1209909
research-article2023

CQXXXX10.1177/19389655231209909Cornell Hospitality QuarterlyPhilander

Article

Are Out-of-State Players the Target
Market? A Geospatial Analysis of the
Gambling Export Strategy in the
United States

Cornell Hospitality Quarterly
2024, Vol. 65(3) 294­–303
© The Author(s) 2023
Article reuse guidelines:
sagepub.com/journals-permissions
https://doi.org/10.1177/19389655231209909
DOI:
10.1177/19389655231209909
journals.sagepub.com/home/cqx

Kahlil S. Philander1

Abstract
Anecdotal evidence suggests that gambling venues are often placed in locations to attract players from outside the
jurisdiction, as part of a gambling export strategy. This strategy may be appealing, as it can simultaneously increase local
economic impacts while decreasing the proportional share of social costs to the host jurisdiction. This study examines
the relative use of the gambling export strategy in the United States by developing a geospatial model of potential U.S.
consumer demand to understand whether casinos are systematically located to attract out-of-state players. Based on
1,481 gambling venues found in the country, our naïve model indicates that 31.7% of potential demand in the United
States is closer to an out-of-state casino than an in-state casino. Our discount model that accounts for distance indicates
that this translates to 20.7% of potential demand in the United States. The results show significant difference at the state
level, with out-of-state potential demand estimates ranging from 0% to 71.2%. These findings suggest that there may be
further development in the U.S. gambling market as jurisdictions seek to recapture their own residents who are gambling
out-of-state.
Keywords
casino demand; gaming; gambling; tourism; geospatial model
Casino location decisions are often controversial.
Stakeholders during site approval processes may have differing views on the value of a development, which often
include a diverse mix of costs and benefits. Detractors may
cite social concerns, including increases in crime, environmental impacts, and problem gambling, while supporters
may cite benefits to economic welfare through jobs, tax revenue, and additions to tourism infrastructure (Eadington,
1996, 1998; Wu & Chen, 2015).
As a function of the mixture of costs and benefits, scholars propose that jurisdictions expanding casino gambling
will have a preference for site locations that can target transborder consumers (Assaf et al., 2013; Calcagno et al., 2010;
Haucap et al., 2022). The reason for this is twofold: (1) An
export-oriented strategy can increase the potential economic output from the venue, particularly for venues in
low-income communities, and (2) an export strategy may
reduce negative social impacts, as many gamblers incurring
harm would not reside in the host jurisdiction. In effect,
what we refer to as the “gambling export strategy” is a form
of regulatory arbitrage, whereby residents from a target
jurisdiction can be serviced without a license to operate in
that jurisdiction. In his seminal treatise on the economics of

casino gambling, Eadington (1999, p. 186) articulates several examples of gambling export markets dating back to
the mid-19th century:
Historically, casinos have often been introduced to capture
economic benefits from “exportin,” casino gaming to customers
from regions where the activity is prohibited. Jurisdictions that
legalized casinos were often resource poor, or under economic
duress. One or both of these factors apply to Monaco (1863),
Nevada (1931), Macao (in the early 20th century), the
Caribbean (1960s), and Atlantic City (1976), and help explain
why many of the newly authorized American jurisdictions
were keen on having casinos. East St. Louis, Illinois, Gary,
Indiana, Tunica County, Mississippi, Shreveport, Louisiana,
New Orleans, and Detroit have experienced varying degrees of
economic distress in recent years. It was believed that casinos—
as long as they could bring about injections of spending from

Washington State University, Everett, USA

Corresponding Author:
Kahlil S. Philander, School of Hospitality Business Management, Carson
College of Business, Washington State University, 915 North Broadway,
Everett, WA 98201, USA.
Email: kahlil.philander@wsu.edu

outside the area—would create jobs, stimulate local investment,
and enhance local government revenues.

This study assesses whether there is a systematic placement of gambling venues in areas targeting transborder
gamblers or whether those locations are idiosyncratic cases
that do not reflect the typical development of retail gambling. Since the jurisdiction hosting the venue has limited
obligations to the well-being of nonresidents, pervasive use
of the gambling export strategy is likely to lead to an overprovision of gambling availability to the targeted jurisdiction and an under-provision of support services. It
additionally may lead to an eventual oversupply of gambling venues, as targeted communities seek to recapture
their residents’ demand.
In this study, the gambling export strategy is assessed
through a spatial modeling of the entire U.S. market at the
zip code level. The share of potential demand accruing to
each gambling property in the United States is estimated
based on site proximity to local populations. The share of
potential demand that is likely to be derived from out-ofstate residents is also estimated. Those figures are then
aggregated at the state level to assess policy differences
across the country, in terms of targeting in-state or out-ofstate patrons.

