determinants of internet poker adoption

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1 ORI GIN AL PA PER

2 Determinants of Internet Poker Adoption

3 Kahlil S. Philander • B. Lillian Abarbanel

45 � Springer Science+Business Media New York 2013

6 Abstract In nearly all jurisdictions, adoption of a new form of gambling has been a

7 controversial and contentious subject. Online gambling has been no different, though there

8 are many aspects that affect online gambling that do not appear in the brick and mortar

9 environment. This study seeks to identify whether demographic, economic, political,

10 technological, and/or sociological determinants contribute to online poker gambling

11 adoption. A theoretical discussion of these categories’ importance to online poker is

12 provided and exploratory empirical analysis is used to examine their potential validity. The

13 analysis revealed support for all of the proposed categories of variables thought to be

14 predictive of online gambling legality.

15 Keywords Internet gambling � Online poker � Gambling policy � Gaming determinants

16 Introduction

17 As the growth of the Internet continues to affect the way that both individuals and busi-

18 nesses interact, countries around the world have been forced to bring new online markets to

19 the forefront of their policy decisions. In particular, the relatively new Internet medium has

20 coupled with the perpetually contentious gambling industry to inspire newsworthy debate

21 on a near-daily basis (Bernhard and Abarbanel 2011). The decision of whether to adopt

22 legal online gambling has proved controversial in many different jurisdictions and has led

23 to different policy decisions among nations. Some countries have taken active steps to

24 legalize various forms of online gambling, while others have chosen to explicitly ban the

A1 K. S. Philander (&)A2 William F. Harrah College of Hotel Administration, University of Nevada, Las Vegas,A3 4505 Maryland Parkway, Box 456037, Las Vegas, NV 89154, USAA4 e-mail: [email protected]

A5 B. L. AbarbanelA6 William F. Harrah College of Hotel Administration, University of Nevada, Las Vegas,A7 4505 Maryland Parkway, Box 456017, Las Vegas, NV 89154, USAA8 e-mail: [email protected]

123

Journal : Small 10899 Dispatch : 6-5-2013 Pages : 15

Article No. : 9382 h LE h TYPESET

MS Code : JOGS-D-12-00097 h CP h DISK4 4

J Gambl StudDOI 10.1007/s10899-013-9382-9

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25 practice—still others have even opted to passively allow or disallow online gambling by

26 way of lack of regulation or imposing past regulation aimed at brick and mortar casinos.

27 Although much research has been done to elicit the determinants of lotteries and brick

28 and mortar casino legality, these studies provide only limited applicability to the online

29 gambling industry. For example, online gambling has few barriers for consumers with

30 access to reliable internet connection, whereas brick and mortar gambling is constrained by

31 physical access requirements. This difference in the medium of delivery has led to many

32 more differences in regulation, player behavior, and policy considerations (McMillen

33 2000; Wood and Williams 2012. For example, while illegal gambling operators of live

34 gaming must maintain relatively discreet profiles in order to avoid detection by judicial

35 authorities, online gambling operators can openly market themselves to online players

36 while operating from safe haven countries. In many jurisdictions, players also can

37 patronize such sites with little threat of legal issues. In part, this has led to the creation of a

38 substantial underground economy in online gaming (e.g., Fiedler and Wilcke 2012).

39 Indeed, there many institutional reasons to suggest that adoption policy determinants

40 will be different than what has been found within conventional lottery and casino literature.

41 As noted by McMillen (2000):

42 With Internet technology and the prospect of digital television broadcasting, gam-

43 bling is now a dynamic and interactive global industry, accessible to gamblers

44 around the world at any time. This development poses a serious challenge to the

45 capacity of any government to prevent citizens from gambling with offshore pro-

46 viders and/or prevent cross-border leakage of gambling revenue (p. 392).4748 The political economy of online gambling may also be quite different, as its user

49 stakeholder group is much different than land based gamblers (e.g., younger, more mas-

50 culine, and more substance use) (Griffiths et al. 2009; Wood and Williams 2011). Wan and

51 Youn (2004) found that people perceived gambling sites likely to have negative effects on

52 others, and that there is an interest in protecting groups perceived as being vulnerable (e.g.,

53 youth) through regulation.

54 In general, there is reason to believe that many of the determinants of offline gambling

55 adoption will apply to online gambling, but that the relationship may not be identical; other

56 factors may also be relevant. This study provides two key contributions to the literature of

57 gambling adoption. First, this study discusses the relevance of existing theory in the brick

58 and mortar literature to online gambling—specifically, poker—extending it to include a

59 category of variables specifically relevant to Internet commerce. Second, through

60 exploratory seemingly unrelated probit regression analysis, we test a set of variables

61 associated with these categories to elicit whether they are predictive of a society’s decision

62 to allow legal online poker.

