J Gambl Stud (2010) 26:89–103
DOI 10.1007/s10899-009-9143-y
ORIGINAL PAPER
Who Plays What? Participation Profiles in Chance
Versus Skill-based Gambling
Matthew Stevens Æ Martin Young
Published online: 24 July 2009
Springer Science+Business Media, LLC 2009
Abstract To determine whether gambling participation falls into skill and chance-based
categories and, if so, to determine the socio-demographic characteristics associated with
these different categories. A cross-sectional analysis of all respondents to the 2005
Northern Territory Gambling Prevalence Survey who gambled in the 12 months prior to
the survey. Factor analysis was employed to determine whether a chance versus skill-based
dichotomy described the structure of gambling participation. Gambler preference groups
were constructed using the median of rotated factor scores. Multinomial logit regression
was then used to determine independent associations between explanatory variables and
categories of gambler preferences. The skill and chance-based dichotomy did describe
player preferences for the sample of adult gamblers in the Northern Territory, Australia.
Gender, age, household income, household structure and the geographic location (access to
gambling opportunities) of respondents were all associated with different degrees of participation in skill and chance based gambling activities. Notably, respondents 35 years and
over were significantly over-represented in the low-skill/high-chance participation group,
and under-represented in the high-skill/low-chance group. It is clear that the term gambling
is a confounding rubric that hides differences both in the type of activity and the sociodemographic profiles of participants. An examination of the latter raises important questions about the role of chance in later life, as well as the role of self-determination in
gambling for other groups, particularly younger men.
Keywords
Chance Skill Gambling Participation Socioeconomic status
M. Stevens (&) M. Young
School for Social and Policy Research, Institute of Advanced Studies, Charles Darwin University,
Ellengowan Drive, Casuarina, NT 0909, Australia
e-mail: [email protected]
M. Young
e-mail: [email protected]
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Introduction
Gambling is an umbrella term that covers a number of activities (i.e. EGMs, lotteries,
cards, betting, internet gambling). It is clear that these activities are vastly different in
terms of their configuration. Situational differences relate to the location of venues and
access to gambling opportunities, while structural characteristics concern the orientations
of the games themselves, such as having a high or rapid staking and as purely chance
versus skill mixes (Griffiths and Delfabbro 2001). Research has tended to examine gambling as a single entity, incorporating a range of activities that are very different, or looked
specifically at individual activities. Interestingly, few studies have examined the relationship between social characteristics and the basic orientations to the gambling activity
(see Dickerson 1993; Volberg and Wray 2007; Walker 1992; Wohl et al. 2005; Young and
Stevens 2009). Previous research has made it clear that the skill versus chance distinction
may be a useful way to categorize gambling activities and to explain patterns of gambling
participation. The analytical value of this distinction lies in its implied critique of the term
‘gambling’ which, because it conflates various forms, may be viewed as a confounding
rubric. The chance–skill distinction may provide a meaningful way to separate gambling
activities and redefine basic orientations towards gambling beyond the generic. It also
provides a conceptual framework for categorizing different gambling activities, categories
amenable to empirical testing to determine if indeed gambling activities are meaningfully
described by the chance–skill binary in terms of peoples gambling activity preferences and
frequency of play. In this paper we specifically explore commonalities between games
based on empirical data (i.e. frequency of play) and identify social partitioning related to
players gambling preferences. As an extension of previous research on the association
between orientations of gambling activities and problem gambling using a sample of
regular gamblers (Young and Stevens 2009), the current paper will assess the extent to
which the skill–chance distinction explains the gambling participation patterns of past-year
gamblers in the Northern Territory of Australia. However, the paper extends beyond a
concern of gambling types and will also examine the association between a range of sociodemographic and economic categories with skill versus chance based gambling.
Skill, Chance and Social Groups
The distinction between skill and chance was first raised by the French sociologist Roger
Caillois (1961) who developed a typology of the types of play in society of which gambling games were one distinctive type. In this context, he developed a binary distinction
between competition or agôn (the ancient Greek word meaning contest or challenge) and
chance or alea (the ancient Greek term for gaming, or playing at a game of chance of any
kind). Agôn stresses the ability of contestants to surmount obstacles and opponents to
achieve victory. In contrast, games of alea, consist of those games where the outcome is
rendered completely independent of the player. In these cases winning is the result of luck
or fate as opposed to triumph over an adversity. While alea and agôn may represent
analytically distinct categories, they are by no means mutually exclusive, in that gambling
is not uniquely associated with aleatory expression. Games such as roulette or dice are
fundamentally chance based games, while games such as poker and blackjack are skill (or
a combination of skill and chance) based games. At a general level, Caillois argued that
games were socially patterned, in that games of chance would be sought out by those
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worse-off within a capitalistic society, who, having fewer opportunities for advancement,
resorts to gambling to change their fortunes.
