research paper gambling topics

108 Gambling Essay Topic Ideas & Examples

🏆 best gambling topic ideas & essay examples, ⭐ good research topics about gambling, 👍 simple & easy gambling essay titles, ❓ research questions on gambling.

• Gambling Benefits and Disadvantages Also in relation to this, the gambling industry offers employment to a myriad of people. This is thus a boost to the economy of the local people.
• Gambling Should Be Illegal Furthermore, gambling leads to lowering reputation of the city in question as a result of the crimes associated. The government is forced to spend a lot of money in controlling crime and rehabilitating addicted gamblers. We will write a custom essay specifically for you by our professional experts 808 writers online Learn More
• Online Gambling Addiction Gambling is an addiction as one becomes dependent on the activity; he cannot do without it, it becomes a necessity to him. Online gambling is more of an addiction than a game to the players.
• Gambling’s Positive and Negative Effects In some cases such as in lotteries, the financial reward is incidental and secondary because the participants drive is to help raise funds for the course the lottery promotes.
• Internet Gambling and Its Impact on the Youth However, it is necessary to remember that apart from obvious issues with gambling, it is also associated with higher crime rates and it is inevitable that online gambling will fuel an increase of crime rates […]
• Comorbid Gambling Disorder and Alcohol Dependence The patient was alert and oriented to the event, time, and place and appropriately dressed for the occasion, season, and weather.
• Gambling: Debate Against the Legislation of Gambling More significantly, the Australian government has so far adopted a conciliatory and indulgent attitude towards most kinds of gambling, which has been the main reason for the rampant growth and proliferation of both forms of […]
• Impact of Internet Use, Online Gaming, and Gambling Among College Students The researchers refute the previous works of literature that have analyzed the significance of the Internet, whereby previous studies depict that the Internet plays a significant role in preventing depression ordeals and making people happy.
• History of Gambling in the US and How It Connects With the Current Times It is possible to note that it is in the Americans’ blood from the sides of Native Americans and the Pilgrims to bet.
• Effects of Gambling on Happiness: Research in the Nursing Homes The objective of the study was to determine whether the elderly in the nursing homes would prefer the introduction of gambling as a happiness stimulant.
• The Era of Legalized Gambling Importantly, they build on each other to demonstrate power in taking risk action and actually how legalizations of the practice can influence character integrity. The conclusive speculation is whether there is a changing definition of […]
• Gambling as an Acceptable Form of Leisure In leisure one should have the freedom to choose which activity to engage in and at what time to do it. Gambling is considered as a form of leisure activity and one has freedom to […]
• Legalization of Casino Gambling in Hong Kong Thus, the problem is in the controversy of the allegedly positive and negative effects of gambling legalization on the social and economic development of Hong Kong.
• Internet Gambling Issue Description There are certain new features added to the gambling game by the internet gamblers, such as proxy gambling, gambling for credit and claim of gambling in the virtual offshore gambling environment.
• The Problem of Gambling in the Modern Society as the Type of Addiction Old people and adolescents, rich and poor, all of them may become the prisoners of this addiction and the only way out may be the treatment, serious psychological treatment, as gambling addiction is the disease […]
• The Psychology of Lottery Gambling This kind of gambling also refers to the expenditure of more currency than was first future and then returning afterward to win the cash lost in the history.
• Gambling, Fraud and Security in Banking By supervising the institutions and banks, there exists openness into the dealings of the banks and this allows investors to get full information about the banks before investing.
• High-Risk Gambles Prevention in Banking The elements of a valid contract between banks and their customers vary according to the context of the contract. This involves the willingness and devotion of both parties to a particular cause.
• Earmark Gambling Revenue Legislation in Illinois The state of Illinois enacted PA 91-40 in 1999, which has affected the gaming industry. The growth in the revenue from gambling has attracted the attention of lawmakers.
• Jay Cohen’s Gambling Company and American Laws Disregarding the controversy concerning the harmful effects of gambling, one might want to ask the question concerning whether the USA had the right to question the policies of other states, even on such a dubious […]
• Fantasy Football: Gambling Regulation and Outlawing Taking this into consideration, it can be stated that fantasy football and its other iterations on sites like Draft Kings is not a form of gambling.
• Gambling and Addiction’s Effects on Neuroplasticity It was established further that blood flow from other parts of the body to the brain is changes whenever an individual engages in gambling, which is similar to the intake of cocaine.
• Gambling and Its Effect on Families The second notable effect of gambling on families is that it results in the increased cases of domestic violence. The third notable effect of gambling on the family is that it increases child abuse and […]
• Online Gambling Legalization When asked about the unavoidable passing of a law decriminalizing online gambling in the US, the CEO of Sams Casino stated that the legislation would not have any impact on their trade.
• Casino Gambling Legalization in Texas In spite of the fact that the idea of legalizing casino gambling is often discussed by opponents as the challenge to the community’s social health, Texas should approve the legalization of casino gambling because this […]
• Economic Issues: Casino Gambling Evidence from several surveys suggests that the competition from various states within the US has contributed to the growth and expansion of casinos. The growth and expansion of casinos has been fueled by competition from […]
• Casino Gambling Industry Trends This will make suppliers known to the rest of the companies operating in the industry. The bargaining power of the supplier in the casino gambling industry is also high.
• Gambling Addiction Research Approaches Therefore, it is possible to claim that the disease model is quite a comprehensive approach which covers several possible factors which lead to the development of the disorder.
• Public Policy on Youth Gambling The outcomes of the research would be useful in identifying the program outcomes as well as provide answers to the whys of youth gambling.