Related Literature
Empirical evidence suggests that residents’ willingness to
support tourism developments is well described by social
exchange theory (Gursoy et al., 2019). As a result, site
selection processes for gambling venues seem likely to be
influenced by local perceptions of the relative costs and
benefits that may accrue from the projects. In this context,
social exchange theory posits that stakeholders reaching a
consensus will tend to do so through an agreement that creates value for all parties (Cropanzano & Mitchell, 2005),
subject to their relative power to influence the outcome
(Musinguzi, 2019; Nunkoo, 2016).
Studies validating social exchange theory’s application
to casino developments were conducted with residents from
South Korea (Lee & Back, 2003; Lee et al., 2010, 2003),
Singapore (Tan et al., 2017), Macau (Wan, 2012), and the
United States (Chhabra, 2008; Lee et al., 2010; Perdue
et al., 1999; Stitt et al., 2005). They generally find that the
most important factor in predicting perceived benefits of
casinos was a perception of positive economic outcomes
from the development. Research by Humphreys (2010) on
media coverage of casino projects concludes that consumers are more likely to be positively influenced by perceptions of entertainment, excitement, and wealth associated
with the development.
In addition to the specific mix of costs and benefits, the
distance of residents from the venue is also likely to

mediate relative valence of impacts (Chhabra, 2012;
Jurowski & Gursoy, 2004). It therefore seems plausible that
as venues become more distant from in-jurisdiction population centers, support will increase for two reasons. First, the
remaining residents near the venue will be more likely to
directly benefit from the economic activity (e.g., local jobs)
(Wenz, 2014). Second, residents farther away will be more
neutral to the development.
Management preferences may not be consistent with
other stakeholders in all circumstances. If firms are attempting to maximize their total addressable market, they would
prefer high population density locations (Ishizaka et al.,
2013; Johnson & Bowen, 1994; Pérez et al., 2022) that are
distant from other gambling venues (Barrow et al., 2016;
Walker & Nesbit, 2014). They therefore may be agnostic to
in-state or out-of-state demand, as long as there is a large
population center nearby. However, in pursuit of an
approved site, they may seek to align themselves with the
needs of other stakeholders, particularly government policymakers (Mottice, 2018).
Other stakeholder interests are less well studied, but
available evidence suggests that public health advocates
may prefer locations away from population centers. Risks
of problematic gambling appear to be related to gambling
frequency (LaPlante & Shaffer, 2007; Potenza et al., 2019),
and gambling frequency was demonstrated to be related to
access convenience (O’Gilvie, 2022; Papineau et al., 2020;
Philander, 2019; Philander et al., 2022; St-Pierre et al.,
2014). Also, higher risks to socioeconomically disadvantaged populations tend to occur in urban areas, which may
further motivate local public health stakeholders to encourage remote locations (Conway, 2015; Pérez et al., 2022).
Today, the United States is relatively well-developed
casino market, with electronic gaming machine and/or cardbased gambling in 42 out of 50 states (American Gaming
Association, 2022). Each state tends to have an idiosyncratic regulatory structure defining the venue features,
which are categorized by the American Gaming Association
(2022) as commercial casinos/racetrack-casinos, tribal casinos, card rooms, and electronic gaming devices in noncasino locations. Together, these components constitute the
U.S. retail gaming market.
The structure of the Indian Gaming Regulatory Act is
relevant to understanding tribal casino locations (for a full
review of the Act and its impacts, see Akee et al., 2015).
The Act requires tribal governments to negotiate compacts
with state government regarding several terms, often including conditions on allowable games, revenue sharing, and
commercial competition (Akee et al., 2015). These compacts vary meaningfully from state to state. The most relevant impact of the Act is the requirement that venues must
generally be located on tribal trust land, although there is a
process for tribal governments to receive approval for subsequently acquired land (Akee et al., 2015), which has

enabled some tribes to locate casinos outside of tribal trust
areas.
The regulated U.S. gambling market also includes lotteries, racetracks without electronic gaming devices, sports
betting, and online gaming. Each vary in design and availability by state. Compared to other markets, the impact of
nonretail gaming on retail gaming in the United States is
likely to be relatively minor. Kearney (2005) examined the
relationship between state lottery spending and other forms
of gambling using micro-level household data and found
that lottery spending tends not to be related to retail gaming.
Instead, it is more related to general household consumption. The distribution of lotteries across the country is also
relatively uniform, with only three states in the contiguous
United States without a state lottery. This suggests that
comparative impacts across states are likely to be null in
most cases.
Online and sports betting is relatively underdeveloped in
the United States. Single-event sports betting was effectively restricted to Nevada prior to 2018 (Roberts &
Gemignani, 2019) and only three states had online casino
games prior to 2020 (American Gaming Association, 2022).
Although 22 new states legalized mobile sports betting
since 2018 (American Gaming Association, 2022), due to
the relatively recent emergence of these markets, most
casino development in the United States predates the expansion of sports betting and online gaming.
This study does not propose specific hypotheses as it is a
descriptive study of the entire market. However, based on
the general alignment of stakeholder interests toward the
gambling export strategy when there are population centers
in nearby jurisdictions, a meaningful share of demand for
U.S. gambling venues is expected to be out-of-state residents, despite adoption of gambling in most U.S. states.

