DREAMING THE NUMBERS:

Ethnocultural Gambling in Ontario

By: Lorne Tepperman Albert Kwan Charles Jones Agata Falkowski-Ham

With the assistance of: David Korn Marion Lynn Janie Wiebe Amy Withers

September 2004

Not for Reproduction or Quotation without Permission of the Authors

Funded by the Ontario Problem Gambling Research Centre

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Table of Contents

Preface: The Purpose of this Research ………. 3

Chapter One: Gambling, Gaming, Games, and Play ………. 8

Chapter Two: Gambling in Canada ………. 33

Chapter Three: Three Gambling Cultures ………. 54

Chapter Four: Individual-level Determinants of Gambling ………. 81

Chapter Five: Neighbourhood-level Determinants of Gambling ………. 108

Chapter Six: A Mixed Model of Gambling Behaviour ………. 124

Chapter Seven: Conclusions and Implications ………. 141

References ………. 146

APPENDIX A: Prevalence Questionnaire ………. 157

APPENDIX B: Statistical Tests Conducted on Gambling Diversity ………. 184

APPENDIX C: Statistical Tests for Ethnic Neighbourhood Gambling Patterns ………. 191

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Preface: The Purpose of this Research

This study builds on an earlier study concerned with the effects of ethnocultural group membership and family life on gambling behaviour. It aims to measure more precisely the extent of ethnic variations, and to examine possible explanations. In the end, we hope to shed light on what has been known for a long time but poorly understood: namely, the place of gambling in different ethnic cultures, the extent and reasons for variation in gambling among Ontario’s larger ethnocultural groups, and the policy implications of this information about ethnocultural variation. We call this monograph Dreaming the Numbers because gambling is, ultimately, about dreaming. Gamblers dream of success, wealth, and popular approval; for them, gambling is a shortcut. Like all dreams, gambling dreams are informed by culture. For some cultures, dreams of success through gambling involve demonstrating skill; for others, they are demonstrations of masculinity or intellect or heavenly blessing. As well, in gambling dreams, people take actions to secure their success. They test their fate, through courage and cunning. All dreams involving action, including gambling dreams, are also informed by culture. Cultures have different ways of acting – indeed, different ways of gambling. Among people who gamble a lot, gambling becomes a central part of life – the thing gamblers think and dream about, whether asleep or awake. They dream and scheme about the best lottery numbers, poker hands, or strategies for winning at blackjack, for example. In these senses, their lives are about “dreaming the numbers,” though as we have said, these dreams are shaped by their ethnocultural heritage. How they dream, and how they enact their dream, depends on where they live and who they gamble with. Much more will be said about these cultural variations in the chapters that follow. A study completed in 2002 by Tepperman, Korn, and Lynn “At Home With Gambling,” funded by the Ontario Problem Gambling Research Centre of Canada, explored the conceptual and measurement issues associated with ethnocultural variation. In that study, we interviewed 60 respondents from each of six ethnocultural groups – Aboriginal, British, Caribbean, Chinese, Latin American, and Russian – using a combination of closed-ended and open-ended survey instruments. Our sample, obtained by advertising, agency referral, interviewer referral, and snowballing – was unrepresentative. Therefore, using these data, we could not speak generally about gambling practices in these six groups. However, the study was useful as an exploratory first step in the direction of the current study. From our earlier study we learned, first, that ethnocultural groups differ in a variety of matters related to gambling. Ethnocultural groups vary in the games they play, where they play them, and with whom they play them (e.g., alone or with others, with strangers or familiars). They hold varying beliefs about luck, risk, winning, losing, gambling and gamblers. Finally, they exhibit a range of gambling practices and gambling-related problems, including other addictive and health-risk behaviours. Second, people teach and learn ethnocultural practices in gambling, as they do in other types of behaviour. Ethnocultural differences result from family and community influences, from early childhood onward. These, in turn, operate within a larger social and cultural context, influenced by such factors as advertising, the mass media, and the proximity of gambling opportunities (e.g., casinos, gambling halls). Third, we learned from the At Home with Gambling study that families and communities both control gambling and stimulate it. Though families and communities are typically the contexts within which people learn the traditions of gambling within their ethnocultural group and, in that sense, come to value gambling, these are typically also the contexts that set limits to risky or costly personal behaviours. As sociologists since Durkheim have argued, social integration is an important source of personal regulation. We need to understand better how family and community life induces responsible

93 gambling behaviour. We also need to understand gambling within the context of stable and unstable, personal and impersonal, social contexts. The theoretically important goals of the current research are to develop a framework for understanding how different ethnocultural groups gamble. In particular, the current analysis enables us to disentangle individual, group and neighbourhood-level influences on gambling behaviour. This is accomplished by combining three data-sets: one exploratory and rich in family and ethnic information, another broad and dense with gambling information, and a third full of representative information about neighbourhood characteristics. Our current study is not primarily concerned with responding to the “problematics” presented by the social and cultural contexts of gambling. It is an analytic and descriptive study. It does not have primarily prescriptive, policy, or therapeutic goals. However, we hope that knowledge derived from this current study will inform health and human services professionals on a range of culturally relevant public health and clinical matters related to gambling. To better understand the factors that affect gambling, we need a clear understanding of the interaction between different levels of influence (individual, family, neighbourhood, community, and society, for example). We need to understand the effects of historical tradition, the dislocating effects of immigration, the marginalizing effects of ethnic segregation, and the psychological effects of unemployment, underemployment, and poverty. When we know more about ethnocultural variations in gambling practice, it will be possible to organize initiatives to support responsible gambling in terms that are culturally appropriate (and understandable) to each particular group. Second, when we know more about the circumstances that lead members of particular groups to drift from responsible gambling to problem (or irresponsible) gambling, seeing the danger signs and taking appropriate steps will be easier. Third, if we know that gambling within the family and community is – for certain groups – more likely to be responsible gambling, we can take steps within those groups to encourage gambling among friends, family, and other community familiars.

Culture and Ethnicity Ethnicity, our key variable, is socially constructed and multi-dimensional. That is, the term “ethnicity” encompasses a variety of characteristics including nationality, religion, and language spoken in the home. Some would even include issues like self-identification, patterns of friendship and mating, and residential location in the definition of ethnicity. Culture and ethnicity are relevant to gambling behavior through culturally produced attitudes towards gambling. Culture affects gambling patterns among members of a cultural group by affecting their ideas, traditions, social practices, customs and laws. As members of a cultural group, we inherit and pass on culture through our institutions, practices, technologies, art forms and modes of discourse (Shweder 1991). Thus, culture affects not only the availability of gambling facilities, but people’s attitudes and beliefs about gambling. A positive attitude towards gambling is related to a tendency to take risks (Kassinove 1998; Kassinove et al 1998). Culture determines the attitude and meaning that gambling has for its members, and in this way, culture affects the specific functions of gambling for different cultural groups (Abt et al 1985). Gambling does not automatically lead to problem gambling (Zuckerman 1999; Murray 1993) and there is an uneven distribution of gamblers (i.e., problem versus non-problem) within different communities. Cultural and family factors are likely the intervening variables between healthy and problem gambling (Raylu and Oei, 1998). The addiction literature suggests that several cultural variables both cause and maintain an addiction and subsequently affect the treatment of addictive disorders among cultural groups. Among the elements of culture and social context that have been found to be relevant to the prevalence of addictive disorders, such as gambling drug and alcohol abuse, are generational status, degree of acculturation, specific ethnic group, and place of birth (Zane et Al. 1998). Blaszczynski et al 94

(1998) argue for the importance of a prior history of gambling in their country of origin as a factor in predicting problem gambling. (See also De La Rosa, et al., 2000; Escobar, et al., 2000; Loue, 1998; Westermeyer, 1999). Cultural values and beliefs, the impact of acculturation, and the likelihood that members of a cultural group will seek professional help all affect the extent of problem gambling in an ethnocultural group. These ethnocultural variations are worth noting, given the importance of immigrant respondents in our studies. Ontario, including the GTA (Greater Toronto Area), is the primary site of the current research. From the 1950’s onwards, Toronto has received the lion’s share of immigrants to Canada. The amount and quality of information about gambling in different ethnocultural groups is enormously variable. However, in general, there is not enough reliable information about any of the ethnocultural groups in which we are interested. This is another reason we have conducted this study.

Design of this Study

Data Sources This study makes use of three sources of data: (1) the exploratory At Home with Gambling study conducted by Tepperman, Korn and Lynn; (2) a survey of gambling in Ontario conducted by Wiebe, Falkowski-Ham and Single (WFS) and reported in “Measuring Gambling and Problem Gambling in Ontario;” and (3) data from the 1996 Census of Canada. A few words are in order about each of these sources. The At Home with Gambling study sampled 360 Ontarians in 2001-2 -- 60 from each of six ethnocultural groups. These are: Aboriginal, British Isles, Caribbean, Chinese, Latin American and Russian. Roughly one-sixth of the total sample was adolescents (n=63) in that study, though in the current study we focus on adults only. As no complete enumeration of these communities exists, we relied on alternatives to random sampling: namely, convenience, self-selection, snowball and targeted sampling. Moreover, as it was difficult to gain access to some of these groups, this was the only practical way of obtaining respondents. We interviewed people who live in a number of different family/household arrangements to investigate whether there is a relationship between gambling behaviour and family/household cohesion, stability and type. From each respondent we collected information on a large variety of topics related to socio-demographic features, ethnocultural identification, gambling beliefs and activities, and family life. As our basic measure of gambling status we used the South Oak Gambling Screen (SOGS) and for the adolescent respondents, the SOGS-RA. The WFS study – or Ontario Prevalence Survey -- was also funded by Ontario Problem Gambling Research Centre. That survey gathered data from a representative sample of the adult population of Ontario. The researchers sampled 5,000 Ontarians in 2001, asking them a variety of questions regarding their involvement in gambling activities, problem gambling behaviour, adverse consequences resulting from gambling, and socio-demographic and other characteristics relating to problem gambling. It used the recently developed Canadian Problem Gambling Index and estimated rates and correlates of gambling. Finally, the neighbourhood level (census tract) data we use in our current study include information from the 1996 Census of Canada for each census tract in the CT-level profile. These were the most recent data available to us in microdata form when we began our work, and they are sufficiently close in time to the other data sources to warrant their use in this study. The key explanatory variables in our current study are ethnicity, immigrant status, family organization, and social class. Multiple measures of each of the variables of interest – a total of 1699 variables in all – are available in the 1996 Census data. For example, measuring "ethnicity" there are the following categories of variable: birthplace, mother tongue, home language, and stated ethnic

95 origin; and for each of these categories, there are many specified variables -- one for each ethnic or language group. Likewise, for immigration and mobility, there are these categories of variables: time of immigration, age at immigration, knowledge of official languages, and mobility status. Related to "social class," there are the following categories of variables: income (employment, total, family), private household income, low income households, housing quality, school attendance, school attainment, industrial categories of workers, occupational categories of workers, labour force status of workers, class of workers. Then, there are the variables that address census tract size and population density, family (and household) size and composition, and age structure within the tract. All of these are potentially important.

Study Design We have designed this study to examine the extent and types of ethnocultural variation in gambling in Ontario, using census data and data from a representative sample survey conducted by Wiebe, Single and Falkowski-Ham. The research questions guiding the research are: (1) What are the specific ethnic variations in gambling behaviour and belief, including patterns of problem gambling as measured by CPGI, indicated in the WSF study? (2) How do these patterns of variation correlate with the patterns observed in the earlier, exploratory study by Tepperman and Korn? and (3) What, generally, can be said about the ethnocultural bases of responsible and problem gambling in Ontario? The report of the WSF project described a variety of gambling behaviours and outcomes. However, it did not cross-tabulate gambling behaviours and outcomes against ethnocultural (or ancestral) origins, though it had asked about these origins. Accordingly, we start by cross-tabulating all of the items that touch on gambling behaviour and gambling outcomes against the ethnocultural origin variable1. We use the resulting data to elaborate on ethnocultural variations identified in the At Home with Gambling study, mentioned above. It is believed that the data from this study provide a richer and more representative picture of ethnic gambling within the six groups of interest. We use these data to explore ethnic variations in gambling more generally, examining, for example, ethnic groups not touched on in the At Home with Gambling study. Our goal is to further understand the range and types of ethnocultural variation in a representative sample, while controlling for a variety of socio-demographic variables such as community size, education, income, marital status, gender, and occupation. We use the geocoded Census data to significantly enrich the amount and quality of data for each respondent, and provide information about the neighbourhood context within which gambling takes place. As to data analysis, we use a variety of multivariate techniques, including regression analysis and hierarchical linear modeling to separate the neighbourhood (census tract) from individual level effects. For illustrative and confirmatory purposes, we combine the results of this statistical analysis with descriptive material from the Report on the At Home With Gambling study. In discussing ethnocultural variations, we do not point a finger of blame at any particular groups, or praise other groups as being particularly meritorious. Instead, we try to understand gambling and gambling problems as thoroughly as possible, and the data presented below show clearly that gambling practices vary from one ethnocultural group to another. What is unknown is the reason for these variations, and how we should respond to gambling problems in the groups where they occur most frequently.

1 Among the 140 questions asked in the WSF study – questions mainly about gambling practices, beliefs, and reactions – is a question on ancestral ethnic or cultural group that permits any of 60 responses. This question reads: To what ethnic or cultural group did you or your ancestors belong on first coming to this country? IF RESPONDENT IS NOT CLEAR SAY “Are you Scottish, Chinese, Greek or something else?” IF RESPONDENT SAYS CANADIAN ASK ” In addition to being Canadian, to what ethnic or cultural group did you or your ancestors belong on first coming to this country?” CIRCLE ALL THAT APPLY. 96

How the monograph is organized In chapter 1, we review the literature on gambling as a long-standing cultural behaviour, in hopes of understanding its role in societies, and its reasons for varying from one culture to another. In chapter 2, we examine gambling in Canadian history and focus on known historical and ethnocultural variations. In chapter 3, we closely examine three ethnocultural groups of particular interest: the British, Chinese and Aboriginals. In chapter 4, we re-analyze data from the WFS Ontario survey of gambling, to examine the extent of ethnocultural gambling in this dataset. In chapter 5, we introduce the census data and look at the ways that neighbourhood characteristics affect individual gambling by people in the WFS survey. In chapter 6, we attempt to distinguish between individual and neighbourhood effects on ethnocultural gambling. Finally, in chapter 7, we pull together our findings and attempt to draw conclusions.

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Chapter One: Gambling, Gaming, Games and Play

This chapter provides a brief overview of gambling as a culturally rooted social activity that has continued throughout human history. As we will see in chapters that follow, many of the central themes of this chapter – among them, people’s motives for gambling and their concerns about gambling – reappear when we examine ethnocultural gambling in Ontario today. Gerda Reith (2002:1), a noted authority on the topic, defines gambling as “a ritual which is strictly demarcated from the everyday world around it and within which chance is deliberately courted as a mechanism which governs a redistribution of wealth among players as well as a commercial interest or ‘house.’” The element of chance is a natural part of any gambling venture, whether the gamble is provided by a casino slot machine, a weekly poker night between friends, or a day at the racetrack. True, scores of books and instructional videos promise “scientifically-verified” methods for beating the house, and dispassionate professional gamblers employ precise systems. They are designed to minimize the role of luck or chance. Nonetheless, risk and the possibility of unpredicted payoffs underlie the thrill recreational gamblers and other fans of games experience, and expect to experience. Anthropologists who study recreation cross-culturally classify games into three groups: games of (1) physical skill, (2) chance, and (3) strategy (see Roberts, Arth, and Bush, 1959; Sutton-Smith and Roberts, 1970). Games of physical skill are often the most basic, may or may not require strategy or rely on chance, and are therefore often the first form of rule-based recreation to arise in a tribal society. Examples include footraces, wrestling, caber tossing, and tennis. In their comparative study of 171 cultural groups, Sutton-Smith and Roberts (1970) note that societies with games of physical skill and no others are typically small in size and located in tropical regions; possess simple technologies and economic systems; lack class stratification and advanced judicial systems; and experience few anxieties and interpersonal conflicts. In games of pure chance, the second type of game, physical skill and strategy (by definition) play no role in determining the outcome -- winning and losing. Dice games, roulette, and bingo are three games whose outcomes rely solely on the random game play – the “roll of the dice.” They may appeal to people because the game’s uncertain outcome mirrors the uncertainty of life itself. As Roberts and Sutton-Smith (1966:143) note, only certain types of cultural group possess games of pure chance:

Games of chance occur in the presence of antecedent conflict, particularly in the areas of sex, aggression, achievement, and possibly responsibility, and a life situation where favorable and unfavorable outcomes may occur in an uncertain way, not easily controlled by either physical skill or strategy, particularly in the areas of environmental setting, food production, social and political interaction, marriage, war, and religion….The motivations produced in this situation are assuaged by play with uncertainty models, and the resulting learning may give individuals and groups strength to endure bad times in the hope of brighter futures.

This conclusion fits well with Gerda Reith’s (2002) observation that games of chance are particularly appealing in a highly uncertain, complex society such as our own. True, uncertainty is an inescapable part of the human condition regardless of cultural membership, whether in a technologically primitive agricultural community or the most developed Western nation. However, many believe that people in large, complex societies experience uncertainty – uncontrollable chance – more often than any other humans in history. This would seem to support the view that modern life is more chaotic than life before, say, the twentieth century. By voluntarily engaging in games of chance, modern players can gain some small measure of influence, by controlling the arena in which their chances play out. Finally, in games of strategy, the third type of game, chance may or may not be involved, and 98 physical skill is excluded as a prerequisite for success. Chess, mah-jong, and poker are popular games of strategy, for example. Roberts, Arth, and Bush (1959:600) assert that these types of games are “models of social interactive systems,” in that they mimic the social world of those who play them. For instance, the language and physical structure of chess contain many elements of a symbolic enactment of war – two armies advancing toward each other, foot soldiers and knights attempting to “capture” their enemies and protect their king. Perhaps, only sufficiently advanced societies can develop such games of strategy, since the purpose of such recreation is to simulate the complexity of their social world. At the least, societies that possess games of strategy also possess advanced subsistence patterns, sophisticated technology, and high levels of political integration, judiciary organization, social stratification, and labour specialization (Sutton-Smith and Roberts, 1970). Most games in large, complex societies like our own are mixtures rather than pure forms of these three types of game. For example, the game of football (whether of the American, European, or Australian variety) combines physical skill with strategy and chance. Gamblers acknowledge the element of chance or luck inherent in a football game, yet often approach the game play with a strategy in mind. Indeed, in some games involving chance, strategy plays a significant role. Winning at poker depends to some degree on chance -- the luck of the draw. However, depending on the variant played, strategy – in the form of selectively bluffing, folding, and/or drawing cards – also plays an important part in determining the winner. In other games, strategies are illusory – mere superstitions. The lights and noise of casino slot machines – whose odds of winning are stacked in favour of the house – hide sophisticated computer programs designed to randomize the payout. Selecting a machine based on, say, whether or not the previous player hit any jackpots, makes little difference to one’s own chance of success. Such a selection shows a belief in the value of strategy where none will actually work. In Man, Play, and Games, Caillois (1962) offers another way of categorizing games and play, depending on which of four variables is dominant: (1) ilinx (vertigo), (2) mimicry (simulation), (3) agon (competition), and (4) alea (chance). Here, “play” includes not only the giddy games of children, with whom the word “play” is most commonly associated, but also with mature recreations enjoyed by adults. In this scheme, games involving vertigo include the stimulus of visual disorientation, as when little children spin around and around in circles. Games involving simulation, which require the adoption of “illusory” identities and imaginary worlds, can be seen in children’s make-believe games and in theatre productions. Games involving agon, such as softball and fencing, involve the dedicated development of skills and discipline. Alea -- the category to which gambling games belong -- ranges from flipping a coin to playing roulette in a Las Vegas casino. Modern gambling contains elements of all four variables. In modern casinos, people are likely to experience sensory disorientation and vertigo (due to lighting, noise and smoke). Some casino games simulate other activities – whether battle, negotiation, or otherwise. All casino games require (sometimes skilful) competition against the house and often against other players. Finally, all casino games rest on the play of chance. Caillois suggests that modern life, which is largely rational and democratic, is characterized by an irresolvable tension between merit (agon or competition) and chance (alea). This shaky balance exists throughout Western social life, from the world of professional sports to that of corporate finance, from a blackjack table to an election campaign. Today, the more “intimate” (that is, internally experienced) games involving vertigo and simulation, once popular social forms of past “Dionysian” societies, have been relegated to the arena of play. There, they function “as cathartic vehicles for the expression of sublimated desires and tensions” (Reith, 2002:4). Games for money, that people “take seriously,” are mainly games combining chance and skill. The attractiveness of chance in a supposedly rational, merit-based society like our own seems paradoxical, yet the appeal to chance surrounds us. Martinez (1977), for example, draws a connection between gamblers and participants in televised game shows. According to Martinez, who defines 99 gambling as “staking something of value on the outcome of an uncertain contingency,”

the offspring between the marriage of gambling and the media is the TV game show. TV game shows are the major media vehicle for gambling. But are the contestants really gambling? ....The contestants do not wager their own money on the shows, but they do invest time and some effort to get to the show. In addition, they wager their faces, their character, by being in front of the camera and audience. Thus, it can be said that the criterion of “something of value” is indeed staked by the contestant (Martinez, 1977:79).

Both gambling activities and game shows play on the materialistic, strike-it-rich attitude pervasive in American society, and indeed throughout the West. Both involve risking a personal investment against the “games-master” (i.e., the dealer or host) in the hope that chance will reward the contestant with a large payoff. Both offer prizes in the form of money and/or an assortment of material goods. In a similar vein, Schwartz (1977) likens the Western attitude towards games to its attitude towards business. The activities of businesspeople are often compared in the media to games to be won. Even Donald Trump, writing in Surviving at the Top (1990: 5), admits, “I’m sometimes too competitive for my own good. If someone is going around labelling people winners and losers, I want to play the game and come out on the right side.” This ethos of corporate winner-takes-all, which characterized much of American high finance in the Eighties (“the Decade of Greed”), was most dramatically portrayed in the movie Wall Street. When changing circumstances forced the story’s hero to question the ethics of devoting a life to the sole pursuit of material gain, Gordon Gekko – the fictional mogul – replied that no amount of winning is ever enough. “It’s not a question of enough, pal. It’s a zero-sum game. Somebody wins. Somebody loses.” Winning and losing – the central concern of gambling – is also the central concern of business, sport, and military activity in our society. Some even think of dating and mating in these terms. What makes gambling unique is the recognition that to some important degree, luck will determine the outcome.

Early Forms of Gambling

As far as we know, people have always gambled. Early games of chance took the form of casting lots and dice. Four-sided gaming sticks dating as far back as 6000 B.C. have been unearthed at archaeological sites in Africa, Asia, and North and South America. Similar objects have been found in the 4000 year-old tomb of Osiris in Egypt (Sifakis, 1990; cited in Reith, 2002:45). The Babylonians, the Etruscans, and the ancient Chinese all played gambling games. The Bible tells us that Roman soldiers cast lots (specifically, the game was likely tabula, a forerunner of backgammon) for Jesus’ garments during the crucifixion. Traces of these forerunners of gambling are still evident. Flipping a coin, possibly the simplest game of chance known today, began with the ancient Greeks. They called the game “night and day,” since they tossed shells that were black on one side and white on the other. Romans preferred a coin version known as “head or ship.” “Bones” as a slang term for dice had its origins in astragali, which used the knucklebones of sheep and cows in primitive gambling games. These early precursors of dice were, like their modern counterparts, marked with dots, although they were irregular in shape and possessed only four sides. Yet changes came quickly. By the time of the Roman Empire, dice were being fashioned out of ivory, stone, wood, amber, and animal or human teeth. Some playing devices had five or eight sides, while others took the form of the six-sided cubes still used today. Some were even pyramid-shaped (Fleming, 1978:2-5). Early civilizations did not distinguish between the casting of lots or dice as a form of gaming 100 and as a ritual for divination. For these pre-scientific cultures, such activities were, like most other life events, both secular and mythical-religious (Reith, 2002). Without a rational understanding of the natural world, most physical events were filled with superstitious meaning. Our knowledge of science -- physics, biology, meteorology, chemistry, geology, and so on -- has rendered much of our daily lives predictable and orderly. However, for people in less developed societies, many observable phenomena -- from fertility to the movement of the sun and moon -- appeared unexplainable and random. Lacking other means to account for the physical realm, they developed spiritual narratives to make sense of the world. These myths assumed the existence of a deity (or deities) whose often frivolous or hot-headed actions were responsible for otherwise unexplainable phenomena in the human world. Chance itself was the observable evidence of a deity’s existence. Before science was able to “disenchant” the world, as sociologist Max Weber has called the process, people attributed chance to causal (natural) events and supernatural meaning to chance events. For example, the Roman poet Ovid begins his classic poem “Metamorphoses” with the line “Now I am ready to tell how bodies are changed into different bodies.” His poem is a vast explanation of the forms nature takes – forms of the moon and stars, plants, animals, and natural geography – in terms of moody, jealous and warring Gods, and the humans who get caught up in divine intrigues. Since the gods made themselves and their wishes known through the randomness of events, gambling rituals could be performed to provoke fate into revealing itself. Rather than remaining passive objects waiting for destiny to act upon them, gamblers became active participants who could “force the hand of chance.” In this context, wagers had a cosmic meaning. The stake acted as evidence of the invoker’s commitment and as a challenge to which chance is compelled to respond:

In a sense the wager was a kind of pact with fate or destiny: a token of the individual’s involvement in the ritual, through which their opinions on the proceedings could be demonstrated….Like the stake in modern games of chance, the wager in ritual brought together the propitiate and the propitiated. (Reith, 2002:45)

As Baudrillard (in Reith, 2002:45) puts it, “The stake is a summons, the game a duel: chance is summoned to respond, obliged by the player’s wager to declare itself either favourable or hostile…. Chance is never neutral; the game transforms it into a player and agonistic figure.” Gambling continued along similar lines throughout the Middles Ages. Dice continued to be the most popular recreation, due to its speed of play and the variety of games that could be based on them. Gradually, playing cards also came into common use. The origins of modern playing cards remain uncertain. According to some sources, they are descended from Htou-Tjen, sheets of oiled paper first used as fortune-telling devices in twelfth-century Korea. Later, they were adopted and modified by the Chinese to include four sets – coins, strings, myriads, and tens of myriads. From this basic configuration would develop our familiar four-suit design (Reith, 2002). Another theory is that cards are (again) of Chinese origin, invented in the twelfth century court of Emperor Seun-Ho as a means for keeping his concubine entertained. They were spread beyond the Orient by gypsies returning from visits to the Far East. A third possibility is that playing cards were developed by the Arabs, and adopted by Europeans during the Crusades (Fleming, 1978:12). Though different in their particulars, all three theories agree that playing cards came into Western culture from outside, probably from the Orient, thanks to the cultural mingling afforded by trade and travel. Whatever their origin, playing cards first appeared in Europe as tarot cards, possibly in Italy or Spain, in the thirteenth and fourteenth centuries. Over the next three centuries, the designs of cards changed to reflect social and cultural interests. In Germany, cards proudly displayed the Copernican universe; in other countries, cards displayed the portraits of reigning families. Depending on the country of origin, the cards’ four suits could be anything from cups, swords, money, clubs, hearts, 101 acorns, bells, and leaves. In the fifteenth century, craftsmen in France – the continent’s leading manufacturer of playing cards at the time – simplified their work by printing the suits in only two colours: red and black. As well, they adopted a standard French system of suits, based on the four classes of French society. Hearts (coeurs) represented the church; spades (piques), the knights and army; diamonds (carreaux), the vassals and merchants; and clubs (trèfles), the farmers and peasants. All the while, people continued to use cards to tell the future, as well as for recreation. Even in the midst of a game, the combinations of suits and numbers that each player held could be of supernatural significance (Reith, 2002).

Gambling in the Seventeenth Century Though gambling has existed everywhere, the motivations and meanings behind gambling have varied over time and space. Gambling practices have always been culturally specific and socially structured. They have always reflected the cultural beliefs and concerns of the time, and the social organization of the society in which they arise. Moreover, political, religious, and public attitudes towards gambling have fluctuated widely, from widespread acceptance to widespread rejection. However, as we will see, there has often been a gap between published formal (or legal and theological) edicts about gambling and the everyday behaviour of ordinary people. Among ordinary people, gambling has always been subject to the same fads and fashions as other kinds of everyday behaviour. Take, for example, the sudden growth of gambling in Europe during the 1600’s. Though gambling was a popular pastime through most of recorded history, it became an obsession in seventeenth-century Europe. Suddenly, gambling was everywhere. The interest in games of chance was higher, the amounts wagered larger, and the condemnation more violent than ever before. The repercussions would last for well over a century. Three discrete but interrelated types of gambling became prominent during this time: (1) speculating in economic ventures, (2) gambling on games of chance, and (3) betting between individuals (Reith, 2002). They remain prominent to the present day. To understand this spurt in gaming activity, we must look to the wider sociohistorical context: namely, the rapidly growing mercantile economy. For the first time in history, money was the universal signifier of value. Almost anything could be bought and sold for cash. The new social order sweeping across the continent was motivated by “the expansion development of international trade, the development of a money economy, the increase in enumeration and the concomitant rise of a numerical, probabilistic Weltanschauung” (Reith, 2002:59). New areas of knowledge included bookkeeping, accounting, actuarial science (for purposes of insurance), and demography – the study of populations. The recently developed scientific method, which laid out a standardized system for exploring the physical environment, was breaking down the world into predictable patterns of behaviour. With experimentation, the discovery of natural laws, and the establishment of scientific bodies of knowledge, people were starting to understand the nature of the universe as well as their place within it. The mathematical fields of statistics and probability theory were creating universal laws to describe the previously unpredictable dynamics of risk and chance. Reason and rational thought were at the heart of all social progress. The influence on gambling was several-fold. First, increased affluence among the new bourgeoisie allowed more people to participate in the games once reserved for the nobility. Second, people came to see parallels between making money in commercial ventures – an activity with its own inherent risks – and making money through games of chance:

Who would be the ideal gamester? Someone who played for real stakes, whose winning meant something to society. Who could that be but the businessperson, whose coins circulated through society as blood through the body, whose fortune swung like the pendulum but always 102

seemed to even out a little to the positive….In the following years, between 1650 and 1830, games of chance shed much of their bad reputation as chance itself became subject to mathematical scrutiny….In the forms of annuities, lotteries and speculation on the new stock exchanges, games of chance appealed to a wider social group than ever before. (Schwartz, 1999:66)

During this time the Bank of England was formed, leading to a widespread circulation of paper money and to “methodical dealing in stocks and shares” (Ashton, 1898: 243; in Reith, 2002:60). Paradoxically, in this era of rationality and scientific progress, activities relying on chance and unpredictability were not looked upon with scorn. Instead, people embraced gambling games with unparalleled zeal. Here, after all, was a chance for ordinary people to test out the much-touted theoretical advances in mathematics and probability theory. During this period, the games themselves changed to reflect the new worldview. The joker or wild card – itself the symbolic representative of chance during the medieval period -- was eliminated from most game play, or demoted to a spare replacement card. There could be no room for such agents of pure unpredictability in the Age of Reason (Reith, 2002:60). Bassette, loo, nap, and other games based on tricks – that is, rounds in which each player plays a card and the best card, as determined by suit or number, wins the bunch – also became popular at this time. Games of medieval origin were, in contrast, based on melds – combinations of cards that only together were of any value. Reith (2002) sees in this shift in the type of favoured games a subtle trend towards individualization stimulated by the mercantile economy:

Released from the rigidity of the great chain of being, individuals were increasingly free to define themselves, not in relation to a fixed, traditional place, but according to the more fluid dynamic of a market economy. Just as one exceptional individual could rise above their station and enjoy commercial success in the wider world, so in the microcosm of games was one particularly good card empowered to break free from its dependence on others and determine the outcome of a game according to its individual value. (Reith, 2002:60)

Betting between individuals also became a popular activity. Unlike gambling with cards or dice, which required a game with rules and structure, a bet between two willing punters could take place over any event, however mundane or unimportant. Virtually any incident whose outcome contained some element of chance – the dates of “marriages, births, deaths; even the colour of a horse” (Reith, 2002:63) – was fair grounds for a wager. Ashton (1898; also Fleming, 1978) describes several such incidents of trivial wagering. In one episode, four members of Parliament tossed their bowler hats into the Thames, staked a crown each on whose hat would be the first to flow downstream to the mill, and ran after them like excited schoolchildren, hooting and hollering. In 1735, the Count de Buckeburg bet that he could ride backwards on a horse from London to Edinburgh. An Irish nobleman in 1788 bet £20,000 that he could walk from Dublin to Constantinople and back again in one year (he won the bet!). An English baronet bet an even larger sum that he could travel to Lapland and return with two Lapp women and a reindeer (the outcome is unknown).

The most morbid gambling fad of the times was betting on when people would die: Wagers might be placed on whether a prominent lord or lady would die before a certain date or whether the king would survive for another year. At a London club, a member once fell to the floor unconscious and two fellow members promptly placed bets on whether or not he would die. The bets were called off when someone insisted on sending for a doctor. If the man had medical help, it was argued, it would not be a fair contest (Fleming, 1978:28-29). 103

Fleming does not say whether a physician was called in time to save the poor man’s life. In Ashton’s account, the man was left alone and later perished, presumably to the satisfaction of those who had bet he would (Ashton, 1898:155). These anecdotes reveal a new tendency to break the world down into “discrete, quantifiable units” (Reith, 2002:63). That so many wagers at the time involved the completion of some task – usually concerning travel within a set period of time – is instructive, for it reveals an appreciation of the ability to measure accurately time and space. Only a society that values the passage of time could recognise the challenge involved and the pride at stake in a race against the clock. Betting of this kind was in line with the broader trend of rejecting chance in favour of rational measures. Once people knew the exact distance between London and Dover, and the precise time needed to travel between the two points, they could even bet on whether a man could travel from London to Dover and back before another man could mark a million dots on a sheet of paper (Ashton 1898:230). This new emphasis on time also helps to explain the frivolous nature of the betting, which was often focused on the minutiae of daily life. The particular activity being wagered on did not matter; what mattered was that it could be reduced to discrete numbers. Moreover, in the mercantile economy of the seventeenth century, money became not only the universal measure of value, but an attributer of value as well (Reith, 2002:64). Simply attaching money (or the prospect of money) to an event or action -- however meaningless in itself -- was enough to fill the latter with value. Thus, a bet such as ‘which year so-and-so would marry’ could just as easily be ‘which year so- and-so would produce an heir’ (or for that matter, ‘whether so-and-so would wear a green dress, or a pink one, to the Christmas festival’). Anyone struck with the sudden urge to wager could easily find or devise some event to bet on, as well as someone to bet against. There was no apparent limit to the insignificance of events that people would wager on. Fleming (1978:29-30) cites an amusing account of John “Bet You A Million” Gates, a famous compulsive gambler living in America during the early 1900’s, who would have felt right at home in seventeenth-century Europe. Besides being an avid poker and bridge player, Gates, a wealthy Chicago executive who made his fortune selling wire fences to the Western settlers, also wagered on prize- fights, horses, and the stock market. Once, while peering out a window during a rainstorm, he bet his companion $1,000 that one raindrop of his choosing could trickle down to the bottom of the window more quickly than the others. On another occasion, Gates and his friend John Drake were dining in a Chicago hotel when one of them came up with the idea of them each moistening a lump of sugar and counting the number of times flies would land on them. The man whose sugar mound attracted the most flies would not only win the bet but also collect an additional $1,000 for each landing he won by. The start of gambling as a widespread cultural practice in the seventeenth century paved the way for the commercialization of games of chance in the years to follow. Gambling as a source of recreation for the working class, and as a moneymaking industry, had come to stay. The common folk gambled mainly for entertainment and occasionally for the chance to earn some quick money, though the bourgeoisie condemned gambling as a horrible social ill distracting the masses from more fruitful pursuits. Meanwhile, the aristocracy gambled for entirely different reasons.

Aristocratic Gambling The Indian religious epic, The Mahabharata, recounts the story of Prince Yudhishthira, a great lover of gambling games. Yudhishthira was universal ruler of the kingdom of Bharata when he became involved in a game of dice with the One Hundred Dhartarashtras, a rival faction of the nobility attempting to dethrone the prince and seize power for themselves. Unwisely, he wagered all of his wealth, including one hundred thousand gold pieces and a thousand elephants, his slaves, and his army, only to lose it all to the Dhartarashtras. Even more unwisely, in a final attempt to win back some of his losses, he staked the freedom of his brothers, his wife, and himself – and lost all that too (The 104

Mahabharata, 1975:128-137). This ancient story of Yudhishthira may belong in the realm of popular folklore, but it reminds us that gambling has always been a popular pastime for the ruling classes. Power and wealth have afforded them great freedom in both the amounts they could wager and the consequences of their gambling. History records the stories of many royal personages who, while under the influence of gambling, found themselves carried away. Among the Roman emperors, all of whom gambled avidly, Caligula was probably the least graceful loser. Enraged by losing at games of dice, he would order random innocent civilians executed and their property seized to compensate for his losses. England’s King Henry VIII was another fabled gambler. So caught up in gambling with a nobleman, he once lost the ownership of England’s largest and most famous set of church bells, the Jesus bells in the St. Paul’s Cathedral tower (Fleming, 1978: 6-7). In France, King Henry IV was allegedly cured of his gambling only after a particularly unlucky session at cards during. To settle his huge losses, he called for money to be brought in. His chief minister, the Duc de Sully, wheeled a small mountain of coins into the games room. Henry, upon realising precisely how much money he had lost in a single evening, allegedly vowed then never to gamble again (in Fleming, 1978:17). Over time, gambling became for the ruling classes a means to display their moral superiority. We know that France’s Henry IV, before he gave up gambling entirely, wagered much and often. For him, gambling was more than just an exciting diversion from the daily business of ruling a kingdom. It was, as Dickerman and Walker (1996) note, one of several avenues through which he could show his mastery and establish himself as a heroic figure worthy of kingship. Through his early years, Henry was known as a risk-taker. Once, while running along the harbour seawall at the town of La Rochelle, the teenager tripped and fell into deep water. Not knowing how to swim, he would surely have drowned had not a sea captain whose ship was sailing by pulled him to safety (Roelker, 1968:405). This time, he was lucky. By the age of fifteen, Henry was in charge of his own small army assigned to lower Navarre. Gradually, he racked up an impressive record of victories, acquiring a reputation as a skilful military leader. Though never a great military strategist, what he lacked in combat acumen Henry more than made up for with sheer courage on the battlefield. Rather than issue orders from the safety of a high vantage point out of range of enemy canons, like other military commanders, Henry regularly rode headlong into the mob, with rapier flashing, much to the awe of his troops, the terror of his foes, and the dismay of his ministers. Henry’s bravery in battle shaded occasionally into recklessness, as in the Battle at Fontaine- Française (1595). There, Henry led his outnumbered cavalry to victory against a superior Spanish army without wearing so much as a helmet or breastplate. His boldness stemmed not so much from a conscious disregard for his own life as from a belief in the virtues of heroism and valour in the face of peril (Dickerman and Walker, 1996). His role as politician and monarch obligated him to place the interests of his state and his people as the highest priority. However, Henry’s own impulses remained unsatisfied. In place of combat, what alternatives were available for Henry, and indeed for the entire class of society raised to view military success as a mark of moral and social distinction?

With opportunities for virtue and acquiring honour and glory through war so sparse after 1598, Henry, like his nobility, turned to substitute and compensatory activities which offered some aspect of the rewards of war: the opportunity to demonstrate his vertu by domination through conflict, with its attendant excitement, risk, and physical exertion. (Dickerman and Walker, 1996:327)

The most obvious of these activities was hunting, which, like war, was a mainly male activity involving weapons, strategy, physical strength, and a conquered enemy. More relevant is another 105 substitute activity that Henry began to pursue with vigour: gambling. One of his counsellors, named Bellièvre, even drew a metaphoric connection between military action and gambling: “War is a game of chance. One wins here and loses there. Otherwise no one would play with us” (in Dickerman and Walker, 1996:329). The similarities only go so far, however. We have already learned that Henry lost vast amounts of money while playing games of chance; now we know why:

At dice or cards Henry IV could reaffirm to himself and others his true self, the heroic man of action. But the dash, the impetuousness, the willingness to risk all, which were such successful traits on the battlefield, were less appropriate to the gaming table, where a cool head and a poker face were more advantageous. (Dickerson and Walker, 1996:330)

Henry IV may have had a particularly keen desire to view gambling as a virtuous pursuit, but he was not alone in that sentiment. All across Europe, the elite classes in the seventeenth and eighteenth centuries were embracing games of chance as a means of showing their daring, refinement, and flair. In the absence of war, they too had reason to seek out an alternative means for proving their superior status over the commoner:

The fact that gambling should stand out as an important symbolic activity for the French nobility…derives from the real and fictive images of the Middle Age in which their fundamental ideal was grounded. If, that ideology tells us, the noble was noble, it was for one reason alone: he or his ancestors had, on the field of battle and with their blood, won the recognition of their king…. [T]he same could not be said of the growing number of nobles who had simply bought their way into the ranks of the second estate. It was the existence of this gap between the explicit ideology of the nobility and the reality of certain of its members that explains the tenacity with which that group clung to a number of compensatory activities in which the battlefield’s challenge as an occasion for glory was reproduced. (Kavanagh, 1994:36- 37)

An authoritative guide to proper court conduct had already been published over seventy years earlier, in Italy. Baldesar Castiglione’s Il Cortegiano (1528) outlined through a series of fictional dialogues the subtle rules and nuances of etiquette, including several passages concerning appropriate behaviour while gambling at cards and dice. If there were ever a cardinal rule for proper behaviour at a court gambling session, it was to never show any concern for “money as money” (Kavanagh, 1994:35). They were members of the aristocracy, after all:

Ideas like “balancing the budget” and “living within one’s means” were bourgeois preoccupations which had no place in the noble ethic. All responsibility for domestic finance was accordingly delegated to household officials, who were simply required to keep the family financially afloat by whatever methods they could devise. (Mettam, 1988:59)

Gambling in polite society evolved into more than a mere opportunity to display one’s noble character and indifference to money. It became a social expectation. The ability to play games of chance was a valuable form of social capital, by conferring status and allowing one access to court life. Skill at dice and cards became so crucial to success in the political arena of the royal court that many parents began hiring “gaming masters” to tutor their children on the finer points of gambling games (Reith, 2002:65). Young members of elite families entering court society were warned against placing any significance on the size of winnings or losses at the table, as the lower classes were seen to do. Indeed, whether one won or lost at all did not truly matter in the end. What ultimately mattered were 106 one’s actions during a game and one’s reaction to the outcome. This is not to say that winning did not matter at all. Winning, after all, was evidence of one’s superior skill and competency, and so winning was better than losing. However, winning was considered honourable only if certain conditions were met: one must never win by cheating, and more importantly, one must never gamble for the purpose of material gain. To focus on the pot, no matter how substantial, was to reveal an obsession with money unbecoming to a member of noble society, and to invalidate any honour conferred by the win. Losing, meanwhile, was no source of shame. It provided more opportunity to display one’s social graces than did winning. Losing money gracefully showed a healthy indifference towards money. Yet, while one should ideally remain indifferent to one’s own financial status, it was at the same time vital that enough funds be found to cover any sums owed to others. Since debts incurred through gambling were not legally binding in seventeenth century France, payment relied solely on the integrity of the debt-holder’s code of honour. Therefore, to pay a debt on time was to reveal one’s character. According to the ideology of the noble class, it was perfectly acceptable (in theory) to wager away one’s entire fortune in a single roll of the dice, as long as one uttered no grumbles about their newly acquired penury. Louis XIV, son and heir of Henry IV, occasionally paid off the debts of those who gambled and lost in his court (Kavanagh, 1994:37). The not-so-implicit message from king to courtier: “Money is no concern for people like us. Lose it all, gain it all back – it matters little either way.” Louis XIV’s court at Versailles was so feverishly possessed by gaming of all sorts that the palace itself became known as ce tripot (“the gambling den”) (Reith, 2002:65). Many went to increasingly desperate lengths to display publicly how much less they concerned themselves over money than their fellow nobles. Some refused to participate in any game where the wager was less than £200 per hand; others lost millions of livres in a single session (in Reith, 2002:65). This progressively more decadent attitude shows up not only in gambling but in other areas of aristocratic life as well. Kavanagh (2000) draws a link between the gambling craze in France, which hit a fever pitch in the eighteenth century following the death of Louis XIV, and the game of power and seduction known as libertinage, which is best known through the numerous theatrical and cinematic adaptations of Choderlos de Laclos’ 1782 novel, Les Liaisons Dangereuses. Gambling, in this view, is a metaphor for the sexual degeneracy that characterized the final days of pre-Revolutionary elites. Libertinage was favoured among aristocrats seeking to entertain themselves by methodically seducing virginal members of the opposite sex through a cat-and-mouse strategy of approach and withdrawal, flirtation and resistance. Love had little to do with the proceedings. Libertinage was merely a game with rules and goals, whose players stalked their prey dispassionately and cynically. The purpose of the whole enterprise was apparently sex; in fact, it was about self-mastery. Deriving inspiration from Descartes’ separation of mind and body, both the gambler and the libertine approached their respective “games” with the view that a person could react to desire in one of two ways: via l’esprit (the mind) or le coeur (the heart). L’esprit was the preferred mode of conduct, as it represented a “faculty of control and domination, a control of self promising the domination of the other.” Le Coeur, meanwhile, promised a “greater intensity of feeling and sentiment, but only at the cost of finding oneself dominated by the object of those desires born of the senses and consolidated by the heart.” For the male libertine, this meant trying to rack up as many meaningless conquests with court women as possible. Ideally, the hunt was a purely intellectual exercise. The skilled libertine had perfected the seduction of women into a science, and went about the task with clinical precision. In Les Liaisons Dangereuses, the Vicomte De Valmont commits the cardinal sin of developing feelings for one of his targets -- of choosing le coeur over l’esprit. This error ultimately proves to be his undoing. The goal of the female “prey,” in turn, was to tease, flirt, and withdraw until they felt that the 107 seducer’s stated desires were motivated by genuine affection rather than the cunningly scripted calculations of l’esprit. For both sexes, then, the objective was to maintain a rational control over their own feelings while trying to convince their prey to surrender to their feelings. Kavanagh finds an equivalent to the libertine perspective in the card game of brelan. Unlike other amusements popular among the eighteenth century French elite, brelan is a “socialized” game; that is, the value of one’s cards (each player holds three) is determined in part by the cards held by the other players. For instance, the ace of spades is a strong card to hold, but only if other players are holding many cards of the same suit. Otherwise, it is insignificant. Here again we see the aristocratic tendency to elevate amusements from mere diversions to tests of character. Bluffing is of utmost importance in this game, since one has little idea of the value of one’s cards until after the betting has ended and the hands are revealed. A façade is erected between each participant – other players must not know of one’s true intentions. Each player seeks to discover the truth from the other players, to read the signs they unintentionally give out, even as they hide their own true nature:

Both the brelan player and the libertine must carefully interpret the implications of their relations to the others around them. Bets and raises, like the cards themselves, will be more or less effective not only in terms of themselves, but as a function of how they are interpreted by those who must react to them….Within the libertine’s enterprise of seduction, as within brelan’s well-executed bluffs, the art of timing is everything. To seduce is to progress neither too slowly nor too quickly towards an outcome which is never really in doubt….The fleeting touch, the clever remark, and the well-placed compliment all derive from the same strategy: that of focusing the target’s inevitable narcissism on the seducer as a vehicle for its indulgence. (Kavanagh, 2000:510)

The gambling spectacle was not limited to the French nobility. Across the channel, a similar scene was playing out in the English courts of the sixteenth through eighteenth centuries (Evans, 2002). Even in distant Russia, Aleksandr Pushkin and other novelists used gambling to display their distaste for receiving monetary payment for their work:

Straddling the discourses of honour and money, [gambling] played an important role in the social image that writers projected during this era of transition from amateur to professional authorship….As an inherently ambiguous activity, ,which ostensibly aimed at profit yet more often ended, for the non-professional, in loss, gambling lent itself particularly well to irony. Casual references to one’s losses at cards could allow a writer like Pushkin to demonstrate gentlemanly distain for the income he increasingly relied upon his writing to provide. (Helfant, 1999:372)

For members of the Russian gentry, and particularly for the men, gambling was an important part of their social life, an activity associated with competence, honour, and “good breeding” (Helfant, 1999:375). As in Western Europe, in Russia concern over money – at the gambling table as well as in daily life – was considered a sign of poor breeding among the respectable classes. For Pushkin, resistance to the rising tide of capitalism was futile. Writing was becoming a commercial industry, and writers were becoming paid professionals whose income relied upon selling their work. Pushkin used gaming to show that although he was obliged to work for money, he held no attachment to his income. Harbouring no ambition for more money, Pushkin was able and willing to gamble it away with ease (Helfant, 1999). As the Industrial Revolution spread across Europe, its characteristic bourgeois class gained power and influence in society. The traditional authority of the aristocracy was threatened by a new 108 social order in which status and power were determined by material wealth – which anyone could acquire – rather than bloodlines, honour, and etiquette. With the bourgeoisie having embraced money as the resource most valued in business society, members of the old nobility trying to differentiate themselves from social classes below had only one direction to go. If the bourgeoisie cared so much for money, then aristocratic elites would care not at all. Gambling became, even more than before, a form of conspicuous consumption, the term that Veblen (1925) would later use to characterize the wasteful spending of the new twentieth century “leisure class” made wealthy through capitalism and industry. Of this, Veblen (1902) writes:

Conspicuous consumption of valuable goods is a means of reputability to the gentleman of leisure. As wealth accumulates on his hands, his own unaided effort will not avail to sufficiently put his opulence in evidence by this method. The aid of friends and competitors is therefore brought in by resorting to the giving of valuable presents and expensive feasts and entertainments … Costly entertainments, such as the potlatch or the ball, are peculiarly adapted to serve this end. The competitor with whom the entertainer wishes to institute a comparison is, by this method, made to sense as a means to the end. He consumes vicariously for his host at the same time that he is a witness to the consumption of that excess of good things which his host is unable to dispose of single-handed, and he is also made to witness his host's facility in etiquette.

Like feasts and entertainments, gambling offers the gentleman of leisure a perfect way to consume wealth uselessly, overcome his competitors with largesse, and prove himself elegantly indifferent to spending. As such, it is a perfect path to “reputability.”

The Crackdown on Gaming For as long as people have enjoyed gambling, others have disapproved of it. The Greek myth of Tyche and Zeus illustrates the general objection to games of chance. In this story, Zeus seduces Tyche, the goddess of good luck, producing a malicious daughter who delights in inventing gambling games that lead to conflicts between players and suicide among losers. Even Plato, normally a voice of reason and measured thought, felt that dice games were invented by a demon named Theuth. The Romans, for their part, associated gambling with quarrelling, lying, cheating, and drunkenness, despite their own passion for playing dice and wagering on chariot races. Eventually, all Roman games were banned except during the December festival of Saturnalia (Fleming, 1978:6). Seneca, clearly disapproving of Claudius’ inveterate gambling ways, not only condemned him to hell in The Apocolocyntosis, but also devised an ironic eternal punishment for the emperor – to forever play dice using a bottomless dice-throwing cup:

When from the rattling cup he seeks to throw The die they trickle through the hole below And when he tries the recovered bones to roll – A gambler fooled by the eternal goal – Again they fool him; through his finger tips Each time each cunning die as cruelly slips A Sisyphus’ rock, before they reach the crest Slips from his neck and roll back to their rest. (Seneca, 1986:232-233)

Europe during the Middle Ages was also home to much gambling, and much restriction and 109 condemnation of gambling. The card game bassett (alternatively, bassette), invented in 1593 by the Venetian Pietro Cellini, was said to be responsible for so many lost fortunes that Cellini was banished from the kingdom (in Fleming, 1978:21). To keep his army focused on its military task during the Crusades of the twelfth century, King Richard Lionheart established a hierarchy of dice-playing privileges. Anyone below the rank of knight was forbidden to play dice for money at any time. Knights and clergymen could gamble, but could lose no more than twenty shillings per day, under threat of a hundred shilling fine, payable to the archbishop. Monarchs had no restrictions placed on their gambling activities, but their attendants were subject to the twenty-shilling limit. The latter’s punishment for breaking the rules was unusually severe: they would be whipped naked in public for three days if they gambled away more than their allotted amount. Later edicts clearly forbade dice playing among the general population at all times (Ashton, 1898). As early as the late fourteenth century, France forbade card playing on workdays. Louis XIV came down especially hard on “vices” among the working class, issuing severe penalties for those caught hiring prostitutes or taking part in gambling games (Riley, 1976). All this occurred even as he and his court frittered away small fortunes on a single roll of dice or a single hand of cards during what Reith (2002:65) refers to as one of many “gambling orgies.” St. Bernard of Siena, an Italian monk living in the 1400’s, denounced cards as an invention of the devil. England issued a royal decree in 1495 banning card games among servants and apprentices at any time other than during the Christmas season. Even then, they were required to play in the presence of their masters (Fleming, 1978:15). Escapees from Europe, the first Pilgrims to New England were likewise opposed to gambling. Through the lens of their Puritan faith, card playing appeared as a waste of time and money. The Puritan approach was driven as much by practical considerations as by religious conviction. Idle pleasures associated with drunkenness, rowdiness, and brawling could hardly help in the establishment of a new colony in an unfamiliar continent separated from the homeland by a treacherous, stormy ocean. Fines were issued to adults caught playing cards; parents and masters were required by law to scold children or servants who gambled. Gradually, in Europe, tolerant gambling attitudes among the aristocracy began to end. In France, the start of the Revolution would end finally the decadent lifestyle of the monarchy and the courts. In England, the Victorian era with its strict moral and religious codes of conduct would foster an atmosphere of intolerance and condemnation of social vices, not the least of which was participating in games of chance. Gambling stood directly in opposition to the principles and values championed by the Victorian middle class:

[It] was profoundly threatening to Enlightenment values, challenging the very idea of man as a rational being, capable of self-discipline, and committed to productive work that would benefit society as a whole. Gaming also undermined Enlightenment ideals of sociability as its “stage,” the gaming table, was not a model of improving civility that subsumed and ameliorated difference, but an agglomeration of individuals, many of whom were prepared to deny social ties and responsibilities in pursuit of chance. (Russell, 2000:483).

