Faith's wager: How religion deters gambling

Accepted Manuscript Faith's wager: How religion deters gambling Kraig Beyerlein, Jeffrey J. Sallaz PII:

S0049-089X(16)30427-6

DOI:

10.1016/j.ssresearch.2016.07.007

Reference:

YSSRE 1952

To appear in:

Social Science Research

Received Date: 6 August 2015 Revised Date:

30 June 2016

Accepted Date: 26 July 2016

Please cite this article as: Beyerlein, K., Sallaz, J.J., Faith's wager: How religion deters gambling, Social Science Research (2016), doi: 10.1016/j.ssresearch.2016.07.007. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Faith’s Wager: How Religion Deters Gambling Abstract

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The relationship between religion and gambling has only rarely been investigated in sociology and related fields. Prior studies have found that religion, broadly defined, deters gambling, with different religious traditions exhibiting varying degrees of deterrence. Our study, a quantitative analysis of a recent representative sample of U.S. adults, theorizes and tests how three different dimensions of religion affect three distinct forms of gambling. Religious tradition and religious service attendance are found to reduce the likelihood of casino gambling and lottery play; while religious salience is the only dimension that constrains online gambling. We argue that these findings reflect variation in the social visibility, time intensity, and broader legitimacy associated with gambling forms, and that this variation is crucial for understanding the deterring effects of faith.

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Faith’s Wager: How Religion Deters Gambling Over the past several decades, the United States has witnessed a surge in opportunities to gamble. Today, Americans spend more per annum on lotteries, casinos, online betting and other

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forms of wagering than on all other entertainment (movies, music, video games, etc.) combined (Chambers 2012; Christiansen 2006) Many worry that gambling is so ubiquitous and alluring as to be addictive, and there is a strong consensus that we need to better understand why some

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people are more able than others to resist the allure of quick riches (National Gambling Impact Study Commission 1999).

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Our approach to this issue is cultural and organizational—in short, sociological. We acknowledge the inherent appeal of gambling, along with the fact that some individuals may be more predisposed to gamble than others. But we define gambling as a social act carried out in concrete situations wherein its meaning is constructed by participants themselves. So while a

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poker table represents for the high roller an area in which to publicly display courage and resolve (Goffman 1967), for the colonial Calvinist it was a shameful symbol of indolence to be avoided—or hidden behind closed doors (Schwartz 2006:141). Individual decisions to gamble

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or refrain from gambling, we argue, must be contextualized within local social settings. The meanings generated in such local settings are not entirely independent of the larger

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social milieu. Gambling has long been considered a “contested commodity” (Radin 2001), such that powerful institutions have pronounced over its meaning and whether it should be permitted at all. Here we find a recent divergence in how two key institutions—organized religions and state governments—frame gambling. On one hand, major religious traditions in America have long defined gambling as “a subverter of the individual character and the socioeconomic order” (Bell 1974: 1), and counseled members to avoid gambling or to do so in moderation. On the

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other hand, states, due to fiscal crises and other factors, now permit and even promote a variety of games (Goodman 1996; Sallaz 2006). Lotteries, long prohibited, are now legal in all but seven states; casinos, once confined to Nevada, are now found in 37 states; and internet gambling,

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largely unregulated, has introduced unprecedented opportunities to wager in the privacy of one’s home.

In this paper, we draw on a recent nationally representative survey of U.S. adults to

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examine if and how religion deters gambling in America today. The few studies on this topic have generally shown that religion, broadly defined, deters gambling, with different religious

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traditions exhibiting varying degrees of deterrence. Ours does as well, but we extend prior studies by analyzing how different dimensions of religion constrain the urge to gamble in unique ways. In particular, we demonstrate that the specific characteristics of a game of chance (in particular, its visibility and time-intensiveness) influence religion’s constraining role. By

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emphasizing the social embeddedness of individual decisions to gamble, our approach leads us to confront and expand on the dominant interdisciplinary understanding of gambling today.

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For a Sociology of Gambling

The expansion of gambling opportunities has brought with it concern about deleterious

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consequences, such as increased rates of bankruptcy, suicide, and crime (Barron et al. 2002; Miller and Schwartz 1998; Wray et al. 2008). A cross-disciplinary field known as “gambling studies” has come to define the epistemic community clustered around this problem-space (Dunne 1985; Knorr Cetina 1999). Sociological voices have generally been absent from this field relative to those of public health, psychiatry, and epidemiology. These fields treat gambling as a medical issue, and so label those who wager heavily as victims of an addiction or

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impulse control disorder (Castellani 2000).1 Practitioners today use screens modeled directly from those used to test for alcoholism to distinguish “healthy” from “pathological” and “at-risk”

for, pathological gambling (Goudriaan et al. 2004; Petry 2012).

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gamblers; while researchers seek biochemical determinants of, and pharmaceutical treatments

We are not the first to express concern that the dominant gambling studies paradigm relies upon an overly-individualistic conception of action (Beckert and Lutter 2009; Johansson et

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al. 2009; Reith and Dobbie 2011; Schull 2012). Even those who work within this field often acknowledge that “sociological factors” (Raylu and Oei 2002: 1009) are too often neglected

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relative to individual-level traits such as impulsivity and depression. For instance, studies have shown that those who are socially isolated (such as the elderly or the divorced) are prone to gambling, as are members of particular subcultures (such as gang-members or gambling industry employees) (Heap 2010; McEvoy and Spirgen 2012; Trevorrow and Moore 1998; Tse et al.

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2012). Such findings suggest the need to model precisely how individual decisions to gamble (or not) are embedded within networks, groups, organizations, and situations. This article moves beyond the gambling studies paradigm by examining how one

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enduring sphere of social life—religion—influences an individual’s likelihood of gambling. Few studies have addressed this relationship explicitly. One established finding is that evangelical

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Protestants possess more anti-gambling attitudes and gamble less often than do Catholics (Diaz 2000; Ellison and McFarland 2011; Ellison and Nybroten 1999; von Herrmann 2002; Welte et al. 2002). And in the most comprehensive study to date, Ellison and McFarland (2011) show that

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The official definition changed from the former to the latter in 1987, when the American

Psychiatric Association revised its Diagnostic and Statistical Manual of Mental Disorders. 3

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active participation in religious life (such as attendance at service) and subjective religiosity lower the probability of gambling (Hoffman 2000). Ellison and McFarland’s (2011) study is notable for how it disaggregates religion into

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“multiple dimensions of religious involvement” (p. 83) and then examines how each does or does not deter gambling. Like others before it, however, their study does not disaggregate the

dependent variable as well. It uses as its indicator of gambling a single survey question as to

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whether one has “gambled for money” (p. 89) in any form whatsoever over the past twelve months. We argue that, like religion, gambling is not monolithic. Overcoming prior data

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limitations, we demonstrate that the spatial and temporal structuring of specific games matter for understanding how religion affects gambling. How each dimension of religion relates to these characteristics is crucial for understanding religion’s overall deterrent effect. In short, our

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approach emphasizes specific rather than general mechanisms.

