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Tourism Management
journal homepage: www.elsevier.com/locate/tourman

Regional impacts of casino availability on gambling problems: Evidence
from the Canadian Community Health Survey

T

Kahlil S. Philandera,b,∗
a
b

School of Hospitality Business Management, Carson College of Business, Washington State University, 915 N. Broadway, Everett, WA, USA
Gambling Treatment and Research Clinic, School of Psychology, University of Sydney, 94 Mallet Street, Camperdown, NSW, Australia

ARTICLE INFO

ABSTRACT

Keywords:
Casino gambling
Exposure
Adaptation
Problem gambling
Tourism policy

Casinos are generally recognized as significant generators of economic impact and tourism, but access is typically controlled in a perceived effort to reduce social harms, particularly those from gambling disorders. Using
data from a representative sample of 50,048 Canadians across four provinces, this study empirically tests theory
of the regional impacts of casino availability on participation in gambling and the development of gambling
related problems. Increased exposure to casinos is found to be related to increases in both participation and
problem gambling risk, despite the observation that all four provinces recently experienced casino expansion and
population-wide declines in problem gambling prevalence rates. The estimates are robust to broad controls for
health and demographic risk factors. The findings suggest that hospitality/tourism planners should consider
wider expansion of gambling paired with increased investment in responsible and problem gambling programs,
to maximize economic impacts while accelerating social adaptation to gambling related harms.

1. Introduction
Casinos are one of the largest components of the hospitality and
tourism sector in terms of economic impact. In the United States, a welldeveloped market, casinos generate more employment than the airline
industry and consumer spending is nearly equal to that of hotel lodging
(Economic Impact of the US Gaming Industry, 2018). In more nascent
gaming markets, jurisdictions often support casino expansion as a core
component of regional or international tourism growth strategies, designed to attract visitors who will spend on both casino games and
complementary hospitality/tourism services (Eadington, 2003;
Henderson, 2006; Ishihara, 2017). Recognition of benefits is not just a
public accounting phenomenon. Residents in local communities typically recognize the positive economic effects that casinos have on their
neighborhood, and this recognition often grows over time (Lee & Back,
2006a; Lee, Kang, Long, & Reisinger, 2010).
Despite the economic benefits of casino gambling, expansion is
often opposed in local communities. While proponents of casino resorts
cite benefits associated with tax revenue, employment, and inbound
tourism (Eadington, 1998, 2011; Philander, 2014; Philander, Bernhard,
Wimmer, Singh, & Eadington, 2015), detractors cite many different
social concerns including crime, money laundering, loss of community
values, and environmental impacts as reasons to reject casino expansion
(Ishizaka, Nemery, & Lidouh, 2013; Lee & Back, 2003, 2006b; Lee et al.,
∗

2010; Wan, 2012; Wu & Chen, 2015). In addition to those objections,
perhaps the most significant concern of casino gambling critics is the
harm associated with gambling disorders (Anders, 1996; Korn, 1999,
2000; Korn, Gibbins, & Azmier, 2003; Philander, Abarbanel, Bernhard,
& Cho, 2017), which are also often described as the originating source
of many of those negative events, due in part to empirical associations,
but also to historical and contemporary views of persons with gambling
disorders as degenerate (Custer, 1984; Hodgins, Stea, & Grant, 2011;
Rose, 1988).
The economic impacts and social concerns of casino gambling are
both so great that casinos are typically regulated and controlled in a
manner different from other industries. This includes locating casinos in
geographically isolated resort communities (e.g. Macau SAR, China;
Atlantic City, USA; and Jeju Island, South Korea), locating casinos along
borders with other jurisdictions (e.g. France casinos along the
Switzerland border, Cambodian casinos along the Thailand border, and
Oklahoma casinos along the Texas border), and restricting entry by
local residents (e.g. Bahamas, Singapore, and Monaco), in an effort to
accrue economic benefits while exporting the negative impacts of
gambling addiction to other jurisdictions (Eadington, 1999). Additional
controls occur at the firm level. These interventions are initiated by
both regulators and firms, and include player self-exclusion (Gainsbury,
2014; Parke & Rigbye, 2014), advertising and marketing restrictions
(Parke, Harris, Parke, Rigbye, & Blaszczynski, 2014), and education

School of Hospitality Business Management, Carson College of Business, Washington State University, 915 N. Broadway, Everett, WA, USA.
E-mail address: kahlil.philander@wsu.edu.

https://doi.org/10.1016/j.tourman.2018.10.017
Received 10 October 2018; Accepted 12 October 2018
Available online 16 October 2018
0261-5177/ © 2018 Elsevier Ltd. All rights reserved.