Upper-class female gamblers in particular were singled out for criticism. Women in eighteenth century society were generally held up as the guardians of virtue and domesticity. Respectable women who gambled undermined this ideal. Men addicted to gambling were portrayed publicly as weak, undisciplined rogues who had abandoned their duties to family and society. Women who were addicted were portrayed even more severely, as people who had betrayed not only society but their own better nature as well. Sir Richard Steele describes his impressions of pathological female gamblers in this way:

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There is nothing that wears out a fine Face like the Vigils of the Card-Table, and those cutting Passions which naturally attend them, Hollow Eyes, haggard Looks, and pale Complexions, are the natural indications of a Female Gamester, Her Morning Sleeps are not able to repair her Midnight Watchings. I have known a Woman carried off half dead from Bassette, and have, many a time grieved to see a Person of Quality gliding by me in her Chair at two a Clock in the Morning, and looking like a spectre amidst a glare of Flambeaux. In short, I never knew a thorough-paced Female Gamester hold her Beauty two Winters together. (Steele, 1713)

One incident in 1796, in which the Chief Justice Lord Kenyon threatened a group of high society female gamblers with the pillory, illustrates how far officials were willing to go to snuff out gambling among women of the upper class. In the late eighteenth century, public gaming among English aristocrats took place primarily at the White’s and Brooks’s Clubs. These clubs were not only gambling dens but also exclusive social institutions where wealthy men could meet to discuss politics, money, and business over drinks. Located near Westminster, they were ideal hangouts for some of society’s most prominent and powerful politicians. White’s Club catered to Tory members, Brooks’s to the Whigs. Both clubs restricted their membership to men (Russell, 2000). Women, however, were fascinated by gambling too, and would not let social disapproval stop them from pursuing their interests. Consider the “Faro ladies” (named after their favourite game). Because their choice of public gambling venues was limited, card games were organized in private homes, often under the pretext of a cultural event such as a musical recital or a play. Faro is a game in which individual players bet against a dealer or banker on the order in which certain cards will be drawn from a specially constructed box. The banker controls the drawing of cards, and often takes a substantial percentage of the winnings. Increasingly, women took on the role of “banker.” There were several reasons why officials – usually men – were so troubled by the activities of the Faro ladies (Russell, 2000). One is that the image of the woman hopelessly addicted to gambling could not be applied so easily to the circle of women who acted as Faro dealers. Individuals in this latter group not only appeared well in control of their own behaviour; they could even boast an income from their role of banker. A second reason is that many people compared these women with brothel- keepers or madams of prostitutes. More disturbing was how easily these free-spirited women dismissed such criticisms, which were intended to shame and humiliate people so labelled. It was one thing for men to have their authority put to the test by women; it was an entirely different issue for them to have their normally effective public rebukes be ignored as well. This was not just a minor squabble between men and women, to be quickly and quietly suppressed; it was the first sign of a gender revolution. But there were other reasons to be wary of Faro ladies. The social gatherings organized by and for women of high standing were traditionally arenas for theatrical and musical performances, for refined discussions of topics suitable for proper ladies, and above all, for the display of sophistication and grace. Using these events instead for high-stakes gambling tarnished the carefully maintained image of the upper class as judges of high culture and fashionable taste. Finally, and relatedly, Faro games in private homes broke down distinctions in social rank in several ways. During any given gambling session, members of all classes intermingled at random, much to the annoyance of upper crust men who spent much of their time and effort trying to keep themselves separate from the rest of the population. With so many reasons for viewing the Faro ladies as a threat to the social and moral stability of eighteenth century London, the severity of the punishment proposed by Chief Justice Lord Kenyon – public humiliation through pillorying – is understandable, given the fervour of the outrage. What is most striking about the nature of the penalty is that pillorying was the traditional sentence for prostitutes, brothel-keepers, sodomites, and confidence tricksters. Russell (2000) theorizes that Lord Kenyon had accepted as true the tacit comparisons drawn 111 between Faro bankers and brothel-keepers, as well as the widespread rumours of same-sex misconduct occurring in the private Faro gaming sessions. The pillory, then, was a particularly apt punishment. It gave the public full view of and access to the offender. Whether the latter was greeted with sympathy or scorn depended largely on the community’s attitudes towards the crime. In some cases, pillory victims were treated humanely by compassionate onlookers; in others, mud-throwing and stoning left the prisoner severely injured or even dead (Russell, 2000:490). In the end, however, the women were let off comparatively lightly – each receiving a fine of £50. The incident of the Faro ladies highlights the growing intolerance of public and private gambling in the eighteenth century. Gambling has always been the target of criticism from some important fraction of society. The late 1700’s and the centuries ahead were no different from any other period in the sense that some people gambled, and others disapproved of it. However, this era, unlike others, was characterized by a particularly well-concerted effort by those in power to extinguish gambling for what they hoped would be the last time. Munting (1989) suggests that the abolition of the English state lotteries in 1826 can be viewed as marking the shift in official gambling attitudes from one of vague disapproval to one of harsh denunciation. Lotteries in the eighteenth century were used to raise capital for a variety of causes, from underwriting state loans to reducing the national debt to funding specific projects and raising revenue directly. For example, lotteries made possible the construction of the Westminster Bridge and the British Museum, the supplying of water to London, and the establishment of public libraries. They repaired the docks and harbours at Dover, Sandwich, Hastings, Romney, and Hythe. They raised millions of pounds in support of the colonies struggling to establish themselves in the New World, and then raised millions more when Britain initiated hostilities with those same colonies during the American War of Independence (in Fleming, 1978; Raven, 1991; Reith, 2002). Yet, by the start of the nineteenth century, concern was growing that state lotteries were morally suspect. Observers during the Victorian era asserted that sweepstakes were little more than thinly veiled, large-scale gambling enterprises – state-sanctioned, legislated, and socially constructive forms of gaming, but gaming nonetheless. No doubt, they celebrated heartily on October 18, 1826, when the final lottery was drawn. If Victorian critics are to be believed, the English lottery ended because “progressive moral argument finally trounced Georgian degeneracy” (Raven, 1991:372). Moralists certainly had much ammunition with which to attack Westminster. The state lottery had been shut down once before, in 1699. Before that, lotteries were a booming business, generating huge revenues that went towards funding government projects. There was a concern that the poorer ranks of society were particularly vulnerable to the dark side of gambling. On this, the political spectrum was united. On the right, proponents of capitalism worried that the working classes were ignoring the Protestant work ethic in favour of idle recreation and the promise of instant wealth. On the left, socialists were concerned that the poor were squandering what little money they had on the sweepstakes instead of on more immediate necessities such as food and clothing. The poor were easily exploited by cunning gambling entrepreneurs, and could not be trusted with the responsibilities of home, work, and family so long as the lottery was around to lure them away from their duties. The only alternative was to remove the source of temptation. This paternalistic attitude held by society’s elites towards the lower classes would repeat itself throughout the history of gambling regulation. Thus, despite gambling’s massive popularity among citizens and healthy profits for the monarchy, it was declared that “all such Lotteries, and all other Lotteries, are common and publick nuisances, and that all grants, patents and licenses for such Lotteries, or any other Lotteries, are void and against the Law” (in Ashton, 1898:227). When the state lottery reappeared over a decade later, several changes had been made to ensure that the poorer ranks were unable to participate. The number of tickets available would be limited to 150,000, each selling for £10, far beyond what the vast majority 112 of the population could afford. Instead of the win-or-lose format used in earlier draws, the new lottery would award annuities rather than a lump-sum prize to winning ticket-holders. Lotteries became an amusement for the rich alone – or so it was intended. In fact, though the lower classes were excluded from buying lottery tickets, their participation in state lotteries simply went underground. Private lotteries with low minimum stakes quickly sprang up to cater to those who could not afford the money for an official ticket. Clever bookies devised several ways of offering smaller bets with smaller payoffs – “insurance” or “little goes,” as they were alternatively known. Some divided lottery tickets into smaller shares that were sold individually to willing bettors. Others placed side bets on which tickets were most likely to appear in the draw itself (Raven, 1991:375). The ethical arguments Victorian moralists offered in favour of (again) abolishing the lottery could therefore point to the vigorous illegal gambling industry surrounding the sweepstakes. This provided evidence of the wantonness and lack of self-control plaguing the masses. Yet, these claims were not enough to sway political opinion (Raven, 1991). Raven cites several parliamentary resolutions put forward by opponents of the lottery in the early 1800’s, each of which were defeated by healthy margins: 47 to 21 in 1816; 72 to 26 in 1817; and 133 to 84 in 1819. While religious and moral conservatives made a lot of noise in public, they were unable to convince Westminster to do away with the state lottery. Raven supports an economic explanation, noting that profits from the lotteries in 1820-22 were significantly lower than in previous years:

As a voluntary revenue device, the lottery was dependent on both public confidence, and, more generally, favourable investment conditions. Offering none of the incentives of the war-loan lotteries, the contractors suffered greatly from the three-year economic slump following a succession of bad harvests and the 1819 resumption of cash payments and currency devaluation….The net annual profit between 1816 and 1823 was £202,500, compared to £407,400 for 1802-15. The average of the final years, 1820-3, was even lower, at £187,300. (Raven, 1991:384)

He concludes:

There is no denying the vigour of hostility to gambling and the lottery during the early nineteenth century, but the verdict that the lottery was defeated by a broad crusade against excess will not do. With greatest anxiety focused on subsidiary gambling, many abolitionists and almost all government supporters were pacified by attempts at greater regulation. On the other hand, the lottery was not abandoned because new revenues provided financial alternatives in the mid-1820s….The lottery could have been abandoned for simple economic reasons long before 1823, when its contribution to the exchequer was so comparatively small. It survived for as long as it did partly because of its value as a flexible emergency device based on voluntary participation, but much more because…it was shackled to commercial interests. Its abolition was triggered not by the new prosperity of the mid-1820s, but by the slump of 1819-22. (Raven, 1991:388-389)

The abolition of the state lottery in 1826, then, was only the beginning of Parliament’s attempts to dismantle the gambling industry. The Act of 1845 removed gambling debts – primarily due to bets on horseracing – from legal enforcement. This had a two-fold purpose. First, it would reduce the number of cases concerning gambling debts that were clogging up the court system. Second, some believed that such an act would reduce the amount of street gambling. After all, without legal consequences for failing to repay debts, bettors who placed wagers on credit with bookmakers would have no incentive to pay back losses. 113

On this second count, the enactment was an utter failure. Not only did gambling not decrease, it actually increased. The statute directly encouraged the development of ready money betting offices where cash payment was the sole means for securing a wager. Forced once again to respond to the ingenuity of the working-class gambling industry, a later bill in 1853 forbade the operation of public betting houses. This act – like others before and after – was clearly discriminatory. Members of the lower ranks of society overwhelmingly frequented public betting houses. Bourgeois men and racetrack betting, meanwhile, remained untouched by parliamentary interference. Naturally, this bill also failed to curtail the prevalence of gambling among the working class. Instead of gathering in public betting houses, bookmakers and bettors simply took the streets; even then, clandestine betting shops were set up in other stores or temporarily in pubs, clubs, and alleyways. Others simply moved over to the racecourse. Even as the government made a concerted effort to curb gambling among the working-class, England witnessed the birth of a full-scale commercial gambling enterprise. Itzkowitz (1988:8) calls the industry of gambling a “very Victorian institution.” Ironically, this new sector of the economy, which the middle-class so despised, relied on the same ethics of hard work, delayed gratification, capitalistic speculation, rational values, and technological advancements that the bourgeoisie insisted was missing among the working-class. In time, gambling would become a central part of working class leisure (McKibbin, 1979), and street bookmakers would be viewed by their peers and community as hard-working, upstanding entrepreneurs (Itzkowitz, 1988). To understand how gambling evolved from a diffuse and locally situated pastime, into a highly-organized, government-regulated, national industry, we must consider the place where large-scale gambling first occurred: the racetrack.

Commercialization of Gambling in the Modern Era Betting on horse races was the first true form of organized mass gambling. As we have seen, betting and gaming of all sorts have taken place throughout history, and the origins of betting on races date as far back as the Roman era, when betting on chariot races was common across the empire. However, in both of these cases, gambling remained for technological and logistical reasons a mainly local event. Until the nineteenth century, the scope of virtually all gambling was narrow – geographically and otherwise. The official state lottery was probably the most widespread gaming event of its time, but even so, its clientele was limited to people who could afford to play. The illegal, secondary forms of gambling associated with the lottery, meanwhile, were largely accessible only to those who lived in or near London. After public betting shops were shut down in 1853, some moved up to Scotland to accept bets by postal mail. Though this manoeuvre allowed bookmakers to sidestep the law – the ban was not extended to Scotland until 1874 – theirs remained a small and remote operation. Racetrack betting became the most popular form of gambling in the Victorian era after the state lottery was dismantled in 1826. Its popularity can be seen today, for instance, in the former British colony of Hong Kong, where horseracing remains a wildly popular sport. Unlike public betting houses and street betting, racecourse gambling had not been made illegal. At first, horseracing suffered from the same geographic limitations that kept other forms of gambling from spreading beyond their immediate vicinity. Many race meets were local events organized by local people for local spectators and punters. While high-stakes wagering occasionally took place, for the most part, this was limited to a small group of wealthy executives who gambled with each other. The arrival of the railway transformed horseracing, just as it transformed so much else. Trains could transport horses, owners, spectators, and punters from one racetrack to the next. Meets could be organized into annual schedules. Major races with large prize winnings and other high-profile events – sure to attract audiences (and profits) from everywhere – could be created and advertised across the network. As a result, the number of active racehorses doubled between 1837 and 1869. It took until the 114 mid-nineteenth century for the critical mass to build up, but by about 1845, a mass gambling industry was clearly developing around horseracing. McKibbin (1979) points to the electric telegraph as another important development in the history of racehorse betting, since it allowed results and starting-price odds to be transmitted quickly from the racetrack to the bookmakers across the nation. Thanks to the railway and the telegraph, horseracing increasingly became England’s national sport (Vamplew, 1976; in Itzkowitz, 1988:9). Bookmakers had been taking bets from racecourse spectators long before the railway connected the meets across the country. The arrival of trains, however, allowed bookies to increase their business from an intermittent pursuit to a full-time operation. To distinguish themselves from other competitors at the track, innovative bookmakers would deck themselves out in showy costumes and call out for the attention of potential bettors. Some stood on platforms and waved colourful signs. A photograph (Itzkowitz, 1988:17) shows one such moustached bet-taker dressed in a flared trench coat, pin-stripe pants, and floppy, wide-brimmed fedora. Around his waist he is carrying a large attaché case, into which he is accepting wagers from the crowd. And consider this excerpt from an advertisement in the Sporting Indicator and Turf Telegraphist:

The gentleman in high boots, sombrero hats, brown velvet coats and vests, with other tasty articles of dress…will attend, in the public ring, the above Meeting. This is on the authority of the gentleman whom racing men generally will recognise as Fred Frazer and his genteel penciller (cited in Itzkowitz, 1988:14)

Off-track bookmakers adopted the opposite strategy. Rather than portray themselves as colourful clowns, those working on the streets preferred to present a public image of sobriety, respectability, and honesty. The bookmaker-as-pillar-of-the-community pose was not simply aimed at duping gullible bettors into placing wagers with them rather than another bookie. Fashioning themselves as business entrepreneurs who pulled themselves up by the bootstrings, many contributed generously to local charitable causes as repayment for support from neighbours. Customers and locals alike viewed them as figures truly deserving of admiration and trust (Itzkowitz, 1988:14-15). Other bookmakers, however, were known to be violent and dangerous, and therefore deserved the wrath of bourgeois and evangelical critics (Huggins, 2000b). A sure sign of the prosperity of the gambling industry in the late nineteenth century was the number of new institutions, in addition to the widespread practise of bookmaking that emerged to support it. The first was the sporting press; the second was the racing tipster. Both were new sources of information, a resource that played a central role in racehorse betting. One of the most attractive qualities of gambling at the racetrack was that it gave its participants some control – or at least the illusion of control – over their fates. Both the sporting press pages and the tipsters offered information about the horses’ or jockeys’ previous performances, about the physical condition of the horses on race day, and about the history and reputations of the trainers and owners. This, combined with each bettor’s individual strategies for placing wagers, imparted the sense that winning involved some degree of skilfulness and effort. In contrast, purchasing a ticket in the state lottery or placing an insurance bet with a bookmaker required no skill. Winning was a matter of pure luck. An added benefit of these new resources was that bettors no longer had to go to a sporting club or to the course itself to get betting tips. Sporting presses and tipsters were available on every street corner from town to town. Together with the abundant number of willing street bookmakers, this dramatically increased the number of bettors who now had access to the track. Turning from the racetrack gambling apparatus to the bettors who sustained it, we find once more the class-based double standard that has long characterized the gambling public. scope of 115 virtually all forms of gambling. Working-class gamblers in the late-Victorian era were usually the target of legislation on gambling between the 1850s and the 1950s. In almost every case, the motivation behind the rulings was to protect the poor from themselves (Itzkowitz, 1988:20). However, in almost every case, the rulings failed in their intended purpose to stop betting. The vast majority of the working classes placed their bets through bookmakers. Since doing so was illegal and unmonitored, it is difficult to describe beyond anecdotal accounts the gambling habits of the working classes and impossible to quantify exactly how many gambled on horse races. However, several modern observers have suggested that it is likely that gambling, along with alcohol and tobacco use, played a significant part in their leisure activities (see McKibbin, 1979; Itzkowitz, 1988; Clapson, 1991; Davies, 1991). Evidently, women participated alongside men with equal enthusiasm. However, betting on horses did not occupy as important a place in their conversations with one another as it did in the conversations between men (Itzkowitz, 1988). Rising wages and shorter workweeks among the lower ranks in the late nineteenth century also contributed to the increased commercialization of all forms of leisure, including gambling (McKibbin, 1982). The evidence suggests that the government’s and the church’s fears of shattered families, suicide, alcoholism, penury, and the downfall of industrial machinery were unwarranted. As McKibbin (1979:158-159) notes:

Invariably total turnover was presented as an accurate measure of the significance of gambling. This, however…is quite misleading, but only in this way was it possible to equate gambling with drink as a proportion of total personal expenditure. Yet, when the appropriate adjustments are made for return on winnings and a redistributive effect, outflow on gambling is seen to be relatively unimportant alongside that on drink or tobacco.

The 1949 Royal Commission on Betting, Lotteries, and Gaming that examined personal spending on “gambling, alcohol, tobacco, and entertainments” found that of the four categories of leisure expenses, gambling were by far the lowest. Those surveyed spent over ten times as much on both alcohol and tobacco as they did on gaming (in McKibbin, 1979:159). A subsequent study by the royal commission found that most ready-money bettors who placed their wagers through illegal bookmakers staked paltry sums relative to more affluent participants. The average wager among ready- money bettors was eight shilling (abbr. “s.”), compared to £3 10s for wealthier credit bettors. Another, perhaps even more surprising, finding is that gambling encouraged rather than precluded saving. Large winnings were often put away rather than reintroduced into the gambling money cycle (in Clapson, 1991:43). The prevalence of racing sheets, sporting presses, tipsters, bookmakers, runners, punters, and other easily observed evidence of turf gambling on the streets belied the ground-level reality of the situation. In truth, the poor gambled a smaller percentage of their income than the middle-classes. Yet, though the working classes were mainly responsible players who avoided excessive betting, what so infuriated the opponents of gambling among the lower ranks was the public nature of their gambling activities:

One need only pass through the streets of a large town, especially when the evening papers are being published on the day of a race or football match, to see how pervading is the gambling instinct among the working classes. Clerks, artisans, common labourers, railway employees, all manifest the greatest eagerness to know the latest betting quotations or the result of the race. The cry of “Winner” as the newsboys run along the street causes as much excitement as a cry of “Fire” or “Stop Thief” might do (Churchill, 1898:73; in McKibbin, 1988:159).

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Critics remained ignorant of the actual gambling behaviours and attitudes of the working classes. As far as behaviour was concerned, the mere appearance of impropriety was evidence enough to justify their legislative and moral double standards. Price (1972) notes that this tendency to make blanket assumptions about an entire population is evident also in the writings of late nineteenth century Western travellers who, while on journeys to the Far East, portrayed gambling practises in China as so epidemic as to appear entrenched in the fabric of daily life:

There is always some stake however small…In public, the very costermongers who hawk cakes and fruit about the streets are invariably provided with some means for determining by a resort to chance how much the purchaser shall have for his money. Here, it is a bamboo full of sticks, with numbers burnt into the concealed end, from which the customer draws; at another stall dice are thrown into an earthenware bowl, and so on. (Giles, 1876:76)

Likewise:

At breakfast-time workmen stream out of their places of employment, and throw dice or lots for their meal at the nearest itinerant cookshop. Coolies, in moments of leisure, while away the time with cards and dice as they sit at the sides of the streets, and the gaming-houses are always full of eager excited crowds, who are willing to lose everything they possess, and more also, in satisfaction of the national craving. (Douglas, 1894:82)

The current body of knowledge about gambling behaviours among the middle-class is even sparser than knowledge about workingmen and women. The evangelical crusade against gambling set its sights squarely on the working-class. It extended its message up to the middle- and upper-ranks of society, but did so with less emphasis on fire and brimstone and more on social disapproval. None of this reduced participation in gaming and betting of all sorts, though it forced gamblers to keep their hobbies hidden. Even as they crusaded against the rampant popularity of gaming in society, the middle- class never denied with any real conviction their own interest and participation in gambling activities. They did not need to; they could be trusted, as far as the church and government were concerned, to manage their own affairs. Most of the spectators at the racetrack were of middle-class origins; working-class punters stuck to bookmakers on the streets. Educated and wealthy bettors also had at their disposal a network of subscription betting houses that accepted wagers on credit. When all else failed, they could take their business to illegal street bookmakers, who in accepting bets from customers did not distinguish between social classes (Huggins, 2000a). Itzkowitz argues that gambling on horse races could very well have served an agonistic function for single, middle-class men:

In a very real sense betting on horse racing was very much like work, even though it was work that few people were as good at as they perhaps thought. By reading the racing press, by studying the predictions of the “experts” who claimed to be able to provide useful information, by discussing the pros and cons of the horses with his or her cronies, and thus by attempting to calculate the outcome of the next race, the Victorian bettor was engaged in an activity that had a rational dimension. (Itzkowitz, 1988:27)

In addition, the middle-class, despite their constant emphasis on respectability and honesty, were hardly models of virtue when it came to gambling. In his study of the Running Rein scandal of 1844, Huggins (1996) finds that immoral conduct was common in the upper ranks of society. Briefly, the scandal refers to the fixed race that took place at the famous Epsom Downs Derby run in 1844, and the subsequent investigations by the persistent Lord George Bentinck, self-appointed guardian of the 117 racetrack. The winner of the race – which was limited strictly to three year olds – was a horse named Running Rein, who was rumoured in the days leading up to the Derby to be actually a four-year-old by the name of Maccabeus. An examination was to be conducted, but by the time they found a veterinarian, the horse in question had vanished. The owner, William Goodman Levy, had earlier fled to France. Rather than pursue the matter across international borders, authorities awarded the prize to the second-place horse, Orlando. The scandal did not end here, however: another horse was later discovered to have been six-years-old. What’s more, the jockey riding the favourite horse was rumoured to have accepted a bribe, and competitors had, the night before, drugged the second favourite horse. As a result of these many revelations, reforms were quickly enacted to prevent further disgraces among the upper-class horseracing establishment. Bentinck, who as a jockey, racehorse owner, and gambler, was himself familiar with the underworld of shady gambling activities, led efforts to clean up the racetrack. Among other things, Bentinck was fed up with never being able to collect on defrauded bets that he had won. However, these reforms proved hollow. Illegal activities continued to benefit the upper-class owners and bettors. Inflated and falsified odds, insider tips, and rigged races remained a common feature of horse racing. In the end, the public’s overwhelming, unwavering interest in gaming would prove too much for the government. By the mid-twentieth century, Government had come to realise that theirs was a losing cause. Contributing to this swing in policy was a broad cultural shift away from the Protestant work ethic that informed much of the state’s political stance. This was as true in Britain as it was in North America, Australia, and many other Western countries (Cosgrave and Klassen, 2001). Changing social attitudes towards work, leisure, play, money, and religion all played important roles in the legitimization of gambling. The hold of religious ethics on economic values began to loosen, allowing capitalism to grow without restraint by puritanical guilt or opposition. Pursuing money as an end in itself had become acceptable in the new ethics of free enterprise. By tapping into the desire for instant gratification, wealth, and consumption, the gambling industry became an attractive pastime for many seeking a quick dollar or, at the least, a quick thrill. To this end, lottery marketing began to target aggressively lower-income people. Scratch-and-win tickets and lottery ticket vendors, for example, are much more readily available in working-class communities than in more affluent neighbourhoods (Nibert, 2000). Scholars of the modern history of gambling dispute whether the evolution of more liberal social attitudes in the 1950s and 60s forced governments to relax their restrictions on gambling, or whether the legislation of state-regulated gaming led to greater levels of acceptance by the public. Cosgrave and Klassen (2001) support the latter view, suggesting that much of the trend towards legitimizing gambling was engineered by the states themselves. The impetus for this, in their view, was economic: governments’ need for revenue to pay for social services. Since direct taxing of citizens is always politically unpopular, the next best thing is to convince the public to voluntarily give up their money. The establishment of a close connection between gambling and fundraising has been critical in swaying public and political opinion in favour of liberalized gaming laws in Canada and elsewhere (Morton 2003). Gambling has been redefined by the state as a form of what Cosgrave and Klassen (2001:8) call “productive leisure,” a type of adult play that offers amusement while at the same time producing revenue for governmental and political agencies. They observe that “the legalization of a variety of forms of gambling has contributed, at least tacitly, to the social acceptance of gambling activity, and for many citizens, lottery players and sports bettors for example, gambling has become a routine aspect of everyday life,” (p. 3). Later, they add:

The social stigma of the gambler as profligate, wasteful, immoral, irreligious or unproductive, 118

originating in particular religious and economic ethics, no longer carries much weight when all citizens are encouraged to gamble, and when gambling is not only consumption, but also a source of revenue for government coffers. (p. 8)

Evidence of this can be seen across the mid-twentieth century Western landscape. The Canadian stance on government-run lotteries was reversed in the 1960s, when additional revenue was needed to fund the upcoming Montreal Summer Olympic Games in 1976. Between 1992 and 1998, revenue from government-regulated gambling operations increased by 167 percent (Marshall, 2000). In the United States, profits from legal gambling ventures grew an enormous 1600 percent between 1975 and 1999 (National Gambling Impact Study, 1999:2). The public spent US$36 billion on state-run lotteries alone, which “as an activity undertaken by state governments…was exceeded only by education, public welfare, highways and health, and it was greater than the total that all states – including states without lotteries – spent on corrections, or on parks and natural resources” (Clotfelter et al., 1999:7). In Britain, off-course cash betting shops – the rebirth of the nineteenth century public betting houses – were reopened in the 1960s, subject to government-regulated licensing. Almost all other forms of gambling were gradually allowed to reappear over the course of an increasingly liberal twentieth century (Munting, 1998). Westminster even considered reintroducing the national lottery in the 1930s, 1960s (going so far as to propose it in the 1968 budget), and the 1970s, before finally taking the plunge in 1994. The first casino in Australia opened in 1973, followed quickly by a dozen more in every state and territory. Today, problem gambling is a growing concern for government officials, who have seen this accompany a steady rise in the gambling participation rates of certain ethnic groups in Australian metropolitan areas (Victorian Casino and Gaming Authority, 2000). Although gambling – and more generally, cultural pursuits and leisure activities – in the modern era has been characterized by a steady march towards greater commercialization in the wake of the global dominance of Western values and lifestyles, not all groups who participate in organized gaming do so for the same reasons. At the individual level, two people purchasing a lottery ticket or wagering at a roulette table may be doing so for entirely different reasons. The same principle holds true as one moves up the sociological ladder to a broader level of analysis.

The Extent of Cultural Diversity

A striking example of the cultural diversity that remains in gambling attitudes and behaviours comes from the peoples of Melanesia and Australia. While gambling, either in a Las Vegas casino or in a friendly poker match between work colleagues, has become for many in the Western world a pursuit motivated primarily by a desire for social bonding, idle pleasure, or monetary gain, Laura Zimmer (1986; 1987a; 1987b), Jane Goodale (1987), and William Mitchell (1988) have each described separate cultures that participate vigorously in games of chance for an entirely different reason. For the Gende people in Madang Province, Papua New Guinea, card playing is serious business. It is also a regular part of their daily lives. Some games last for hours, while others last for entire weeks. Virtually everyone in the community participates, including school and church officials, councillors, and village leaders (Zimmer, 1986:246). The main purpose of such intense play is not material gain – the average income of villagers who are employed (in itself an already small percentage of the whole population) is low. Nor is the purpose of gambling merely social interaction. Rather, gambling contributes “to a more equitable distribution of cash throughout the community” and participants are as concerned to “decrease relative income differences between themselves and others as they [are] to increase their gross income” (Zimmer, 1986:247; emphasis from original text). The Gende region is underdeveloped in comparison with other areas of the Melanesian 119 highlands. With few cash crops available, little revenue flows into the community. Given this harsh and unstable economy, Gende card playing has become an innovative exchange system that redistributes cash reserves throughout the community, to reduce income discrepancies and ensure that social relationships are reinforced and reciprocated. Nobody ever really loses in a Gende card game, because they do not associate losing the game with losing money. Once money is introduced into the game system, one no longer has any legitimate claims to it. Simply, all participants can play for available money. Failing to break even does not mean one has truly “lost”; likewise, coming away with more than one began with is not the same as “winning.” The prime rule is that winners are expected to always be willing to allow losers an opportunity to win back their losses. Another important observation is that Gende card players are expected to invest their winnings in some way. This may involve purchasing farm animals or trade goods to be resold for profit, or travelling to the nearby towns to obtain more money, or even reinvesting their winnings in other card games in order to further increase their income. This ensures that they are increasing their wealth and that they will have money available when others come seeking a rematch. For those with a relatively large income – primarily those who are employed in more developed towns outside the village – they have absolutely nothing to lose. If they happen to win in a gaming session, this is even better. Yet, even if they lose some of their money, the reciprocal nature of the exchange system itself ensures that they have not really lost anything. They have simply converted their excess income into a mutually recognised social relationship of future give-and-take. This arrangement benefits everyone. The players with cash can invest it in ventures to improve their own financial situation, and the players who lost know that they can always approach the winners, should they ever need money in the future. Wealthy players who lose on purpose are viewed as generous and kind, especially when they lose to someone who is already heavily indebted to them. Such losses provide the grateful winner with capital to invest; the profits from investment, in turn, can go towards repaying the wealthy player. By structuring the repayment system in this way, the social relationship between debt-holder and debtor is maintained without the need for dominance or humiliation of one party by the other. When the processes involved here operate at the level of an entire community, Gende card playing reduces income inequalities and promotes social cohesion. The wealthy have nothing to lose by voluntarily giving up some of their excess income for the time being. Indeed, their public image suffers if they do not. Losses reduce their immediate cash reserves, and increase those of the poorer players. Since the relationships are governed by an ethic of reciprocity, all parties involved know that should fortunes be reversed in the future, those with newfound wealth will have the opportunity to become “generous losers” themselves. Those who are struggling will be able to call upon past relationships for assistance (Zimmer, 1986; 1987b). Another aspect of Gende card playing has to do with male initiation practises. The preferred game among young, unmarried village men is Last Card, whose game play is characterized by sudden reversals of fortune that can see an underdog emerge victorious in a very short period. Just as gambling became, for the seventeenth century European aristocracy, a compensatory ritual that offered some of the rewards of military service, Last Card has become for young Gende males a supplementary part of the traditional male initiation ceremony:

In recent years, male initiation has proven to be an inadequate means for young men to attract a network of supporters or to acquire an education which will fit them for success in today’s changing circumstances. With the acceptance of cash into the Gende exchange system and the rising costs of exchange payments, young men are judged worthy of support (including access to garden land) more on the basis of their current or potential earning power than on any other single factor....[P]laying Last Card is a means for acquiring capital…[G]ames of Last Card also 120

provide a context or arena in which young men display personal attributes or talents and distinguish themselves from other young men in the eyes of older men and women as well as potential marriage partners. (Zimmer, 1986b:32-33).

Zimmer (1986b) draws five parallels between the game of Last Card and the traditional male initiation into adulthood: both (1) help initiate to attract a network of financial and emotional supporters from among the village’s older men and women; (2) require the building of partnerships between youths for the provision of mutual assistance, companionship, and protection during the initiation; (3) offer opportunities for initiates to display their abilities and character; (4) provide initial resources to assist in the pursuit of future goals; and (5) act as occasions for attracting potential mates. Monetary gain is only one reason why young Gende men participate in card games. The main reason is its social aspect. Far from being a leisurely and irrelevant diversion, Last Card offers economically disadvantaged men an opportunity to prove themselves worthy of the support of their elders, the respect of their peers, and the affections of potential brides. Jane Goodale (1987) finds much of the same investment of meaning and economic reciprocity in the gambling activities of the Tiwi community in northern Australia. Like the Gende, the Tiwi do not acknowledge the Western distinction between work and play. Nor, do they view the former as being a moral virtue and the latter as being a form of idle wastefulness. Instead, Goodale (1987:8) argues that for the Tiwi, cards are viewed as tools useful in acquiring a socially desirable resource (i.e., money), just as “steel axes, fish-hooks, shotguns, motor boats as well as new kinds of knowledge and skill” were used as tools for “seeking subsistence and life on the land and in the seas surround their islands.” Tiwi society is situated on Melville and Bathurst Islands in North Australia. Roughly, one-third of the working-age population is employed, primarily as low-wage workers in the township. Unemployment insurance, child allowances, widow and old age pensions, and various other social security payments comprise most of the remaining sources of income that enter the community. This income is initially divided unequally in favour of those who are employed – typically the males – and those receiving government benefits. However, because the Tiwi are “almost militantly egalitarian” in their emphasis on equal opportunities for all, they have, like the Gende, adopted card playing as a way of redistributing resources more equally among the population (Goodale, 1987:16). By introducing a large fraction of the income entering the community into the gaming system, all residents gain an equal access to the community’s cash. Skill at card playing -- rather than age, education, training, or employment -- becomes the sole determinant of where that money ends up. The money is further distributed among close kin, who only need to ask to receive a share of the wealth. Moreover, money is not the only resource people gambling over. Beer is perceived by the Tiwi as another symbol of success in white society, and is therefore used as a substitute for cash in the aptly titled card game of Beer-Up. The frequency of card playing and the reasons for participation in gambling games is also uniquely gendered in Tiwi society. Before contact with European settlers, Tiwi women were the primary providers of food for their families. Goodale sees their participation in card games as a modern extension of this maternal instinct. In hunter-gatherer societies, the women (as gatherers) provided most of the food consumed by the community. Men, as hunters, could not be counted upon to consistently return with food, although when they were successful, their kills could be distributed to a wider kinship network than could the food supply contributed by women. Aspects of card playing among the Tiwi are similar to this gendered contribution to communal welfare. Tiwi women play cards more often than men, and they are more skilled and successful at it. They often organize casual games within their extended households to teach children how to become skilled players themselves, knowing that the income used only gets recirculated within the immediate kin group. Games involving distant relatives or even strangers become more consequential, however, as 121 they introduce the possibility of real money being won and lost. This revenue is important because it contributes directly to the subsistence of the household. For many women, card playing is their sole source of income. In comparison, most men play cards only when high-stakes gambling is involved. Like male hunters returning with a large kill, male gamblers are striving to bring prestige to their respective communities. Women’s small (but consistent) winnings from card playing are used with little fanfare to supply the household with necessities, whereas men’s large (but rarer) winnings are sources of group and individual pride (Goodale, 1987).

Closing Remarks

As we have seen, gambling has a long history and humans have found various ways to gamble. They have also, periodically, attacked gambling and gamblers as dangerous, sinful, or otherwise undesirable members of the community. The view a particular culture holds of gambling at any particular time is specific to that time and culture, and subject to variation. Likewise, we have seen that within cultures, different subgroups – rich versus poor, men versus women, urban versus rural, etc. – gamble in different ways, to different degrees; and people in power (or holding moral authority) view their gambling practices in different light. In individualistic cultures, gambling represents a means by which individuals can win a fortune or demonstrate the courage and luck. In collective cultures, gambling represents a means by which individuals can entertain themselves together and redistribute their bounty among familiars. In class- differentiated societies, gambling represents a means by which “upper classes” can flaunt their wealth and middle-classes can impose moral judgments on the poor. In these societies, the coming and going of laws against gambling reflects shifts in the interests of the powerful and, increasingly, shifts in State interest. In relatively class-less societies, gambling continues to have little economic, political or moral significance. It is a matter of harmless entertainment. We now turn to a closer examination of gambling in Canada, and specifically, to ethnocultural variations in Canadian gambling. There, we will see many of the same themes discussed here emerge in a Canadian context. To the extent that ethnicity and class have merged in Canada’s “vertical mosaic,” gambling has been both class- and ethnic-differentiated behaviour. Legislation against gambling has been both a means of controlling the poor and excluding the foreigner.

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Chapter Two: Ethnicity and Gambling in Canada

In this chapter, we begin to examine the impact of ethnicity on gambling behaviour in Canada, past and present. We understand “ethnicity” to be a social and cultural – not biological – factor in people’s behaviour. As such, ethnicity – short for “ethnocultural ancestry” – reveals a lot about the social context of gambling since gambling behaviour is often learned from friends or in the family home. From a sociological point of view, ethnicity – like “race” – is socially constructed. Both are ideas we have about others and ourselves that affect how we perceive and interact with one another. They are similar in that both terms imply some kind of common biological origin that ties people together. People who share a common ethnicity or race are usually considered to be related by "blood" or to have had some common ancestor. They differ in that ethnic identity is likely to form among people with a common culture, language, religion, or national origin. Members feel they are culturally and socially united, and that is how others see them. Racial groups, on the other hand, are identified on the basis of presumed physical traits, especially appearance. A race could include members from many ethnic and social backgrounds and is defined in terms of shared appearance rather than shared history or culture. Neither “race” nor “ethnicity” is a purely objective concept. Interethnic and interracial contact has been taking place for thousands of years. As a result, no supposed racial group is genetically pure and racial divisions do not reflect genetic realities. They reflect the assumptions, biases, or stereotypes with which people categorize one another. Likewise, no cultural group has escaped influence by other cultural groups. This is evident in our languages. English, for example, shows the cultural and linguistic influence of Greek, Latin, French, German, Arabic, and a variety of other languages, both modern and ancient. So, when we talk about an ethnocultural group, or its beliefs and traditions, we are talking about the product of centuries if not millennia of cultural mixing. Yet, somehow, we give the group culture a distinct name, as though it were completely separate – as though, for example, English and French cultures had not influenced Aboriginal culture, or Welsh, Scottish, and Irish cultures had not influenced English culture. Doing this is as arbitrary as designating a mixed race child either “black” or “white.” Consider the child of a mixed race couple, with one white and one black parent. Is this child black or white? In the United States, such a child would almost certainly be considered black. Yet the child is as much white as black. In much of Latin America, the child would be considered white or as the member of a category that falls between black and white. The truth is, the child is black or white or some mixture of the two not because of innate biological traits but because of how the people in a society identify and categorize him or her. Thus, being “black” or “white” is a social construction. The same is true of ethnicity. Daniel Johnson, former premier of Quebec, firmly and unhesitatingly considered himself a Francophone Québécois, extremely proud of his Québécois family's long and illustrious history. However, clearly among his many Francophone ancestors was a "Johnson" and a long line of English ancestors. In other words, race and ethnicity are social constructs, not biological facts. Yet, even though they are not biologically meaningful, group markers such as an ethnic surname or physical appearance carry social meanings to which people respond. If people see themselves, and others see them, as members of a unique biological category, then in practice they form a race. Race is real then in the sense that its effects are real. As we will see, people often choose their friends and spouses, their places of work and lodging – they even hire workers and elect representatives -- based on perceptions of "race." Likewise, if people see themselves, and others see them as members of a unique ethnocultural category – as Hungarian or Jewish or Aboriginal, for example – then in practice they form an ethnic group; and if other people treat them as members of a

123 group, ethnicity becomes real because its effects are real. Thus, ethnicity and race are both reflective and constituted concepts. Ethnic and racial relations are social relationships. Sometimes these are individual relations, but usually they are relationships among groups. Minority people who suffer from political and economic bias often react by organizing themselves as groups. In doing so, they hope to gain political power, change the laws, or gain an economic edge. Those who already enjoy political or economic power are also organized in order to retain what they have. We have to “read in” all of these cultural, social, economic and political aspects of ethnicity when we study any aspect of ethnic behaviour, including ethnic gambling in this case. We also understand “gambling” to have multiple meanings that are dependant upon the social context. As we saw in the previous chapter, people play many different kinds of games and game appeal varies over time and location. Mah-jong, horse racing, dice games, Scalaforti, and sports betting, for example, are all associated with particular social/cultural contexts. Although all contain a basic element of risk-taking, as McMillen (1996: 9) notes, “gambling has no intrinsic meaning; rather, its meaning always depends on the socio-historical context in which it occurs.” “Problem gambling” too is a socially constructed concept whose definition has varied with time. For instance, the gambling behaviours of the French and English nobility in the seventeenth and eighteenth centuries would likely be deemed problematic and even pathological by today’s standards, yet the notion of “problem gambling” did not even exist at that time. Lavish wagers on games of chance were considered in that particular context to be not only desirable but also necessary. Moreover, as we shall see, what is considered “problem gambling” varies with culture and religion. For Protestant moral reformers in the 1800s and early 1900s, any wager or lottery, no matter its size or purpose, was morally abject; for those participating in these games, they were innocuous sources of recreation and social fun. Still, in order to enable research, basic concepts must be operationally defined. The majority of gambling researchers and public health officials today agree that problem gambling exists whenever an individual’s gambling activities result in harm to themselves and/or their families, and possible to the larger community. For the purposes of this study, “problem gambling” is defined as gambling defined by the gambler and/or others as excessive and resulting in social or economic problems. Sociological theories about gambling behaviour can be broadly categorized as those that focus on the behaviour of the individual and those that focus on the behaviour of the state.

I) Behaviour of the individual: Whether legal or illegal, gambling participation has had a long tradition in Canada, and several explanations as to why people gamble have been put forward. Traditional practices theories note that as different groups come to Canada, they bring with them their own beliefs and practices about many behaviours, gambling being but one of them. Variations in gambling activity are therefore simply expressions of the multitude of lifestyles and traditions occurring simultaneously in any multicultural society. Upward mobility explanations highlight the instrumental (rather than social or traditional) value of gambling. Deprived of other opportunities for economic advancement, some groups (or individuals within groups) may gamble – lawfully or not – to make money or acquire social status. Lastly, social marginality factors highlight the reasons (e.g., political exclusion or geographic isolation) as to why some groups are less committed to the gambling values of the dominant group, and therefore less likely to adopt those views.

II) Behaviour of the state: Given that gambling has always existed in Canada, what is perhaps more interesting is the historical fluctuation in the government attitudes towards gambling and the subsequent laws enacted to govern the activity. There has always been a dissonance between official prohibitive laws and the gambling behaviour of its constituents. This conflict, which has gender, class, and ethnicity implications, can be understood from two general perspectives. 124

Culture-based analyses focus on the differences in the system of beliefs, values, and practices adopted by the dominant British Protestant group and that of other groups, including the Catholic Francophones and later-arriving non-British and non-Protestant populations. This paradigm stresses the importance of the different meanings cultures impose upon gambling in understanding deviations from the law. Politics-based explanations stress not only cultural incongruities but differences in political status and power. Not only are the values of the dominant social group enshrined in the law, but, in the fashion of a classic positive feedback loop, the law itself becomes a means of legitimizing and furthering their domination. The state, and more specifically the law, is a political tool used to exert power, stigmatize, control, and subordinate competitors for status and power. Thus, according to this view, gambling laws that restrict certain behaviours are instruments designed to maintain a status quo that is conducive to the political needs of those in power. Both of these perspectives – of the individual and of the state – will contribute to the discussion of gambling in Canada that comprises the remainder of this chapter. For now, it is important to remember that these explanations are not mutually exclusive. Behaviour at the individual level is necessarily nested within the larger context of the political and social environments; likewise, abstract agendas at the government and legislative level do not exist in a vacuum, but have very real consequences in the real world. One theoretical approach may be more applicable than the other depending on the circumstances of the context and the intentions of the researcher, but even the most microsociological of paradigms has implications on a macrosociological level, and the same is true in the other direction as well. In fact, in most cases, some combination of theories is necessary. We begin the chapter with a review of gambling in Canadian history, and follow this with an overview of the quantitative literature on the prevalence of gambling across different cultural backgrounds. Cross-cultural prevalence surveys are of great use because they reflect on questions from differing perspectives. We highlight comparisons across cultures, because they are useful in determining whether ethnicity has an impact on gambling behaviour. In Chapter Three, the focus in narrowed through a series of “vignettes” of selected ethnocultural groups, showing the extent and types of variation in ethnic gambling within these communities. In the chapters that follow, we examine ethnic variations more systematically, using a new dataset that combines data from an Ontario prevalence survey (Wiebe et al., 2001) with Canadian census data.

Gambling, Law, and Government in Canadian History

Throughout Canada’s history of gambling, two facts have remained constant: gambling has always been a popular and widely enjoyed pastime, and the laws concerning gambling and their enforcement have been ambiguous and irregularly enforced. Gambling has never been completely illegal. What is considered legal has changed over time; likewise, public attitudes towards games of chance have fluctuated over time and across regional, sociocultural, economic, and ethnic lines. Canada’s earliest gambling laws evolved from the country’s British roots, beginning in 1388 when the English Monarch banned all games except archery for fear of losing his skilled archers to “idle games of dice” (Glickman, 1979). When the Criminal Code of Canada was enacted in 1892, public gambling was officially outlawed, with only a few exceptions. Religious or charity bazaars were permitted, pursuant to municipal approval, to conduct fundraising raffles so long as the prize being rewarded was valued at less than $50 and had first been put up for public sale. Agricultural fairs and exhibitions were also allowed to offer carnival-style games of chance, as long as the profits from these amusements were designated to support the activities of the associations who organized and hosted these events. Both of these exceptions facilitated non-commercial and non-profit ends. The third and most glaring exception to Canada’s legal policy on gambling, however, was the provision allowing organized betting to take place at government-chartered racetracks. In Canada as in elsewhere, the 125 colonial elite favoured gambling on horse racing, a aristocratic sport with echoes of the old military mode of war in which cavalry mattered. Given that Canada’s Criminal Code evolved from the British statute and common law imported through colonization, it is no surprise that the values and ideas of the old country, Britain, continued to flourish on the government level. Legal exemptions aside, abstract inclusiveness at the legislative level did not translate into uniform implementation at the community level. Despite its illegal status, gambling was a pervasive activity enjoyed by rich and poor alike. Generally speaking, players of different social classes wagered on different games in different venues, but the basic fact that gambling of one sort or another could be found in every level of society is well-documented (see Morton, 2003). Enforcement to the letter of the law would therefore have been impossible. Instead, gambling activities were largely tolerated by police, with only occasional enforcement against specific social groups. Among the gambling public, those with power and influence – predominantly British middle- class men – found ways to justify their indulgence in the eyes of the law, as in the case of horseracing and private men’s clubs. Those lacking these resources – the working class, ethnic minorities, and to some extent, women – were forced to exercise secrecy and caution, and remain vigilant against the occasional police crackdown. Cultural bias rendered class bias. Wamsley (1998: 80) describes the elite Rideau Club in Ottawa, for example, where Members of Parliament regularly met to gamble. Clearly, their behaviour was socially acceptable, at least in their own eyes. Racetracks regulations also created and reinforced these distinctions by permitting operation only during the day, effectively excluding working-class gamblers who could not easily leave their workplaces during the day to wager at the tracks. Having fewer such material restrictions, the upper class could visit the racetrack without fear of losing their jobs or pay and gamble as they pleased. Such hypocrisy was noticed, however. For example, during the gambling debate in the 1930s, the Halifax Citizen commented that “if it was wicked and frightfully wrong for Mr. Workingman to gamble a dime on ‘bingo,’ it was equally wrong for Mr. Leisure-Class to gamble ten or twenty-five dollars on a horse via the gambling machines known as the ‘parimutuels’” (cited in Morton, 2003: 12). In this sense, gambling laws not only reinforced class distinctions but regulated the lives of the lower classes by shaping their work and leisure pursuits. Suzanne Morton (2003) examines Canada’s conflicted historical relationship with gambling and the numerous discrepancies that existed between gambling legislation and policing. In Morton’s view, this ambiguity reveals how the public in general and anti-gambling advocates in particular connected the gambling discourse to cultural values like hard work, and social institutions like the family and religion. The opposition to gambling came from several directions. Many from the business community expressed concern that gambling houses lured recreation-seekers away from more legitimate venues such as movie theatres. Others scorned gambling because it violated the fundamental tenets of Canada’s emerging capitalist free-market economy, which celebrated the virtues of labour, enterprise, and merit. Gambling, with its lure of instant riches, offered (at least in theory) the same material rewards as capitalism while demanding little effort, skill, and self-discipline – in effect, getting “something for nothing.” Most arguments, however, were moralistic rather than economic, derived from the Protestant beliefs of the Anglo-Saxon middle-class. Coming from a religious tradition that placed high value on hard work, delayed gratification, and humble pleasures, many in positions of power in Canada questioned the work ethic and moral virtue of the common labourer who rejected conventional social norms in favour of the psychological thrills and promises of instant fortunes offered by gambling games. Finally, many anti-gambling advocates – the majority of whom were white Canadians with a northern European ancestry – associated gambling problems with ethnicity. The racial and ethnic stereotyping frequently used to bolster arguments calling for the condemnation of gambling suggests that scornful attitudes toward gambling were as often reflective of views on Canada’s immigration 126 policies and the minority groups themselves as they were of views on gambling behaviour per se. Gambling, then, was never necessarily a crime, sin, or vice; it only became so within particular contexts, dependent largely on the people involved and the interests they wished to protect. When it came to gambling, there was clearly a social “double standard.” Historians of Canada’s political culture have identified a few key groups in Canada’s early history that played a significant role in shaping the early social and political culture, particularly its values and what it deemed legitimate. These groups were the French, who set up the conservative ancien régime in Quebec, the English who were Protestants and liberals, and the United Empire Loyalists -- British Americans who were less revolutionary than their American counterparts. Both culturally and politically, Canadians have been more inclined towards socialistic ideas and values than their American counterparts. In part, this is because French Canada’s almost feudal ancien régime bears a similarity to the way that socialists view the role of government and the responsibility of upper classes. Both socialists and conservatives root social life in the community, not the individual (on this, see Bell and Tepperman, 1979). The “redness” of our Tories and the acceptability of socialists in Canada are due in large part to the early French immigrants who brought over a culture that espoused conservative values, extolling the whole of a society over its parts. In this Burkean conception of conservatism, the upper class has obligations to the poor to help the whole work efficiently, and vice versa, as the brain and hands work together to keep the mouth full. This conservative tradition may have been further supported by the arrival of United Empire Loyalists. While Canada’s cultural makeup had this internal complexity brought forth by the two founding nations of British and French peoples, the former group eventually became politically dominant. In 1763, with the end of the Seven Years’ War, Britain gained control of North America, establishing British North America. From then on, the tenets of Protestantism and liberalism “imported” by British colonists would dominate Canada’s political culture. This cultural background is useful in understanding the dominant view of activities like gambling in this early period. Where gambling is concerned, Protestantism and liberalism complement each other. Both emphasize the individual and his/her own responsibility for life choices. Both sets of ideas encourage self-discipline, rather than state intervention; in other words, self-control and personal responsibility are central to a well-functioning society. Yet, as we saw in the last chapter, it was common for British society to strictly regulate morality, especially where middle- and working-class people were concerned (a characteristic common to many Protestant societies), and gambling was very much a moral issue in these early days. Again, both conservatives and socialists find common ground on this issue. Former Conservative Prime Minister Arthur Meighen once stated that “gambling is an attempt to get by chance what should be earned by industry, to obtain the rewards of doing well by doing ill.” J. S. Woodsworth, founder of the Co-Operative Commonwealth Federation (CCF; precursor of the modern-day New Democratic Party) echoed similar sentiments when he observed that “lotteries divert attention from real values and suggest that only those who get something for nothing succeed” (cited in Morton, 2003: 25). Clergymen and moral reformers at the forefront of the anti-gambling movement further stirred up public fears by claiming that gambling, already a stigmatized behaviour was quickly becoming an out-of-control vice that threatened the basic social institutions of family, religion, and community. This Protestant ideology, and in particular, its notion of the work ethic, was enshrined in the Criminal Code and informed the central arguments put forth by Canada’s anti-gambling advocates for much of the twentieth century.

Capitalism, Work Ethic and Gambling

The discourse on gambling in Canada has paralleled the development of the modern capitalist economy through the common link of the “Protestant ethic.” The early stage of capitalism spanning 127 the eighteenth (in America), nineteenth, and early twentieth centuries developed in conjunction with the Industrial Revolution, a central component of which was production. Simply put, people were required to work hard and with consistency in order to maintain the machinery of the emerging factory system. In order for industry to flourish, a new ideology of work, one that celebrated labour for its own sake, was needed to replace the pre-existing medieval Christian notion of work (that is, secular work, in contrast to the spiritual work of the Church) as intrinsically lacking in redemptive value. The foundation for this shift in the understanding of work was first laid out during the Protestant Reformation, particularly in the writings of theologians Martin Luther and John Calvin, over two century ago. Luther rejected the Christian notion of sacred work as being superior to secular work, and put forth the idea that any person whose vocation contributed to the welfare of society – farmers, bakers, blacksmiths, etc. – was already acting in the service of God. This placed a spiritual and moral incentive on productive labour: salvation was made attainable through a life dedicated to toil and sacrifice. Calvin expanded on Luther’s doctrines by explicitly linking success in worldly endeavours with spiritual grace. He argued God had predestined some people (whom he termed the Elect) to enjoy eternal salvation; the rest were simply damned. However, who was saved, and who was not? According to Calvin, there was no sure-fire way to tell, since predestination was assigned randomly according to His unknowable will. At best, the possibility of spiritual redemption could be guessed at through a favourable interpretation of one’s daily actions. Those who were worked hard, sacrificed, and were successful in their ventures were seen as possible members of the Elect, their accomplishments and profits taken as signs of having been previously blessed and chosen by God Himself. The result was a reoriented view of work as a religious obligation that, as society became increasingly secularized over time, was appropriated into the economic ideology of Industrial-Era capitalism. Work became a social and moral, if not outright spiritual, duty – it was good for the individual, and it was good for society. Eternal salvation was replaced in the profane economic sphere by financial wealth and social prestige as the dominant incentives for a life of labour and enterprise, and the unfettered pursuit of profit was raised to the level of virtue. It was this conceptualization of hard work, along with the financial wealth that was presumed to follow it, as markers of one’s social esteem that made gambling so odious. The idea that gamblers got “something for nothing,” expending little effort and producing no work in exchange for accumulating wealth, inflamed the sensibilities of the Protestant middle-class. Since work for its own sake was so central a tenet in the conservative Protestant worldview, work’s seemingly blasé rejection by gambling enthusiasts was interpreted as an affront to their entire moral system. Gambling was situated in opposition to the work ethic, and by implication, to a person’s family and social responsibilities. The business community raised additional concerns regarding the potential consequences of a gambling epidemic. Perhaps recognizing the dull and repetitive nature characteristic of the assembly line factory jobs of the Industrial Era, employers insisted that gambling opportunities threatened to undermine the fragile work ethic of their employees. According to this line of argument, gambling threatened to derail the entire economic engine of the country. But what was equally troubling to the status quo, and what prevented the gambling debate from resolving itself into a straight-forward prohibition of a dangerous vice, was that gambling also highlighted the internal contradictions of a capitalist economy (Morton, 2003: 28). The work ethic aspect of capitalism emphasizes thrift, labour, and industry. Wealth is not to be horded or flaunted, but rather, reinvested into one’s enterprise in a quest to improve production and maximize profit. However, also central to the capitalist spirit is the fondness for risk-taking, for adventurism and speculation – characteristics shared by businessmen and gamblers alike. Life insurance, land speculation, and commodity futures were all denounced at various times by various groups as little more than legitimized forms of gambling, though the most common target for such criticism was that 128 vital pillar of the new economy, the stock market. After all, the principles and strategies behind trading in shares were little different from those behind many gambling games, a similarity that was not lost on many individuals inhabiting on both sides of the gambling debate (Morton, 2003: 28). Those who were in favour of legalizing gambling activities, especially lotteries intended as fundraisers for social service organizations, pointed to the double standard inherent in a Criminal Code that outlawed behaviour in one social arena (whose members, by virtue of their class, ethnicity, and/or gender, were socially and politically marginalized) while allowing it to continue in another. Since they represented charitable and benevolent groups whose work benefited the larger community, their appeals deftly sidestepped any counterarguments invoking themes of moral or social decay. Others, while supportive of the legislative prohibition on gambling, objected the class discrimination embedded in the Criminal Code. It was absurd, as one argument went, that a wealthy businessman could wager a small fortune on the stock market without fear of prosecution, while a humble worker who spent a comparatively insignificant sum on a lottery or bingo game was technically in violation of the law. Whether the voices of protest came from the pro- or anti-gambling side, the basic contention was the same: at present, what made some species of gambling legal and other species illegal was labelling based on economic and political interests rather than any inherent difference between them. Despite their ideological disparities, what both sides demanded above all was consistency in the laws.

Women, Gambling, and Bingo Games

Both commercialized gambling and the opposition to gambling have historically been activities performed by men. On the one side, the operations and establishments most closely associated with gambling (e.g., taverns, sporting clubs, brothels, and the like) catered primarily to the interests of the male population. Men were also more likely to be the sole breadwinners of the household, so they had more immediate access to the disposable income required for participation in games of chance. On the other side, the occupation of social offices endowed with enough political power to mount a sustained and vocal attack against any undesirable behaviour was for much of Canada’s history assigned according to patriarchal guidelines. The most prominent critics of gambling – politicians, law enforcers, the clergy, and business owners – were therefore always men. In this male- dominated environment, women were effectively excluded from participating in any great numbers in most gambling activities, with one exception: bingo. Women had fewer opportunities to gamble, but their interest in games of chance was no less than that of their male counterparts. Bingo became an ideal and convenient outlet for indulging in an otherwise inaccessible pleasure. The game proved particularly popular among married, working-class women whose domestic and economic situations left few options for recreation and leisure. For many players, bingo was foremost a social event, an opportunity to spend the evening outside of the home, chatting with friends and hopefully bringing back a small prize for the family. Nor were the prizes insignificant – they included useful items such as toasters and irons in addition to small cash jackpots. Particularly for players from a working-class background for whom expensive modern household conveniences remained permanently beyond their reach, the chance to win an electric sewing machine or a vacuum cleaner added an element of material glamour to their bingo nights that was otherwise lacking in their lives (Morton, 2003: 97). As bingo became increasingly popular during the inter-war years, its growing presence fell under the scrutiny of the anti-gambling squads. For many years, the growing bingo craze was largely overlooked by law enforcement officials despite its classification in the Canadian Criminal Code as a lottery and hence an illegal activity. Although police at the municipal level likewise ignored other forms of unlawful gaming, bingo presented an especially anomalous case within the gambling world. Morton, (2003: 91-92) identifies three reasons for this ambivalence: 129

First, games were usually not commercial but were organized by volunteers for charitable goals. Second, two of the institutions that drew heavily on bingo as a source of revenue – the Catholic Church and the Canadian Legion – were paragons of good citizenship. The fact that they supported and hosted the games in their own facilities made it difficult for many to consider bingo deviant….Third, there was the law. Under certain conditions bingo, as a lottery, was legal. The Criminal Code of 1892 permitted lotteries in a charity or religious bazaar when the article being given away was not valued at more than $50 and had been first offered for public sale.