Multiple Worlds of Gambling

Although all forms of gambling share an essential definition—risk voluntarily undertaken for

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economic gain—there are important distinctions. In theorizing how religion influences the decision to gamble, we believe that two characteristics of the gambling context are crucial: its

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spatiality (the degree to which it exposes the gambler to public view) and its temporality (the time commitment it entails). The forms of gambling chosen for our study—casinos, lotteries, and online gambling—thus not only represent three of the main types of gambling available in America today, they also provide significant variation in terms of spatiality and temporality. To start, consider casino gambling, which we argue is the most socially visible and timeintensive of the three forms. Spatially, a casino is a physically discrete structure which one must

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traverse some public area in order to access—for this reason, casino gambling is often classified as “destination gambling” (Eadington 1998). For those wishing to conceal their gambling from others, destination gambling poses multiple problems. One’s family members and neighbors

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could notice one’s car pulling into the driveway, or hear one enter the door after the walk from the bus station, after a night at a casino. At the casino itself, hundreds or even thousands of

the gambler will be seen by someone whom they know.

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people will be present at any given time as consumers and employees, increasing the odds that

Most casino games, furthermore, are time-intensive. They take place over prolonged

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sessions during which one’s bankroll fluctuates while being slowly whittled away. Coupled with having to travel to and from the casino, the sequential nature of these sessions means that casino gambling eats up a lot of time and is difficult to conceal from others. Memoirs of those who have recovered from “addictions” to slot machines and table games attest to the great lengths that they

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go through to conceal their casino-going from significant others, including the wearing of disguises and the creation of elaborate stories to account for their disappearance during multi-day gambling “binges” (Schull 2012; Sojourner 2010).

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In contrast to destination gambling at casinos, lotteries have been labeled the quintessential form of “convenience gambling” (Clotfelter and Cook 1991). While people must

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decide to go to a casino, invest significant time to get there, and appear publicly before others, lotto play can be integrated into everyday life. Lottery outlets are located in grocery stores, gas stations, and other establishments which one could patronize for various legitimate (i.e., nongambling related) reasons. Lotto gambling is also quick. One can stop to purchase a tank of gas or carton of milk then purchase a scratch-off ticket or a ticket for the weekly powerball drawing right at the counter. It follows then, that, compared to casinos, lotto play is much easier to

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conceal by those who wish to keep their gambling secret. Lottery play is low in both time intensity and social visibility. The final form of gambling on which we focus—online betting—is considered a new

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form of convenience gambling (for example, see Stevens 2014). With a credit card or an online payment, people today can wager from the privacy of their computers on a variety of outcomes, from poker games to rugby matches to presidential elections. Ongoing advances in mobile

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technology (such as wi-fi enabled tablets, cellular phones and televisions) are proliferating opportunities to gamble via the internet.

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In terms of visibility, online gambling seemingly offers near-total anonymity for those concerned to hide their gambling from others.2 Wolfe and Owens (2009: 5) summarize the situation thus: online bettors do not “even need to come face-to-face with other people.” The question of time commitment is less clear. As LaBrie et al. (2008) showed, some people visit a

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betting site to make a single quick transaction, while others play for hours on virtual poker tables. Still, it seems safe to say that online gambling is closer to convenience than destination gambling in that the setup costs (logging on to a website) are lower than physically commuting

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to a casino.

Figure 1 summarizes our classification of the three gambling forms along the dimensions

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of time intensiveness and spatial visibility. The result is a grouping of, on one hand, lotteries and online gambling, and, on the other, casinos. One potential limitation to our theoretical schema is that it does not differentiate between lotteries and online gambling. We classify both as 2

Similar arguments have been made as to how the internet affords consumers of pornography

(Edelman 2012), paid sex (Bernstein 2007), and illegal drugs (Christin 2012) low public visibility. 6

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convenience gambling, in that they are less visible and time intensive than casinos. Yet lotteries are an established gambling form that is endorsed and often operated by state governments,

issues to which we will return later in the paper.

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Figure 1. Two Worlds of Gambling

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whereas online gambling is new and its status as a licit activity may yet be unsettled. These are

Linking Dimensions of Religion to Types of Gambling In this section, we disaggregate religion into three dimensions— religious tradition affiliation, organized religious participation, and subjective religiosity—and ask how each influences the three forms of gambling discussed above. In so doing, we explicate specific hypotheses as to how the spatial and temporal structuring of these gambling forms likely shape the deterrent power of religion.

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Religious Tradition Religious traditions in the United States vary in the extent to which they frame gambling (for

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example, see Bell 1974; Ellison and McFarland 2011) and other risky economic behaviors (Keister 2003; Keister 2011) as sinful. The most well-documented example of divergent

religious framings is the different theological teachings of Catholicism and Protestantism (Reith

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2002). Historically, the Catholic Church has not condoned gambling, but nor has it labeled it a dangerous sin. As the New Catholic Encyclopedia states: “Gambling…though a luxury, is not

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considered sinful except when the indulgence in it is inconsistent with duty” (quote from Bell 1974: 57).3 And in fact, according to (Carpenter 2009: 291), a third of state-level bingo licenses go to the Catholic Church. In contrast, Protestant leaders, dating back to the founding of the Republic, have cast gambling into a deadly sin framework. Cotton Mather himself regularly

grace (Tanner 2009).

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sermonized that gambling was a sinful attempt to discern God’s will and evidence of a lack of

As Protestantism developed and splintered in the United States, different denominations

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varied in whether they retained the Puritans’ harsh framing of gambling. Today, due in large part to their commitment to biblical literalism, the evangelical Protestant tradition is more likely to

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apply a strict “sin frame” to gambling (Ellison and McFarland 2011) as they have done for other so-called vices, such as alcohol, drugs, and pornography (see Sherkat and Ellison 1997; Skeel and Stuntz 2009). Mainline Protestant denominations, in contrast, have generally toned down the 3

In addition, the Catholic Church, more so than other traditions, has integrated gambling into the

culture of parish life in the form of fund-raising through Bingo and raffles (Johnson and Meier 1990). 8

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anti-gambling stance and “vice” framework (Ellison and McFarland 2011: 84) 4. Less is known about how black Protestantism—another major religious tradition in America (Steensland et al. 2000)—frames gambling but this tradition tends to espouse liberal views on social issues,

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especially those related to economics and wealth (Wald and Calhoun-Brown 2011).5 In sum, it is reasonable to assume that the evangelical Protestant tradition advances the strongest anti-

gambling frame, Catholics the most tolerant, with mainline and black Protestants in between.