63 Review of Literature

64 Early History of Online Wagering Adoption

65 The first occurrence of money being wagered using the Internet dates back only 15 years,

66 with the sale of lottery tickets from the International Lottery in Liechtenstein for a manual

67 drawing that occurred on October 7, 1995 (Romney 1995). A few months later in January

68 1996, Intertops.com, based and licensed in Antigua, became the first online casino to

69 accept a wager (Business Wire 2005). Online gambling grew rapidly and by 2007, online

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70 gambling sites were operating in 45 different jurisdictions (Casino City 2010; Schwartz

71 2006). The first online poker room, Planet Poker, opened in 1998 and was quickly followed

72 by numerous others (Williams and Wood 2007). The World Series of Poker Main Event,

73 the world’s largest live poker tournament, became a popular televised event in 2003, when

74 its winner, Chris Moneymaker, gained his entry to the tournament by winning a satellite

75 event on an online poker room. This ‘‘Moneymaker Effect’’ inspired a rush of interest in

76 the game, and thousands more hopefuls took to computers to seek their fortunes in online

77 poker rooms (Grohman 2006).

78 Despite this rapid growth, the percentage of the general population gambling on the

79 Internet remained small when compared to the overall percentage of gamblers. In 2010, the

80 United Kingdom Gambling Commission (2011) reported that the prevalence of gambling

81 in the country was at a rate of 73 %. During that same time period, Internet gambling was

82 reported by 13 % of survey respondents (United Kingdom Gambling Commission 2011).

83 This percentage, however, was still a large increase over the short time during which online

84 gambling had been available to the public. In 2006, for example, Internet gambling was

85 reported by only 3.1 % of respondents, a number that nearly doubled by the end of the

86 following year (United Kingdom Gambling Commission 2010). Governments worldwide

87 struggled to keep up throughout this rapid expansion of online poker rooms and casinos,

88 attempting to develop an understanding of these new gambling venues, which easily

89 spanned across national borders and became a policy challenge.

90 Defining Gaming Adoption Policy

91 In response to the use of the Internet for gambling, government policy tends to take four

92 general forms—actively allow, actively prohibit, passively allow, or passively prohibit

93 (Policy Department European Economic and Scientific Policy. European Parliament 2008).

94 Those governments who actively allow or actively prohibit have passed legislation which

95 deals explicitly with Internet gambling. Those who passively allow or prohibit have not

96 enacted any specific legislation, or have chosen to apply pre-existing gaming regulation to

97 this new gambling medium (Policy Department European Economic and Scientific Policy.

98 European Parliament 2008). In the United Kingdom, for example, online gambling is

99 actively allowed while in Cyprus1 it is passively allowed.

100 In predominantly Muslim countries where Sharia Law is followed, Internet gambling is

101 passively prohibited since the Muslim religion bans all forms of gambling (Al Agha 2007).

102 Thus, there is no need to create legislation to explicitly ban Internet gaming. In an attempt to

103 enact enforceable laws, the United States took a more vague approach with the Unlawful

104 Internet Gambling Enforcement Act of 2006, which prohibits the transfer of funds from a

105 financial institution to Internet gambling sites for ‘‘unlawful Internet gambling’’ (H.R. 4954

106 2006). Interactive Media Entertainment and Gaming Association, Inc. (iMEGA) v. Attorney

107 General of the United States (2009) determined that ‘‘unlawful Internet gambling’’ referred

108 to the status of brick and mortar gambling policy in the state in which the wager was placed.

109 Academic Policy Literature

110 Literature on gambling adoption first began with a focus on the political economy of lottery

111 legalization at the US state level. Winn and Whieker (1989) found support in their study of

1FL01 1 At the time of this writing, Cyprus grants online gambling licenses under current law but Government is1FL02 actively discussing tightening their regulations.

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112 state lottery adoption that economic factors and socio-cultural factors contributed in some

113 way to legalization. Specifically, they found that income levels, tax revenue per capita, and

114 the state’s share of local/state taxes were significant economic predictors, while mem-

115 bership in certain religious groups and geographic regions were important socio-cultural

116 variables. The authors also tested a limited set of political factors, but found only weak

117 evidence of their importance.