Caillois’s distinction has been developed in the context of gambling studies, although
other researchers have questioned the use of a skill–chance dichotomy as a useful distinction, particularly given that many games contain elements of both skill and chance
(Langer 1975; Rogers 1998; Walker 1992; Wohl and Enzle 2002; Wohl et al. 2005). For
example, the assumption of skill is often conflated with the illusion of control (Walker
1992), a concept related to sympathetic magic, a belief by gamblers that they possess luck
of a sort that may be intentionally harnessed as a means of control over chance events
(Nemeroff and Rozin 2000). As a case in point, Wohl and Enzle (2002) found that when
playing a wheel of fortune, gamblers who were able to handle balls with numbers corresponding to the wheel scored highest on a scale measuring their perceived chance of
winning. Scores were lower by those who could only sight the numbered balls, and lower
again by those who did not touch or sight the balls. In short, gamblers believed they could
transfer luck to the game being played either through seeing or touching a related object.
However, while the categories of skill and chance may be contestable, nonetheless provide
a starting point from which to develop a typology based on the fundamental orientation of
the games themselves.
Delfabbro (2000), in a review of the Australian literature, found that males preferred
skill-based gambling such as racetrack betting and casino table games, although there were
few differences between genders in past-year participation and frequency of gambling.
However, female gamblers were more likely to gamble as a form of escapism, and Delfabbro further emphasized the importance of matching by frequency and activity when
comparing motivational reasons for gambling by males and females (Delfabbro 2000). In a
national survey of gambling in the US, past-year gamblers were more likely to be male,
have a decreasing participation in both skill and chance-based games with age (though not
for casino/track betting), and to participate in casino/track betting for respondents with an
Asian ethnicity (Welte et al. 2002). Some studies have examined the skill versus chance
dimensions more indirectly, through their association with the role of gender in participation. For example, Volberg (2003), using a selected sample of prevalence surveys across
several U.S. jurisdictions, noted increased participation by women gambling in four states
after the expansion of slot machines into less gendered environments (e.g. social clubs and
convenience stores). Volberg also noted that participation in games of skill (e.g. casino
table games) was more often played by males, while games of chance (e.g. bingo) were
more often played by women. In the Australian context, Hing and Breen (2001), in a study
comparing female and male members of six large clubs in Sydney, found little difference
in frequency of play between males and females for electronic gambling machines (EGMs)
in clubs, though significant differences were found when comparing frequency of play in
casinos with males dominating these spaces. However, Hing and Breen (2001) did find that
females tended to maximize the time spent playing by selecting machines with lower
minimum bets available. These studies suggest that the skill–chance distinction has a
gender base, and that gambling participation patterns may reveal quite different motivational profiles.
More recently, Young and Stevens (2009), using a sample of regular gamblers (defined
as those individuals who gambled weekly on any activity other than lotteries or instant
scratch lotteries) in the Northern Territory, Australia, found that players displayed a
general preference for either chance-based games or skill-based games as revealed by a
factor analysis of frequency of weekly play for eight gambling activities. Regular gamblers
who participated more frequently in chance-based gambling were more likely to be: older,
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female, separated/divorced, a single parent, a couple with children, and have good access
to gambling venues. Regular gamblers who participated more frequently in skill-based
games were more likely to be male, full-time workers, living alone or in a groups/share
house, and single. Neither group in the Young and Stevens (2009) study showed a significant association with problem gambling, although individual activities within each
grouping were significantly associated with problem gambling (i.e. electronic gaming
machines, casino table games and private games). As an extension of this research on the
association between gambling activities and problem gambling using the sample of regular
gamblers (Young and Stevens 2009), we assess the extent to which the skill–chance
distinction explains the gambling participation patterns of past-year gamblers in the
Northern Territory of Australia (i.e. all gamblers). However, the paper extends beyond a
concern with categorization to an analysis of the social groups that are involved with
different types of gambling. To do this, the analysis will examine the association between a
range of socio-demographic and economic categories and skill versus chance based
gambling. Several key questions guide the analysis:
• Do the categories of skill and chance describe the structure of gambling participation as
measured by frequency of play?
• Can different groups of gamblers be defined on the basis of their participation patterns
(i.e. skill versus chance-based gamblers)?
• Are skill and chance-based gambling associated with different social profiles?