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We expect religious tradition to influence all forms of gambling. Traditions are

conceptualized as communities whose framings constitute “taken for granted” understandings of

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gambling. These understandings will be internalized by members and then guide their behavior across a variety of gambling settings. Those affiliated with an evangelical Protestant denomination such as the Southern Baptist Church or Assemblies of God should be more likely to interpret all forms of gambling as sinful, and thus to refrain from participating in them. This

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motivates our first hypothesis, which is formally stated as:

Hypothesis 1a: Because the evangelical Protestant tradition theologically frames

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gambling as unambiguously “sinful,” members of this tradition should be less likely to engage in all three types of gambling relative to black and mainline Protestants, and

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especially Catholics and the non-religiously affiliated.

There are some exceptions, such as the United Methodist Church’s strong and consistent

opposition to gambling. 5

To our knowledge, black Protestant denominations have not made any official statements

proclaiming gambling as immoral and sinful. 9

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It is possible that members of different religious traditions vary in their gambling not because of specific cultural framings but because of differences in organized religious behavior and subjective religiosity. As we explain below, these religious dimensions are expected to have

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important deterring effects on gambling behaviors. Members of different traditions vary in how often they attend religious services and the extent to which religion guides their daily lives. For instance, Catholics attend religious services less frequently than do evangelical Protestants (for

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example, see Smith et al. 1998), and it might be this extra free time that explains why they are more likely to go to casinos. Yet while we expect these other religious dimensions to explain

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some of the religious tradition effects, because of the significance of cultural framings about gambling associated with traditions, it is predicted that:

Hypothesis 1b: Even after controlling for religious service attendance and religious

should persist.

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salience, tradition differences in casino gambling, lottery playing, and Internet betting

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Organized Religious Participation

The second dimension of religion on which we focus is participation in religious congregations.

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We predict that the more people attend religious services, the less likely they will be to engage in destination gambling such as betting at casinos. We do not predict, however, that greater rates of service attendance will deter convenience gambling. Regular participation in congregational life requires time, thereby reducing opportunities

to do other time-intensive activities. Attending religious services not only involves sitting in the pew for an hour or so, but also traveling to and from one’s place of worship and socializing and

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engaging in organized activities (e.g., Bible studies) before and after services.. Active members of congregations simply do not have as much free time for extended gambling sessions at casinos. They also face greater risks of being exposed as a gambler from people who are

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unlikely to support this behavior. Congregations are important settings in which people form relationships, and, consequently, people active in them are likely to have a greater number of religious ties (for example, see Scheitle and Adamczyk 2009). It follows that those who actively

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participate in church life should have more sets of eyes from the faith community that could see them as they publicly gamble and formally or informally sanction them for doing so. Our

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prediction here accords with prior research showing that the presence of anti-gamblers in one’s circle of associates significantly decreases the likelihood of gambling (for example, see Meisel et al. 2013; Reith and Dobbie 2011).

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Hypothesis 2a: Controlling for all other factors, religious service attendance should have a significant negative effect on destination gambling such as casino gambling.

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No prior studies have considered whether the deterring power of organized religious participation holds for convenience gambling. According to the logic of our argument, active

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participation in religious congregations should not deter convenience gambling such as lottery play and online betting. While casino gambling is inherently high on both time commitment and social visibility, lotteries and online gambling can be done quickly and covertly. We thus predict that the constraining efficacy of organized participation will work only for destination (casino) and not convenience (lotto and online) gambling:

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Hypothesis 2b: Religious service attendance should not affect lottery play and Internet betting.

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Subjective Religiosity

Finally, we predict that subjective religiosity, or how salient religion is in people’s personal lives, will deter destination but not convenience gambling. Individuals with higher levels of

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religious saliency look to faith when making daily decisions. It thus makes sense that greater religious salience should prevent destination gambling in that this activity requires extensive

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planning and deliberation. For instance, one may see a commercial for a local casino and initially feel tempted to go there and gamble. There are a variety of steps that first must be taken, however, such as putting gas in the car and withdrawing cash from an ATM machine, during which one will have time to mull over the initial decision to travel to the casino. The

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more personally devout should be more likely to take advantage of these “veto points” inherent to destination gambling. Of course, religious salience is correlated with religious tradition and religious service attendance, both of which are expected to influence destination gambling. But

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for reasons stated in this paragraph, even after controlling for them, subjective religiosity should

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still deter casino gambling.

Hypothesis 3a: Religious salience will deter destination (i.e., casino) gambling and this effect will hold even in the presence of religious tradition and religious service attendance.

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Will the deterring efficacy of religious salience also operate for convenience gambling such as lotteries and internet wagering? We predict not. Convenience gambling is intentionally structured so as to produce impulsive and irrational decisions (Clotfelter and Cook 1991). It is

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ubiquitous throughout society, entails only seconds to play, and marketed to appeal to base

desires. Simply put, even those who use religion to guide their daily life decisions should not be able to muster the will power to resist the constant “temptations” offered by modern convenience

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gambling. We thus predict the following:

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Hypothesis 3b: Subjective religiosity will not deter convenience gambling (i.e., lottery play and online wagering).

Figure 2 summarizes our predictions for the different dimensions of religion and the different

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forms of gambling:

Figure 2: Predicted Pattern of Religion’s Deterring Effects

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Casino

Lottery

Online

X

X

X

Attendance

X

O

O

Salience

X

O

O

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Tradition

Note: O = no expected effect; X = expected deterring effect

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DATA, MEASURES, AND METHODS Data Sources To test the above hypotheses, we draw on the 2010 Science of Generosity Survey (SGS). This

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web-based survey collected information about various charitable activities, personality characteristics, and—crucially for our analysis—gambling behaviors from a nationally

representative sample of nearly 2,000 U.S. adults aged 23 years and older. 6 SGS respondents

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were drawn from Knowledge Networks’ (KN) Knowledge Panel (KP). KP is a probability-based online non-volunteer panel containing respondents 18 years or older from over 50,000

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households from all 50 states and the District of Columbia. KP panelists are recruited through a combination of random-digit-dial (RDD) and address-based-sampling (ABS) methods that generate a sampling frame covering approximately 97% of all households in the United States (for more information, see Dennis 2010). This panel is increasingly being used to collect data for

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social science projects, including recent waves of the American National Election Studies (ANES). Research has shown that the KP is representative of the United States as a whole and that the data collected from it are highly accurate (Callegaro and Disogra 2008; Chang and

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Krosnick 2009).