118 Berry and Berry (1990) estimate an empirical model on US lottery adoption to test

119 whether Mohr (1969) theory can be supported that suggests that innovation is explained by:

120 ‘‘the motivation to innovate, the strength of obstacles against innovation, and the avail-

121 ability of resources for overcoming such obstacles’’ (p. 111). In their analysis, Berry and

122 Berry (1990) find empirical support for a theory that internal determinants, such as the

123 political and economic environment within the state, and external determinants (termed

124 regional diffusion in the article), such as activity by neighboring states, both influence

125 adoption. The authors then note that this joint explanation supports the theory proposed by

126 Mohr (1969).

127 Pierce and Miller (1999) find some evidence that determinants of lottery legalization

128 differ, depending on the specific product. In particular, general lotteries determinants differ

129 from lotteries whose revenue is earmarked for education. This finding suggests that it is

130 likely appropriate in the online gaming realm to focus on individual games, such as poker,

131 rather than study the entire set of online gaming products. There is much empirical evi-

132 dence that jurisdictions may legalize some online gaming products, while banning others

133 (Casino City 2010).

134 Other evidence in the lottery adoption literature has been found supporting the pre-

135 dictive validity of personal income variables (Caudill et al. 1995; Coughlin et al. 2006;

136 Davis et al. 1992; Erekson et al. 1999), religion variables (Caudill et al. 1995; Davis et al.

137 1992; Erekson et al. 1999), fiscal health variables (Coughlin et al. 2006; Davis et al. 1992;

138 Erekson et al. 1999), political variables (Coughlin et al. 2006; Garrett 1999) and regional

139 diffusion variables (Caudill et al. 1995; Coughlin et al. 2006; Davis et al. 1992; Erekson

140 et al. 1999).

141 Prior literature on brick and mortar casino policy adoptions has helped to identify

142 several categories of predictors variables that are likely pertinent to the adoption of Internet

143 poker. Calcagno et al. (2010) used a tobit model to identify several predictors of United

144 States state casino policy adoption, using a set of variables that was similar to the internal

145 determinants and regional diffusion approach first used by Berry and Berry (1990), and

146 supported by theory put forth by Mohr (1969). Calcagno et al. (2010) found significant

147 empirical results in the categories of fiscal (economic) variables, political party variables,

148 intra- and inter-jurisdiction competition variables, and demographic variables. The repe-

149 ated intra- and inter-jurisdictional competition variable is worth noting, because it is

150 reflective of the neighboring state effect, as was found in the lottery adoption literature, and

151 has been seen in recent debates over expanding Pennsylvania gaming.2 How this same

152 phenomenon manifests itself in the borderless Internet, where every country is able to act

153 as neighbor to any other country in the world, is part of the exploratory empirical focus of

154 this study.

155 Furlong (1998) also analyzed determinants of United States casino policy adoption.

156 Using a logistic regression model, he inferred that adoption is affected by ideological

157 identification, per capita tax collections, and longitudinal changes in job growth (Furlong

2FL01 2 Concern that Atlantic City was drawing Pennsylvania residents and their disposable income away from2FL02 the state was among the primary drivers of expanded gaming in Pennsylvania.

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158 1998). Unlike Calcagno et al. (2010), who found the Baptist religion to be a significant

159 demographic indicator, Furlong (1998) did not find that religious affiliation demonstrated

160 any statistically significant effect on casino policy adoption.

161 The authors located only one study that focused on countries as a jurisdiction (as

162 opposed to states), in which Richard (2010) applied a logistic regression model to 13

163 countries in Europe, Southeast Asia, and South and North America. Richard (2010) model

164 found that income level, unemployment, tourism targeting, and religious intensity were

165 significant contributors to a country’s gambling legality.

166 Predictive Variables in Internet Poker

167 Based on the theoretical and empirical discussion from the literature outlined above, we

168 put view online poker adoption as a function of five key categories of variables:

169 1. Demographic

170 Since poker is a game played against other players, not the house, a part of the political

171 discussion regarding regulation of poker has surrounded the need for a poker population to

172 reach critical mass so there are enough players in the pool.3 As such, a jurisdiction’s

173 population could play an important role in poker regulation. These variables have previ-

174 ously been found to be significant indicators of gaming adoption (Calcagno et al. 2010;

175 Furlong 1998; Richard 2010; Winn and Whieker 1989). Calcagno et al. (2010) also found

176 religion to be a significant indicator in land-based gaming, and many lottery adoption

177 studies also noted its importance, which warrants the inclusion of a demographic category

178 for religion.