• Gambling and Gaming Industry Compulsive gambling Compulsive gambling refers to the inability to control an individual’s urge to engage in gambling activities. Other gambling activities in the state are classified as a misdemeanour.
• Revision of Problem Gambling The reasoning behind the researchers’ decision to focus on the social and financial factors of gambling within the UK is because of the significant increase in gambling-related problems within Britain.
• Impact of Gambling on the Bahamian Economy Sources from the government of The Bahamas indicate that the first of gambling casinos in the name of the Bahamian Club opened for business from the capital of Nassau towards the close of the 1920s […]
• Positive Aspects of Gambling It is therefore essential for sociologist to understand the positive aspects of gambling and the impact that it has on our lives. However, it is essential to control the level of gambling.
• Argument for Legalization of Gambling in Texas The subject of gambling is that the gambler losses the money offered if the outcome of the event is against him or her or gains the money offered if the event outcome favors the gambler.
• Gambling in Kentucky: Moral Obligations vs. the Economical Reasons The industries that added to the well-being of the state and its GNP since the day the state was founded and throughout the past century was the coal mining, which contributed to the state’s income […]
• Gambling in Ohio The purpose of this project is to investigate the history of gambling in Ohio, its development in the 1990s, and its impact on ordinary human lives in order to underline the significance of this process […]
• Gambling Projects: Impact on the Cultural Transformations in America The high rates of unemployment and low earning levels may coerce residents to engage in gambling, hoping that they would enrich themselves.
• Gambling in Four Perspectives A gambler faces the challenge of imminent effect of addiction to the effects of gambling every time he continues with the exercise of gambling something that may take long to drop.
• Gambling Discusses Three Causes or Effects of Gambling and Their Impact on Society
• Taking the Risk: Love, Luck, and Gambling in Literature
• Financial Crime and Gambling in a Virtual World
• Management and Information Issues for Industries With Externalities: The Case of Casino Gambling
• Internet Gambling Consumers Industry and Regulation
• Human Resource Management for Gambling Industry
• Youth Gambling Abuse: Issues and Challenges to Counselling
• Differentiate Between Investment Speculation and Gambling
• The Gambling and Its Influence on the Individual and Society
• Assessing the Differential Impacts of Online, Mixed, and Offline Gambling
• Gambling Taxation: Public Equity in the Gambling Business
• Risk and Protective Factors in Problem Gambling: An Examination of Psychological Resilience
• Behavioral Accounts and Treatments of Problem Gambling
• Cognitive Remediation Interventions for Gambling Disorder
• Gambling: The Problems and History of Addiction, Helpfulness, and Tragedy
• Casino Gambling Myths and Facts: When Fun Becomes Dangerous
• Associations Between Problem Gambling, Socio-Demographics, Mental Health Factors, and Gambling Type
• Gambling With the House Money and Trying to Break Even: The Effects of Prior Outcomes on Risky Choice
• Differences in Effects on Brain Functional Connectivity in Patients With Internet-Based Gambling Disorder and Internet Gaming Disorder
• Gambling Addiction Hidden Evils of Online Play
• Diagnosing and Treating Pathological Addictions: Compulsive Gambling, Drugs, and Alcohol Addiction
• The Pleasure Principle: Gambling and Brain Chemistry
• Gambling, Geographical Variations, and Deprivation: Findings From the Adult Psychiatric Morbidity Survey
• Behavior Change Strategies for Problem Gambling: An Analysis of Online Posts
• Economic Recession Affects Gambling Participation but Not Problematic Gambling: Results From a Population-Based Follow-up Study
• Different Gambling Consequence Western Civilisation Countries Sociology
• Gambling Among Culturally Diverse Older Adults: A Systematic Review of Qualitative and Quantitative Data
• Decision-Making Under Risk, but Not Under Ambiguity, Predicts Pathological Gambling in Discrete Types of Abstinent Substance Users
• Gambling: The Game Where Everyone Is a Loser
• The Problems and Issues Concerning Legalization of Online Gambling
• Gambling Attitudes and Beliefs Associated With Problem Gambling: The Cohort Effect of Baby Boomers
• Pathological Compulsive Gambling: Diagnosis and Treatment of a Medical Disorder
• Accounting and Financial Reports in the Gambling Monopoly – Measures for a Moral Economic System
• Gender, Gambling Settings and Gambling Behaviors Among Undergraduate Poker Players
• Gambling Among Young Croatian People: An Exploratory Study of the Relationship Between Psychopathic Traits, Risk-Taking Tendencies, and Gambling-Related Problems
• Buying and Selling Price for Risky Lotteries and Expected Utility Theory With Gambling Wealth
• Charitable Giving and Charitable Gambling: Cognitive Abilities, Non-cognitive Skills, and Gambling Behaviors
• Compulsive Buying Behavior: Characteristics of Comorbidity With Gambling Disorder
• Motivation, Personality Type, and the Choice Between Skill and Luck Gambling Products
• Gambling and the Use of Credit: An Individual and Household Level Analysis
• Why Will Internet Gambling Prohibition Ultimately Fail?
• How Do Binge Drinking, Gambling, and Procrastinating Affect Students?
• What Motivates Gambling Behavior?
• Does Charitable Gamble Crowd Out Charitable Donations?
• What Should the State’s Policy Be On Gambling?
• How Does Gambling Effect the Economy?
• What Does the Bible Say About Gambling?
• Are There Gambling Effects in Incentive-Compatible Elicitations of Reservation Prices?
• How Does the Gambling Affect the Society?
• Does Indian Casino Gambling Reduce State Revenues?
• How Does the Stigma of Problem Gambling Influence Help-Seeking, Treatment, and Recovery?
• Why Are Gambling Markets Organised So Differently From Financial Markets?
• Can Expected Utility Theory Explain Gambling?
• What Is the Attraction to Gambling?
• Should Sports Gambling Be Legalized?
• Who Gets Hurt From Gambling?
• How Do Gambling Addiction Affect Families?
• Does Individual Gambling Behavior Vary Across Gambling Venues With Differing Numbers of Terminals?
• Why Gambling Should Not Be Prohibited or Policed?
• How Has Gambling Become the Favorite Distraction of Americans?
• Should Age Restriction for Gambling Be Increase?
• Are There Net State Social Benefits or Costs From Legalizing Slot Machine Gambling?
• What Are the Possible Circumstances for Gambling?
• How Do Habit and Satisfaction Affect Player Retention for Online Gambling?
• Does DRD2 Taq1A Mediate Aripiprazole-Induced Gambling Disorder?
• How Does the Online Gambling Ban Help Al Qaeda?
• Does Pareto Rule Internet Gambling?
• Should Governments Sponsor Gambling?
• What Are the Problems Associated With Gambling?
• Why Isn’t Congress Closing a Loophole That Fosters Gambling in College?
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126 Gambling Essay Topic Ideas & Examples