Methodology
Data
A list of every U.S. gambling venue was obtained from the
World Casino Directory (Morrison, 2019). The directory of
venues includes listings for commercial casinos, tribal casinos, card rooms, and licensed slot parlors. Each venue in the
directory and its address was queried into the Here.com
REST API (Guide—HERE Geocoding & Search API, n.d.)
to cross-validate the entry in their dataset of registered businesses and retrieve the geolocation coordinates of the property (Weber & Péclat, 2017). Venues that did not return a
listing were manually reviewed for accuracy using Google
Search. The list was then cross-referenced against the
American Gaming Association (2022) list of states with
casinos or cardrooms and if any jurisdictions were missing,
the states were manually reviewed for venues, inputted into
the casino list, and then re-queried into the Here.com REST

Cornell Hospitality Quarterly 65(3)
API. In total, 1,480 venues were included in the database.
The complete list is provided in the Supplementary
Materials.
Household population and income data by zip code tabulation area were obtained from the U.S. Census Bureau,
2016–2020 American Community Survey 5-Year Estimates
(U.S. Census Bureau, 2022). Values for Puerto Rico and the
U.S. Virgin Islands were removed. There were 33,579 zip
codes in the final data set, including 836 with no residents.
The mean adult population size of inhabited zip codes was
7,333. Geographic shapefiles of zip code tabulation areas
were obtained from data.gov (General Services
Administration, 2021). Nonpublic datasets and code to replicate the results are provided in the study Supplementary
Materials.

Data Analysis
The Euclidian distance in miles was computed from every
zip code tabulation area (zip code) centroid to every gambling venue, using the geosphere package in R (Hijmans,
2011). We use Euclidian distance due to computational constraints in retrieving travel distances or commute times at a
national level. For example, the Google Maps Geocoding
API provides credit for 5,000 queries of travel commute
times each month and with 33,579 populated zip codes and
1,476 gaming venues, it would take 1,032 years or $2.48
million paying per query, to estimate the complete set of
possible pairs. Since commute times and Euclidian distance
were found to have a correlation of 0.981 in Philander et al.
(2022), we expect relatively little impact on our findings
from this method.
Relative gross income available from each zip code is
estimated as the sum of all household income in the zip
code (U.S. Census Bureau, 2022). This aggregate value
reflects the product of income per capita—a proxy for discretionary spending ability—and population size—a proxy
for the addressable market of consumers. The total value
therefore serves as a relative index of potential demand
available in each region. Zip codes with higher relative
household income should have higher relative potential
demand.
Potential demand from each zip code is allocated to the
nearest gambling venue as a mechanism for determining
which venue is most likely to capture that potential demand.
Therefore, potential demand for a given venue is defined as
the sum of potential demand from zip codes where it is the
nearest venue based on its Euclidian distance.
Formally,
n

PDi =

min

∑ PD ∀ ∈ i ∈ m
( arccos ( sin∅ sin∅ + cos∅ cos∅ cos ( ∆λ )))
k

k

k =1

i

k

i

k

i−k

Figure 1.

U.S. Gambling Venue Locations.

Note. Circle plots denote each location with radius size based on the share of out-of-state potential demand (model 2). Clusters of venues with large
out-of-state potential demand appear along the New York Tri-state area, the Mississippi River, the Texas border, and the Nevada/California border.

Where, PDi is the potential demand for venue i, PDk is the
potential demand from zip code k, m is the set of all venues,
n is the set of all zip codes, ∅ is a latitude coordinate, and
λ is a longitude coordinate. Since venues are identified by
their geolocation coordinates and not their zip code, there
are no ties for the shortest distance to a venue from any
given zip code centroid.
Two different methods are used to allocate potential
demand on a relative basis:
1.

2.

A naïve model, which does not adjust potential
demand from each zip code as a function of distance
and is intended to represent the total available U.S.
consumer demand;
A discount model, which reduces potential demand
based on distance from the zip code to the gambling
venue and is intended to represent a reduction in
potential demand for far away venues. Discounting
potential demand based on proximity and allocating
potential demand based on the nearest property is
consistent with modeling techniques used in actual
casino site selection processes (e.g., Barrow &
Borges, 2014; Cummings et al., 2017; Davis, 2006;
Morley et al., 2014).

Potential demand is discounted using estimates produced
from the data and visit frequency model in Philander et al.
(2022). The visit frequency model estimates the frequency

of venue visitation as a function of a resident’s travel time
from the nearest gambling property. The model is adapted
to produce an index value of potential demand, based on the
projected number of venue visits as a function of distance.
The original study by Philander et al. (2022) was estimated
using travel time as the access variable. This study re-estimates that model but substitutes distance instead travel time
(see Supplementary Materials). Travel times would not be
computationally scalable to the size of our U.S. national
study. The distance values are transformed using the natural
logarithm to represent the effect of distance impacting
demand at a decreasing rate. Figure 1 illustrates the gambling venue locations with visualizations of the estimated
share of proximity discounted potential demand from outof-state (model 2).
For venue i, potential demand ( PDi ) is distinguished
based on whether it originates from an in-state zip code or
an out-of-state zip code. The share of in-state potential
demand for state S is given by the sum of potential demand
from in-state zip codes over the sum of potential demand
PDi ∀i ∈ S i ∩ S k
from all zip codes: α S =
, where S i is
PDi ∀i ∈ S i