179 2. Economic

180 Economic indicators are viewed as important following the common political argument

181 that legalization of gambling will bring in substantial tax revenues for governments facing

182 budget shortfalls (for multiple examples, see Bernhard and Abarbanel 2011). Several

183 authors have proposed that governments design gaming tax policies to generate public

184 revenue, rather than for any particular welfare maximizing interests (Smith 1998; Walker

185 and Jackson 2008; Eadington 1999). In prior research of casino adoption and lottery

186 adoption, several economic indicators were found to be significant.

187 3. Political

188 There have been discussions in the literature that legalization of gambling often relies

189 on aspects of the political mechanism, such as the ability to reach political compromise

190 (Eadington 2003). Again, we also observed that both the lottery adoption literature and the

191 casino adoption literature found that political variables were important for legalization.

192 There is also more general theory supporting the importance of political considerations in

193 adoption, such as the Mohr’s innovation theory (1969), previously put forth in the gaming

194 literature by Berry and Berry (1990).

195 4. Cultural

196 Abarbanel (2012) examined the impact of cultural values on gambling policy in

197 numerous jurisdictions, and found that policy varies greatly across culture. As this study

3FL01 3 See, for example, Sierory’s (2011) summary of the online poker panel at the 2011 iGaming North America3FL02 Conference.

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198 examines jurisdictions around the World, it seems plausible that culture will be a more

199 important variable than it has been in the prior gaming adoption literature that has tended

200 to solely focus on the US.

201 5. Technological Infrastructure

202 Finally, as a category of variables that is uniquely important to online gaming adoption,

203 we suggest that technological ability is an important category of variables for predicting

204 Internet poker legalization. The importance of technology in the development of gambling

205 practices has been expressed previously in the literature by Griffiths (1999) and Griffiths

206 and Parke (2002) This derives from the obvious importance of ease of access to the product

207 in determining public policy; that is, in the absence of access to the Internet, no policy is

208 necessary to control constituent behavior.

209 Empirical Methodology

210 The following analysis is intended to explore potential validity of the category of variables

211 outlined above in predicting public policy (in terms of adoption) regarding online poker.

212 As this study uses an exploratory regression procedure (based on a stepwise variable

213 exclusion procedure) we note that our purpose it not provide a strong causation argument,

214 but rather is to simply provide evidence that existing empirical results from the brick and

215 mortar literature may be generalizable to online poker, and to provide further direction for

216 future empirical work—that may be designed to test for individual variable causation.

217 Data

218 Fifty-seven countries were examined based on the availability of data on the legality of

219 online poker as of the end of 2011. Data indicating public policy on Internet poker was

220 restricted to sources generally considered by the online gaming community to be reliable.

221 In total twelve sources consisting of government reports, academic presentations, online

222 gambling databases, and government legislation (Balestra and Krafcik 2010; Bianchi 2011

223 Casino City 2010; Gainsbury 2010; GamingZion 2011; OPAP 2011; Policy Department

224 European Economic and Scientific Policy. European Parliament 2008; RecentPoker 2009;

225 Tanzania Gaming Act 2003; Wery 2010; Wiebe and Lipton 2008; Williams and Wood

226 2009). Predominantly Muslim countries in which Sharia Law dictates policy were not

227 included in the final data set as all gambling is prohibited, and the countries’ inclusion

228 would not add any more explanatory value to the model for non-Sharia Law adhering

229 countries. In countries where gambling policy is purview of state/provincial governments,

230 rather than the Federal government, adoption in some form was coded as a positive

231 adoption value.4

232 Coding of the dependent variables distinguished between whether online poker was

233 legal for the player and whether online poker was legal for operators. The two binary

234 dependent variables (one for operator and one for players) were coded with a ‘‘1’’ value

235 indicating the country legally permitted online poker, and a ‘‘0’’ value otherwise. The

236 independent variable data were compiled from several sources. ‘‘Appendix’’ lists the

237 variables in our model, along with brief definitions and data sources.

4FL01 4 In particular, the US and Canada were coded on this basis.

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238 We recognize that further refining the model with categorical institutional variables that

239 identify characteristics of countries may improve the predictive accuracy of the models.

240 Such an approach, however, would make the empirical model less pertinent as many

241 countries in the sample would be perfectly identified by such use of categorical variables.

242 This would not contribute to the overall model understanding with regard to policy

243 adoption determinants. The purpose of this aspect of the study is to explore likely cate-

244 gories of determinants, not perfectly identify the sample. We note that the effects of many

245 potential institutional variables are partially captured through various political and cultural

246 variables identified as part of the analysis.