Inside This Article

Gambling is a popular pastime that has been around for centuries. Whether it's placing bets on sports games, playing poker at a casino, or buying lottery tickets, the thrill of risking money in the hopes of winning big is something that many people enjoy.

If you're looking for essay topics related to gambling, you're in luck. We've compiled a list of 126 gambling essay topic ideas and examples to help inspire your next paper. From the ethics of gambling to the impact of online gambling on society, there are plenty of angles to explore in this fascinating topic.

• The history of gambling
• The psychology of gambling addiction
• The ethics of gambling
• The impact of gambling on society
• The economics of gambling
• The role of luck in gambling
• The legality of online gambling
• The relationship between gambling and crime
• The effects of gambling on mental health
• The role of gambling in popular culture
• The impact of gambling on families
• The regulation of gambling
• The social stigma of gambling
• The role of gambling in politics
• The impact of gambling on the economy
• The link between gambling and substance abuse
• The role of gambling in sports
• The impact of gambling on indigenous communities
• The relationship between gambling and religion
• The effects of gambling advertising
• The impact of gambling on tourism
• The relationship between gambling and technology
• The role of gambling in education
• The impact of gambling on the environment
• The link between gambling and mental illness
• The role of gambling in history
• The impact of gambling on the brain
• The relationship between gambling and poverty
• The effects of gambling on relationships
• The role of gambling in the criminal justice system
• The impact of gambling on youth
• The link between gambling and suicide
• The role of gambling in healthcare
• The impact of gambling on the elderly
• The relationship between gambling and gender
• The effects of gambling on personal finances
• The role of gambling in international relations
• The impact of gambling on education
• The link between gambling and public health
• The role of gambling in social welfare
• The impact of gambling on mental health services
• The relationship between gambling and social services
• The effects of gambling on community development
• The role of gambling in urban planning
• The impact of gambling on rural communities
• The link between gambling and economic development
• The role of gambling in environmental conservation
• The impact of gambling on cultural heritage
• The relationship between gambling and human rights
• The effects of gambling on social justice
• The role of gambling in international development
• The impact of gambling on global health
• The link between gambling and international trade
• The role of gambling in sustainable development
• The impact of gambling on climate change
• The relationship between gambling and poverty reduction
• The effects of gambling on gender equality
• The role of gambling in conflict resolution
• The impact of gambling on peacebuilding
• The link between gambling and human security
• The role of gambling in disaster response
• The impact of gambling on humanitarian aid
• The relationship between gambling and international law
• The effects of gambling on global governance
• The role of gambling in international organizations
• The impact of gambling on regional cooperation
• The link between gambling and international relations
• The role of gambling in diplomacy
• The impact of gambling on conflict prevention
• The relationship between gambling and peacekeeping
• The effects of gambling on peacebuilding
• The role of gambling in post-conflict reconstruction
• The impact of gambling on transitional justice
• The link between gambling and human rights
• The role of gambling in international criminal justice
• The impact of gambling on international humanitarian law
• The relationship between gambling and international human rights law
• The effects of gambling on international refugee law
• The role of gambling in international environmental law
• The impact of gambling on international trade law
• The link between gambling and international investment law
• The role of gambling in international economic law
• The impact of gambling on international financial law
• The relationship between gambling and international banking law
• The effects of gambling on international tax law
• The role of gambling in international competition law
• The impact of gambling on international antitrust law
• The link between gambling and international intellectual property law
• The role of gambling in international labor law
• The impact of gambling on international human rights law

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• Published: 04 February 2021

The association between gambling and financial, social and health outcomes in big financial data

• Naomi Muggleton   ORCID: orcid.org/0000-0002-6462-3237 1 , 2 , 3 ,
• Paula Parpart 2 , 3 , 4 ,
• Philip Newall   ORCID: orcid.org/0000-0002-1660-9254 5 , 6 ,
• David Leake 3 ,
• John Gathergood   ORCID: orcid.org/0000-0003-0067-8324 7 &
• Neil Stewart   ORCID: orcid.org/0000-0002-2202-018X 2  

Nature Human Behaviour volume  5 ,  pages 319–326 ( 2021 ) Cite this article

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• Social policy

Gambling is an ordinary pastime for some people, but is associated with addiction and harmful outcomes for others. Evidence of these harms is limited to small-sample, cross-sectional self-reports, such as prevalence surveys. We examine the association between gambling as a proportion of monthly income and 31 financial, social and health outcomes using anonymous data provided by a UK retail bank, aggregated for up to 6.5 million individuals over up to 7 years. Gambling is associated with higher financial distress and lower financial inclusion and planning, and with negative lifestyle, health, well-being and leisure outcomes. Gambling is associated with higher rates of future unemployment and physical disability and, at the highest levels, with substantially increased mortality. Gambling is persistent over time, growing over the sample period, and has higher negative associations among the heaviest gamblers. Our findings inform the debate over the relationship between gambling and life experiences across the population.

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Genome-wide association studies

Data availability.

The data that support the findings of this study are available from LBG but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are available from the authors upon reasonable request and with permission of LBG.

Code availability

Data were extracted from LBG databases using Teradata SQL Assistant (v.15.10.1.9). Data analysis was conducted using R (v.3.4.4). The SQL code that supports the analysis is commercially sensitive and is therefore not publicly available. The code is available from the authors upon reasonable request and with permission of LBG. The R code that supports this analysis can be found at github.com/nmuggleton/gambling_related_harm . Commercially sensitive code has been redacted. This should not affect the interpretability of the code.

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Acknowledgements

We thank A. Trendl and H. Wardle for comments on an earlier draft of this manuscript. We thank R. Burton, Z. Clarke, C. Henn, J. Marsden, M. Regan, C. Sharpe and M. Smolar from Public Health England and L. Balla, L. Cole, K. King, P. Rangeley, H. Rhodes, C. Rogers and D. Taylor from the Gambling Commission for providing feedback on a presentation of this work. We thank A. Akerkar, D. Collins, T. Davies, D. Eales, E. Fitzhugh, P. Jefferson, T. Bo Kim, M. King, A. Lazarou, M. Lien and G. Sanders for their assistance. We thank the Customer Vulnerability team, with whom we worked as part of their ongoing strategy to help vulnerable customers. We acknowledge funding from LBG, who also provided us with the data but had no other role in study design, analysis, decision to publish or preparation of the manuscript. The views and opinions expressed are those of the authors and do not necessarily reflect the views of LBG, its affiliates or its employees. We also acknowledge funding from Economic and Social Research Council (ESRC) grants nos. ES/P008976/1 and ES/N018192/1. The ESRC had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and affiliations.

Department of Social Policy and Intervention, University of Oxford, Oxford, UK

Naomi Muggleton

Warwick Business School, University of Warwick, Coventry, UK

Naomi Muggleton, Paula Parpart & Neil Stewart

Applied Science, Lloyds Banking Group, London, UK

Naomi Muggleton, Paula Parpart & David Leake

Department of Experimental Psychology, University of Oxford, Oxford, UK

Paula Parpart

Warwick Manufacturing Group, University of Warwick, Coventry, UK

• Philip Newall

School of Health, Medical and Applied Sciences, CQ University, Melbourne, Australia

School of Economics, University of Nottingham, Nottingham, UK

John Gathergood

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Contributions

P.P. and P.N. proposed the initial concept. All authors contributed to the design of the analysis and the interpretation of the results. J.G. and N.S. wrote the initial draft; all authors contributed to the revision. N.M. and P.P. constructed variables and N.M. prepared all figures and tables. D.L. established collaboration with LBG. D.L., J.G. and N.S. secured funding for the research. P.N. conducted a review of the existing literature.

Corresponding author

Correspondence to Naomi Muggleton .

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N.M. was previously, and D.L. is currently, an employee of LBG. P.P. was previously a contractor at LBG. They do not, however, have any direct or indirect interest in revenues accrued from the gambling industry. P.N. was a special advisor to the House of Lords Select Committee Enquiry on the Social and Economic Impact of the Gambling Industry. In the last 3 years, P.N. has contributed to research projects funded by GambleAware, Gambling Research Australia, NSW Responsible Gambling Fund and the Victorian Responsible Gambling Foundation. In 2019, P.N. received travel and accommodation funding from the Spanish Federation of Rehabilitated Gamblers and in 2020 received an open access fee grant from Gambling Research Exchange Ontario. All other authors have no competing interests.