∑
∑

the set of venues i in state S, and where S k is the set of zip
codes k in state S. The share of out-of-state demand is given
by 1− α S .
We compute the arithmetic mean of out-of-state potenαS
, where N S is
tial demand across states: 1 − α S = 1 −
NS

∑

Cornell Hospitality Quarterly 65(3)

the count of all states with casinos, and we compute a
weighted mean to assess out-of-state potential demand for
m
PDi ⋅ αi
i =1
,
the United States as a whole: 1 − µ = 1 −
m
PDi

differences. West Virginia casinos are generally located
along its border near neighboring state population centers
while California casinos are generally located near its highdensity urban regions away from interstate borders.1

where αi is the share of in-state potential demand for venue

Discussion

∑
∑

i =1

k

PDi ∀ k ∈ S
.
PDi
The market is modeled in aggregate, but also in subsets
for venues with hotels and venues that are tribal-owned
(Morrison, 2019). A more detailed description of venue features may improve forecasts of actual revenue but would
not be useful for our purposes of understanding location
decisions by stakeholders as the venue design decisions are
endogenous to location. Venue features are a function of
local market demand conditions and a more amenity rich
venue would only yield a sufficient return if there was an
available target population with sufficiently low competitive pressures. In such circumstances, our naïve model
should capture these patrons, as it makes no assumptions
about constraints on catchment area size. It appears to be
reasonable that our approach will capture market dynamics
as many casino feasibility studies tend to solely model the
business opportunity based on measures of regional income
and population size (e.g., Convergence Strategy Group,
2021; Union Gaming Analytics, 2019; WhiteSand Gaming,
2014).
i:

Results
Across all states, the arithmetic mean share of out-of-state
revenue per venue is 0.145 (SD = .294) using the naïve
model and 0.132 (SD = .280) using the discount model.
After weighing the means by total demand to determine the
U.S.-wide effect, the estimated share of out-of-state revenue increased substantially using both the naïve method (M
= .317) and the discount method (M = .207).
Figure 2 shows the share of potential demand estimated
from out-of-state consumers for all jurisdictions in the dataset. On a state-by-state basis, there is little difference
between the naïve and discount methods, but there is substantial variation from one state to another. The absence of
a difference between the two methods suggests that competitive effects may be more likely to be the binding constraint on potential demand catchment areas, rather than a
lack of access entirely by residents. A visual analysis of the
subset of venues with hotels suggests that these properties
tend to have a larger share of potential demand from out-ofstate. Similarly, the subset of venues with tribal ownership
tend to have greater shares of out-of-state potential demand.
The results show wide dispersion in the share of out of
state revenue by state, ranging from 0% to 71.2%. Maps of
West Virginia and California in Figure 3 illustrate these

This study contributes to the literature through a rigorous
analysis of the gambling export strategy in the U.S. casino
market. Evidence from the analysis suggests that the share
of consumers being targeted by neighboring state casinos is
meaningful. Nearly one-third of potential demand (31.7%)
is closer to an out-of-state casino than an in-state casino,
which when discounted based on proximity translates to
over a fifth (20.7%) of estimated U.S. potential demand.
That is, we estimate that roughly one out of five dollars
spent on venue-based gambling in the United States ends up
in an out-of-state casino. These findings suggest meaningful use of the gambling export strategy in the United States,
but not an exclusive or dominant use in venue placement
strategy.
On a state-by-state basis, there is more nuance in the
results. There is substantial variation among jurisdictions in
the share of potential demand that is coming from out-ofstate, ranging from a low of 0.0% in Texas to a high of
71.2% in West Virginia. It remains an open question what is
causing these differences. There may be a general preference for the gambling export strategy but geographic constraints for large states with few highly populated neighbors
may narrow the scope of viable locations. For example,
Florida has greater access to large population centers instate compared to out-of-state and this may explain its small
share of out-of-state potential demand. In general, it appears
as though states with larger land masses (e.g., California,
Texas, Montana, Arizona, New Mexico, and Wyoming)
mostly draw from in-state.2 Empirically, the gambling
export strategy seems to be used more often by small or
medium sized jurisdictions, which is likely to be a function
of interstate travel time.
In the subset of venues with hotels, we observed a relatively greater share of potential demand from out-of-state.
This finding is consistent with hotels being used to make
the venue more attractive for travelers from farther distances, as a property with lodging can more easily be a multiday trip. The subset of venues with tribal ownership also
had relatively greater shares of out-of-state potential
demand. This may partially reflect constraints of having to
locate the venue on tribal trust land.
One implication of the findings is that there may be further development in the U.S. gambling market as jurisdictions seek to recapture their own residents that are gambling
out-of-state. For instance, as part of the discussion in the
newest state to introduce casino gaming, the Chairman of
the Massachusetts Gaming Commission specifically

Figure 2.