However, the flagrant disregard for the law eventually proved too difficult to ignore. Again, the Protestant perspective informed the main attack, with a secondary criticism provided by the business community. If the Protestant work ethic defined the responsible man’s roles as those of breadwinner, husband, and father, then the corresponding roles for the respectable woman were mother, homemaker, and consumer (Morton, 2003). And just as gambling was seen as a temptation that undermined male duties in the social realm, so it was seen as a threat to female responsibilities in the domestic realm. Indeed, opponents asserted that female gamblers posed a greater threat to social stability than did male gamblers since, as the primary caregivers in the home, they were presumed to have greater influence over their children’s development. A mother infected with the gambling bug thus not only harmed herself, but also corrupted future generations of potentially hard-working citizens. Furthermore, the money used for bingo games was in most cases earned by a male breadwinner who, as the argument went, could only presume that his hard-earned wages were being used responsibly by his wife to feed and clothe the family rather than being squandered on frivolous games of chance. Women who gambled therefore also violated the trust between husband and wife. Accusations such as these were bolstered by increasingly frequent anecdotes in the 1940s and 1950s of negligent, gambling-obsessed mothers dragging their unsuitably young children to bingo parlours (“dens of iniquity and depravity for young people,” as one critic described such establishments), or else leaving them at home alone for the evening without proper supervision or dinner (Morton, 2003: 95-96). For businessmen, the concern was one of competition for consumer dollars. Butchers, grocers, coal dealers, and other merchants who conducted business on credit claimed that women gamblers frequently spent their money on bingo rather than on settling their debts. Additionally, as mentioned above, bingo prizes often took the form of household appliances, cooking equipment, and even groceries, commodities that were previously purchased from vendors. For instance, bingo organizers frequently handed out turkeys, hams, geese, and ducks as prizes in the weeks leading up to Christmas, Thanksgiving, or Easter, a tradition that was inevitably followed by an uproar from butchers claiming that the loss of customers was threatening to send their businesses into bankruptcy (Morton, 2003: 97- 98). Throughout the 1940s and 1950s, an increasing rate of crackdowns on bingo was matched paradoxically by an increasing popularity of the game. Juries almost never convicted bingo-players, either out of sympathy for the defendant or an interest in the game themselves. Eventually, the ubiquity of bingo gaming rendered the law unenforceable. Recognizing the futility of the struggle, the provinces began seeking other means for controlling the activity. British Columbia, for example, reached a mutually satisfactory resolution to the issue in 1957 by instituting a 10 per cent “amusement tax” on the revenues of all bingo games (Morton, 2003: 105).

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Ethnicity and Gambling

Within the white male Protestant-dominated discourse on gambling, the criticisms levelled against female bingo-players in Canada both stemmed from and were reinforced by their status as “Others.” Other groups so designated within this dominance hierarchy included Roman Catholics and racial and ethnic minorities. To be labelled as an outsider is to be vulnerable to prejudice, stereotyping, and scapegoating by the dominant group; this distortion allowed English-speaking Protestants to “explain the popularity of gambling – ‘others’ were the culprits – without having to address the ambivalence within their own culture” (Morton, 2003: 108). The continued friction between Protestants and Roman Catholics took their primary form in the political tensions between French-speaking Quebec and the rest of the country, but was also evident in a more subtle form within the anti-gambling discourse. As we have seen, Protestants took a hard-line stance against gambling, declaring it a violation of an individual’s responsibilities towards work and family. Furthermore, Protestant objections were codified into law. The Roman Catholic church, while not unanimously in favour of gambling, recognized their local parishioner’ financial needs and conveniently turned a blind eye to bingo games and other fundraising activities. This divergent approach to a common vice, while being only one example in a long list of ideological differences between the two groups, did little to reduce the gulf that separated them. The ideological clashes between religious groups resulted in a low-level tension that permeated all levels of Canadian society. In contrast, the tenuous relations between White Anglo-Saxon Canada and the budding ethnic populations were both immediate and blunt. From the start, gambling was associated with immigration communities, a legacy that lingers to this day despite recent studies showing that the two most enthusiastic gambling nations are, in order, Australia and Great Britain (Morton, 2003: 108). Gambling was without a doubt practised by ethnic minorities, but then gambling also crossed all racial, class, and gender boundaries. In truth, illegal gaming was popular in all levels of Canadian society, but for the critics of gambling, some groups were more to blame than others. The relationship between gambling and ethnicity reflected larger issues about immigration and minority groups in the country. Although Canada’s multiculturalism is today a source of national pride and global admiration, its immigration policies of the past were far from egalitarian. Rather, like other New World countries populated first by colonialists and later by successive waves of immigrants, Canada has grappled with its own history of institutional and popular discrimination against ethnic minority groups. Racial stereotyping assigns moral opprobrium to individual character. More so than any other ethnic group in the early half of the twentieth century, gambling (among other vices) was associated with the Chinese. Thousands of young men from China were welcomed to Canada beginning the mid- 1800s as cheap labourers who participated in the British Columbia gold rush, and later in the building of the Canadian Pacific Railway. However, once the CPR work was completed in 1885, public attitudes shifted as the white population found itself in competition with the Chinese for employment. Racial tensions soon turned into outright racism, and the Chinese became synonymous with immorality and vice (Anderson, 1995). “John Chinaman,” the derisive appellation for the typical working-class Asian immigrant in Canada, was seen not only a fanatical gambler, but also by turns an opium fiend, a visitor of brothels, and a petty criminal. Chinese women were assumed to be prostitutes or concubines, or else madams who lured young white women into a life of harlotry. Chinatowns across the country were condemned by moralists as dens of iniquity, congested and infested slums whose storefronts hid all manners of “wickedness unmentionable.” According to historian Kay Anderson,

In Vancouver, the “heathen Chinee” was known for inveterate gambling. Successive officers of the city police certainly accepted this label, and they pursued Chinatown’s gambling vigorously 131

for five decades, from the 1890s until the late 1940s, when the extent of the harassment became embarrassingly transparent even to the city. Until then, however, it was rare to find a year that the Chinese Times and the rest of the local press did not report at least one raid on Chinatown’s gambling quarters…Just as raids on opium dens vindicated white people’s assumptions about the moral laxity of the Chinese, civic scrutiny of gambling in Chinatown sprang from and confirmed popular assumptions about a generically addicted “Chinaman.” And one vice bred another, as Alderman McIntosh charged in 1915. Chinese gambling and opium required constant vigilance, he said, because they were associated not only with white women slavery, but also with tuberculosis. (Anderson, 1995: 101)

This treatment was motivated by a general distain for Chinese immigrants, whom many from the white population viewed, often without any first-hand interaction, as threats to their economic prosperity and comparatively civilized standard of living. Whether justified or not, the Chinese, with their strange customs and shadowy neighbourhoods, were relegated to a second-class tier of society from their first arrival into Canada until the mid-twentieth century. Such was the fear of the “Yellow Peril” permanently establishing itself in Canada that by the late nineteenth century, the government was moved to impose several measures designed to limit immigration from Asiatic countries. The passing of an 1885 bill designed to restrict Chinese entry led to the creation of two official streams of foreign applicants: those of Chinese origin, who were subject to restricted quotas and a $50 head tax (which would be raised in 1903 to $500, which for the average Chinese labourer was the equivalent of two years’ wages); and everyone else, who were covered under the general Immigration Act. In 1923, Parliament passed the Chinese Immigration Act, barring virtually all Chinese from coming to Canada. Between then and 1947, when the Act was repealed, fewer than 50 Chinese immigrants entered the country. It was the only time in Canadian history where an entire people were officially excluded explicitly on the basis of race. Was there any truth to the claims of the white Protestant majority, or were their accusation merely the product of moral panic and racial prejudice? Though it is important not to minimize or ignore the xenophobia lurking beneath many of the anti-gambling advocates’ criticisms of the Asian population, certain elements of the Chinese community did contributed to a gambling-friendly culture. For instance, chance and fortune are central concepts in many Asian rituals and celebrations, and gambling as a recreational activity in China can be traced back millennia. Although excessive gaming was still frowned upon, moderate gambling on games of fan-tan, mah-jong, and lotteries was generally deemed acceptable. The demographic characteristics of the Chinese in Canada were also important. Most of the Chinese immigrants entering the country up until the early 1900s were young men who had endured the trans-Pacific journey alone with hopes of economic prosperity in mind. The goal for many was to earn their riches in the New World before returning home to China; the grim reality that greeted them was more often characterized by low wages, cramped and unsanitary housing conditions, racial prejudice, legal harassment, social isolation, and loneliness. Combined with the economic and political discrimination that prevented labourers from bringing their families over from China, the Chinese quarters in Vancouver, Victoria, Toronto, and elsewhere soon became “bachelor communities,” overflowing with unattached, working-age men with little to do for leisure except gamble, drink, and socialize with one another. As one individual later reminisced, “There was no family, everyone was single…If you went to the gambling house, you could talk and laugh…Where else could we go?” (in Li, 1988: 81). The Canadian majority was therefore at least equally culpable in creating an environment in which gambling was a rare outlet for marginalized foreigners, as the Vancouver branch of the Chinese Benevolent Association pointed out in 1924 (Morton, 2003: 129). Although the gambling problem was most closely associated with Chinese Canadians, they were not the only ethnic group to be subjected to moral condemnation and discriminatory prosecution. 132

The Jewish population, centred mostly in Montreal, was also often linked to gambling, especially bookmaking. Within the Jewish community itself, moderate gambling was generally accepted, though by no means was approval unanimous. Still, when the occasional gambler struck it rich, he (and it was inevitably a “he”) reinvested their profits in local businesses such as theatres and restaurants, thereby blurring the distinction between illegal and legal enterprises and earning the respect and admiration of the community. However, gambling’s links to the criminal underworld left its Jewish patrons vulnerable to the occasional (and usually wild) accusations of French-Canada’s anti-Semitic factions. The May 11, 1933 edition of Le Patriote, the voice piece of the fascist National Christian Social Party, alleged that the heads of Canada’s organized crime syndicates were invariably Jewish, the implication being that local Jewish bookmakers were in fact gangsters and thugs. Ethnic prejudice was largely responsible for the discriminatory targeting of gambling in Chinese and Jewish communities, but when it came down to finding a rationale for condemning their activities, the critics invoked the standard Protestant ethic arguments. The stereotype of an inherently degenerate Chinese character was already well ingrained in public opinion, but the greater fear was that the corruption would spread to the remaining Canadian populations. Gambling and opium, which in the minds of the critics were assumed to go hand-in-hand, lured young men and women from a life of discipline and hard work to one of depravity and wretchedness. In fact, law enforcers largely tolerated Chinatown gambling, along with the other vices such as prostitution and opium smoking, so long as fellow Chinese only patronized the establishments offering such activities. A Vancouver chief of police stated in 1928 that gambling dens in Chinese neighbourhoods was not a major concern “because it is among themselves and as long as the whites keep out of it no one suffers. If anyone suffers it is themselves” (cited in Morton, 2003: 122). Only after claims were made that gaming clubs were beginning to attract “native” (i.e., white) clientele did public anxiety increase and police raids become commonplace. In the popular imagination, gambling (and deviant or questionable behaviours, more generally) is often associated with ethnic groups (and socially disadvantaged populations, more generally). Certainly, the link is to a degree an illusory one, created though negative stereotyping by the dominant majority to maintain their own favourable position within the unequal power hierarchy; but this cannot be the full explanation, since it reduces minority groups into passive subjects whose self-identities are defined completely by hostile others. Despite the inequalities that separate them, marginalized groups are as active in determining their actions and outcomes as those who marginalize them. It is true that, contrary to stereotype, not all minority group members are inveterate gamblers; but it is also true that many minority group members do gamble, and they do so for a variety of reasons. Two theories have been proposed by sociologists as possible explanations for this relationship between gambling and ethnicity. One suggests that disadvantaged social groups share the same core values as the majority but, due to systemic barriers of inequality, are forced to seek out alternative – and occasionally illegitimate – avenues for achieving wealth, prestige, and other markers of success in life. The other suggests that marginalized populations reject even the core values of the dominant group, forming instead a subculture within the larger community, defined by its own set of moral and behavioural standards.

Upward Mobility and Gambling

Despite admonitions against gambling, participation was still widespread among certain ethnic groups. Yet, their participation may have nothing to do with more primitive, irrational thinking, but rather, may be a rational response to social and economic circumstances. If Reith (1999) is correct in saying that gambling is an interaction with chance, then it can also be a great equalizer. From this perspective, pure chance, where a random dice toss – as opposed to effort or skill – determines the 133 winner, can level a playing field and overcome disabilities and inequalities due to birth and unequal circumstance. Minorities often face an unfair playing field, especially when they first immigrate to a new country, in terms of employment, housing, and immigration policy. For them more so than for members of the dominant social group, gambling may seem to be a fair, attractive, and rational way to overcome economic and social disparities. Gambling may therefore have been one channel of upward mobility for new immigrants. This possibility is consistent with the theory of criminal behaviour put forward by sociologist Daniel Bell (1992: 111 et passim). According to Bell, criminal activity is one of the few ways that minorities can advance in a prejudiced society. His theory proposes that immigrant criminal activity is due not to a lack of character or a fondness for crime, but to the lack of legitimate earning opportunities for politically, socially, and economically excluded minorities. Bell notes that America immigrant groups established themselves through entrepreneurial criminal activities – consider the clichéd but relevant example of the Jewish-American and Italian-American mobster of the early twentieth century. While legitimate business opportunities were hard to come by, members of many immigrant groups could create their own businesses and gain capital through criminal activities like prostitution, gambling, bookmaking, and smuggling.

Social Marginality and Gambling

If certain groups were excluded from legitimate opportunities and thus, motivated towards criminal outlets to service, it also meant that these groups had less motivation to embrace the dominant group’s culture and values. Jews, Chinese, and other excluded minority groups had little at stake in the dominant culture’s view of gambling (Morton, 2003: 110). The value systems espoused by Protestant and even Catholic Canadians precluded the values and practices of these groups, so they had little reason to embrace their values. In this respect, they were “strangers” to the country, in the sense that sociologist Georg Simmel (1950) meant the term. The word stranger comes from Latin roots related to “extraneous” and meaning “outsider.” According to Simmel, the stranger, in sociological terms, is a person living within a community and, therefore, affecting to some degree the life of the place, but not given status as one of the in-group by the natives. He is within the group but not entirely of the group. He or she is one who accepts such a lack of identification with the group within which he lives and lacks the compulsion to submit to prevailing forms of subtle social control (e.g., gossip, shame). Three aspects of the social position of the stranger define it in sociological terms. First, the person is what Robert Park (1921) called “marginal”: partly inside and partly outside the group. He is, to a greater or lesser degree, a man without a country, though he will accept some elements of both his former and present places of abode. Second, the stranger enjoys a particular combination of nearness and remoteness (or social distance) from group members. Despite structural centrality or importance to the functioning of the community, the stranger is culturally marginal. Such a person at once attains certain freedoms denied natives or members of the in-group, and is denied the freedom of participation which is the unquestioned right of such members. Finally, because of this marginality and social distance, and a relative mental and physical mobility, the stranger is free to accept or reject native values and conventions. A key feature of the stranger is detachment and objectivity, freedom from habit, piety, and precedent. One of the most enduring stereotypes about early Chinese immigrants was their inassimilable nature (Ward, 1978). According to the “natives,” the Chinese were too immoral, too unsanitary, and too foreign; they simply could not adopt a Western way of life. There is little truth to this claim, either then or now; the “inassimilable-ness” of the Chinese was a consequence more of the hostile environments that greeted them at the public and institutional levels than of any innate “Chinese-ness.” 134

Victims of constant discrimination, separated from family and loved ones, and lacking few genuine opportunities for social recognition or advancements, foreigners had few incentives to adopt the value system of a majority who viewed them as second-class citizens. Life for the Chinese in Canada in the early half of the twentieth century was therefore rootless and transient. For many, the decision to come to Canada was one of economics – they would make their fortunes in the gold rush or as labourers, and then they would return home in China. Few intended to stay permanently; those who did found themselves unwelcome; additionally, restrictive immigration policies prevented them from bringing their families with them. The Chinese Immigration Act was intended to control the foreign population in Canada, but it also had the effect of hindering those already here from adopting Canadian values and identities. The acceptability of gambling among the “Others” becomes, in this perspective, symbolic of their rejection of mainstream Canadian culture. In lieu of assimilation into the dominant society, socially marginalized groups establish their own communities. For some, this alliance is merely ideological; for others, it also takes the physical form of ethnic neighbourhoods. Urban enclaves are functional in many ways. They centralize resources catering to the specific needs and interests of the group, such as restaurants, grocery stores, banks, medical offices, and social clubs. They become attractive hubs for newly arriving immigrants. Perhaps most importantly, by concentrating group members in a small physical space, it also makes them the majority, at least in this particular sphere. Such geographic concentration provides psychological comfort that can only be truly appreciated by the individual who has always existed in the larger community as a minority. For outsiders, “Chinatown” was a place of exoticism, danger, and/or seediness; for the Chinese inhabitants, it was a self-contained, self-defined society with its own practices and daily rhythms, and governed by its own values and beliefs.

Changes in Gambling Attitudes and Laws

In 1969, the prohibitive gambling stance was repealed, moving the government towards a more permissive approach towards gambling. The Canadian Criminal Code was amended to permit the provincial and federal governments – alone or together – to conduct lottery schemes, broadly defined as “games of chance.” Charitable or religious organizations, fair, exhibits, and public places of amusement could also continue to operate lottery schemes but under provincial authorization and licensing regulations. In 1985, the provinces consolidated control over gambling provision with an amendment giving them exclusive jurisdiction. The fund-raising capacity of lotteries was an important factor in the government’s decision to revise the Criminal Code. However, the 1969 amendment was also a response to the growing consensus that, as with alcohol, prohibition was simply ineffective in protecting society from the evils of gambling, which were characterized not only in moral but also social terms (e.g., organized crime) (Morton, 2003). Gambling could be better controlled through strict licensing and regulation (Joint Committee of the Senate and House of Commons on Capital Punishment, Corporal Punishment and Lotteries, 1956, 20-22; cited in Osborne, 1992). Although decriminalization was part of a larger law reform strategy to scale back government involvement, particularly for vice and victimless crimes, it still reflected the state’s ongoing paternalistic desire to closely monitor and regulate potentially harmful conduct within limits (Osborne, 1992). Nonetheless, despite these moral connotations, gambling was becoming more acceptable from both official and social perspectives. Gambling was consistent with an emerging new citizen ideal, one accompanied by different social, religious, and economic norms brought on by the increasing pluralization and secularization of Canadian society. While the story of gambling’s normalization and acceptance is unique in some ways, it also bears a close resemblance to the story of much behaviour that Canadians came to accept over the 135 course of the century. In Canadian bedrooms, new forms of sexual behaviour – non-missionary sex, non-heterosexual sex, non-monogamous relationships, and sex before marriage – became far less stigmatized, especially after the 1960s. Divorce was made easier to obtain, and common-law relationships were increasingly viewed as an acceptable alternative to marriage. Recreational drug use likewise became more tolerated, the current debate over marijuana decriminalization being the most recent development in this ongoing trend. Even social and commercial activities on Sabbath days became legal (Morton, 2003: 169). These changes did not take place without a fight. The debate was about social inclusion and social status as much as it was about gambling per se. One obvious reason for the change in Canadian views on pleasure in general, and gambling in particular, is the growth of a highly diverse, secular society. With immigration continuing strongly throughout the twentieth century, colonial elites and their traditional Anglo-Saxon values no longer held sway in Canada’s largest cities. The dominance of English-speaking Protestants waned due to the effects of immigration by people from other cultural backgrounds. In this way, the fight over gambling in Canada was like the fight over Prohibition in the United States in the 1920s and early 1930s. Joseph Gusfield (1963) argues that the successful lobbying effort organized by the American Temperance Movement that led to Prohibition was an example of what he calls “status politics.” Status politics reveal “a struggle between groups for prestige and social position.” Defending their position in the status order is as important to people as protecting or expanding their economic power; indeed, the two are often related. In the case of Prohibition, the struggle was between the native-born, white, Protestant, small-town middle class, which had run the United States up through the 19th century, and the foreign-born and mostly Catholic immigrants who poured into American cities at the turn of the twentieth century. American Protestants had made a virtue of “temperance,” but the new immigrants saw nothing wrong with the use of alcohol. For this reason, small-town, middle-class Americans came to identify immigrants with the production, distribution, and consumption of alcohol. Symbolically, the attempt to impose temperance through Prohibition was a bid to turn the clock back to a time when the United States was a uniform society dominated by middle-class Protestants. Its goal, however unconscious, was to show the immigrants who ran the country, but it proved unenforceable and generally unpopular. Rural small-towners had lost the battle and, in doing so, they lost their symbolic status. Likewise, with gambling in Canada, staunch Protestant moral conservatism slowly gave way to pluralism and secular permissiveness. With widespread acceptance and participation, the government could also begin using gambling as a revenue-generating tool for nation-building. The state had long been aware of gambling’s potential for raising money; recall that the original 1892 Criminal Code exempted small charity lotteries from legal prosecution. Government-sanctioned lotteries already enjoyed a long tradition of use by numerous colonial states in the past to generate the funds needed to support infrastructure construction projects or military campaigns. Still, the invisible hand of the old Protestant Puritanism, which had guided legislative policy for the much of Canada’s early history, ensured that legal non-commercial lotteries remained the exception rather than the rule. Its grip, however, had loosened considerably by the mid-twentieth century, and as the welfare state expanded and social service costs grew, a more pragmatic approach to gambling was adopted by a government in search of new sources of revenue. A progressively pluralistic, secular population shifted public attitudes away from the conservative Protestantism of the past, compelling the Canadian government to amend its policy on gambling, among other prohibited activities. Official tolerance in turn led to even greater public acceptance, creating a cycle whose momentum would eventually lead to the widespread acceptability and availability of recreational, commercial gambling opportunities. Also contributing to increasingly permissiveness towards gaming was the emergence of a distinction between the occasional “problem” gambler, whose lack of control posed a threat to both 136 themselves and society, and the recreational gambler, whose self-restraint could be trusted. Increasingly, the standard of acceptability for gambling, as for alcohol and other vices, was one of moderation – participation is fine, but don’t overdo it. Problem gambling became medicalized as an individual addiction rather than as a weakness inherent in any specific social group or sub-group, freeing the majority of the population from the stigma of participating in lotteries and casino games. Today, “pathological gambling” is listed as an impulse control disorder under the American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorders (DSM-IV), and its diagnostic criteria resembles closely those of other addictive illnesses, such as substance dependence disorder (American Psychiatric Association, 1994). The less severe form known as “problem gambling” is not listed as a condition in the DSM-IV, but is widely considered by medical officials to be a very real public health concern and is used by most gambling surveys to distinguish between destructive and benign recreational gaming patterns.

Gambling in Canada Today

Over the past two decades, the availability of gambling in a variety of forms has increased markedly in Canada (Ladouceur, 1996), and in North America more generally. Prior to the 1970s, legal gambling in Canada was limited to occasional charity bingos and raffles, midway games of chance, pari-mutuel wagering on horse races, private non-commercial card games, and friendly bets between individuals (Campbell & Lowman, 1988). Thirty years later, legalized gambling in Canada has expanded to include slot machines, video gambling devices, casinos, large-scale bingo halls, and sports and off-track horse race betting. A recent report indicates that the total net revenue from government- run lotteries, video lottery terminals, and casinos across Canada increased from $2.7 billion in 1992 to $11.3 billion in 2002 (Statistics Canada, March 2003). The percentage of revenues that provincial and territorial governments receive from gambling ranges between nearly zero and 6 percent of their total revenues (see Table 2.1 below). Across Canada, the share of total revenue from gambling increased from 1.9 percent in 1992 to 5.1 percent in 2001. In 1992, the average gambling expenditure per person 18 years and over was $130. By 2001, that number had risen to $447. (Statistics Canada, March 2003).

Table 2.1: Percentage of Total Revenue from Gambling: 1992 and 2001

1992 2001 Alberta 1.6 5.4 British Columbia 2.2 3.6 Manitoba 2.5 5.5 New Brunswick 2.7 3.5 Newfoundland & Labrador 2.3 4.9 Nova Scotia 2.8 6.0 Ontario 1.9 6.0 Prince Edward Island 2.7 3.1 Saskatchewan 1.1 4.8 Quebec 1.8 5.0 Yukon, NWT & Nunavut 0.3 0.3 Canada 1.9 5.1 Statistics Canada (March, 2003)

Consistent with the growth and normalization of gambling in Canada, we see that gambling has become a regular activity for the majority of adults. In the Canadian Community Health Survey 137 conducted in 2002 by Statistics Canada, the results found that three-quarters of Canadian 15 years and older had gambled that year, an estimated 18.9 million people. Across Canada, purchasing lottery tickets is the most common gambling activity (65 percent), followed by instant-win tickets (36 percent) and going to a casino (22 percent). As gambling opportunities expand and become increasingly available to a wider spectrum of the population, concerns have been raised about the potential social and economic fallout of a permissive gambling culture. Pathological gambling has been listed in the DSM-IV since 1980, in recognition of the addictive potential of lotteries, slot machines and video lottery terminals (VLTs), and casino card games. Numerous provincial prevalence studies, including the general population of adults, adolescents, and older adults, have been conducted to examine the prevalence of problem gambling. In the only national study conducted to date, 0.5 percent of Canadians 15 years and older were found to have severe gambling problems, and another 1.5 percent had problems classified as “moderate.” Research had consistently found clear trends in the demographics and gambling styles of problem gamblers in Canada. The National Council of Welfare (1996) reviewed the results of eight Canadian adult prevalence studies in an attempt to identify the profile of a problem gambler. The review identified a consistent pattern between problem gambling and being male, single (including widowed or divorced), and under the age of 30 years. In the Ontario prevalence study, young adults (18-24 years of age) were almost twice as likely as the general population to have moderate to severe gambling problems (7 percent vs. 3.8 percent) (Wiebe et al., 2001). Problem gamblers were also more likely to be unemployed or underemployed (Ipsos-Reid and Gemini Research, 2003; Patton et al., 2002; Smith and Wynne, 2002; Wynne, 2002; Department of Health and Wellness, 2001). Similarly, in his review of prevalence studies, Korn (2000) observed that being male, young, and having a concurrent substance abuse or mental illness placed people at greater risk for gambling-related problems. In terms of gambling activities, relationships have been observed between engaging in problematic levels of gambling and playing slots or VLTs. This finding supports the observed relationship between so-called “continuous games” and problem gambling (Dickerson, 1993). Continuous games are characterized by a short time span between placing a wager and obtaining the outcome. The sequence of wager-play-outcome occurs rapidly and can be repeated frequently, as with slots and VLTs. In a study conducted with gambling treatment clients in Australia, the majority had sought help did so as the consequence of an addiction to electronic gambling machines (Australian Productivity Commission, 1999). A number of studies have specifically examined the link between psychological states and gambling levels (Jacobs, 1986; 1987; Rosenthal, 1993). Pathological gambling often occurs in conjunction with other behavioural problems, including substance abuse, mood disorders, and personality disorders (Blaszczynski & Steele, 1998; NORC, 1999). The joint occurrence of two or more psychiatric problems, termed comorbidity, is an important, though complicating factor in studying the basis of this disorder. Is problem or pathological gambling a unique pathology that exists on its own or is it merely a symptom of a common predisposition, genetic or otherwise, that underlies all addictions? Life stressors have also been identified as an important component in the development of gambling problems. The General Theory of Addictions (Jacobs, 1986) proposes that certain personality characteristics and life events influence the development of gambling problems. Jacobs suggests that excessive gambling may result from a history of negative childhood experiences. Additionally, personal vulnerability has been linked by some researchers to negative childhood experiences of inadequacy, inferiority, low self-esteem, and rejection (McCormick et al., 1987; McCormick et al., 1989). A study by Taber et al. (1987) has found that out of 44 individuals admitted to an inpatient gambling treatment program, 23 percent had experienced severe trauma during their lives and another

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16 percent reported moderately heavy trauma. Furthermore, those with traumatic experiences also reported higher rates of substance abuse, depression, and anxiety than those without such experiences.

Ethnic Variations in Canadian and International Prevalence Studies

The survey-based literature on gambling has grown tremendously over recent years (McGowan et al., 2000). Psychologists and sociologists have primarily carried out this research, following a (quantitative) positivist orientation (McGowan et al 2000: 13). As a result, quantitative prevalence and incidence studies dominate our knowledge of ethnocultural variations in gambling, comprising 90 percent of research in this domain (McGowan et al 2000: 14). Furthermore, these studies focus on pathological/problem gambling and not what Abt and Smith (1984) call “gambling as play.” During the 1980s and 1990s, the South Oaks Gambling Screen (SOGS; Lesieur and Blume, 1987) was the predominant measure of pathological gambling. Its popularity was due to the extensive validity and reliability testing it underwent to verify its robust diagnostic power (Shaffer et al., 1997). However, it has not been without criticism. One of the main critiques has centred on the fact that it was developed in a clinical setting, but has subsequently been used in general population studies (Culleton, 1989). In 1997, an inter-provincial group of government agencies appointed with the responsibility of reducing problem gambling commissioned the Canadian Centre on Substance Abuse to design and test a new instrument for measuring problem gambling in non-clinical settings (i.e., the public community). The outcome was the development of a new measurement instrument, the Canadian Problem Gambling Index (CPGI), which has been validated in a Canada-wide survey (Ferris & Wynne, 2001). The CPGI also has the additional benefit of allowing for qualitative analysis of problem gambler subtypes (Patton et al., 2002). Since 2001, the CPGI has been the instrument of choice for conducting problem gambling prevalence studies with the Canadian general population, including the current study. As noted earlier, survey research has found several social characteristics that are consistently associated with the presence of gambling and problem gambling. Gender, age, and financial circumstances are all known to be important demographic correlates of gambling behaviour (Volberg and Abbott, 1997), a finding that is consistently found in virtually every Canadian prevalence study conducted to date. However, the role of ethnicity has yet to be thoroughly documented. Only a handful of prevalence studies have included ethnocultural ancestry as a variable of interest (e.g., Smith and Wynne, 2002; Wynne, 2002); of these, the discussion is often limited to problem gambling among Aboriginal subpopulations (e.g., Patton et al., 2002). In British Columbia, Ipsos-Reid and Gemini Research (2003) recently conducted a survey to, firstly, estimate the prevalence of gambling and problem gambling among British Columbians, using both the SOGS and CPGI measures, and secondly, to determine demographic information about gamblers in the province. The researchers included region, age, gender, education, employment, children at home, and marital status in their analyses, but chose not to include ethnicity for several reasons. Firstly, the purpose of their study was to analyze pathological gambling across the entire B.C. population. Secondly, many of the ethnic groups contained only a handful of subjects within the total survey sample of 2,500, too small to be representative of those in the general population. Thirdly, the study was conducted in English, which possible discouraged those lacking English proficiency from participating. Finally, the researchers argued that ethnicity, unlike other demographic characteristics, is best investigated using focused studies that target specific ethnic communities (Ipsos-Reid and Gemini Research, 2003: 15). While the British Columbia report gives no data on ethnicity, it at least recognized the importance of ethnicity in gambling research, underscoring the relevance of the questions we are asking in this study. A 2001 report on gambling in New Brunswick marked the third wave of that province’s 139 commitment to gambling research (Department of Health and Wellness, 2001: 1-2). The researchers used a geographically stratified random sample of 2,677 New Brunswick residents and carried out interviews over the phone. Researchers pre-tested the CPGI in both its French and English variations. The results revealed that French-speaking participants, who comprised 22 percent of the study sample, were more likely to have ever gambled and to have gambled in the past month than English-speaking participants (Department of Health and Wellness 2001: 2-10). There were no differences between French- and English-speaking participants in terms of rates of problem gambling. The study did not record respondents’ ethnicity. The preliminary data from the gambling studies that have included ethnocultural variation as a variable are encouraging, and suggest a need for more systematic research into this area. For instance, in Smith and Wynne’s (2002) study conducted through telephone interviews with 1,803 Albertan participants, subjects were asked to select their ethnicity from a list of 54 choices, including “Canadian.” The five largest groups in the study were British (including Irish, English, Welsh and Scottish), German, French, Ukrainian, and Aboriginal (including First Nations and Métis). The majority of respondents in all five ethnic groups were gamblers – defined in the study as people who have ever-gambled – with the British subgroup at 82.8 percent, Germans at 83.8 percent, French at 86.1 percent, Ukrainians at 95.6 percent, and Aboriginals at 83.9 percent (Smith and Wynne 2002: 23). Among the five groups, only the Aboriginal respondents showed a higher-than-average risk for developing a gambling problem, with nearly 20 percent exhibiting moderate to severe problem gambling behaviours. With only 33 subjects in the Aboriginal group, the authors cautioned that the sample should not be viewed as representative of Aboriginal in the community. However, further studies involving Aboriginal subjects have found corroborating results, supporting the theory that problem gambling is a significant social issue in many Aboriginal populations (Wardman et al., 2001). Statistics from the North West Territories’ 2002 Alcohol and Drug Survey, for example, found that among Aboriginals, who make up slightly less than half of the population, 50.8 percent gambled more than $20 per week, compared to 13.9 percent of non-Aboriginals (North West Territories Bureau of Statistics, 2002). Likewise, in Wynne’s (2002) prevalence study of Saskatchewan gamblers, Aboriginal players were more likely than others to either already have a gambling problem or be at risk of developing one in the future. Because the evidence is still accumulating, Wynne has been careful in calling the recorded gambling rates among adolescent and Aboriginal populations “apparent.” A 2002 report from Manitoba examined the prevalence of gambling and problem gambling among 3,000 adults in the province (Patton et al., 2002). Ethnocultural discussion was limited only to the Aboriginal subgroup, which the study identified as being at greater risk than the general population of developing gambling problems. However, the Manitoba sample was too small to estimate reliable prevalence rates of problem or pathological gambling among First Nations people, although it was large enough to show that First Nations people gambled differently than non-First Nations people. Among adults, being an Aboriginal member increases the likelihood that a person will gamble more than 10 hours a month and spend more than $100 a month on gambling (Patton et al., 2002: 2). The Chinese Family Service of Greater Montreal (1997) conducted a gambling survey using the SOGS tool among a non-random sample of Chinese clients enrolled in its community service programs. They found that 32 percent of respondents had gambled in the previous year, of whom 4.7 percent were classified as problem gamblers and 1.7 percent as probable pathological gamblers. Male gender, duration of residence in Canada, a low level of formal education, and unemployment were associated with greater risk of problem gambling. The Impact of Gaming on Specific Cultural Groups (IGSCG), commissioned by the Victoria Casino and Gaming Authority, randomly sampled ethnic communities in Victoria, Australia (Cultural Partners Australia Consortium, 2000). Data was collected in telephone interviews in the language of the participants’ choice. Four ethnocultural groups – Vietnamese, Greek, Arabic speakers, and Chinese – were subsequently examined in further detail. 140

The results suggest that problem gambling is more common in the ethnic communities studied than in the general population. Overall, the researchers found that 11 percent of Chinese, 11 percent of Vietnamese, 9 percent of Greek, and 7 percent of Arabic respondents attained a SOGS score of 5 or more, compared to 1.5 percent among the general population. The results also found that the Chinese more so than other ethnic groups view gambling as an innocuous recreational activity. 62 percent of the Chinese sample either strongly agreed or agreed with the statement “Generally, gambling is an acceptable activity in our community,” while only 3 percent strongly disagreed with the statement. In contrast, 35 percent of the Greek, 31 percent of the Vietnamese, and 13 percent of the Arabic-speaking sample strongly disagreed. In fact, when combined, the data regarding Greek, Vietnamese, and Arabic-speakers’ attitudes towards gambling opportunities in the community – including responses to the questions, “Gambling is too widely accessible in Victoria,” and “Gambling related problems have become worse in the last few years” – reveal greater anti-gambling sentiments than the general population (Victoria Casino and Gaming Authority, 2000). This is a surprising finding in light of these groups’ higher-than-expected scores on the SOGS measure. It shows that while problem gambling may be a significant issue in many ethnic communities, it does not necessarily result from a universally-accepted permissive attitude towards gaming. In fact, opposition to gambling may be stronger in these populations than elsewhere precisely because many have experienced first-hand the adverse consequences of gambling addiction. Abbott and Volberg (1994) provide a cross-national analysis of demographic variables associated with problem gambling in the United States, Canada, and New Zealand. They found that demographic characteristics associated with problematic levels of gambling include unemployment, having parents with gambling problems, being male, and being a younger adult. As well, the New Zealand Maori and the Chinese were identified as ethnocultural groups with a higher-than-average incidence of problem and pathological gambling.

Criticisms and Concerns

Increasingly, researchers have called for more attention to be paid to ethnocultural variations in gambling and problem gambling. For example, in their comprehensive review of the socio-cultural domain of gambling literature between 1980 and 2000, McGowan et al. (2000) advise that future prevalence research must give a high priority to research on “understudied subgroups,” including First Nations peoples and other visible minority communities. One possible explanation for the current paucity of research in this area is that ethnocultural differences in gambling behaviour are difficult to measure. As discussed at the beginning of this chapter, the terms “ethnicity” and “culture” are themselves socially constructed, variable, and to a degree open to interpretation. Their lack of precision can present operational challenges. In addition, “problem gambling” as a research construct is meaningful only in relation to “normal gambling.” “Problem gambling” is not an absolute condition; rather, it occupies the extreme region of what might be termed a “gambling behaviour spectrum.” The boundary separating benign recreational gaming from its more pathological form is therefore necessarily ambiguous, and while the American Psychiatric Association’s DSM-IV criteria for “pathological gambling” and the scoring schemes of the SOGS and the CPGI are not arbitrarily set, they are nevertheless relative to a socially constructed norm. Consequently, there remains a lack of full consensus among researchers as to even the appropriate diagnostic benchmark for defining “problem gambling.” Some studies (e.g., Victoria Casino and Gaming Authority, 2000; Volberg, 1994), for instance, have used a SOGS score of 5 as an indicator of problem gambling, while others (e.g., Blaszczynski et al., 1998; Dickerson et al., 1996) have argued for a threshold of 10. On the DSM-IV, the currently accepted threshold is five “yes” answers to its list of ten “persistent and maladaptive behaviours.” On the CPGI, a score of 8 or higher (out of a possible 27) is indicative of problem gambling (Ferris and Wynne, 2001). 141

The use of the SOGS and the CPGI within ethnic communities is also problematic for issues of translation. Both screening tools were developed in English and have yet to be validated in other languages and among ethnic minority groups (Walker and Dickerson, 1996). In one study of Chinese gambling (Blaszczynski et al., 1998), the SOGS survey was translated into Mandarin to accommodate subjects whose English language skills were poor. While the intent was to maximize response rates and facilitate the gathering of accurate data, flaws in the translation process and a lack of validation of the Chinese version before its administration led to problems when the data was returned. Upon analysis, it became clear that four questions within the SOGS section had different meanings in the English and Mandarin versions. While this particular study was only a preliminary pilot survey, and the investigators were able to salvage their data, this case serves as a reminder for researchers to remain vigilant against overlooking seemingly minor semantic differences in translation that nevertheless have the potential to invalidate entire bodies of research. One precaution worth taking is for translations of survey instruments to be back-translated into the original language in order to confirm consistency of semantics and wording (Kinzie and Mason, 1987; Pernice, 1994). In addition to translation, other aspects of language also become issues whenever ethnocultural research is conducted. Researchers need to maintain consistent phrasing when comparing prevalence findings, as Walker and Dickerson (1996) point out. There is a big difference between the current incidence of pathological gambling and lifetime incidence of pathological gambling. Gambling behaviours in one’s country of birth may also differ from gambling behaviours in one’s adopted country, so it is difficult to know how to interpret data about “lifetime” gambling. Indeed, “gambling” itself has different meanings in different cultures. Gambling also means different things to different people at different times (McMillen, 1996). Researchers must be aware of these variations when interpreting their data. For example, Blaszczynski et al. (1998) note that in the Chinese community that they studied, respondents may understand gambling to include activities like lotteries but not traditional cultural activities like mah-jong. Yet, in the SOGS survey, both activities fall under the gambling umbrella, leaving it to the researchers’ to distinguish these activities when analysing the resultant data. Others have also cited the self-reporting techniques used in gambling prevalence studies as potential confounding factors (Ipsos-Reid and Gemini Research, 2003; Blazczynski et al., 1998). Despite assurances of confidentiality, under-reporting remains a concern whenever respondents are probed for their participation in socially stigmatized or potentially compromising behaviours. Accordingly, under-reporting has been documented in everything from illicit drug use (Kim and Hill, 2003), domestic violence (Heckert and Gondolf, 2000), and drinking and driving (Sommers et al., 2002) to fetal abortion (Jagannathan, 2001), unsuccessful dieting (Muhlheim et al., 1998), and being late for work (Koslowsky and Dishon-Berkovits, 2001). In an initial study of the reliability and validity of self-reporting among problem gamblers, Hodgins and Makarchuk (2003) found that most respondents were generally honest in interviews, whether assessments were conducted face-to-face or by telephone, but cautioned that further research is needed to confirm their findings. Social stigma may also be responsible for difficulties reported by researchers in enrolling a sufficiently representative number of minority group subjects for participation in studies involving ethnocultural variables (see Cheung and Spears, 1992; Pernice, 1994). Lin (1982) notes that the Chinese have traditionally stigmatized psychiatric disorders, and presumably this has made Chinese people reluctant to participate in addiction research. Or, as Blaszczynski and his colleagues, put it,

Individuals who engage in unrestrained gambling are regard as failures and heavy gamblers are seen as exceedingly undesirable marriage partners. “Compulsive gambling” is seen as comparable to other “antisocial behaviours” such as child abuse and social apathy….The negative perception toward pathological gambling is further reinforced by the reluctance of the psychiatric profession to recognize the behaviour as a mental health problem as evidenced by its 142

decision to exclude pathological gambling from the Chinese Classification of Mental Disorders…the social stigma associated with mental illness and the fear of “losing face” in public…a marked reliance on family support and management in preference to consulting professional organizations…and a reluctance to approach mainstream health services because of language and cultural differences. (Blaszczynski et al., 1998: 362-363)

Likewise, Pernice (1994) points out the unique challenges facing researchers whose work involves refugees and immigrants. Many in these special populations have endured political and social upheaval in their home countries and hardships in linguistic and cultural adaptation in their adopted ones. A lingering distrust of authority and formal inquiries, borne of experience with oppressive governments, often make them wary of responding to solicitations for participation in social science research.

Concluding Remarks

In this chapter, we have reviewed how gambling has historically been understood in Canada; and presented a brief summary of the current gambling behaviour of Canadians. We have also seen the role that ethnicity has played in shaping the country’s gambling discourse, past and present. Historical records have provided rich qualitative and anecdotal knowledge about the role of culture in shaping gambling attitudes and practices, but to date, little direct and systematic quantitative research has been conducted on ethnocultural variations in gambling behaviour. Our goal in this study is to use two sources of current survey data – individual-level data from an Ontario gambling prevalence study (Wiebe et al., 2001) and community-level data from the 2002 Canadian Census – to gain more precise information about ethnic variations in gambling behaviour, and also to investigate for interaction and intensification effects of neighbourhood variables on individual gambling. In the next chapter, we use data from a variety of sources to construct vignettes of gambling among three ethnocultural communities – the British, Chinese, and Aboriginal populations – the goal being to illustrate the roles of tradition and culture on the behaviour of three groups known for their enthusiastic gambling. This material will serve as a preparation for the statistical analysis of survey data beginning in Chapter Four.

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Chapter Three: Three Gambling Cultures

In this chapter, we examine data from a variety of sources about three ethnocultural groups of particular interest: the British, Chinese and Aboriginal populations. Our goal is to identify what may be traditional ethnocultural gambling practices. We will look more closely at these ethnocultural variations in a Canadian context when we begin to examine our survey data in the next chapter. As we will see then, these three groups continue to show marked differences in gambling behaviour, as revealed in survey data from Ontario. The information in this chapter comes mainly from a literature review and, secondarily, from exploratory survey data collected in the Toronto-based At Home With Gambling study conducted by Tepperman, Korn and Lynn (2002).

The British

Gambling in Britain Literature on current gambling in Britain is divided into two types: historical literature that describes (among other things) the class distinctions in gambling behavior, and survey-based literature that uses methods like the South Oaks Gambling Survey (SOGS) to discern the prevalence of gambling in Britain. However, the latter literature is of limited use to us, since it does not routinely distinguish between people of English cultural ancestry and people of other cultural ancestries who also reside in the United Kingdom. Thus, it provides little information about ethnocultural variation per se. As a result, we are mainly reliant on historical and other “soft” analyses of British gambling as a cultural phenomenon. As we saw in the preceding chapters, gambling has always been infused with class distinctions. In England, although both the rich and poor gambled, they did so in different ways. Among the wealthy, card games like Blacks and Reds were always popular, as was wagering on horseracing. There was no legal prohibition or backlash against these types of gambling. “Gentlemen” gamblers were presumed to be in no danger of becoming addicted, unlike the working classes, who could not be trusted to practice self-restraint without the safeguard of legal enforcement. Even the words used by Englishmen to describe betting – “laying a wager” versus “punting” – was a signifier of their social class, and therefore of whether their gambling was to be considered harmless leisure or not (Clapson, 1990). Clapson (1992) argues that gambling – regardless of one’s class – was viewed historically as a form of leisure. Indeed, it was one of the few recreational activities available to the poor and rich alike, since the amount of a wager could range from a pittance to a fortune. Despite the fears of pundits and critics, betting was mostly harmless, even for the working-man, since the limited incomes of the working class prohibited excessive wagering. Furthermore, contrary to the paternalistic attitudes of the social and political elite, most working class gamblers displayed a remarkable degree of self-control in moderating their gambling habits, such that few would be considered by today’s standards as “problematic” or “pathological” (Clapson 1992). Still, vocal anti-gambling movements flourished in Britain and succeeded in convincing the government to proscribe virtually all forms of betting from 1853 to 1960. However, gambling did not disappear in the face of this legislation. In fact, it did the opposite; growing in popularity, it became a quintessential aspect of the stereotypical Englishman. Gambling activities evolved during this time from casual wagers between friends to an organized industry of bookies and punters. By the time the gambling legislation was revisited in 1960, domestic anti-gambling sentiments were being viewed increasingly as “un-English.” Recognizing both the futility of attempting to prohibit a nearly universal leisure activity and the revenue potential of a state-run gaming industry, the British government gradually revised its policies 144 on public gambling. As the number and kinds of proscribed forms of gambling gradually dwindled during the 1950s and 1960s, and the state began playing a larger role in regulating the industry, a new round of debate about the social costs of legalized gambling and whether gaming was too accessible to vulnerable populations (i.e., women, children, the poor) emerged. For example, the 1960 Betting and Gaming Act legalized bingo, which led more or less immediately to the exponential increase in the number of bingo shops. Predictably, there followed a backlash against the vast amounts of money that Britons were spending on this newly permissible activity. Some even described bingo as a “cretinous pastime” (The Times, September 14 1961), referring to the view held by many opponents that gamblers were not evil so much as they were mentally and morally underdeveloped. Gambling’s status as a widespread leisure activity made progressive Britons wonder whether universal education had resulted in making “better” people, as they had hoped and expected (Dixey 1996: 137). In response to these concerns, the laws were refined in 1969. However, care was taken to not eliminate bingo since “half a million or so women who play each day should not be exposed to the temptations of hard gaming” (Gaming Board Report 1969: 9). Bingo was and continues to be especially popular among working-class women. Dixey (1996) notes that many women gambled to increase their personal incomes, with little risk to the family’s financial well-being. McKibben (1979), Harrison (1975), and Clapson (1992) have noted that working- class gambling is usually a controlled activity, meaning that only the little “extra” money available within a household budget is spent on betting, and the losses or gains are therefore seldom significant. Bingo gains and losses are typically small, providing an ideal solution to the working classes’ needs for recreational gambling. The social nature of the game also appealed to working-class women in the 1960s (Dixey 1996: 139-141). Prior to 1960, the cinema was one of the few acceptable places for women to go in a group. Bingo, by nature a collective activity, offered players a similar opportunity for socialization. Similarly, Dixey argues that the residential housing patterns of post-war London, which emphasized high rise apartments rather than single-family houses with adjoining front- and backyards, along with the growing popularity of television, led to a decline in “chance” opportunities for socializing (1996: 140- 141). Women who could not satisfy their sociability needs through increased visits to the local pub could head instead to their local bingo halls. In 1968, as Parliament attempted to pass the Gaming Act, the House of Lords hotly debated the issue of aid to pathological gamblers. The result was a rule that made gamblers wait for days before gambling, limited to two the number of slot machines that could be located in one club, and put restrictions on casino cheque cashing (Kelly 1988). However, as Kelly (1988) notes, the debaters failed to address the growing importance of slot machines. Today, machines are still allowed in public arcades, which are especially popular with British youngsters. Betting (slot) machines are as easy to operate as an arcade video game machine and for some children much more exciting. There is growing concern that, like cigarette advertisements targeted at children, exposure to these betting machines at such a young age will foster a new generation of problem gamblers in England. Gambling researcher Rachel Volberg (2000) has also traced the development of the gambling industry in Britain, and notes that the gaming clientele increased significantly since the legalisation of previously prohibited gambling activities. She argues that the National Lotteries, revived after decades of proscription, stimulated the popular demand for more gambling opportunities. Between 1965 and 1995, gambling followed the model of “un-stimulated demand,” meaning that the gaming industry was by and large a passive entity, content in merely catering to players’ interest. This changed, however, in 1994, when the National Lottery began a weekly draw (Kent 1997). A second weekly draw was soon introduced, along with scratch tickets and other new games. Responding to the cries of “unfair” by the gambling industry, the government soon allowed casinos, football pools, charitable gambling, arcades, and betting shops to operate (Kent 1997; McQueen 2000). The effect was cyclic: as gambling becomes commonplace, new products, gimmicks, and ads 145 were used to bring in even more bettors. Meanwhile, the government, having grown accustomed to the revenue that the industry generates, did little to hinder its growth. Older forms of gambling, such as racetrack betting, successfully lobbied the government for fewer restrictions in order to remain competitive in an increasingly crowded field populated by flashy casinos and ostentatious charitable lotteries (Gerstein et al. 1999). Volberg and others have also noted that “event frequency” – the frequency of possible bets during a period – is a key component of any gambling problem (Abbott and Volberg 1999; Griffiths 1999). So, for example, people who gamble daily are more likely to be probable pathological gamblers than people who gamble monthly (Volberg and Moore 1999). Volberg (2000) expresses particular concern about video, internet, and other machine gambling possibilities, where, as in the case of computer blackjack, a person can play a complete game every 15 seconds. Indeed, so addictive are stand-alone video lottery terminals (VTLs), found most often in bars, pubs, restaurants, and other non- casino (and alcohol-friendly) establishments, that they have been described by Statistics Canada as the “crack cocaine” of gambling. In Britain, the effect of gambling deregulation on gaming rates is impossible to quantify, as there are no prevalence data from the period before deregulation. The first national survey of gambling in Britain found that 72 percent of the population participated in at least one gambling activity each year and 65 percent played the National Lottery. 0.8 percent of the population – approximately 333,000 people – are estimated to be problem gamblers (Sproston, Erens and Orford 2000). In comparison, problem and pathological gambling rates are slightly higher in the US and Canada. Between 1 to 2 percent of adults over the age of 18 in the US and Canada are estimated to be pathological gamblers and 2 to 4 percent more adults are problem gamblers (Schaffer et al.1997). In New Zealand and Australia the numbers are similar. In response to Volberg’s plea to slow down, Sproston, Erens and Orford (2000) write that Volberg’s (2000) concerns over “event frequency” are misguided. They note that few people – roughly, 1 percent of all Britons – participate in any kind of Internet gambling, for example. However, they also acknowledge that Internet gambling is on the rise.

British People in Canada

From 1763 to 1867, Canada was an official British colony (and, some would argue, an unofficial one for many years after Confederation). Until 1980, the largest single ethnocultural group in Canada originated in the British Isles, owing to immigration from the England, Scotland, Ireland, Wales, and the United States of America. Thus, the story of gambling in Canada is – in the first instance – the story of British gambling in Canada. However, the British influence goes beyond gambling and includes also the British form of government, and British-style opposition to gambling. Canada’s British roots are evident, to take one example, in its government structure, which is in many ways a copy of the system that exists in Britain. Both have two chambers of Parliament: the House of Commons and the Senate (known in England as the House of Lords). The Governor-General is the Monarch’s representative in the Canadian government. Other than the importance of federalism, whereby the provinces have separate zones of jurisdiction from the national government, Canada was created largely in the image of Britain. The dominance of British rule and tradition in Canada meant that immigration regulations frequently favoured British immigrants for much of the country’s early history. Between 1896 and 1914, Canada saw the first large wave of peasants and farmers, comprising in total 1.4 million people. Brits were particularly numerous in this group. In fact, Britain was the single largest source country for immigrants to Canada until 1980. Two main changes took place in the 1960s that affected the place of British natives in Canada. First, there was a decline in the relative number of migrants from Britain. Twenty-five percent of 146 immigrants entering the country between 1950 and 1960 claimed British heritage. During the next decade, that percentage dropped to 21 percent; and the decade after that, to 13 percent. This decline was partly the result of a revised Canadian immigration policy that, in the hopes of recruiting a wider variety of skill-sets, stopped rating migrants on the basis of their ethnicity, religion, or country of origin. Since then, the number of British immigrants in proportion to non-British immigrants has gone down, compared with the number of immigrants originating in other areas of the world, in particular South and East Asia. Second, in the 1960’s, the Royal Commission on Biculturalism and Bilingualism was set up to examine the state of English and French language and culture. Understandably, members of Canada’s many other cultures objected to their apparent exclusion from notice. In time, the Royal Commissioners published three volumes that “invented” multiculturalism as a Canadian concept. This Royal Commission, unlike many others in Canada’s history, has had a significant effect. Today, top civil servants must be able to speak both English and French, and government services across Canada are expected to be available in either language. Additionally, the government has sponsored numerous programs to aid new immigrants in learning the official languages. These initiatives have all promoted Canada’s two founding cultures. However, the federal government has also spent millions of dollars preserving other immigrant cultures in Canada. As yet, there are no published statistics to estimate the prevalence of gambling among people of British or English descent in Canada. However, a preliminary sense of British-Canadian gambling behaviour in Canada can be determined from exploratory survey data gathered by Tepperman, Korn and Lynn (2002).

From the At Home With Gambling Study: It should noted at the onset that the data discussed below, taken from Tepperman, Korn, and Lynn (2002), require cautious interpretation due to the study’s small sample size and the non-random methods by which the participants were selected. However, as an exploratory survey, the results are useful as an indicator of possible trends warranting further study. The purpose of the following discussion is to present a set of initial qualitative findings on ethnocultural differences in gambling behaviour that are supported quantitatively by results from the present study, to be discussed in subsequent chapters. Tepperman, Korn, and Lynn (2002) classified respondents according to their ancestry, not their place of birth. Respondents of British ancestry may not necessarily have been born in Britain, though some were. British Isles respondents were culturally and socially varied, representing a mixture of English, Welsh, Scottish, and Irish ancestries, with varying numbers of generations in Canada. These respondents were mainly Christians, and several made links between Christianity (Catholicism and Protestantism) and gambling, remarking that, for Christians, gambling was not condoned:

I think that certainly that kind of Christian – in the sense of Catholic, Protestant, Anglican background – that it was a behaviour that was not condoned, particularly, I mean, it would have been frowned upon and looked at as a weakness and a sin and all those kinds of things.

Well, it is frowned upon right? If you’re a Protestant, it’s like drinking too much; so I guess it would be something you had to watch.

As well, these respondents remarked on the history of aristocratic and social gambling in Britain, and some respondents appeared to see their own group’s gambling behaviour in relation to that aristocratic tradition. Many pointed out that the Royal Family were themselves keen sporting fans:

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The Queen mother [had] always been a racing fan, you know, things like that, so I think it’s not frowned upon.

When you get to the track you get the women with their beautiful hats. The racetrack in that situation is more of a social. It is men that take their wives with them. This is part of a social event, like the Queen [went] with the Queen mother…This is part of a social thing.

Many British-ancestry respondents also distinguished between the sporting events on which the British traditionally gambled in England – e.g., horse and dog racing, darts, soccer, and polo – and those popular for gamblers in Canada – e.g., hockey and baseball. Many of those who had lived in the British Isles pointed out how prevalent off-track betting establishments were in the UK compared to Canada:

[There is] much higher gambling in England than here…Definitely. There’s an off-track betting shop in the way there’s a Tim Horton’s here. I mean you can’t swing a dead cat without hitting a Tim Horton’s [here]. And in the area where I lived in London that would have been true of an OTB…There you can walk in and it’s like they’re giving odds on whether what’s her name Spice girl (sic) is going to marry the hot soccer player right up to the day before it happened; and the odds are shortening as the day is getting closer and the press is still good, you know.

Two-thirds of these respondents believed that there were gender differences with respect to gambling within British culture:

For some reasons, what I noticed is that women seem to go to the casinos and the Bingos but they don’t seem to go to the horse races. Like not in the same ratio. If you go to a Bingo I would say 90 percent of them are women. If you go to the horse races there is 90 percent men there up until they got the casinos and horses combined.

Some attributed this preference to women’s concern with safety and a preference to gamble alone:

Yeah, it’s mostly women [at the casino]. Maybe [it’s] because you don’t have to talk to anybody. Maybe it’s because you can go there alone and play Bingo or the slot machines and you can feel comfortable going alone to do those things. And like, I know when you go to Las Vegas…well you’re as comfortable as a woman walking around gambling there…But I mean you walk in here [Off-Track Betting] and it’s really a male dominated scene.