Methods
Sample and Data Collection
In October, 2005, the Northern Territory of Australia conducted its first population-wide
gambling prevalence survey (Young et al. 2006; Young et al. 2008). A telephone survey
was used to obtain estimates of gambling participation, type of activity, frequency of play,
and prevalence of problem gambling for the adult (18 years and over) Northern Territory
population. In addition, data were collected on attitudes to gambling and socio-demographic and socioeconomic characteristics of respondents. The survey used a two-stage
screening process to select respondents in the population based on whether they had
gambled in the past year and whether they were a weekly gambler (excluding lotto and
instant scratch tickets). The sample was then stratified by region, age and gender. A total of
5,264 people were screened with all regular gamblers, one in four non-regular gamblers
and one in two non-gamblers sampled. This method produced 1,893 completed interviews,
of which, 667 were of non-gamblers, 850 of non-regular gamblers, and 376 regular
gamblers (those gambling at least once per week excluding lotteries and instant scratch
tickets). The 1,893 completed interviews were weighted to the Northern Territory estimated resident population (Nnon-gamblers = 37,283, Nnon-regular gamblers = 90,583, Nregular
gamblers = 10,359, N = 138,225) based on the stratification variables. As this analysis only
concerns people who had gambled, all non-gamblers were removed, as were observations
with missing data and respondents who visitors to the Northern Territory. The latter being
removed as they were found to be gambling significantly more frequently than respondents
that were Northern Territory residents. The final sample used in the following analyses
consisted of a sample 1,172 people weighted to 97,525 to represent the resident Northern
Territory adult population who had gambled in the previous 12 months from when the
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survey was enumerated. No significant differences (Fisher Exact tests) were found between
socio-demographic variables between those included and those excluded from the analyses. All analyses use the weighted sample with standard errors (and confidence intervals)
adjusted according to the sample design. The characteristics of the weighted sample are
presented in Table 1.
The Structure of Gambling Activities
Information about participation and frequency of play for the 12 months preceding the
survey was collected from respondents. The following eight activities are used in the
analysis: electronic gaming machines (EGMs); instant scratch lotteries; lotteries; keno (a
form of continuous electronic lottery); racetrack betting (i.e. betting on horses and dogs);
table games played at casinos (e.g. blackjack, roulette); sporting event betting; and private
games (e.g. cards). Frequency of play was then standardised for each activity to give
weekly frequency of play. Using weekly frequency of play for the eight gambling activities, all respondents who gambled on these activities were subjected to a factor analysis
(principal components factor method). It was hypothesized that activities would load on
separate factors defined by the level of skill involved in the game, and hence the player’s
ability to affect or predict the outcome. By this logic, games of chance would include
lotteries, instant scratch tickets, keno, and EGMs, while games of skill would include
racetrack betting, casino table games, sports event betting, and private card games. It
should be acknowledged, however, that games of skill could be played in a pure chance
way and that this distinction in players participation may also be an outcome of the factor
analysis and could potentially blur the binary skill–chance dichotomy hypothesized.
The number of factors retained in the solution was based on the size of the Eigen values
(which indicate the amount of variance explained) and through assessment of the component scree plot. A varimax (orthogonal) rotation was applied to the retained factors to
reduce the number of negative loadings occurring on each factor and improve interpretability (Bryant and Yarnold 1995). The propensity to gamble on the activities for each
factor was represented by factor scores which were calculated for each of the retained
rotated factors.
Socio-Demographic Associations with Gambler Groups
To identify groups of gamblers on the basis of their participation profiles, factor scores
were converted to binary variables using the median score as a cut-point. That is,
respondents were grouped according to whether they scored high (i.e. greater than or equal
to the median) or low (i.e. less than the median) for each factor score. Therefore, for a
