qualitative survey of
Canadian EGM gamblers similarly found widespread mis-
understanding around the return-to-player, [8] as has
other qualitative work from the UK [9]. Return-to-player
information is also displayed by law on EGMs in the
Australian state of Victoria [10]. An experimental study
of Australian undergraduates also found a widespread mis-
understanding of the return-to-player [10].
This paper investigates the issue of equivalently framed
gambling warning labels experimentally. Participants were
either given a return-to-player wording or a novel house-
edge reframing of the same information, e.g. ‘This game
keeps 10% of all money bet on average’.Foreachexperi-
ment, a pre-registered hypothesis and analysis plan, study
materials, results and analysis output files are available
from https://osf.io/7avnz/. Experiment 1 was run on
31 May 2019, where it was hypothesized that house-edge
framing would lead to a lower perceived chance of winning
than return-to-player framing across a range of typical av-
erage payouts. Experiment 2 was run on 2 June 2019,
where it was hypothesized that gamblers would answer a
four-alternative multiple-choice question correctly more
often with a house-edge than return-to-player label.
EXPERIMENT 1
Participants
A total of 399 UK nationals aged 18 years or older were re-
cruited via Prolific Academic and paid £0.50 each. Partic-
ipants took an average of 3.3 minutes to complete the
study, so this translated to £9.09/hour. Participants were
50.5% female (0.75% preferred not to answer), had a
mean age of 33.9 years (SD = 10.9), a mean problem gam-
bling severity index of 3.7 (SD = 4.7) and gambled an aver-
age of 58.0 days during the last year (SD = 80.4). No other
demographic information was collected.
Participants had earlier indicated to ProlificAcademic
that they had experience in playing at least one on-line vir-
tual casino gambling game.
Design and materials
Using G*Power version 3.1, [11] with the design below, we
estimated that to achieve 95% power, with alpha = 0.01,
three measurements (corr = 0.5) and a small effect size
(f= 0.10), at least 347 participants were required.
On each trial participants were presented with some
short introductory text about on-line gambling and then
a warning label. Figure 1a shows an example from the
return-to-player condition.
Throughout three trials, the magnitude of the house-
edge (return-to-player) was varied to check whether any
potential framing effect was moderated by average payout
size. These were: 5 (95%), 10 (90%) and 15% (85%), re-
spectively. These percentage values were based on the
existing norms for gambling products. Prior EGM research
suggests a house-edge range of 5–9% (US [12]), 4–15%
(Canada [13]) and 7–15% (Australia [14]).
Procedure
Participants were randomly allocated to either the return-
to-player or house-edge condition and completed the three
trials in random order. Participants then completed an at-
tention check with the same warning label, but where
the percentage corresponded to 95% in the house-edge
condition and 5% in the return-to-player condition (which
are implausibly unfair games). Our pre-registered analysis
plan states that any participant giving a higher perceived
winning chance on this attention check than on any previ-
ous trial would be excluded from the analysis, as they may
not have been paying attention.
After the main experimental trials, age, gender, return-
to-player warning label understanding, problem gambling
severity index (PGSI) [15] and last-year gambling fre-
quency (‘On how many days over the last 12 months have
you gambled?’) were collected in random order. The mea-
sure of return-to-player warning label understanding
(which was given to participants in both conditions) is
shown in Fig. 1b (correct answer: ‘For every £100 bet on
this game about £90 is paid out in prizes’).
Measures
The dependent variable was the gambler’sperceived
chances of winning, as measured by a seven-point Likert
scale (see Fig. 1a).
Results
In total, nine participants failed the attention-check ques-
tion, and were excluded from the analysis (four in the
house-edge condition and five in the return-to-player
condition).
Data were analysed using a mixed-effects model, to ac-
count for the shared variance between a participant’sre-
sponses across different trials. Responses were regressed
on the independent variables of framing (two levels,
between-participants) and magnitude (three levels,
2Philip W. S. Newall et al.
© 2020 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction Addiction
within-participants) and their interaction. In addition, a
random intercept for participants was included in the
model. The fitting was performed using the afex package
[16] in R.