[Top] Share of Potential Demand from Out-of-State Consumers Using the Naïve and Proximity-Based Discounting
Models. Both Models Show Similar Results with Large Variation among States in the Estimated Share. [BottomLeft] Shares of Potential Demand Using the Naïve Model, for all Properties and Properties with a Hotel. [BottomRight] Shares of Potential Demand using the Naïve Model, for All Properties and Tribal-Owned Properties.

Note. California venues are generally found near its urban population areas in the Bay Area, Sacramento, Greater Los Angeles, San Diego County, and
other municipalities along the Interstate 5 Highway, and out-of-state consumers account for only 2.0% of the potential demand. West Virginia venues
are generally located along its borders, near population centers of neighboring states such as Washington DC, Pittsburg, PN, and Akron, OH, and outof-state consumers account for 71.2% of the potential demand. Total income density = total income / land area in square meters.

articulated the importance of recapturing resident spending
(“Research Update: Plainridge Park Casino Economic
Impacts Released for First Year of Operation,” 2017):
The principal motivation for the Legislature in crafting the
Gaming Law was to recapture the approximately one billion
spent annually by Massachusetts residents at out-of-state
casinos. This report demonstrates emphatically that we are in
the process of accomplishing that important objective.

Some evidence suggests that new properties near existing
targets of interstate demand are not accretive to the overall
market (Condliffe, 2012). Should that continue, it seems
likely that supply may become oversaturated as transborder
venues become redundant to in-state venues located closer

to core population areas. Repetti and Jung’s (2014) findings
suggest that this occurred in Atlantic City when Pennsylvania
opened Philadelphia-area casinos.
Policymakers and operators targeting out-of-jurisdiction
players should therefore be forward looking in understanding the game theory of their development strategy and consider what the response of their neighbors is likely to be
over the useful life of the development. Futures studies
could also consider how similar dynamics may emerge in
high value gambler segments where air travel is the primary
mode of travel, such as with junket operators.
There are several limitations of this study that could be
considered in future work. Most notably, this study did not
consider subjective measures of site quality that may modify the realizable geographic market. Many distinctions are

Cornell Hospitality Quarterly 65(3)

Figure 3.

Plots of Gambling Venues in California (left) and West Virginia (right) with Nearby States.

Note. California venues are generally found near its urban population areas in the Bay Area, Sacramento, Greater Los Angeles, San Diego County, and
other municipalities along the Interstate 5 Highway, and out-of-state consumers account for only 2.0% of the potential demand. West Virginia venues
are generally located along its borders, near population centers of neighboring states such as Washington DC, Pittsburg, PN, and Akron, OH, and outof-state consumers account for 71.2% of the potential demand. Total income density = total income / land area in square meters.

idiosyncratic, specific to a state or region, but improvement in determining what differentiates properties will
lead to better estimates of potential demand. Since our
naïve model makes no assumptions about constraints on
catchment area size, this limitation does not affect our
conclusion.
Future work could account for different types of venue
features (e.g., integrated resorts vs. local cardrooms) or
competitive forces (e.g., casino brands) and provide more
precise estimates of realized demand as a function of competitive effects between venues. Since design decisions
are likely to be endogenous to the location decision, this
may require a different methodological approach. Future
work may also need to consider the competitive dynamics
of online gaming. Although it is an open question how
retail and online casino gaming compete for customers
(Marionneau & Nikkinen, 2017; Philander et al., 2015),
states may elect to use online casino games to recapture
demand rather than retail properties. This dynamic is
likely to be more important in the future than in our retrospective study, as our data predates the widespread expansion of online gaming and mobile sports betting in the
United States.

To better understand location preferences relative to
opportunities, future studies could simulate how much
out-of-state demand would be gained and how much instate demand would be lost if existing properties were
relocated nearer to borders. Qualitative studies could
review reports or media coverage during the periods of
site selection debate to understand how location preferences may have evolved throughout the early development
processes.
Last, it seems likely that the proximity demand model
underestimates the true out-of-state demand, as fly-in tourists to some destinations like Las Vegas are unlikely to be
adequately captured. A mixed methods study that includes
behavioral data of gamblers would improve the validity of
these findings. Although we do not estimate actual demand
or spending, our proxy using census population and income
data closely mirrors actual feasibility studies, and therefore
may be a better measure of stakeholder intention than postdevelopment revenue.
Author Contributions
Kahlil S. Philander —All content.