2-factor solution, there are four possible groups: (1) low score on factor 1 and 2 (LL), (2)
low score on factor 1 and high score on factor 2 (LH), (3) high score on factor 1 and low
score on factor 2 (HL), and (4) high score on factor 1 and 2 (HH). To identify associations
between gambler groups and socio-demographic variables, multinomial logit regression
models (also known as polytomous logistic regression) were constructed. The multinomial
logit model is an extension of logistic regression with the difference being that the outcome
variable may have three or more, as opposed to two, possible outcomes. For the current
analysis the outcome variable of interest (i.e. gambler groups) will have either four outcomes (the previously described four groups) or eight outcomes (where groups are based
on a 3-factor solution). In this model, one of the outcome variable categories is selected as
the comparison category and associations between it and the other categories are assessed
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Table 1 Sample characteristics using weighted data for regular, non-regular and all gamblers
Regular
(N = 9,627)
% (SE)
Non-regular
(N = 87,898)
% (SE)
All gamblers
(N = 97,525)
% (SE)
Darwin
50.2 (3.0)
53.9 (1.6)
53.5 (1.2)
Alice Springs
11.4 (1.6)
11.3 (0.8)
11.3 (0.6)
Katherine
7.2 (1.0)
4.4 (0.4)
4.7 (0.3)
Tennant Ck/Nhulunbuy
6.1 (1.3)
4.5 (0.6)
4.7 (0.5)
25.2 (3.5)
25.9 (1.8)
25.8 (1.3)
18–24
15.2 (2.8)
14.8 (1.5)
14.9 (1.2)
25–34
18.9 (2.7)
23.7 (1.4)
23.2 (1.1)
35–44
18.2 (2.1)
21.7 (1.3)
21.3 (1.1)
45–54
21.2 (2.4)
23.4 (1.6)
23.2 (1.4)
55 or more
26.4 (2.7)
16.4 (1.2)
17.4 (1.1)
Female
30.9 (2.7)
49.4 (1.5)
47.6 (1.1)
Male
69.1 (2.7)
50.6 (1.5)
52.4 (1.1)
90.4 (2.3)
89.9 (1.7)
89.9 (1.5)
9.6 (2.3)
10.1 (1.7)
10.1 (1.5)
96.2 (1.5)
95.8 (1.0)
95.8 (0.9)
3.8 (1.5)
4.2 (1.0)
4.2 (0.9)
Some university
20.6 (2.8)
30.1 (2.0)
29.2 (1.8)
Some tertiary
13.3 (2.0)
12.2 (1.3)
12.3 (1.2)
Some secondary
62.9 (3.3)
56 (2.2)
56.7 (2.0)
3.2 (1.4)
1.7 (0.6)
1.8 (0.6)
Full-time
74.4 (2.9)
69.4 (2.0)
69.9 (1.8)
Part-time
8.4 (1.6)
12.8 (1.4)
12.3 (1.2)
Home duties
3.9 (1.4)
5.1 (0.8)
5.0 (0.7)
Student
2.4 (1.4)
3.2 (0.8)
3.1 (0.7)
Retired
6.7 (1.7)
3.5 (0.6)
3.8 (0.6)
Pensioner
3.3 (0.9)
3.3 (0.8)
3.3 (0.7)
Unemployed
1.0 (0.4)
2.8 (1.2)
2.6 (1.1)
Location
Rest of NT
Age in years
Gender
Indigenous status
Non-Indigenous
Indigenous
Language spoken at home
English
Other than English
Highest education
Some primary
Labor force status
HH income pa
Less than $40,000
11.3 (2.1)
10.8 (1.6)
10.8 (1.4)
$40,000–$59,999
13.1 (2.6)
12.5 (1.4)
12.5 (1.3)
$60,000–$79,999
13.3 (2.2)
17.0 (1.7)
16.7 (1.6)
$80,000–$99,999
21.1 (3.0)
21.7 (1.9)
21.6 (1.7)
$100,000–$124,999
15.7 (2.3)
20.0 (1.6)
19.5 (1.5)
$125,000 or more
25.5 (3.0)
18.0 (1.9)
18.8 (1.8)
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Table 1 continued
Regular
(N = 9,627)
% (SE)
Non-regular
(N = 87,898)
% (SE)
All gamblers
(N = 97,525)
% (SE)
39.1 (1.9)
Household type
Couple with children
30.3 (3.0)
40.1 (2.1)
Single parent
5.1 (1.2)
7.2 (1.7)
7.0 (1.5)
Lone person
12.7 (2.1)
13.6 (1.5)
13.5 (1.4)
Couple with no children
31.8 (3.1)
28.3 (2.0)
28.6 (1.8)
Group household
15.3 (2.9)
7.4 (1.2)
8.2 (1.1)
4.7 (1.9)
3.5 (0.9)
3.6 (0.8)
Other
Marital status
Married
67.2 (3.2)
66.1 (2.1)
66.2 (1.9)
Separated
7.9 (1.4)
7.3 (0.9)
7.4 (0.8)
Widowed
2.3 (0.7)
1.6 (0.4)
1.6 (0.3)
22.6 (2.9)
25.0 (2.1)
24.7 (1.9)
Single
according to the explanatory variable (i.e. socio-demographic category). For current purposes, the low-low group was used as the reference category for the outcome variable as
this group represents respondents who scored below the median on all factors (i.e. gambled
less frequently).
The strength of the association in multinomial logit regression is assessed using relative
risk ratios. Scores above one represent an increased risk for a given category of the
explanatory variable, while scores below one represent decreased risk. The reference
category for explanatory variables is set at 1.00. Each reference category was selected to
maximize differences within the categories of each explanatory variable. Unadjusted
associations between socio-demographic variables and the factor score groups were calculated. Variables showing a moderate association (p \ 0.20) were included in a single
model which was subjected to backward elimination procedure with removal of variables
with p-values[0.05. This procedure produced a multivariable adjusted model of the sociodemographic profile of the gambler groups defined on the basis of their factor scores. All
analyses were carried out using Stata v9.2 using weighted data and the standard errors
and confidence intervals presented account for the stratified survey design.