Other respondents saw gambling as a male-centered activity:

Oh I would definitely say it’s a male thing because I know that when I was growing up in Scotland off-track betting places—like it was men, like it was a male domain.

I would say that even now most of the betting places are male dominated. They’re definitely male-dominated.

Even when high male participation was not stressed, gambling was nevertheless linked to traditional and distinctive gender roles:

I’ve known a lot of women who like to gamble, but women have been the ones to sort of try to 148

maintain the peace in life, in their relationships, in their families etcetera and that’s such a heavy responsibility. So they’ve kind of squashed that down a bit, whereas men are just allowed to do it: “Oh sorry, I just went out and blew however many dollars.” So I think that women like to do it but from a societal side they weren’t allowed to do it in the same way.

In terms of my generation, gambling was worse for men because they had the money and they probably gambled the rent and women didn’t used to work so they could only gamble the egg money.

Most men gamble in our culture [speaking of Scotland] and the women don’t. Yeah—the women, it goes back to that the women are at home with the kids. And a lot of women weren’t even allowed to work. Their husbands didn’t approve of them working. And so they were kind of in a cocoon.

Some British-ancestry respondents saw these traditions as changing and suggested that women were now gambling in part due to an increased accessibility of gambling venues:

Men like to gamble more than women do. And I think it’s always been like that. It might be changing now because of the fact that we have casinos: we have these accessible places to go where we never had that before. I mean when I used to play poker nobody ever invited the wives or their girl friends to go and play poker at somebody’s house. But now we have casinos all over the place. So environmentally and socially you have these places for women to go and enjoy themselves that you didn’t have before.

Another respondent suggested that the track was a way for women to meet companions:

[Some women] - you know their husbands have passed away and now they’re looking for something to do. They would like to have companionship but you can’t find one easily and so they go to the track and meet people.

Finally, one respondent offered a completely different assessment of gender differences in gambling activities:

I don’t think there’s any difference. It’s all about handing over the money.

The answers to question about why people from the British Isles gamble reflected a wide range of motivations, including a need for excitement, a desire to relieve stress or alleviate poverty, or a confession of gambling addiction:

It’s a thrill…I do it for the excitement, really. I mean I am not trying to fool myself. You know you can’t make a living out of it. But it gives you a thrill to win. It’s a thrill to win. For $2.00 you can own a horse for three minutes and you can scream on him to make him win.

I know doctors that gamble; you know they’re addicts and they are gamblers but they don’t drink and they say it’s stress related – it’s a stress release for them. That’s what they say.

I think that poverty has a lot to do with it, you know; it’s like the chance that you might get some money and be able to change your life, you know.

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I believe you are born with it or you are not. Because some people are introduced to gambling in their earlier years or teenage years and they had no interest in doing it. And other people, you just take them once, like myself, to the horse race and that’s it and you love it. It’s like an addiction.

When asked about what it would take to quit their gambling behaviours, many of these respondents adopted an individualistic attitude, although some conceded that counselling services and/or family intervention might prove useful:

If they had trouble at home, you know, then I think they would stop. Yes, I think that is the biggest factor or can be a great factor.

I don’t think anything is going to persuade you…once you’re a gambler, you’re a gambler. Let’s face it; unless you go for help you’re not going to quit.

Ah…it’s like any addiction. You’ve got to find your own personal boundary…Like I am in the stage where I never ask people for help and maybe a little bit of help might be useful to shorten my recovery to the stage that I am going to decide to quit. Once I’ve decided that, maybe some kind of counseling can help me to get there, okay, but I have to help myself.

The Chinese

As we saw in Chapter One, gambling in Asia has a long and complex history. The parts of Asia that have been influenced heavily by Islam generally do not gamble much, since it is condemned by the Koran. However, people in Eastern and Southern Asia – mainly from a Confucian tradition – love to gamble. Generally, the literature reports that Chinese people enjoy gambling and consider it a healthy social activity. Gambling games of many kinds have a long history in China itself. Historically, gambling was widespread among the “idle rich” as well as among the lower classes. By the sixteenth century, the streets of Beijing were occupied by thousands of beggars who lived from hand to mouth, often gambling at dice for a living. In areas governed by rural traditions, gambling continued throughout the first half of the twentieth century not only as a popular pastime, but also as a component of ritual and ceremony. In the People’s Republic of China today, casinos remain illegal today. However, there are signs that gambling may become more acceptable to the state government. When the island of Macao, long a haven for high-stakes gambling, was transferred from Portuguese control to Chinese authority in 1999, some speculated that the casinos would be closed. Instead, they were allowed to continue operating, and Macau today remains a popular Asian tourist destination (Pulsudski 1999). Similarly, studies of Chinese communities in Hong Kong and Taiwan frequently mention gambling and its broad public appeal. Gambling also plays an important economic role in contemporary Hong Kong, where in addition to mah-jongg, racetrack betting is a billion dollar industry. Even on the mainland, gambling remains a popular and socially (if not legally) acceptable activity. In Taiwan, the only legal form of gambling is by buying lottery tickets, and this has rapidly become a very popular form of gambling. Attitudes along with behaviours have changed in relation to gambling. Where previously frequent or extreme gambling in China was seen as pathological, it is now viewed as simply not a good thing to do. Excessive gambling may be morally suspect, but it is not considered a medical disease. To emphasize this change in perspective, pathological gambling has been removed from the Chinese Classification of Mental Disorders (The Wager 1997). For the Chinese medical community, gambling 150 is an activity in which participation is freely chosen and responsibility remains in the hands of the player, rather than an illness that one succumbs to (see, for example, Blaszczynski et al. 1998: 363). According to Luk and Bond (1992), Chinese people see pathological gambling as an anti-social activity rather than as a mental health issue. A pathological gambler is believed to lack a sense of responsibility to society and is understandably considered to be an undesirable marriage partner.

Current Research on Chinese Gambling The literature on Asian gambling sometimes makes the error of failing to specify the differences among Asian cultures (Cheung 1993). However, increasingly, there is a literature that addresses specific Asian communities, including the Chinese. Whether the research approach is an anthropological case study (e.g., Nonini 1992), non-representative sampling (e.g., GAMECS 1999; Blaszczynski et al. 1998), or a stratified random sample survey (IGSCG 2000), the findings for Chinese gamblers are fairly consistent. Hsu (1981) reports that Chinese people tend to gamble with others they know and meet together regularly. In short, gambling for Chinese people is a form of sociability. Studies of contemporary Chinese societies in Hong Kong and Taiwan often mention gambling and its broad public appeal. Journalistic accounts also cite gambling as a prominent economic activity in contemporary Hong Kong. There, in addition to mah-jongg, racetrack betting is an industry of economic significance. Nonini (1992) finds class differences in the ways that Chinese people view gambling. Working- class Chinese tend to favour lotteries and mah-jongg, but the business-class rarely plays either. This difference is explained by Nonini as a rational decision on the part of the business class, who do not wish to lose their capital in games of chance. In contrast, the working-class Chinese residents see lotteries as the only possibility of earning extra money. Chinese people view gambling as a pleasant social activity to be shared with friends and family, not as a form of escapism. It is also practiced during times of celebration and entertainment, to foster good luck and happy times. The Chinese take great pleasure and fun from gambling. Members of this community gamble optimistically – they expect to win – which is congruent with their use of gambling to celebrate, rather than to escape from unpleasant conditions. One Australian study, entitled “Gambling Among Members of Ethnic Communities in Sydney” (GAMECS 1999), used a snowball sample of 142 Chinese-speaking people in Sydney who gambled at least once a week, and concluded that gambling is an important and socially accepted part of Chinese culture (GAMECS 1999). Gambling was seen by 80 percent of respondents as “an important activity for members of the Chinese community,” compared to only 55 percent of the general Australian population (GAMECS 1999). Although the GAMECS study provides many insights into the gambling patterns and attitudes of Chinese-Australians, the reliability and validity of its results are hindered by its use of a convenience sampling technique. To correct these shortcomings, the Impact of Gaming on Specific Cultural Groups (IGSCG) Project Report (2000) surveyed a random sample from several ethnocultural communities in Victoria, Australia, using the language of the respondent’s choice. The researchers found that 62 percent of Chinese respondents agreed with the statement that “generally gambling is an acceptable activity in our community” and only 3 percent strongly disagreed with the statement (IGSCG 2000: 120). The study also confirmed that gambling is widely considered by the Chinese to be a social, rather than an individual or escapist, activity.

Rates of Gambling and Probable Pathological Gambling Volberg et al. (1996) report that among Chinese-speaking people in Australia, the prevalence of gambling is lower than it is in the general population of Australia. However, those Chinese-Australians who do gamble are more likely to have a gambling problem. Other studies have confirmed that the rate of problem gambling in the Chinese-Australian community is higher than the rate of problem gambling in the Australian population as a whole (see Dickerson et al. 1996), and is comparable to the Chinese 151 problem gambling rates found in Canada (CFSGM 1997) and Hong Kong (Chen et al.1993). Blaszczynski et al. (1998) surveyed a Chinese-speaking community in Australia, using a convenience sample of an area in South Western Sydney. Since the sample is not random, and the response rate was only 27.4 percent, the results cannot be considered representative of any Chinese group. However, they may be suggestive of trends that warrant further research. A majority of respondents in this survey were born overseas, mainly in South East Asia (most frequently Vietnam, Cambodia, Malaysia, and Laos). The mean length of residency in Australia was 11 years. Respondents who reported having gambled in their home country were more likely than others to have spent money on gambling in Australia. Almost 8 percent of respondents had a SOGS score of over 5 and 2.9 percent of respondents had a SOGS score of at least 10, indicating the presence of problem and probably pathological gambling, respectively. Those who had gambled in their country of origin are more often to be probable pathological gamblers. The researchers also found that those who have a gambling problem were more likely to report that they have a relative who also had a gambling problem. Despite these results, the majority of respondents described themselves as non-gamblers.

Gender Differences within the Community Chinese men and women reportedly gamble differently and experience different rates of problem gambling. In Hong Kong, differences in the prevalence of problem gambling among male and female populations are well-documented. Chen et al. (1993) reported on the community of Shatin in Hong Kong. Using the DSM-III criteria for measuring pathological gambling, the authors found that almost 3 percent of men were probable pathological gamblers, compared with less than 0.2 percent of women. Similar gender differences were also found by Blaszczynski et al. (1998). Males were also more likely to be problem gamblers according to a survey of users of family services in Montreal (CFSGM 1997). Gender differences in gambling behaviour among Taiwanese players have also been found by Chen et al. (1993) and by Hwu et al (1989). Note, however, that Blaszczynski et al. (1998) found a smaller than expected discrepancy between men’s and women’s rates of probable pathological gambling in their Australian sample, raising the possibility that the availability of gambling activities for women, and presumably the risk of developing a problem gambling habit, is greater in Australia than in Hong Kong or Taiwan. However, this does not explain the observed gap between male and female Chinese gamblers in Montreal, whose immigrant culture is similar in important ways to the one observed in Australia.

A Few Words of Caution Taken together, the literature described above suggests that among the Chinese, gambling (in moderation) is a popular, normative, and social activity that becomes a concern only when it threatens to become pathological. Even then, excessive gaming is considered a personal choice and a character flaw rather than as an addictive disorder requiring medical or psychiatric intervention. However, several caveats must be kept in mind when interpreting the results of these studies. Any form of research in which ethnic or cultural “others” form part or all of the subject pool is vulnerable to biases and flaws resulting from a lack of cultural sensitivity. For instance, Blaszczynski et al. (1998: 366) note that some Chinese-Australian gamblers do not consider lotteries to be a form of gambling. They found that while a majority of the respondents in their study claimed to have never gambled, some also admitted that they participated in lotteries. Lotto play alone, however, never led to a score of more than 5 on the SOGS. The finding that people who play lotto exclusively are not at risk of becoming problem gamblers is supported by congruent findings by Dickerson et al. (1996) for the entire Australian population. These findings appear to support the respondents’ assertion that, despite admitting to playing the lotto, they never truly “gambled.” Blaszczynski and his colleagues conclude that at least among this population, “lighter” forms of gambling are not considered problematic and therefore not really gambling at all. 152

In addition, language becomes an issue when the target subjects are not native English- speakers. Prevalence surveys often miss immigrant respondents who are uncomfortable or unfamiliar with the English language. Blaszczynski et al. (1998) translated their survey instrument into mandarin, and respondents were given the choice between taking the survey in English or mandarin. However, two questions from the mandarin survey were mistranslated, and the error was discovered only after the results were translated back into English. The verb tenses caused respondents answering the mandarin- language survey to answer differently from respondents answering the English-language survey. Blaszczynski et al. (1998) conclude that in cross-cultural, multilingual studies, questionnaires should be translated not only from x to y but back to x again prior to its administration, as a check to avoid mistranslations. Collecting data on particular ethnic groups may also be difficult. For example, Chinese people attach a stigma to psychological illness (Lin 1982), so respondents may be unwilling to admit to a psychological problem or addiction. Likewise, some immigrants, having experienced political oppression in their homelands, may distrust authority, including officious-seeming researchers. This may make them reluctant to answer survey questions that probe into their private lives (Pernice 1994). Blaszczynski et al. (1998: 374) suggest that network building with community groups may help to avoid some of these problems of trust: “[I]t is imperative that key stakeholders in the community under study be included at all levels of planning an implementation phase of surveys in order to ensure that as representative a sample as possible is obtained.” Stigma-related underreporting is a concern not only for researchers, but also for public health professionals. A failure to admit one’s gambling problem has an obvious impact on how effectively social and health services deliverers can provide treatment. The social stigma surrounding problem gambling discourages gamblers from both reporting and asking for help for fear of losing their sense of pride and self-respect (Lewis-Fernandez and Kleinman 1994). Fortunately, the importance of family in Chinese culture may provide an alternative avenue of treatment for those struggling with problematic gambling behaviours. In the Chinese community, people are more likely to seek help for problems from families rather than from professional outsiders (Tseng et al. 1995). Recognizing this, Luk and Bond (1992), among others, have argued for a tailoring of treatment programs that actively involve family members rather than isolate the gambler in one-on-one sessions with the therapist. A final challenge facing researchers is the lack of culturally-verified measurement instruments. The SOGS has not been validated for the Chinese population and may prove in the future to be an unacceptable tool in that culture. Another tool that has been specifically validated for Chinese respondents is the Diagnostic Interview Schedule (DIS) (Hwu et al. 1986a; Hwu et al. 1986b).

Chinese Gambling in Canada

The first Chinese arrived in Canada during the second half of the 1800s. They came primarily from the province of Guangdong in Southern China and worked in Canada as labourers building the railroad. They were predominantly single men, without the comforts and responsibilities of family life. Today, the Chinese are recognized for having built the cross-Canada railroad, which played an integral role in the expansion of Canadian territory out to the west in the late nineteenth century. Back then, however, the Chinese contribution to the Federation was greeted not with gratitude but with suspicion, fear, and hatred. Canadian authorities A succession of increasingly proscriptive immigration policies, bearing names such as the 1885 Act to Restrict and Regulate Chinese Immigration (popularly known as the Chinese Head Tax) and the 1923 Chinese Exclusion Act, ensured that labourers were prevented from bringing their families over from China, with the predictable outcome being that the demographic profile of Chinese Canadians during the first half of the twentieth century remained predominantly male, single, and aging. In 1951, four years after the Chinese Exclusion Act was 153 repealed, almost 80 percent of the Chinese in Canada were men. Of the male population, almost half were over 45 years old, and only 5.5 percent were children under the age of nine (Ng 1993: 54). Though the dominant (i.e., white) culture viewed gambling as wrong, in part because it damaged families, this attitude held little sway in the Chinese community, where families were demographically uncommon. Moreover, the racism explicit in the immigration policies of the government was matched by racism in other areas of life as well, including access to employment. As a result, Chinese Canadians may have been reluctant to adhere to the dominant group’s values that offered them no benefits. Gambling was viewed within the community as a lesser evil, a solace for the bachelor Chinese man, alone due to circumstances beyond his control. In fact, their demographic homogeneity was used as an argument in favour of the continued existence of gambling houses. By the 1930s, the young labourers who had come to British Columbia in the late nineteenth century had reached their golden years and, lacking families to care for them, were in need of community support. The provincial government, however, was not keen on adding to the list of welfare recipients foreigners who were mostly shunned by the rest of the community. Capitalizing on this ambivalence, an advocate for a gambling club that was being shut down argued that, without the club, out-of-work elderly Chinese men would have no place to stay warm. In this way, social clubs served the important function of providing these men with tea, beds, and a social network that rooted them to their community (Morton 2003: 123-124). The Chinese communities’ attitude toward gambling was a complex one. On the one hand, gambling and business in Chinese culture are intertwined (Morton 2003: 121). In North American culture, gambling is merely a leisure activity; in Chinese culture, gambling is a means of sociability and also a way of displaying wealth. A businessman who wants to display his success will likely gamble (Basu 1991: 228, 244-246). This kind of ostentatious display – which sociologist Thorstein Veblen termed “conspicuous consumption” – is common among many cultures past and present, as we saw in Chapter One. High-stakes gambling is understood as one of many signals that a person is well off, and able to afford to risk his wealth by gambling. This positive view of gambling is echoed in The Concubine’s Children, Denise Chong’s biographical account of her grandparents’ and parents’ lives in early twentieth century Nanaimo (Chong 1994). Historically, gambling was seen within the Chinese community of Nanaimo, British Columbia as acceptable, so long as the money eventually returned to the community (Chong 1994: 43). Of course, not all Chinese Canadians were equally in favour of games of chance. Like any group of reasonable size, there were degrees of tolerance and differences of opinion. However, it is clear that, as a whole, the Chinese were well at ease with the gambling in their midst. Chinese Canadians were also not unaware of the disapproval with which the dominant Anglo- Canadians viewed their gambling habits, leading to occasional tensions within the Chinese community between accepting their own reality and toeing the line of the white majority. In the 1920s, a graduate student of sociology at McGill University wished to research gambling in Montreal’s Chinatown. Realizing that a young, white academic poking around in a Chinese-run gaming den was unlikely to establish much researcher-participant trust, the prospective scholar asked a fellow student of Chinese background to infiltrate a gaming club and act as an incognito fly-on-the-wall on his behalf. The Chinese student declined, claiming that “it would be indiscreet to mingle with people who frequent the clubs” (Robert 1928: 80). Whatever its actual behaviour, the Chinese community faced pressure to appear to hold the dominant belief that gambling was bad. Selective policing reinforced the stereotype in Canada of gambling as a social problem created by the presence of “other” ethnic groups. In Montreal, the majority group – French Catholics – encouraged heavy policing of Jewish and Chinese gambling establishments (Morton 2003: 109). The technique of shaming, whereby the names of people caught gambling would be published in local newspapers, was used to discourage “respectable” citizens (i.e., whites) from frequenting gaming dens. 154

The Chinese community in Montreal also received much harsher than average policing. There, police did not follow the usual pattern that allowed people caught in gambling establishments to give false names (Morton 2003: 115). In Winnipeg, police used the same shaming techniques but protected gamblers of higher social status by offering them fake and “foreign” sounding names to be published in the press (Saturday Night 1918: 1). Any reader glancing over such a list of names could therefore easily draw the mistaken conclusion that all illicit gamblers were foreign-born. Anderson (1995), in her description of Vancouver’s Chinatown, considers gambling to be one of the many petty crimes that were policed in varying degrees of harshness depending on the ethnicity of the offender. Annual raids on the gambling and drug use establishments through the nineteenth and early twentieth centuries, reported diligently by local presses, ensured plenty of ammunition to justify the racist views held by many whites against Chinese Canadians (Anderson 1995: 101). For their part, the Chinese community rejected the “addicted Chinaman” stereotype. Anderson (1995) cites the 1901 Royal Commission on Chinese Immigration testimony of Won Alexander Cumyow, who acknowledged that there was a great deal of gambling in the Vancouver Chinese community but that:

some do not gamble for large amounts, but more commonly, the play is for amusement only and for small sums to pass the time as this is done in the common room of the boarding house. If a police raid is made and any are caught playing, all are arrested for gambling and looking on. If the same course were pursued in relation to white men, gamblers would be caught in barrooms and of course all who were at the bar would be arrested as onlookers (Anderson 1995: 101).

Two important issues are confirmed in Cumyow’s testimony: the predominantly social nature of Chinese gambling, and the discriminatory double-standard with which gambling-law enforcement was applied. In Chinese gambling houses, the gamblers were mainly Chinese and male. There was more racial and class mixing in the larger clubs and less of it in the smaller ones. One study of smaller clubs in Toronto’s “Lower Ward” district during the 1960s found most of the clientele to be of similar demographic profiles reflective of the working-class roots of the area (Garry and Sangster 1970: 286). Larger establishments in Toronto, Montreal, and Vancouver boasted a more diverse patronage, thanks to their more public visibility and their ability to translate higher revenues into more luxurious and inviting venues (Leung 1975: 20). By attracting a mixture of ethnicities and classes, gambling houses, in addition to violating the moral standards against public gaming, also broke down the social rules that usually kept disparate subgroups of the population from leisurely intermingling. Over time, changing demographics played a role in changing the attitudes toward gambling within the Chinese Canadian community. Morton (2003) notes that due to the Chinese Exclusion Act, there were two demographically different communities in Chinatown. Those who arrived after 1947 contrasted sharply with those already present in 1923. The new Chinese immigrants, who were better educated than their elders, disapproved of gambling, even by their elders (Ng 1993). Ng (1993: 63-64) notes that during the 1950s, an essay contest on the subject of Chinese recreation facilities resulted in two winning essays, both of which discouraged gambling. The justice system also perceived these class, sex, and age cleavages within Chinese Canadian society. Those Chinese who were deemed “good” by authorities lived out the middle class’s ideal life of hard work and family (Morton 2003: 127). As the Chinese community became more integrated into Canadian culture, it also became increasingly opposed to gambling, viewing it in a way that was congruent with the dominant culture’s value system. At the same time that these changes were occurring within the Chinese community, an enlightened Canadian government was also busy striking down at all legislative levels the 155 discriminatory gambling laws erected in the first half of the century. The events of World War II had taught many about the terrible consequences of racial persecution, and few government officials were willing to risk the accusation of discriminating against visible minorities. Selective police raids of ethnic gaming enclaves diminished in frequency, and in 1949, Vancouver municipal laws were altered so that Chinese Canadians could gamble legally (Morton 2003: 211 n. 28). However, not all games were permitted and conflicts between the police and the community continued (Morton 2003: 127). Current research on Chinese gambling in Canada finds a high rate of problem gambling. A non-random survey of the prevalence of problem gambling among Chinese residents of Montreal finds that only one-third of respondents had gambled in the past year. However, of the total sample, 4.7 percent were classified as problem gamblers and 1.7 percent were found to be probable pathological gamblers. Consistent with studies discussed earlier, gender differences prevailed: males were more likely than females to be problem gamblers. Problem gambling among Chinese immigrants were also associated with lower educational achievement and a lack of activity in the labour force. The longer someone has been a resident of Canada, the more likely he or she is a problem gambler (Chinese Family Services of Greater Montreal (CFSGM) 1997). This last finding was corroborated by a survey conducted in Toronto’s Chinese community, which found that Chinese people who had been in Canada for between 11 and 15 years had the highest rates of gambling involvement, compared to those who had either only recently immigrated to Canada and those who have lived here for more than 15 years (Kwan 1997).

From the At Home With Gambling Study: The qualitative data collected by this study provide an insightful glimpse into how individuals of a specific ethnic community perceive gambling and gambling problems. Interviews with members of the Chinese community in Toronto reveal that many still experience, or had experienced as children, gambling as a normal and common part of their lives. Chinese respondents in this sample largely agreed that mah-jongg was the most “typical” traditional Chinese game. Other games mentioned were card games such as “Pai Gow” and “Fan Tan,” and horse-racing:

Mahjong is very popular, that is, commonly played by the working class people, I mean those who are working at factory or the like.

It’s thrilling. Men and women, old and young, they all like to play this game despite educational background. Playing mahjong is acceptable to all. In China, these people include senior officials, intellectuals, housewives, aged ladies, and those without jobs. It seems people from all walks of life can accept this form of gambling.

One respondent pointed out that mah-jong was usually played at home and among friends, as compared to the more public games typically played in Canada. It was also noted that in Canada, most people gambled against the casino – that is, either against a dealer or the mechanized wheels of a slot machine, neither of whom are known to the player. Even in casino card games such as poker, where one gambles against the other players at the table, the relationships are usually anonymous. The difference between this kind of “individualized” and “public” gambling in the West may help explain why the Chinese respondents so often point to the damaging affects that social gambling can have on families and friendships:

The difference is that Chinese people normally play mahjong and poker with friends and each time they need four people to play along. In Western world, however, people gamble on their own. They play slot machines themselves, or they play with dealers. I think the Western ways 156

of gambling are more reasonable. Chinese people gamble against their friends and this more or less harms the friendship among these when some of them win but others have to “say Uncle.” Such a situation doesn’t happen among Western people because they don’t gamble against their friends.

About a third of the Chinese adults respondents believed that there are no gender differences with respect to gambling. Occasionally, however, gendered ideas were expressed:

Generally women are more sensible and disciplined than men.

Women are more careful and consider their family and others.

It’s normal for men to gamble since they work outside.

For those who believe that there are gender differences, these were most often framed in terms of levels of “daring” or risk-taking, although this quality is more often attributed to both male and female gamblers:

Men generally like taking risk so they are more likely to [be] involved in gambling.

Not too many women gamble, but if they do, they could be more daring than men.

They are both very daring when they gamble.

Men and women could be equally daring and become desperate gamblers.

Most men go to the casinos and the horse racing fields. Most women play mahjong. But if they are addicted to gambling, they could be more daring than men. They could forsake their families.

Men are more daring gamblers.

They are both very daring when they gamble. I saw a woman in the casino. She placed a bet of $8,000. She lost it. She put another bet of $8,000. Even those working in the casinos were stunned. When a gambler sits at the gambling table, he or she does not even remember his or her father’s name.

The theme of “daring” arises in other respondent’s discussions of gambling, for example, another respondent states:

Westerners are not as daring as the Chinese in gambling.

The theme of women being the caretakers in the family emerges as a caution against women gambling too much or risking too much money:

Women seem to be smaller gamblers than men. This is probably because they care for their families and are afraid of losing too much money.

If they become obsessed, they can’t manage their money and they would not care about their

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families.

When women gamble, they don’t care about their families.

I think there are differences between men and women in gambling behaviours. Women are usually more careful and tend not to bet a big amount of money, and they are considerate and care for their kids. They won’t forget to look after their children when they gamble. I noticed some women who were playing mahjong. They ended it quickly when they knew their children were coming back. Men are different. They tend to put big amounts of money. They are likely to take a risk and forget their children.

As these and other answers show, strong and often positive linkages are made between daring, or “risk-taking,” and gambling; however, the links between risk-taking and the loss of family, or not caring about children, are very negative. When asked why people in their culture gamble and what would persuade them to stop, Chinese respondents spoke of the changes in leisure time after immigration to Canada, the long history of gambling in Chinese culture, as well as the degree to which gambling patterns are passed down in the family:

There are few places in China for entertainment. Before 1985 we only had one day off each week and had to occupy the time for housework. Now it’s two days off per week. To make up for it being difficult to find places for recreation, colleagues, friends and relatives sit together play mahjong and poker followed by a meal.

Gambling in China has a long history. This is partly because there are not enough ways of entertainment. Also gambling has become trendy as a kind of recreation activity. Gambling behaviour also passes down from one generation to another. Many families have gambling history.

Some suggest that gambling is a status symbol, indicating financial success, class difference, as well as a way to make money:

Well, people who are gambling are those who have more time, and want to make a fortune, like upstarts, and earn more money in the shortest time and without hard working.

I think to Chinese, people are gambling for money. But it depends on the social classes. Rich people, they have more money; they seek stimulation and excitement; they go to gamble.

Many people believe showing up in a casino is a symbol of distinction. Many rich people go to casino, eating, drinking and playing.

Some interviewers, as key informants, suggested that people with a Chinese background gambled because they felt that life was otherwise dull. In addition, a language barrier prevented some Chinese from communicating effectively with people from other cultural backgrounds. Gambling in a casino – which is purpose-built to be sensually stimulating, and which requires little verbal communication on the part of its players – might therefore seem to be an ideal solution for these particular gamblers. In terms of what would make them quit, respondents pointed to the need for the community to find alternative ways to entertain themselves. They also indicated that the threat of family destruction might be a sufficiently dire consequence to make someone quit gambling. Finally, they recognized the 158 addictive nature of some forms of gambling, suggesting that it represents a serious problem that requires outside help:

Gambling is a kind of thrill. Unless better ways of entertainment are found, gamblers will remain unmoved to persuasion.

I think the best way of helping people to quit gambling is to let them know the consequences of gambling, which often lead to a broken family. It is a disaster for the family, and also for family members.

Aboriginal People in Canada

Aboriginals in Canada More so than for any other ethnic group, an understanding of Aboriginal gambling in Canada requires first an understanding of the historical relationship between Native peoples and white Europeans settlers. Aboriginal ancestors arrived in the Americas by some estimates as far back as 13,000 years ago, on foot across a land bridge over the Bering Strait, which separates Siberia from Alaska. No written records survive from the earliest periods of settlement. However, most experts agree that in North America, many of the Native peoples became hunters and trappers, food-plant gatherers, or fishermen. They lived in small communities or bands that varied widely in cultural practices and beliefs, but which nevertheless had certain structural features in common. These included a close tie to nature, a migratory lifestyle with no land ownership, little division of labour or inequality, no capital or wage labour, small settlements and no cities, and oral cultural traditions (i.e., no written record). Despite differences in language and culture, Native peoples lived in small, stable communities whose members had similar personal histories and intimate enduring relationships. A strong sense of belongingness, along with moral custodians who stood guard over the group's traditions, promoted the sharing of moral values. Particularly important was an ethos of sharing. Rights to conduct commercial relations with other tribes were marks of distinction for a family, but the true measure of prestige and worth within a community was the ability and willingness to distribute one’s wealth to others. Indeed, generosity was not so much a virtue as it was a requirement – greed and self-interest were condemned by community members. The trade of commodities was a well-established practice among First Nations peoples long before European incursions. What differentiated the two groups was intent and motive – Indians were interested in communal prosperity, while Europeans were interested in private acquisition and profit. After several abortive attempts at exploration by the Norse at the turn of the second millennium C.E., contact between “Europeans” (more specifically, Basque whalers, English fishers, Dutch traders, and French missionaries) and the people of the Americas began in earnest in late fifteen century (Miller 2000). The Europeans – in Canada, primarily the French, led by Jacques Cartier in the sixteenth and seventeenth centuries – were motivated to establish and maintain permanent and harmonious relations with Natives in the New World by four objectives: fish, furs, exploration, and evagelization (Miller, 2000). Meanwhile, early attempts at colonization, of laying claim to the land in the name of France, were strongly opposed by the Indians, who made clear that they were the natural possessors of the New World. Trade and even exploration into the interior were acceptable, but colonization was not. Old World ships first appeared in the waters off Newfoundland in the fifteenth century in search of fish products (the Christian calendar contained 57 fasting days in addition to Fridays and Saturdays in which believers abstained from consuming meat, resulting in a European diet highly dependent on fish). Later, explorers arrived seeking a safe route to Asia via the waterways of the New World. Fur trading, which many associate with early European-Native contact, was established initially as a

159 secondary enterprise during the pursuit of these two primary aims. The commercial relationship was at first mutually beneficial for both Europeans and Native peoples. For French fur traders, the most coveted pelts were known as castor gras d’hiver (greasy winter beaver), which were beaver furs that had been taken in the winter and worn for a year by Indians (the year’s wear-and-tear removed the pelt’s coarse guard hairs, leaving behind a supple, smooth layer of downy, felt filaments much prized by French haberdashers and their clientele). For the Indians, mirrors, glass beads, and iron products such as kettles and knives, which were until then unknown to them, were highly desired. The only metal used by Indians before the introduction of iron was copper, which was sufficiently malleable for decorative purposes but was otherwise too soft for more practical usage, such as in the construction of containers and arrow heads. Objects constructed of iron and steel were more durable and efficient. Glass objects and mirrors, in addition to serving a variety of utilitarian functions, were also revered as fetish objects in Native religious ceremonies. Both sides, then, must have felt that they were trading with fools – on the one hand, Indians obtained new decorative and utilitarian items that greatly improved the quality of their lives in exchange for was to them used, worthless hides; and on the other, the French received an enthusiastic and lucrative supply of high quality pelts in exchange for simple, cheap, and taken-for-granted technologies like mirrors and iron pots (Miller, 2000). Early trade between Aboriginals and Europeans was mutually beneficial, even amiable, but it was not without conflict or harm. Although Native peoples undoubtedly benefited from European technology, commercial trade was for them a mixed blessing. Along with metal tools and ornamental novelties, the French and English brought to the New World alcohol, guns, and disease. Alcohol – introduced to Indians in the Maritimes and along the St. Lawrence Seaway in the form of French brandy – created a social problem, but not a severe one initially. Certainly, alcohol fuelled some violence within the Native population, but the close-knit nature of the community ensured that alcoholism remained a rare occurrence. On the role of alcohol is undermining Indian solidarity, scholars have offered diverging perspectives. Some have argued that alcohol led to the erosion of First Nations society, breaking down traditional systems of authority and connectedness. Others have suggested that Aboriginal culture was already in the process of disintegration, and that escalating rates of alcoholism were merely symptoms of a greater social malaise. At best, one can state with certainty only that alcohol was associated with a depressed and declining Native society. The direction of causality has not yet been established, and indeed may never be known. Guns would also prove to be an important introduction into Indian society. As weapons for hunting, they were a great resource for fur trappers who needed to ensure a constant supply of pelts for European buyers. As weapons for war, however, they were also grimly effective. Native peoples were not strangers to war, but prior to the availability of firearms, conflicts between hostile tribes or bands were fought with bows and arrow, clubs, and knives. These weapons, though potentially deadly in the hands of a skilled warrior, were by design incapable of bringing about mass devastation. Fighting between warring Indian tribes was more often than not a local and small-scale affair, conducted using strategies of stalking, ambushes, and skirmishes, in contrast to the marching armies and battlefield tactics of European militaries. The introduction of the musket, however, allowed for a level of warfare previously unknown to Native life. Still, given the inaccuracy of these early guns, it is important not to overstate their devastating potential. Not until the nineteenth century would improved firearms offer Natives a reliably deadly alternative to their indigenous weaponry. Perhaps most devastating to First Nations society was the inadvertent introduction of Old World diseases for which they harboured no immunity. By some estimates, measles, smallpox, influenza, yellow fever, and other diseases killed half or more of the 200,000 to 300,000 members of the Aboriginal community in Canada during the seventeenth, eighteenth, and early nineteenth centuries (MacInnes, 1945). Across the Americas, European diseases did more to weaken Native opposition to conquering nations than did any invading military’s battlefield victories (Diamond, 1998). 160

In Canada, the Huron lost upwards of half their numbers, primarily the very young and the old, to measles between 1634 and 1640, as a consequence of welcoming Jesuit missionaries and French traders into their towns (Miller 2000: 58). To lose the youth and the elderly in such high numbers was particularly devastating for Native society, since the former represented the future bearers, and the latter, the preservers, of their traditions and cultural heritage. In effect, diseases did much to splinter First Nations peoples from their history, cutting them off from their past and their future. While the Spanish army was busy annihilating the ancient empires in the south, Aboriginals and Europeans in Canada continued to conduct trade in relative peace through the sixteenth and much of the seventeenth centuries. But harmony and partnership were not to last. Cooperation turned slowly to coercion (Miller 2000). War began brewing between European powers in the eighteenth century. Both French and British military officials, preparing for eventual war against one another, advised a strengthening of all battlefronts, including North America. Military alliances with the natives, then, became an important part of the overall war strategy for both parties, for several reasons. Firstly, despite a ravaging of their population by foreign-borne diseases, Natives still vastly outnumbered Europeans in Canada, provide a ready source of militia for the two European rivals, neither of whom were eager to dispatch a large commitment of their own ranks overseas. Secondly, Natives were familiar with the land, well-adapted to its terrain, and experienced in the guerrilla-type warfare that best suited battle in the wilderness. Together, these two facts made the Indians a formidable military presence. Military partnerships, which played only a minor role in the primarily economic and commercial relationship between Natives peoples and Europeans in the sixteenth and seventeenth centuries, became much more prominent in the eighteenth century. The British eventually proved victorious in the Seven Years’ War against the French, and the signing of the Peace of Paris and the Royal Proclamation in 1763 laid down the foundations for a new policy towards First Nations groups. The territory of New France was partitioned out between Indian tribes and colonial agriculturalists, and the British set up authority along the towns of the St. Lawrence. Trade, exploration, and evangelization resumed once more, but the relationship between Indians and Europeans were forever altered by this period of military intervention. French Canadiens, the primary trading partners of many Native communities prior to the war, had been perceived as less of a threat to Native life, since they were seen to be present in the New World for strategic and commercial purposes only. The British, meanwhile, appeared more interested in agricultural settlement and hence permanent colonization, a notion that Natives, with their close spiritual and practical ties to the land, were particularly wary of. Their suspicions proved correct. With the French presence greatly diminished, British authorities no longer needed to maintain a military partnership with their Native allies. Instead, Indians were increasingly viewed as an obstacle to agricultural expansion, and policy-makers began considering courses of action to remove this complication. Two solutions immediately presented themselves: annihilation or assimilation. Annihilation was ruled out because of cost (economic and human) and opposition from overseas humanitarian groups, and also because of a history of peaceful negotiations between Indians and Europeans in British North America. Unlike the more violent actions occurring to the south, relations between whites and Indians in New France had always been relatively amicable. Mindful of the problems that would result from a unified Native rebellion, British authorities chose instead to adopt a strategy of assimilation. If Indians could not be exterminated, they could at least be made to disappear within the folds of a European-based society. Of course, what the British considered “assimilation” was in fact an attempt to eradicate an ancient culture and coerce a people into adopting its Anglo-European values, beliefs, and social structures – violence of a different sort. Such a program was made possible because white immigrants arriving from Europe and America were beginning to outnumber the indigenous population. Furthermore, dealings with Native communities in the past were conducted with a diplomacy that 161 recognized the various Indian tribes as powerful and equal nations. With the dissolution of military alliances, however, and a growing white population that reduced the Native groups to a minority in Upper Canada, British-Canadian civil authorities adopted a paternalistic policy that would concentrate the indigenous population onto reserves, create a system of schooling to instruct them on the European way of life, and subject them to the Christian doctrine. Over the next several centuries, a series of treaties, commissions, statutes, and legislations were enacted to further these aims. Still, here as well, caution must be taken in making conclusions about the treatment of Aboriginals by non-Aboriginals. It is often claimed that whites arrived in Canada and immediately destroyed the Native way of life, instituting a system of oppression that stole Aboriginal children from their parents, forcefully removed indigenous peoples from their lands, and crushed any opposition standing in the way. This popular interpretation, while perhaps containing a grain of truth, nevertheless reduces Native peoples to passive players whose outcomes are entirely beyond their control. A more accurate depiction of history requires a more nuanced analysis to adequately captures the complex reality of that period of time. Natives were on the whole enthusiastic about their children receiving a Euro-Canadian education, at first in the form of day schools and later in the form of residential schooling. They recognized that their homeland was in the midst of change, and that education into the encroaching whites’ way of life was necessary for their continued survival. The ability to converse in English, an understanding of their beliefs and history, and a knowledge of their technologies were all useful tools that would allow the Indian clans to adapt to their new neighbours. What the Native chiefs had no interest in was assimilation. They desired an education to improve, but not to fundamentally alter, their traditional way of life (Miller 2000). Hindsight tells us that residential schools had the hugely negative effect of isolating Native children from their parents and elders, which represented both a symbolic and real severing of ties to their histories. Run by missionaries who incorporated their own evangelical agendas into the curriculum, these schools were intended to erase pupils’ indigenous roots and erect in their place a thoroughly Euro-Canadian identity. In addition, after decades of silent suffering, stories of rampant sexual and physical abuse of Aboriginal children have begun to surface in recent years, an additional and devastating injury to an already unjust and unbearable situation. The high rates of alcoholism, drug abuse, and suicide among Aboriginal communities, which far exceed national norms, must be viewed within the context of this near-epidemic of childhood and pre-adolescent trauma. The Act for the Gradual Civilization of the Indian Tribes, passed by the Canadian legislature in 1857, further attempted to assimilate Natives into Canadian society by preparing the groundwork for the eventually enfranchisement – that is, the dropping of Indian status and the adoption of British North American citizenship – of First Nations peoples. In the process of conferring full citizen status to Indians, this Act also had the effect of defining Indians as non-citizens. The Gradual Civilization Act also dismissed the First Nations desire to retain the right to hold their land communally, paving instead a path that would lead to the partitioning of Indian reserve lands into individually held units, to be appropriated slowly for Canadian agricultural settlers in “land surrender treaties” at a later date. The significance of this legislative direction lies in the fact that Native identity and culture is intimately tied to their land. Across the world, indigenous populations have conferred special status to the natural surroundings that sustain them. Mother Nature is indelibly weaved into their rituals, their mythologies, their cultural beliefs and practices. Communal sharing of land reflects a cultural standard that acknowledges the sanctity of the earth. To own land is to claim supremacy over it; in contrast, the communal sharing of the environment’s natural resources is a statement of Native beliefs about their roles within the overall system, as simple stewards of a land that cannot be owned. By the arrival of the twentieth century, Natives had become largely irrelevant to both the Canadian peoples and their elected government in Ottawa. Since Confederation, Parliament had 162 continued to pursue the same policy of assimilation that began over a century ago, and if anything, had intensified their attempts to “civilize” the indigenous populations. Indian cooperation in these programs had long since turned into resistance and in some cases, outright defiance, as in the Louis Riel-led Red River Resistance of 1869-1870 and the bloody North-West Rebellion of 1885. The 1880 amendments to the Indian Act established the Department of Indian Affairs (DIA) and imposed an elected council on First Nations peoples irregardless of their interest in formal politics. This sort of meddling in the political affairs of Aboriginal communities was greeted with resistance and protest, but the Canadian government had momentum and history on its side. World War II marked a turning point in ethnocultural relations in Canada, and indeed, in much of the world. The reality of the Holocaust, the scale of which was understood fully only in the years after hostilities in Europe had come to an end, had at least the redeeming effect of highlighting the offensiveness and indefensibility of Canada’s own discriminatory policies towards certain ethnic minority groups. At the social and economic levels, Natives in Canada were living as second-class citizens. The high rates of poverty, incarceration, and social deviance experienced by Indians on and off their reserves was becoming a problem that could no longer be ignored by the federal government. The 1969 White Paper on Indian Policy, released by then-Indian Affairs Minister Jean Chrétien under the Trudeau government after a year’s consultation with Aboriginal chiefs, was intended to address these issues. Like so many pieces of social legislation, however, it was both well-intentioned and massively flawed. It argued that the social and economic problems facing First Nations peoples were the result not of past government policy mistakes, nor of generations of explicit racial discrimination, nor of systematic physical and cultural marginalization, but because of the special legal status conferred to Indians. The solution, then, was to gradually do away with recognizing the distinct status of Native Canadians so that they can immerse themselves fully in the web of Canadian life. Such a conclusion, however, was merely a thinly disguised restatement of the assimilation policies first introduced by British-Canadian authorities in the early nineteenth century, one that completely disregarded the wishes and interests of Aboriginals to improve their collective lives while preserving their cultural identities. It was an answer to the Aboriginal question that simply ignored the Aboriginals. For the Indian leaders, the proposal was deflating, proof that little had changed in over a century of political interference by Ottawa. What they were demanding, increasingly vocally since the early 1970s, was the right of self- government, and along with it the control over their social welfare and their children’s education. The story of the Alkali Lake Reserve in British Columbia, for example, provides moving evidence suggesting that this solution works. The approximately four thousand Natives living on this impoverished Indian reserve went from 100 percent alcohol addiction in 1971 – in large part the consequence of decades of physical and sexual abuse experienced at residential schools during childhood – to 95 percent sobriety in 15 years. The successful method of treatment included addiction counselling by Natives, sharing aboriginal experiences, re-learning the traditional culture, and practicing aboriginal rituals. Such community formation is best accomplished through self-government, which promotes Native solutions to Native problems, faster recovery of Aboriginal traditions, less outside interference by outsiders, and Native economic development. Moreover, much sociological research on First Nations peoples (c.f. Frideres; Satzewich) has looked to the role of the state and state policy in perpetuating dependency as a cause of aboriginal deviance. To these observers’ eyes, Native culture was not so much destroyed as it was prevented from adapting to a new way of life. The ability to self-govern would be a first step in allowing this long-overdue evolution to take place.

Native Peoples Today Today, the government recognizes three main groups of Canada’s native peoples: Indians, 163

Eskimos, and Métis. Each of these groups includes several subgroups. The first main group are registered or status Indians, who belong to bands. They fall under the rule of the Indian Act. Indians who lack band membership can still sign up with the Department of Indian Affairs and Northern Development. About 70 percent of Canada's roughly 620,000 status Indians live on one of nearly 2,300 reserves. Most of the other 30 percent have moved to cities, in search of better economic opportunities. A subgroup of Native people, non-status Indians, lost their Indian status through marriage to non-Indians or as the children of such marriages. There were fewer than 100,000 such Indians in the Canadian Census of 1991 and many do not live on reserves. Bill C- 31, passed in 1985 by the federal government, allows some non-status Indians to regain their status. So far, about 70,000 have done so. A second main group, the Eskimo people, include the Inuvialuit of the western Arctic and the Inuit of the eastern Arctic and Labrador. Most of these roughly 57,000 people still live in small Native communities. Unlike the Indian peoples, they have not moved to big cities in large numbers. The Inuit people have a cultural system unlike that of most Canadians. Even their system of writing differs from the main Canadian system. Like other aspects of their culture, this difference makes it hard for people to converse across the Native/non-Native divide. The Inuit are the most distinct of Canada's Native peoples. A third main group, the "Métis," are the offspring of Native-French couples who never obtained registered Indian status. The 1991 Census counted fewer than 100,000 Métis. Most live in small rural communities or among the non-Native people of larger communities. Although few live in ethnically separate communities, the Métis maintain an identity that is distinct from that of Indians and Eskimos. Irrespective of their group membership, many Native communities across Canada continue to experience social and economic conditions usually associated with Third World conditions. A 1998 study by the Department of Indian Affairs found that when United Nations’ quality-of-life criteria were applied to status Indians, the standard of living on reserves was closer to that of Brazil than of Canada. Signs of depravation and inadequacy are ubiquitous. For example, Natives receive little schooling and few obtain a post-secondary education. They are much more likely than white Canadians to live in poverty and their urban unemployment rates are higher than average. They make up a significant percentage of Canada’s homeless. On reserves, many of their homes fail to meet national health and safety standards. Overcrowding is common, facilitating the spread of infectious diseases through the population. Native peoples are much more likely than other Canadians to die of infectious (especially respiratory and gastro-intestinal) diseases, or from accidents, poisoning and violence, and less likely to die of chronic or so-called “lifestyle” diseases, such as cancer or stroke. Finally, Native peoples also have the highest rates of alcoholism, suicide, and crime in Canada. Given these conditions, rates of mortality are much higher than the national average, and life expectancy at birth, much lower. At the same time, the Native population has grown very quickly through high rates of childbearing. As a consequence, it has become the youngest of Canada's population groups, and some of the troubles it faces today – such as violence and suicide – are due in part to the large fraction of young people. Perhaps most indefensible of all is the fact that these problems have been present for over a century, during which time the federal government did little to aid the peoples whom they praised in their political rhetoric as “citizens plus.” Federal and provincial governments have made several recent attempts – somewhat half- heartedly, many believe – to deal with these problems. For example, the federal government provides a range of services at almost no cost to Native peoples. But, for the most part, these efforts have failed. In return for the "handouts" they get, the Native peoples pay a high social cost – primarily, disrespect from the white society and little control over their own lives. As well, they confront barriers that others do not. For example, because they don't own property on reserves, they don't pay property taxes. Yet, the flipside of this supposed “benefit” is that they also cannot secure mortgages or business loans, 164 without which, home-ownership and economic improvement through entrepreneurialism are virtually impossible. Many Natives continue to feel that Canadian government has little commitment to solving their problems, and little idea of how to go about doing so. The five volume Report of the Royal Commission on Aboriginal Peoples, published in November of 1996, called for sweeping changes to the treatment of Native peoples. Among other things, it recommended an acknowledgment of past mistakes by the Government of Canada, an inquiry into the effects on Native children of residential schooling, an inquiry into the harm done to Native peoples by relocations, the overhaul of land treaty and land claim settlements, arrangements to stimulate Aboriginal economic development and, perhaps most important, measures to establish Aboriginal self-determination (including the creation of an Aboriginal parliament called the House of First Peoples). In their formal response to these recommendations in 1998, Parliament reiterated the report’s call for the turning of a new leaf in Aboriginal/non-Aboriginal relations, apologized for the scandal of residential schooling, and promised to commit new federal funding to improving the lives of Native peoples across Canada. Whether this marks a genuine and lasting effort towards a tolerance, cooperative, and equal partnership between the Canadian government and Aboriginal groups remains to be seen.

Aboriginal Gambling Gambling has a long history in Aboriginal culture, with its origins in the quasi-religious ceremonies of ancestral tribal groups. In cultures like the Iroquoians, ritualistic gambling was an integral part of the ritual practice and was believed to influence the outcome of the ceremony (Salter 1979). Gambling for more recreational purposes was also practiced by Native members, though the distinction between ritual and leisure was never made explicit. For these pre-Industrial societies, the spiritual and the profane were simply two sides of the same coin. Contemporary gambling in an Aboriginal context has both supporters and critics. Proponents often emphasize the economic benefits derived from Indian-run casinos, pointing to success stories like the Oneida and the Mille Lacs band of Ojibwe, which have used tax-free revenues from gaming facilities to fund social health and welfare program for their respective communities (see Wardman, el- Guebaly, and Hodgins 2001). Similarly, the Foxwoods Resort and Casino in Mashantucket, Connecticut enjoys a reputation as one of the most successful examples of its kind. Opened in 1992 by the Mashantucket Pequot Nation, the sprawling 315,000-foot complex offers 350 gaming tables, 5,800 slot machines, a 3,200-seat bingo hall, and a Keno lounge, as well as numerous restaurants and bars, a 4,000-seat arena, theatre, arcade, concert hall, nightclub, shopping mall, and health spa. The resort, which currently employs over 11,000 employees, has generated enough revenue for the tribe to buy back reserve territory. Foxwoods has even been credited with luring back the tribal population, many of whom had left in the 1970s in search of better economic opportunities in urban centres. At one point, the reserve was populated only by two elderly women; today, the territory is again thriving, self-esteem and pride are high, and the economic outlook for the Pequot Nation remains bright. The general argument behind these success stories runs as follows: casinos operated by First Nations peoples on reserves stimulate local economic development, create much needed jobs for Indians, attract outside commercial investments, and generate revenues that allow Aboriginals to become less dependent on federal and provincial aid, and by corollary, increase their political autonomy. Ultimately, according to this perspective, gambling operations – the “New Buffalo,” according to some – offer Natives the same independence that the famous buffalo herds once did (see Kelley, 2001; Napoli 2002; Wardman, el-Guebaly, and Hodgins 2001). Indeed, the monetary potential is substantial – in the United States, where the reservation gaming industry has a more established history than in Canada, annual revenues from Indian casinos have increased from $212 million in 1988 165 to over $6.7 billion in 1997 (National Gambling Impact Study Commission 1999). Anders (1996) estimates that the Fort McDowell American Indian casino in Maricopa County, Arizona, has been responsible for generating nearly 2,500 new jobs and over $80 million in regional output. The presence of an on-reserve casino can also be a boon for local and state coffers. Since 1993, the Foxwoods Resort has generated from its slot machine revenues alone over $1 billion for the State of Connecticut. On the other side of the fence, critics, while acknowledging the financial incentives of Indian- run casinos and the importance of reliable revenue in improving the Aboriginal condition, express concern about the potential harm that could result from introducing gambling opportunities into an already vulnerable population. In general, there is public support across Canada for the licensing of gambling facilities on First Nation reserves. In a nation-wide survey of Canadian attitudes towards gambling, Azmier (2000) found that 52 percent agreed with the statement that “governments should license gambling on Aboriginal/Indian Reserves.” However, the same report found that only 45 percent believed that gambling would create economic opportunities on Aboriginal territories. A study of attitudes in the Western provinces concurred, finding that nearly 3 in 5 respondents did not believe on-reserve casinos would benefit Aboriginals (cited in Kelley 2001). Nor are all Native tribes amenable to the idea of opening casinos within their jurisdictions. In the 1990s, for instance, the Navajo Indian Nation rejected via referenda on two separate occasions proposals to build Indian-run casinos on their reserves, citing concerns about the negative impact of gaming on their youth development, their cultural values, and their Native identities. Opponents of Native-run gambling have pointed to crime, community conflict, cultural deterioration, and a loss of traditional values as some of the negative outcomes that result from allowing gaming facilities onto reserves. Peacock, Day, and Peacock (1999) also report that some tribal members have expressed anxieties over the growing materialism among Aboriginal youths, which stands in stark contrast to the traditional values of cooperation, generosity, and communality that once characterized First Nations culture. More immediately, concerns have been raised about the observed increases in gambling problems among Aboriginals subsequent to the opening of on-reserve casinos, a negative consequence that outweighs any economic benefit that such facilities might produce. A literature review of gambling among North American Native adult populations found prevalence rates ranging from 5.8 percent to 19 percent for problem gambling and 6.6 percent to 22 percent for pathological gambling (Wardman, el-Guebaly, and Hodgins 2001). Studies have also found that Aboriginal adolescent gamblers are more likely than other groups to gamble on a frequent basis (Wardman, el-Guebaly, and Hodgins 2001; Stinchfield 2000). In one such study, a sample of 78,582 Grade 9 and 12 students enrolled in Minnesotan public schools were given an anonymous questionnaire that covered a variety of demographic and health related issues. The author reported ethnic differences in gambling patterns: American Indians were more likely to gamble on a daily or weekly basis than were either Caucasians or Asian Americans (Stinchfield 2000). Other researchers have replicated the finding that the level of problem gambling among Aboriginals is significantly higher than that of the Canadian population as a whole (e.g., Auger 1999). In general, Aboriginal people show higher rates of problem and pathological gambling, poorer mental health status, as well as higher rates of substance-related problems compared with the general population. Although research on this point still needs to be conducted, it is probable that these outcomes are related, and that as a cluster of individual pathology, is itself related to the socioeconomic depression experienced by many Aboriginal communities. Reservation life has been linked to a greater risk of pathological gambling (Hewitt 1994). The specific aspect(s) of life on reserves that contribute to higher rates of gambling problems have yet to be elucidated, but it seems reasonable to speculate that the lack of educational and recreational opportunities, the shortage of income and employment sources, 166 and the geographic and social isolation of reserves from the rest of Canada can increase one’s susceptibility to gambling addiction. The breakdown of extended family ties, the repercussions of abuse experienced in residential schools, and the systematic discrimination levelled against people of an Aboriginal background are also potential contributors that warrant further investigation.

From the At Home With Gambling Study: Aboriginal respondents in the qualitative study by Tepperman, Korn, and Lynn’s (2002) acknowledged that many members of their group gamble:

I think a lot of aboriginal people gamble a lot. You can ask anybody, just about anyone, just about any aboriginal person.

I don’t know, I do hear there are a lot of people that do have a problem; we hear people talking about it.

Several non-problem and moderate gamblers among the adult Aboriginal respondents argued that there was a unique historical and cultural context to Aboriginal gambling:

[Traditionally, there were the] stick games, like when they had celebrations in the north-west, they would have pow-wows, they would have a log, and a lot of these people would sit around and each one would have a stick, it is like the log was a drum. They played a song on it, and with the other hand they would pass a baton around, like behind, and they would bet on where it would stop, when the song was over. There are lots of games, like the moccasin games, you know the old shell game. They would take three moccasins and put something under it and shake it around and people would bet on it.

Others reported that gambling was not traditional and indigenous, but rather was introduced by Europeans in the early days of contact:

I think that for First Nations people, cigarettes, pop, alcohol, gambling, all of those things are very addictive behaviours. For First Nations people, if you look at history, First Nations people never really had …these kinds of behaviours. Nothing against Europeans, but when Europeans came it seems to be something that was in their backgrounds for many, many years. So it was something introduced.

Often, however, these practices are traced to the relatively new presence of casinos on the reserves:

Until the casinos went up I hadn’t heard anything.

I think since they put the [casino] there, it’s more accessible.

Many of the respondents raised the topic of on-reserve casino presence spontaneously and with mixed feelings:

Yeah, well you’ve got the casinos on the Rez; it puts a lot of people to work, it spreads money onto different reserves, they don’t touch every single one, but they touch a lot so that’s good for making money.

Now with the casinos, we have a lot of things we didn’t have before. We have cocaine dealers 167 now. We had one of the workers, when the [coffee shop] was opened, while she was putting out the garbage, she was raped and beaten. That was right down in the heart of [town]. That person got away. They never found him. It’s almost like we are made spectacles of…Of course a lot of good things have happened too, like we have a water treatment plant, we have a dump, we are all on the same water line – water was a real problem before – and we have a day care for the school, and the arena for the kids and stuff. But when you weigh down both factors, my fear is that one day something is going to happen to one of the kids and I don’t think the price is worth it.