Figure 2 presents the mean perceived chance of win-
ning across all levels of the factors. Error bars in the figure
depict 95% confidence intervals (CIs) based on the model
fit. There was a significant effect of condition, F
(1,
388)
=19.03,P<0.001, showing that perceived chances
of winning were higher under the return-to-player frame.
There was also a significant effect of magnitude, F
(2,
776)
=244.85,P<0.001, showing that perceived chances
Figure 1 Example of the (a) main stimulus screen and (b) measure ofreturn-to-player understanding. The main stimulus screen (a) looked identical
to participants in both conditions, except in the house-edge condition the main label was altered to, e.g. ‘This game keeps 10% of all money bet on
average’. Participants in both conditions answered the measure of warning label understanding as shown in (b)
Equivalent gambling warning labels 3
© 2020 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction Addiction
of winning were higher for higher values of the return-to-
player. The interaction between the two variables was sig-
nificant, F
(2, 776)
=4.74,P= 0.009. Despite this interac-
tion, inspection of the means in Fig. 2 shows that
responses differed significantly between the two conditions
across all magnitude levels.
An additional model was run to observe if these effects
remained if gamblers’characteristics were taken into ac-
count. The model included fixed effects of PGSI and
gambling frequency. We tested for the presence of signifi-
cant two-way interactions between magnitude, condition,
PGSI and gambling frequency. An analysis of variance
(ANOVA) table is displayed in Table 1. As can be seen, the
only new statistically significant interaction term was be-
tween PGSI and condition (F=5.34,P= 0.021). Closer in-
spection of the marginal effects revealed a trend such that
those with higher PGSI scores gave higher responses in the
house-edge condition (marginal trend = 0.21, 95%
Figure 2 Mean perceived chance of winning in experiment 1. Perceived chances of winning: 7 = very high chance of coming out ahead, 4 = neither
high nor low chance of coming out ahead, 1 = very low chance of coming out ahead. Error bars represent 95% confidence intervals
Table 1 Mixed-model analysis of variance (ANOVA) table.
Model 1 Model 2
Variable F-value P-value F-value P-value
Condition 19.03 (<0.001) 21.15 0
Magnitude 244.85 (<0.001) 245.72 (<0.001)
Magnitude × condition 4.74 (0.009) 4.92 (0.008)
Problem gambling severity <0.01 (0.994)
Gambling frequency 5.94 (0.015)
Problem gambling severity × condition 5.34 (0.021)
Gambling frequency × condition 0.04 (0.843)
Problem gambling severity × magnitude 2.25 (0.106)
Gambling frequency × magnitude 1.18 (0.307)
Problem gambling severity × Gambling frequency 0.42 (0.517)
F-values and P-valuesin parentheses, for a model that compares experimentally manipulated variables (model 1) and a model that adds individual difference
variables and two-way interactions (model 2), showing main effects and interactions, with interactions denoted by *.
4Philip W. S. Newall et al.
© 2020 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction Addiction
CIs = 0.06; 0.48), but lower responses in the return-to-
player condition (marginal trend = 0.21, 95%
CIs = 0.46; 0.04.
Overall, 47.4% of participants responded correctly to
the multiple-choice question of return-to-player under-
standing. As can be seen in Table 2, the most-commonly
given incorrect answers were: ‘90% of people who play this
game will win something’(23.8%) and: ‘This game will
give out a prize 9 times in 10’(23.8%).
Discussion
Participants rated their perceived chances of winning as
higher in the return-to-player condition than the house-
edge condition. Perceived chances of winning are subjec-
tive, however, and hence there is no ‘correct’response to
experiment 1. Experiment 2 was designed to address this
limitation, by assessing whether participants would answer
the four-alternative multiple-choice question correctly
more often with a house-edge than return-to-player label.
EXPERIMENT 2
In total, 407 participants were recruited (56.8% female,
mean age = 33.7 years, mean PGSI = 3, SD = 4.6, mean
days gambled over previous 12 months = 55.6, SD = 78.3).