Declaration of Conflicting Interests
The author(s) declared the following potential conflicts of interest
with respect to the research, authorship, or publication of this article:
Declarations: No funding was received to assist with the preparation
of this manuscript. Financial interests: K.P. received research funds
from the Washington State Gaming Commission, Manitoba Gambling
Research Program, UNLV International Center for Gaming
Regulation, U.S.-Japan Business Council, Wynn Resorts, Board of
Regents of the Nevada System of Higher Education. He received consulting payments from British Columbia Lottery Corporation, Eilers
& Krejcik Gaming, Seminole Hard Rock Entertainment Inc.,
Responsible Gambling Council of Canada, the Commonwealth of
The Bahamas, West Virginia Lottery, Indiana Gaming Commission,
Intralot, and iDevelopment and Economic Association. He received
expert witness payments from Crown Resorts Ltd, Jones Ward PLC,
and Scientific Games Corp. Nonfinancial interests: K.P. is a member
of the editorial boards at International Gambling Studies, the UNLV
Gaming Research and Review Journal, and the Brief Addiction
Science Information Source.

Funding
The author(s) received no financial support for the research,
authorship, or publication of this article.

ORCID iD
Kahlil S. Philander

https://orcid.org/0000-0002-0747-0772

Data Availability Statement
Code to replicate the analysis is available in the supplementary
materials.

Supplemental Material
Supplemental material for this article is available online.

Notes
1.

2.

Visualizations of all states can be retrieved from the related
web app located at: https://o1ufqz-kphilander.shinyapps.io/
casino/
We thank an anonymous reviewer for this insight.

References
Akee, R. K. Q., Spilde, K. A., & Taylor, J. B. (2015). The Indian
Gaming Regulatory Act and its effects on American Indian
economic development. Journal of Economic Perspectives,
29(3), 185–208. https://doi.org/10.1257/jep.29.3.185
American Gaming Association. (2022). State of the states 2022:
The AGA analysis of the commercial casino industry (pp.
1–125). https://www.americangaming.org/resources/state-ofthe-states-2022/
Assaf, A. G., Cvelbar, L. K., & Pahor, M. (2013). Performance
drivers in the casino industry: Evidence from Slovenia.
International Journal of Hospitality Management, 32, 149–
154. https://doi.org/10.1016/j.ijhm.2012.05.006

Barrow, C. W., & Borges, D. R. (2014). Gravity models and
casino gaming: A review, critique, and modification. UNLV
Gaming Research & Review Journal, 18, 51–84.
Barrow, C. W., Borges, D. R., & Meister, A. P. (2016). An empirical framework for assessing market saturation in the U.S.
casino industry. Gaming Law Review and Economics, 20(5),
397–411. https://doi.org/10.1089/glre.2016.2057
Calcagno, P. T., Walker, D. M., & Jackson, J. D. (2010).
Determinants of the probability and timing of commercial casino legalization in the United States. Public Choice,
142(1–2), 69–90. https://doi.org/10.1007/s11127-009-9475-2
Chhabra, D. (2008). Social exchange theory in resort and nonresort casino settings. Anatolia, 19(1), 155–160. https://doi.
org/10.1080/13032917.2008.9687060
Chhabra, D. (2012). Exploring social exchange theory dynamics
in Native American casino settings. UNLV Gaming Research
& Review Journal, 11(2), 31–48. https://digitalscholarship.
unlv.edu/grrj/vol11/iss2/3
Condliffe, S. (2012). Pennsylvania casinos’ cannibalization of
regional gambling revenues. UNLV Gaming Research &
Review Journal, 16(1), 45–58. https://digitalscholarship.unlv.
edu/grrj/vol16/iss1/3
Convergence Strategy Group. (2021). City of Richmond Casino
RFQ/P—Market analysis. City Council Informal Meeting.
https://www.rva.gov/sites/default/files/2021-03/Casino%20
RFQ%20RFP%20Presentation%20-%20Market%20
Analysis.pdf
Conway, M. (2015). Vulnerability modeling of casinos in the United
States: A case study of Philadelphia. Applied Geography, 63,
21–32. https://doi.org/10.1016/j.apgeog.2015.05.015
Cropanzano, R., & Mitchell, M. S. (2005). Social exchange theory: An interdisciplinary review. Journal of Management,
31(6), 874–900. https://doi.org/10.1177/0149206305279602
Cummings, W. E., Walker, D. M., & Cotti, C. D. (2017). The
effect of casino proximity on lottery sales: Evidence from
Maryland. Contemporary Economic Policy, 35(4), 684–699.
https://doi.org/10.1111/coep.12225
Davis, P. (2006). Spatial competition in retail markets: Movie
theaters. The RAND Journal of Economics, 37(4), 964–982.
https://doi.org/10.1111/j.1756-2171.2006.tb00066.x
Eadington, W. R. (1996). The legalization of casinos: Policy
objectives, regulatory alternatives, and cost/benefit considerations. Journal of Travel Research, 34(3), 3–8. https://doi.
org/10.1177/004728759603400301
Eadington, W. R. (1998). Contributions of casino-style gambling
to local economies. The ANNALS of the American Academy
of Political and Social Science, 556(1), 53–65. https://doi.
org/10.1177/0002716298556001005
Eadington, W. R. (1999). The economics of casino gambling.
Journal of Economic Perspectives, 13(3), 173–192.
General Services Administration. (2021). TIGER/line shapefile,
current, nation, U.S., 2020 5-digit ZIP code tabulation areas.
https://www.census.gov/geographies/mapping-files/timeseries/geo/tiger-line-file.html
Guide—HERE Geocoding & Search API. (n.d.). HERE developer. https://developer.here.com/documentation/geocodingsearch-api/dev_guide/index.html