247 Model Specification

248 The variable of interest, legality of online poker, is modeled with a probit using a standard

249 nonlinear maximization algorithm for model fit. It takes the form:

250 y* = a ? demographic’ b1 ? economic’ b2 ? political’ b3 ? cultural’ b4 ? techno-

251 logical’ b5 ? e.

252 In which y* represents the legality of online poker, with a value of ‘1’ indicating legal

253 and ‘0’ indicating not legal; demographic is a vector of demographic variables, economic

254 is a vector of economic variables; political is a vector of political indices; cultural is a

255 vector of cultural indices; and technological is a vector identifying technological infra-

256 structure variables. Individual elements of the explanatory variable vectors are as described

257 in ‘‘Appendix’’. bj (j = 1,…,5) are vectors of unknown parameters associated with each of

258 the four classes of predictor variables; a represents random effects, assumed to be iid

259 *N(0, ra2); and e are the contemporaneous error terms assumed to be iid *N(0,1).

260 Since two very similar models are estimated in this study (one with legal status of online

261 poker for players as the dependent variable, and another with legal status of online poker

262 for operators as the dependent variable) we use two steps in our estimation procedures to

263 adequately explore feasible predictors, while improving efficiency. First, we estimate

264 separate probit models using a stepwise regression procedure with backwards elimination

265 (and list-wise deletion) with a significance cut-off criterion at the a = 0.1 level. Then, to

266 increase the efficiency of our estimates, we estimate a seemingly unrelated biprobit (SUR)

267 model, which simultaneously estimates predictive models for both dependent variables.

268 This SUR model is identified using the variables that were found to be statistically sig-

269 nificant in the step one regressions. The simultaneous estimation procedure increases the

270 efficiency of our measurements when the error terms are correlated (Greene 2003). Finally,

271 we list the variables that remain significant after a Bonferroni adjustment, based on the

272 number of multiple comparisons (predictor variables). Although this is an exploratory

273 analysis, we provide the Bonferroni corrected significance level to identify the most robust

274 results.

275 Results

276 The initial probit models identified several independent variables as being related to the

277 observed distribution of online poker legality in the selected countries. Model fit results are

278 summarized in Tables 1, 2, 3. The standard errors of the model were corrected for any

279 arbitrary forms of heteroscedasticity using the Huber/White/sandwich estimator (StataCorp

280 LP 2009.

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281 In the jointly estimated model (Table 3), the classification precision for both player and

282 operator online legality was 73.7 %, exceeding the threshold of 25 % better than maximum

283 chance accuracy (69.0 % for players and 64.7 % for operators). The pseudo-R2 value was

284 0.38. Five variables—population, non-Christian share of the population, Rule of Law,

285 Hofstede Masculinity, and Internet users—were found to be significantly related to both

286 dependent variables. Unemployment rate was found to be significantly related to player

287 legality and the Regulatory Quality index is significantly related to operator legality.

288 After a Bonferroni correction to provide a conservative perspective on the stated

289 relationships, the non-Christian, rule of law, masculinity index, and Internet users variables

290 all found some measure of significance. This conservative outlook does not necessarily

291 mandate the exclusion of the other variables, but highlights the most robust results. As this

292 is an exploratory study, both the typical and the conservative significance levels are

293 reported in Table 3.

294 In general, the seemingly unrelated model produced estimates that were similar in

295 magnitude to the independent models, but with smaller standard errors. For example, even

296 though the coefficient estimate on the unemployment rate variable moved closer to zero,

297 the statistical significance increased due to a much lower z-stat.

298 The new category of variables proposed as a gaming legality determinant (Techno-

299 logical) produced one significant variable, Internet users per 100 people. This variable is

300 also significant after the Bonferroni correction, further validating its importance.

301 Discussion

302 The results from exploratory testing of online gambling legality predictors produced results

303 that were consistent with the expectation that demographic, economic, political, cultural,

304 and technological variables were all important determinants of Internet poker policy.

305 While we certainly caution drawing strong causal arguments from this exploratory anal-

306 ysis, we do find that variables consistent with those found in previous literature on lotteries

307 and land-based casinos were statistically significant.

308 The negative relationship between population and online gambling policy may reflect

309 the number of small countries that have sought to license online gaming to increase net

310 exports from larger markets. It has often been the case that small economies are the first to

311 adopt legal gaming, in order to export the industry to foreign visitors. As stated by

312 (Eadington 1999).