No other demographic information was collected. Partici-
pants were paid £0.25 and took an average of 2.0 minutes
to complete the study, which translates to £7.50/hour. Par-
ticipants were given either a return-to-player or house-
edge warning label (both equivalent to a house-edge of
10%), and asked to complete the multiple-choice measure
of understanding used in the previous experiment. In the
previous experiment this measure of understanding was
given to all participants with the return-to-player warning
label only, but here understandingof both labels (return-to-
player and house-edge) was assessed.
Results
In total, 66.5% (95% CI = 60.0%, 73.0%) of participants in
the house-edge condition answered the understanding
question correctly, which was shown by logistic regression
to be significantly more than the 45.6% (95% CI = 38.8%,
52.4%) of participants in the return-to-player condition
(z=4.22,P<0.001). Table 2 provides a breakdown of re-
sponses to this measure across the two experiments. The
house-edge condition was associated with a large shift
away from the incorrect response: ‘This game will give
out a prize 9 times in 10’.
A model was run to observe if this effect remained if
gamblers’characteristics were taken into account. A logis-
tic regression model was run controlling for PGSI and gam-
bling frequency and including interaction terms between
experimental condition and these two individual difference
variables. There was an additional significant main effect of
PGSI, whereby gamblers with higher PGSI levels were
more likely to answer the question correctly in either label
condition [z=2.18,P=0.030,oddsratio(OR)=1.08].
However, neither the interaction term on PGSI severity
(z=1.31, P= 0.190) nor gambling frequency
(z=0.60, P= 0.545) was statistically significant. There-
fore, the house-edge warning label was understood more
clearly by all gamblers.
GENERAL DISCUSSION
The present findings contribute to the literature on gam-
bling warning labels [17]. Gamblers’perceived chances of
winning were significantly lower under the house-edge
warning label than a return-to-player warning label in ex-
periment 1. Perceived chances of winning are subjective,
and hence there is no ‘correct’response to experiment 1.
Experiment 2 addressed this limitation, and showed that
more gamblers correctly understood the house-edge label
than the return-to-player label. Given the international ev-
idence base showing that return-to-player information is
frequently misunderstood, [6,8,10] these results suggest
that it would be better to display house-edge information
instead in jurisdictions such as the United Kingdom [4] or
Victoria, Australia [10].
Measures of gambling behaviour in a realistic gambling
task would help to provide further support to the practical
policy relevance of these results. Warning labels on UK
EGMs and virtual on-line gambling games, however, are
currently only found on low-prominence help screens,
Table 2 Responses to the measure of warning label understanding.
Response Experiment 1
Experiment 2
(return-to-player condition)
Experiment 2
(house-edge condition)
‘90% of people who play this game will win something’23.8% 18.1% 16.3%
‘This game will give out a prize 9 times in 10’23.8% 32.8% 10.3%
‘If you bet £1 on this game you are guaranteed to win 90p’5.0% 3.4% 6.9%
Correct response: ‘For every £100 bet on this game about £90 is paid
out in prizes’
47.4% 45.6% 66.5%
Equivalent gambling warning labels 5
© 2020 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction Addiction
which many regular gamblers have not even seen [6]. To-
bacco control research suggests that effective warning la-
bels must be more prominent [18]. Additional changes
may be required to yield measurable changes in gamblers’
behaviour in real gambling environments. More research
is also required for gamblers at the highest levels of
problem gambling severity, and to explore other gambler
subtypes who might respond differently to the framing
manipulation.
While these results suggest that house-edge informa-
tion is a better way to communicate gambling risks, even
better information formats are surely possible. For example,
graphical risk representations can be more effective than
equivalent numerical information [19]. House-edge infor-
mation might be even better understood with visual aids.
These results provide evidence for a novel framing effect
in gambling warning labels. This further supports the view
that gambling policy should reflect behavioural scientific
insights [20–22].
Declaration of interests
In 2018, P.W.S.N. was included as a named researcher on a
grant funded by GambleAware, and in 2019 received
travel and accommodation funding from the Spanish
Federation of Rehabilitated Gamblers. E.A.L. was co-
investigator on a grant funded by the Alberta Gambling Re-
search Institute that ended in February 2019.
Acknowledgements
We thank the Behavioural Science Global Research Priori-
ties fund from the University of Warwick for funding and
Depi Alempaki and Derek Webb for their helpful ideas.
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