For the SGS, 3,072 KP panelists were invited to participate. Of those, 1,997 completed it

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for a completion rate (COMR) of 65%.7 On average, it took respondents about an hour to finish The minimum legal age to engage in gambling varies between 18 and 21, depending upon the

state and the specific game played. Surveying adults over the age of 22 thus generally controls for opportunity to gamble. 7

Calculated based on Callegaro and DiSorga’s (2008) formula. In addition to reporting the

COMR, these scholars recommend that researchers using probability-based Internet panels report 14

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the SGS. A post-stratification weight was created to correct for several known demographic differences between the SGS sample and Current Population Study (CPS) benchmarks due to

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issues related to non-response.8

Measures

Gambling. It is notoriously difficult to obtain precise measures of individuals’ past gambling

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behaviors. Most transactions are in cash and do not generate receipts; while people themselves tend to do a poor job recollecting the details of their gambling activities, such as how often one

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gambles, how much one normally wagers, and how long is one’s typical gambling session (Shaffer and Hall 1996; Shaffer et al. 2010; Volberg 1996). Two common measures used in survey research to make meaningful comparisons across individuals are lifetime prevalence and past-year prevalence. The former derives from questions asking whether respondents have ever

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the recruitment rate (RECR), the profile rate (PROR), and the final cumulative response rate (CUMRR1). For the SGS, the RECR = .182; the PROR = .554; and the CUMRR1 = .066, which is comparable to that of the ANES. Although this response rate likely seems low in RDD

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standards, comparing the response rates of probability-based Internet surveys to those of RDD

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surveys is not advisable since they are substantively different (Callegaro and Disogra 2008 3341). Additionally, response rates in themselves do not indicate the severity (or lack thereof) of non-response bias (Groves 2006 3344; Groves and Peytcheva 2008 3345), nor do higher ones necessarily indicate greater accuracy of response (Yeager et al. 2011 3343). 8

The variables that comprise the SGS-specific post-stratification weight are age, race/ethnicity,

education, marital status, geographic region, metropolitan area, home internet access, and volunteer status. Weight trimming was 1.0% and 99.0% and the overall design effect was 1.150. 15

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engaged in gambling; the latter from questions asking whether respondents have done so in the past year. The SGS measures past-year prevalence, which is suitable for our purposes. Lifetime prevalence rates would group together someone who gambled once during a trip to Las Vegas

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decades ago and someone who visits a casino several times each year. Very fine-grained

measures, such as self-reporting of weekly participation rates, or of typical losses per session, are generally considered unreliable (Rodgers et al. 2009). Past-year prevalence, in contrast,

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meaningfully distinguishes between those who gamble somewhat regularly from those who do so infrequently or never. In addition, ours is one of few empirical studies to differentiate among

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three forms of gambling.

We used questions asking respondents if at any point in the past twelve months they had gambled at a casino (i.e., “a large gambling hall with many different kinds of games, for example the kind of casino that you might find in a resort hotel, on tribal land, or on a riverboat”), played

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the lottery (“bought a lottery ticket for games like Lotto or Powerball, dailies like pick-4, or instants and scratch-offs”), and bet online (“placed a wager for money on any type of game using a computer on the Internet?”). We chose casinos, lotteries and online betting because they are

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large and growing segments of the gambling market (Welte et al. 2002). Lottery play was the most common form of gambling in our sample. Over 50% of SGS respondents played the lotto in

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the last year. Just over 25% of respondents gambled at a casino, while less than 5% gambled on the Internet. These figures are in line with other representative surveys of Americans’ gambling behavior (The National Gambling Impact Study 1999; Pavalko 2000; Welte et al. 2002).9 9

The National Gambling Impact Study (1999) found past year prevalence rates of 52% for

lottery and 29% for casinos; Pavalko (2000) found 56% and 32%; and Welte (2002) found 66% and 27%. 16

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Religion. Using the Steensland et al. (2000) classification scheme, we distinguished the following religious traditions: evangelical Protestant, black Protestant, mainline Protestant, Catholic, and the unaffiliated.10 Religious service attendance, our measure of organized religious

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participation, was an eight-point ordinal variable for how often people normally attended

religious services (not counting weddings, baptisms, and funerals). Answers ranged from never to more than once a week. The last religious dimension was subjective religiosity. It was

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measured with the following question “How important is your religious faith in providing

guidance in your day-to-day living?” The five answer categories ranged from not important at all

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to extremely important.

Controlling for Opportunity to Gamble. Not everyone has an equal opportunity to gamble. People who live in one of the seven states that do not have a lottery, for instance, have far fewer opportunities to engage in this form of gambling. Yet while the broader scholarship has

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argued that gambling opportunity is an important factor to consider (Jacques et al. 2000; LaPlante and Shaffer 2007; Welte et al. 2004), few studies have collected the necessary data to control for it. Holding opportunity constant is particularly important when studying the

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relationship between religion and gambling. Religious traditions, for instance, tend to be

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geographically clustered in ways that are germane to gambling availability. Catholics are

Religious groups (e.g., Hindus, Muslims, Mormons, or Christian Scientists) that did not fit one

of the major religious traditions were put into a “catchall” category. None of the groups in this category were large enough to analyze on their own. The heterogeneity of this category renders it substantively meaningless, so we do not interpret any of its effects. It is only included in the models so that there is a clear reference group for the religious tradition effects.

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concentrated more highly in the Northeast, proximate to Atlantic City and several large Tribal casinos in Connecticut, while stretches of the South, an evangelical Protestant hotbed, are casino free.

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We collected various supplementary data in order to incorporate measures of

opportunities for each form of gambling. For lottery play, we coded whether respondents lived in a state that had some sort of sponsored lottery. For Internet gambling, we included a variable for

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whether respondents had Internet access in their homes. Measuring the availability of casinos was more difficult. We first built a database of every U.S. casino in operation (except those in

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the state of Nevada) in 2010 (the year the survey was in the field) and its county location. We then linked this database to respondents’ county location to create a binary variable for whether at least one casino was in operation in their county. Because of the ubiquity of casinos in Nevada, we also included a dichotomous variable for whether respondents lived in this state.

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Other Control Variables. Based on prior research, we included a number of demographic variables often found to be associated with gambling (Forrest and McHale 2012; Ladd and Petry 2002; Shaffer et al. 1999; Volberg 1994). Dichotomous measures for sex (1 = male), race (1 =

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nonwhite), region (1 = South), urban/rural (1 = live in metropolitan area), employment status (1= working), and education (1 = four-year college degree or more) were included in all model

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specifications. Health was operationalized with a seven-point ordinal variable for the extent to which respondents’ daily activities were limited because of their physical condition (higher scores indicated that they were less limited). A six-point category ordinal variable for political liberalism/conservatism was included, with higher scores reflecting more conservative political views. Household income was a 19-category variable ranging from less than $5,000 to more than $175,000. Strong-tie network size was measured with a variable that asked respondents how

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many people (up to 5) they felt closest to. Age was a continuous measure for how old respondents were. In addition, we used scales to control for three psychological characteristics that have been demonstrated to correlate with gambling and heavy gambling: sensation-seeking,

[Table 1 here]

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Methods

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all variables used in our analysis.