Gursoy, D., Ouyang, Z., Nunkoo, R., & Wei, W. (2019). Residents’
impact perceptions of and attitudes towards tourism development: A meta-analysis. Journal of Hospitality Marketing &
Management, 28(3), 306–333. https://doi.org/10.1080/19368
623.2018.1516589
Haucap, J., Nedic, R., & Şimşek, T. (2022). An empirical analysis of German casino locations (DICE). Heinrich-HeineUniversity Düsseldorf, Düsseldorf Institute for Competition
Economics.
Hijmans, R. J. (2011). Introduction to the “geosphere” package
(1.2-19). https://citeseerx.ist.psu.edu/viewdoc/download?doi
=10.1.1.205.120&rep=rep1&type=pdf
Humphreys, A. (2010). Semiotic structure and the legitimation
of consumption practices: The case of casino gambling.
Journal of Consumer Research, 37(3), 490–510. https://doi.
org/10.1086/652464
Ishizaka, A., Nemery, P., & Lidouh, K. (2013). Location selection
for the construction of a casino in the Greater London region:
A triple multi-criteria approach. Tourism Management, 34,
211–220. https://doi.org/10.1016/j.tourman.2012.05.003
Johnson, L., & Bowen, J. (1994). Riverboat site selection. UNLV
Gaming Research & Review Journal, 1(2), 79–94. https://
digitalscholarship.unlv.edu/grrj/vol1/iss2/8
Jurowski, C., & Gursoy, D. (2004). Distance effects on residents’
attitudes toward tourism. Annals of Tourism Research, 31(2),
296–312. https://doi.org/10.1016/j.annals.2003.12.005
Kearney, M. S. (2005). State lotteries and consumer behavior.
Journal of Public Economics, 89(11), 2269–2299. https://doi.
org/10.1016/j.jpubeco.2004.07.004
LaPlante, D. A., & Shaffer, H. J. (2007). Understanding the influence of gambling opportunities: Expanding exposure models
to include adaptation. American Journal of Orthopsychiatry,
77(4), 616–623. https://doi.org/10.1037/0002-9432.77.4.616
Lee, C. K., & Back, K. J. (2003). Pre- and post-casino impact of
residents’ perception. Annals of Tourism Research, 30(4),
868–885. https://doi.org/10.1016/S0160-7383(03)00060-4
Lee, C. K., Kang, S. K., Long, P., & Reisinger, Y. (2010).
Residents’ perceptions of casino impacts: A comparative study. Tourism Management, 31, 189–201. https://doi.
org/10.1016/j.tourman.2009.02.011
Lee, C. K., Kim, S. S., & Kang, S. (2003). Perceptions of casino
impacts—A Korean longitudinal study. Tourism Management,
24, 45–55. https://doi.org/10.1016/S0261-5177(02)00048-1
Marionneau, V., & Nikkinen, J. (2017). Market cannibalization within and between gambling industries: A systematic
review. Journal of Gambling Issues, 37, 1–35. https://doi.
org/10.4309/jgi.2018.37.1
Morley, C., Rosselló, J., & Santana-Gallego, M. (2014). Gravity
models for tourism demand: Theory and use. Annals of Tourism
Research, 48, 1–10. https://doi.org/10.1016/j.annals.2014.05.008
Morrison, K. (2019). World casino directory. https://www.worldcasinodirectory.com/
Mottice, C. M. (2018). The political economy of casino licensing. In S. Haeffele (Ed.), Knowledge and incentives in policy:
Using public choice and market process theory to analyze
public policy issues (pp. 187–206). Rowman & Littlefield.
Musinguzi, D. (2019). A review of the application of social
exchange theory in tourism research. In D. Gursoy & R.