Results
Gambling Participation
Table 2 displays participation rates (i.e. previous 12 months) and summary statistics for
weekly frequency of play for the eight gambling activities used in the analysis. Chancebased gambling forms including lotteries (played by 72% of gamblers in 12 months preceding the survey) and scratch lotteries (39%) enjoyed the highest participation, followed
by EGMs (37%), and keno (31%). For the skill-based forms of gambling, participations
rates were highest for racetrack betting (26%), followed by casino table games (14%),
sports betting (7%) and private games (5%). The distribution for each activity varied
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Table 2 Participation and summary statistics for eight gambling activities (N = 97,525)
Gambling activity Participationa Weekly frequencyb of play
% (SE)
Mean (SE) Minimum 50%
75%
90%
Maximum
(median) percentile percentile
EGMs
36.5 (2.0)
0.09 (0.01) 0.00
0.00
0.04
0.25
Scratch lotteries
39.2 (2.1)
0.10 (0.01) 0.00
0.00
0.06
0.25
7.00
Lotteries
72.2 (2.0)
0.42 (0.02) 0.00
0.12
0.50
1.00
21.00
Keno
30.8 (1.9)
0.09 (0.01) 0.00
0.00
0.04
0.19
10.00
Racetrack
25.8 (1.8)
0.06 (0.01) 0.00
0.00
0.02
0.06
7.00
Table games
14.1 (1.6)
0.01 (0.00) 0.00
0.00
0.00
0.03
4.00
Sporting events
7.1 (0.9)
0.02 (0.00) 0.00
0.00
0.00
0.00
7.00
Private games
4.8 (0.7)
0.02 (0.01) 0.00
0.00
0.00
0.00
4.00
a
Participation in the 12 months preceding the survey
b
Weekly frequency of play indicates number of times played per week
7.00
significantly and all displayed highly skewed distributions in terms of their weekly frequency of play. Lotteries and keno were the activities that recorded the highest weekly
frequency of play.
The structure of Gambling Activities
Table 3 presents the gambling activity factor loadings for the varimax rotated factor scores
for the 2- and 3-factor solutions. The unrotated solution produced three Eigen values
greater than one. Factor 1 clearly represented games of skill (i.e. racetrack betting, betting
on sporting events, and playing table games at a casino). Factor 2 represented chance
games (i.e. EGMs, instant scratch lotteries, and keno). Factor 3 represented lotteries, which
had a positive loading, as well as private games, which had a negative loading. This factor
accounted for a considerably lower amount of variation compared with the first two factors
as indicated by an Eigen value of 1.09. The emergence of factor 3 is most likely due to the
Table 3 Rotated factor loadings for 2- and 3-factor solutions (N = 97,525)
Gambling activity
EGMs
Instant scratchies
Factor 1(skill)
Factor 2
(chance)
Factor 3
(skill/chance)
Factor 1
(skill)
Factor 2
(chance)
0.13
0.76
-0.16
0.35
0.60
-0.17
0.54
0.25
-0.12
0.60
Lotto
0.02
0.28
0.64
-0.16
0.53
Keno
0.14
0.54
0.16
0.19
0.55
Racetrack
0.74
0.01
0.16
0.60
0.05
Table games
0.58
0.31
-0.24
0.68
0.14
Sporting events
Private games
Variance
0.69
0.01
-0.07
0.65
-0.06
-0.01
0.27
-0.69
0.31
-0.06
1.43
1.40
1.09
1.55
1.33
% Variation
17.9%
17.5%
13.6%
19.4%
16.7%
Cumulative %
17.9%
35.4%
49.0%
19.4%
36.1%
Note: Bold fonts indicate loadings C0.40 or B-0.40 for gambling activity for that factor
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patterns of associations that emerge when a large number of the sample and in particular
the non-regular gamblers, who participate in lotteries (72% in the past year) and a few
other gambling forms occasionally. Lotteries are fundamentally chance, and this was
reflected in the two-factor solution, from which the chance–skill distinction emerged more
clearly. Factor 1 (skill) included casino table games, betting on sporting events, and race
betting, all with loadings above 0.6. Factor 2 (chance) included EGMs, instant scratch
lotteries, keno and lotteries (all with loadings above 0.5). Given this clarity, the limited
variance explained by the third factor, and the desire to keep the number of gambler groups
to an interpretable number, a decision was made to define groups on the basis of the two
factor solution.
Socio-demographic Associations with Gambler Groups
Table 4 shows the distribution of respondents across the four gambler groups. Approximately 34% of respondents scored below the median on both factor scores (group 1), while
14% scored above the median for both factors (group 4). The remainder of respondents
were distributed nearly equally across the other two groups (low on factor 1 and high on
factor 2, and high on factor 1 and low on factor 2). The four emergent groups were group 1
(low chance–low skill), group 2 (high chance–low skill), group 3 (low chance–high skill),
and group 4 (high chance–high skill).