Some respondents also emphasized the link between gambling and poverty:

I think that the majority of aboriginals are living in poverty, they have this hope of getting out of that…having money for once, so gambling probably does appeal to them.

I think a lot of First Nations people see gambling as a possible way out of poverty.

Among the severe adult gamblers, several respondents linked gambling to other addictions like alcoholism, and these two problems to feelings of alienation and a desire to escape:

Some of them are just lost. Because I know like, when I see some of them, like some of them are heavy drinkers or drug addicts.

There are addictions that go together, and it is almost as though all of those things are an escape for the problems, the deeper problems these people experience.

What gambling is for me, knowing what I do when I gamble, is to escape. A lot of aboriginal people have issues that they have not dealt with and gambling is a form of escape.

When asked why Aboriginal people gamble, many respondents pointed out the importance of their own heritage and the influence of Europeans:

Gambling is a part of [our culture]. It is a part of our mid-winter ceremonies…the Peace Bowl Game. It’s a game that we play at the end of our mid-winter ceremonies…a gambling game. You are supposed to bring something that is very near and dear to you heart, be it moccasins, rattles, dress clothes, anything. The teams are divided up into clans…the prizes get handed out to the winning team…they say that if you lose something you will get it back in the Spirit world…They usually start at about one or two and keep going until four or five [a.m.], and if there is no winner, then we start the next morning and we can go for the whole day.

I think it was a behaviour introduced by Europeans. You take away a way of life that was always known to a person and introduce something that looks new, it’s gonna look exciting; it’s gonna look fun, good and tempting, and that’s what happened. They started out with alcohol. Alcoholic behaviour can lead to gambling.

Others cited the centrality of poverty, addiction, and boredom, and the belief that gambling represented the only possibility of escape from the cycle of depression and disappointment:

Always the hope of winning; we have issues with poverty.

I don’t know what could replace gambling as a social activity; obviously nothing can replace 168

gambling financially, because the only way you could win quick money is by gambling.

Because a lot of Aboriginal people have been put into a situation where they cannot succeed on their own, or by their own merit, and they have to look for other ways where they can afford some sort of success…We could go into other reasons, such as governmental structure, on how they kind of systematically devised some sort of plan that just works non-stop, they just keep going around in a circle, so they get trapped in a situation they can’t get out of unless they win the big one.

Some respondents believed that Aboriginal people could quit gambling only by drawing on their own heritage, understanding the power of healing circles, and rediscovering themselves as a unified people. They also acknowledged the powerful economic incentive of having casinos on reserves, but suggested that since the social and personal costs appear to cancel out any financial gains, a moratorium on future construction should be put in place to allow current social problems to be mended. Others raised the notion of gambling as a group activity that must be addressed as such. Some believed that if Aboriginals could develop a sense of their own abilities and a recognition of other possibilities for success, they would independently leave gambling behind:

I believe there are historical maps of pain that people are trying to make their way through, whether it is personal history or cultural history. They are trying to find their map through so they can have a good life.

I think that if they would deal with the history that is behind us and realize that we are like everybody else, we are not different and no less. I think that would help build self-esteem. If more reserves had what we have here, we have such a low rate of unemployment, so that people have more pride in themselves and in their families. My biggest fear was when the casino came that they would be handing out royalties, like they did out west, and people would not want to work.

Across the gambling continuum, respondents were nearly split between the view that there were no differences between men and women with respect to gambling, and the view that men and women preferred different forms of gambling. Occasionally, a respondent’s first reaction was that no differences existed, but on further reflection, would often state that gender differences appear in terms of what types of gambling women and men were likely to prefer. Those who agreed that gambling is a gendered activity generally agreed also that women tend to prefer bingo whereas men prefer sport betting, card games, horse race wagering, and gambling in casinos. Usually, these reflections went beyond reports of simple preference of games to include some social and structural commentary:

Well the difference might be more women would play Bingo than go to the casino, because Bingo is more accessible than the casino. If someone doesn’t have the transportation they can hitch a ride with someone else to the local Bingo hall. It would be harder to go to the casino. I think men are more comfortable going to a lot of place by themselves. Women might feel more comfortable going with a friend.

Some respondents had the view that women in their ethno-cultural group were more addicted to gambling than men; however, men were described as more aggressive and as more willing to play at higher stakes:

For some reason I think it is that women tend to gamble more, but from what I’ve seen in our 169

own community is that when men gamble they gamble a lot more than women…The stakes are higher. They go for the bigger game, and women will go spend thirty or forty dollars every night, where a man will go in one night and spend three or four thousand dollars.

There was also a perception that whereas traditionally bingo was seen as a women’s game, indeed a grandmother’s game, it was now attracting both men and women players:

I grew up thinking Bingo was an old woman’s thing, something done in church basements, you know, it was a place where old ladies go, you know, Gab and Dab. I’m sitting there with my brother, over 40, and him and I are at Bingo and his wife is at home…and I look at him and say, “Did you ever think you’d see the day that you and I are at Bingo together?”

Summary

Chapter One began our discussion of gambling in Canada with a brief history that established the link between games of chance and human cultures generally. Chapter Two narrowed the spotlight to Canada, asking how ethnicity has influenced gambling policies, attitudes, and behaviours in Canada’s past and present. This chapter, Chapter Three, has further focused our attention by presenting three case studies that examine the ethnocultural variation in people’s understanding of gambling. Together, these chapters have provided a backdrop for the next section of the book. There, we analyze quantitative survey data on ethnocultural variations in gambling behaviour in Ontario, using data from the Ontario Prevalence Survey and the Canadian census. Much of the data presented in this chapter was taken from a 2002 study conducted with members of six major ethnic communities in Toronto (Tepperman, Korn, and Lynn 2002). This exploratory study was intended to lay the basic groundwork for the present study. To that end, the study relied simply on a variety of non-random sampling techniques. As a result, it could not claim to be either representative or comprehensive. It could, however, elicit interesting insights stated in the respondents’ own workds. Despite its methodological limitations the study hinted, through the words of its respondents, at the existence of community-level ethnocultural differences in gambling attitudes and behaviours. In so doing, it highlighted an area of gambling research that warrants more thorough investigation; hence the present research. Within the context of this monograph, this exploratory interview data serves as a bridge between the historical analysis of cultural variations in gambling attitudes presented in earlier chapters and the quantitative analysis of ethno-cultural variations presented in chapters that follow. In the next three chapters, we examine statistical analyses drawn from high-quality survey data. Supported by historical evidence and qualitative interviewing, these survey data make for a powerful statement about the presence of a sociological reality: namely, ethno-cultural variations in gambling in Ontario.

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Chapter Four: Individual level determinants of gambling

The overall goal of this study is to measure and explain the influence of ethnocultural membership on gambling habits and the propensity for developing gambling problems. As we will see in this chapter and others that follow, the data from an Ontario prevalence survey support our earlier findings that gambling is indeed different, in degree and type, in different ethnocultural groups, and thus ethnocultural membership does measurably influence gambling. Why would different ethno-cultural groups have different gambling patterns – patterns that vary in degree and type? Four different explanations will be considered in this chapter: cultural, demographic, types of games, and ecological explanation. The cultural explanation, which we have explored to this point, suggests that different ethnic groups, through their beliefs and practices, encourage different gambling practices. The history of these beliefs and practices is long lost, but the beliefs and practices are preserved and passed down, generation after generation, through ethnic families and ethnic institutions: through language, literature, religion, and communal institution. This explanation will be examined largely by default. We have already noted interesting differences cultural and gambling differences between the English, Chinese and Aboriginals, for example. We will assume that these are purely cultural differences if they are not “explained away” by other factors. Another factor that may explain away the cultural influence is demography. The demographic explanation of variations in gambling asserts that ethnocultural differences are merely a proxy for other demographic differences. In short, it holds that groups A and B differ because of their sociodemographic differences and not because of the ethno-cultural values. To determine the validity of this explanation, we will examine whether differences between ethno-cultural groups disappear when we control statistically for important sociodemographic characteristics. A third factor that may explain away differences in the degree and type of gambling is a modified cultural explanation that focuses on the different games that different groups play. The games explanation argues that ethnocultural groups differ not because they hold different values but because they play different (kinds of) games which vary in their addictiveness. Playing more addictive games increases the overall frequency of gambling and the likelihood that gambling problems will develop. For whatever historical reason, ethno-cultural groups differ in their likelihood of playing such highly addictive games. To determine the validity of this explanation, we will examine whether differences between ethno-cultural groups disappear when we control statistically for the differences in games they play. Finally, a fourth factor that may explain differences in the degree and type of gambling is one that focuses on neighbourhood context. The ecological explanation argues that ethnocultural differences are merely a proxy for the makeup of neighbourhoods – the social ecology within which people live. For example, it argues that members of ethnocultural groups A and B differ not because of cultural differences but because members of group A tend to live in neighbourhoods different from – for example, poorer, less educated, more unemployed, more recently immigrated, or more rural – members of group B. Some neighbourhoods, by this reasoning, provide either more opportunity or more encouragement for gambling. To determine the validity of this explanation, we examine whether differences between ethnocultural groups disappear when we control statistically for neighbourhood differences. This explanation will be discussed in greater detail in the next chapter, where we introduce neighbourhood-level data to predict individual-level gambling behaviour. This chapter, then, will examine individual level differences among different ethnic groups and their gambling habits, as well as their probability of developing gambling problems. We will particularly focus on ethnocultural variations and their relation to socio-demographic differences and differences in the games they play.

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Design and Methodology

Data Collection The analysis in this chapter is based on data collected as part of the 2001 Ontario Prevalence Survey (Wiebe, Single and Falkowski-Ham, 2001). The survey was conducted with a random sample of 5,000 adults 18 years of age and older from Ontario. Data collection was conducted by Viewpoints Research Inc. Telephone numbers were selected from a database based on a random selection of live residential numbers from the Ontario regions. This sample selection technique ensures that listed numbers, numbers listed after directories have been published, and unlisted numbers are included in the sample. Potential households were selected through the use of Random Digit Dialing (RDD), and within each household, the closest birthday method was used to select a respondent. If someone in the household met the age requirements, the interviewer asked to speak to that person. If more than one person met the age requirements, the person whose birthday came next was asked to complete the interview (a process used to randomize the selection within each household). Only one individual from each household was asked to participate. At the end of each interview, participants were asked if they would be willing to be re-interviewed in the future. Of the total sample, 91% (n=4,209) agreed. The sample was stratified by region, age and gender to ensure adequate representation on these variables.2 In the present study, data are weighted to reflect the distribution of the population. Among the households with a known eligible respondent, the response rate was 37%, the refusal rate was 62%, and 1% resulted in incomplete interviews. Response rates for general problem gambling surveys in Canada range from 65% in Ontario (Ferris and Stirpe, 1995) to 25% in British Columbia (Gemini Research, 1994). The response rate achieved in this study is toward the lower range. Survey research professionals in the United States and Canada have found that response rates for telephone surveys in the general population have declined in recent years as individuals in the general population become more reluctant to participate in this type of research (Gemini Research, 1994). The over-sampling of older adults may have also contributed to the low response rate. That is, older adults may be more suspicious of telephone solicitations and as such, less likely to participate in a study. Unfortunately, demographic information from those who refused to participate was not collected. The sample size provides reasonably exact estimates of population means on key variables. For example, 5,000 cases provide a 95% confidence interval for a sample estimate of 50% (i.e., the estimate with the largest range of variation) of plus/minus 1.4%. Thus, one can be reasonably certain (95 out of 100 times) that the true population mean falls between 48.6% and 51.4% when the sample mean is 50% on a particular variable. For smaller sample means, the range of confidence intervals is even smaller. The standard error of estimates and associated confidence intervals in the sampling design may be slightly larger due to the stratified sampling design, but the sample is of sufficient size to ensure reasonably robust and generalizable results. A major restriction of any cross-sectional design is that, strictly speaking, causal inferences are not possible with data from only one point in time. Observed statistical relationships only signify associations between variables. In order to infer a causal relationship, a longitudinal research design is required. An important limitation associated with telephone surveys is that the results may not be generalizable to the population at large, particularly those who do not have access to a telephone or refuse to participate. This is particularly a concern given the low response rate associated with this study. However, as indicated above, the demographic characteristics of both the weighted and

2 Stratification by region was conducted according to the seven provincial District Health Council (DHC) regions so that the resultant descriptive statistical data and analysis may be useful to DHS advisory councils in planning/evaluating gambling- related programs and services. 172 unweighted samples compare well with the demographic characteristics of the general population of Ontario.

Variables

Ethnicity: The Main Independent Variable The ethnic groups selected for this secondary analysis are: Native Indian, Dutch, English, Canadian, Chinese, East Indian, French, German, Irish, Italian, Polish, Scottish and Ukrainian. An ethnic group was selected for further study if 40 or more respondents identified themselves as a member of that particular group and no other: for example, as having Polish ancestry. It was thought that forty respondents was a lower limit in providing reliable results at the ethnic-group level of analysis. Table 4.1 shows the variety of ethnic identities given by respondents, and the counts of people with single and multiple ethnicities in each category. Respondents in the survey could choose more than one ethnicity, and many did. Respondents who chose two or more ethnicities – for example, both Czech and Polish -- are said to have multiple ethnicities. These terms -- single ethnicity and multiple ethnicities -- will be used throughout the monograph. All cross tabulations were conducted on respondents with single ethnicity, while multivariate regression analyses were conducted on respondents who were identified as having multiple ethnicities. As a result, group counts are larger in the regression analyses than in the cross- tabulations, because respondents with multiple ethnicities are included. For example, the number of Polish respondents in our analysis increases from 71 to 115 when we move from single to multiple ethnicities. In descending order of size, the largest ethnocultural groups in our sample are the following: English/British, Irish, Scottish, French, German, Canadian, Italian, Dutch, Polish, Chinese, Ukrainian, Native Canadian/Aboriginal, and East Indian. In each instance, we have listed the number giving the identification as a single ethnicity, followed by the number given the identification as one multiple ethnicities (Table 4.1).

Table 4.1: Number of respondents identifying themselves with each ethnocultural group

Ethnicity Stated Single Stated Ethnicity Stated Single Stated Ethnicity (N) Multiple Ethnicity (N) Multiple Ethnicity (N) Ethnicity (N) Native Indian/Inuit 50 134 Korean 2 3 Australian 8 14 Lebanese 6 9 Austrian 5 19 Macedonian 3 5 Bahamian 0 3 Metis 2 4 Bangladeshi 3 8 New Zealander 0 2 Black/African 39 52 Nigerian 0 1 Dutch/Netherlands/ 119 187 Norwegian 6 18 Holland English/British 1053 1731 Pakistani 10 11 Canadian 175 336 Pilipino 18 20 Chilean 2 3 Polish 71 115 Chinese 61 71 Portuguese 36 46 Croatian 9 14 Russian 35 56 Czech 10 20 Scottish 292 778 Danish 16 33 Serbian 5 10 East Indian 44 52 Sikh 5 6 El Salvadorian 0 Slovakian 8 10 Ethiopian 4 7 Somalian 1 1

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Ethnicity Stated Single Stated Ethnicity Stated Single Stated Ethnicity (N) Multiple Ethnicity (N) Multiple Ethnicity (N) Ethnicity (N) Finnish 27 42 Spanish 16 20 French 232 481 Sri Lankan 4 5 German 198 419 Swedish 12 34 Greek 23 25 Tamil 2 2 Guyanese 5 9 Trinidadian 11 14 Haitian 0 2 Ukrainian 58 95 Hungarian 28 44 Vietnamese 4 6 Inuit 3 4 Welsh 12 45 Irish 299 763 Yugoslavian 15 24 Israeli 8 11 Other 267 Italian 150 211 Jamaican 27 32 Japanese 6 13 Jewish 38 48

CPGI: The Main Dependent Variable The core of the survey questionnaire is the Canadian Problem Gambling Index (CPGI), which consists of four main sections: gambling involvement, problem gambling behaviour, consequences of problem gambling behaviour, and correlates of problem gambling behaviour (Ferris and Wynne, 2001). The complete questionnaire can be found in Appendix A. The CPGI problem gambling severity index has nine items, which include: chasing losses, escalating to maintain excitement, borrowing/selling to get gambling money, betting more than one can afford, feeling guilty, being criticized by others, harm to health, financial difficulties to one’s household, and feeling one might have a problem with gambling. The first four items are behavioural items; the last five are consequences of gambling. Most of the CPGI items are adapted from widely accepted SOGS or DSM scales for measuring problem gambling. The exceptions are “harm to health” and “financial difficulties to one’s household,” which are original to the CPGI. Nine of the items are scored, placing an individual at one of four levels. Level 1, which consists of a score of 0, constitutes the problem-free gambling group. Level 4, a score of 8 or greater, represents the most severe problem gambling group. The creators of the CPGI labeled Levels 1 to 4 as non-problem gambling, low-risk gambling, moderate-risk gambling and problem gambling (Ferris and Wynne, 2001). However, we were uncomfortable with these labels, particularly low risk and moderate risk. There is very limited information on the progression of gambling problems. Until more is learned, through longitudinal studies, a decision was made to use the following labels: non-problem gamblers, at risk, moderate problems and severe problem gamblers. The CPGI has received extensive psychometric testing (Ferris and Wynne, 2001). Reliability of the new measure was shown to be good, with a co-efficient alpha of 0.84. Test-retest analysis produced an acceptable correlation of 0.78. Validity was tested a number of ways. Face/content validity was addressed through continual feedback from numerous gambling experts. A test of criterion validity was achieved by comparing the CPGI to DSM-IV and the SOGS. It was found that the CPGI was highly correlated with these two measures (0.83). Construct validity was demonstrated by expected correlations between CPGI scores and money spent on gambling, gambling frequency, and number of adverse consequences reported. Many of the analyses contained in this report examine differences associated with gambling level, which includes non-gamblers and the four gambling levels measured by the nine scored items. The scored questions on the CPGI are not asked if individuals indicate that they have not gambled on

174 any of the 17 gambling activities in the past year, or if an individual states that “I do not gamble” twice; these individuals are labeled as non-gamblers.

Gambling Participation

Gamblers vs. Non-gamblers We have focused our research by looking mainly at respondents who acknowledge that they gamble. That said, how gamblers and non-gamblers differ is also of interest. Below, we outline the differences between gamblers and non-gamblers along ethnic and socio-demographic lines. Data in Table 4.2 show that gamblers are significantly more likely to live in Northern or Eastern Ontario, while non-gamblers are more likely to live in Toronto or South Western Ontario. Gender does not significantly differentiate gamblers from non-gamblers. However, age does; respondents who are 18-24 years old or 35-49 years old are the most likely age groups to gamble. Only English, German and French ethnic membership is significantly associated with gambling status. The vast majority (82.7%) of respondents reporting a single ethnicity are at least occasional gamblers; only 17.3% are non-gamblers. Although all ethnic groups are likely to gamble, the proportions gambling in the past year vary dramatically from one ethnocultural group to another. The highest percentage of non-gamblers is found within the Chinese community (24.3%), while French are the most likely ethnic group to have gambled in the past year (88.1%).

Table 4.2: Gambling participation by Sociodemographic characteristics (weighted, single ethnicity)

Demographic Characteristics Non-gamblers Gamblers Total Population 17.3% 82.7% Gender Male 16.5% 83.5% Female 18.1% 81.9% Age *** 18-24 15.1% 84.9% 25-34 17.9% 82.1% 35-49 14.0% 86.0% 50-59 16.1% 83.9% 60 + 25.8% 74.2% Marital Status *** Married/living with partner 17.4% 82.6% Widowed 25.4% 74.6% Divorced/separated 14.3% 85.7% Single, never married 16.4% 83.6% Educational attainment *** Some high school 21.5% 78.5% Completed high school 17.9% 82.1% Some post-secondary 15.8% 84.2% Completed post-secondary 15.7% 84.3% Completed post-graduate 18.0% 82.0% Income *** <$20, 000 22.1% 77.9% < $30, 000 18.6% 81.4% < $40, 000 18.5% 81.5% < $50, 000 15.1% 84.9% < $60, 000 14.0% 86.0%

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Demographic Characteristics Non-gamblers Gamblers $60, 000 + 12.5% 87.5% Region ** East 13.5% 86.5% Central East 16.1% 83.9% Toronto 19.8% 80.2% Central West 18.0% 82.0% Central South 18.4% 81.6% South West 19.2% 80.8% North 11.3% 88.7% N 560 2682

Table 4.2: Gamblers vs. Non-gamblers, by Ethnicity (weighted, single ethnicity)

Ethnicity Non-gamblers Gamblers Native 22.9% 77.1% Dutch 23.4% 76.6% English * 14.5% 85.5% Canadian 14.6% 85.4% Chinese 26.2% 73.8% East Indian 20.0% 80.0% French * 11.9% 88.1% German * 24.3% 75.7% Irish 14.5% 85.5% Italian 12.1% 87.9% Polish 10.0% 90.0% Scottish 19.6% 80.4% Ukrainian 12.5% 87.5% N 561 2682

CPGI Scores In this section we use CPGI scores to examine different levels of gambling activity by members of different socio-demographic groups. We specifically look at variations in gambling by gender, marital status, education, employment status, income, family composition and region. The data in Table 4.3 show significant differences in male and female gambling. The average CPGI scores are much higher for men than they are for women. The pattern is clear: men are more often gamblers, and more often problem gamblers, than women. Additionally, the table also shows that young, single respondent with less then $30,000 household income living in Toronto tend score high on CPGI items.

Table 4.3: CPGI Score, by Socio-demographic variables (weighted, single ethnicity, unclassified deleted)

Demographic Characteristics Average CPGI score-gamblers Average CPGI score non- only gamblers included All participants 0.4243 0.1862 Gender *** *** Male 0.5263 0.2835 Female 0.3158 0.0847 Age *** *** 18-24 0.7070 0.4577

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Demographic Characteristics Average CPGI score-gamblers Average CPGI score non- only gamblers included 25-34 0.4405 0.1927 35-49 0.4233 0.2319 50-59 0.3807 0.1663 60 + 0.1993 -0.1053 Marital Status ** *** Married/ 0.3649 0.1353 Widowed 0.1956 -0.1059 Divorced/separated 0.4638 0.2573 Single, never married 0.5977 0.3451 Educational attainment Some high school 0.5024 0.1817 Completed high school 0.3996 0.1573 Some post-secondary 0.6427 0.3892 Completed post-secondary 0.3639 0.1542 Completed post-graduate 0.3947 0.1608 Income * <$20, 000 0.6221 0.2773 < $30, 000 0.6228 0.3232 < $40, 000 0.3428 0.0938 < $50, 000 0.5619 0.3377 < $60, 000 0.3560 0.1659 $60, 000 + 0.3508 0.1906 Region * * East 0.2696 0.1036 Central East 0.4056 0.1852 Toronto 0.6498 0.3400 Central West 0.4241 0.1783 Central South 0.3929 0.1395 South West 0.2771 0.0347 North 0.3462 0.1954 N 2727 3274

CPGI Level The data in Table 4.4 confirm these socio-demographic variations. Women and men consistently have different CPGI scores. Men more often are gamblers and more often are found in the higher levels of the CPGI. Older people (those aged 60+) are less likely to gamble and if and when they do they are less often found in the higher levels of the CPGI. Conversely, the youngest cohort (i.e., respondents aged 18-24) are less likely to be non-problem gamblers and more likely to be found in the higher levels of the CPGI. Marital status reveals a similar picture. Like older people, married people often do not gamble. If and when they do, they are less likely to be found in the higher levels of the CPGI. By contrast, single, divorced, and separated people are more often found toward the top end of the CPGI scale. If and when single people gamble, they more often fall into the “at-risk” category of the CPGI. That is to say, they are more likely to run into gambling “problems.” Level of educational attainment has a non-linear relationship with gambling, as measured by the CPGI scores. However, when examining CPGI levels and educational attainment, Table 4.4 shows that people with some post secondary school are most likely to fall into the “severe” gambling category.

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Respondents with only some high school, or who have completed post-graduate education, are the most likely to be non-gamblers. Income also has a non-linear relationship with CPGI. Having a high income does not appear to increase or decrease one’s CPGI score. People with $20,000 or less per year are most likely to be non- gamblers. However, this group -- if it gambles -- is more often found in the at-risk, moderate and severe (problem-gambling) categories. People in the higher income brackets appear to gamble most “responsibly,” if they gamble at all. Although they are not highly represented in the non-gambling category, neither are they found often in the problem levels of the CPGI. It is the low-middle earners who appear most likely to fall into the problem gambling levels of the CPGI. These results lead to a few conclusions. First, since many predictor variables are correlated with one another, we need multivariate techniques of data analysis to separate out their individual effects on gambling behaviour. Second, the relations between predictor variables and CPGI are often non-linear. This may point to problems of non-normality in CPGI, of which more will be said later, or in one or more predictor variables. It may also point to interactions between predictor variables. So, for example, people who are without a spouse or children at home and are old (i.e., over 60) or young (i.e., under 25) may have a different gambling profile than people who are without a spouse or children at home and are middle-aged (e.g., 35-55). We will save any speculation on the reasons for this for another occasion. At this point, note simply that socio-demographic variables predict strong variations in gambling behaviour. These must be taken into account when we consider the effects of ethnocultural identification.

Table 4.4: CPGI levels, by Socio-demographic Characteristics (weighted, single ethnicity)

CPGI Levels Demographic Non-gamblers Non-problem At risk Moderate Severe Characteristics gamblers problem problem Gender *** Male 15.9% 68.0% 11.5% 3.3% 1.3% Female 17.6% 70.7% 8.6% 2.7% 0.4% Age *** 18-24 14.6% 59.2% 19.8% 5.0% 1.4% 25-34 17.2% 67.6% 11.5% 2.8% 0.9% 35-49 13.4% 72.5% 10.1% 3.0% 1.0% 50-59 15.4% 73.4% 6.8% 3.5% 1.0% 60 + 25.4% 67.6% 4.9% 2.0% 0.2% Marital Status *** Married/living with partner 16.8% 71.9% 8.1% 2.3% 0.9% Widowed 25.3% 67.6% 4.1% 2.9% 0.0% Divorced/separated 14.1% 69.8% 12.3% 3.2% 0.6% Single, never married 15.8% 63.1% 15.3% 5.0% 0.9% Educational attainment *** Some high school 21.4% 63.5% 9.7% 4.7% 0.7% Completed high school 17.4% 68.7% 10.5% 2.8% 0.7% Some post-secondary 15.5% 66.5% 12.7% 3.8% 1.5% Completed post-secondary 15.4% 70.2% 10.8% 3.2% 0.5% Completed post-graduate 16.8% 72.5% 7.6% 1.9% 1.2% Income *** <$20, 000 21.3% 57.4% 14.8% 6.1% 0.4% < $30, 000 18.6% 63.3% 12.1% 4.2% 1.9%

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CPGI Levels Demographic Non-gamblers Non-problem At risk Moderate Severe Characteristics gamblers problem problem < $40, 000 18.4% 70.2% 6.8% 3.9% 0.6% < $50, 000 14.3% 69.7% 10.8% 3.8% 1.3% < $60, 000 13.9% 72.1% 11.1% 2.5% 0.3% $60, 000 + 11.8% 75.7% 9.0% 2.6% 0.9% Region ** East 13.0% 76.9% 8.2% 1.4% 0.5% Central East 15.7% 69.8% 11.2% 2.9% 0.4% Toronto 18.8% 63.1% 12.3% 3.9% 1.9% Central West 17.3% 68.6% 10.3% 2.9% 0.9% Central South 18.2% 70.4% 7.2% 3.6% 0.6% South West 18.9% 68.3% 9.8% 3.0% 0.0% North 11.1% 75.8% 9.1% 3.6% 0.4% N 548 2269 331 99 28

Gambling Diversity Measure People and groups differ in the games they play, and the total number of games they play. Different gambling activities may be more or less accessible to different groups, for example sports betting may interest some groups but not others; and VLT’s are harder for rural people to access. Table 4.5, shows the variety of gambling activities that respondents reported participating in and the number of times respondents played each game. Only one activity – purchasing lottery tickets during the year – was shared by a large majority (nearly two thirds) of the population. Raffle tickets are nearly as popular as lottery tickets, with half the sample reporting they bought raffle tickets in the past year. Scratch tickets and slots machines (or VLTs) – the latter normally played at casinos – are also popular, with nearly a third of respondents reporting they played these during the past year. By contrast, some gambling activities are extremely rare in the Ontario population. These include sports betting with a bookie, Internet gambling, and the use of slot machines and VLTs outside casinos. Due to the low volumes of people who gamble on internet and bet on sport with a bookie, these activities will be removed from the calculation of the gambling diversity measure. Additionally, since previous research has shown that respondents who invest in stocks differ from those who gamble on other activities, this activity will not be considered when calculating the gambling diversity measure.

Table 4.5: Frequency of Participation in Various Gambling Activities (weighted sample)

Gambling Overall Daily At least once At least once Less than once Never (N) Activities participation a week a month a month in past year Lottery tickets 64.6 0.5 22.0 15.2 27.1 35.4 (4993) Raffle tickets 51.0 0.1 0.8 6.9 43.2 49.0 (4987) Scratch tickets 31.6 0.4 3.6 8.8 18.8 68.5 (4998) Slot machines 28.3 0.2 0.5 2.7 24.9 71.7 (4992) or VLTs Outcome of 13.2 0.1 0.9 1.7 10.5 86.8 (4995) sporting event Arcade or video 11.8 0.3 0.9 2.1 8.6 88.2 (4997) games Games of skill 10.2 0.2 1.5 1.9 6.6 89.8 (4996)

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Card or board 10.0 0.1 0.7 1.7 7.4 90.0 (4998) games with friends, etc. Casinos out of 9.5 0 0.0 0.4 9.1 90.5 (4995) the province Bingo 8.5 0.1 1.5 1.5 5.4 91.5 (4999) Casino table 7.2 0.1 0.2 0.7 6.3 92.7 (4997) games Speculative 6.4 0.2 0.5 1.6 4.2 93.6 (4990) investments Sport Select 6.0 0.1 1.0 1.5 3.4 94.0 (4997) Horse races 5.4 0.1 0.3 0.4 4.7 94.6 (4999) Slot machines 2.2 0.0 0.1 0.5 1.6 97.7 (5000) /VLTs other than at casinos Internet 0.6 0.0 0.1 0.1 0.4 99.4 (4999) Sports with 0.4 0 0.1 0.2 0.1 99.6 (4998) bookie

As we noted earlier, 87% of all respondents report having participated in at least one gambling activity during the previous year. The majority of respondents who gamble participated in only a few different activities. Graph 4.1 shows the distribution of frequencies with which people play different games. Most scores are low, since most people who play any games play few games. By summing all gambling activities that respondents participated in the past 12 moths, we can construct a “gambling diversity measure” which runs from a low of 0 – meaning zero (or no) gambling activities in the past 12 months to a high of fourteen such activities (see Graph 4.1). All tests conducted on this constructed measure can be found in Appendix B. The item-to-scale correlations – that is, yes-no correlations between the sum total and the 14 items making it up -- are only moderately high. As a result, the scale validity (Cronbach alpha) is only a = .6366 -- reliability level that could likely be increased by eliminating items with a lower item-to- total correlation and/or lower frequency in the sample. Despite this, gambling diversity measure correlates moderately high with CPGI score (r =.383, p<0.001) and therefore suggests that it is a good measure of gambling problems. In tables below, we use the top-coded at 5, however, additional analysis of Gambling Diversity (e.g., ANOVA) can be found in Appendix B. As the data in Graph 4.1 show, nearly half (49%) of all respondents participated in two types of gambling or fewer, and 68.6% participated in three types of gambling or fewer. By contrast, 4.1% of respondents who gamble participated in seven or more gambling activities. While gambling a little is common, the mean number of gambling activities that gamblers participate in is only 2.73.

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Graph 4.1: Frequency Distribution of Gambling Activities (Weighted sample)

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Graph 4.2: Gambling Diversity Measure - Top Coded at 5 Categories

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Most of all, gambling diversity is significantly greater among young people than among old people. Indeed, gambling diversity diminishes monotonically from the youngest age category down to the highest age category. In short, young people play a larger number of different games than older people. This strong association between age and gambling diversity makes us wonder whether the other correlations observed are mere reflections of the age effect. This could be the case where the variables marital status, education, employment status, and income are concerned. Generally, younger people (ages 18-25) will be single or cohabiting (rather than married), have a middling (incomplete) level of education, be students or hold full-time jobs, and have middling-level incomes.

Table 4.6: Number of gambling activities by socio-demographics (weighted, single ethnicity)

Gambling Diversity Measure Demographic Characteristics 0 1 2 3 4 5 All participants 17.3% 15.9% 18.6% 18.3% 12.5% 17.4% Gender *** 181

Male 16.5% 14.9% 17.3% 17.9% 13.2% 20.1% Female 18.1% 17.0% 19.9% 18.6% 11.8% 14.6% Age *** 18-24 15.1% 12.0% 13.4% 16.5% 14.8% 28.0% 25-34 35-49 17.9% 13.0% 13.8% 19.4% 13.2% 22.8% 50-59 60 + 14.0% 15.7% 20.1% 19.4% 14.6% 16.2% Marital Status *** Married/ 17.5% 16.0% 18.8% 19.9% 12.1% 15.6% Widowed Divorced/separated 25.6% 19.0% 22.6% 13.7% 7.7% 11.3% Single, never married Educational attainment *** Some high school 21.5% 18.2% 17.8% 17.5% 11.1% 13.8% Completed high school Some post-secondary 17.8% 14.9% 17.8% 17.7% 14.5% 17.3% Completed post-secondary Completed post-graduate 15.8% 13.2% 19.4% 17.1% 11.6% 23.0% Income *** <$20, 000 21.5% 18.2% 17.8% 17.5% 11.1% 13.8% < $30, 000 < $40, 000 17.8% 14.9% 17.8% 17.7% 14.5% 17.3% < $50, 000 < $60, 000 15.8% 13.2% 19.4% 17.1% 11.6% 23.0% $60, 000 + Region ** East 13.6% 15.3% 20.6% 20.8% 14.0% 15.7% Central East Toronto 16.1% 14.2% 18.0% 20.3% 15.0% 16.5% Central West Central South 19.8% 18.7% 18.6% 14.8% 10.3% 17.8% South West North 18.0% 16.4% 19.6% 18.0% 11.5% 16.5% N

Ethnicity and Gambling

CPGI Score The distribution of CPGI scores, like the variation in gambling--non-gambling itself, varies from one ethnocultural group to another. Graphs 4.3-4.15 display the CPGI scores of the 14 largest ethnocultural groups in our sample. Each graph follows the same general pattern, with a large number of people falling within the lower levels of the CPGI scale. Beyond the modal category, the number of respondents drops dramatically and consistently, with few respondents recording high CPGI scores. Groups following this typical pattern are the Dutch, Chinese, English, East Indian, French, Italian, German, Polish and Irish. By contrast, the graphs of the Native, Canadian, Scottish, and Ukrainian groups show a larger-than-average number of people scoring higher CPGI scores after the initial drop – hence, a “flatter” distribution. As a result, their graphs decline less steeply than average. Note, finally, that the Native, Dutch and Chinese groups have larger-than-average numbers of respondents scoring between 1 and 4 on the CPGI measurement.

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Graph 4.3: Native gamblers n=37 Graph 4.4: Dutch gamblers n=82

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Graph 4.5: English gamblers =817 Graph 4.6: Canadian gamblers n=140

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Graph 4.7: German gamblers n=134 Graph 4.8: Irish gamblers n=234

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Graph 4.9: Italian gamblers n=127 Graph 4.10: Polish gamblers n=61

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Graph 4.11: Chinese gamblers n=42 Graph 4.12: East Indian gamblers n=30

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Graph 4.13: French gamblers n=191 Graph 4.14: Scottish gamblers n=216

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Graph 4.15: Ukrainian gamblers n=44

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As one recalls, the average CPGI score for respondents with single ethnicity is .4243. Graph 4.16 illustrates the CPGI score variation between the different ethnocultural groups. The Chinese and Native respondents are significantly above the mean, indicating a higher propensity to problem gambling. The English and Irish are significantly below the mean. Irish respondents tend to have the lowest CPGI scores, and therefore are significantly more likely than the general population to be non- problem gamblers or at risk gamblers.

Graph 4.16: Average CPGI score (weighted, single ethnicity)

Irish ** 0.1

Dutch 0.2

English 0.3

French 0.3

German 0.3

Scottish 0.4

Canadian 0.4

Ukrainian 0.4

Italian 0.6

Polish 0.7

East Indian * 0.8

Chinese *** 1.3

Native *** 1.4

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

CPGI Level In this section we look at ethnocultural variations in CPGI level. Table 4.7 shows the percentage of each ethnocultural group that falls into each level of the CPGI. In almost every group, the majority of gamblers – roughly, 50-80 percent -- are non-problem gamblers, with two exceptions: the Chinese and Native groups. Only 40.6% of Chinese gamblers and 45.3% of Native gamblers are non- problem gamblers. These two groups consistently have a higher-than-average number of respondents falling into the at-risk, moderate, and severe problem gambling categories.

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Several ethnocultural groups fall outside the typical pattern. The East Indian respondents, while having a majority in the non-problem category, have above-average numbers in the severe and moderate problem gambling categories as well. The same general pattern applies to Ukrainian respondents, except that no Ukrainian respondents fall into the severe category. Asterisks in the table indicate patterns that deviate significantly from the norm. The Native and Chinese gamblers have a significantly higher proportion of problem gamblers than average. Other groups – the Dutch, French and Irish, but especially the English and Italian respondents – have significantly fewer problem gamblers than average.

Table 4.7: CPGI Level, by Ethnic Identification (weighted, single ethnicity, unclassified deleted)

Gambling Level Ethnicity Non-gamblers* Non-problem At risk Moderate Severe Total Native *** 20.8% 45.3% 20.8% 11.3% 1.9% 1.6% Dutch * 22.5% 65.8% 10.8% 0.9% 0.0% 3.4% English ** 13.8% 74.1% 9.6% 1.9% 0.6% 28.3% Canadian 13.6% 75.3% 6.2% 3.7% 1.2% 5.0% Chinese *** 26.6% 40.6% 23.4% 7.8% 1.6% 2.0% East Indian 20.0% 62.5% 10.0% 5.0% 2.5% 1.3% French * 11.0% 79.3% 7.0% 2.2% 0.4% 6.9% German 23.5% 67.1% 6.5% 2.4% 0.6% 5.2% Irish * 14.5% 78.1% 6.3% 1.1% 0.0% 8.2% Italian ** 12.2% 65.4% 17.9% 3.8% 0.6% 4.8% Polish 10.1% 63.8% 18.8% 5.8% 1.4% 2.1% Scottish 19.3% 68.9% 7.5% 3.9% 0.4% 7.8% Ukrainian 12.5% 68.8% 14.6% 4.2% 0.0% 1.5% N 548 2270 332 99 27 3276

Gambling Diversity Measure Graph 4.17 shows the number of gambling activities that respondents participate in, by ethnicity. On average, respondents who gamble participate in roughly three activities (mean=3.1). Interestingly, Chinese respondents, who tend to score higher-than-average on the CPGI, gamble in the smallest number of ways. This in contrast to Native respondents, who diversify their gambling activities and enjoy 4.2 activities on average, and they tend to participate in more gambling activities then any other ethnic group. Additionally, this graph shows that Italian respondents participate in many different gambling activities, however, despite their gambling diversification, they do not develop gambling problems.

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Graph 4.17: Number of Gambling Activities by Ethnicity (weighted, single ethnicity, gamblers only)

Chinese * 2.6

East Indian 2.8 Dutch 2.9

German 2.9 Ukrainian 3.1 English 3.1 Irish 3.2 Polish 3.2 Canadian 3.2

Scottish 3.3 French 3.3

Italian ** 3.6 Native *** 4.2

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

This section found many interesting differences related to gambling experiences among the ethnic groups studied. However, considering the large effects of sociodemographic variables on CPGI score and gambling diversity, we will want to know whether ethnic variations found in this section persist when controlled for age and gender, specifically. Most important, we wonder whether age explains the observed correlation between ethnocultural background and gambling diversity. If younger people are more likely to gamble variously and extremely, ethnocultural groups with higher fertility (and/or more recent immigration by young people) will show the highest levels of gambling diversity and gambling problems. If so, we will be unable to ascribe to value differences the observed ethnic variations in gambling. They will be due exclusively to differences in demographic composition.

Gambling Preferences as a Factor in Ethnic Gambling

We noted at the beginning of this chapter that another possible explanation for the ethnic variation in gambling is that members of different ethnic groups typically play different games, and these games may vary in their addictiveness. As Volberg (2000) points out, games differ in terms of their speed of play. For example, black jack games on the Internet may take a mere 15 seconds, whereas bingo games may take 10 minutes or even an hour. This variable is called “event frequency.” Games with a higher event frequency tend to be more addictive. If Polish people, for example, prefer games with a higher event frequency, such as playing blackjack on the Internet, their choice of game may make problem gambling more likely in the Polish respondent group. For this reason, we must find out which ethnic groups play which games. To determine whether some gambling activities are specific to an ethnic group, while controlling for demographics, we carry out a binary logistic regression, in which the dependent variable is ethnicity (as a dichotomy, e.g., Native Yes/No) (see Appendix B for detailed tables). Here, we control for socio-demographic variables because virtually all types of gambling are predicted by gender, age, marital status, educational attainment, job status, and region of the Province. So, for example, though nearly one respondent in three bought a scratch ticket in the past year, women were significantly more likely to buy them than men; young people, more likely to buy them than older people; single people more likely to buy them than married or widowed people; less-educated people more likely to buy them than more-educated people; and people in the North or Central South more 187 likely to buy them than people in Toronto. In effect, we use gambling games here to predict ethnicity, while holding constant socio-demographic variables such as gender, marital status, age, education, and household income. Among other results, we find that Native respondents are more likely than average to play bingo and gamble in casinos outside the province; English respondents are more likely to bet on sporting events; Chinese respondents are more likely to bet on table games and less likely play scratch tickets, raffles, or slot machines; East Indian respondents are less likely (than average) to play raffles; French respondents are more likely to gamble on scratch tickets and less likely to gamble on sporting events; German respondents are less likely to play the lottery; and Italian respondents are more likely to play slot machines and gamble on the outcome of sporting events. However, there are too many (i.e. 17) distinct game categories to consider separately, for each of 14 ethnic groups. Accordingly, we compress the data through the use of a common scaling technique, factor analysis. In short, factor analysis allows us to form clusters of correlated gambling activities. We then will examine ethnic variations in game clusters, and variations in CPGI by game clusters. Our goal is to determine whether the correlation between ethnicity and CPGI disappears when we introduce game clusters into the analysis. We factor analyze the 14 game categories, using the principal components method, followed by a varimax rotation with Kaiser normalization. This method is typically used to identify the main factors, or clusters, in a body of intercorrelated data, and to maximize the statistical independence of the clusters so identified. The analysis pulls out four factors that, together, account for 43.5% -- or nearly half – of all the variation in the 14-game data, providing us with a very good summary of the material. The factors are as follows:

• Factor 1: Casino Betting -- casinos out of province, slots in casinos, casino table games and horse races • Factor 2: Ticket Betting -- lottery, raffles, scratch tickets, and bingo • Factor 3: Social Betting -- games of skill, arcade/video games, cards/board games with friends and VLTs • Factor 4: Sports Betting -- sports with bookie, outcome of sporting events

Statistics on these factors and on the total variance they explain are contained in Appendix B. (The names chosen for these factors try to capture the essence of each cluster of activities, but do not always succeed.) Data in Table 4.8 show the results of correlating the CPGI with scores derived from these four factors. Once again, this will be a weak model, owing to problems associated with using CPGI in multivariate (regression) analysis. Despite this, the data show statistically significant effects of the following games on CPGI (for gamblers only): Casino Betting (r= .150**), Ticket Betting (r= .080**), Social Betting (r =. 161**), where ** indicates statistical significance at the p < .01 level. The only form of betting that is not predictive of high CPGI scores is Sports Betting.

Table 4.8: Pearson Correlation between CPGI and Factor Scores

Factor Score Factor 1: Casino Betting .150*** Factor 2: Ticket Betting .080*** Factor 3: Social Betting .161*** Factor 4: Sports Betting .011 N 3857

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The results in Table 4.9 support this analysis, by showing the mean factor scores by CPGI levels. Note that factor scores are always normally distributed, with a mean of zero and a standard deviation of 1.0. Thus, a factor score of, say, 2, means that someone is two standard deviations above the mean, which (in turn) means that 95% of the population has a lower score on that variable. This is meaningful when we note, in Table 4.9, that among respondents scored “Problem gamblers” by CPGI, the mean score on Sports Betting is 0.144. In short, problem gamblers are very unlikely to be sports bettors. Among problem gamblers, the mean score on Casino Betting is also well above the mean, at 1.445. By contrast, problem gamblers score below the mean on Ticket Betting and near the mean on Internet Betting and Social Betting. Said another way, problem gamblers do not buy lottery tickets very often. They also do not participate in what we are calling “social betting”; they tend to favour sports betting and casino betting.

Table 4.9: CPGI Level by Average Factor Scores

Factor 1 Factor 2 Factor 3 Factor 4 Gambler Type Casino Betting Ticket Betting Social Betting Sports Betting Non-problem gamblers -0.019 0.166 -0.084 0.066 Low Risk gamblers 0.369 0.314 0.518 0.198 Moderate Risk gamblers 0.486 0.649 0.797 0.089 Problem gamblers 1.445 0.332 0.772 0.144 Total 0.056 0.202 0.024 0.083

Table 4.10 shows the correlations between factors 1 through 4 and the 13 ethnic groups of interest. • Factor 1: Casino Betting -- for example, is uncorrelated with any ethnic group. That is, no ethnic group is particularly or uniquely committed to Casino Betting. • Factor 2: Ticket Betting -- has a significant positive correlation with Chinese and East Indian peoples and a negative correlation with Canadian and Native people. That is, Canadian and Native respondents are unlikely to gamble by buying tickets; by contrast, Chinese and East Indian respondents are likely to gamble by buying tickets. • Factor 3: Social Betting -- is positively correlated with English and negatively correlated with the Native. That is, Native respondents are unlikely to gamble by playing games of skill, or board games, while English are likely to do so. • Factor 4: Sports Betting -- is positively correlated with Native and French and negatively correlated with English, Italian and Ukrainian people. That is, English, Italian and Ukrainian respondents are unlikely to gamble on sports, while Native and French are likely to do so.

Table 4.10: Pearson Correlation between Ethnicity and Factor Scores (weighted, multiple ethnicity)

Factor 1 Factor 2 Factor 3 Factor 4 Ethnicity Test Casino Betting Ticket Betting Social Betting Sports Betting Native Pearson Correlation -0.018 -0.060 -0.039 0.050 Sig. (2-tailed) 0.271 0.000 0.019 0.003 Dutch Pearson Correlation 0.013 -0.015 0.011 0.028 Sig. (2-tailed) 0.437 0.354 0.499 0.088 English Pearson Correlation 0.000 -0.009 0.068 -0.031 Sig. (2-tailed) 0.979 0.580 0.000 0.062 Canadian Pearson Correlation 0.013 -0.044 0.010 0.019 Sig. (2-tailed) 0.426 0.009 0.558 0.242

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Factor 1 Factor 2 Factor 3 Factor 4 Ethnicity Test Casino Betting Ticket Betting Social Betting Sports Betting Chinese Pearson Correlation -0.002 0.064 -0.012 0.012 Sig. (2-tailed) 0.905 0.000 0.463 0.484 East Indian Pearson Correlation -0.011 0.028 -0.021 0.018 Sig. (2-tailed) 0.505 0.091 0.215 0.284 French Pearson Correlation 0.017 -0.042 -0.013 0.052 Sig. (2-tailed) 0.297 0.011 0.442 0.002 German Pearson Correlation 0.030 -0.005 -0.006 -0.011 Sig. (2-tailed) 0.074 0.749 0.724 0.521 Irish Pearson Correlation 0.030 -0.025 0.013 -0.002 Sig. (2-tailed) 0.071 0.136 0.443 0.900 Italian Pearson Correlation -0.035 -0.021 -0.027 -0.044 Sig. (2-tailed) 0.036 0.210 0.100 0.008 Polish Pearson Correlation 0.003 0.004 -0.029 -0.008 Sig. (2-tailed) 0.861 0.796 0.076 0.629 Scottish Pearson Correlation -0.011 -0.009 -0.019 0.001 Sig. (2-tailed) 0.508 0.591 0.252 0.963 Ukrainian Pearson Correlation -0.005 -0.021 0.016 -0.050 Sig. (2-tailed) 0.752 0.208 0.348 0.002

Some correlations are statistically significant; however, overall, there are no strong correlations between ethnicity and games played. Thus, it is unlikely that games played “explain away” the connection between ethnicity and CPGI. What we have found in this chapter is a modest but consistent and statistically significant correlation between ethnicity and gambling problem level. This correlation, as we have seen, is not likely due to games played. In the case of some ethnic groups – for example, the English and Chinese – it may be largely due to socio-demographic variables that characterize the ethnic group in question. However, socio-demographic variables do not explain away the high level of problem gambling among Native respondents. Thus, at least in the case of Natives, and perhaps to a lesser degree in some other groups, gambling may be a result of ethnic cultural values. Alternately, it may be a result of neighbourhood availability and encouragement of gambling. To address this question, we need to look at neighbourhood level variables.

Determinants of Gambling Levels

Linear Regression Models As we have seen, ethnicity is a significant correlate of both CPGI and Gambling Diversity. People’s ethnocultural origins influence the amount and kind of gambling they participate in. However, ethnocultural identity is also correlated with other sociodemographic variables – for example, gender, age, marital status, educational attainment, household income, and region -- which, in turn, are correlated with gambling practices. To determine whether ethnocultural origins truly influence gambling, we need to control for these sociocultural variables while examining the effect of ethnicity on gambling; to do that, we need to use methods of multivariate analysis. A regression analysis calculates the equation of the line that, with the least error, describes the relation between the independent variables entered and the dependent variable – in this case, gambling level. The regression model also reveals how much variance each set of predictor variables explains in total, and how much each predictor variable contributes to this “explanation.” In the linear regression

190 analyses that follow, all regressions are performed with weighted data, using dummy variables to identify multiple ethnicities. That is, dummy (yes/no) variables are included to identify respondents who list the following ethnic identities: Native, Dutch, English, French, Canadian, Chinese, East Indian, Irish, Italian, Polish, Scottish and Ukrainian. When the dependent variable is the number of gambling activities that a respondent engaged in, we include the (ethnically) unclassified respondents. However, when the dependant variable is CPGI score, unclassified respondents are excluded from the analysis. Non-gamblers are excluded from both types of analysis, unless otherwise indicated. Some forms of regression analysis have proven less useful than others, due to their various sensitivities. Linear regression is not completely useful in the analyses that follow, since many of the key variables -- particularly, the CPGI score -- are not normally distributed, as linear regression requires. Various transformations of the scores -- the raw or unaltered CPGI score, a logged CPGI score, and Square Root of CPGI score -- do not solve this problem. All are far from being linear or being normally distributed (see Appendix B for Regression results and histograms). Table B6 in Appendix B shows the results of regressing the raw CPGI scores on the ethnocultural variables. Overall, the model is statistically significant at the level of p<0.001. Moreover, some of the individual ethnocultural variables are significant: specifically, the Native, English/ British, Chinese and French groups are significantly different from the mean. The relationship between CPGI score and being Native or Chinese is a positive one, meaning that these groups have higher-than- average gambling scores. The relationship between CPGI and being English/ British and French is a negative relationship, meaning they have lower-than-average gambling scores. The strength of the relationship between being French or English and CPGI score is less strong than the strength of the relationship between being Native or Chinese and CPGI score. None of the other variables is significant at the 5% level. These ethnocultural results support our earlier findings about ethnocultural variation. However, the model explains only 1% of the variation in CPGI; that is, the adjusted R-square is = 0.010. In hopes of explaining more variance in the model by normalizing the dependent variable, we take the natural logarithm of the CPGI scores. Since one cannot log a negative number or a zero, non- gamblers and unclassified respondents are eliminated in this regression analysis. In short, this transformation does not improve the analysis and this model is even weaker than the first, explaining nearly none of the variation. The model as a whole is not statistically significant and only the variable English/British is significant. The results are reported in Table B7 in Appendix B. Transforming the dependent variable by taking the square root of CPGI slightly improves the amount of explained variance. Again, since one cannot have a number less then zero, the non-gamblers and unclassified are excluded from the analysis. This model explains nearly 2% (the adjusted r-squared is 0.019) of the variation in CPGI and is significant at p<0.001. Almost half the independent (ethnocultural) variables are statistically significant, indicating ethnocultural influences on gambling: they are Native, English/ British, Chinese, East Indian, French, German and Irish. As before, Native, Chinese and East Indian identifications all have a positive relationship with CPGI scores while English/ British, German and Irish all have slightly negative relationships. The results are reported in Table B8 in Appendix B. In hopes of further improving the power of the model, we try switching the dependent variable from CPGI – which is far from normally distributed – to Gambling Diversity (or mean number of gambling activities), which is much closer to a normal distributed and is highly correlated with CPGI. Table A9 shows the results of this analysis. Here, all cases were included. The new model explains only 0.7% of the variation in the dependent variable, though it is significant at p<0.001. The results vary somewhat from the pattern that CPGI scores revealed. When we look at the number of gambling activities, English/British, Canadian, French, German and Italians are significantly different from the average. English/British, Canadians, French and Italians play a larger-than-average number of games, 191 whereas Germans play fewer. We then try to improve this model by taking the square root of the number of gambling activities. Once again, however, we find that the model explains just 0.7% of the variation, though the model is significant at p<0.001. Now, Canadian, Chinese and Native ethnic groups are significantly different from the mean. Canadians and Natives appear to play more varied games, whereas Chinese play less varied games.

Logistic Regression Models Since linear regression models are unable to deal effectively with non-normal dependent variables – especially, with CPGI – we switch to using logistic regression models to analyze the effects of ethnocultural identification on gambling. A logistic regression model does much the same thing as a linear regression model, but it does not require the variables used to be continuous. In fact, they may even be nominal variables, like being a problem gambler or non-problem gambler. While this shift loses some information by shifting to categories in the dependent variable, we eliminate the problem with normality. So, in the analyses that follow, we have lost some statistical power but, overall, gained the ability to work with a non-normal dependent variable. Now, instead of a continuously distributed dependent variable – CPGI score-- we look at dichotomous (two-category) dependant variables: gamblers versus non-gamblers, and problem versus non-problem gamblers. Since gambling is, in most forms, legal and enjoyed by large majorities of all the groups we are studying, the key difference of interest is how and why some gamblers are problem gamblers and some are not. Therefore, in the logistic regression analyses that follow we compare non-problem and problem gamblers. To do this, we code non-problem gamblers as zero and all other gamblers -- i.e., the CPGI levels at-risk, moderate and severe probable problem gamblers -- as 1. The regression is done in two blocks. In the first block are all the ethnic identifications of interest. In the second block are both the ethnic identifications and socio-demographic variables of interest. We add each sociodemographic variable in block two incrementally, so that we can tell whether ethnicity is still significant. For ethnicity, the “other” category is the reference (or comparison) category; for socio-demographic variables, the last category for each variable is the reference (or comparison) category. These regressions use weighted data. The model (block 1) containing only ethnic variables is statistically significant at p<0.000, and explains between 2.0% and 3.4% of the variance in the dependent variable CPGI. In particular, five ethnic identifications significantly predict gambling levels:

• Native respondents are twice as likely (2.505 times more likely) more likely than other respondents to be problem gamblers; • English respondents are 0.720 times less likely than others to be problem gamblers; • Chinese respondents are over three times more likely (3.186) times more likely) than other respondents to be problem gamblers; • French respondents are 0.614 times less likely than others to be problem gamblers; and • German respondents are 0.658 times less likely than others to be problem gamblers.

Block 2, as we have said, includes all the ethnic groups, plus a number of socio-demographic variables: region, gender, age, marital status, educational status, household income and age of the respondent. Our purpose, as indicated at the beginning of this chapter, is to determine whether the influence of ethnicity on gambling is spurious and will disappear once we control for other socio- demographic variables. By adding these socio-demographic variables to the model, we increase the amount of variance explained by all the independent (predictor) variables. Now, predictor variables in the model explain 192 between 4.5 to 7.8% of the variance in the dependent variable, depending on whether we use the Cox and Snell measure (0.045) or the Negelkerke measure (0.078). Also, the ability of the model to predict problem gamblers has increased. For complete regression tables see Appendix B. In the final model of the regression:

• Native people are still 2.2 times more likely than other respondents to be problem gamblers; • English respondents are 0.788 times less likely then others to be problem gamblers; • Chinese respondents are nearly three times (2.8) more likely to be problem gamblers; • French respondents are still .586 times less likely then others to be problem gamblers. • German respondents are still .647 times less likely then others to be problem gamblers.

Additionally, some of the sociodemographic variables added to the model significantly contributed to the final model. In particular, male respondents are 1.3 times more likely to be problem gamblers, while married individuals are 0.639 times less likely to be problem gamblers. Also, as age and educational attainment increase the likelihood of having gambling problems declines.