Table 1 Probit regression model to predict online poker legality for players

b Robust S.E. Marginal effects

Population (millions) -0.009*** 0.004 -0.003

Non-Christian share of population -2.072** 0.878 -0.718

Unemployment rate 0.098* 0.052 0.034

Rule of law -1.096** 0.451 -0.380

Hofstede masculinity index -0.023* 0.052 -0.008

Internet users per 100 people 0.067*** 0.020 0.023

Model constant -1.893 1.224

* Significant at a = 0.1, ** significant at a = 0.05, *** significant at a = .01; n = 57; pseudo R2 = 0.37

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313 Historically, casinos have often been introduced to capture economic benefits from

314 ‘‘exporting’’ casino gaming to customers from regions where the activity is pro-

315 hibited. Jurisdictions that legalized casinos were often resource poor, or under

316 economic duress. One or both of these factors apply to Monaco (1863), Nevada

317 (1931), Macao (in the early 20th century), the Caribbean (1960s), and Atlantic City

318 (1976) (p. 186–187).319320 Much in the way that the neighboring state effect was important to adoption of lotteries

321 and casinos, small countries may be the neighbors that lead to adoption by other (larger)

322 countries. For example, the loss of gaming related income to foreign providers is fre-

323 quently an argument put forth in support of legalized online poker in the US (Stewart

Table 2 Probit regression model to predict online poker legality for operators

b Robust S.E. Marginal effects

Population (millions) -0.010** 0.004 -0.004

Non-Christian share of population -2.524*** 0.905 -0.980

Rule of law -2.417*** 0.853 -0.939

Regulatory quality 1.495* 0.821 0.581

Hofstede masculinity index -0.029** 0.013 -0.011

Internet users per 100 people 0.064*** 0.021 0.025

Model constant -0.418 0.933

* Significant at a = 0.1, ** significant at a = 0.05, *** significant at a = .01; n = 57; pseudo R2 = 0.40

Table 3 Seemingly unrelated biprobit model results

Variables DV: legal for players DV: legal for operators

Population (millions) -0.008**(-2.33)

-0.010**(-2.11)

Non-Christian share of population -2.503***b

(-3.14)-2.538***b

(-3.13)

Unemployment rate 0.068**(2.42)

N/A

Rule of law -1.148***a

(-2.61)-2.204***b

(-3.27)

Regulatory quality N/A 1.306**(2.41)

Hofstede mas culinity index -0.022**(-2.10)

-0.030***(-2.66)

Internet users per 100 people 0.068***c

(3.46)0.058***b

(3.17)

Model constant -1.554-0.049

(-1.49)(-0.06)

z-stats given in parentheses; n = 57

Pre-Bonferroni correction: * significant at a = 0.1, ** significant at a = 0.05, *** significant at a = 0.01

Post-Bonferroni correction: a significant at a = 0.1, b significant at a = 0.05, c significant at a = 0.01

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324 2011). Another reason for the importance of population size is that many small countries

325 may lack the political, technological, and economic infrastructure to effectively restrict this

326 industry.

327 The negative relationship between non-Christianity prevalence in the population and the

328 dependent variables may reflect a strong anti-gambling sentiment shared by many reli-

329 gions. Although predominantly Muslim countries were excluded from this study, religions

330 like Buddhism and Hinduism also discourage gaming, and in countries where those reli-

331 gions are more prevalent, we may be more likely to see the religious morality influence

332 gambling policy decisions.

333 The Hofstede masculinity index (MAS) variable is negatively related to the legal status

334 of online poker. The MAS dimension describes distribution of roles between the genders,

335 and is a strong correlate of religious involvement (Hofstede 1998, 2009). In countries with

336 stricter religious institutions, gender roles are more traditionally rigid, thus generating a

337 higher MAS dimension score. As we can see demonstrated in the significance of the non-

338 Christian variable, firm religious principles tend to impact online poker legality negatively.

339 Masculine traits also include materialism/material success, self-centeredness, and indi-

340 vidual achievements, which have been previously linked to gambling behavior (Fang and

341 Mowen 2009), though this does not appear to overcome the other influences on policy-

342 making.