Table 5 sets out results for the final multivariate adjusted logit model based on the four
groups obtained from the 2-factor solution. Five variables remained in the final model
including location, age, gender, household income and household type. The variables
education and marital status, both of which displayed significant bivariate associations,
were eliminated from the model in the stepwise elimination procedure. All relative risk
ratios for the outcome variable use the gambler type group low-skill and low-chance
(group 1) as the reference category.
Moving down column 2 (i.e. group 2 -low skill and high chance) in Table 5, four of the
five socio-demographic variables displayed significant multivariate adjusted associations
with this group. Respondents living in Katherine (a small regional centre with an estimated
resident population of 9,000 people) had a significantly lower risk (0.47, 95% confidence
interval 0.25–0.89) of falling into this group compared with respondents living in Darwin
and Alice Springs (the two largest urban centers in the Northern Territory with populations
of 96,600 and 27,000 respectively (Australian Bureau of Statistics 2007) and which host
the only two casinos in operation in the Northern Territory). Older respondents (i.e.
35 years and over) were more associated with low skill and high chance participation
compared with younger (i.e. 18–24 years) respondents. This was particularly so for
respondents aged 55 years and over, whom were seven times more likely to fall into this
group than 18–24 year olds. Respondents living in high income households ($125,000 or
Table 4 Distribution of median
cut factor score groups
(N = 97,525)
2-factor
solution groups
Skill
(Factor 1)
Chance
(Factor 2)
% (SE)
1. LL
Low
Low
33.6 (1.9)
2. LH
Low
High
24.4 (1.6)
3. HL
High
Low
28.1 (2.0)
4. HH
High
High
13.9 (1.3)
Total
–
–
100.0
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Table 5 Multivariable multinomial logit regression model for median factor score groups from the 2-factor
solution (N = 97,525)
Group 2
(Lskill–Hchance)
RRR (95% CI)
Group 3
(Hskill–Lchance)
RRR (95% CI)
Group 4
(Hskill–Hchance)
RRR (95% CI)
Location
Darwin-Alice Springs
1.00
1.00
1.00
Katherine
0.47 (0.25–0.89)
0.31 (0.14–0.68)
0.59 (0.27–1.30)
Tennant Ck/Nhulunbuy
0.89 (0.46–1.72)
1.11 (0.50–2.45)
1.66 (0.75–3.67)
Rest of NT
0.57 (0.31–1.05)
1.22 (0.70–2.13)
0.63 (0.31–1.27)
Age in years
18–24
1.00
0.74 (0.34–1.61)
0.54 (0.23–1.23)
25–34
2.68 (0.97–7.43)
1.00
1.00
35–44
3.36 (1.27–8.88)
0.57 (0.32–1.02)
0.68 (0.35–1.34)
45–54
3.25 (1.22–8.66)
0.50 (0.29–0.89)
0.59 (0.30–1.13)
55 or more
7.01 (2.65–18.5)
0.39 (0.18–0.85)
0.71 (0.35–1.43)
Female
1.00
1.00
1.00
Male
1.08 (0.72–1.61)
1.94 (1.26–2.99)
2.48 (1.59–3.87)
Gender
HH income per annum
Less than $40,000
1.84 (0.77–4.42)
3.47 (1.31–9.15)
1.04 (0.41–2.64)
$40,000–$59,999
2.04 (0.94–4.42)
1.05 (0.46–2.35)
0.72 (0.32–1.60)
$60,000–$79,999
1.00
1.00
1.00
$80,000–$99,999
0.85 (0.42–1.72)
1.68 (0.87–3.25)
1.81 (0.89–3.69)
$100,000–$124,999
1.30 (0.68–2.50)
1.23 (0.60–2.55)
0.59 (0.28–1.25)
$125,000 or more
2.23 (1.07–4.66)
1.69 (0.78–3.69)
1.65 (0.76–3.55)
Household type
Couple with children
1.00
1.40 (0.81–2.40)
1.00
Single parent
0.57 (0.25–1.34)
2.33 (0.91–5.95)
1.46 (0.61–3.51)
Single person
0.56 (0.29–1.11)
1.20 (0.57–2.56)
1.00 (0.46–2.21)
Couple with no children
0.57 (0.35–0.93)
1.00
1.80 (1.07–3.05)
Group household
0.52 (0.19–1.46)
2.87 (1.13–7.32)
3.13 (1.19–8.26)
Other
0.45 (0.12–1.64)
3.20 (0.93–11.0)
2.71 (0.85–8.62)
Note: Bold fonts indicate a significant association (p B 0.05) relative to the reference category denoted by
1.00
Reference category for the dependent variable (i.e. column headings) is low participation in chance and skill
based forms of gambling
more) were 2.23 (1.07–4.66) times more likely to fall into group 2 compared to respondents living in households with the median income ($60,000–$79,999). All household
types showed lower risks for falling into group 2 compared with couples with children,
although only couples with no children showed a significantly lower risk (0.57, 0.35 to
0.93) of falling into group 2 compared to their childless counterparts.