Table 4.11: Logistic Regression: Gambling Level by Multiple Ethnicity and Sociodemographics

B S.E. Wald df Sig. Exp(B) Block 1: Native * 0.918 0.223 16.893 1 0.000 2.505 Dutch -0.006 0.248 0.001 1 0.981 0.994 English -0.328 0.108 9.305 1 0.002 0.720 Canadian -0.304 0.190 2.559 1 0.110 0.738 Chinese 1.159 0.305 14.391 1 0.000 3.186 East Indian 0.645 0.382 2.850 1 0.091 1.907 French -0.488 0.175 7.811 1 0.005 0.614 German -0.419 0.194 4.664 1 0.031 0.658 Irish -0.257 0.140 3.376 1 0.066 0.773 Italian 0.393 0.196 3.993 1 0.046 1.481 Polish 0.213 0.266 0.638 1 0.424 1.237 Scottish -0.228 0.140 2.663 1 0.103 0.796 Ukrainian 0.149 0.306 0.236 1 0.627 1.160 Constant -1.542 0.085 330.105 1 0.000 0.214 Block 2: Final Model Native * 0.783 0.231 11.483 1 0.001 2.187 Dutch 0.015 0.250 0.004 1 0.953 1.015 English -0.239 0.109 4.782 1 0.029 0.788 Canadian -0.291 0.192 2.294 1 0.130 0.747 Chinese 1.026 0.316 10.512 1 0.001 2.790 East Indian 0.625 0.392 2.548 1 0.110 1.869 French -0.534 0.176 9.182 1 0.002 0.586 German -0.436 0.196 4.922 1 0.027 0.647 Irish -0.234 0.142 2.717 1 0.099 0.791

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B S.E. Wald df Sig. Exp(B) Italian 0.354 0.200 3.146 1 0.076 1.425 Polish 0.337 0.270 1.555 1 0.212 1.400 Scottish -0.195 0.141 1.908 1 0.167 0.823 Ukrainian 0.198 0.311 0.406 1 0.524 1.220 Gender Male 0.279 0.097 8.233 1 0.004 1.322 Female (reference) Marital Status 18.475 3 0.000 Married -0.447 0.116 14.859 1 0.000 0.639 Widowed -0.115 0.253 0.207 1 0.649 0.891 Separated -0.084 0.170 0.243 1 0.622 0.920 Single (reference) Age -0.020 0.004 30.159 1 0.000 0.980 Educational Attainment -0.177 0.037 22.353 1 0.000 0.838 Constant 0.057 0.235 0.059 1 0.809 1.059

To summarize to this point, compared with linear regression, logistic regression significantly improves our predictive power – as measured by the total amount of variance explained. At first, the groups identified as problem gamblers – Native and Chinese -- continue to be identified as problem gamblers in block 1 logistic analysis. The same groups identified as non-problem gamblers – particularly, the English – continue to be identified as non-problem gamblers. Additionally, French and German respondents are now identified as non-problem gamblers. After we introduce the socio-demographic variables into our analysis, in block two, the amount of variance explained by the model more than doubles. This means that these socio-demographic variables have an independent effect on gambling, over and above any effect they may exercise through ethnicity. That said, the predictive power of ethnicity diminishes for the Native and Chinese groups, as well as for the other ethinic groups identified in block 1. Chinese people continue to run a higher-than- reference risk of problem gambling, as they are three times more likely then other respondents to develop gambling problems. The predictive power of ethnicity remains significant but “b” (beta) decline approximately when sociodemographic variables are added to the model. For example, for the Native respondents betas declines from 2.5 to 2.2 – a decline of 12.0%, while for the Chinese respondents betas decline from 3.2 to 2.8—a decline of 12.5%. (There is small decline in the predictive power of ethnicity for the French and Germans after the socio-demographic variables are included.) This says that an important part of the supposed ethnic influence we have been tracking is due to compositional features of the particular ethnic group. This is slightly true for the Native respondents, and much more true for the English and Chinese respondents. So, it is useful for us to consider the statistically significant socio-demographic variables in block two. Understanding them will help us to understand why we thought ethnicity made a different, where the English and Chinese respondents are concerned. Note first that age has a statistically significant effect on problem gambling (p = .000), with younger people being more likely to have problems. Second, gender has a statistically significant effect (p= .004) in the model, with males 1.322 times more likely than females to be problem gamblers. Third, marital status has a significant effect on gambling status (p=.000). In particular, married respondents are less likely to have gambling problems (p=.000). Finally, educational attainment is significantly related to gambling levels (p = .005). The odds of being a problem gambler decrease as the educational attainment of the respondents increases.

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In this model, the influence of (Ontario) regional variations and household income disappears once we hold socio-demographic variations constant. Note that the effects of age, gender, income and education – though related to one another in the “real world” – exercise independent influences in this model. Thus, education and income have distinct, important influences on gambling. Likewise, age and income have distinct, important influences on gambling. As a result, we can reasonably say that poor, uneducated young men run the highest risk of problem gambling, for four different reasons (i.e., income, education, age and gender). Second, the effects of income, education, age and gender should be able to account compositionally for the earlier apparent effects of ethnicity in the English and Chinese groups. Said another way, the lower-than-average risk of problem gambling among English respondents must, logically, be explainable by an above-average mix of English respondents with higher-than-average income, higher-than-average education and older-than-average age, who are also more often women than men. Conversely, the higher-than-average risk of problem gambling among Chinese respondents must, logically, be explainable by an above-average mix of Chinese respondents with lower-than- average income, lower-than-average education and younger-than-average age, who are also more often men than women. Note once again, however, that compositional (socio-demographic) variables cannot explain away the particularity of the Native gambling pattern.

Multinomial Logistic Regression In order to develop a finer-grained model, with more levels of variation in the dependent variable (i.e., three levels versus two, as in the logistic regression), we also use multinomial logistic regression analysis. In this analysis, we employ the gambling levels associated with CPGI score. The “reference (or comparison) category” is “non-problem gambling.” “Moderate” and “severe” levels of problem gambling are combined into one category and, along with at-risk gambling, are compared to the reference category of non-problem gambling. The model is significant, and explains between 5.6- 8.7% of variance (Cox and Snell=0.056, Negelkerke=0.087). The results of this analysis are congruent with the previous analysis. Native respondents remain 1.947 times (p = .016) more likely than other respondents to be at-risk of gambling problems, rather than to be non-problem gamblers. Chinese respondents are 2.711 times more likely to be at-risk of gambling problems (p = .004). At the other end, Canadian respondents are .584 times (p = .029) less likely than others to be at-risk gamblers, rather than non-problem gamblers. French respondents are .661 times (p = .043) less likely to be at-risk gamblers. The results are similar but slightly different when we examine the tendencies of different ethnic groups to be moderate or severe problem gamblers, rather than non-problem gamblers. Then, we see that Native respondents are 2.982 times (p = .002) more likely than other respondents to have moderate or severe gambling problems, rather than to be non-problem gamblers. At the other extreme, English respondents are .630 times (p = .022) less likely than other respondents to have moderate or severe gambling problems, rather than to be non-problem gamblers.

Table 4.12: Multinomial Logistic Regression: Gambling Levels by Multiple Ethnicity and Sociodemographics

At risk gamblers B Std. Error Wald df Sig. Exp(B) Intercept -0.534 0.347 2.367 1 0.124 AGE -0.023 0.004 28.832 1 0.000 0.977 Educational Attainment -0.138 0.045 9.433 1 0.002 0.871 Household Income -0.015 0.020 0.557 1 0.455 0.985

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At risk gamblers B Std. Error Wald df Sig. Exp(B) Native * 0.666 0.277 5.802 1 0.016 1.947 Dutch 0.206 0.269 0.587 1 0.443 1.229 English -0.144 0.125 1.330 1 0.249 0.866 Canadian -0.538 0.246 4.763 1 0.029 0.584 Chinese 0.997 0.350 8.116 1 0.004 2.711 East Indian 0.402 0.473 0.723 1 0.395 1.495 French -0.414 0.203 4.174 1 0.041 0.661 German -0.345 0.222 2.411 1 0.120 0.708 Irish -0.173 0.162 1.142 1 0.285 0.841 Italian 0.434 0.223 3.787 1 0.052 1.544 Polish 0.462 0.299 2.393 1 0.122 1.587 Scottish -0.283 0.169 2.793 1 0.095 0.753 Ukrainian 0.037 0.391 0.009 1 0.925 1.038 Region East 0.124 0.269 0.212 1 0.645 1.132 Central East 0.465 0.255 3.324 1 0.068 1.592 Toronto 0.393 0.245 2.564 1 0.109 1.481 Central West 0.283 0.255 1.236 1 0.266 1.328 Central South 0.149 0.279 0.286 1 0.593 1.161 South West 0.299 0.263 1.296 1 0.255 1.349 North (reference) 0.000 . . 0 . . Gender Male 0.256 0.112 5.194 1 0.023 1.292 Female 0.000 . . 0 . . Marital Status Married -0.401 0.139 8.352 1 0.004 0.670 Widowed -0.002 0.294 0.000 1 0.994 0.998 Divorced/separated -0.076 0.197 0.148 1 0.700 0.927 Single (reference) 0.000 . . 0 . .

Moderate/severe gamblers B Std. Error Wald df Sig. Exp(B) Intercept -1.162 0.498 5.433 1 0.020 AGE -0.015 0.006 5.573 1 0.018 0.985 Educational Attainment -0.256 0.068 14.367 1 0.000 0.774 Household Income -0.036 0.031 1.347 1 0.246 0.964 Native * 1.093 0.346 9.957 1 0.002 2.982 Dutch -0.739 0.599 1.523 1 0.217 0.478 English -0.462 0.201 5.264 1 0.022 0.630 Canadian 0.158 0.286 0.306 1 0.580 1.171 Chinese 0.743 0.517 2.068 1 0.150 2.102 East Indian 0.797 0.572 1.936 1 0.164 2.218 French -0.630 0.332 3.610 1 0.057 0.532 German -0.600 0.376 2.545 1 0.111 0.549 Irish -0.345 0.263 1.721 1 0.190 0.708 Italian 0.036 0.370 0.009 1 0.923 1.036 Polish -0.009 0.533 0.000 1 0.986 0.991 Scottish 0.076 0.233 0.108 1 0.742 1.079 Ukrainian 0.608 0.454 1.789 1 0.181 1.836 Region East -0.692 0.431 2.584 1 0.108 0.500

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Moderate/severe gamblers B Std. Error Wald df Sig. Exp(B) Central East 0.133 0.360 0.137 1 0.711 1.143 Toronto 0.432 0.329 1.728 1 0.189 1.541 Central West -0.019 0.359 0.003 1 0.957 0.981 Central South 0.211 0.372 0.323 1 0.570 1.235 South West -0.066 0.380 0.030 1 0.862 0.936 North (reference) 0.000 . . 0 . . Gender Male 0.404 0.173 5.485 1 0.019 1.498 Female 0.000 . . 0 . . Marital Status Married -0.349 0.211 2.735 1 0.098 0.706 Widowed -0.316 0.448 0.499 1 0.480 0.729 Divorced/separated -0.097 0.293 0.110 1 0.741 0.908 Single (reference) 0.000 . . 0 . . a This parameter is set to zero because it is redundant. * Note: For all ethnic groups, the “other” category is the reference group.

The socio-demographic results are similar to what we saw earlier, using simple logistic regression. Focusing on the at-risk comparison with non-problem gamblers, males are 1.292 times more likely than females to be at-risk gamblers, rather than non-problem gamblers. As age and educational attainment increase, the likelihood of being at risk of gambling problems declines. Finally, respondents who are married are .670 times less likely than those who are single to be at-risk gamblers, rather than non-problem gamblers. When looking at moderate/severe comparison with non-problem gamblers, males are 1.498 (p=.019) times more likely then females to experience moderate or severe gambling problems as opposed to having no gambling problems. Older respondents and those with higher education are less likely to have moderate or severe gambling problems as compared to those having no problems.

Conclusions to this point Owing to the non-normal distribution of CPGI – our key dependent variable -- we have had to apply a variety of multivariate techniques of analysis to these data. The results have been consistent, however. None of the resulting models, though statistically significant, have explained much variance in the dependent variable. After adding in the socio-demographic variables, the influence of ethnic identification on gambling has diminished in almost all cases. At the same time, ethnic patterns of gambling have been consistent even when they have not been statistically significant. The Chinese respondents continue to show an above-average tendency to gambling problems, and the English respondents a below-average tendency, even after the inclusion of socio-demographic variables. The same is true to a lesser degree of certain other ethnic groups in this analysis: the French, Germans, and Canadians, among others. Conceivably, for some of these groups – especially smaller groups like the Chinese and East Indians – the sample size is too small to yield powerful results when we add in more predictor variables. In Chapter 6 we deal with this problem by imputing mean values to variables where values are missing, in this way retaining the required total number of cases.

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Chapter Five: Neighborhood Variations

To determine how neighbourhood influenced respondents’ gambling behaviour, we used Canadian Census data from 1996 to provide information on neighbourhood characteristics. In the Ontario Prevalence survey, all respondents listed an address with a 6-digit postal code. Using these postal codes, we linked individual respondents to their respective Enumeration Areas in the 1996 census. An Enumeration Area is defined by Statistics Canada as a “Small area composed of one or more neighbouring blocks, used by Statistics Canada for distributing questionnaires to households and dwellings. All of Canada is divided into enumeration areas.” Typically, an enumeration area is the geographic area canvassed by one census representative… For efficient and effective questionnaire drop-off and canvassing, EAs are as compact as possible, and the boundaries follow visible features such as streets and rivers when possible. The number of dwellings in an EA generally varies between a maximum of 650 in large urban centres (census metropolitan areas and census agglomerations with census tracts) to a minimum of 125 in rural areas.” (from http://www.statcan.ca/english/census2001/dict/geo024.htm, accessed on November 27, 2003.) Census Data for 1996, accessible by both postal code and Enumeration Area, were available in electronic form at Robarts Research Library. There, the Ontario Prevalence Survey data were linked to roughly 1600 socio-demographic variables describing each enumeration area, creating an enlarged database for analysis (Access http://www.statcan.ca/english/census96/define.html#census agglom for a listing of all the available variables.) The Census Data we used, collected in 1996 (five years before the Ontario Prevalence Survey data) were the most recent data available, by postal code, at the time we conducted this research. Moreover, in many ways, the 1996 data were preferable to data from 2001, since Enumeration Areas increased in size between 1996 and 2001. Statistics Canada report, “EAs in large urban centres (census metropolitan areas and tracted census agglomerations) contain a maximum of 650 dwellings [in 2001], an increase of 210 dwellings from the 1996 Census.” The smaller areal units provide a better sense of what we normally mean by a “neighbourhood.” The variables we selected from among those available in the Census database were as similar as possible to the variables in the Ontario Prevalence Survey. Additionally, we recoded the census data to have roughly the same age categories, income categories, employment categories and, most important of all, ethnocultural identification categories, as we had in the Ontario Prevalence Survey. See Appendix B below for a detailed definition of the Census variables used in this analysis, and the ways they were recoded for our use. In the analyses that follow, all measures of gambling behaviour are taken from the Ontario Prevalence Survey, while all ethnic and socio-demographic variables are taken from the Census Enumeration Area (“neighbourhood”) data. Thus, a correlation (reported below) of r = .037* between Native ethnicity and CPGI score means that respondent CPGI scores are significantly and upwardly (at p< .05) influenced by the proportion of people in the neighbourhood who are of Native ancestry. Likewise, a correlation of r = -.063** between English ethnicity and CPGI score means the respondent CPGI scores are significantly and downwardly (at p < .01) influenced by the proportion of people in the neighbourhood who are of English ancestry.

Ethnic and Socio-demographic Compositions of Neighbourhoods

As we have just seen, the gambling variables – CPGI and gambling diversity – are influenced by both the ethnicity measures and the socio-demographic variables. Likely, this is because these two sets of influences are correlated with each other. Support for this conjecture is found in Table 11.1. There it is evident that the ethnic composition of a neighbourhood is significantly correlated with virtually every socio-demographic 198 variable we have examined. For the purpose of illustration, we confine ourselves to three ethnocultural groups of particular interest: the Native or Aboriginal population, English, and Chinese. The data in Table 5.1 tell us that, as the proportion of neighbourhood residents who are Native increases, statistically speaking, there is a concomitant increase in the proportion of:

• Males (r = .069**) • Never married residents (r = .124**) • Separated residents (r = .107**) • Divorced residents (r = .126*) • Unemployed residents (r = .209*) • Residents not in the labour force (r = .089**) • Young residents aged 18-24 (r = .075**) • < High school educated people (r = .178**) and a concomitant decrease in the proportion of:

• Females (r = -.074**) • Married residents (r = -.148**) • Employed residents (r = -.143**) • Some post secondary education (r = -.100**) • Complete post secondary educ (r = -.154**) • Residents over the age of 40 (-.039** < r < -.079**) • Higher household incomes r = -.087**)

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Table 5.1: Neighbourhood Ethnicity by Neighbourhood Socio-demographics (correlations)

East Native Dutch English Canadian Chinese Indian French German Irish Italian Polish Scottish Ukrainian Gender Male .069** .082** -.044** .098** .046** .028* .057** .051** 0.002 .069** .034* -0.011 .046** Female -.074** -.082** .042** -.096** -.045** -0.028 -.053** -.053** 0 -.072** -.034* 0.009 -.050** Marital status Never married/single .124** -.193** -.292** -.308** .224** .096** 0.005 .226** -.139** -.042** .037** -.199** -0.013 Married -.148** .193** .174** .240** -.069** 0.014 -.041** -.078** .088** .157** -0.01 .141** .034* Separated-still married .107** -.104** -.047** 0.004 -.100** 0.006 .072** -.093** -.043** -.189** 0.012 -.066** -.045** Divorced .126** -.088** .070** .046** -.148** -.130** .140** -.131** .046** -.216** .033* .029* -0.023 Widowed 0.006 -0.023 .084** -.040** -.104** -.121** -0.019 -.100** .028* -.101** -.067** 0.027 -.029* Employment Employed -.143** .135** .068** .081** -0.009 0.019 -.051** 0.006 .112** .048** .085** .150** .067** Unemployed .209** -.131** -.211** -.045** .030* .132** .110** 0.006 -.173** -.062** -0.019 -.211** -.040** Not in the labour force .089** -.095** -0.008 -.075** 0.002 -.060** 0.018 -0.008 -.067** -.029* -.077** -.098** -.056** Age 18-24 .075** 0.017 -.094** .046** .102** .103** .090** .092** -.060** .045** -.064** -.047** -.041** 25-29 0.021 -.143** -.232** -.268** .170** .095** -.034* .165** -.139** 0.011 .063** -.170** -0.025 30-34 0.012 -.096** -.203** -.124** .112** .096** 0.009 .102** -.120** -0.014 .086** -.148** 0.009 35-39 0.004 -.040** -.101** .057** .064** .047** .073** .057** -.047** 0.002 .088** -.036* .068** 40-44 -.045** 0.017 0.026 .090** .063** .042** 0.023 .057** .056** -0.017 .088** .059** .083** 45-49 -.073** 0.023 .088** .051** .058** 0.005 -0.01 .071** .098** .032* .061** .102** .064** 50-54 -.079** .042** .148** .041** -0.019 -.028* 0.006 -0.005 .126** 0.025 0.004 .128** .049** 55-59 -.040** 0.015 .087** -0.022 -0.02 -.071** -0.017 -0.009 .059** .102** -.047** .047** -0.001 60 or more -.039** -0.013 .144** -.065** -.105** -.162** -.067** -.092** .072** -.039** -.062** .080** -0.012 Education - Less then high school .178** .122** .058** .271** -.188** -.055** .098** -.192** -.052** -0.019 -.120** -.054** -.059** Completed high school -0.002 .107** .102** .257** -.130** -0.007 .117** -.138** 0.02 -.034* -.034* .046** -.041** Some post secondary -100** -.159** -.124** -.267** .211** .134** -.061** .214** -.044** .029* .108** -.049** .028* Completed post secondary -.154** -.067** .090** -.134** .148** 0.026 -.070** .155** .175** -.040** .119** .188** .088** Household income -.087** -0.004 .059** .111** -.032* 0.007 .091** 0.007 .062** .055** .074** .096** .062**

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By contrast, as the proportion of neighbourhood residents who are English increases, statistically speaking, there is a concomitant increase in the proportion of:

• Females (r = .042**) • Married residents (r = .193**) • Employed residents (r = .068**) • Residents with some high school (r = .058**) • Completed post secondary educ (r = .090**) • Residents completed high school (r = .107**) • Residents aged 45 and over (.087** < r < .148**) • Higher household incomes (r = .059**)

As the proportion of neighbourhood residents who are English decreases, statistically speaking, there is a concomitant increase in the proportion of:

• Males (r = -.044**) • Single residents (r = -.292**) • Separated residents (r = -.047**) • Unemployed residents (r = -.211**) • Residents < age 40 (-.094** < r < -.232**) • Some post secondary educ (r = -.124**)

Note, then, that the neighbourhoods with higher proportions of English residents are socio- demographically almost the exact opposite of neighbourhoods with higher proportions of Native residents. Finally, consider the pattern of Chinese residency. As the proportion of neighbourhood residents who are Chinese increases, statistically speaking, there is a concomitant increase in the proportion of:

• Males (r = .046**) • Single residents (r = .224**) • Unemployed residents (r = .030*) • Residents < age of 35 (.102** < r < .170**) • Residents aged 35-50 (.058** < r < .064**) • Residents with post secondary educ (.148** < r < .211**)

And a concomitant decrease in the proportion of:

• Females (r = -.045**) • Married residents (r = -.069**) • Divorced residents (r= -.148**) • Elderly residents (r = -.105**) • High school or less education (-.130** < r < -.188**) • Higher household incomes (r = -.032*)

In some respects, the Chinese pattern is like the Native pattern. Both groups live in neighbourhoods characterized by a relatively young, disproportionately male population, with more than average unemployment and low average household incomes. Unlike the Native neighbourhoods, however, the Chinese neighbourhoods have a higher average level of formal education. The Chinese 201 neighbourhoods are poor despite the educational attainment of their residents – likely, the result of recent immigration and job discrimination based on race or foreign credentials. The Native neighbourhoods are poor because of low educational attainment, unemployment and, perhaps also, because of higher rates of marital dissolution. Though all of the correlations in this table are low, they are all statistically significant and identify socially important differences in the ethnic populations of Ontario. Because of the high correlations between ethnicity and socio-demographic characteristics, gambling activities in Ontario also vary from one kind of neighbourhood to another, due to socio-demographic differences. These differences in gambling are displayed in Table 5.2 below. However, rather than describe everything in this table, note a few key features. Consider the gambling that is characteristic of neighbourhoods in which larger than average fractions of residents are (a) employed, (b) married, or (c) highly educated (i.e., completed a post-secondary degree.

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Table 5.2: Respondent Gambling Activities by Neighbourhood Socio-demographics

Casinos Table Sport sporting Cards/ board Games Arcade/ Sports with out of Lottery Scratch Raffles Races Bingo Slots games VLTs Select events (friends) of skill video Internet bookie Stocks province Gender Male 0.002 .040** 0.025 0.011 0.000 -0.010 0.005 0.024 0.022 0.021 0.003 .046** .052** 0.000 0.012 .032* 0.012 Female 0.001 -.041** -0.021 -0.013 -0.003 0.013 -0.004 -0.023 -0.021 -0.020 -0.005 -.043** -.052** -0.001 -0.016 -.034* -0.010 Marital status Never married/single -0.021 0.001 -.065** -0.011 0.012 0.012 .044** .063** .052** .034* 0.025 .077** .104** 0.001 -0.004 .055* .035* Married 0.013 0.011 .084** .038** -.042** -0.013 -0.015 -.033* -0.051 0.000 -0.008 -.043** -.040** -0.004 0.016 -0.008 -0.012 Separated- .030* .033* -.052** -.036* .044** 0.027 0.003 0.013 .030* -0.011 0.007 0.020 0.012 0.006 -0.016 -.029* -0.013 Divorced .028* 0.023 -.058** -.038** .045** 0.008 -0.025 -0.001 .040** -0.023 -0.016 0.007 -0.017 0.005 -0.019 -0.025 -0.012 Widowed -0.021 -0.047 -0.028 -.032* 0.028 -0.006 -.030* -.037* -0.008 -.039** -0.017 -.046** -083** -0.001 -0.015 -.051** -0.026 Employment Employed 0.000 0.023 .036* .050** -.059** -0.020 0.003 0.019 -0.017 .035* -0.011 0.020 .053** -0.005 0.010 .038** 0.016 Unemployed 0.020 .030* -.045** -.034* .036* 0.025 .034* 0.022 .044** 0.023 0.008 .041** .033* 0.016 -0.019 -0.027 -0.010 Not in the labour force -0.005 -.037** -0.023 -.041** .050** 0.013 -0.013 -0.024 0.004 -.042** 0.010 -.034* -.066** -0.001 -0.004 -.033* -0.015 Age 18-24 -0.006 0.027 0.003 0.008 .031* 0.014 0.008 .049** .042** .034* 0.021 .068** .054** -0.005 -0.003 0.028 0.010 25-29 -0.018 0.019 -.070** -0.009 0.011 0.026 .038** .051** .047** .040** 0.028 .070** .094** 0.007 0.006 .050** 0.018 30-34 0.022 .050** -.057** 0.018 -0.007 0.012 0.008 .030* .039** .040** 0.007 .042** .087** 0.004 -0.001 0.017 -0.008 35-39 .043** .080** 0.017 0.014 -0.019 -0.008 -0.001 0.007 0.021 0.023 -0.018 0.024 .063** 0.009 -0.005 0.001 0.016 40-44 .037* 0.028 .046** 0.023 -.046** -0.011 0.014 -0.005 -0.017 0.009 -0.019 0.008 0.016 -0.001 0.004 .034* 0.019 45-49 0.012 -0.034 .054** .037* -0.040 -.038** -0.006 -0.001 -.029* 0.005 -0.017 0.000 -0.003 0.000 0.007 0.017 0.028 50-54 0.005 -.043** .042** .040** -0.028 -0.011 0.012 -0.011 -.035* 0.000 -.029* -0.017 -0.024 -0.009 0.002 0.021 .033* 55-59 -0.008 -.045** .037* -0.006 -0.011 -0.011 0.004 -.041** -0.020 -.030* -0.002 -.035* -.080** -0.011 0.021 0.021 0.017 60 or more -.041** -0.072 -0.015 -0.024 0.006 0.000 -0.024 -.049** -0.022 -.049** -0.004 -.056** -.097** -0.004 -0.004 -.035* -0.009 Education Less then high school .047** .058** -0.006 -0.026 .088** 0.021 -.041** -0.013 0.019 -.033* -0.004 -0.024 -.055** 0.006 -0.005 -.093** -.052** Completed high school .056** .042** 0.025 0.000 0.024 0.013 -0.022 0.004 0.008 -0.008 -0.012 -0.027 -0.027 -0.001 -0.016 -.045** -.029* Some post secondary -0.021 -0.011 -0.012 .029* -.031* 0.010 .052** .045** 0.028 0.019 .033* .037* .064** -0.004 0.004 .048** .051** Completed post secondary -.034* -.049** .035* 0.028 -.095** -.029* .035* 0.004 -.033* .040** -0.001 0.011 .043** -0.005 0.002 .091** .053** Household income

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Neighbourhood Gambling Participation

The CPGI Score Just as we did in the last chapter, we now examine the influence of various socio-demographic variables on gambling. We examine the same socio-demographic variables as before, this time at the neighbourhood level. We do so in the anticipation that ethnic and socio-demographic variables will prove to be correlated, and we will need to control for socio-demographic variables – as we did in the last chapter – to partial out the unique impact of ethnocultural variation. The data in Table 5.3 show socio-demographic variations by neighbourhood. Specifically, respondents with high CPGI scores are likely to be found in neighbourhoods with a larger than average proportion of single (i.e., never-married) people (r = .060**) and separated people (.078**), unemployed people (r = .048**), young people aged 18-24 (r = .044**) and ages 25-29 (r = .036*), and people with less than a high school education (r = .036*). Respondents with low CPGI scores are likely to be found in neighbourhoods with a larger than average proportion of married people (-.052**), employed people (r = -.035*), people who have completed post-secondary education (r = -.042**), and older people aged 50-54 (r = -.033*), 55-59 (r = -.062**), and 60 or more (r = -.039*). The results are similar, whether we include non-gamblers or not.

Table 5.3: Respondent CPGI score, by neighbourhood socio-demographics (correlation, unweighted, total population)

CPGI score CPGI score Gamblers only Non-gamblers included Gender Male .010 .016 Female -.015 -.020 Marital status Never married/single .060** .054** Married -.052** -.040** Separated-still married .078** .068** Divorced .024 .019 Widowed -.027 -.036* Employment Employed -.035* -.022 Unemployed .049** .044** Not in the labour force .020 .010 Age 18-24 .044** .039** 25-29 .036* .037* 30-34 .007 .016 35-39 -.004 .009 40-44 -.014 -.006 45-49 -.022 -.010 50-54 -.033* -.024 55-59 -.062** -.054** 60 or more -.039* -.046** Education Less then high school .036* .025 Completed high school -.007 .000 Some post secondary .011 .016

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CPGI score CPGI score Gamblers only Non-gamblers included Completed post secondary -.042** -.025 Household income -.029 -.018 N 3772 4613

CPGI Level The data in Table 5.4 tell a similar story, using ANOVA to isolate the socio-demographic variables significantly influencing CPGI levels. They show that problem gambling is less likely among respondents who live in neighbourhoods where a higher-than-average proportion of residents have completed a post-secondary education, are employed, married, and 55 years of age or older. Problem gambling is more likely in neighbourhoods where a higher-than-average proportion of residents are unemployed, younger than age 30, and single (never married) or separated.

Table 5.4: Respondent CPGI Level, by neighbourhood socio-demographics (ANOVA, unweighted, total population)

Non-gamblers Non problem At risk Moderate Severe Total Gender Male .4845 .4864 .4878 .4897 .4903 .4863 Female .5154 .5138 .5125 .5099 .5077 .5138 Marital status Never married/single .2965 .2961 .3097 .3144 .3490 .2982 *** Married ** .5317 .5379 .5228 .5152 .4802 .5345 Separated-still married .0345 .0343 .0375 .0382 .0438 .0348 *** Divorced .0664 .0659 .0669 .0689 .0731 .0662 Widowed .0709 .0658 .0625 .0622 .0557 .0663 Employment Employed * .6004 .6076 .5996 .5895 .5622 .6048 Unemployed ** .0571 .0568 .0603 .0647 .0701 .0575 Not in the labour force .3410 .3344 .3387 .3446 .3658 .3365 Age 18-24 * .0869 .0866 .0908 .0891 .0947 .0872 25-29 ** .0740 .0751 .0806 .0835 .0822 .0756 30-34 .0875 .0896 .0903 .0911 .0883 .0893 35-39 .0847 .0867 .0868 .062 .0868 .0863 40-44 .0769 .0779 .0763 .0752 .0791 .0775 45-49 .0703 .0716 .0698 .0701 .0684 .0712 50-54 .0544 .0552 .0532 .0535 .0495 .0548 55-59 * .0458 .0459 .0439 .0441 .0375 .0456 60+ * .1861 .1761 .1657 .1715 .1410 .1766 Education Less then high school .6922 .6812 .7062 .7027 .7359 .6864 Completed high school .1408 .1425 .1459 .1420 .1357 .1424 Some post secondary .2054 .2080 .2118 .2207 .2048 .2082 Completed post .4127 .4246 .4016 .4067 .3939 .4197 secondary *** Household income 324.7503 330.0500 325.3951 332.7778 296.7241 328.5454 N 841 3203 405 135 29 4613

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Gambling Diversity Measure The pattern is slightly different when we examine the socio-demographic influences on number of gambling activities (or what we have called “gambling diversity”). The data in Table 5.5 show that respondents have higher levels of gambling diversity if they live in a neighbourhoods with higher-than- average proportions of males (r = .036*), residents below the age of 45 and especially between the ages of 18-24 (r = .063**), 25-29 (r = .050**) or 35-39 (r = .051**), residents who are single (r = .056**), and residents with only some post-secondary education (r = .038*). They have lower levels of gambling diversity if they live in neighbourhoods with higher-than-average proportions of females (r = -.036*), widowed residents (-.067**), residents not in the labour force (r = -.040*), and residents aged 60 or more (-.080**).

Table 5.5: Number of Respondent Gambling Activities by Neighbourhood Socio-demographics (correlation, unweighted, total population)

Number of gambling Number of gambling activities— activities—gamblers only non gamblers included Gender Male 0.036* 0.040** Female -0.036* -0.038** Marital status Never married/single 0.056** 0.044** Married -0.014 -0.002 Separated-still married 0.015 0.014 Divorced -0.011 -0.011 Widowed -0.067** -0.068** Employment Employed 0.026 0.027 Unemployed 0.038* 0.033* Not in the labour force -0.040* -0.038** Age 18-24 0.063** 0.050** 25-29 0.050** 0.045** 30-34 0.042** 0.043** 35-39 0.051** 0.055** 40-44 0.032* 0.031* 45-49 -0.018 -0.002 50-54 -0.018 -0.007 55-59 -0.042** -0.032* 60 or more -0.080** -0.077** Education Less then high school 0.010 0.002 Completed high school 0.012 0.017 Some post secondary 0.038* 0.037* Completed post secondary -0.012 0.004 Household income 0.020 0.028 N 4071 4931

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Ethnic Neighbourhood Gambling Patterns

The CPGI score The data in Table 5.6 show that average CPGI scores -- which measure the presence of a gambling problem -- increase significantly with increases in the fraction of Native people (r = .037*) in the neighbourhood (See endnote for definition of ethnic categories used in Census of 1996). Conversely, CPGI scores decrease significantly with increases in the fraction of Irish (r = -.067**), English (r = -.063**), Canadian (r = -.052), Dutch (r = -.048**), Scottish (r = -.047**), and French (- .032*) ancestry people in the neighbourhood. The sizes of correlations are roughly the same whether we include non-gamblers or not.

Table 5.6: Respondent CPGI Score by Neighbourhood Ethnicity (correlation, unweighted, total population)

Ethnicity CPGI Score—gamblers only CPGI score-non-gamblers included Native .037* .045** Dutch -.048** -.054** English -.063** -.054** Canadian -.052** -.039** Chinese .026 .023 East Indian .027 .009 French -.032* -.005 German .023 .024 Irish -.067** -.060** Italian .008 .015 Polish .020 .025 Scottish -.047** -.046** Ukrainian -.018 -.005 Total 3772 4613

CPGI Level In Table 5.7, ANOVA is used to measure the strength and significance of ethnic composition as an influence on respondent gambling. The data show that in neighbourhoods where the fraction of Native people is high, at-risk, moderate, and severe problem gamblers are disproportionately common. In neighbourhoods where the fractions of Dutch, English, Canadian, French, Irish, and Scottish people are high, at-risk, moderate, and severe problem gamblers are disproportionately rare. To a lesser but still significant degree, problem gamblers are disproportionately common where the fraction of East Indian people is high, and rare where the fraction of Polish people is high.

Table 5.7: Respondent CPGI Level by Neighbourhood Ethnicity (ANOVA, unweighted, total population)

Gambling Levels Ethnicity Non-gamblers Non-problem At risk Moderate Severe Total Native** .0157 .0184 .0214 .0304 .0162 .0185 Dutch *** .0455 .0419 .0364 .0360 .0242 .0418 English*** .3137 .3164 .2939 .2795 .2564 .3125 Canadian *** .2593 .2673 .2485 .2371 .2055 .2629 Chinese .0300 .0295 .0346 .0362 .0499 .0304 East Indian * .0254 .0207 .0234 .0283 .0324 .0221 French *** .1111 .1304 .1211 .1117 .1022 .1253

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Gambling Levels Ethnicity Non-gamblers Non-problem At risk Moderate Severe Total German .0261 .0272 .0331 .0305 .0468 .0278 Irish *** .1767 .1773 .1640 .1506 .1351 .1750 Italian .0580 .0620 .0680 .0664 .0669 .0620 Polish* .0340 .0356 .0379 .0458 .0358 .0358 Scottish*** .1963 .1954 .1777 .1686 .1676 .1931 Ukrainian .0252 .0279 .0262 .0276 .0187 .0272 N 841 3203 405 135 26 4613

Gambling Diversity Measure The data in Table 5.8 examine the number of gambling activities respondents participate in, as a function of the proportions of different ethnic groups in the neighbourhood. The data show that respondents’ number of gambling activities increase significantly with increases in the fraction of Native people (r = .051**), French people (r = .066**), and Italian people (r = .052*) in the neighbourhood. They decrease significantly with increases in the fraction of Irish people (r = -.045**), English people (r = -.047**), and Dutch people (r = -.031*) in the neighbourhood. The sizes of correlations are similar whether we include non-gamblers or not.

Table 5.8: Number of Respondent Gambling Activities by Neighbourhood Ethnicity (correlation, unweighted, total population)

Ethnicity Number of gambling Number of gambling activities— activities—gamblers only non gamblers included Native 0.051** 0.055** Dutch -0.031* -0.032* English -0.047** -0.036 Canadian 0.003 0.019 Chinese 0.012 0.019 East Indian 0.006 -0.006 French 0.066** 0.081** German -0.016 -0.021 Irish -0.045** -0.043** Italian 0.052** 0.055** Polish 0.023 0.027 Scottish -0.037* -0.032* Ukrainian 0.038* 0.039** N 4088 4951

The data in Table 5.9 show marked variations in the types of games respondents play, as a function of the ethnic makeup of their neighbourhoods. So, for example, respondents living in neighbourhoods with a larger-than-average fraction of Native people are more likely than average to buy lottery tickets (r = .035*), scratch tickets (r = .068*), and raffle tickets (r = .046**), and to gamble at casinos outside the province (r = .068**). By contrast, respondents living in neighbourhoods with a larger-than-average fraction of English people are more likely than average to buy raffle tickets (r = .066**), less likely than average to gamble at casinos outside the province (r = -.035*), and less likely than average to play table games (r = -.051**), sport select (r = -.039**) or arcade/video games (r = - .051**). Again for example, respondents living in neighbourhoods with a larger-than-average fraction of Chinese people are more likely than average to play table games (r = .032*), arcade/video games (r = .032*) or speculative stocks (r = .055**) and less likely than average to buy scratch tickets ((r = - .051**) or raffle tickets (r = .031*).

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Table 5.9: Respondent Gambling Activities by Neighbourhood Ethnicity

East Gambling levels Native Dutch English Canadian Chinese Indian French German Irish Italian Polish Scottish Ukrainian Total Lottery .035* -0.03 -0.02 0.015 0.011 0.004 .055** 0.016 -.047** 0.022 0.011 -.033* 0.011 4857 Scratch .068* 0.013 -0.002 .054** -.051** -.039** .090** -.049** -0.003 0.009 0.008 -0.004 0.021 4805 Raffles .046** 0.009 .066** .067** -.031* -.063** .057** -0.025 .065** -0.001 0.007 .062** 0.006 4837 Races -0.02 0.002 -0.023 -0.024 -0.001 .031* -0.012 0.006 -0.023 0.022 0.025 -0.001 0.019 4742 Bingo 4750 Slots .035* -.039** -0.019 0 -0.023 -0.003 .043** -0.016 -0.016 0.028 0.005 -0.02 0.014 4698 Table games -0.02 -.041** -.051** -.043** .032* 0.025 -0.023 .037* -.073** .045** 0.011 -.044** 0.014 4637 VLTs .041** -0.02 -0.028 -0.024 0.027 0.023 0 0.028 -.036* 0.006 -0.006 -.037* -0.003 4672 Sport Select 0.026 -0.01 -.039** -.042** 0.014 0.001 0.016 0.013 -.051** 0.043 .039** -0.055 -0.005 4698 Outcome of sporting events 0.018 -0.03 -0.014 -0.022 0.013 -0.019 0.025 0.013 -0.007 0.027 0.024 0.001 0.022 4708 Cards with friends 0.015 -0 -0.005 -0.005 0.007 0.01 -0.012 0.008 -0.017 -0.008 -0.008 0.002 0 4711 Games of skill 4711 Arcade/video games 0 -0.02 -.051** -.084** .032* .041** -0.025 .031* -.051** 0.021 0.008 -.036* 0.012 4723 Internet 0.008 -0 -0.014 0.01 0.005 -0.007 -0.015 0.008 -0.012 -0.006 0.014 -0.013 0.005 4655 Sports with bookie -0.01 -0.01 -0.02 -.030* -0.003 -0.004 -.033* -0.001 -0.024 .050** 0.02 -0.012 -0.002 4661 Stocks -0.01 -.053** -0.019 -.037* .055** 0.008 -0.025 .060** -0.019 .032* 0.002 -0.014 0.011 4744 Casinos out of province .048** -.042** -.035* -0.023 0.004 0 0.025 0.01 -0.02 0.005 0.004 -0.007 .039** 4698

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What these data tell us is, that in neighbourhoods with different ethnic profiles, people gamble in different ways. This is likely because, in different neighbourhoods, different kinds of games are available, encouraged, taught and learned. In the next chapter, when we carry out hierarchical modeling, we will find out whether respondents gamble the way they do because they reflect the ethnic composition of their neighbourhood, are influenced by the neighbourhood’s ethnic composition, or both. (Then, for example, we will be able to tell how much difference there is in gambling by, say, Chinese respondents in mainly Chinese neighbourhoods, compared with say, East Indian respondents in mainly Chinese neighbourhoods, or Chinese respondents in mainly East Indian neighbourhoods.) What is clear from these data is that ethnic neighbourhood influences appear similar to ethnic identifications. So, for example, Native respondents tend to have high CPGI scores; so do respondents (of whatever ethnicity) who live in neighbourhoods with large numbers of Native residents. English respondents tend to have low CPGI scores; so do respondents (of whatever ethnicity) who live in neighbourhoods with large numbers of English residents. In short, so far we are seeing congruence between the individual-level Ontario Prevalence Data, and the neighbourhood level Census Data. This congruence gives us confidence that the results are reliable. Neighbourhoods in which a higher than average proportion are employed are neighbourhoods in which people are more likely than average to bet on raffles (r = .036*), races (r = .050**), sporting events (r = .035*), arcade or video games (r = .053**), and stocks (r = .038**). They are less likely than average to bet on bingo games (r = -.059**). Neighborhoods in which a higher than average proportion are married are neighbourhoods in which people are more likely than average to bet on raffles (r = .084**) and races (r = .038**). They are less likely than average to bet on bingo games (r = -.042**), VLTs (r = - .033*), games of skill (r = -.043**), or arcade or video games (r = -.040**). Finally, neighbourhoods in which a higher than average proportion are highly educated are neighbourhoods in which people are more likely than average to bet on raffles (r = .035*), table games (r = .035*), sporting events (r = .040**), arcade or video games (r = .043**), speculative stocks (r = .091**), and casinos out of province (r = .053**) Certain games have distinctive age profiles. For example, betting on VLTs, sport select, sporting events, games of skill, and arcade or video games are more common in neighbourhoods where there are higher than average fractions of residents under age 35. Betting on raffles is more common in neighbourhoods with higher than average fractions of residents over age 40. Because particular games are correlated with the socio-demographic features of a neighbourhood, they are also correlated with the ethnic composition of a neighbourhood.

Factor Analysis In Table 5.10, we simplify the analysis by using the four game factors established in the last chapter. Recall that the four factors are as follows:

• Factor 1: Casino Betting - casinos out of province, slots in casinos, casino table games and horse races • Factor 2: Ticket Betting - lottery, raffles, scratch tickets, and bingo • Factor 3: Social Betting - games of skill, arcade/video games, cards/board games with friends and VLTs • Factor 4: Sports Betting - sports with bookie, outcome of sporting events

The data in Table 5.10 show that the frequency of game factors or clusters also varies with the ethnic composition of a neighbourhood. For example, in neighbourhoods where Native residents are disproportionately numerous, we find a disproportionate tendency for respondents to bet on tickets (r=0.091**) and engage in social betting (r=0.040**). In neighbourhoods where English residents are 120 disproportionately numerous, we find a disproportionate avoidance of casino gambling (r = -.042**) and social games (r = -.062**). Finally, in neighbourhoods where Chinese residents are disproportionately numerous, we find a disproportionate avoidance of ticket betting (r = -.055**) and a disproportionate tendency to play social games (r = .031*).

Table 5.10: Respondent Gambling Factors by Neighbourhood Ethnicity

Ethnicity Factors 1 (casino) 2 (tickets) 3 (social) 4 (sports) Native 0.012 0.091** 0.040** -0.025 Dutch -0.050** 0.003 -0.027 -0.002 English -0.042** 0.010 -0.062** -0.001 Canadian -0.039* 0.075** -0.051** -0.027 Chinese 0.014 -0.055** 0.031* 0.011 East Indian 0.024 -0.047** 0.021 -0.013 French -0.007 0.130** 0.018 -0.012 German 0.021 -0.050** 0.028* 0.014 Irish -0.042** 0.000 -0.065** -0.009 Italian 0.027 0.004 0.012 0.040** Polish 0.001 0.005 0.006 0.038* Scottish -0.021 -0.010 -0.060** 0.009 Ukrainian 0.034 -0.002 -0.015 0.017 N 4353 4353 4353 4353

Conclusions to this point What we would conclude from the data so far is that gambling patterns vary from one neighbourhood to another, depending both on the ethnic makeup of the neighbourhood and other socio- demographic features of the neighbourhood. The first finding – that neighbourhood gambling varies by ethnicity – would lend support to the idea that different ethnic groups gamble differently and have different gambling problems as a result. The second finding – that neighbourhood gambling varies by socio-demographic variables associated with ethnicity – leads us to realize that we cannot draw inferences about ethnicity without controlling for the associated socio-demographic factors. We do so in the next section.

Multivariate analysis of gambling We recall difficulties in conducting multivariate analysis of CPGI scores encountered in the last chapter. Accordingly, we begin directly with binary logistic regression, a mode of analysis that does not rely on the normality of the dependent variable. The results in Table 5.11 compare problem and non-problem gamblers, and examine the influence of neighbourhood ethnic composition on respondent gambling levels, while controlling for neighbourhood socio-demographic composition. A stepwise selection method was used to add a number of socio-demographic variables to the model. The final model was significant with the likleyhood ratio 29.5747, df=2, p<0.001) and Gamma of 0.145. In brief, the results show that when we control for neighbourhood socio-demographic composition, the effects of neighbourhood ethnic composition disappear with the exception of those neighborhood that are predominantly Native, Canadian or Scottish. That is Native neighborhoods are 1.017 times more likely to be problems gamblers then non-problem gamblers, while Canadian and Scottish neighborhoods are .990 and .982, respectively, time less likely to be problem gamblers. Additionally, none of the sociodemographic variables were significantly related to the dependent variable.

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Table 5.11: Logistic Regression (Block 2—Final Model)

B S.E. Wald df Sig. Exp(B) Intercept -1.1680 0.1174 98.9150 1 .000 Native .0173 .00823 4.4242 1 .0354 1.017 Canadian -.00104 .00427 5.9146 1 .015 .990 Scottish -.0178 .00565 9.9031 1 .0016 .982

The fact that virtually none of the socio-demographic influences are statistically significant in this neighbourhood analysis when controlling for ethnic compositional variables may be due to several possibilities. First, the model may be so over-determined by intercorrelated variables that their relative importance cannot be distinguished. Second, there may be so many interactions between the predictor variables that there are no statistically significant main effects in the model, only interaction effects. Note that, in Ontario, we do not have homogeneous ethnic neighbourhoods, which means that a combination of ethnicity and socio-demographic variables determines one's neighbourhood. Note, finally, that our zero-order results (reported earlier in this chapter) tend to have low significance levels and the relationships are not very strong. This may be another reason they disappear when other influences are controlled. The results in Table 5.12 are similar. Here, we use multinomial logistic regression to compare at-risk and moderate or severe problem gamblers with non-problem gamblers. Since multinomial logistic regression does not give an option for stepwise selection, the model was run twice, once with just ethnicity and second time with ethnicity and demographics. The final model was significant with the likelihood ration of 69.3223, df=5, p=0.001, with Gamma of .199. None of the ethnic influences we have been particularly tracking – those of Native, English and Chinese – are statistically significant in this neighbourhood analysis. At the same time, Canadian and Irish ethnic neighborhoods are .985 and .988, respectively, less likely to have moderate or severe gambling problems. Age distribution of the neighbourhoods seem to marginally explain gambling levels, showing that neighbourhood with young residents or those who are separated but still married tend to experience gambling problems, while neighborhoods with higher education are less likely to experience no gambling problems.

Table 5.13: Multinomial Logistic Regression

B Std. Error Wald df Sig. Exp(B) Intercept -1.1558 .1174 96.8978 1 .001 Canadian -.015 .00432 12.0352 1 .001 .985 Irish -.0126 .00629 3.9897 1 .0458 .988 Percent separated but still legally married .1766 .0441 16.0351 1 .001 1.193 Age 18-24 .1329 .0447 8.8553 1 .0029 1.142 Completed Post Secondary -.1445 .0496 8.4862 1 .0036 .865

We also examined the effects on gambling of average age at immigration and average year of immigration in the neighbourhood, using both binary regression and multinomial regression techniques. Although all of the models were significant, average age of immigration and average year of immigration in the neighbourhood were not significantly related to gambling levels (for more detailed information about the results please check Appendix C).

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Concluding Remarks

The results presented in this chapter, for the most part, support the results in the previous chapter. They support the earlier finding that gambling varies due to both ethnic and socio- demographic variables, and that ethnic and socio-demographic variables are, themselves, correlated. Second, they support the earlier finding that, once we control for the effect of socio-demographic variables, the effects of ethnic variables are diminished as predictors of gambling. Additionally, the analyses in this chapter show that neighbourhood-level variables influence gambling, just as individual-level variables do. The fact that they both influence gambling in similar ways suggests, but does not prove, that our respondents live in neighbourhoods alongside people very much like themselves. In the next chapter, we will attempt to disentangle individual-level and neighbourhood-level influences on gambling, using a method of multivariate analysis designed for this purpose.

Endnote

Source: http://www.statcan.ca/english/census96/define.html#census agglom, Accessed November 23, 2003

Definitions of Ethnicity

“Ethnicity” refers to the ethnic or cultural group(s) to which the respondent's ancestors belong. Ethnic or cultural origin refers to the ethnic "roots" or ancestral background of the population, and should not be confused with citizenship or nationality.

Comparability of ethnic origin data between the 1996 Census and previous censuses has been affected by several factors including changes in the question format, wording, examples, instructions and data processing, as well as by the social environment at the time of the census.

In 1996, comparability with previous census data will be particularly affected by the change in format and the examples provided on the questionnaire. While the 1991 Census question included fifteen mark-in categories and two write-in spaces, in 1996, respondents were required to write in their ethnic origin(s) in four write-in spaces. Twenty-four examples were provided.

The change in format to an open-ended question in 1996 is likely to affect response patterns especially for groups which had been included as mark-in response categories in 1991. In addition, the presence of examples such as "Canadian", which were not included in previous censuses, may also affect response patterns.

Since 1986, an instruction to specify as many ethnic groups as applicable has been included in the ethnic origin question. This has affected data comparability for all ethnic groups and categories because of the increase in multiple responses. Prior to the 1981 Census, only the respondent's paternal ancestry was to be reported. If multiple ethnic origins were provided, only one origin was captured, resulting in one ethnic origin per respondent. In 1981, multiple origins were allowed and a write-in space was added to the question, although respondents were not instructed to provide more than one origin. In1986, respondents were permitted to write in up to three origins other than those shown in the mark-in circles. In 1991, they were permitted to write in up to two additional origins. In 1996, four write-in spaces were provided on the questionnaire and up to six ethnic origins were captured.

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Chapter Six: A Mixed Model of Gambling Behaviour

In chapter 4, we saw that problem gambling correlates with a number of individual characteristics. They include age in that, like some other risky behaviours, problem gambling has a somewhat higher incidence among young adults than among people over forty. Social class background and aspects of ethnic identification are also statistically linked to problem gambling. Here, however, the interpretation here is less straightforward, since social class and ethnicity have both individual (or household) and neighbourhood (or community) aspects. In chapter 5, we saw that problem gambling also correlates with a variety of neighbourhood characteristics. US researchers have made much use of the concept of “neighbourhood disadvantage”, considered as the incidence of low income in the neighbourhood (obtained from Census figures) combined with the percent of African Americans, the percent of single parent families and the percent of single person households. There is also a literature on neighbourhood based ethnic communities that, in the extreme case, constitute “ethnic enclaves”. Such communities are often composed of as yet incompletely unassimilated immigrants who stick together for economic, linguistic, religious and sentimental reasons. It is not surprising then that many economic characteristics of households are intimately bound up with neighbourhood and that ethno-cultural aspects of families are often linked with those of the community. In this chapter, we introduce a mode of analysis that can examine individual and neighbourhood level influences simultaneously. This is important because there are several possible explanations of any linkage between problem gambling and neighbourhoods. One line of thinking, “geographical determinism,” assumes that physical proximity to gambling facilities leads to increased risk of becoming a problem gambler. A related but different argument is that some gambling facilities – such as casinos – are physically located in or near particular kinds of neighbourhoods that may be characterised by higher levels of prostitution, alcohol consumption and other indicators of neighbourhood deprivation or disorganization. We can also note that, for a variety of historical reasons, some casinos in North America have been owned by aboriginal (North American Indian) bands and /or located on aboriginal reserves and that this will tend to make casino gambling easily available to at least some aboriginal communities. We shall see that, at least in the case of gambling, neighbourhood ethnic composition is sometimes linked to the presence of gambling facilities. Against this kind of geographical determinism, others might argue that not all gambling has to take place in casinos and, even if it does, one important aspect of compulsive gambling is that the gambler is prepared to travel long distances in order to indulge her or his habit. Neighbourhoods could still be important influences on gambling behaviour but this would be because of their influence on their residents’ attitudes, not because of the location of gambling facilities. From this standpoint, an individual’s risk of engaging in problem gambling might be increased by local cultural norms that approve of gambling, or decreased by other local cultural norms that stress responsible behaviour with respect to family and community. For example, if gambling is part of traditional Chinese culture and Chinese communities traditionally tolerate problem gambling, then a Chinese person’s problem gambling should be enhanced by residing in a community with many other Chinese. If, on the other hand, Chinese communities tolerate gambling but also stress that it should not harm the interests of the family, then a Chinese person’s problem gambling should be diminished if she or he lives in a Chinese community. A similar argument will hold for Native Indians, though in this case the situation is complicated by the fact that some casinos are sited in or near Indian reservations so that neighbourhoods with large percentages of Native Indians should enhance aboriginal ethnicity but should also make gambling an easily accessible leisure activity. This chapter looks at problem gambling using the Multi-Level Modeling conceptual framework in an argument whose essentials can be verbally framed as: 124

Problem gamblers become and/or remain so because of individual-level characteristics such as age, educational level, income and ethnicity and because of neighbourhood-level characteristics such as physical proximity to casinos, neighbourhood deprivation, the presence of particular ethnic communities, etc.

A statistical technique known as Hierarchical Linear Modeling (HLM) is useful for formalizing this verbal conceptual framework and for estimating neighbourhood effects upon problem gambling while controlling for effects at the individual level. We present the basic equations for HLM as endnote (1) below. Consider the reasons why we might expect that neighbourhood characteristics should be linked with gambling behaviour. As we argued above, neighbourhoods differ in a variety of ways that might affect gambling behaviour, the most obvious possibilities being their physical proximity to casinos, their average levels of education and income and other things related to “neighbourhood advantage” or “neighbourhood disadvantage”, and their ethnic composition. As we shall see below, it is possible to construct useful measures of neighbourhood characteristics by using small area statistics from the 1996 Census Enumeration Area Profile. As noted in chapter 5, we have used Postal Codes as our empirical indicator of neighbourhood membership. Each respondent in our study provided the postcode for her or his home address. We use the first three characters of these postcodes to define of neighbourhoods and this leads to multilevel analyses with some 3,636 respondents from 460 neighbourhoods in Ontario. (The reduction from the original 5,001 respondents is largely due to the elimination of those who did not gamble at all.) The most populated neighbourhoods (K0K and N1H contributed between 33 and 32 respondents respectively while the least populated ones contributed only one. Several authors have discussed the issue of optimal sample design for multi-level modeling, usually in the context of studying the effects of schools upon their pupils. Hox (2002: 175) recommends that, if there is strong interest in the effects of the grouping factor, in our case neighbourhoods, there should be at least 100 groups with at about 10 individuals per group. We used Statistics Canada’s public use “1996 Enumeration Area Profile” as a source for measures of neighbourhood characteristics. Census Enumeration Areas are the small number of streets for which a given Census taker is expected to collect data. The Enumeration Area Profile includes a wide range of measures: age, gender and family status variables from the standard census form (100% coverage) as well as income3, education, ethnic origin, visible minority status, etc. from the long form (20% sample coverage) that asks more detailed questions.