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Muggleton, N., Parpart, P., Newall, P. et al. The association between gambling and financial, social and health outcomes in big financial data. Nat Hum Behav 5 , 319–326 (2021). https://doi.org/10.1038/s41562-020-01045-w

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BRIEF RESEARCH REPORT article

Gambling behavior and risk factors in preadolescent students: a cross sectional study.

• Department of Psychology, Niccolò Cusano University, Rome, Italy

Although gambling was initially characterized as a specific phenomenon of adulthood, the progressive lowering of the age of onset, combined with earlier and increased access to the game, led researchers to study the younger population as well. According to the literature, those who develop a gambling addiction in adulthood begin to play significantly before than those who play without developing a real disorder. In this perspective, the main hypothesis of the study was that the phenomenon of gambling behavior in this younger population is already associated with specific characteristics that could lead to identify risk factors. In this paper, are reported the results of an exploratory survey on an Italian sample of 2,734 preadolescents, aged between 11 and 14 years, who replied to a self-report structured questionnaire developed ad hoc . Firstly, data analysis highlighted an association between the gambling behavior and individual or ecological factors, as well as a statistically significant difference in the perception of gambling between preadolescent, who play games of chance, and the others. Similarly, the binomial logistic regression performed to ascertain the effects of seven key variables on the likelihood that participants gambled with money showed a statistically significant effect for six of them. The relevant findings of this first study address a literature gap and suggest the need to investigate the preadolescent as a cohort in which it identifies predictive factors of gambling behavior in order to design effective and structured preventive interventions.

Introduction

In recent years, addiction has undergone changes both in terms of choice of the so-called substance and for the age groups involved ( Echeburúa and de Corral Gargallo, 1999 ; Griffiths, 2000 ). Although addiction is a condition associated to substance abuse disorder, it also determines other conducts that can significantly affect the lifestyle of subjects ( Schulte and Hser, 2013 ).

In the last edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) ( American Psychiatric Association, 2013 ), the pathological gambling behavior has been conceptualized differently than in previous editions, as a result of a series of empirical evidence indicating the commonality of some clinical and neurobiological correlates between pathological gambling and substance use disorders ( Rash et al., 2016 ). The new classification into the “ Substance-Related and Addictive Disorders ” category supports the model of behavioral addictions in which people may be compulsively and dysfunctionally engaged in behaviors that do not involve exogenous drug administration, and these conducts can be conceptualized within an addiction framework as different expressions of the same underlying syndrome ( Shaffer et al., 2004 ).

Despite the fact that in many countries gambling is forbidden to minors, in recent years, there has been a marked increase in this behavior among younger people so that from surveys conducted in different cultural contexts it emerges that a percentage between 60 and 99% of boys and between 12 and 20 years have gambled at least once ( Splevins et al., 2010 ). The increasing number of children and underaged youth participating in games of chance for recreation and entertainment is attributable to the legalization, normalization, and proliferation of gambling opportunities/activities ( Hurt et al., 2008 ).

Several studies have shown that the percentage of young people who gamble in a pathological way is significant and even greater than the percentage of adult pathological gamblers ( Blinn-Pike et al., 2010 ). Using the definitions of at-risk and problem gambler that directly refer to the diagnostic criteria for pathological gambling, the review of Splevins et al. (2010) showed that a percentage of adolescents between 2 and 9% can be classified within the category of problem gamblers, while between 10 and 18% are adolescents who can be considered at-risk gamblers.

The first comprehensive review on problematic gambling in Italy noted a lack of large-scale epidemiological studies and of a national observatory regarding this issue ( Croce et al., 2009 ). More recent studies regarding the Italian national context are now available. A survey carried out with 2,853 students aged between 13 and 20 years showed that 7% of adolescents interviewed were classified as pathological gamblers ( Villella et al., 2011 ), while the study conducted by Donati et al. (2013) indicated that 17% of adolescents showed problematic gambling behaviors.

As far as ecological factors are concerned, the crucial role of family and play behavior of friends has been widely documented. In particular, a strong association between parents’ and children’s gambling behavior has emerged ( Hardoon et al., 2004 ), and it has been highlighted that the spread of gambling in the group of friends influences the practice of gambling among adolescents ( Gupta and Derevensky, 1998 ).

Traditionally, gambling in youth was considered as related to poor academic achievement, truancy, criminal involvement, and delinquency. More recently, investigators have examined the relationship between gambling and delinquent behaviors among adolescents in a systematic way, shifting the understanding beyond the explanation that delinquency associated with problem gambling is merely financially motivated by gambling losses ( Kryszajtys et al., 2018 ). This suggests that young players may have more general problems of conduct than specific criminal behavior.

Conversely, in relation to poor academic achievement, it has been highlighted that problem gambling in adolescence affect students’ performance mainly by reducing the time spent in studying ( Allami et al., 2018 ).

Although the phenomenon of gambling has been widely analyzed in the adult population and there are numerous studies on the adolescent population, the data in the literature suggest that gambling may be a phenomenon already present in preadolescence and needs to be analyzed. In fact, the lowering of the age of onset of problematic behaviors related to pathological gambling raises a question about the presence of gambling in preadolescents, as more exposed to the use of the Internet, smartphones, and tablets as tools that could encourage this type of conduct. A series of studies ( Shaffer and Hall, 2001 ; Vitaro et al., 2004 ; Winters et al., 2005 ; Kessler et al., 2008 ) have highlighted how adult pathological players started playing significantly earlier from a non-pathological player’s chronological point of view.

Nevertheless, it has been seen in the literature as, within the population of those who start playing before the age of 15, only 25% maintain the same frequency of play even in adulthood ( Vitaro et al., 2004 ; Delfabbro et al., 2009 , 2014 ).

In the review by Volberg and colleagues, it was shown how teenagers tend to prefer social and intimate games, such as card games and sports betting, while only a small percentage of teenagers are involved in illegal age gambling activities ( Volberg et al., 2010 ).

Pathological and problem players seem to be more involved in machine gambling (such as slot machines and poker machines), non-strategy games (such as bingo and lottery or super jackpot), and online games; they play in different contexts such as the Internet, school, and dedicated rooms ( Rahman et al., 2012 ; Yip et al., 2015 ).

It has been seen that online gambling is particularly attractive for young people due to its extreme accessibility, the large number of events dedicated to gambling, accessibility from the point of view of the economic share invested, and the multisensory experience and high level of involvement reported by young people ( Brezing et al., 2010 ; King et al., 2010 ).

Considering what is present in the literature, it is evident that the phenomenon of pathological gambling in adulthood is linked to a series of risk factors already present in adolescence. At the same time, the progressive lowering of the age at the beginning, which has been seen to be one of the main risk factors, makes it necessary to analyze the presence of the phenomenon of gambling in preadolescents, an analysis that at this time cannot count on the support of validated tools and questionnaires.

Considering that young people spend part of their time playing, it is necessary to distinguish between what is considered a game and what is considered gambling, even if not in a pathological way.