Column 3 shows associations between socio-demographic variables and those respondents who fall into the high skill and low chance group (group 3). The location association
with group 2 was similarly present for this group, with respondents living in Katherine
having a lower risk (0.31, 0.14–0.68) of being high skill and low chance gamblers
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99
compared with respondents living in Darwin and Alice Springs. Age showed a significant
association with this group, with respondents aged 45 years and over having a reduced risk
compared with 25–34 year olds (note that the reference category is different to that used
for group 2). Males were nearly two times (1.94, 1.26–9.15) more likely to be high skill and
low chance gamblers compared with females. All household income categories showed
increased risk of falling into this group compared with the median household income,
though only respondents in households with the lowest income showed a significant
increased risk (3.47, 1.31–9.15). Group households showed a significant increased risk
(2.87, 1.13–7.32) of falling into group 3 compared with couples with no children (note the
different reference category compared with group 2).
Only two socio-demographic variables, gender and household type displayed a significant association with group 4, which included respondents who were high skill and high
chance gamblers. Males were 2.48 (1.59–3.87) times more likely to be high frequency
indiscriminate gamblers compared with females. Group households had an increased risk
(3.13, 1.19–8.26) of falling into group 4, and couples with no children also had an
increased risk (1.80, 1.07–3.05) of falling into this group.
Discussion
The Structure of Gambling Activities
In terms of the structure of gambling activities, the factor analysis supported the relevance
of skill and chance as a conceptual framework. Individual activities were located on one of
two main factors, representative of skill and chance, respectively. Thus, rather than
viewing games as unique, the structure of participation indicates that games display a
commonality contingent on whether the force of skill or chance dominates the game.
Therefore, skill or chance based games may be considered as separate categories with some
empirical support. These findings were consistent with the analysis by Young and Stevens
(2009) who, using a sample of regular (weekly) gamblers, found that participation in
gambling activities broadly reflected a preference for either skill or chance based games. In
fact, the chance–skill based distinction was more discernible for the analysis of all gamblers, compared with regular gamblers (Young and Stevens 2009). This may reflect that
regular gamblers are more opportunistic gamblers in that they play whatever activities are
available, and participation is more reflective of supply than it is of demand (Marshall
2005). Indeed while there exists a general structure of skill and chance, the distinction is
undoubtedly blurred at the level of the game as many gamblers are known to play chancebased games with the illusion of control (Wohl and Enzle 2002). For example, not all
casino table games are skill based, though knowledge of the game (and probabilities
associated with winning) may be used to improve a gamblers odds of winning in these
games (e.g. roulette). So, the moderate loadings of casino table games and private games
on factor 2 (chance) in the three-factor solution may represent this ‘blurriness’ between
skill and chance-based games. This point is also borne out in the two-factor solution with
EGMs showing a moderate loading on factor 1 (skill).
However, while the skill–chance distinction does appear to explain the factor loadings
of the activities, this does not suggest they are the only dimensions by which games may be
categorized. Reith (1999) makes the point that the gambling landscape may be delineated
by various additional categories including the rate of play of a game, the player’s relation
to a game, the spatial organization and the social integration of the site, and the
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socioeconomic characteristics of players (Dickerson 1993; Home Office 1996; Walker
1992; Young and Tyler 2008). As such, given the distinction in playing preferences found
in the analysis of all gamblers and for regular gamblers (Young and Stevens 2009), the
skill–chance binary offers opportunities for further empirical testing, as well as potential
expansion to include a range of other activity-specific domains. In addition, the results do
not to suggest that the pattern of participation is attributable solely to the structure of
demand. As the results in Table 2 have shown, participation is mediated by the supply of
gambling opportunity, a structure that favors chance-based gambling forms. This may be
due to both to the relative ease with which chance-based forms may be engaged in
compared to the more skill-based ones, as well as the more general availability of games of
chance, particularly lotteries and EGMs (Young and Tyler 2008). Thus, the gambling
participation patterns presented here are most likely to be a product of the interaction
between demand and the supply of different gambling products.
Skill, Chance and Social Patterns
Given the factor structure that emerged, it was possible to define gambler groups on the
basis of skill or chance preferences and to examine the socio-demographic differences
between these groups. While the analysis conducted herein differed somewhat to that
carried out in earlier work by the authors, there was considerable overlap in the social
groups that were associated with skill and chance based preferences for regular gamblers
(Young and Stevens 2009). In particular, chance-based gamblers were associated with an
urban location, older age groups, high household income, and couple with no children
households for past-year gamblers in the Northern Territory, Australia. In previous work
by the authors using only the sample of regular gamblers from the same dataset, these same
associations were present, though female regular gamblers showed a significant association
with chance-based gambling (Young and Stevens 2009). Thus, participation in chance may
be more closely related to factors including accessibility, stage in the lifecycle, and disposable income rather than socio-economic status per se (Dickerson 1993; Griffiths and
Delfabbro 2001; Young and Tyler 2008). On the contrary, low household income was
associated with group 2, which contained the low chance and high skill frequency gamblers, while high household income was associated with high chance and low skill frequency gamblers. While research does suggest richer groups do gamble less on some
activities than their economically disadvantaged counterparts, they still do engage with
chance to a considerable degree (Delfabbro and Le Couteur 2008). It is clear that while the
orientation towards gambling is socially patterned, these patterns are complex and are not
fully accounted for by a simple class-based, compensatory explanation.