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extroversion, and depression (Johansson et al. 2009).11 Table 1 presents descriptive statistics for

Because each gambling measure is dichotomous, we used logistic regression to analyze how the different dimensions of religion affect them. A series of models were conducted to test the

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Questions about whether respondents liked to explore strange places, do frightening/dangerous

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things, exciting things even if they break the rules, and if they thought of themselves as impulsive were used to represent sensation-seeking. There were seven possible answer categories

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for these questions, ranging from strongly agree to strongly disagree. Higher scores reflected greater sensation-seeking. The extroversion scale was measured with questions about how

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“talkative,” “reserved,” “shy or inhibited,” or “outgoing or sociable” respondents were. The same answer categories were used for these questions as the ones above for sensation-seeking. Questions employed for the depression scale asked respondents about the extent to which they felt sad or down, hopeless, had problems sleeping, found little interest or pleasure in doing things, had a poor appetite, felt tired, and had thoughts about hurting oneself. For these questions, respondents had five answer choices, ranging from never to very often. Higher scores on the depression scale indicate higher levels of depression 19

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various hypotheses specified above. The first model included only control variables and measures of religious tradition, with evangelical Protestants as the reference category. We then

separately and then jointly to assess their net effects.

RESULTS

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added variables for religious service attendance and religious salience. They are entered

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Table 2 presents coefficients from logistic regression models predicting casino gambling. All models control for the variables previously described (see Table A1 for their results).12 We begin

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with the religious tradition effects (Model 1). Consistent with hypothesis 1a, we find that black Protestants, mainline Protestants, Catholics, and the non-religiously affiliated all significantly increase the probability of this form of gambling relative to evangelical Protestants. Being Catholic has the strongest effect. Controlling for other factors, the odds of Catholics gambling at

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a casino are over 2 times that of evangelical Protestants (exp[.776 = 2.173).

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[Table 2 here]

In the second model, we add the variable for religious service attendance. It has a

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significant constraining effect on casino gambling, in line with hypothesis 2a. For each unit increase in religious service attendance, the odds of gambling at a casino are reduced by 7% 12

To make sure that the different gambling opportunity variables (e.g., proximity to casinos for

casino gambling) were not suppressing the religious effects, we ran models excluding them. Since the results were basically the same, we do not show models without controlling for the gambling opportunity variables. 20

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(exp[-.074] = .929). The next model replaces attendance with religious salience. Like its organizational counterpart, there is a significant negative relationship between importance of faith and casino gambling. However, the fourth model in Table 2 shows that when religious

deterring effect remains, which runs counter to Hypothesis 3a.

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service attendance and importance of faith are entered together, only the former’s significant

Comparing the coefficients of the religious traditions across the different models, we see

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that those significant in the first model generally continue to be significant in the other models. This finding supports Hypothesis 1b. The one exception to this pattern is the variable for non-

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religiously affiliated. Its coefficient falls to nonsignificance and drops by almost half in size (.565 to .276) between Model 1 and Model 4. It thus seems that the difference in casino gambling between those claiming no religious affiliation and evangelical Protestants is mainly a function of religious service attendance and importance of faith. Not surprisingly, auxiliary

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analyses showed that the non-religious had much lower levels of religious service attendance and religious salience to evangelical Protestants (results available upon request). Shifting to lottery play, the first model in Table 3 below shows that black Protestants,

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mainline Protestants, and Catholics are more likely to engage in this form of gambling relative to evangelical Protestants, controlling for other factors (see Table A2). This supports hypothesis 1a.

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Contrary to this hypothesis, however, there is no significant difference between evangelical Protestants and the non-religiously affiliated. With the exception of the mainline Protestant effect, the religious tradition effects observed in Model 1 remain intact across the other models, which is consistent with hypothesis 1b. The mainline-evangelical Protestant difference disappears when religious service attendance is entered (Model 2), suggesting that organized religious activity explains this initial difference. In auxiliary analyses, we find that, consistent

21

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with other research, evangelical Protestants have a higher rate of religious service attendance

[Table 3 here]

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than their mainline counterparts. Given this result, hypothesis 1b is only partially supported.

In contrast to Hypothesis 2b, religious service attendance has a significant negative effect

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on playing the lotto (we predicted a null finding). This effect holds across all models. Looking at the final model in Table 3, we see that for every unit increase in religious service attendance, the

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odds lotto play are reduced by 11% (exp[-.113] = .893). Consistent with hypothesis 3b, religious salience does not significantly affect the probability of lottery play in the full model. Internet betting is the final gambling outcome analyzed.13 In contrast to the other forms, we observe no religious tradition differences in Table 4 when holding other factors constant (see

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Table A3), which goes against hypothesis 1a. Supporting hypothesis 2b, the second model in this table shows a non-significant effect for religious service attendance. The only dimension of religion that significantly deters Internet gambling is religious salience. Holding all other factors

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constant, each unit increase in importance of faith decreases the odds of online betting by 21%

13

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(exp[-.240] = .787). This finding is the opposite of what we predicted in hypothesis 3b. [Table 4 here]

Because Internet wagering had a low participation rate (4%), we re-ran our model using

penalized maximum likelihood with the firth method/program in Stata (firthlogit) given its utility for analyzing rare events (Leitgöb 2013). The results from this method/program were very similar to the ones presented in Table 4 based on traditional logistic regression. 22

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DISCUSSION Figure 3 summarizes how well our predicted pattern of effects matched with what we actually observed. On the whole, our results demonstrate that religion continues to be a constraining

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force for gambling activity in the United States. They also suggest that our theoretical

framework captured some but not all of the salient characteristics of gambling, which have

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implications for religion’s deterrent power.

Predicted Effects Casino

Lotto Online

Tradition

X

X

X

Attendance

X

O

O

Salience

X

O

O

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Figure 3: Predicted vs Actual Pattern of Religious Effects

Actual Effects

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Note: O = no expected effect; X = expected deterring effect.

Casino Lotto Online

Attendance

X

Salience

O

X

O

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X

X

O

O

X

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Tradition

Hypotheses 1a and 1b were mostly supported. We had predicted that long-standing

differences in how religious traditions theologically frame gambling would affect the likelihood of all forms of gambling. In particular, we predicted that affiliation with evangelical Protestantism would have the strongest deterring effect, insofar as this tradition’s theology

23

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explicitly labels gambling in any form as a sin. Looking at casinos and lotteries, this was certainly the case. Even when controlling for religious service attendance, religious salience, and gambling availability, evangelical Protestants were generally less likely to gamble at casinos and

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play the lottery than other groups. The best explanation for these results is that evangelical

Protestants have internalized at a deep level their tradition’s doctrine condemning gambling. Had we been able to measure such internalization in the form of biblical literalism, for example, this

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likely would have explained a large part of the evangelical Protestant effect (Ellison and

McFarland 2011). Mainline and black Protestants, who have tempered the sin framework,

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engage in casino gambling and lottery play less often than do evangelical Protestants; while Catholics, whose doctrine is the most tolerant of gambling, participate in these two forms of gambling the most. In short, the broad cultural framings that different religious traditions espouse about gambling activity matter when it comes to casino gambling and lottery play (the

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two largest sectors of the national gambling industry).