K.S. Philander

programs designed to prevent the development of problems (Wood &
Griffiths, 2014; Wood, Wohl, Tabri, & Philander, 2017).
While the concerns over the harms from gambling preoccupy many
public debates and industry efforts, there is an absence of well tested
theory describing whether the presence of regulated casinos will increase the prevalence of gambling problems in a local community. This
debate perpetuates because rates of problem gambling are largely unchanged over multiple decades, despite the broad expansion of regulated casino gambling (Welte, Barnes, Tidwell, Hoffman, & Wieczorek,
2015). To effectively develop casino-related tourism strategies, policies,
and programs, a better understanding of marginal impacts of casino
properties on regional gambling-related harms is important. Little empirical work has carefully assessed the impact of gambling expansion on
regional gambling problems. In this study, a large representative health
survey collected by Canada's national statistical agency, Statistics Canada, is connected to geographic information of casino locations, to
estimate the impact of casinos on local residents' likelihood of gambling
and of developing problems with gambling.

There are few studies that have tested exposure or adaptation effects. In a short review, Tong and Chim (2013) found mixed results in
terms of the relationship between proximity and problem gambling
risk, which may be a function of methodological limitations in the literature. The most illustrative empirical literature is from Jacques &
Ladouceur and colleagues. In two studies, the authors exploited a natural experiment of a Quebec casino opening by comparing respondents
from the local municipality to a similar city without expansion (Jacques
& Ladouceur, 2006; Jacques, Ladouceur, & Ferland, 2000). They observed that problem gambling rates fell in both cities but fell further in
city without a casino. Subsequently, they conducted a repeated measures study of a Canadian region during a period of gambling expansion
and found a relationship between casino proximity and gambling participation (Sévigny, Ladouceur, Jacques, & Cantinotti, 2008). They did
not find a relationship between proximity and problem gambling, but
that study controlled for relatively few confounding variables. One of
the few studies using a large and representative data set is Pearce,
Mason, Hiscock, and Day (2008), which focused on a national sample of
New Zealand residents. The authors found casino proximity-based impacts on problem gambling but could not control for individual-level
health risk factors. This is an important omission, as gambling disorder
is highly related to other mental health and addiction issues, and is
often not an individual's primary diagnosis (Hodgins et al., 2011;
Johansson, Grant, Kim, Odlaug, & Götestam, 2009).

1.1. Exposure and adaptation theories
Gambling disorder is a behavioral addiction, characterized by high
involvement in gambling in terms of time and/or money spent on the
activity, along with continued play despite substantial negative outcomes personally, socially, and/or financially (Hodgins et al., 2011). As
casino gambling expanded throughout the 20th century, many researchers and pundits viewed gaming expansion as monotonic in its
impacts on gambling participation and problems – that is, with an increased number of casinos would come an increased amount of societal
harms (Abbott et al., 2013; Korn, 2000; Volberg, 2004). This came to be
known as the “exposure model”, and related inferences served as a
reason for limiting the expansion of casino gaming across jurisdictions.
A more nuanced perspective on the theoretical relationship between
casino expansion and gambling disorder was introduced by LaPlante
and Shaffer (2007), who describe exposure to gambling as similar to
other public health toxins. Individuals are susceptible to infection, with
some persons having higher risks than others, but they are also capable
of social and behavioral responses that can reduce harmful impacts
(McGuire, 1964). Persons exposed to gambling may therefore develop
problems if there is exposure and they are adequately vulnerable, but
individual and social behaviors may adapt to recover from problems
and prevent future harms. Conceptually, adaptation may be observed
through reduced frequency of play, more informed decision making,
recognition of risks, better treatment access and delivery, and/or more
responsible play (LaPlante & Shaffer, 2007; Prentice & Zeng, 2018).
Based on the theorized relationship between casino availability and
gambling behavior, two null hypotheses emerge:

2. Methodology
2.1. Model
In this study, the likelihood of gambling and the severity of gambling problems are modeled as a function of RE, along with risk-factors
associated with individual health (IH) (self-perceived health categories,
self-perceived mental health categories, reported mood disorder, reported anxiety disorder, self-perceived stress categories, and alcohol
consumption frequency categories) and demographic variables (DE)
(sex, province of residence, marital status, income group, age group).
RE is adapted from the model proposed by Shaffer et al. (2004):

RE =

+

1 f (D ) +

2 f (P ) +

3 f (T ) +

4 f (X 4 ) + …+ n f (Xn ) +

Where RE is determined by a constant ( ) , the dose or exposure quantity
(D), the potency or types of gambling (P), the duration of exposure or
time with gambling (T), additional public health factors ( Xi ), where
i (4, n) , and the model error term ( ) . Participation in gambling is
modeled as a function of RE, IH, DE, and an error term, µg :
Participation = f (RE , IH , DE , µg ) . Problem gambling risk is modeled as
a function of RE, IH, DE, and an error term, µpg :
Problem Gambling Risk = f (RE , IH , DE , µpg ) . Participation is modeled
with a binary dependent variable and estimated using a logit function
while problem gambling risk is modeled with an ordinal dependent
variable and estimated using an ordered logit function.

H1. Participation in gambling is unrelated to the number of gambling
venues
H2. Gambling problems are unrelated to the number of gambling
venues

2.2. Data

To better describe the mechanisms by which individuals may be
exposed to gambling, Shaffer, LaBrie, and LaPlante (2004) propose a
regional exposure (RE) model, and operationalize component parts of
exposure to better frame the impacts of gambling availability on gambling problems. The authors posit gambling disorder's relationship to
regional exposure can be functionally described as related to dosage
(the number of gambling venues), potency (the types of gambling offered) and duration (the length of time that gambling has been available. While this study focuses only on casino-gaming, potency effects
are ignored, but a third null hypothesis is added to account for duration
effects:

Individual response data is taken from the Canadian Community
Health Survey (CCHS) microdata file (Canadian Community Health
Survey - Annual Component Study Documentation, 2017). The CCHS is
a large cross-sectional survey of Canadian residents aged 12 or older,
which focuses on health status and health determinants and is designed
to provide reliable estimates at the health region level. The individuallevel response rate is 87.3%. In the 2013/2014 CCHS release, an optional gambling-related question module was included for four provinces: Quebec (QC), British Columbia (BC), Manitoba (MB), and Saskatchewan (SK). Across the four provinces, 50,048 respondents
received this module. The problem gambling prevalence rates for the
four provinces are provided in Table 2. While problem gambling prevalence rates are not available by health region in earlier versions of the

H3. Gambling problems are unrelated to the length of casino
availability

K.S. Philander

Table 1
Prevalence rates of gambling categories from 2002 CCHS and 2013/2014
CCHS.

QC 2002
QC 2013/
BC 2002
BC 2013/
MB 2002
MB 2013/
SK 2002
SK 2013/

Non-problem/
Non-gambler

Low-risk

Moderate-risk

Problem
gambler

Count

96.86%
97.92%

2.22%
1.57%

0.53%
0.25%

0.40%
0.27%

20,916

94.85%
96.64%

4.17%
2.87%

0.59%
0.34%

0.39%
0.16%

13,910

93.40%
95.05%

4.89%
4.06%

0.94%
0.46%

0.76%
0.43%

92.26%
95.14%

5.20%
4.03%

0.79%
0.58%

1.08%
0.24%

Table 3
Estimated effect of casino availability on participation (Logit model).
(1)

Casinos per 100,000
Casinos per 100,000 sq.
Self-perceived health

PGSI group
Gamble
Casinos per 100,000
Casinos per 100,0002
Years w/casino(s)
Self-perceived health
Self-perceived mental health
Mood disorder
Anxiety disorder
Self-perceived stress
Alcohol freq.
Sex
Province
Marital status
Income group
Age group

Std. Dev.