Ethnic Origin Ethnic origin is the key independent variable in this analysis, and we have various measures of it. The long form of the Census is administered to one in every five households and includes questions on self-reported ethnic origin as well as on self-assessed visible minority status. The Census question on ethnic origin permits single responses (e.g. “Chinese” or multiple responses (e.g. “Chinese and French” and we have counted the prevalence of each ethnicity in each neighbourhood by adding together the single and multiple responses. For this reason the prevalence of various ethnic origins in any given neighbourhood can add to over 100%. The major ethnic origin groups are: North American Indian; Dutch; English; Canadian; Chinese; East Indian; French; German; Irish; Italian; Polish; Scottish and Ukrainian. The Enumeration Area Profile includes an alternative indicator of the prevalence of North American Indians. This is based on the Aboriginal Origin question rather than on the Ethnic Origin

3 Unlike the 1996 Census Tract Profile, the 1996 Enumeration Area Profile does not include specific indicators of low income. 125 question and, while the two indicators are highly correlated, they have different thresholds. In the sub- sample we used for analysis, some 41% of respondents were from neighbourhoods with no North American Indians self-identified on the Census ethnic origin question, while a much higher percentage, 69% were from neighbourhoods with no North American Indians self-identified on the Census aboriginal origin question. The aboriginal origin question is seems to be more specific and, because of this, fails to capture an ethnic identification that might be more psychological. Our data show geographical concentrations for certain ethnic origin groups. Some enumeration areas are 100% North American Indian and these are presumably reserves. Italians also display some concentration by ethnic origin, the highest prevalence in our data being 94%, as do Ukrainians (highest prevalence 86%), Chinese (highest prevalence 83%), Germans (highest prevalence 74%). “English” ethnic origin has its highest prevalence in an enumeration area where 84% of Census respondents so identify and “Canadian” has its highest prevalence at 73%. “Visible minority status” is another powerful indicator of ethnicity and is measured in the Census through a question administered to members of the non-aboriginal population. Respondents to the long form of the Census are asked whether they consider themselves as belonging to a visible minority and, the results are coded by Statistics Canada into ten categories (plus a further category for multiple visible minorities). The standard list of visible minority categories includes, “Black”, “Chinese”, “South Asian”, “Korean”, “Japanese”, “Southeast Asian”, “Filipino”, “Arab/West Asian”, “Latin American” and a residual group. We have constructed a variable that indicates the prevalence of visible minorities in the neighbourhood, as well as another variable that combines the prevalence of the various Asian visible minorities. Generally speaking, at least for Ontario, the prevalence of visible minorities in a neighbourhood indicates relatively recent immigration. Sadly it is still the case that concentrations of the “Black” visible minority group are statistically linked with lower average income levels. As we shall see, the prevalence of the “Black” visible minority is also correlated with an increased risk of problem gambling.

Indicators of Neighbourhood Deprivation US researchers typically measure “neighbourhood deprivation” by using Census indicators of the average income in the neighbourhood, the percentage of single parents, the percentage of African- Americans, percentage of single-person households, etc. Not surprisingly these indicators tend to overlap with each other: low income neighbourhoods tend, on average, to have higher percentages of Blacks as well as higher percentages of single parents, single person households, divorced or separated persons, etc. The general pattern is similar north of the Border except that the Black visible minority population is much more heterogeneous in Canada and that the aboriginal population is proportionately larger and at least as disadvantaged.

Analyses of the Data

The Hierarchical Linear Model Scott Menard has referred to the Hierarchical Linear Model (HLM) family as the method that ‘revolutionized our analysis of multilevel and longitudinal data’ (Menard, 2002). The basic theory behind HLM is simple enough: partition the social world into different strata, and see how the higher level groupings, affect lower levels that are nested within them. In the context of gambling research, we would like to see how neighbourhood and community characteristics influence the individual level risk of problem gambling for example. We ask the following questions: is the increased risk of problem gambling among Aboriginal and Chinese respondents something that is linked with ethnicity due to influences at the individual or community level, or does it better reflect neighbourhood deprivation or other economic characteristics of neighbourhoods where Aboriginals and Chinese tend to cluster. 126

The underlying assumptions of hierarchical linear modelling are presented below as Endnote (1). The main results from this multilevel analysis are: (1) the “fixed effects” of individual-level and group-level predictors upon problem gambling. The symbols for these effects are usually given as γ00, γ10, γ20 etc. and they have the same interpretation as any other partial regression coefficients; and (2) the variances of the random intercepts and random slopes, typically known as the “random effects”. Significant random intercepts imply that important explanatory variables have not yet been included at the neighbourhood level, while significant random slopes imply that the researcher has failed to include “cross-level interactions” in the explanatory model. The ideal outcome from multi- level modeling is where the researcher can show that the inclusion of meaningful explanatory variables explains away what had been statistically significant random intercepts or random slopes in earlier versions of the multi-level model. We do this below. The results Analyses below are reported for a standard group of 3636 respondents. Of original 5,001 respondents 3772 reported that they gambled and could be matched with census data about their neighbourhoods. Table 6.1 below shows the mean values of the most independent important variables we will be examining below, and in where appropriate, also the standard deviations.

Table 6.1: Mean Values of Key Variables

N Mean Std. Dev. Ethnic Origin Aboriginal 3636 0.030 English 3636 0.367 Chinese 3636 0.013 French 3636 0.105 German 3636 0.082 Irish 3636 0.159 Other 3636 0.243 Age 3588 47.525 16.089 Female 3772 0.508 Education 3755 3.286 1.329 Household Income 3181 4.370 1.823 Employment Status Unemployed 3772 0.029 Employed 3772 0.613 Other 3772 0.358 Marital Status Divorced/Separated/Widowed 3250 0.221 Other 3772 0.597 Binge Drinking 3772 1.290 1.722 Overall Poor Health 3772 1.699 0.811 Region East 3772 0.140 Central/East 3772 0.150 Toronto 3772 0.220 Central/West 3772 0.164 Central/South 3772 0.108 South/West 3772 0.131 North 3772 0.086 Enumeration Area Characteristics Neighbourhood % American Indian Ethnic 3765 0.019 0.044 Origin* 127

N Mean Std. Dev. Neighbourhood % Black visible minority 3765 2.606 5.588 Neighbourhood % Home Language Neither 3765 9.977 12.948 English nor French Neighbourhood % Men Aged 25-34 Years 3772 16.711 7.297 Neighbourhood % Separated but still Legally 3772 3.488 1.793 Married Neighbourhood % Unemployed* 3610 9.335 5.820

Missing data on the self-reports of ethnic origin further reduced this to the 3636 respondents used in data analysis for this chapter. Most of them reported few or no problems with their gambling but around 15% reported some degree of problem gambling. The CPGI scale quantifies this and has a positively skewed distribution that we have capped or “top coded” at a score of 10 in order to avoid our results being affected by the small number of cases with extreme scores. We have attempted to increase the validity of significance tests by using a logged version of the top-coded CPGI scale in hierarchical regression analyses and, in addition, by using a coarser ordered classification of problem gamblers (no problems, some problems, severe problems) in cumulative logistic regression analysis. The difference between these two analytic approaches is that the hierarchical regression analysis uses Normal distribution theory in order to carry out tests of statistical significance while the cumulative logistic regression method does not. While the hierarchical regression analysis uses more of the fine detail in the problem gambling scale the cumulative logistic regression approach should yield results that are more robust. We will demonstrate results that are similar, using each of these data analysis methods. Similar results will convince us we have found reliable results. In order that we could use each of a large number of predictor variables in the analysis of all 3,636 respondents, we replaced missing values by their means, for several independent variables. The variable most affected by this was household income where, as is common in survey research, there was a high item response rate in this case 455 out of 3772 respondents gave no household income information and were allocated the mean household income reported by all other respondents. We expect this to weaken some of our tests for the effect of household income on problem gambling. Note that, because we are using a more powerful method of data analysis, particularly suited for examining multi-level data of the kind we have here, the results will differ slightly from those discussed in chapters 4 and 5 where we used single-level methods of data analysis. Specifically, we will show with the present multi-level method, where mean values are imputed for missing values, that ethnic variables – both as direct effects and in interaction with other variables – influence the likelihood of problem gambling. To further increase the strength of our statistical analysis, we standardized the logged CPGI scale as well as each of our continuous independent variables to a mean of zero and standard deviation of one. However, we coded categorical predictors as dichotomies. Thus, for example, the effect of age is estimated as the number of standard deviation units of the logged CPGI score that is associated with one standard deviation unit increase in age. In a similar way the effect of being female is estimated as the gender difference in problem gambling measured in standard deviation units of the logged CPGI score. The effects of the different categories of marital status are measured by comparison to the group of respondents who were married or living common law, while the effects of region are measured by comparison to the group of respondents living in Toronto.

Correlational Analysis Analyses reported in the previous chapter showed that at least three neighbourhood level socio- demographic variables – age, family structure, and education -- have small but statistically significant

128 effect on the risk of problem gambling. Neighbourhood age and family structures seem to be implicated in problem gambling since people in neighbourhoods with higher prevalence of 18-24 year olds, or with higher prevalence of maritally separated individuals, have higher odds of being problem gamblers. People in neighbourhoods with a higher than average prevalence of university-educated individuals have lower odds. Pearson correlations with the CPGI scale are 0.049 for the prevalence of younger people, 0.069 for the prevalence of the maritally separated and -0.044 for the prevalence of the university educated. We have noted in Chapter 5 that ethnic variations also influence gambling patterns. Correlations between problem gambling scores and the prevalence of those with “English” ethnic origin had been - 0.076, indicating a lower risk of problem gambling scores for neighbourhoods with more people identifying themselves as of “English” ethnic origin. There were similar correlations for the prevalence of “Irish” and “Scottish”. Indeed, when we created an index of the prevalence of any of “English”, “Irish”, “Scottish”, “Dutch” or “German” ethnic origins, the correlation with the CPGI scale was -0.092 (and -0.104 for the logged version of the CPGI scale). The corresponding correlations with the prevalence of Chinese and North American Indian ethnic origin are 0.037 and 0.043. As noted in the previous chapter, these small correlations become non-significant when adjusted for the effects of neighbourhood level socio-demographic variables. Digging deeper into the neighbourhood measures that are available in the Census profile, we find that prevalence of visible minorities in a neighbourhood -- particularly the “Black” visible minority -- increases the risk of problem gambling, the correlations with the CPGI score being 0.086 for the visible minorities index and 0.097 for the prevalence of the Black visible minority. An index of the prevalence of New Canadians is also correlated with elevated risk of problem gambling (r = 0.098 and r = 0.104 when we use the logged version of the CPGI scale), though this index includes the prevalence of visible minorities as one of its ingredients. We already noted that the neighbourhood presence of ethnic origins that are British, Dutch or German is negatively correlated with the logged CPGI score (r = -0.104) and that the prevalence of people with high levels of education is linked with lower incidence of problem gambling and we find that higher average family income is similarly correlated (r = -0.064) These results suggest that, while the correlations are small, the main neighbourhood variables linked with problem gambling are those linked with the presence of recent immigrants and visible minorities, particularly the “Black” visible minority. We must bear in mind that our index of New Canadians overlaps with the prevalence of visible minorities (being composed of the prevalence of visible minority groups, of those whose home language is neither English nor French and those who immigrated in the 15 years since the 1996 Census). Neighbourhoods with higher incidence of New Canadians are, on average, socio-economically disadvantaged but neighbourhood indicators such as average family income and the incidence of university educated people have rather small correlations with problem gambling. Several characteristics of neighbourhoods have significant correlations with problem gambling. The index of New Canadians has a correlation of 0.10 with problem gambling. This means that we find somewhat elevated scores on problem gambling in neighbourhoods characterised by visible minority groups, people whose home language is neither English nor French and people who immigrated to Canada in the 15 years since 1980. Breaking down this index into its components shows that problem gambling is associated with the prevalence of people whose home language is neither of Canada’s official languages (r = 0.108) as well as of people who immigrated to Canada between 1981 and 1986. Using the visible minorities index alone, we find a correlation of 0.09 with problem gambling while using the percentage “Black” visible minority, we find a correlation of 0.10. The prevalence of Chinese in the neighbourhood has a significant but much smaller correlation with problem gambling. The same is true for the prevalence of Aboriginals though, as we shall see, this neighbourhood level characteristic becomes a significant predictor of problem gambling once other variables have been controlled. 129

Sociologists and data analysts have long known about the dangers of assuming that neighbourhood level correlations reflect corresponding causal processes at the individual level. (This is the so-called “ecological fallacy.”) Thus, the finding that neighbourhoods with higher percentages of recent immigrants, people whose home language is neither English nor French, or “Black” visible minorities have somewhat higher incidence of problem gambling does not necessarily mean that recent immigration, home language or self-identification as Black cause people to become problem gamblers. It does however suggest that problem gambling is somewhat more common in certain kinds of neighbourhoods. Hierarchical Regression Analysis (Multilevel Modeling with Random Intercepts) We have used random intercepts models in regression analyses that predict problem gambling scores (the logged CPGI scale). The results are summarized in Table 6.2 below.

Table 6.2: Hierarchical Mixed Model

Model 1 Model 2 Model 3 Model 4 R-squared -- -- 0.077 0.10 Bayesian Fit Index (Smaller is Better) 10256 10143 10124 10078 Intercept 0.07 0.06 0.05 0.067 Unexplained Neighbourhood Variation (intercept) 0.095 0.062(ns) 0 0.095 Ethnic Origin Aboriginal 0.46 0.29 0.26 0.23 English -0.14 -0.12 -0.10 -0.11 Chinese 0.60 0.58 0.54 0.44 French -0.17 -0.19 -0.17 -0.16 German -0.12 -0.14 -0.13 -0.12 Irish -0.10 -0.10 ns ns Other Ethnic Origins reference category Age* -0.08 -0.08 -0.08 Female -0.09 -0.07 -0.07 Education* -0.06 -0.07 -0.06 Household Income -0.03 ns ns Interaction: Age*Education 0.04 0.04 0.04 Employment Status Unemployed 0.36 0.35 0.40 Employed ns ns ns Not in the workforce reference category Binge Drinking* 0.06 0.06 0.07 Overall Poor Health* 0.09 0.09 0.09 Interaction: Age*Poor Health -0.06 -0.06 -0.06 Marital Status Married/Partner reference category Divorced/Separated/Widowed ns ns ns Single never married 0.11 0.12 0.13 Neighbourhood Characteristics Neighbourhood % American Indian Ethnic Origin* 0.05 ns Neighbourhood % Black Visible Minority* 0.05 0.06 Neighbourhood % Home Language Neither English nor 0.08 0.07 French* Neighbourhood % Men Aged 25-34 Years -0.05 ns Neighbourhood % Separated but still Legally Married 0.05 ns Neighbourhood % Unemployed* ns ns

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Model 1 Model 2 Model 3 Model 4 Cross-Level Interactions 0.13 Aboriginal Self-Identification * % American Indian Ethnic Origin Binge Drinking * % Men Aged 25-34 Years 0.06 Binge Drinking * % Separated but still Legally Married -0.07 Chinese Self-Identification * % Black Visible Minority 0.40 English Self-Identification * % Black Visible Minority -0.08 Single Never Married * % Men Aged 25-34 Years -0.13 Single Never Married * % Separated but still Legally 0.10 Married Unemployed * % American Indian Ethnic Origin -0.32 Unemployed * % Men Aged 25-34 Years 0.30 Unemployed * % Unemployed -0.19 Respondents=3636: Neighbourhoods=460 * These Predictors have been standardized to mean= 0 and standard deviation = 1 The Bayesian fit index is the Bayes Information Criterion (BIC), which is like the adjusted R-squared in that it adjusts for the number of predictors used. Household income is not a significant predictor of CPGI, once employment status is included as an explanatory variable.

As discussed above, postal codes are a reasonable empirical indicator of neighbourhoods and map reasonably well to the Census Enumeration Area profile from Statistics Canada. Using the three- character postal code with our 3636 respondents meant that we covered 460 neighbourhoods over Ontario. (We have experimented with using the four-character postal code, yielding 1357 neighbourhoods and find similar results). Random intercepts models provide estimates of the “fixed effects” of the predictor variables upon the criterion, just as in conventional regression analysis. They also yield an estimate of the as yet unexplained variation that lies between clusters of observations (in our case neighbourhoods). The smaller the between-neighbourhoods residual variation, the more convinced we will be that we have included all relevant neighbourhood level predictors in our model. We report our results by estimating five equations of increasing complexity. In Table 6.2, Model 1 predicts CPGI from self-reported ethnic origin alone. Model 2 adds in a series of social and demographic characteristics such as age, gender, years of schooling, household income, marital status and region. Model 3 predicts CPGI scores from all the above-mentioned predictors plus the main effects of relevant Census characteristics of neighbourhoods. As we shall see, the key neighbourhood variables are the percent whose home language is neither English nor French, the percent of American Indian ethnic origin and the percent self-identifying as being “Black” visible minorities. Finally, Model 4 adds in several interaction terms that are “cross-level” in the sense that each of them reflects the interaction between an individual respondent characteristic and a neighbourhood level variable.

Self-Reported Ethnic Origin on Problem Gambling in a Random Intercepts Model Self-reported ethnic origin continues to have a small but highly significant association with problem gambling. As in previous chapters we find that people reporting Aboriginal and Chinese ethnic origin have higher than average CPGI scores, while those of English, Irish, Scottish, French or German ethnic origin are somewhat lower than average. The difference between Chinese respondents and those of “Other” ethnic origins is 0.6 of one standard deviation in the logged problem gambling score, while the difference between Aboriginal respondents and the “Other” group is 0.46 of one standard deviation. The Model 1 estimate for yet unexplained between-neighbourhood variation in problem gambling is 0.10 and since this is significantly larger than might reasonably be attributed to chance, we

131 enhance the predictive model by adding further predictors, preferably ones that show some differences between neighbourhoods. Accordingly, Model 2 adds a series of standard social and demographic predictors including marital status (where married/common law is the reference category). Consistent with results reported in previous chapters, problem gambling is negatively correlated with age, being female, having more schooling and having higher household income. People who are single and never married tend to have somewhat higher CPGI scores than married people. Addition of these extra individual-level predictors has reduced the unexplained between neighbourhood variation to 0.07 and rendered it “non-significant” at the conventional 5% level. However, their inclusion reduces the estimated effects of self-reported ethnic origin variables only slightly. Thus, ethnic origin continues to be important. Model 3 adds selected neighbourhood level predictors to the prediction equation. While several other Census characteristics of neighbourhoods are correlated with problem gambling, we find that the three variables having independently significant associations are the percentage of people whose home language is neither English nor French, the percent North American Indian Ethnic Origin and the percent “Black” visible minority. The result of adding these predictors is further to reduce the unexplained between-neighbourhood variation as well as further reducing the size of the estimated coefficients for self-reported ethnic origin and other individual-level predictors. However, most ethnic origins remain statistically significant influences on CPGI. Model 4 introduces several “cross-level” interactions. Each of these is the combination of an individual level variable with one at the neighbourhood level. For example, the combination of Aboriginal ethnic self-identification and living in a neighbourhood with a higher percentage of North American Indians is found to increase the expected CPGI score over and above what was predicted from the main effects of these two variables. Similarly the combination of English ethnic self- identification and living in a neighbourhood with higher percentages of the “Black” visible minority decreases the expected CPGI score from what might otherwise have been predicted. Self-identification as being of Chinese ethnic origin shows significant cross-level interactions with both the previously mentioned neighbourhood level predictors: percent Aboriginal and percent Black. These two interaction terms are both positive. The unexplained between-neighbourhood variation remains non-significant and, while German and Irish ethnic origins have become non-significant predictors of problem gambling, most individual- level predictor variables have much the same estimated effects as before, the most obvious change from the previous model being that the neighbourhood level variable of percent North American Indian has now become non-significant. However, as variables on the individual level, ethnic origins continue to be significant influences on CPGI for the Aboriginal, English, Chinese, French, German and Irish groups. The Bayesian Information Criterion (BIC) shows that model 4 fits much better than any of the previous ones, though a parallel analysis with standard least squares regression yields a squared multiple correlation of only 0.103. This indicates that there is still a lot for us to learn about the antecedents of problem gambling. To summarize the results of this hierarchical mixed model analysis, when we control simultaneously for a variety of individual and neighbourhood level characteristics, ethnic origins continue to be significant influences on problem gambling. The Chinese and Aboriginal individuals are more likely than average to have gambling problems, while French, German and English individuals are less likely than average to have gambling problems. Additionally, Aboriginal individuals in Aboriginal neighbourhoods and Chinese individuals in Black visible minority neighbourhoods are more likely than average to have gambling problems.

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Cumulative Logistic Regression Analysis As noted earlier, we also used a more robust, “ordinal regression” technique to confirm the general pattern of the hierarchical linear modeling. In this approach the CPGI scale was collapsed into three ordered categories: “unproblematic gambling”, “at risk” and “problem gambler”. The associations between this ordered outcome and predictive variables are expressed as an odds ratio: the factor that multiplies the odds that a respondent will be in the next highest category. We shall see, for example, that the gambling tendency for people who self-identify as Chinese is expressed as an odds multiple of around 3.0: - the odds that Chinese people will be in a higher gambling category is three times the odds for people in the “Other” ethnic origin groups. In similar fashion, women’s lesser tendency for problem gambling is expressed as an odds-multiple of around 0.70. This means that the odds that they will be in a higher gambling category are 70% of the male odds. The approach is a generalization of binary logistic regression as used in a previous chapter but since our three-fold classification identifies low, middle and high levels of problem gambling, the analysis is more sensitive. We use it here because it does not make the normal distribution assumption required by hierarchical regression techniques and so we are surer of our results when they are confirmed by a method that makes fewer assumptions.

Table 6.3: Cumulative Logit Regression Analysis: Predicting Odds of Being in the Next Highest of 3 Categories of Problem Gambling Scale

Model 1 Model 2 Model 3 Model 4 Gamma Correlation Measure 0.21 0.39 0.41 0.42 Odds Odds Odds Odds Multiple Multiple Multiple Multiple Ethnic Origin Aboriginal 2.55 2.03 1.85 1.60 ns English 0.69 0.72 0.76 0.76 Chinese 3.00 2.55 2.59 2.52 French 0.59 0.59 0.61 0.61 German 0.64 0.62 0.64 0.66 Irish 0.74 0.75 0.79 0.81 ns Other Ethnic Origins Age* 0.78 0.78 0.77 Female 0.82 0.83 0.83 Education* 0.85 0.86 0.86 Household Income* 0.89 0.91 0.90 ns Interaction: Age*Education 1.10 1.09 1.09 Employment Status Unemployed 1.78 1.77 1.86 Employed 1.06 1.05 1.04 Not in the workforce reference category 1.20 1.20 1.22 Binge Drinking* 1.21 1.20 1.19 Overall Poor Health* 0.88 0.89 0.89 Interaction: Age*Poor Health 1.78 1.77 1.86 Marital Status Married/Partner reference category Divorced/Separated/Widowed 1.21 1.19 1.21 Single never married 1.29 1.33 1.35 Region

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Model 1 Model 2 Model 3 Model 4 East 0.55 0.71 0.74 ns Central/East 0.92 1.13 1.18 ns Toronto Central/West 0.76 0.94 0.95 ns Central/South 0.77 0.98 0.99 ns South/West 0.76 0.97 1.03 ns North 0.66 0.80 0.90 ns Neighbourhood Characteristics Neighbourhood % American Indian Ethnic Origin* 1.12 1.01 ns Neighbourhood % Black Visible Minority* 1.10 1.10 Neighbourhood % Home Language Neither English 1.18 1.18 nor French* Neighbourhood % Men Aged 25-34 Years 0.86 0.91 Neighbourhood % Separated but still Legally Married 1.15 1.14 Neighbourhood % Unemployed* 0.92 0.94 ns Cross-Level Interactions Aboriginal Self-Identification * % American Indian 1.23 Ethnic Origin Binge Drinking * % Men Aged 25-34 Years 1.14 Binge Drinking * % Separated but still Legally 0.84 Married Chinese Self-Identification * % Black Visible 1.37 ns Minority English Self-Identification * % Black Visible 0.88 ns Minority Single Never Married * % Men Aged 25-34 Years 0.73 Single Never Married * % Separated but still Legally 1.19 ns Married Unemployed * % American Indian Ethnic Origin 0.77 ns Unemployed * % Men Aged 25-34 Years 1.66 Unemployed * % Unemployed 0.64 ns N=3636 The results of this analysis confirm our earlier findings in earlier chapters, and earlier in this chapter using hierarchical linear modelling. Overall, our data analyses show that the key explanatory variables for understanding problem gambling are: • age (younger persons being more likely to be problem gamblers) • employment status (the unemployed being more likely to be problem gamblers), • binge drinking (binge drinkers more likely to be problem gamblers), • health (less healthy people being more likely to be problem gamblers)

Most important for our present purposes, ethnic identification continues to be an important predictor of problem gambling, even after we control for both individual and neighbourhood level variables (in Model 4 of Table 6.3). Holding constant the other socio-demographic factors already mentioned, Chinese respondents are 2.5 times more likely to be problem gamblers, compared with the reference (“other”) ethnic group. By contrast, the English, French and German are only about 60-70% as likely to be problem gamblers as the reference group – indeed, only about one-quarter to one-third as likely as the Chinese respondents. This indicates a true, strong ethnic effect where the Chinese are concerned.

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By contrast, the Aboriginal respondents, initially more likely than the reference group to be problem gamblers, are no longer significantly different from the reference group, once we control for other socio-demographic variables. The effect of Aboriginal ancestry per se shrinks with each successive model, indicating that the effect of Aboriginal ancestry is spurious: the reflection of other socio-demographic factors including age, education, household income, employment status, and marital status. Additionally, it is correlated with other variables measured directly in the model: namely, overall poor health and binge drinking. To reiterate, we found these results using the Normal theory hierarchical regression model and confirmed them with the cumulative logistic regression approach. At the neighbourhood level, only the Census percentage of persons whose home language is neither English nor French has a consistently significant main effect (tending to increase the risk of problem gambling) though the Census percentage of “Black” visible minority identification is also implicated, as is the Census percentage of North American Indians.

Strengthening the predictive power We were able to increase the explained variation in problem gambling by introducing two statistical interaction terms at the individual level (age by education and age by health status4) as well as a larger number of “cross-level interactions” (Snijders and Bosker (1999: 74-5; Hox (2002: 19; 53). Cross-level interactions are so called because they involve explanatory variables from different levels. They are policy relevant because they identify those individual-level characteristics whose effects depend upon neighbourhood contexts. While the results of statistical analysis are always to some degree uncertain, we can be reasonably sure that aboriginal ethnic identification, binge drinking, being single-never-married and being unemployed are four explanatory variables that are definitely involved in cross-level interactions in the prediction of problem gambling. We know this because we used two very different analytic methods to identify statistically significant cross-level interactions involving these variables5. Five cross-level interactions are significant both for the normal distribution (mixed) model and the cumulative logistic regression. They are as follows:

(1) Binge Drinking by Neighbourhood Age Structure People who engage in binge drinking have greater odds of problem gambling. Additionally, binge drinkers who live in neighbourhoods with a higher than average percent of male population aged 25-35 have extra greater odds of problem gambling (i.e., 13% higher than expected). We have no explanation for this finding.

(2) Binge Drinking by Neighbourhood Percentage Maritally Separated People who engage in binge drinking have greater than average odds of problem gambling. However, binge drinkers who live in neighbourhoods with a lower than average percentage of maritally separated individuals has lower than expected odds of problem gambling (i.e., only 85% of the expected level). We have no explanation for this finding.

(3) Marital Status (Single-never-married) by Neighbourhood Percentage Maritally Separated People who are divorced or separated have higher odds of problem gambling. Additionally, people who are divorced or separated and who live in neighbourhoods with a lower percent of the male

4 Older people have lesser odds of problem gambling. Unhealthy people have greater odds of problem gambling. People who are both older and unhealthy have extra lesser odds of problem gambling. 5 We are less sure about the reliability of some other cross-level interactions, for example those involving Chinese and English ethnic self-identification, since these appear in the multilevel Normal model but not in the cumulative logistic regression analysis. 135 population aged 25-35 have lower than expected odds of problem gambling (i.e., only 78% of the expected level).

(4) Employment Status (unemployed) by Neighbourhood Age Structure Unemployed respondents have higher odds of reporting problem gambling. Additionally, unemployed respondents who live in neighbourhoods with a higher percentage of men aged 25-35 have much higher odds of problem gambling (i.e., 67% above the expected level).

(5) Employment status (unemployed) by Neighbourhood Unemployment Level Unemployed respondents have, as noted, higher odds of reporting problem gambling. However, unemployed respondents who live in neighbourhoods with a low percentage of people who are unemployed have much lower odds of problem gambling (i.e., only 60% of the expected level.)

(6) Aboriginal Ethnic Identification by Neighbourhood Percentage North American Indian Most important of all for our present purposes, people who self-identify as Aboriginal have higher than average odds of problem gambling. Aboriginals who live in neighbourhoods with a higher than average percentage of aboriginals (North American Indians) have higher than expected odds of problem gambling (i.e., 26% higher than expected).

The cross level interaction between aboriginal ethnic identification and % North American Indian means intensified Aboriginal (Indian) cultural norms: Aboriginals are more Aboriginal when surrounded by other Aboriginals. This should work for Chinese too and it does not. Aboriginal ethnic identification may be more situational than is Chinese ethnic identification, and more variable over time. Statistics Canada was recently talking about the apparent rise in aboriginal ethnic identification in the 2001 Census.

Discussion

The data in Table 6.4 report the findings of Model 5 – a summary based on Model 4 with the non- significant variables removed and the remaining effects re-estimated. They permit us to summarize the key influences on problem gambling in this sample used in the Ontario Prevalence Survey. As we can see, compared with the expected level, the likelihood of problem gambling is increased by the following individual level variables:

• Unemployment (85% increase) • Single status (37% increase) • Binge drinking (23% increase) • Poor health (18% increase);

By the following neighbourhood level variables:

• Neighbourhood % separated but still legally married (21% increase) • Neighbourhood % Home Language neither English nor French (19% increase)

And by the following cross-level interactions:

• Unemployment * % men Aged 25-34 years (67% increase) • Aboriginal self-identification * % American Indian Ethnic Origin (26% increase) • Binge drinking * % men Aged 25-34 years (13% increase) 136

Table 6.4: Cumulative Logit Regression Analysis: Predicting Odds of Being in the Next Highest of 3 Categories of Problem Gambling Scale

Model 5 Gamma Correlation Measure 0.41 Odds Multiple Ethnic Origin Aboriginal 1.63 English 0.77 Chinese 3.09 French 0.59 German 0.65 Other Ethnic Origins reference category Age* 0.78 Female 0.83 Education* 0.86 Household Income* 0.90 Interaction: Age*Education 1.09 Employment Status Unemployed 1.85 Employed 1.06 Not in the workforce reference category Binge Drinking* 1.23 Overall Poor Health* 1.18 Interaction: Age*Poor Health 0.88 Marital Status Married/Partner reference category Divorced/Separated/Widowed 1.17 Single never married 1.37 Neighbourhood Characteristics Neighbourhood % American Indian Ethnic Origin* 0.98 Neighbourhood % Black Visible Minority* 1.09 Neighbourhood % Home Language Neither English nor French* 1.19 Neighbourhood % Men Aged 25-34 Years 0.87 Neighbourhood % Separated but still Legally Married 1.21 Neighbourhood % Unemployed* 0.94 Cross-Level Interactions Aboriginal Self-Identification * % American Indian Ethnic Origin 1.26 Binge Drinking * % Men Aged 25-34 Years 1.13 Binge Drinking * % Separated but still Legally Married 0.85 English Self-Identification * % Black Visible Minority Single Never Married * % Men Aged 25-34 Years 0.78 Unemployed * % American Indian Ethnic Origin Unemployed * % Men Aged 25-34 Years 1.67 Unemployed * % Unemployed 0.60 Model 5 – estimated with non-significant variables removed N=3636

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On the other hand, compared with the expected level, the likelihood of problem gambling is decreased by the following individual level variables:

• Age – older people gamble less (only 78% of expected level) • Female – females gamble less (only 83% of expected level) • Education – higher educated people gamble less (only 86% of expected level) • Household income – people with more money gamble less (only 90% of expected level) • Interaction between age and poor health – sick older people gamble less (only 88% of expected level)

By the following neighbourhood level variables:

• Neighbourhood % of men aged 25-34 years – (only 87% of expected level) • Neighbourhood % unemployed – (only 94% of expected level)

And by the following cross-level interactions:

• Unemployed people in low-unemployment neighbourhoods – (only 60% of expected level) • Never married people in neighbourhood with low % men aged 25-34 years – (only 78% of expected level) • Binge drinker in neighbourhood with low percentage separated but legally married – (only 85% of expected level)

Finally, and most important, we have clear evidence that – controlling for all other individual and neighbourhood level variables, ethnic variables influence gambling clearly and decisively. The data in Table 6.4 demonstrate that, other things controlled, Chinese ethnic origin produces three times as high a level of problem gambling as expected (or 200% higher than expected). Aboriginal ethnic origin produces a level of problem gambling 63% higher than expected. Additionally, living in a neighbourhood with a high percentage of black visible minority increases the odds of problem gambling 9% above what is expected. Living in a neighbourhood with a high percentage of recent immigrants – people whose home language is neither English nor French – increases the odds of problem gambling 19% above what is expected. Aboriginals who live in neighbourhoods with a high proportion of Aboriginals are 26% more likely to be problem gamblers than expected. However living in a neighbourhood with a high proportion of Aboriginals does not pose gambling problems for other ethnic groups. The effect of such neighbourhoods overall is a 2% reduction in overall gambling risk (i.e., only 98% of the expected level). By contrast, English, German and French ethnic origins produce levels of problem gambling that are only 77%, 65%, and 59% of the expected level, respectively. These results show a persistent pattern of association between ethnicity and problem gambling. We draw essentially the same conclusions by applying two different statistical methods to the same data. Problem gambling is more common among men than women, among younger people than among older, among single (never married) people than among those with partners and more common among those with lower education and the unemployed, among binge drinkers and those with poor health. That said, these social and demographic factors do not explain away the persistent trend for Chinese and to a lesser extent Aboriginals to be more at risk for problem gambling than other ethnic origin groups. Furthermore, analysis of neighbourhood correlates clearly shows that neighbourhoods with

138 higher percentages of the “Black” visible minority tend to be linked to our respondents having higher problem gambling scores. Neighbourhood characteristics are clearly relevant to problem gambling not just because some neighbourhoods are disadvantaged and include people generally more disposed to social problems but also because some neighbourhoods interact with individual level characteristics. In some cases these “cross level interactions” suggest that certain neighbourhoods can be protective against individual level characteristics that might otherwise lead to problem gambling. In other cases, the neighbourhood level characteristics seem to make the individual effects stronger. The age structure of neighbourhoods seems to be implicated in problem gambling in some ways that we do not yet fully understand. The regressions carried out so far show that the percentage of men who are aged 25-34 seems to be an indicator of something about neighbourhoods that is very relevant to problem gambling. Since young men are particularly likely to gamble, the concentration of such men increases the likelihood of gambling in the neighbourhood. To conclude, we have shown that – both as an individual identifier and as a neighbourhood characteristic – ethnic ancestry is a predictor of problem gambling. In the next chapter, we draw conclusions and consider some policy implications.

Endnotes 1 The basic theory of Hierarchical Linear Modeling is intuitively simple but the equations can quickly become complicated. Consider the standard one-variable regression equation to predict problem gambling (Y) from one of age, educational level, income or ethnicity (X) for the i-th person in the j-th neighbourhood.

Yij=π0i+ π1iXij+eij (1)

Where Yij=Outcome for respondent i in neighbourhood j

π 0j = intercept estimate for the Y, X relationship within neighbourhood j

π 1j = slope estimate for the Y, X relationship within neighbourhood j

Xij= predictor for individual i in neighbourhood j eij = normally distributed error term for respondent i in neighbourhood j This is for level 1, the within-neighbourhood level. To model differences between neighbourhoods (level 2) we need to create two more equations, one to predict within-neighbourhood intercepts (π 0j) and one for within-neighbourhood slopes (π 1j). For simplicity, we include no additional explanatory variables at level 2, giving us the following equations:

π 0j = γ00 + u0j (2)

π 1j = γ10 + u1j (3)

Where π 0j = same as equation 1: intercept estimate for the Y, X relationship within neighbourhood j

π 1j = same as equation 1: slope estimate for the Y, X relationship within neighbourhood j u0j = random effect of neighbourhood j on the intercept π 0 u1j = random effect of neighbourhood on the slope π 1

γ00 = grand mean score of variable Y

γ10 = grand mean increase in variable per one unit incremental increase in variable X

The random disturbance terms u0j and u1j acknowledge that there may be variation in intra neighbourhood coefficients (intercepts and slopes) that cannot be explained by our knowledge of the neighbourhood the person is from. If there were explanatory variables at level 2 (equations 2 and 3), these random terms would also acknowledge any unobserved heterogeneity not captured by those level 2 equations). The importance of these random disturbance terms is measured by their variances which might be small and non- significant or quite large. If there are non-significant variances for the random effects of neighbourhood membership on

139 intercepts and on slopes the model simplifies to standard forms of multiple regression in which all the neighbourhood effects are accounted for by measured explanatory variables either at the individual or the neighbourhood level. On the other hand, if there are significant variances for the random effects of neighbourhood membership on intercepts (“significant random intercepts”), this means that the neighbourhood differences on measured explanatory variables do not fully explain the between-neighbourhood variation in problem gambling. If there are significant variances for the random effects of neighbourhood membership on slopes “significant random slopes”), this means that certain within- neighbourhood relationships vary according to neighbourhood: for example the effect of age on problem gambling might be higher in some kinds of neighbourhood than in others. This finding could simply be reported or the researcher could go one further step and show that one or more additional explanatory variables, in the form of “cross-level interactions” suffice to explain away the significant random slopes. To better assess this, the above equations are combined to produce:

Yij= (γ00 + u0j )+ (γ10 + u1j )Xij+eij (4) This is often simplified and stated as:

Yij= γ00 + γ10 Xij + ε ij (5) where ε ij is a complex error term consisting of u0j + u1jXij +eij. The presence of this complex error term (embodying error from levels 1 and 2) makes these equations unsuitable for ordinary least squares estimation with standard regression routines. (Diggle et. al. 1994; Goldstein, 1995; Little et. al, 2001; Raudenbush, 1995). Equation 5 is the basic equation for a multilevel model, in which we predict outcomes for level 1 (individuals) within level 2 (neighbourhoods). This is for only one predictor variable at level 1 (labeled X), and no variables at level 2. At level 1, the error terms are correlated with one another (since it is the same neighbourhood), and they may or may not be at level 2, depending on whether neighbourhoods are independent of each other or are further clustered into regions, etc.

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Chapter Seven: Conclusions and Implications

Summary of Findings

In Chapter 1 we showed that gambling has a long history in human affairs. Likely, gambling is a universal activity, because it serves a variety of social functions. According to the Centre for Addictions and Mental Health, CAMH, people from different cultural backgrounds gamble:

• To socialize • To escape problems and isolation • To socialize with members of cultural groups • To regain social status • To celebrate religious festivals

(accessed on the website www.gamb-ling.com/flash/english/pdf/ethno.pdf )

Not least, gambling satisfies people’s desires for competition, skill testing and excitement; it offers an opportunity for social interaction; and it provides people a reason for the exchange and the reallocation of resources from richer to poorer. Additionally, gambling has long served as a means of demonstrating bravery, honour, and superior social status, especially, among men of leisure. What is also important is to note that, as an aspect of culture, gambling has always varied somewhat from one society to another, in terms of the types of games played and the occasions when gambling is considered proper. In Chapter 2 we showed that gambling also has a long history in Canadian society. Some Canadians have always gambled. At the same time, other (non-gambling) Canadians have always sought to impose controls and penalties on Canadians who do gamble. There is a long history of Canadian moralizing about gambling. Typically, state controls and penalties have been aimed primarily at the poor and at (ethnic) immigrant minorities. Since the 1960s, this has changed in Canada as elsewhere. With the growth of multiculturalism as an official ideology, immigrant bashing has become less acceptable. And, with the need for additional revenues, governments have moved to tax and even sponsor gambling events. In Chapter 3 we showed that gambling in Canada has varied from one ethnic group to another. There, we looked in depth at three particular groups: the English, the Chinese, and the Aboriginals. We examined the English and the Chinese both in Canada and in their original home countries. We found that the Chinese and Aboriginal peoples tend to gamble a lot more than average and are more likely than average to show signs of gambling problems. By contrast, people of English background – despite a long history of gambling by both working class and aristocratic men (discussed in earlier chapters) – gamble less than average and are less likely than average to show signs of gambling problems. The question to be answered is whether these ethnic variations are due to cultural differences or other factors. In Chapter 4, we began to answer this question by re-analyzing data from the Ontario Prevalence Survey. We found that members of different Ontario ethnic groups do indeed score differently (on average) on the CPGI. Again, we find Chinese and Aboriginal people scoring significantly higher than average, with more gambling problems, and English people scoring significantly lower than average, with fewer gambling problems. In Chapter 5, we continued addressing this problem through the use of neighbourhood level data from the 1996 Census of Canada. Here, we found that living in neighbourhoods (i.e., Enumeration Areas) with different ethnic mixes predicts a respondent’s score on CPGI. The strength of prediction is weaker overall than when we use individual level data, indicating weaker neighbourhood effects than 141 individual level effects. That said, the same ethnic compositions emerge to predict high and low CPGI scores. In particular, neighbourhoods with a higher than average Aboriginal residency are likely to have higher than average CPGI scores. In Chapter 6, we completed our analysis of these data by examining the interactions between the individual effects and the neighbourhood effects. We found, using sophisticated and powerful tools of multivariate data analysis, that ethnic ancestry does indeed influence gambling patterns, positively and negatively. Other things being equal people of Chinese ancestry are more likely than average to show gambling problems. Other things considered, people of English, Irish and French ancestry are not. Finally, the tendencies of Aboriginal people to show gambling problems are both a reflection of socio-demographic disadvantage and of their tendencies to other forms of poor health and addiction. Additionally, we found interesting – indeed, sometimes perplexing – cross-level interactions between individual level and neighbourhood level characteristics. The fact is, for certain kinds of people, living in certain kinds of neighbourhoods increases the likelihood of problem gambling. We are far from knowing all the reasons why.

Conclusions

To repeat, gambling is part of culture. Since cultures vary from one society to another, different cultural groups gamble in somewhat different ways, to somewhat different degrees. Our analysis has shown that some ethnic variations in gambling in Canada are not ascribable to cultural differences, since they disappear – they are washed out – when we control for social, economic and demographic differences. How can we account for this anomaly that, for these groups, culture seems very important yet, in the Canadian ethnic context, it is not very important. First, there is the possibility that earlier studies – largely by historians and anthropologists studying particular isolated cases – have exaggerated the extent and importance of cultural uniqueness. Case studies – as we know from social science critiques of medical and psychotherapeutic research – always run the risk of exaggerating difference. Comparative studies and surveys are the necessary corrective. We have applied this corrective and found no significant cultural effect. Second, there is the possibility that cultural differences in gambling, and attitudes toward gambling, are indeed significant where immigrants originated. However, immigration and the homogenizing tendencies of Canadian society (e.g., public education, exposure to English language mass media) water down these cultural traditions and, in this way, reduce ethnic variation within Canada. What start out as cultural differences abroad, in Canada survive only briefly, in a weakened form. As a result, in an immigrant society like Canada (or Australia, Israel or the USA), cultural differences are not a good way of explaining variations in gambling. That said, there is no denying that different ethnic groups show different patterns. We have often remarked on the extreme gambling practices of Aboriginal Canadians. Even if these practices are, ultimately, explained by social, demographic and economic factors, there is good reason to continue considering ethnic variation. That is because each of ethnic groups display a particular mix of social, demographic, and economic characteristics. The Aboriginals, for example, are a high fertility, mixed urban and rural group, with much less education on average, less community cohesion, and little history of upward economic mobility. They are a comparatively young, uneducated population. When we feed their social, economic and demographic features into the ancestral Aboriginal cultures, we get gambling problems. However, the Chinese also have problems and their cultural and demographic profile is quite different. This leads us to wonder whether these two seemingly different cultural groups actually have some important commonalities. Consider two such commonalities we shall call (a) the tendency to affiliate, and (b) the tendency to protect insiders. 142

The Tendency to Affiliate Northern European (Protestant) culture is known for its celebration of individual responsibility, whether in the economic realm (i.e., a person’s responsibility for his/her own success) or the spiritual realm (i.e., a person’s responsibility for his/her own salvation). By contrast, many cultures celebrate group cohesion. Indeed, they teach their members to deal with adversity by means of family, kinship and communal affiliation. They are more collectivistic, more traditional, and less individualistic. Group rituals and activities assume an important role in the lives of these people. Gambling is an activity with great communal and kinship importance for both Chinese and Aboriginal people. It provides a regular occasion for kinsmen to gather together, enjoy each other’s company, and pass the time. Perhaps, in these cultures, group members learn to deal with stress and anxiety by gambling in a group setting. As social, economic and demographic factors increase the stress level, gambling – along with other collective activities – serves to reduce the feeling of stress. Thus, culture does not so much cause gambling, much less excessive gambling; it merely channels surplus anxiety into gambling activities, within cultures already accustomed to social gambling. Gambling problems emerge only when, for social, economic and demographic reasons, stress is relatively unrelenting.

The Tendency to Protect Insiders Some cultures maintain an exclusionary (tribal) ethic that gives a preference to insiders – members of their own group – over outsiders. Often, this is a result of exclusion they experience themselves, because of racial, religious or other discrimination. The preference to insiders may take the form of protection: i.e., you never hurt a kinsman or member of the community. What this means, in principle, is that you try to protect community members against others, and against themselves. If, for example, they are at risk of taking a bad job or making a bad marriage, drinking to excess or gambling away more than they can afford, you advise them, caution them, or put obstacles in their way. In the area of gambling, this might mean refusing to accept a bet beyond what a person can afford. Or, it might mean conspiring to lose to a loser after having won too many bets from him in a row. In respect to gambling, this tendency to protect insiders results in a redistributive ethic that we have discussed earlier. Anthropologist Laura Zimmer-Tamakoshi has shown how, in Melanesia, gambling is used to balance (in a certain, perhaps mostly symbolic sense) relationships that had become unbalanced by unequal exchange in a changing economic environment. In these circumstances, gambling problems emerge only when group members gamble outside their group and do not receive the accustomed protection. Casino gambling, for example, would be particularly dangerous for such people. Casinos certainly do not undertake to protect players, nor do they redistribute the winnings to the kinsmen and community members of losers.

Implications

We agree entirely with the conclusions stated below, accessed on the website www.gamb- ling.com/flash/english/pdf/ethno.pdf :

Working in partnership and/or very closely with ethno-cultural communities is a key element for developing effective and alternative mental health practices to address the issue of problem gambling. As A. Blaszczynski, concludes in his article “Gambling Problems in a Multicultural Society,” of research done in Australia,

In order to achieve better outcomes for ethnic clients, therapists must accommodate cultural differences and specific cultural needs of ethnic clients. The cultural background of ethnic clients provides them with a context for actions and interactions 143

with each other. This context will have embedded in it culturally based beliefs, values, attitudes and role requirements. Awareness of the influence of culture will allow therapists to gain more relevant information from ethnic clients by asking more appropriate questions and allow for more culturally appropriate interventions.

These practices must include:

• Outreach • Developing partnerships and/or coalitions • Prevention and awareness programs • Counseling in various languages • Language-specific publications • Increased collaboration with service agencies working specifically with ethno-cultural groups

Ways counselors can test and modify their own attitudes and perceptions:

• Counselors need to increase self-awareness and develop relevant skills • Focus on the individual. • Learn the cultural and religious beliefs, and traditions related to gambling. • Consider the element of stress. • Consider the role of family in the person’s culture. • Understand the concept of “time” (provide extra time, longer appointments, flexibility in keeping/missing appointment, lateness, etc.) • Understand the family’s structure and expected roles within each culture, • Find out about the historical cultural perspective and context of gambling. • Learn about the perception of gambling in the client’s culture. • Know the common forms of gambling of client’s particular culture.

That being said, it would be wrong to conclude, as some do, that problem gambling is the same for every culture, since gambling is a universal human activity and ethnic variations are wiped out by controlling for socio-demographic factors. Before we can dismiss ethnic culture as a causal factor that interacts with socially structured inequalities and stresses to produce problem gambling, we must test two hypotheses suggested by this research. First, we need to test the “tendency to affiliate” hypothesis. This is the hypothesis that different ethnic groups vary in their use of gambling as a social activity, and that this social activity is particularly common when the group is experiencing problems, isolation, homesickness, exclusion, and feelings of lost status. Individual group members learn, in this way, to channel their personal stress into gambling activity. We would theorize that groups that gamble a lot when they are collectively under great pressure are most likely to produce individuals who register high problem gambling scores when they are individually under the most pressure. Second, we need to test the “tendency to protect insiders” hypothesis. This is the hypothesis that different ethnic groups vary in their tendency to protect members against their own weakness or vulnerability. By this reckoning, problem gambling only emerges when individuals, accustomed to such protection, begin to gamble outside the group with strangers, in casinos, or with members of other ethnic groups. To study this question would require studying groups of gamblers from different ethnocultural groups, gambling inside and outside their group environment. In closing, we agree that it is important to avoid mistakes of the past, when immigrant minorities were labelled “vicious” or “disreputable” because they gambled, drank alcohol, or behaved in other ways the Anglo-Saxon majority could not readily accept. Likely, gambling is here to stay, and 144 our goal is to minimize the harm of problem gambling, not to eliminate gambling per se. That said, some groups are apparently more prone to problem gambling than other groups, likely for a combination of social, demographic, economic and cultural reasons. These groups must be helped in culturally sensitive ways, as we have suggested. They must also be better understood, and must gain a better understanding of their own problems; that is where research of the present kind comes in. Now we need to study the role of informal control in relation to gambling. Gambling is a social behaviour, in the sense that most people learn to gamble, and practice gambling, in the presence of other people. Little is known about the social processes that regulate gambling, however. Do groups, using informal controls, regulate the gambling behaviour of individuals? If so, to what extent do groups regulate gambling behaviour through such means as personal influence, argument, persuasion, ridicule, threats of exclusion, and so on? What kinds of controls are most common? Most effective? Which group members are most likely to apply these controls, and with how much success, in which situations? What kinds of gambling behaviour are typically regulated by the informal behaviour of family, friends, acquaintances, workmates, employees, neighbours, health professionals (e.g., doctors, nurses), and social service professionals (e.g., social workers)? Sociologists have studied the operation of informal control in a wide variety of settings, including families, prisons, schools, and workplaces. For example, an early classic piece of research -- the so-called Hawthorne Studies of Western General Electric -- found that work groups exercise informal control over their members to limit productivity. In stable work groups, individuals do not respond individually and automatically to rewards for higher productivity; rather, they respond to group norms regulating productivity. In everyday life, we all exercise informal social control, through the application of interpersonal rewards and punishments – for example, through encouragement, threat, praise, blame, and gossip (among other means). All processes of interpersonal influence and persuasion are informal means of social control. In families, schools, workplaces, and small communities, people regularly use informal control to keep social order. Informal control works because (almost) everyone seeks the approval and cooperation of others. Thanks to informal control, most people obey the rules of their groups most of the time. The police and courts are needed only in extreme cases. Guilt, shame, and fear of exclusion work particularly well where people consider "saving face" to be important and when they want to please their close friends, relatives and workmates. To repeat, little is known about the role of informal control over gambling in different ethnic groups. The next step in our research on ethnic groups is to better understand how communities and families exert such control, given ethnically various notions about the value and appropriateness of gambling under different circumstances.

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APPENDIX A: Prevalence Questionnaire

VIEWPOINTS RESEARCH (N = 5000) LD CODE: 4350 INTERVIEWER: ______PHONE: ( ______) - ______- ______

GENDER: Male ...... 1 Female...... 2

(1) Hi, my name is (first & last) and I’m calling from Viewpoints Research. We are a professional public opinion research company and today we’re calling a random sample of 5,000 Ontario residents on behalf of the Canadian Centre on Substance Abuse and the Canadian Foundation on Compulsive Gambling. These two organizations are conducting a study on the gambling activities and attitudes of adult Ontarians and we would like to include your views. For the purposes of this study we would like to speak to the person living in your household who is 18 or over, and whose birthday will come next. Would that be you? IF NO, ASK TO SPEAK TO THE PERSON WHO DOES MEET THE REQUIREMENTS. IF THE PERSON WHO MEETS THE REQUIREMENTS IS NOT AT HOME, ASK FOR ANY PERSON WHO IS 18 OR OVER. IF NO ONE PRESENTLY AT HOME QUALIFIES, ARRANGE A TIME TO CALL BACK. (2) Some of the survey questions may be sensitive. The survey will ask you questions about: • The types of gambling activities you participate in, and the amount of time and money spent on gambling • Any problems you have experienced from your own or someone else’s gambling • Use of alcohol and other drugs • Your background such as level of education, marital status • Your general well-being (3) The study will provide important information on the nature of gambling among Ontarians and related service needs. In order to provide more in-depth information on issues related to gambling, a small number of respondents (3%) will be contacted in a couple of months for another telephone interview. If you are one of the respondents who is contacted for another interview, you may choose not to participate at that time. (4) If you want further information on this study, you may call a toll free number (1-888-391- 1111). (5) The survey will take approximately 20 minutes. You can quit the survey at any time, or refuse to answer any question. All of your answers will remain confidential; you will not be identified in any report that may arise from this study. Only the researchers on this project will have access to all of the information collected. If the data is shared with other researchers in the future, all identifiers would be removed. (6) Would you be willing to participate?

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DO NOT ASK Q1. START AT Q2.

Q1 In the past year, have you gambled, for example by buying a lottery or raffle ticket, betting on horse races or bingo, playing a slot machine or video lottery terminal in a casino or elsewhere, playing other games in a casino, betting on a sports event, playing cards or other games for money or bet on the internet? Yes ...... 1 No...... 2 GOTO Q964 Don't know...... 3 Refused...... 4

Q2 In the past 12 months, how often did you spend money on Lottery tickets like the 649, Super 7, Pick 3 or POGO? Would you say daily, at least once a week (but not daily), at least once a month (but not weekly), less than once a month or never? Daily...... 1 At least once a week...... 2 At least once a month...... 3 Less than once a month...... 4 Never...... 5 GOTO Q6 (DO NOT READ) I do not gamble ...... 6 GOTO Q6 Don't know...... 7 Refused...... 8 GOTO Q6

Q3 On a typical occasion when you spend money on a lottery ticket, how much money do you spend, not including winnings? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. $______... Don't know...... 2 Refused...... 3

Q4 On a typical occasion when you spend money on a lottery ticket, how much money do you win? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Wins $______.... Don't know...... 2 Refused...... 3

Q5 On a typical occasion when you spend money on a lottery ticket, how much money do you lose? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Loses $______.... Don't know...... 2 Refused...... 3

Q6 In the past 12 months, how often did you spend money on instant win or scratch tickets like break open, pull tab or Nevada strips? Would you say daily, at least once a week (but not daily), at least once a month (but not weekly), less than once a month or never?

158

Daily...... 1 At least once a week...... 2 At least once a month...... 3 Less than once a month...... 4 Never...... 5 GOTO Q10 (DO NOT READ) I do not gamble ...... 6 GOTO Q10 Don't know...... 7 Refused...... 8 GOTO Q10

Q7 On a typical occasion when you spend money on such instant win or scratch tickets, how much money do you spend, not including winnings? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. $______... Don't know...... 2 Refused...... 3

Q8 On a typical occasion when you spend money on such instant win or scratch tickets, how much money do you win? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Wins $______.... Don't know...... 2 Refused...... 3

Q9 On a typical occasion when you spend money on such instant win or scratch tickets, how much money do you lose? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Loses $______.... Don't know...... 2 Refused...... 3

Q10 In the past 12 months, how often did you bet or spend money on raffles or fundraising tickets? Would you say daily, at least once a week (but not daily), at least once a month (but not weekly), less than once a month or never? Daily...... 1 At least once a week...... 2 At least once a month...... 3 Less than once a month...... 4 Never...... 5 GOTO Q14 (DO NOT READ) I do not gamble ...... 6 GOTO Q14 Don't know...... 7 Refused...... 8 GOTO Q14

Q11 On a typical occasion when you spend money on raffles or fundraising tickets, how much money do you spend, not including winnings? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. $______... Don't know...... 2 Refused...... 3

159

Q12 On a typical occasion when you spend money on raffles or fundraising tickets, how much money do you win? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Wins $______.... Don't know...... 2 Refused...... 3

Q13 On a typical occasion when you spend money on raffles or fundraising tickets, how much money do you lose? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Loses $______.... Don't know...... 2 Refused...... 3

Q14 In the past 12 months, how often did you bet or spend money on horse races (i.e. live at the track or off track)? Would you say daily, at least once a week (but not daily), at least once a month (but not weekly), less than once a month or never? Daily...... 1 At least once a week...... 2 At least once a month...... 3 Less than once a month...... 4 Never...... 5 GOTO Q18 (DO NOT READ) I do not gamble ...... 6 GOTO Q18 Don't know...... 7 Refused...... 8 GOTO Q18

Q15 On a typical occasion when you spend money on horse races, how much money do you risk (not including winnings)? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. $______... Don't know...... 2 Refused...... 3

Q16 On a typical occasion when you spend money on horse races, how much money do you win? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Wins $______.... Don't know...... 2 Refused...... 3

Q17 On a typical occasion when you spend money on horse races, how much money do you lose? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Loses $______.... Don't know...... 2 Refused...... 3

Q18 In the past 12 months, how often did you bet or spend money on bingo? Would you say daily, at least once a week (but not daily), at least once a month (but not weekly), less than once a month or never?