But we were surprised to find no religious tradition variation for gambling on the internet. All else being equal, evangelical Protestants are no more or less likely to wager online

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than are Catholics, black or mainline Protestants, or the non-religious. Our data do not tell us why this is the case, but we posit that the newness of online gambling likely means that religious

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traditions have not yet had time to frame it definitively. Casinos and lotteries have long been sources of contention within denominations, such that religious leaders have had adequate time to consider and preach about how they fit or do not fit within broader theological doctrines and teachings.

Online gambling, in contrast, is very new, which may explain the lack of a tradition effect. It may be that religious groups have not yet bounded it within traditional framings. It is

24

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possible that online gamblers do not think of their activity as gambling. This certainly represents an agenda for future research: are denominational bodies and leaders aware of online gambling, and if so, which bodies and leaders are most concerned and advocate against it? Will religious

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tradition effects emerge for this new form of gambling over time? Because of the newness and privacy of this activity, qualitative studies would be especially helpful in answering these questions.

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Turning to religious service attendance, we had predicted that more frequent attendance would constrain destination gambling (casinos), but not convenience gambling (lotteries and

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online wagering). Our reasoning was that active participation in congregational life congregations takes time and exposes people to more conservative social networks, which would raise the costs and risks of time-intensive and publicly-visible forms of gambling. Here our predictions (Hypotheses 2a and 2b) held up for casino gambling (which was constrained by

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attendance) and online gambling (which was not). More frequent religious service attendance, however, also suppressed lottery play. But why did it do so? One possibility is that lotteries have been incorrectly defined as convenience gambling;

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that is, as a form of gambling that is invisible and without costs of time. Those who play the popular weekly “powerball” drawings often purchase numerous tickets which are tangible pieces

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of paper that must be retained throughout the week. This means that for lottery gamblers seeking to conceal their play from significant others, there is an omnipresent danger that a family member or friend may discover the tickets as evidence of gambling. Furthermore, there are several qualitative studies which suggest that lottery play is less individualized and more grouporiented than we think (Hedenus 2014). For instance, lottery players often form groups and pool their money to purchase a large number of tickets, which makes this gambling known throughout

25

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one’s networks (Guillén et al. 2012). Moreover, lotto players tend to monitor TV broadcasts, websites, and newspapers announcing the drawings, thereby risking being exposed as a gambler (Beckert and Lutter 2013; Howland 2001).

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Our predictions concerning religious salience (Hypotheses 3a and 3b) received the least support. We had assumed that those who use faith to make daily decisions would resist

destination gambling, but not convenience gambling. Instead we found just the opposite.

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Relying on faith to guide one’s daily life had no effect on one’s propensity to gamble at casinos or to play the lottery, but it did deter gambling on the internet. In fact, religious salience was the

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only dimension of religion to constrain online wagering.

A possible explanation for these unexpected findings is that there are other relevant characteristics of gambling besides their spatiality and temporality that shape religion’s constraining power. One is legitimacy. What distinguishes casinos and lotteries from online

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wagering is that state governments have only sanctioned the only the former gambling acts. Online betting is not technically illegal; rather, it is so new that it remains essentially unregulated. 14 As one scholar (Rose 2012) puts it, there is a “maze of confusion and

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contradiction that now clutters the legal scene” surrounding internet betting. Because casinos and lotteries are state-sanctioned today, it could be that Americans do

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not consider indulging in them as incompatible with a personal life of faith. Indeed, casino and lottery proprietors go to great lengths to offer alternative frames allowing gamblers to think of their gambling as “good works.” State governments, for instance, advertise lottery play as a way to fund ailing school systems, while, while Indian casinos convey to patrons that their losses go to funding social services for impoverished tribes (Clotfelter and Cook 1991; Sallaz 2006). But 14

To the best of our knowledge, no U.S. consumers have been arrested for wagering online. 26

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no such counter-frames are offered by online gambling proprietors, who are located offshore and who cannot claim to use revenues for good works. It could also be that potential gamblers worry that online gambling is risky. Because online gambling websites are not officially sanctioned,

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they do not offer consumers the same regulatory protections as do casinos and lotteries, for

instance against credit card fraud or rigging of the games. Research has shown that greater

levels of subjective religiosity are associated with greater risk aversion (for example, see Miller

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and Hoffman 1995), which could help explain why religious salience does deter online gambling.

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In sum, our findings suggest that online gambling, as it is currently organized and regulated, is fundamentally different from both casinos and lotteries. It is not entirely impervious to religion as a constraining force, but it is only religious salience that shows a statistically significant effect in our model. The rate of participation in online gambling among our sample

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(4%) was much lower than for casinos (29%) and lotteries (56%). It thus seems reasonable to assume that, barring outright prohibition by the federal government, there is potential for this segment of the gambling market to expand. If this occurs, and the percentage of Americans who

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gamble online increases, future research should investigate whether the unique pattern of religious deterrence of online gambling persists, or if it converges toward the pattern

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characteristic of casinos and lotteries (in which tradition and participation rather than salience do the work of constraining gambling).

Conclusion

On the whole, our study supports the general theme of prior research showing that religion influences the gambling behavior of Americans. Following Ellison and McFarland (2011), we

27

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treated religion as a multidimensional construct, focusing on tradition, attendance, and salience. But going beyond these scholars, we also divided gambling into three distinct types—casinos, lotteries, and online wagering—and hypothesized how each dimension of religion would

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influence each type of gambling. Notably, ours study is the first to also control rigorously for opportunity across all three gambling types. Based on theories and findings, the deterring power of religion seems to work through multiple pathways. Essential to understanding these

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pathways, is the spatiality and time-intensiveness inherent in specific gambling games. In addition, future research should consider how the power of religion might depend on the

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legitimacy of gambling operators, which likely explains some of our unanticipated observations. Our paper has implications for the cross-disciplinary field of gambling studies which, as previously noted, has suffered from a paucity of sociological perspectives. Sociology, in fact, has itself long been conflicted regarding the nature of gambling. Symbolic interactionists,

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writing in the 1960s and 1970s, sought to rescue the activity from the clutches of economists and utilitarians, who maintained that it was fundamentally irrational. Goffman and others demonstrated that gamblers were not unreasonable recluses, but members of distinctive

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subcultures: poker players, pool hustlers, and cockfighters were seen as constituting social worlds unto themselves. Now, the pendulum has swung back and the accepted model of

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gambling once again focuses on factors internal to the individual actor—not so much their irrationality as their underlying pathology. Rather than situating gamblers in their lifeworld, this emergent literature descends into the gamblers’ psyches and even into their genomes, seeking determinants of pathological gambling. This study does not negate such determinants, but it does suggest that, net of any individual-level propensities, a person’s embeddedness in larger social institutions matters. In

28

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the current case, we find that religion, broadly defined, influences a person’s gambling behavior. Religion, in this formulation, exists as a social fact: it is external to the individual, and it is endowed with a causal efficacy. Here is a positive argument for supporting civic life and social

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institutions of all sorts as a means of curtailing the impulse to engage in the many new forms of state-sanctioned convenience gambling.