Min

Max

50,048
50,048
50,048
50,048
50,048
49,986
49,963
49,972
49,966
49,827
49,767
50,048
50,048
50,048
50,048
50,048

0.040
0.468
0.523
0.704
9.311
2.593
2.953
0.076
0.064
2.661
2.856
1.555
2.281
2.401
3.379
9.605

0.244
0.499
0.656
1.461
8.492
0.997
0.929
0.265
0.244
1.012
2.306
0.497
1.283
1.294
1.431
4.222

2.946
8.679

DV: Gamble

DV: Gamble

DV: Gamble

1.974
(0.073)
0.808∗∗∗
(0.013)

1.355
(0.068)
0.909∗∗∗
(0.018)

No
No
No
No
No
50,048
.007

Yes
Yes
Yes
Yes
Yes
50,048
.052

1.333∗∗∗
(0.068)
0.917∗∗∗
(0.018)
0.967∗∗
(0.011)
1.013
(0.012)
0.977
(0.039)
1.036
(0.044)
1.045∗∗∗
(0.011)
1.077∗∗∗
(0.005)
Yes
Yes
Yes
Yes
Yes
49,313
.057

∗∗∗

∗∗∗

Mood disorder
Anxiety disorder
Self-perceived stress
Alcohol Freq.
Sex
Province
Marital status
Income
Age
Observations
Pseudo R2

Table 2
Summary statistics.
Mean

(3)

Self-perceived mental health

Note: Prevalence rates are limited to respondents aged 15 or older to compare
similar age groupings.

Count

(2)

Odds ratios reported in main results. Robust standard errors in parentheses.
*p < 0.05, **p < 0.01, ***p < 0.001.

This reflects direct effects of play at casinos, but also reflects potential
changes in social norms towards all gambling, as a result of regional
exposure. The problem gambling severity index (PGSI group), as
amended by Currie, Hodgins, and Casey (2013), is used to measure
gambling problems and includes four ordinal categories: non-problem/
non-gambler, low-risk, moderate-risk, and problem gambler. Summary
statistics for variables used in the analysis are provided in Table 1. All
estimates are produced using StataMP 15 (StataCorp LLC, 2017).
3. Results

CCHS, the 2001 study did provide rates by province, which are also
shown in Table 2. Across all provinces, the proportion of problem and
moderate-risk gamblers dropped by 50.0% over the 12-year period:
z = 7.20, p < 0.001.
Casino location and opening date information is primarily taken
from records at the Alberta Gambling Research Institute (2018), but is
supplemented with information about BC electronic gaming machine
casinos known as community gaming centres (Local Government Share
of Provincial Casino and Community Gaming Centre Revenue, 2018)
and individual property searches on Google Maps that resulted in the
removal of racino locations in Quebec due to closure. In total, there are
56 properties across four provinces by 2014, which are mapped onto
the provinces’ 53 health regions. The majority of those properties, 34
total, opened after 2002.
Dose is measured using the concentration of casinos in respondents’
health regions, operationalized as the number of casinos per 100,000
residents, with linear and quadratic terms to allow for reduced returns
to scale. Duration is measured as the number of years in which the
health region has had a casino. Potency is not estimated directly as there
are no suitable proxies, but is controlled for by using provincial indicator variables, as other non-casino gambling offerings are regulated
provincially and tend to be widely distributed across health regions if
offered (e.g. retail lottery, video lottery terminals, online gambling).
As the CCHS microdata file does not report individual game participation, Gamble is defined as participation in any form of gambling.

Results of the logit model estimating casino availability effects on
gambling participation (Gamble) are provided in Table 3. Presence of
demographic control variables is indicated. In all models, casino concentration is positively related to participation in gambling with decreasing returns to scale. This implies a rejection of null hypothesis H1.
The estimates in model (3) suggest that the incremental impacts of
casinos on participation will not dissipate until 3.334 casinos exist per
100,000 residents and this is higher than any health region in the data
set. As shown in the fully specified model (3), self-perceived health,
self-perceived stress, and alcohol consumption are all statistically significant and of the correct sign. Self-perceived mental health, mood
disorders, and anxiety disorders are unrelated to gambling participation.
Results of the ordered logit model estimating casino availability
effects on problem gambling severity (PGSI group) are provided in
Table 3. In addition to participation, casino concentration is found to be
related to gambling related problems, with decreasing returns to scale.
This implies a rejection of null hypothesis H2. In model 3, the estimates
suggest that the incremental impacts of casinos will not dissipate until
3.460 casinos exist per 100,000 residents, which again is higher than
any health region in the data set. The proportional odds ratios suggest
that at one casino per 100,000 residents, there is a 47.8% increase in
the likelihood of belonging to the moderate-risk or problem gambling
categories, versus the non-problem or low-risk categories. Self-