160

Daily...... 1 At least once a week...... 2 At least once a month...... 3 Less than once a month...... 4 Never...... 5 GOTO PREAMBLE BEFORE Q22 (DO NOT READ) I do not gamble ...... 6 GOTO PREAMBLE BEFORE Q22 Don't know...... 7 Refused...... 8 GOTO PREAMBLE BEFORE Q22

Q19 On a typical occasion when you spend money on bingo, how much money do you spend, not including winnings? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. $______... Don't know...... 2 Refused...... 3

Q20 On a typical occasion when you spend money on bingo, how much money do you win? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Wins $______.... Don't know...... 2 Refused...... 3

Q21 On a typical occasion when you spend money on bingo, how much money do you lose? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Loses $______.... Don't know...... 2 Refused...... 3

I would like to ask you whether you have bet on coin slot machines or other electronic gambling machines such as video lottery terminals in casinos. Video Lottery Terminals or “VLTs” refer to gambling machines where coins are not dispersed. Q22 In the past 12 months, how often did you bet or spend money on coin slot machines or video lottery terminals in a casino? Would you say daily, at least once a week (but not daily), at least once a month (but not weekly), less than once a month or never? Daily...... 1 At least once a week...... 2 At least once a month...... 3 Less than once a month...... 4 Never...... 5 GOTO Q26 (DO NOT READ) I do not gamble ...... 6 GOTO Q26 Don't know...... 7 Refused...... 8 GOTO Q26

Q23 On a typical occasion when you spend money on coin slot machines or video lottery terminals in a casino, how much money do you spend, not including winnings? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR.

161

$______... Don't know...... 2 Refused...... 3

Q24 On a typical occasion when you spend money on coin slot machines or video lottery terminals in a casino, how much money do you win? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Wins $______.... Don't know...... 2 Refused...... 3

Q25 On a typical occasion when you spend money on coin slot machines or video lottery terminals in a casino, how much money do you lose? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Loses $______.... Don't know...... 2 Refused...... 3

Q26 In the past 12 months, how often did you bet or spend money on games other than slot machines in a casino such as poker, blackjack, roulette or keno? Would you say daily, at least once a week (but not daily), at least once a month (but not weekly), less than once a month or never? Daily...... 1 At least once a week...... 2 At least once a month...... 3 Less than once a month...... 4 Never...... 5 GOTO Q30 (DO NOT READ) I do not gamble ...... 6 GOTO Q30 Don't know...... 7 Refused...... 8 GOTO Q30

Q27 On a typical occasion when you spend money on games other than slot machines in a casino such as poker, blackjack, roulette or keno, how much money do you spend, not including winnings? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. $______... Don't know...... 2 Refused...... 3

Q28 On a typical occasion when you spend money on games other than slot machines in a casino such as poker, blackjack, roulette or keno, how much money do you win? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Wins $______.... Don't know...... 2 Refused...... 3

Q29 On a typical occasion when you spend money on games other than slot machines in a casino such as poker, blackjack, roulette or keno, how much money do you lose? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR.

162

Loses $______.... Don't know...... 2 Refused...... 3

Q30 In the past 12 months, how often did you bet or spend money on coin slot machines or video lottery terminals other than at casinos? Would you say daily, at least once a week (but not daily), at least once a month (but not weekly), less than once a month or never? Daily...... 1 At least once a week...... 2 At least once a month...... 3 Less than once a month...... 4 Never...... 5 GOTO Q34 (DO NOT READ) I do not gamble ...... 6 GOTO Q34 Don't know...... 7 Refused...... 8 GOTO Q34

Q31 On a typical occasion when you spend money on coin slot machines or video lottery terminals other than at casinos, how much money do you spend, not including winnings? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. $______... Don't know...... 2 Refused...... 3

Q32 On a typical occasion when you spend money on coin slot machines or video lottery terminals other than at casinos, how much money do you win? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Wins $______.... Don't know...... 2 Refused...... 3

Q33 On a typical occasion when you spend money on coin slot machines or video lottery terminals other than at casinos, how much money do you lose? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Loses $______.... Don't know...... 2 Refused...... 3

Q34 In the past 12 months, how often did you bet or spend money on Sport Select (e.g Pro Line, Over/Under, Point Spread)? Would you say daily, at least once a week (but not daily), at least once a month (but not weekly), less than once a month or never?

163

Daily...... 1 At least once a week...... 2 At least once a month...... 3 Less than once a month...... 4 Never...... 5 GOTO Q38 (DO NOT READ) I do not gamble ...... 6 GOTO Q38 Don't know...... 7 Refused...... 8 GOTO Q38

Q35 On a typical occasion when you spend money on Sport Select (e.g Pro Line, Over/Under, Point Spread), how much money do you spend, not including winnings? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. $______... Don't know...... 2 Refused...... 3

Q36 On a typical occasion when you spend money on Sport Select (e.g Pro Line, Over/Under, Point Spread), how much money do you win? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Wins $______.... Don't know...... 2 Refused...... 3

Q37 On a typical occasion when you spend money on Sport Select (e.g Pro Line, Over/Under, Point Spread), how much money do you lose? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Loses $______.... Don't know...... 2 Refused...... 3

Q38 In the past 12 months, how often did you bet or spend money on sports pools or the outcome of sporting events? Would you say daily, at least once a week (but not daily), at least once a month (but not weekly), less than once a month or never? Daily...... 1 At least once a week...... 2 At least once a month...... 3 Less than once a month...... 4 Never...... 5 GOTO Q42 (DO NOT READ) I do not gamble ...... 6 GOTO Q42 Don't know...... 7 Refused...... 8 GOTO Q42

Q39 On a typical occasion when you spend money on sports pools or the outcome of sporting events, how much money do you spend, not including winnings? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR.

164

$______... Don't know...... 2 Refused...... 3

Q40 On a typical occasion when you spend money on sports pools or the outcome of sporting events, how much money do you win? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Wins $______.... Don't know...... 2 Refused...... 3

Q41 On a typical occasion when you spend money on sports pools or the outcome of sporting events, how much money do you lose? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Loses $______.... Don't know...... 2 Refused...... 3

Q42 In the past 12 months, how often did you bet or spend money on cards or board games anywhere other than at casinos (at home, friends’ homes, work, card rooms, etc.)? Would you say daily, at least once a week (but not daily), at least once a month (but not weekly), less than once a month or never? Daily...... 1 At least once a week...... 2 At least once a month...... 3 Less than once a month...... 4 Never...... 5 GOTO Q46 (DO NOT READ) I do not gamble ...... 6 GOTO Q46 Don't know...... 7 Refused...... 8 GOTO Q46

Q43 On a typical occasion when you spend money on cards or board games anywhere other than at casinos (at home, friends’ homes, work, card rooms, etc.), how much money do you spend, not including winnings? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. $______... Don't know...... 2 Refused...... 3

Q44 On a typical occasion when you spend money on cards or board games anywhere other than at casinos (at home, friends’ homes, work, card rooms, etc.), how much money do you win? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Wins $______.... Don't know...... 2 Refused...... 3

Q45 On a typical occasion when you spend money on cards or board games anywhere other than at casinos (at home, friends’ homes, work, card rooms, etc.), how much money do you lose? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR.

165

Loses $______.... Don't know...... 2 Refused...... 3

Q46 In the past 12 months, how often did you bet or spend money on games of skill such as pool, bowling or darts? Would you say daily, at least once a week (but not daily), at least once a month (but not weekly), less than once a month or never? Daily...... 1 At least once a week...... 2 At least once a month...... 3 Less than once a month...... 4 Never...... 5 GOTO Q50 (DO NOT READ) I do not gamble ...... 6 GOTO Q50 Don't know...... 7 Refused...... 8 GOTO Q50

Q47 On a typical occasion when you spend money on games of skill such as pool, bowling or darts, how much money do you spend, not including winnings? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. $______... Don't know...... 2 Refused...... 3

Q48 On a typical occasion when you spend money on games of skill such as pool, bowling or darts, how much money do you win? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Wins $______.... Don't know...... 2 Refused...... 3

Q49 On a typical occasion when you spend money on games of skill such as pool, bowling or darts, how much money do you lose? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Loses $______.... Don't know...... 2 Refused...... 3

Q50 In the past 12 months, how often did you bet or spend money on arcade or video games? Would you say daily, at least once a week (but not daily), at least once a month (but not weekly), less than once a month or never?

166

Daily...... 1 At least once a week...... 2 At least once a month...... 3 Less than once a month...... 4 Never...... 5 GOTO Q54 (DO NOT READ) I do not gamble ...... 6 GOTO Q54 Don't know...... 7 Refused...... 8 GOTO Q54

Q51 On a typical occasion when you spend money on arcade or video games, how much money do you spend, not including winnings? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. $______... Don't know...... 2 Refused...... 3

Q52 On a typical occasion when you spend money on arcade or video games, how much money do you win? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Wins $______.... Don't know...... 2 Refused...... 3

Q53 On a typical occasion when you spend money on arcade or video games, how much money do you lose? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Loses $______.... Don't know...... 2 Refused...... 3

Q54 In the past 12 months, how often did you bet or spend money gambling on the internet? Would you say daily, at least once a week (but not daily), at least once a month (but not weekly), less than once a month or never? Daily...... 1 At least once a week...... 2 At least once a month...... 3 Less than once a month...... 4 Never...... 5 GOTO Q58 (DO NOT READ) I do not gamble ...... 6 GOTO Q58 Don't know...... 7 Refused...... 8 GOTO Q58

Q55 On a typical occasion when you spend money gambling on the internet, how much money do you spend, not including winnings? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. $______... Don't know...... 2 Refused...... 3

167

Q56 On a typical occasion when you spend money gambling on the internet, how much money do you win? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Wins $______.... Don't know...... 2 Refused...... 3

Q57 On a typical occasion when you spend money gambling on the internet, how much money do you lose? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Loses $______.... Don't know...... 2 Refused...... 3

Q58 In the past 12 months, how often did you bet or spend money gambling on sports with a bookie? Would you say daily, at least once a week (but not daily), at least once a month (but not weekly), less than once a month or never? Daily...... 1 At least once a week...... 2 At least once a month...... 3 Less than once a month...... 4 Never...... 5 GOTO Q62 (DO NOT READ) I do not gamble ...... 6 GOTO Q62 Don't know...... 7 Refused...... 8 GOTO Q62

Q59 On a typical occasion when you spend money gambling on sports with a bookie, how much money do you spend, not including winnings? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. $______... Don't know...... 2 Refused...... 3

Q60 On a typical occasion when you spend money gambling on sports with a bookie, how much money do you win? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Wins $______.... Don't know...... 2 Refused...... 3

Q61 On a typical occasion when you spend money gambling on sports with a bookie, how much money do you lose? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Loses $______.... Don't know...... 2 Refused...... 3

Q62 In the past 12 months, how often have you made short-term speculative stock or commodity purchases such as day trading, not including long-term investments such as mutual funds or RRSPs? Would you say daily, at least once a week (but not daily), at least once a month (but not weekly), less than once a month or never?

168

Daily...... 1 At least once a week...... 2 At least once a month...... 3 Less than once a month...... 4 Never...... 5 GOTO Q66 (DO NOT READ) I do not gamble ...... 6 GOTO Q66 Don't know...... 7 Refused...... 8 GOTO Q66

Q63 On a typical occasion when you spend money on short-term speculative stock or commodity purchases, how much money do you spend, not including winnings? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. $______... Don't know...... 2 Refused...... 3

Q64 On a typical occasion when you spend money on short-term speculative stock or commodity purchases, how much money do you win? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Wins $______.... Don't know...... 2 Refused...... 3

Q65 On a typical occasion when you spend money on short-term speculative stock or commodity purchases, how much money do you lose? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Loses $______.... Don't know...... 2 Refused...... 3

Q66 In the past 12 months, how often did you bet or spend money gambling in casinos out of province (e.g. at Las Vegas or Atlantic City or casinos in other Canadian provinces)? Would you say daily, at least once a week (but not daily), at least once a month (but not weekly), less than once a month or never? Daily...... 1 At least once a week...... 2 At least once a month...... 3 Less than once a month...... 4 Never...... 5 GOTO Q70 (DO NOT READ) I do not gamble ...... 6 GOTO Q70 Don't know...... 7 Refused...... 8 GOTO Q70

Q67 On a typical occasion when you spend money gambling in casinos out of province, how much money do you spend, not including winnings? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR.

169

$______... Don't know...... 2 Refused...... 3

Q68 On a typical occasion when you spend money gambling in casinos out of province, how much money do you win? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Wins $______..... Don't know...... 2 Refused...... 3

Q69 On a typical occasion when you spend money gambling in casinos out of province, how much money do you lose? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Loses $______.... Don't know...... 2 Refused...... 3

IF NEVER TO ALL GAMBLING OR SAID DO NOT GAMBLE TWICE GOTO Q94

Q70 On average, how many hours or minutes do you normally spend each month on all of these gambling activities? Please give the total amount of time spent on gambling in an average month. IF ONLY MINUTES, ENTER 0 FOR HOURS. Hours______Minutes______More than 8 hours ...... 3 Don't know...... 4 Refused...... 5

Q71 What, if any, are some of the benefits you receive from gambling? (READ CATEGORIES, CHECK ALL THAT APPLY) It’s an opportunity to socialize...... 01 I get to be around others, decreased isolation ...... 02 I can forget about my problems ...... 03 It’s exciting, it’s fun...... 04 It decreases my boredom...... 05 I can win money...... 06 Other (specify below) ...... 07 (DO NOT READ) None...... 08 Don't know...... 09 Refused...... 10

______

Q72 In the past 12 months, how much money have you spent on any type of gambling, not including winnings? (ENTER NUMBER OF DOLLARS). ROUND UP TO NEAREST DOLLAR. $______... Don't know...... 2 Refused...... 3

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Q73 In the past 12 months, how much money have you won on all types of gambling? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Wins $______.... Don't know...... 2 Refused...... 3

Q74 In the past 12 months, how much money have you lost on all types of gambling? ENTER NUMBER OF DOLLARS. ROUND UP TO NEAREST DOLLAR. Loses $______.... Don't know...... 2 Refused...... 3

Some of the next questions may not apply to you, but please try to be as accurate as possible. Thinking about the last 12 months, would you say you never, sometimes, most of the time or almost always … ROTATE. Never Some- Most of Almost DK REF times the time always Q75 Bet more than you could really afford to lose? 1 2 3 4 5 6 Q76 Need to gamble with larger amounts of money 1 2 3 4 5 6 to get the same feeling of excitement? Q77 Go back another day to try to win back the 1 2 3 4 5 6 money you lost? Q78 Borrow money or sold anything to get money to 1 2 3 4 5 6 gamble? Q79 Feel that you might have a problem with 1 2 3 4 5 6 gambling? Q80 Feel gambling has caused you any health 1 2 3 4 5 6 problems, including stress or anxiety? Q81 Have people criticizing your betting or telling 1 2 3 4 5 6 you that you have a gambling problem, regardless of whether or not you think it is true? Q82 Feel your gambling has caused financial 1 2 3 4 5 6 problems for you or your household? Q83 Feel guilty about the way you gamble or what 1 2 3 4 5 6 happens when you gamble?

Next, we explore some of your beliefs about gambling, as well as any early experiences you have had with gambling or betting money. For each of the following, please tell me if you strongly agree, agree, disagree or strongly disagree? ROTATE. Str Agree Dis- Str Dis- DK REF Agree agree agree Q84 After losing many times in a row, you are more 1 2 3 4 5 6 likely to win. Do you strongly agree, agree, disagree or strongly disagree?

171

Str Agree Dis- Str Dis- DK REF Agree agree agree Q85 You could win more if you used a certain 1 2 3 4 5 6 system or strategy.

Q86 Do you remember a big win when you first started gambling? Yes ...... 1 No...... 2 Don't know...... 3 Refused...... 4

Q87 What, if any, are some of the problems you have experienced from gambling? READ CATEGORIES AND CHECK ALL THAT APPLY Income loss / debt ...... 1 Relationship problems...... 2 Health problems ...... 3 Work problems...... 4 Loneliness / increased isolation ...... 5 Other (specify below) ...... 6 (DO NOT READ) None...... 7 Don't know...... 8 Refused...... 9

______

Q88 Do you remember a big loss when you first started gambling? Yes ...... 1 No...... 2 Don't know...... 3 Refused...... 4

Q89 In the last 12 months, have you used alcohol or drugs while gambling? Yes ...... 1 No...... 2 Don't know...... 3 Refused...... 4

Q90 In the last 12 months, have you gambled while drunk or high? Yes ...... 1 No...... 2 Don't know...... 3 Refused...... 4

Q91 Have you ever engaged in petty crime or other criminal activities to support your gambling? Yes ...... 1 No...... 2 Don't know...... 3 Refused...... 4

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Q92 In the last 12 months, if something painful happened in your life, did you have the urge to gamble? Yes (did gamble, had the urge to gamble) ...... 1 No...... 2 Nothing painful has happened ...... 3 Don't know...... 4 Refused...... 5

Q93 Have you seriously thought about or attempted suicide as a result of your gambling? Yes ...... 1 No...... 2 Don't know...... 3 Refused...... 4

RESUME FOR NON GAMBLERS. GAMBLERS CONTINUE

Q94 Have you been to a casino in the last year? Yes ...... 1 No...... 2 GOTO Q96 Don't know...... 3 GOTO Q96 Refused...... 4 GOTO Q96

Q95 And what are the main reasons you go to a casino? READ LIST. CIRCLE ALL MENTIONS. Enjoyment of gambling...... 1 To win money ...... 2 To watch others gamble ...... 3 Musical entertainment and shows...... 4 To drink alcohol...... 5 Other (specify below) ...... 6 (DO NOT READ) Don't know...... 7 Refused...... 8

______

Q96 Has anyone in your family ever had a gambling problem? Yes ...... 1 No...... 2 Don't know...... 3 Refused...... 4

Q97 I’d like to ask you some questions about cigarette smoking. Have you ever smoked cigarettes? Yes ...... 1 No...... 2 GOTO Q101 Don't know...... 3 GOTO Q101 Refused...... 4 GOTO Q101

Q98 At the present time, do you smoke cigarettes daily, occasionally or not at all?

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Daily...... 1 Occasionally...... 2 Not at all...... 3 GOTO Q101 Don't know...... 4 GOTO Q101 Refused...... 5 GOTO Q101

Q99 Have you smoked at least 100 cigarettes in your life? Yes ...... 1 No...... 2 Don't know...... 3 Refused...... 4

Q100 How many cigarettes do you usually smoke each day? (One pack = 25 cigarettes, 1 small pack=20 cigarettes.) Less than one a day ...... 1 Enter number of cigarettes/ day ______...... 2 Don't know...... 3 Refused...... 4

Q101 Now I would like to ask you some questions about drinking alcohol. In these questions, when we use the word “drink” it means one 12 ounce bottle of beer or glass of draft, one five once glass of wine or one straight or mixed drink with one and a half ounces of hard liquor. During the past 12 months, have you had a drink of any alcoholic beverage? Yes ...... 1 GOTO Q103 No...... 2 Don't know...... 3 Refused...... 4 GOTO Q103

Q102 Did you ever have a drink of any alcoholic beverage? Yes ...... 1 GOTO Q106 No...... 2 GOTO Q106 Don't know...... 3 GOTO Q106 Refused...... 4 GOTO Q106

Q103 How often, if ever, did you drink alcoholic beverages during the past twelve months: Would you say it was more than once a day, about every day, four to five times a week, two to three times a week, once a week, two to three times a month, once a month or less than once a month? More than once a day...... 01 About everyday...... 02 4 to 5 times a week...... 03 2 to 3 times a week...... 04 Once a week...... 05 2 to 3 times a month...... 06 Once a month ...... 07 Less than once a month...... 08 Don't know...... 09 Refused...... 10

Q104 On those days when you drank, approximately how many drinks did you have?

174

Number of drinks/ day ______...... 1 Don't know...... 2 Refused...... 3

Q105 About how often during the past twelve months would you say you had five or more drinks at the same sitting or occasion: Would you say it was everyday, about everyday, 3 or 4 times a week, once or twice a week, 2 or 3 times a month, about once a month, 6 to 11 times a year, 1 to 5 times a year or never in the past year? Everyday ...... 01 About everyday...... 02 3 or 4 times a week ...... 03 Once or twice a week...... 04 2 or 3 times a month...... 05 About once a month ...... 06 6 to 11 times a year ...... 07 1 to 5 times a year ...... 08 Never in the past year...... 09 Don't know...... 10 Refused...... 11

Q106 Some people use marijuana or hash in private, with friends or in other situations. Have you ever in your lifetime used marijuana or hash? Yes ...... 1 No...... 2 GOTO Q109 Don't know...... 3 GOTO Q109 Refused...... 4 GOTO Q109

Q107 Have you used marijuana or hash in the past twelve months? Yes ...... 1 No...... 2 GOTO Q109 Don't know...... 3 GOTO Q109 Refused...... 4 GOTO Q109

Q108 How many times, if any, have you used marijuana or hash during the past twelve months: would you say more than once a day, about everyday, four to five times a week, two to three times a week, once a week, two to three times a month, once a month, less than once a month or never? More than once a day...... 01 About everyday...... 02 4 to 5 times a week...... 03 2 to 3 times a week...... 04 Once a week...... 05 2 to 3 times a month...... 06 Once a month ...... 07 Less than once a month...... 08 Never...... 09 Don't know...... 10 Refused...... 11

175

Q109 Some people use cocaine in private, with friends or in other situations. Have you ever in your lifetime used cocaine? Yes ...... 1 No...... 2 GOTO Q112 Don't know...... 3 GOTO Q112 Refused...... 4 GOTO Q112

Q110 Have you used cocaine in the past twelve months? Yes ...... 1 No...... 2 GOTO Q112 Don't know...... 3 GOTO Q112 Refused...... 4 GOTO Q112

Q111 How many times, if any, have you used cocaine during the past twelve months: would you say more than once a day, about everyday, four to five times a week, two to three times a week, once a week, two to three times a month, once a month, less than once a month or never? More than once a day...... 01 About everyday...... 02 4 to 5 times a week...... 03 2 to 3 times a week...... 04 Once a week...... 05 2 to 3 times a month...... 06 Once a month ...... 07 Less than once a month...... 08 Never...... 09 Don't know...... 10 Refused...... 11

Q112 Some people use the drug MDMA, more commonly known as “Ecstasy”. Have you ever in your lifetime used the drug MDMA, more commonly known as “Ecstasy”? Yes ...... 1 No...... 2 GOTO Q115 Don't know...... 3 GOTO Q115 Refused...... 4 GOTO Q115

Q113 Have you used the drug MDMA, more commonly known as “Ecstasy”in the past twelve months? Yes ...... 1 No...... 2 GOTO Q115 Don't know...... 3 GOTO Q115 Refused...... 4 GOTO Q115

Q114 How many times, if any, have you used the drug MDMA, more commonly known as “Ecstasy” during the past twelve months: would you say more than once a day, about everyday, four to five times a week, two to three times a week, once a week, two to three times a month, once a month, less than once a month or never?

176

More than once a day...... 01 About everyday...... 02 4 to 5 times a week...... 03 2 to 3 times a week...... 04 Once a week...... 05 2 to 3 times a month...... 06 Once a month ...... 07 Less than once a month...... 08 Never...... 09 Don't know...... 10 Refused...... 11

Q115 Some people use heroin, LSD or other psychodelics. Have you ever in your lifetime used heroin, LSD or other psychodelics? Yes ...... 1 No...... 2 GOTO Q118 Don't know...... 3 GOTO Q118 Refused...... 4 GOTO Q118

Q116 Have you used heroin, LSD or other psychodelics in the past twelve months? Yes ...... 1 No...... 2 GOTO Q118 Don't know...... 3 GOTO Q118 Refused...... 4 GOTO Q118

Q117 How many times, if any, have you used heroin, LSD or other psychodelics during the past twelve months: would you say more than once a day, about everyday, four to five times a week, two to three times a week, once a week, two to three times a month, once a month, less than once a month or never? More than once a day...... 01 About everyday...... 02 4 to 5 times a week...... 03 2 to 3 times a week...... 04 Once a week...... 05 2 to 3 times a month...... 06 Once a month ...... 07 Less than once a month...... 08 Never...... 09 Don't know...... 10 Refused...... 11

Q118 Has anyone in your family ever had an alcohol or drug problem? Yes ...... 1 No...... 2 Don't know...... 3 Refused...... 4

Q119 Have you ever felt you might have an alcohol or drug problem?

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Yes ...... 1 No...... 2 Don't know...... 3 Refused...... 4

Q120 If you or someone close to you had a gambling problem, who might you go to for help? READ CATEGORIES AND CIRCLE ALL THAT APPLY Family ...... 01 Friend ...... 02 Family doctor ...... 03 Minister / priest / rabbi...... 04 Social worker / psychologist / psychiatrist ...... 05 Gambling counselor ...... 06 Other...... 07 (DO NOT READ) No one...... 08 Don't know...... 09 Refused...... 10

Q121 Are you aware that there is a toll free gambling help line in Ontario? Yes ...... 1 No...... 2 Don't know...... 3 Refused...... 4

Q122 To your knowledge, are there gambling counseling services available in your community? Yes ...... 1 No...... 2 Don't know...... 3 Refused...... 4

Q123 How would you rate your current health status. READ RESPONSES Very good...... 1 Good...... 2 Fair ...... 3 Poor ...... 4 Very poor ...... 5 (DO NOT READ) Other (specify below) ...... 6 Don't know...... 7 Refused...... 8

______

Q124 In the past 12 months, if something painful happened in your life, did you have the urge to have a drink of alcohol?

178

Yes (did have a drink, had an urge to drink)...... 1 No...... 2 Nothing painful has happened ...... 3 Don't know...... 4 Refused...... 5

Q125 In the past 12 months, if something painful happened in your life, did you have the urge to use drugs or medication? Yes (did use/ had an urge to use drugs/ medication) ...... 1 No...... 2 Nothing painful has happened ...... 3 Don't know...... 4 Refused...... 5

Q126 In the past 12 months, have you been under a doctor’s care because of physical or emotional problems brought on by stress? Yes ...... 1 No...... 2 Don't know...... 3 Refused...... 4

Q127 In the past 12 months, have you felt seriously depressed? Yes ...... 1 No...... 2 Don't know...... 3 Refused...... 4

Q128 Have you ever experienced problems as a result of someone else’s gambling? Yes ...... 1 No...... 2 Don't know...... 3 Refused...... 4

Q129 How many people, if any, could you turn to for support or help if you had a serious personal problem? ENTER NUMBER OF PEOPLE Number of people ______...... 1 Don't know...... 2 Refused...... 3

Finally, we would like to ask you some basic demographic questions. Like all your other answers, this information will be kept strictly confidential. Z1 RECORD GENDER (DO NOT READ) Male ...... 1 Female...... 2

Z2 In what year were you born? ENTER THE YEAR

179

______(before 1981) ...... 1 GOTO Z4 1981 and after ...... 2 GOTO Z4 Don't know...... 3 Refused...... 4

Z3 Instead of giving us your exact year of birth, could you please tell us to which of the following age categories you belong? READ LIST. IF RESPONDENT STILL DK/ REFUSES: I understand that your age is a private matter, but could you tell me if you are 60 years or older, or if you are younger than 60. IF SAYS 60 OR OLDER CODE AS A '5'. IF SAYS UNDER 60, CODE AS '8'. 18 to 24 ...... 1 25 to 34 ...... 2 35 to 49 ...... 3 50 to 59 ...... 4 60 or over ...... 5 Don't know...... 6 Refused...... 7 Under 60 ...... 8

Z4 Currently are you married, living with a partner, widowed, divorced, separated or have you never been married? Married (incl widowed and divorced who remarried) ...... 1 Living with a partner...... 2 Widowed (not remarried)...... 3 Divorced or separated (not remarried)...... 4 Single, never married ...... 5 Don't know...... 6 Refused...... 7

180

Z5 To what ethnic or cultural group did you or your ancestors belong on first coming to this country? IF RESPONDENT IS NOT CLEAR SAY “Are you Scottish, Chinese, Greek or something else?” IF RESPONDENT SAYS CANADIAN ASK ”In addition to being Canadian, to what ethnic or cultural group did you or your ancestors belong on first coming to this country?” CIRCLE ALL THAT APPLY. Native Indian, Inuit ...... 01 Australian...... 02 Austrian...... 03 Bahamian ...... 04 Bangladeshi...... 05 Black / African...... 06 Dutch / Netherlands / Holland ...... 07 English / British ...... 08 Canadian...... 09 Chilean ...... 10 Chinese...... 11 Croatian...... 12 Czech...... 13 Danish ...... 14 East Indian...... 15 El Salvadorian...... 16 Ethiopian...... 17 Finnish...... 18 French...... 19 German...... 20 Greek...... 21 Guyanese...... 22 Haitian...... 23 Hungarian...... 24 Inuit ...... 25 Irish ...... 26 Israeli...... 27 Italian ...... 28 Jamaican...... 29 Japanese ...... 30 Jewish...... 31 Korean...... 32 Lebanese...... 33 Macedonian...... 34 Metis ...... 35 New Zealander ...... 36 Nigerian...... 37 Norwegian...... 38 Pakistani...... 39 Philipino...... 40 Polish...... 41 Portugese...... 42 Russian...... 43 Scottish...... 44 181

Serbian ...... 45 Sikh ...... 46 Slovakian...... 47 Somalian...... 48 Spanish...... 49 Sri Lankan...... 50 Swedish ...... 51 Tamil ...... 52 Trinidadian...... 53 Ukrainian...... 54 Vietnamese...... 55 Welsh ...... 56 Yugoslavian ...... 57 Other (specify below) ...... 58 Don’t know ...... 59 Refused...... 60

______Z6 What is the highest level of education you have completed? Some high school / junior high or less...... 1 Completed high school ...... 2 Some post secondary school ...... 3 Completed post secondary school...... 4 Completed post graduate education ...... 5 Don't know...... 6 Refused...... 7

Z7 What is your present job status? Are you employed full time, employed part time, unemployed, a student, retired or a homemaker? IF RESPONDENT GIVES MORE THAN ONE ANSWER, RECORD THE ONE THAT APPEARS FIRST ON THE LIST. Employed full time (30 or more hrs/wk) ...... 01 Employed part time (less than 30 hrs/wk) ...... 02 Unemployed...... 03 Student – employed part or full time ...... 04 Student – not employed...... 05 Retired...... 06 GO TO Z9 Homemaker...... 07 GO TO Z9 Other (specify)______...... 08 GO TO Z9 Don't know...... 09 GO TO Z9 Refused...... 10 GO TO Z9

Z8 What type of work do you currently do (or do you do when you are employed)? Job title. ______

Z9 Could you please tell me how much income you and other members of your household received in the year ending December 31st 1999. Please include income form all sources such as savings, pensions, rent and employment insurance as well as wages? We don’t need the exact amount: could you tell me which of these broad categories it falls into.. READ LIST.

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Less than $20,000 ...... 01 Less than $30,000 ...... 02 Less than $40,000 ...... 03 Less than $50,000 ...... 04 Less than $60,000 ...... 05 Less than $70,000 ...... 06 Less than $80,000 ...... 07 Less than $90,000 ...... 08 Less than $100,000 ...... 09 Less than $120,000 ...... 10 Less than $150,000 ...... 11 $150,000 or more ...... 12 Don't know / Refused...... 13

Z10 How many people under the age of 18 live with you? None...... 01 One...... 02 Two ...... 03 Three ...... 04 Four ...... 05 Five ...... 06 Six ...... 07 Seven or more ...... 08 Don't know...... 09 Refused...... 10

Z11 Can I just confirm that the first three digits of your postal code are ______Z12 How important is religion in your life? Would you say it is very important, somewhat important, not very important or not important at all? Very important ...... 1 Somewhat important ...... 2 Not very important...... 3 Not at all important ...... 4 Don't know...... 5 Refused...... 6

Z13 We hope to speak to some people again. May we call you for a short follow up? Yes ...... 1 No...... 2 GOTO Z15 Don't know / Refused...... 3 GOTO Z15

Z14 (IF YES) Can I have your first name or initials so that I make sure that it is you I speak to when I call back? ______

Z15 May I just confirm that your phone number is (READ NUMBER DIALED) ( ______) ______- ______I'd like to thank-you for taking the time to participate in this survey and to advise you that my supervisor may be calling you later to verify your participation.

183

APPENDIX B: Statistical Tests Conducted on Gambling Diversity

Table B1: Item to Total Pearson Correlation with Gambling Diversity Measure (weighted)

Game Gambling Diversity Measure Lottery .555*** Scratch .558*** Raffles .491*** Horse races .323*** Bingo .322*** Slots at casino .577*** Casino table games .413*** VLTs .230*** Sport select .364*** Outcome of sporting events .425*** Cards/board games with friends .372*** Games of skill .364*** Arcade/video games .319*** Casinos out of province .423 N 4599 *** Correlation is significant at the 0.001 level (2-tailed).

Table B2: Cronbach’s Alpha: Reliability Analysis

Reliability Coefficients

N of Cases = 4598.8 N of Items = 14 Alpha = .6366

Table B3: Number of gambling activities by socio-demographics (single ethnicity)

Demographic Characteristics Mean number of gambling Mean number of gambling activities—gamblers only activities –non-gamblers included All participants 3.1589 2.7160 Gender *** *** Male 3.3247 2.8723 Female 2.9844 2.5532 Age *** *** 18-24 3.7852 3.3881 25-34 3.6157 3.1305 35-49 3.1998 2.8617 50-59 3.0140 2.6180 60 + 2.6634 2.1440 Marital Status *** *** Married/ 3.1016 2.6658 Widowed 2.7358 2.1639 Divorced/separated 2.9892 2.6645 Single, never married 3.5479 3.0927

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Demographic Characteristics Mean number of gambling Mean number of gambling activities—gamblers only activities –non-gamblers included Educational attainment * ** Some high school 2.8845 2.3922 Completed high school 3.1511 2.7181 Some post-secondary 3.3726 2.9417 Completed post-secondary 3.2105 2.7868 Completed post-graduate 3.1141 2.6631 Income *** *** <$20, 000 2.8468 2.3759 < $30, 000 2.9782 2.4982 < $40, 000 2.8884 2.4829 < $50, 000 3.1508 2.7474 < $60, 000 3.4962 3.1288 $60, 000 + 3.3307 3.0071 Region East 3.1483 2.7912 Central East 3.2278 2.7302 Toronto 3.1076 2.5925 Central West 3.0757 2.6868 Central South 3.1552 2.7313 South West 3.1424 2.6711 North 3.3909 3.0449 N 2625 3053

Table B4: Factor Analysis: Total Variance Explained

Initial Eigenvalues Extraction Sums of Squared Rotation Sums of Squared Loadings Loadings Compo- Total % of Cumulative Total % of Cumulative Total % of Cumulative nent Variance % Variance % Variance % 1 2.413 17.238 17.238 2.413 17.238 17.238 1.658 11.840 11.840 2 1.341 9.580 26.819 1.341 9.580 26.819 1.621 11.577 23.417 3 1.251 8.934 35.753 1.251 8.934 35.753 1.438 10.272 33.689 4 1.088 7.769 43.521 1.088 7.769 43.521 1.377 9.832 43.521 5 .990 7.068 50.590 6 .947 6.762 57.352 7 .890 6.358 63.710 8 .876 6.259 69.969 9 .823 5.882 75.850 10 .783 5.592 81.443 11 .708 5.058 86.500 12 .667 4.763 91.264 13 .637 4.553 95.816 14 .586 4.184 100.000 Extraction Method: Principal Component Analysis.

185

Table B5: Factor Analysis: Rotated Component Matrix

Component 1 2 3 4 Casinos out of province .770 Casino table games .642 slots at casino .642 .374 Horse races .395 .307 Scratch .716 Lottery .689 Bingo .528 -.369 Raffles .423 .329 Games of skill .651 VLTs .529 Cards/board games with friends .474 Arcade/video games .449 Outcome of sporting events .721 Sport select .579 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

Table B6: Linear Regression: CPGI score, multiple ethnicity

Unstandardized Standardized Coefficients Coefficients B Std. Error Beta t Sig. (Constant) .483 .043 11.113 .000 Native .449 .137 .054 3.268 .001 Dutch/ Netherlands/ Holland -.134 .128 -.017 -1.048 .295 English /British -.150 .053 -.049 -2.828 .005 Canadian -7.947E-02 .093 -.014 -.857 .391 Chinese .700 .202 .057 3.463 .001 East Indian .412 .237 .029 1.737 .082 French -.166 .079 -.035 -2.105 .035 German -.137 .089 -.025 -1.543 .123 Irish -.125 .067 -.031 -1.862 .063 Italian 3.162E-02 .112 .005 .281 .779 Polish 7.718E-02 .151 .008 .512 .609 Scottish -2.586E-02 .068 -.006 -.383 .702 Ukrainian 5.980E-02 .172 .006 .348 .728 a Dependent Variable: CPGI score

Table B7: Linear Regression: Logged CPGI score, multiple ethnicity*

Unstandardized Standardized Coefficients Coefficients B Std. Error Beta t Sig. (Constant) .584 .052 11.182 .000 Native .160 .131 .051 1.227 .220 Dutch/ Netherlands/ Holland -.137 .159 -.036 -.862 .389

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Unstandardized Standardized Coefficients Coefficients B Std. Error Beta t Sig. English /British -.141 .069 -.089 -2.036 .042 Canadian .145 .124 .049 1.167 .244 Chinese 9.782E-02 .162 .025 .605 .545 East Indian .172 .222 .032 .774 .439 French -5.375E-02 .114 -.020 -.473 .636 German 3.291E-02 .128 .011 .256 .798 Irish -3.598E-02 .093 -.016 -.385 .700 Italian -8.808E-02 .121 -.031 -.726 .468 Polish 1.769E-02 .160 .005 .110 .912 Scottish .104 .093 .047 1.125 .261 Ukrainian .117 .184 .026 .637 .524 Dependent Variable: Ln CPGI score * Model was not significant with F{13, 585}=1.002, p=.448

Table B8: Linear Regression, Square Root CPGI score, multiple ethnicity

Unstandardized Standardized Coefficients Coefficients B Std. Error Beta t Sig. (Constant) .267 .017 15.379 .000 native .247 .055 .074 4.496 .000 Dutch/ Netherlands/ Holland -3.127E-02 .051 -.010 -.610 .542 English /British -6.461E-02 .021 -.053 -3.057 .002 Canadian -4.725E-02 .037 -.021 -1.275 .202 Chinese .369 .081 .075 4.574 .000 East Indian .199 .095 .034 2.100 .036 French -9.389E-02 .031 -.050 -2.986 .003 German -7.033E-02 .035 -.033 -1.983 .047 Irish -5.875E-02 .027 -.036 -2.190 .029 Italian 5.231E-02 .045 .019 1.164 .244 Polish 7.127E-02 .060 .020 1.184 .237 Scottish -2.756E-02 .027 -.017 -1.021 .307 Ukrainian 6.741E-02 .069 .016 .983 .326 a Dependent Variable: Square Root of CPGI score (non gamblers and unclassified deleted)

Table B8: Linear Regression: Gambling diversity, multiple ethnicity

Unstandardized Standardized Coefficients Coefficients B Std. Error Beta t Sig. (Constant) .803 .010 79.018 .000 native 4.632E-02 .034 .021 1.375 .169 Dutch/ Netherlands/ Holland -3.365E-02 .029 -.017 -1.142 .253 English /British 4.134E-02 .013 .052 3.305 .001 Canadian 7.658E-02 .023 .052 3.387 .001 Chinese -8.294E-02 .044 -.028 -1.865 .062 East Indian -2.512E-02 .055 -.007 -.460 .645

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French 4.336E-02 .019 .035 2.303 .021 German -4.390E-02 .020 -.033 -2.157 .031 Irish 1.887E-02 .016 .018 1.190 .234 Italian 5.831E-02 .027 .033 2.174 .030 Polish 7.596E-02 .036 .032 2.090 .037 Scottish -1.493E-02 .016 -.014 -.946 .344 Ukrainian 3.017E-02 .041 .011 .734 .463 a Dependent Variable: gambling status

Table B9: Linear Regression, Square Root Gambling Diversity, multiple ethnicity

Unstandardized Standardized Coefficients Coefficients B Std. Error Beta t Sig. (Constant) .803 .010 79.018 .000 native 4.632E-02 .034 .021 1.375 .169 Dutch/ Netherlands/ Holland -3.365E-02 .029 -.017 -1.142 .253 English /British 4.134E-02 .013 .052 3.305 .001 Canadian 7.658E-02 .023 .052 3.387 .001 Chinese -8.294E-02 .044 -.028 -1.865 .062 East Indian -2.512E-02 .055 -.007 -.460 .645 French 4.336E-02 .019 .035 2.303 .021 German -4.390E-02 .020 -.033 -2.157 .031 Irish 1.887E-02 .016 .018 1.190 .234 Italian 5.831E-02 .027 .033 2.174 .030 Polish 7.596E-02 .036 .032 2.090 .037 Scottish -1.493E-02 .016 -.014 -.946 .344 Ukrainian 3.017E-02 .041 .011 .734 .463 a Dependent Variable: SQGAM

Table B10: Linear Regression: Gambling Diversity Top-coded at 5 by multiple ethnicity

Unstandardized Standardized Coefficients Coefficients B Std. Error Beta t Sig. (Constant) 2.346 .046 50.818 .000 Native .423 .153 .042 2.762 .006 Dutch/ Netherlands/ Holland -.107 .134 -.012 -.803 .422 English /British .144 .057 .040 2.539 .011 Canadian .290 .103 .043 2.828 .005 Chinese -.559 .202 -.042 -2.768 .006 East Indian -.146 .248 -.009 -.590 .556 French .164 .085 .029 1.915 .056 German -.151 .092 -.025 -1.639 .101 Irish 9.258E-02 .072 .020 1.286 .199 Italian .494 .122 .062 4.059 .000 Polish .283 .165 .026 1.715 .086 Scottish -2.378E-03 .072 -.001 -.033 .974 Ukrainian .387 .187 .031 2.076 .038 a Dependent Variable: Gambling Diversity Top-coded at 5

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Table B11: Logistic Regression: Gambling Level by multiple ethnicity and sociodemographic variables. Results from Block 2.

Steps Variables: B S.E. Wald df Sig. Exp(B) Step 1 Native .831 .226 13.532 1 .000 2.296 Dutch -.006 .248 .001 1 .981 .994 English -.273 .108 6.386 1 .012 .761 Canadian -.274 .191 2.052 1 .152 .760 Chinese 1.027 .308 11.150 1 .001 2.794 East Indian .520 .384 1.828 1 .176 1.682 French -.522 .175 8.908 1 .003 .594 German -.411 .195 4.463 1 .035 .663 Irish -.230 .141 2.656 1 .103 .795 Italian .327 .198 2.745 1 .098 1.387 Polish .244 .267 .829 1 .362 1.276

Scottish -.192 .140 1.878 1 .171 .825

Ukrainian .166 .308 .290 1 .590 1.180

AGE -.020 .003 40.051 1 .000 .980

Constant -.633 .162 15.197 1 .000 .531 Step 2 Native .752 .228 10.855 1 .001 2.122 Dutch .000 .249 .000 1 .999 1.000 English -.259 .109 5.701 1 .017 .771 Canadian -.309 .192 2.596 1 .107 .734 Chinese 1.060 .311 11.636 1 .001 2.887 East Indian .584 .388 2.274 1 .132 1.794 French -.557 .176 10.037 1 .002 .573 German -.449 .196 5.252 1 .022 .638 Irish -.244 .141 2.989 1 .084 .783 Italian .344 .198 3.004 1 .083 1.410 Polish .317 .269 1.384 1 .239 1.372

Scottish -.199 .141 1.995 1 .158 .820

Ukrainian .169 .309 .298 1 .585 1.184

Educational attainment -.187 .037 25.235 1 .000 .830

AGE -.024 .003 52.707 1 .000 .977

Constant .139 .223 .387 1 .534 1.149 Step 3 Native .738 .229 10.341 1 .001 2.091 Dutch .020 .250 .007 1 .935 1.021 English -.253 .109 5.392 1 .020 .777 Canadian -.306 .192 2.533 1 .112 .737 Chinese 1.016 .315 10.383 1 .001 2.762 East Indian .664 .392 2.876 1 .090 1.942 French -.550 .176 9.763 1 .002 .577 German -.447 .196 5.204 1 .023 .640 Irish -.247 .142 3.045 1 .081 .781 Italian .345 .199 2.999 1 .083 1.412 Polish .315 .270 1.363 1 .243 1.370 Scottish -.197 .141 1.940 1 .164 .822

Ukrainian .164 .310 .280 1 .597 1.178 Marital Status 19.345 3 .000 Married -.471 .115 16.647 1 .000 .625 Widowed -.219 .249 .773 1 .379 .803

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Steps Variables: B S.E. Wald df Sig. Exp(B) Separated -.111 .169 .428 1 .513 .895 Single (reference) Age -.020 .004 30.231 1 .000 .980 Educational Attainment -.177 .037 22.405 1 .000 .838 Constant .239 .227 1.111 1 .292 1.270 Step 4 Native .783 .231 11.483 1 .001 2.187 Dutch .015 .250 .004 1 .953 1.015 English -.239 .109 4.782 1 .029 .788 Canadian -.291 .192 2.294 1 .130 .747 Chinese 1.026 .316 10.512 1 .001 2.790 East Indian .625 .392 2.548 1 .110 1.869 French -.534 .176 9.182 1 .002 .586 German -.436 .196 4.922 1 .027 .647 Irish -.234 .142 2.717 1 .099 .791 Italian .354 .200 3.146 1 .076 1.425 Polish .337 .270 1.555 1 .212 1.400 Scottish -.195 .141 1.908 1 .167 .823

Ukrainian .198 .311 .406 1 .524 1.220

Gender

Male 0.279 0.097 8.233 1 0.004 1.322

Female (reference) Marital Status 18.475 3 0.000 Married -0.447 0.116 14.859 1 0.000 0.639 Widowed -0.115 0.253 0.207 1 0.649 0.891 Separated -0.084 0.170 0.243 1 0.622 0.920 Single (reference) Age -0.020 0.004 30.159 1 0.000 0.980 Educational Attainment -0.177 0.037 22.353 1 0.000 0.838 Constant 0.057 0.235 0.059 1 0.809 1.059 a Variable(s) entered on step 1: Z2AGE_1. b Variable(s) entered on step 2: Z6_1. c Variable(s) entered on step 3: Z4NEW. d Variable(s) entered on step 4: GENDER

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APPENDIX C: Statistical Tests for Ethnic Neighbourhood Gambling Patterns

Multinomial Logistic regression with Age at immigration added to the model

Ordered Value gamble2f Total Frequency 1 1 164 2 2 405 3 3 3196

Probabilities modeled are cumulated over the lower Ordered Values. NOTE: 7 observations were deleted due to missing values for the response or explanatory variables.

Stepwise Selection Procedure

Step 0. Intercepts entered:

Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied.

Residual Chi-Square Test Chi-Square 134.9455 DF 50 Pr > ChiSq <.0001

Step 1. Effect hlangnof entered:

Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied.

Score Test for the Proportional Odds Assumption Chi-Square 0.9025 DF 1 Pr > ChiSq .3421

Model Fit Statistics Criterion Intercept Only Intercept and Covariates AIC 3885.137 3850.207 SC 3897.604 3868.907 -2 Log L 3881.137 3844.207

R-Square 0.0098 Max-rescaled R-Square 0.0152

Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood 36.9307 1 <.0001 Ratio Score 41.0223 1 <.0001 Wald 40.0523 1 <.0001

Residual Chi-Square Test Chi-Square 92.0728 DF 49

191

Pr > ChiSq 0.0002

Step 2. Effect V54N entered:

Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied.

Score Test for the Proportional Odds Assumption Chi-Square 0.8118 DF 2 Pr > ChiSq 0.6664

Model Fit Statistics Criterion Intercept Only Intercept and Covariates AIC 3885.137 3836.224 SC 3897.604 3861.158 -2 Log L 3881.137 3828.224

R-Square 0.0140 Max-rescaled R-Square 0.0217

Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood 52.9133 2 <.0001 Ratio Score 57.3677 2 <.0001 Wald 56.2753 2 <.0001

Residual Chi-Square Test Chi-Square 73.9258 DF 48 Pr > ChiSq 0.0095

Step 3. Effect AGE1B entered:

Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied.

Score Test for the Proportional Odds Assumption Chi-Square 1.1279 DF 3 Pr > ChiSq 0.7703

Model Fit Statistics Criterion Intercept Only Intercept and Covariates AIC 3885.137 3829.087 SC 3897.604 3860.254 -2 Log L 3881.137 3819.087

R-Square 0.0163 Max-rescaled R-Square 0.0254

Testing Global Null Hypothesis: BETA=0

192

Test Chi-Square DF Pr > ChiSq Likelihood Ratio 62.0504 3 <.0001 Score 66.8097 3 <.0001 Wald 64.9598 3 <.0001

Residual Chi-Square Test Chi-Square 65.8925 DF 47 Pr > ChiSq 0.0357

Step 4. Effect ABORIG entered:

Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied.

Score Test for the Proportional Odds Assumption Chi-Square 2.0935 DF 4 Pr > ChiSq 0.7186

Model Fit Statistics Criterion Intercept Only Intercept and Covariates AIC 3885.137 3825.336 SC 3897.604 3862.737 -2 Log L 3881.137 3813.336

R-Square 0.0178 Max-rescaled R-Square 0.0277

Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood 67.8008 4 <.0001 Ratio Score 73.1195 4 <.0001 Wald 72.5130 4 <.0001

Residual Chi-Square Test Chi-Square 59.6455 DF 46 Pr > ChiSq 0.0853

Step 5. Effect EDUC4A entered:

Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied.

Score Test for the Proportional Odds Assumption Chi-Square 3.3088 DF 5 Pr > ChiSq 0.6525

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Model Fit Statistics Criterion Intercept Only Intercept and Covariates AIC 3885.137 3822.262 SC 3897.604 3865.897 -2 Log L 3881.137 3808.262

R-Square 0.0192 Max-rescaled R-Square 0.0298

Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood 72.8749 5 <.0001 Ratio Score 78.1131 5 <.0001 Wald 77.3561 5 <.0001

Residual Chi-Square Test Chi-Square 54.9985 DF 45 Pr > ChiSq 0.1460

NOTE: No (additional) effects met the 0.05 significance level for entry into the model.

Summary of Stepwise Selection Effect Step Entered Removed DF Number In Score Chi-Square Wald Chi-Square Pr > ChiSq 1 hlangnof 1 1 41.0223 . <.0001 2 V54N 1 2 16.4013 . <.0001 3 AGE1B 1 3 9.3869 . 0.0022 4 ABORIG 1 4 6.3452 . 0.0118 5 EDUC4A 1 5 5.0428 . 0.0247

Variable

Step Label 1 Percent home language neither English nor French 2 EA percent separated but still legally married 3 EA- age 18-24 4 Aboriginal population as % of total population 5 EA- completed post secondary (ratio)

Analysis of Maximum Likelihood Estimates Parameter DF Estimate Std Error Wald Chi-Square Pr > ChiSq Intercept 1 1 -3.1549 0.0813 1505.1394 <.0001 Intercept 2 1 -1.7704 0.0472 1409.5079 <.0001 V54N 1 0.1613 0.0445 13.1216 0.0003 AGE1B 1 0.1212 0.0451 7.2130 0.0072 EDUC4A 1 -0.1085 0.0486 4.9970 0.0254 ABORIG 1 0.0802 0.0339 5.5999 0.0180 hlangnof 1 0.2286 0.0402 32.3827 <.0001

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Odds Ratio Estimates Effect Point Estimate 95% Wald Confidence Limits V54N 1.175 1.077 1.282 AGE1B 1.129 1.033 1.233 EDUC4A 0.897 0.816 0.987 ABORIG 1.084 1.014 1.158 hlangnof 1.257 1.162 1.360

Association of Predicted Probabilities and Observed Responses Percent Concordant 57.1 Somers' D 0.189 Percent Discordant 38.2 Gamma 0.198 Percent Tied 4.8 Tau-a 0.050 Pairs 1884944 c 0.594

Multinomial Logistic regression with year of immigration added to the model

Model Information Data Set WORK.GAMBLESTD Response Variable gamble2f Number of Response Levels 3 Number of Observations 3765

Value gamble2f Frequency 1 1 164 2 2 405 3 3 3196

Stepwise Selection Procedure

Step 0. Intercepts entered:

Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied.

Residual Chi-Square Test Chi-Square 137.2557 DF 52 Pr > ChiSq <.0001

Step 1. Effect hlangnof entered:

Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied.

Score Test for the Proportional Odds Assumption Chi-Square 0.9025 DF 1 Pr > ChiSq 0.3421

195

Model Fit Statistics Criterion Intercept Only Intercept and Covariates AIC 3885.137 3850.207 SC 3897.604 3868.907 -2 Log L 3881.137 3844.207

R-Square 0.0098 Max-rescaled R-Square 0.0152

Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 36.9307 1 <.0001 Score 41.0223 1 <.0001 Wald 40.0523 1 <.0001

Residual Chi-Square Test Chi-Square 94.5109 DF 51 Pr > ChiSq 0.0002

Step 2. Effect V54N entered:

Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied.

Score Test for the Proportional Odds Assumption Chi-Square 0.8118 DF 2 Pr > ChiSq 0.6664

Model Fit Statistics Criterion Intercept Only Intercept and Covariates AIC 3885.137 3836.224 SC 3897.604 3861.158 -2 Log L 3881.137 3 828.224

R-Square 0.0140 Max-rescaled R-Square 0.0217

Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 52.9133 2 <.0001 Score 57.3677 2 <.0001 Wald 56.2753 2 <.0001

Residual Chi-Square Test Chi-Square 76.4105 DF 50 Pr > ChiSq 0.0095

Step 3. Effect AGE1B entered:

Model Convergence Status

196

Convergence criterion (GCONV=1E-8) satisfied.

Score Test for the Proportional Odds Assumption Chi-Square 1.1279 DF 3 Pr > ChiSq0.7703

Model Fit Statistics Criterion Intercept Only Intercept and Covariates AIC 3885.137 3829.087 SC 3897.604 3860.254 -2 Log L 3881.137 3819.087

R-Square 0.0163 Max-rescaled R-Square 0.0254

Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 62.0504 3 <.0001 Score 66.8097 3 <.0001 Wald 64.9598 3 <.0001

Residual Chi-Square Test Chi-Square 68.3970 DF 49 Pr > ChiSq 0.0349

Step 4. Effect ABORIG entered:

Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied.

Score Test for the Proportional Odds Assumption Chi-Square 2.0935 DF 4 Pr > ChiSq 0.7186

Model Fit Statistics Criterion Intercept Only Intercept and Covariates AIC 3885.137 3825.336 SC 3897.604 3862.737 -2 Log L 3881.137 3813.336

R-Square 0.0178 Max-rescaled R-Square 0.0277

Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 67.8008 4 <.0001 Score 73.1195 4 <.0001 Wald 72.5130 4 <.0001

Residual Chi-Square Test 197

Chi-Square 62.1164 DF 48 Pr > ChiSq 0.0828

Step 5. Effect EDUC4A entered:

Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied.

Score Test for the Proportional Odds Assumption Chi-Square 3.3088 DF 5 Pr > ChiSq 0.6525

Model Fit Statistics Criterion intercept Only Intercept and Covariates AIC 3885.137 3822.262 SC 3897.604 3865.897 -2 Log L 3881.137 3808.262

R-Square 0.0192 Max-rescaled R-Square 0.0298

Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 72.8749 5 <.0001 Score 78.1131 5 <.0001 Wald 77.3561 5 <.0001

Residual Chi-Square Test Chi-Square 57.4150 DF 47 Pr > ChiSq 0.1420

NOTE: No (additional) effects met the 0.05 significance level for entry into the model.

Summary of Stepwise Selection Step Effect Entered Effect DF Number Score Chi- Wald Chi- Pr > ChiSq Removed In Square Square 1 hlangnof 1 1. 41.0223 <.0001 2 V54N 1 2 16.4013 . <.0001 3 AGE1B 1 3 9.3869 . 0.0022 4 ABORIG 1 4 6.3452. 0.0118 5 EDUC4A 1 5 5.0428. 0.0247

Variable Step Label 1 Percent home language neither English nor French 2 EA percent separated but still legally married 3 EA- age 18-24 4 Aboriginal population as % of total population 5 EA- completed post secondary (ratio)

198

Analysis of Maximum Likelihood Estimates Parameter DF Estimate Std Error Wald Chi-Square Pr > ChiSq Intercept 1 1 -3.1549 0.0813 1505.1394 <.0001 Intercept 2 1 -1.7704 0.0472 1409.5079 <.0001 V54N 1 0.1613 0.0445 13.1216 0.0003 AGE1B 1 0.1212 0.0451 7.2130 0.0072 EDUC4A 1 -0.1085 0.0486 4.9970 0.0254 ABORIG 1 0.0802 0.0339 5.5999 0.0180 hlangnof 1 0.2286 0.0402 32.3827 <.0001

Odds Ratio Estimates Effect Point Estimate 95% Wald Confidence Limits V54N 1.175 1.077 1.282 AGE1B 1.129 1.033 1.233 EDUC4A 0.897 0.816 0.987 ABORIG 1.084 1.014 1.158 hlangnof 1.257 1.162 1.360

Association of Predicted Probabilities and Observed Responses

Percent Concordant 57.1 Somers' D 0.189 Percent Discordant 38.2 Gamma 0.198 Percent Tied 4.8 Tau-a 0.050 Pairs 1884944 c 0.594

199