According to King et al., “gaming is principally defined by its interactivity, skill-based play, and contextual indicators of progression and success. In contrast, gambling is defined by betting and wagering mechanics, predominantly chance-determined outcomes, and monetization features that involve risk and payout to the player” ( King et al., 2015 ).

Primarily, the objective of this study is to verify the presence, the possible extent, and the characteristics of the phenomenon of gambling as defined before in a population of preadolescents (percentage, distribution by gender) to see if the population of preadolescent players shows the same characteristics as those found in larger populations at the age level (adolescents and adults). Secondly, the study aims to verify any differences in the perception of the game between those who play and those who do not, in order to identify additional specific characteristics.

In addition, on the basis of what is highlighted in the literature with respect to the risk factors detected in adults and adolescents, the study aims to assess whether and which of these factors can be predictive of the phenomenon of preadolescent gambling.

Finally, always in line with the identification of possible prodromal factors of gambling, the study wants to analyze the differences with respect to the types of games preferred by preadolescent players to assess any similarity with what emerged in the adolescent population.

In addition, the study aims to verify whether preadolescent players show the same game-level preferences highlighted in the literature as risk factors for the development of a real game disorder ( Rahman et al., 2012 ; Yip et al., 2015 ).

Materials and Methods

The investigation followed the Ethical Standards of the 1994 Declaration of Helsinki, and the study was approved by the Departmental Research Authorization Committee of Niccolò Cusano University and the Italian Ministry of Labour and Social Policy. In a prospective study of gambling perception, behavior, and risk factors, youth aged 11 to 14 years were recruited from 47 schools situated in 18 regions of Italy. The respondents’ survey was composed by 2,734 preadolescents (1,256 female and 1,452 male), enrolled in the 6, 7, and 8 grades across all national areas (18 provinces out of 20 Italian regions).

The administration of the survey was approved by the school boards of all the institutes involved, and all parents signed the informed consent and authorization to process personal data of their children. The self-report questionnaire was proposed and filled out in the classroom during school time.

The complete questionnaire developed ad hoc by the authors for the survey is composed of 19 items, 6 related to demographic characteristics of the sample and the remaining tighter focused on gambling behaviors and information related to the context of the subject. An excerpt of all the analyzed questionnaire items is provided in the appendix to facilitate the understanding of the Likert scale administered (see Supplementary Data Sheet 4 ).

After data screening, which excluded incomplete/invalid questionnaires, the sample presented the following characteristics: gender, 1,312 male (53%) and 1,163 female (47%); nationality, 93% Italian and 7% others; age: M = 12.36, SD = 0.95, distributed in 11 years old n = 541 (21.9%), 12 years old n = 803 (32.4%), 13 years old n = 841 (34.0%), and 14 years old n = 290 (11.7%).

Gamblers were defined as individuals who showed gambling behaviors in the previous year, classified as the ones who answered “yes” to the question “In the last twelve months did you game and gamble money playing any game?”

In the first sets of analysis, data were examined to determine whether there was an association between the gambling behavior and individual or ecological factors measured on nominal, continuous, or ordinal scales. Variable dependence was assessed as appropriate using chi-square for nominal variables, t -test for comparing groups on two continuous variables (e.g., age), or the sound nonparametric Mann-Whitney U test to confront two ordinal variables (e.g., Likert 5/4-point scale from fully agree to fully disagree). The decision to apply nonparametric tests was made considering the correlational research design of the survey and the non-previously validated questionnaire as the tool for collecting data. Moreover, the utilization of nonparametric analysis gives the most accurate estimates of significance in case of non-normal data distributions and variables of intrinsic ordinal nature as the ones obtained from Likert items in the questionnaire ( Laake et al., 2015 ).

For the same reason, a Friedman test was run to determine if there were differences in the playing rates of gamers concerning different games of chance, because this nonparametric test determines if there are differences between more than two variables measured on ordinal scales, e.g., when the answers to the questionnaire items are a rank ( Conover, 1999 ). The different categories of game taken into account were “videopoker, slot machine e video slot,” “lotto, lottery and superjackpot,” “Scratch card,” “Sport bets,” and “Daily fantasy sports.”

The second set of analyses examined the probability of being in the category “gamblers” of the dependent variable given the set of relevant independent variables already identified in base of preliminary analysis results and substantive literature support. More specifically, the following variables measured by the questionnaire were analyzed: gender, inappropriate school behavior, parent with gambling behavior, and troubles with parent – videogame-related and gambling-related. In this perspective, model selection in the multivariate logistic regression is aimed to the understanding of possible causes, knowing that certain variables did not explain much of the variation in gambling could suggest that they are probably not important causes of the variation in predicted variable. Moreover, introduction of too many variables could not only violate the parsimony principle but also produce numerically unstable estimates due to overfitting ( Rothman et al., 2008 ).

Individual characteristics of participants who gambled (gamblers) versus participants who did not gamble (nongamblers) are shown in Supplementary Table S1 .

Gamblers were more likely males, older, and showed a higher record of inappropriate behavior at school in the past. Moreover, the parents of these students presented a higher proportion of gambling behavior and family conflicts related to playing videogames or gambling. As shown in Supplementary Table S2 , the two groups also differed significantly on the variable “online gambling without money.”

Subsequently, several Mann-Whitney U tests were run to determine if there were differences in the perception of many gambling’s facets (measured through self-report scores) between gamblers and nongamblers. To analyze the perception of the game and any differences between players and nonplayers have been isolated four variables measured through the following items: “loosing money because of gambling,” “becoming rich through gambling,” “gambling is funny,” “gambling is an exciting activity.” The distributions of the perception scores for gamers and not gamers on these four items were similar, as assessed by visual inspection. Median perception of gambling as a risk was statistically significantly lower in gamblers (3) than in nongamblers (4), U = 344, z = −4.59, p < 0.001, as well as the difference between median perception scores of gambling as an habit was statistically significantly lower in gamblers (3) than in nongamblers (4); U = 357, z = −3.48, p < 0.001. Statistically significant differences were also found between the median perception scores of gamblers and nongamblers on the variable “ losing money because of gambling ” [lower in gamblers (3) than in nongamblers (4); U = 327, z = −6.27, p < 0.001] and “ becoming rich through gambling ” [higher in gamblers (2) than in nongamblers (1); U = 519, z = 9.879, p < 0.001].

Differently, on two similar items regarding the perception of gambling as an entertaining activity and as an exciting activity, the distributions for gamblers and nongamblers were not similar, as assessed by visual inspection. One of the two items concerned the perception of gambling as an entertaining activity; the Mann-Whitney U test revealed that scores for gamblers (mean rank = 1.8) were significantly higher than for nongamblers (mean rank = 1.14; U = 608, z = 17.52, p < 0.001). The last item concerned the perception of gambling as an exciting activity; the Mann-Whitney U test revealed that scores for gamblers (mean rank = 1.7) were significantly higher than for nongamblers (mean rank = 1.16; U = 569, z = 14.23, p < 0.001).