K.S. Philander

perceived health, self-perceived mental health, self-perceived stress,
and alcohol consumption are all statistically significant and of the expected sign, while mood disorders, and anxiety disorders are unrelated
to gambling problems in this model.
Table 5 introduces duration (Years with casino) to models (2) and (3)
from Tables 3 and 4 The models show a small but significant effect size
of duration on participation in gambling and gambling problems. This
implies a rejection of null hypothesis H3. The linear casino availability
term remains significant in all models, but the quadratic term is not
significant in the fully specified models. To assess potential multicollinearity issues, variance inflation factors (VIF) were estimated. In
model (4), the VIF is 1.50 for the Years with casino variable. The only
VIF above 1.5 for a non-demographic control variable was the expected
high values for linear (16.42) and squared (11.57) casino per 100,000
variables. When only the linear variable is used (model results not
shown), the VIF falls to 1.86 and mean VIF for all variables is 2.25, well
below recommended maximums (Hair, Black, Babin, Anderson, &
Tatham, 2010).

Table 4
Estimated effect of casino availability on problem gambling risk (Ordered logit
model).

Casino per 100,000
Casinos per 100,000 sq.
Self-perceived health

(1)

(2)

(3)

DV: PGSI
Group

DV: PGSI
Group

DV: PGSI Group

2.841∗∗∗
(0.295)
0.721∗∗∗
(0.0349)

1.793∗∗∗
(0.238)
0.846∗∗
(0.0451)

1.733∗∗∗
(0.234)
0.853∗∗
(0.047)
0.831∗∗∗
(0.025)
0.853∗∗∗
(0.027)
1.197
(0.115)
1.040
(0.110)
1.129∗∗∗
(0.033)
1.064∗∗∗
(0.013)
Yes
Yes
Yes
Yes
Yes
49,313
.052

Self-perceived mental
health
Mood disorder
Anxiety disorder
Self-perceived stress
Alcohol freq.
Sex
Province
Marital status
Income
Age
Observations
Pseudo R2

No
No
No
No
No
50,048
.009

Yes
Yes
Yes
Yes
Yes
50,048
.049

4. Discussion
This study demonstrated a relatively robust relationship between
casino availability, participation in gambling, and problem gambling
risk levels in large representative data set of four Canadian provinces. It
also demonstrated a relationship between duration of casino exposure,
gambling participation, and problem gambling risk. While this may
appear to support policy interventions to restrict casino availability, it
should also be considered that overall levels of problem gambling
prevalence fell in each province between 2002 and 2013/2014 CCHS
studies, despite the number of available casinos more than doubling in
that period. These casinos were also not generally located near foreign
borders or with local access restrictions, which are tactics thought in
other jurisdictions to attenuate local resident demand.
As a whole, the empirical findings provide evidence for exposure
and adaptation forces described by LaPlante and Shaffer (2007). While
casino concentration is associated with increased participation and risk
of gambling problems in the 2013/2014 CCHS data set, the drop in
prevalence rates from the 2002 study suggests some population-wide
adaptation. LaPlante & Shaffer describe this adapation as potentially
including “social learning, waning of novelty effects, increases in
harmful consequences, developed interventions, and new interests that
preclude engaging in the initially harmful activity.”
One practice that appears particularly supportive of adaptation is
spending by the provincial governments on firm-centric responsible
gambling programs and treatment-centric problem gambling programs. In
the 2013/2014 fiscal year, the four provinces distributed between 1.2%
and 3.7% of non-lottery gaming revenue to related programs. As several
forms of problem gambling treatment have shown evidence of effectiveness (Hodgins et al., 2011), and evidence is beginning to emerge around
the effectiveness of previously untested responsible gambling problems
(Ladouceur, Shaffer, Blaszczynski, & Shaffer, 2016), the relatively high
levels of investment may have influenced the population-wide adaptation.
This would at least partially explain the drop in problem gambling prevalence despite the empirical observations in this study that increased
casino availability is associated with increased harms.
As tourism planners consider the role of casinos in their economy
and community, both the long-run trends in problem gambling prevalence should be weighed, along with the regional aspects of exposure.
Based on the observations in this study, a more socially optimal and
responsible strategy to casino expansion may not be geographic or local
access restrictions, but may instead be wider expansion paired with
increased investment in responsible gambling and treatment programs.
This will lead to greater economic impacts and will likely lead to faster
and greater social adaptation to gambling harms.