The socio-demographic associations with the skill-based group (group 3) included nonurban residence, younger age (i.e. 25–34), male gender, low household income, and residence in a group household. Skill-based gambling, evidently attracts a well-defined
demographic of younger males on low incomes. It is evident that rather than participating
in pure chance, as in chance-based gambling, this group attempts to use skill to achieve a
desired outcome. Consideration of the activities involved (i.e. casino table games, racebetting, and sports betting) suggest these are generally male-dominated, and occur in maledominated environments (i.e. casinos, racetracks, betting shops). This may be due to the
gendered nature of gambling spaces or an innate attractiveness of skill to this group, who
then find skill-based spaces for expression (Adams et al. 2007; Worthington et al. 2007).
Therefore, while it is possible that there are male dominated spaces which dictate the types
of gambling activity occurring (e.g. racetrack betting), there were no corresponding female
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spaces (and associated activities) included in this study or rather these places are nongender specific (Volberg and Wray 2007; Welte et al. 2002). This could potentially be
remedied by the inclusion of bingo in the current analysis, which is a more traditionally
female space (Oliveira and Silva 2001; Volberg 2003).
Group 4, the smallest group, included those gamblers who participated heavily in both
chance and skill-based gambling (14% of respondents) did not display clear preferences in
terms of skill or chance but gambled on both. Group 4 gamblers were associated with male
gender, group household type, and couple with no children households. Compared with the
social characteristics of gamblers in groups 2 and 3, the gamblers in group 4 exhibited less
social patterning. For example, both age and household income were not significant,
indicating that this group of frequent gamblers in both chance and skill-based games come
from a spectrum of ages and income types, but tended to be males living in group and
couple with no children households (Delfabbro and LeCouteur 2008; Volberg 2003;
Walker 1992). While the gamblers in groups 2 and 3 clearly fell on one side of the fence in
terms of activity orientation, group 4 is more of a puzzle in that the individuals within it
combined both chance-based and skill-based gambling. This emphasizes the fact that some
players may have dynamic orientations towards the game, being able to adapt to a range of
gambling types. The non-significance of the geographic variable for this high-skill and
high-chance group may indicate that they source whatever form of gambling activity is
available to them. While casino table games are not available in the larger regional towns
of the Northern Territory, racetrack betting and private card games are readily available, as
are EGMs, keno, lotto and instant lotteries. Group 4 is the group that may be of most
interest in terms of gambling-risk or problem gambling as they appear not to discriminate
in preferred activity. Indeed, problematic gambling is consistently associated with economic, gender, and social class divisions (Abbott et al. 2004; Clarke et al. 2006; Currie
et al. 2006; Productivity Commission 1999; Volberg et al. 2001; Welte et al. 2002; Welte
et al. 2004; Young et al. 2008), although recent research has suggested this is activitybased and not directly related to skill or chance forms (Young and Stevens 2009). Additional analysis by the authors using the 2005 Gambling Prevalence Survey data showed
that indeed problem gambling estimates increased with the number of activities participated in on a weekly basis (Young et al. 2008).
Conclusions
It is evident from the empirical investigation of gambling patterns in the Northern Territory
of Australia that skill and chance are useful in explaining the patterns of gambling frequency for different activities. Skill and chance effectively described the structure of
gambling participation, and provided a mid-scale level of conceptualization between the
conflating term ‘gambling’ and the specificities of individual games. In addition, the
current results indicate that skill and chance are socially patterned. However, these patterns
are complex and are not fully accounted for by simple socioeconomic-based explanations.
While research does suggest richer groups do gamble less frequently on some activities
than their economically disadvantaged counterparts, they still do engage with chance to a
considerable degree. Other variables that were associated with the skill–chance distinction
included gender, access to gambling opportunities, and the stage in the lifecycle (including
income and household structure). Areas for further investigation of the social contexts of
gambling include the relationship between male gender and skill-based gambling as well as
the relationship between increasing age and chance-based gambling. This research may
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assist in linking gambling participation more closely to broader social changes western
society, such as the ageing of the population and the increasing availability a range of
gambling forms.
Acknowledgements The authors wish to acknowledge the Community Benefit Fund of the Northern
Territory Government, Australia, for funding the research.
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