Net of one’s gender, marital status, level of depression, degree of impulsivity, and other

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personal traits, participation in religious life generally reduces the likelihood of gambling.

Simply put, spending time at congregations means less time to go to a casino. Being integrated

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into the life of religious institutions means one is less likely to risk being seen as a gambler buying lotto tickets. And even the most secluded form of gambling on the Internet appears to be curtailed when we consider how much personal guidance people derive from religion. For those concerned that mass gambling has been a Faustian bargain, our overall conclusion that gambling

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varies depending on different indicators of “faith” suggests new pathways for ameliorating

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ordinary and perhaps even heavy gambling.

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Table 1. Descriptive Statistics for Variables Used in Analysis

0.288 0.557 0.039

Religion Variables Evangelical Protestant Black Protestant Mainline Protestant Catholic No religious affiliation Religious service attendance Importance of faith

0.244 0.071 0.199 0.239 0.120 3.402 3.352

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EP

TE D

Control Variables No casino in county Casino in county Nevada resident No state lottery Home internet access Nonwhite Male Age Bachelor or higher degree Household income Married/living with a partner Widowed or divorced Never married No children in household Working Live in Southern state Live in metropolitan area Physical health Depression scale Political views Number of strong ties Extroversion scale Sensation-seeking scale

0.453 0.497 0.194

0.430 0.257 0.399 0.427 0.325 2.606 1.387

SC

Dependent Variables Casino gambling Lottery play Internet betting

S.D.

RI PT

Mean

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0.801 0.177 0.022 0.086 0.722 0.300 0.491 48.840 0.299 11.184 0.637 0.189 0.174 0.368 0.561 0.363 0.830 4.899 3.534 4.162 3.776 4.566 3.832

0.399 0.382 0.147 0.280 0.448 0.458 0.500 15.579 0.458 4.347 0.481 0.392 0.379 0.482 0.496 0.481 0.376 2.012 0.781 1.410 1.710 1.015 1.262

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Table 2. Unstandardized Coefficients from Logistic Regression Models

RI PT

Predicting Casino Gambling

Model1

Model 2

Model 3

Model 4

Black Protestant

0.519**

0.461+

0.496**

0.460+

(0.242)

(0.241)

(0.241)

0.343**

0.383**

0.333+

(0.168)

(0.171)

(0.170)

(0.172)

0.776***

0.706***

0.715***

0.694***

(0.161)

(0.162)

(0.163)

(0.163)

0.565***

0.340

0.306

0.276

(0.195)

(0.207)

(0.227)

(0.227)

(0.241) 0.447***

M AN U

Mainline Protestant

Catholic

TE D

No religious affiliation

Religious service attendance

-0.074***

-0.064**

(0.024)

(0.028)

AC C

EP

Importance of faith

SC

Predictor Variables

-0.108**

-0.039

(0.049)

(0.057)

Note: Numbers in parentheses are standard errors; N = 1,914 in all models. All models include controls for gambling opportunities and individual-level traits (see Table A1) ***p<0.01; ** p<.05 (two-tailed tests); + p<.05 (one-tailed test)

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Table 3. Unstandardized Coefficients from Logistic Regression Models

RI PT

Predicting Lottery Play

Model 1

Model 2

Model 3

Model 4

Black Protestant

0.470**

0.391+

0.445**

0.391+

(0.217)

(0.220)

(0.218)

(0.220)

0.214

0.289**

0.210

(0.149)

(0.147)

(0.149)

1.095***

0.999***

1.020***

0.993***

(0.150)

(0.152)

(0.152)

(0.153)

0.076

0.287

0.268

0.318

(0.172)

(0.184)

(0.203)

(0.203)

Mainline Protestant

0.366**

Catholic

No religious affiliation

-0.118***

-0.113***

(0.021)

(0.025) -0.146***

-0.019

(0.045)

(0.053)

EP

Importance of faith

TE D

Religious service attendance

M AN U

(0.145)

SC

Predictor Variables

AC C

Note: Numbers in parentheses are standard errors: N= 1,906 for all models All models include controls for gambling opportunities and individual-level traits (see Table A2) ***p<0.01; ** p<.05 (two-tailed tests); + p<.05 (one-tailed test)

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Table 4. Unstandardized Coefficients from Logistic Regression Models

Predictor Variables

Model 1

Model 2

Model 3

Model 4

-0.078

-0.122

-0.124

-0.123

(0.609)

(0.610)

(0.610)

(0.611)

-0.617

-0.669

-0.666

(0.501)

SC

Black Protestant

(0.507)

(0.506)

(0.509)

0.501

0.432

0.327

0.329

(0.367)

(0.375)

(0.378)

(0.379)

0.291

0.127

0.248

0.246

(0.425)

(0.454)

(0.499)

(0.500)

-0.544

No religious affiliation

-0.057

-0.003

(0.060)

(0.070) -0.236**

-0.240+

(0.116)

(0.136)

EP

Importance of faith

TE D

Religious service attendance

M AN U

Mainline Protestant

Catholic

RI PT

Predicting Internet Betting

AC C

Note: Numbers in parentheses are standard errors; N = 1,903 for all models All models include controls for gambling opportunities and individual-level traits (see Table A3) ***p<0.01; ** p<.05 (two-tailed tests); + p<.05 (one-tailed test)

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Table A1. Unstandardized Coefficients from Logistic Regression Models Predicting Casino Gambling, Control Variables

Nonwhite Male Age Bachelor or higher degree Household income Widowed or divorced Never married

Working Live in Southern state

Live in metropolitan area

EP

Physical health

AC C

Depression scale Political views

Number of strong ties Extroversion scale

Sensation-seeking scale Constant

Model 4

0.297** (0.137) 2.026*** (0.485) 0.206 (0.135) -0.196* (0.113) 0.008* (0.005) -0.432*** (0.134) 0.065*** (0.016) 0.341** (0.161) 0.488** (0.149) 0.013 (0.132) 0.198 (0.126) -0.436*** (0.121) -0.052 (0.147) 0.027 (0.030) -0.110 (0.080) -0.021 (0.040) 0.028 (0.035) 0.051 (0.060) 0.227*** (0.048) -3.390*** (0.534)