For this reason, a Friedman test was run to determine if there were differences in the playing rates of gamers concerning different games of chance, because this nonparametric test determine if there are differences between more than two variables measured on ordinal scale, i.e., when the answers to the questionnaire items are a rank ( Conover, 1999 ). The students who stated to have gambled money in the previous 12 months were asked in the following question about the frequency they played different group of games.

Pairwise comparisons were performed ( IBM Corporation Released, 2017 ) with a Bonferroni correction for multiple comparisons. Gambling/playing rate was statistically significantly different in the five groups of games, χ 2 (4) = 226.693, p < 0.0005. The values of post hoc analysis are presented in Supplementary Table S2 , and the Pairwise Friedman’s comparisons revealed relevant statistically significant differences in playing rates of gamers. In fact, the category of game of chance constituted by “videopoker, slot machine e video slot” (mean rank = 2.46) is preferred to all other kinds of game of chance, except “lotto, lottery and superjackpot” (mean rank = 2.50). In the case of “Lotto, lottery, SuperJackpot,” this category of game of chance is preferred to “Scratch card” (mean rank = 3.30) in a statistically significant way, but it is also statistically less played in comparison to “Sport bets” (mean rank = 3.35) and “Daily fantasy sports” (mean rank = 3.40). None of the remaining differences were statistically significant.

Regarding the second set of analyses, Supplementary Table S3 provides the model used in the binomial logistic regression performed to ascertain the effects of key variables on the likelihood that participants played game of chance with money. The logistic regression model was statistically significant, χ 2 (7) = 326, p < 0.001. The model explained 23.0% (Nagelkerke R 2 ) of the variance in the predicted variable (gambling behavior) and demonstrated a percentage accuracy in classification (PAC) equal to 86.6%. Sensitivity was 22.5%, specificity was 97.6%, positive predictive value was 62.2%, and negative predictive value was 87.9%. Of the seven predictor variables only six were statistically significant: gender, inappropriate school behavior, parents with gambling behavior, troubles with parents – videogames related, online gambling without money, and age (as shown in Supplementary Table S3 ). Analysis showed that male had 2.96 times higher odds to be gamers than females (OR = 0.337; 95% CI 0.248–0.458), and increasing age was associated with an increased likelihood of gambling behavior. Also, inappropriate school behavior (OR = 1.859; 95% CI 1.395–2.477), parents with gambling behavior (OR = 3.836; 95% CI 2.871–5.125), troubles with parents – videogames related (OR = 1.285; 95% CI.510–3.236), and online gambling without money (OR = 2.297; 95% CI 1.681–3.139) increased the likelihood of gambling. By contrast, the “Troubles with parents – gambling related” variable was not statistically significant, probably because of the extremely unbalanced case ratio between the two modalities.

The first objective of this study was to evaluate the presence or absence and the consequent extent of the phenomenon of gambling in a population of preadolescents and to understand which factors are associated to the progressive lowering of the age of onset.

Consistently with the literature on the adult and adolescent population, the evidence presented thus far supports the idea that even in the preadolescent population players tend to be predominantly males ( Hurt et al., 2008 ; Splevins et al., 2010 ; Villella et al., 2011 ; Dowling et al., 2017 ).

One of the more significant findings to emerge from this study is that players of game of chance have a significantly different perception of the game than nonplayers, i.e., they see the game as “less risky” and perceive less risk of losing money through the game. In addition, confirming this “altered” perception, they show higher values than nonplayers in the perception of being able to become rich through the game ( Hurt et al., 2008 ; Dowling et al., 2017 ). Gamblers have a perception of the game as exciting and fun, a tendency which increases with age. This pattern seems to confirm what is expressed in the literature regarding the theme of sensation seeking and its connection with the development of gambling disease ( Dickson et al., 2002 , 2008 ; Hardoon and Derevensky, 2002 ; Messerlian et al., 2007 ; Blinn-Pike et al., 2010 ; Shead et al., 2010 ; Ariyabuddhiphongs, 2011 ; Lussier et al., 2014 ).

Even more importantly, some possible predictive factors of gambling emerged among the variables analyzed: thus, the phenomenon of gambling was associated with problems of school conduct, problems with parents related to the use of video games and, interestingly, also to the presence of parents who are gamers.

Since there are no validated tools in the literature for the diagnosis of preadolescent gambling, the analyses were conducted on those who were “gamblers” according to what was previously stated. It is therefore of particular relevance that the sample of preadolescent gamblers shows descriptive characteristics and predictive factors similar to those highlighted by the literature on adolescent gamblers with a diagnosis of gambling.

In this sense, the analysis of the most frequently used game types is particularly important.

With respect to the game categories analyzed, with the exception of “Lotto, lottery, SuperJackpot,” the category that is most frequently chosen by the sample of gamblers is that of “videopoker, slot machine e video slot.”

These data are of particular relevance considering that some studies in the literature have shown that adult pathological players have shown in previous ages a strong preference for these types of games. Although it is necessary to investigate with further studies the reasons underlying the choice of this type of game by preadolescents, this fact suggests that the phenomenon of preadolescent gambling has a number of aspects and characteristics common to those identified by the literature in the analysis of the precursors of pathological gambling.

There are some issues to take under consideration in framing the present results. Regarding the sample, although the numerous participants and the geographical representativeness of the population, the sample was not randomly selected. Therefore, we cannot exclude that subjects were unbalanced on unobserved, causally relevant concomitants. Although the methodology allows prediction, it should be noted that causality cannot be established from this survey, because the research design does not properly establish temporal sequence. In addition, only self-report measures and not thoroughly validated scales were used, as the objective of this study was to conduct an exploratory survey on the characteristics of the phenomenon, and there were some dichotomous variable with uneven case ratios. Furthermore, some constructs related to gambling behavior (e.g., impulsivity) and neurocognitive functioning were not analyzed in designing this first study; although in the wider research program, it is intended to explore also these factors.

Notwithstanding these limitations, the present study makes some noteworthy contributions to the understanding of the phenomenon of gambling and its characteristics in a population (preadolescents) which is still not very explored in the literature.

In particular, one significant finding is that the lowering of the age has not substantially changed what has been established in the literature with respect to the phenomenon in adolescents: the characteristics of players in terms of gender are substantially unchanged in the comparison between adolescents and preadolescents.

Moreover, from the analyses carried out, it appears that those that the literature has highlighted as risk factors of gambling in adolescence and adulthood are already present in younger players and may be predictive factors of gambling conduct already in preadolescence.

The data show, moreover, that the perception of gambling for those who play is significantly different from those who do not play, and specifically on aspects related to attractiveness, the low perception of risk and the possibility of getting rich easily. Finally, even with respect to an analysis carried out on different types of games, what emerged from the literature as additional risk factors for adolescents and adults is already present in preadolescence.