Cornell Hospitality Quarterly 65(3)
Nunkoo (Eds.), The Routledge handbook of tourism impacts
(pp. 498–506). Routledge.
Nunkoo, R. (2016). Toward a more comprehensive use of social
exchange theory to study residents’ attitudes to tourism.
Procedia Economics and Finance, 39, 588–596. https://doi.
org/10.1016/S2212-5671(16)30303-3
O’Gilvie, P. J. (2022). The impact of casino proximity on northeast urban communities: A literature review. Humanities and
Social Sciences Communications, 9(1), Article 1. https://doi.
org/10.1057/s41599-022-01055-1
Papineau, E., Robitaille, É., Samba, C. P., Lemétayer, F., Kestens,
Y., & Raynault, M.-F. (2020). Spatial distribution of gambling exposure and vulnerability: An ecological tool to support health inequality reduction. Public Health, 184, 46–55.
https://doi.org/10.1016/j.puhe.2020.03.023
Perdue, R. R., Long, P. T., & Kang, Y. S. (1999). Boomtown
tourism and resident quality of life: The marketing of gaming to host community residents. Journal of Business
Research, 44(3), 165–177. https://doi.org/10.1016/S01482963(97)00198-7
Pérez, L., Rodríguez, A., & Shmarev, A. (2022). Gambling outlets
location in urban areas: A case study of Madrid. Journal of
Gambling Studies, 38, 1229–1242. https://doi.org/10.1007/
s10899-021-10094-3
Philander, K. S. (2019). Regional impacts of casino availability on
gambling problems: Evidence from the Canadian community
health survey. Tourism Management, 71, 173–178. https://
doi.org/10.1016/j.tourman.2018.10.017
Philander, K. S., Abarbanel, B. L. L., & Repetti, T. (2015).
Consumer spending in the gaming industry: Evidence of complementary demand in casino and online venues. International
Gambling Studies, 15(2), 256–272. https://doi.org/10.1080/14
459795.2015.1042002
Philander, K. S., Tabri, N., Wood, R., & Wohl, M. (2022).
Casino proximity, visit frequency, and gambling problems.
International Gambling Studies, 22(3), 459–479. https://doi.
org/10.1080/14459795.2022.2035423
Potenza, M. N., Balodis, I. M., Derevensky, J., Grant, J. E., Petry,
N. M., Verdejo-Garcia, A., & Yip, S. W. (2019). Gambling
disorder. Nature Reviews Disease Primers, 5(1), Article 1.
https://doi.org/10.1038/s41572-019-0099-7
Repetti, T., & Jung, S. (2014). What happened to Atlantic City’s
GAMING VOLUME? UNLV Gaming Research & Review
Journal, 18(2), 23–38.
Research Update: Plainridge Park Casino economic impacts
released for first year of operation, Analysis concludes $100M
in out-of-state spending recaptured. (2017). Massachusetts
Gaming Commission. https://massgaming.com/blog-post/
research-update-plainridge-park-casino-economic-impactsreleased-first-year-operation-analysis-concludes-100m-statespending-recaptured/
Roberts, J., & Gemignani, G. (2019). Who wore it better: Federal
v. State Government regulation of sports betting. UNLV
Gaming Law Journal, 9, Article 7.
Stitt, B. G., Nichols, M., & Giacopassi, D. (2005). Perception
of casinos as disruptive influences in USA communities.
International Journal of Tourism Research, 7(4–5), 187–200.
https://doi.org/10.1002/jtr.530

St-Pierre, R. A., Walker, D. M., Derevensky, J., & Gupta, R.
(2014). How availability and accessibility of gambling
venues influence problem gambling: A review of the literature. Gaming Law Review and Economics, 18(2), 150–172.
https://doi.org/10.1089/glre.2014.1824
Tan, A.-L., Lee, T. J., & Kim, J. J. (2017). Resident perceptions of
casinos in a newly developed casino destination. International
Journal of Tourism Sciences, 17(1), 15–31. https://doi.org/10
.1080/15980634.2016.1274175
Union Gaming Analytics. (2019). City of Chicago casino financial feasibility analysis. Illinois Gaming Board. https://www.
igb.illinois.gov/FilesPressReleases/Union%20Gaming%20
Feasibility%20Study.pdf
U.S. Census Bureau. (2022). American community survey 20162020 5-year data release. Census.Gov. https://www.census.
gov/newsroom/press-kits/2021/acs-5-year.html
Walker, D. M., & Nesbit, T. M. (2014). Casino revenue sensitivity
to competing casinos: A spatial analysis of Missouri: Casino
revenue sensitivity to competition. Growth and Change,
45(1), 21–40. https://doi.org/10.1111/grow.12035
Wan, Y. K. P. (2012). The social, economic and environmental
impacts of casino gaming in Macao: The community leader

perspective. Journal of Sustainable Tourism, 20, 737–755.
https://doi.org/10.1080/09669582.2011.636818
Weber, S., & Péclat, M. (2017). A simple command to calculate
travel distance and travel time. The Stata Journal, 17(4), 962–
971. https://doi.org/10.1177/1536867X1801700411
Wenz, M. (2014). Valuing casinos as a local amenity: Valuing
casinos as a local amenity. Growth and Change, 45(1), 136–
158. https://doi.org/10.1111/grow.12036
WhiteSand Gaming. (2014). Market feasibility study expanded gaming in Maine [Final Report]. Maine State Legislature. https://
lldc.mainelegislature.org/Open/Rpts/kf3992_z99w55_2014.pdf
Wu, S.-T., & Chen, Y.-S. (2015). The social, economic, and environmental impacts of casino gambling on the residents of
Macau and Singapore. Tourism Management, 48, 285–298.
https://doi.org/10.1016/j.tourman.2014.11.013

Author Biography
Kahlil S. Philander is an Assistant Professor in School of
Hospitality Business Management at Washington State University.
His research focuses on public policy and consumer behavior in
the gambling industry.