0.300** (0.138) 1.961*** (0.487) 0.254* (0.136) -0.219* (0.113) 0.010** (0.005) -0.393*** (0.135) 0.066*** (0.016) 0.351** (0.162) 0.498*** (0.149) 0.042 (0.133) 0.199 (0.126) -0.418*** (0.122) -0.051 (0.147) 0.029 (0.030) -0.089 (0.080) 0.002 (0.041) 0.036 (0.036) 0.058 (0.060) 0.217*** (0.048) -3.371*** (0.535)

0.299** (0.137) 2.009*** (0.486) 0.255* (0.137) -0.220* (0.113) 0.009* (0.005) -0.425*** (0.135) 0.064*** (0.016) 0.352** (0.162) 0.495*** (0.149) 0.033 (0.133) 0.194 (0.126) -0.423*** (0.122) -0.079 (0.147) 0.025 (0.030) -0.106 (0.080) -0.007 (0.041) 0.036 (0.036) 0.060 (0.061) 0.219*** (0.048) -3.067*** (0.553)

0.301** (0.138) 1.964*** (0.487) 0.266* (0.137) -0.225* (0.113) 0.010** (0.005) -0.395*** (0.136) 0.066*** (0.016) 0.354** (0.162) 0.499*** (0.149) 0.046 (0.133) 0.198 (0.126) -0.416*** (0.122) -0.061 (0.148) 0.028 (0.030) -0.091 (0.080) 0.004 (0.041) 0.038 (0.036) 0.060 (0.061) 0.216*** (0.048) -3.255*** (0.561)

TE D

No children in household

Model 3

RI PT

Nevada resident

Model 2

SC

Casino in county

Model1

M AN U

Predictor Variables

Note : Numbers in parentheses are standard errors; N = 1,914 in all models. ***p <0.01; ** p <.05; * p <.10 (two-tailed tests)

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Table A2. Unstandardized Coefficients from Logistic Regression Models Predicting Lottery Play, Control Variables Model 1

Nonwhite Male Age Bachelor or higher degree Household income

Never married No children in household Working

Depression scale

AC C

Political views

EP

Physical health

Number of strong ties Extroversion scale

Sensation-seeking scale Constant

-0.880*** -0.889*** -0.865*** -0.886*** (0.189) (0.190) (0.189) (0.190) 0.113 0.192 0.178 0.198 (0.124) (0.126) (0.126) (0.127) -0.077 -0.109 -0.110 -0.112 (0.102) (0.103) (0.103) (0.103) 0.003 0.005 0.003 0.005 (0.004) (0.004) (0.004) (0.004) -0.734*** -0.680*** -0.729*** -0.681*** (0.121) (0.122) (0.122) (0.123) 0.003 0.005 0.001 0.005 (0.014) (0.014) (0.014) (0.014) -0.207 -0.207 -0.198 -0.205 (0.146) (0.147) (0.146) (0.147) -0.055 -0.054 -0.054 -0.054 (0.137) (0.138) (0.138) (0.138) -0.010 0.039 0.020 0.041 (0.120) (0.121) (0.121) (0.122) 0.140 0.144 0.136 0.143 (0.113) (0.114) (0.113) (0.114) -0.010 0.016 0.005 0.017 (0.104) (0.105) (0.104) (0.105) -0.013 -0.007 -0.044 -0.011 (0.126) (0.127) (0.127) (0.128) -0.040 -0.038 -0.044 -0.038 (0.027) (0.027) (0.027) (0.028) -0.050 -0.020 -0.045 -0.021 (0.073) (0.074) (0.073) (0.074) -0.104*** -0.064* -0.083* -0.062* (0.037) (0.037) (0.037) (0.038) 0.037 0.055* 0.049 0.056* (0.032) (0.032) (0.032) (0.032) 0.110** 0.120** 0.124** 0.121** (0.055) (0.055) (0.055) (0.055) 0.092** 0.076* 0.080* 0.075* (0.043) (0.043) (0.043) (0.043) -0.232 -0.194 0.204 -0.139 (0.464) (0.466) (0.484) (0.491)

TE D

Live in Southern state Live in metropolitan area

Model 4

M AN U

Widowed or divorced

Model 3

RI PT

No state lottery

Model 2

SC

Predictor Variables

Note : Numbers in parentheses are standard errors: N= 1,906 for all models *** p <.01; ** p <.05; * p <.10 (two-tailed tests)

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Table A3. Unstandardized Coefficients from Logistic Regression Models Predicting Internet Betting, Control Variables Model 1

Model 2

Model 3

Model 4

Home internet access

0.755** (0.350) 0.559* (0.287) 0.310 (0.273) -0.029** (0.011) -0.156 (0.334) 0.001 (0.036) 0.043 (0.466) 0.297 (0.326) 0.343 (0.291) -0.539* (0.291) -0.413 (0.283) -0.391 (0.340) 0.005 (0.071) -0.186 (0.177) -0.091 (0.093) -0.086 (0.076) 0.095 (0.146) 0.178 (0.113) -2.329* (1.244)

0.755** (0.351) 0.588** (0.288) 0.296 (0.273) -0.028** (0.011) -0.118 (0.337) 0.001 (0.036) 0.046 (0.465) 0.311 (0.325) 0.352 (0.291) -0.525* (0.291) -0.405 (0.283) -0.380 (0.341) 0.004 (0.071) -0.173 (0.177) -0.075 (0.094) -0.083 (0.076) 0.104 (0.146) 0.169 (0.113) -2.270* (1.245)

0.740** (0.351) 0.669** (0.292) 0.259 (0.274) -0.028** (0.011) -0.129 (0.336) -0.003 (0.036) 0.067 (0.466) 0.318 (0.326) 0.374 (0.291) -0.524* (0.292) -0.381 (0.284) -0.438 (0.342) -0.003 (0.072) -0.172 (0.179) -0.065 (0.095) -0.072 (0.077) 0.126 (0.147) 0.163 (0.114) -1.654 (1.286)

0.740** (0.351) 0.669** (0.292) 0.259 (0.274) -0.028** (0.011) -0.131 (0.337) -0.003 (0.036) 0.067 (0.466) 0.317 (0.326) 0.373 (0.291) -0.525* (0.293) -0.381 (0.284) -0.439 (0.343) -0.003 (0.072) -0.172 (0.179) -0.065 (0.095) -0.072 (0.077) 0.126 (0.147) 0.164 (0.114) -1.649 (1.291)

Age Bachelor or higher degree Household income Widowed or divorced Never married No children in household

Live in Southern state

TE D

Working

Live in metropolitan area

EP

Physical health Depression scale

AC C

Political views

Number of strong ties Extroversion scale

Sensation-seeking scale Constant

SC

Male

M AN U

Nonwhite

RI PT

Predictor Variables

Note : Numbers in parentheses are standard errors; N = 1,903 for all models *** p <.01; ** p <.05; * p <.10 (two-tailed tests)

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