343 The negative coefficient estimate on the rule of law index, which captures the extent to

344 which agents abide rules of society, may be capturing governments that recognize that they

345 have no ability to enforce anti-gaming laws. That is, since the index reflects the likelihood

346 of crime, a jurisdiction may not choose to prohibit online gambling since the ban would be

347 impossible to enforce. As an example of this type of behavior, consider the Kahnawake

348 tribe in Canada (excluded from the estimation sample). The criminal code of Canada

349 outlaws gaming except when provided by provincial governments, but the Mohawk

350 community of Kahnawake has claimed a right to gaming due to some historical precedents

351 of First Nations communities in the country. Accordingly, the Kahnawake gaming com-

352 mission has become one of the largest regulators for online gaming in the World, hosting

353 many gaming servers (Belanger 2011).

354 The positive relationship between Internet users and legality could be partially

355 explained by a need-based policy action by technologically developing nations. In par-

356 ticular, countries whose populace has limited access to computers, and therefore to online

357 gaming, have little need to take action to legalize and regulate the industry. Online poker,

358 therefore, may tend to be passively disallowed in a jurisdiction and will only be legalized if

359 Internet prevalence rates reach a certain critical mass. This explanation should not be

360 considered a uniform description of countries’ reactions to increased technological infra-

361 structure, as some jurisdictions may have a negative reaction towards development of

362 online poker. In 2007, for example, when Germany felt the state monopoly on offline

363 gaming was threatened by the rapidly increasing presence of online gambling sites, the

364 government issued a ban on all online gambling (Cabot et al. 2011). Since January 1, 2012,

365 Germany has reversed this position in some states (Rohan and Scuffham 2011), perhaps

366 suggesting that the ban was more about creating tighter (legal) regulation of a quickly

367 growing industry than a long-term negative policy outlook.

368 The two variables which were only significant predictors of one dependent variable—

369 unemployment for the player legality model and regulatory quality for the operator legality

370 model—are interesting in that they reflect issues that pertain primarily to that dependent

371 variable subgroup. The unemployment variable reflects the aggregate ability for individ-372 uals to find work. Therefore, it may not be surprising that it effects whether online poker is

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373 legal for individual players. Similarly, the regulatory quality variable captures ‘‘the ability

374 of the government to formulate and implement sound policies and regulations that permit

375 and promote private sector development’’ (Kaufmann et al. 2010 p. 4), and as gaming is a

376 highly regulated industry, it seems sensible that a certain level of regulatory structure

377 would be needed to effectively legalize online poker for operators.

378 Conclusion

379 The findings from this study provide a basis from which future research on gambling

380 adoption, and in particular, online gambling, can be built. As with past theoretical and

381 empirical studies on gambling policy adoption, demographic, economic, political, and

382 cultural factors were all found to be important determinants of adoption of legal online

383 poker—with minor variations between player legality and operator legality. In addition,

384 this study also proposed and found empirical evidence supporting the importance of

385 technological variables in the online gaming policy adoption model. The inclusion of

386 variables that accurately reflect access in the online market are important in the online

387 poker market, and are also likely relevant in other online gaming variants.

388 Limitations and Future Research

389 A binary variable is clearly an imperfect measure of public policy, and fails to capture

390 many subtleties in policy design and debate. As mentioned in the literature review, some

391 countries have actively legalized or banned online poker, while others chose a more

392 passive approach to regulation. Some countries provided other challenges to data analysis,

393 as they may permit online poker, but restrict the quantity or scope of operator licenses. The

394 precise legal status of online poker in these countries is therefore challenging to fully

395 capture in any sort of quantitative variable, let alone a binary variable.

396 In addition to continuing to test the findings from this study in replicated research,

397 future studies should develop methodologies to explain the differences in active and

398 passive policy decisions. Alternative explanatory variables, such as non-discrete brick and

399 mortar measurements (e.g., slot machines per capita) and cultural social responsibility

400 concerns (e.g., survey results of populace legal gambling preferences), should be tested in

401 future studies, as theories of online gambling adoption continue to evolve.

402 Restrictions on data availability also limited our ability to examine the timing of policy

403 changes, as well as the sample of countries examined. In many cases, an oversimplification

404 of the variable design has been used due to limits of data availability. For example, other

405 religious denominations besides the Christian/non-Christian distinction (in addition to the

406 excluded Sharia Law countries) may be important (Grichting 1986; Diaz 2000), but reli-

407 able statistics with more detailed religion values for each country is not obtainable through

408 census offices, and sampling would require large surveys from each country.

409 A study that included temporal variation could be used to provide more evidence of

410 causation and may also reveal statistically significant variables that failed to be captured by

411 this cross-section analysis. The adoption of other forms of online gambling, such as lot-

412 teries, slot machines, sports betting, and pari-mutuel wagering, should be explored, as this

413 study focused only on poker.