The findings of this study focus on the need to investigate the preadolescent age group in order to identify specific predictive factors of gambling in order to structure effective and structured preventive interventions and the parallel need to structure a standardized tool for the diagnosis of gambling in this specific population.

Data Availability

The datasets generated for this study are available on request to the corresponding author.

Ethics Statement

The study was carried out according to the principles of the 2012–2013 Helsinki Declaration. Written informed consent to participate in the study was obtained from the parents of all children. The study was approved by the IRB of the Department of Psychology of Niccolò Cusano University of Rome.

Author Contributions

NV and GF designed and performed the design of the study and conducted the literature searches. CD, MC, and GP provided the acquisition of the data, while FM undertook the statistical analyses. NV, CP, and FM wrote the first draft of the manuscript. All authors significantly participated in interpreting the results, revising the manuscript, and approved its final version.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/article/10.3389/fpsyg.2019.01287/full#supplementary-material

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Keywords: gambling, risk factors, preadolescence, addiction, prevention

Citation: Vegni N, Melchiori FM, D’Ardia C, Prestano C, Canu M, Piergiovanni G and Di Filippo G (2019) Gambling Behavior and Risk Factors in Preadolescent Students: A Cross Sectional Study. Front. Psychol . 10:1287. doi: 10.3389/fpsyg.2019.01287

Received: 15 February 2019; Accepted: 16 May 2019; Published: 12 June 2019.

Reviewed by:

Copyright © 2019 Vegni, Melchiori, D’Ardia, Prestano, Canu, Piergiovanni and Di Filippo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Nicoletta Vegni, [email protected]

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• Published: 27 August 2020

Risk factors for gambling and problem gambling: a protocol for a rapid umbrella review of systematic reviews and meta-analyses

• Caryl Beynon 1 ,
• Nicola Pearce-Smith 1 &
• Rachel Clark   ORCID: orcid.org/0000-0003-2800-2713 1  

Systematic Reviews volume  9 , Article number:  198 ( 2020 ) Cite this article

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Gambling and problem gambling are increasingly being viewed as a public health issue. European surveys have reported a high prevalence of gambling, and according to the Gambling Commission, in 2018, almost half of the general population aged 16 and over in England had participated in gambling in the 4 weeks prior to being surveyed. The potential harms associated with gambling and problem are broad, including harms to individuals, their friends and family, and society. There is a need to better understand the nature of this issue, including its risk factors. The purpose of this study is to identify and examine the risk factors associated with gambling and problem gambling.

An umbrella review will be conducted, where systematic approaches will be used to identify, appraise and synthesise systematic reviews and meta-analyses of risk factors for gambling and problem gambling. The review will include systematic reviews and meta-analyses published between 2005 and 2019, in English language, focused on any population and any risk factor, and of quantitative or qualitative studies. Electronic searches will be conducted in Ovid MEDLINE, Ovid Embase, Ovid PsycInfo, NICE Evidence and SocIndex via EBSCO, and a range of websites will be searched for grey literature. Reference lists will be scanned for additional papers and experts will be contacted. Screening, quality assessment and data extraction will be conducted in duplicate, and quality assessment will be conducted using AMSTAR-2. A narrative synthesis will be used to summarise the results.

The results of this review will provide a comprehensive and up-to-date understanding of the risk factors associated with gambling and problem gambling. It will be used by Public Health England as part of a broader evidence review of gambling-related harms.

Systematic review registration

PROSPERO CRD42019151520

Peer Review reports

Gambling is increasingly being identified as a public health problem [ 1 , 2 ]. Harms associated with gambling are wide-ranging and include harms not only to the individual gambler but to their families and close associates as well as wider society [ 3 , 4 ]. The global prevalence of problem gambling has been reported to range from 0.7 to 6.5%, and studies from across Europe have reported a high participation in gambling [ 5 ]. In 2018, a survey conducted in England by the Gambling Commission reported that almost half of the respondents had participated in gambling in the 4 weeks prior to being surveyed [ 6 ]. In addition, 0.7% of respondents were classified as ‘problem gamblers’ and an additional 1.1% of respondents were classified as ‘moderate risk’ gamblers, defined as ‘those who experience a moderate level of problems leading to some negative consequences’ [ 6 ]. The threshold for being considered a ‘problem gambler’ within this particular survey is high—a person has to score 8 or more on the Problem Gambling Severity Index (PGSI) or 3 or more according to the Diagnostic or Statistical Manual-IV [ 7 ]. So the number of people experiencing problem gambling could well be higher.

Risk factors are traits or exposures that increase the possibility that an individual will develop a condition and can be fixed or variable [ 8 ]. The risk factors for gambling and problem gambling are broad and have been reported in numerous systematic reviews and primary studies. At an individual level, risk factors include (but are not limited to) fixed biological factors, such as gender and impulsivity, and behavioural factors such as levels of participation in gambling, excessive use of alcohol and use of illicit drugs and propensity towards violent behaviour [ 9 ]. Broader factors related to the family environment [ 10 ] and gambling availability have also been identified [ 11 ]. A scoping search identified a number of systematic reviews and meta-analyses of risk factors for problem gambling, largely focused on specific risk factors or types of risk [ 9 , 10 , 11 ] although one focused on specific populations [ 12 ]. No systematic reviews, meta-analyses or umbrella reviews were identified examining all risk factors for all populations. In order to understand the breadth of possible risk factors driving gambling and problem gambling behaviours, there is a need to collate this review-level evidence. This work is part of a broader review examining gambling-related harms [ 13 , 14 ].

The overall aim of this umbrella review is to identify the risk factors associated with gambling and problem gambling. The research questions are as follows:

What risk factors are associated with gambling?

What risk factors are associated with different levels of gambling intensity?

This review adopted a rapid review methodology [ 15 ] to identify, appraise and synthesise systematic reviews and meta-analyses, defined here as an ‘umbrella’ review [ 16 ]. The use of existing systematic reviews and meta-analyses enables a broad examination of best available evidence in a timely way and is useful for addressing the high-level questions set out for this review, where multiple risk factors are expected to be identified. This review protocol is being reported in accordance with reporting guidance provided in the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) statement [ 17 ] (see checklist in Additional file 1 ). The protocol is registered on PROSPERO (CRD42019151520). The review will be conducted using EPPI-Reviewer 4.

Definitions of terms

There are multiple definitions of the term ‘gambling’, but for the purpose of this review, gambling is defined (as set out by the Gambling Act 2005) as ‘… any kind of betting, gaming or playing lotteries. Gaming means taking part in games of chance for a prize (where the prize is money or money’s worth), betting involves making a bet on the outcome of sports, races, events or whether or not something is true, whose outcomes may or may not involve elements of skill but whose outcomes are uncertain and lotteries (typically) involve a payment to participate in an event in which prizes are allocated on the basis of chance.’ [ 4 ].