Proportional odds ratios reported in main results. Robust standard errors in
parentheses.
*p < 0.05, **p < 0.01, ***p < 0.001.

Table 5
Estimated effect of casino duration on participation and problem gambling risk.

Years with casino
Casino per 100,000
Casinos per 100,000
sq.
Self-perceived
health
Self-perceived m.
health
Mood disorder

(1)

(2)

(3)

(4)

Logit

Logit

Ordered logit

Ordered logit

DV: Gamble

DV: Gamble

DV: PGSI
Group

DV: PGSI
Group

1.007∗∗∗
(0.00135)
1.182∗∗
(0.0672)
0.950∗
(0.0204)

1.007∗∗∗
(0.00136)
1.159∗
(0.0666)
0.960
(0.0208)
0.967∗∗
(0.0105)
1.013
(0.0121)
0.977
(0.0387)
1.038
(0.0436)
1.044∗∗∗
(0.0107)
1.077∗∗∗
(0.00481)
Yes
Yes
Yes
Yes
Yes
49,313
.057

1.008∗
(0.004)
1.551∗∗
(0.231)
0.886∗
(0.051)

1.008∗
(0.004)
1.517∗∗
(0.229)
0.891
(0.053)
0.831∗∗∗
(0.025)
0.853∗∗∗
(0.027)
1.196
(0.115)
1.041
(0.111)
1.129∗∗∗
(0.033)
1.064∗∗∗
(0.013)
Yes
Yes
Yes
Yes
Yes
49,313
.052

Anxiety disorder
Self-perceived stress
Alcohol freq.
Sex
Province
Marital status
Income
Age
Observations
Pseudo R2

Yes
Yes
Yes
Yes
Yes
50,048
.053

Yes
Yes
Yes
Yes
Yes
50,048
.039

Odds ratios and proportional odds ratios reported in main logit and ordered
logit results, respectively. Robust standard errors in parentheses.
∗
p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.

K.S. Philander

4.1. Limitations/future research

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This study examined a large sample of individuals, but all came
from a single country and may not be widely generalizable in different
nations or cultures. Similar work in other jurisdictions, or with other
forms of gambling, would be useful contributions. While this study
controlled for most individual level risk-factors associated with gambling disorder, the cross-sectional analysis is subject to typical concerns
around endogeneity and reverse causality. Future studies that examine
longitudinal data sets would provide better evidence around causality.
Last, more research is needed for tourism planners to understand what
adaptive programs warrant increased investment, as the specific dynamics of adaptation are largely unknown.
Conflicts of interest
No external funding was received for this study.
KP was previously the Director of Social Responsibility at the British
Columbia Lottery Corporation. In the past three years, he has received
funds from the British Columbia Lottery Corporation, Washington State
Gaming Commission, Manitoba Gambling Research Program,
Responsible Gambling Council of Canada, UNLV International Centre
for Gaming Regulation, U.S.-Japan Business Council, Wynn Resorts,
West Virginia Lottery, Indiana Gaming Commission, Board of Regents
of the Nevada System of Higher Education, and iDevelopment and
Economic Association. He has received reimbursement for travel from
the National Council for Problem Gambling, International Association
of Gaming Advisors, National Centre for Responsible Gambling, North
American State and Provincial Lottery Association, Evergreen Council
on Problem Gambling, Global Gaming Expo Asia, and Alberta Gambling
Research Institute.
Author contributions
KP completed all aspects of this study.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://
doi.org/10.1016/j.tourman.2018.10.017.
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K.S. Philander

Kahlil S. Philander, Ph.D. Kahlil Philander is an Assistant
Professor at Washington State University's Carson College
of Business, and an Honorary Lecturer at the School of
Psychology at the University of Sydney. His research interests are in the socio-economic impacts of gambling.

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