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414 Appendix

415 See Table 4.

Table 4 Variable descriptions and data sources

Variable Description Data source

Legal status of onlinepoker for players(dependent variable)

Legality of online poker for players in2011; ‘1’ denotes legal, ‘0’ denotesnot legal

Cabot et al. (2011), Casino City(2010), Policy Department EuropeanEconomic and Scientific Policy.European Parliament (2008), andRose and Owens (2009)

Legal status of onlinepoker for operators(dependent variable)

Legality of online poker for at least oneoperator in 2011; ‘1’ denotes legal,‘0’ denotes not legal

Demographic

Population Total domestic population, 2010 International Monetary Fund,September 2011 world economicoutlook database

Non-Christian share ofthe population

Share of the population not belonging toa Christian denomination

Central intelligence agency, the worldfactbook

Median Age Estimated median age, 2010 Central Intelligence Agency, TheWorld Factbook

Economic

Real GDP (USD) GDP expressed in billions of USDollars, 2010

International Monetary Fund,September 2011 World EconomicOutlook Database

General governmentrevenue aspercentage of GDP

Taxes, social contributions, grantsreceivable and other revenue,expressed as a percentage of GDP;2010

International Monetary Fund,September 2011 world economicoutlook database

Unemployment Country unemployment rate, 2010 International Monetary Fund,September 2011 world economicoutlook database

Current account deficitas share of GDP

The balance of the current account(deficit or surplus) in a countrydivided by the country’s grossdomestic product; 2010

International Monetary Fund,September 2011 world economicoutlook database

Political

Governmenteffectiveness

Reflects the quality of public services,civil service, their independence frompolitical pressures, and agovernment’s commitment to qualitypolicy

The World Bank Group, TheWorldwide Governance Indicators,2011 Update

Regulatory quality Ability of the government to createpolicy that supports private sectordevelopment

The world bank group, the worldwidegovernance Indicators, 2011 update

Rule of law Extent to which the population hasconfidence in and abides by the rulesof society

The world bank group, the worldwidegovernance indicators, 2011 update

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416417418 References

419 Abarbanel, B. L. (2012). Cultural indicators of internet sports betting policy. UNLV Gaming Research &420 Review Journal, 16(1), 77–85.421 Al Agha, S. (2007). Money laundering from Islamic perspective. Journal of Money Laundering Control, 10,422 406–411.423 American Gaming Association. (2011). State of the states: The AGA survey of casino entertainment.424 Washington, DC: Author.425 Balestra, M., & Krafcik, C. A. (Eds.). (2010). Casino city’s internet gambling report (11th ed.). Newton,426 MA: Casino City Press.427 Belanger, Y. (Ed.). (2011). First nations gaming in Canada. Winnipeg, MB: University of Manitoba Press.428 Bernhard, B. J., & Abarbanel, B. L. L. (2011). GlobalScan: Problem gambling around the world from429 Alderney to Zurich. Las Vegas, NV: International Gaming Institute.

Table 4 continued

Variable Description Data source

Cultural

Power distance index Extent to which less powerful membersof organizations are willing to acceptthat power is distributed unequally.High values indicate that a society hasa higher tolerance for inequality ofpower

Itim international—Geert Hofstedecultural dimensionsa

Individualism index Degree to which individuals areintegrated into groups. High valuesindicate relatively loose bonds withothers, while low values indicate amore collectivist society.Collectivism refers to groups, notpolitical states.

Itim international—Geert Hofstedecultural dimensions

Masculinity index Representation of differentiation anddiscrimination between genders. Highvalues indicate the male dominates asignificant portion of the society andpower structure


Uncertainty Avoidanceindex

Degree to which a culture programs itsmembers to feel either uncomfortableor comfortable in unstructuredsituations. High values indicate lowertolerance of uncertainty


Technological infrastructure

Internet users per 100people

Estimated number of internet users,using any device, per 100 people,2010

International telecommunicationsunion, world telecommunicationdevelopment report

Fixed broadband per100 people

Number of fixed broadbandsubscriptions, per 100 people, 2010


Mobile subscriptionsper 100 people

Number of mobile cellularsubscriptions, post-paid and prepaid,per 100 people, 2010


Data from the ‘Central Intelligence Agency, The World Factbook’ relies on country level census data thatvary in terms of the respective census dates. The fifth Hofstede Cultural Dimension, the Long-TermOrientation Index, was not included in the model due to the low number of countries with computed values

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