There is no single definition for ‘harmful’ or ‘problem’ gambling, and this can be measured in several ways. For example, reports prepared for the Gambling Commission estimate problem gambling according to scores derived from 2 different instruments: the Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV) and the Problem Gambling Severity Index (PGSI). The DSM-IV contains 10 diagnostic criteria and possible scores are between 0 and 10; a score of 3 or over indicates problem gambling. The PGSI contains 9 diagnostic criteria and a score of between 0 and 27 is possible; a score of 1–2 is ‘low risk’, 3–7 is ‘moderate risk’ and 8 and over is ‘problem gambling’ [ 7 ]. In the USA, the South Oaks Gambling Screen (SOGS) is commonly used, where positive answers to three out of twenty gambling-related questions are considered indicative of problem gambling [ 18 ]. In order to capture the breadth of literature available, no one definition will be adopted and this review will include papers which define ‘harmful’ or ‘problem’ gambling in different ways.

In the context of this review, a risk factor is defined as any factor investigated as being associated with gambling (including initiation, escalation, urge or intensity), either causally or otherwise. Where the evidence shows the link to be causal (rather than an association), this will be reported.

Inclusion and exclusion criteria

Inclusion and exclusion criteria have been developed using an adapted version of the PICO (population, intervention, comparison, outcome) framework, as set out in Table 1 .

It is expected that two types of study will be identified for inclusion: (i) those that focus on the gambling population and explore all risk factors and (ii) those that focus on a specific risk factor.

Additional inclusion criteria:

Language: English (other languages will not be included, due to the team’s inability to translate)

Publication date: 1 January 2005–4 September 2019. 2005 was selected as a cut-off as in this year the Government issued proposals to reform the law on gambling [i.e. the Gambling Act] and the Economic and Social Research Council/Responsibility in Gambling Trust provided £1 million of funding for research on problem gambling—significantly increasing capacity for research on this topic in England [ 19 ].

Publication type: peer reviewed and grey literature

Setting: reviews of studies which are based within the Organisation for Economic Co-operation and Development (OECD). Where studies set in non-OECD countries are also included, more than half of included studies must be from OECD countries and inclusion/exclusion will be considered on a case-by-case basis.

Search strategy

A comprehensive search will be undertaken using multiple methods to identify both published and grey literature. The search strategy was developed by a Senior Information Scientist in PHE and quality assured by a second Information Scientist.

Electronic searches

The following databases will be searched: Ovid MEDLINE, Ovid Embase, Ovid PsycINFO, Social Policy and Practice, Social Care Online, NICE Evidence and SocIndex via EBSCO. The number of papers retrieved from each database will be recorded. The full MEDLINE search is presented in Additional file 2 ; this will be adjusted for use in other databases. The search will look for terms in the title, abstract, author key words and thesaurus terms (such as MeSH Medical Subject Headings in MEDLINE) where available. The review filter will be used for all databases except for SocIndex (which does not have a validated one). For SocIndex, a set of search terms will be created in order to restrict the search to systematic reviews and meta-analyses.

Grey literature

Reports and other relevant literature that may not be published in databases will be sought by searching Google and websites such as those listed here (years 2005 to 2019). If a website provides a review summary, effort will be made to find the full study report.

Gamble Aware InfoHub

Gambling Commission

GambLib (Gambling Research Library)

National Problem Gambling Clinic

Gordon Moody Association

Gamblers Anonymous

Gambling Information Resource Office Research Library

Advisory Board for Safer Gambling

Gambling Watch UK

Australian Gambling Research Centre

Gambling Research Exchange Ontario

Citizens Advice Bureau

Be Gamble Aware

Problem Gambling, Wigan Council

Gambling Compliance

Child Family Community Australia

International Centre for Youth Gambling Problems and High-Risk Behaviours

Gambling and Addictions Research Centre

Alberta Gambling Research Institute

Responsible Gambling Council

Problem Gambling Foundation of New Zealand

Gambling Commission New Zealand

Victorian Responsible Gambling Foundation

Handsearching

Reference lists of retrieved papers will be searched for additional relevant papers which fulfil the inclusion/exclusion criteria. In addition, if any umbrella reviews are identified, the reference lists will be scanned for inclusion.

Consultation with experts

Once a list of included studies is available, this will be shared with the project Expert Reference Group to check for additional studies. This group includes national and international topic experts.

Screening and selection procedure

A pilot screen will be undertaken whereby each reviewer will independently screen the same 100 randomly selected references/papers and indicate which should be included/excluded. Reviewers will obtain the full paper if this is needed for them to make their assessment. Any discrepancies indicate inconsistencies in understanding of the inclusion/exclusion criteria between reviewers, and this stage will allow these to be identified, discussed and resolved. If necessary, the inclusion/exclusion criteria will be modified, and the changes will be recorded in a decision log.

References will be divided between four reviewers. The title/abstract of every reference will be screened independently by two reviewers (‘review pairs’) according to the inclusion/exclusion criteria, and each reference will be coded as either ‘included’ or ‘excluded’. EPPI-Reviewer will be used to measure inter-rater agreement for all reviewer pairs; agreement of 90% or over will be considered acceptable. If the agreement is less than 90%, the reason will be explored and rectified and screening will be repeated, in line with the guidance from the National Institute for Health and Care Excellence (NICE) on title/abstract screening [ 20 ].

The full articles of the remaining references will be obtained. Full articles will be divided between reviewers and screened using inclusion/exclusion codes set up in advance by the Project Team. Ten percent of the papers screened by each reviewer will be reviewed independently by a second reviewer using the ‘parent’ codes: include and exclude (i.e. rather than specific exclusion codes such as ‘date’, ‘geography’, ‘study type’). A threshold of 80% agreement will be considered acceptable in line with criteria outlined in the AMSTAR 2 (Assessing the Methodological Quality of Systematic Reviews) tool [ 21 ]. A decision on what steps should be taken if the agreement is less than 80% will be made by the Project Team should this situation arise.

Data extraction

Data extraction tables will be used to extract the relevant information from each study. These will include the following information: authors, date, country, the PICO-S elements and the relevant results. Authors will be contacted by the reviewers to ask for missing information or clarification where necessary, and where information is considered essential. Data extraction tables will be pilot tested before being used and signed off by the Expert Reference Group. All reviewers will extract the data from a set of eligible studies; 10% of all papers will be randomly selected and the data from these will be extracted independently by a second reviewer. Agreement between reviewers for data extraction will be checked to ensure this is acceptable (at least 80%). A decision on what steps should be taken if the agreement is less than 80% will be made by the Project Team should this situation arise. The Cochrane PROGRESS-Plus tool [ 22 ] will be used to extract data on the broad dimensions of inequality.

Quality assessment (risk of bias)

The quality of systematic reviews will be assessed using the AMSTAR2 checklist [ 21 ]. Each paper will be independently assessed by two reviewers, and disagreements will be resolved through discussion. If required, a third person will be brought in to resolve ongoing disagreements.

Method of synthesis

Given the broad scope of this review, included studies are likely to be heterogeneous, and therefore, a narrative analysis will be conducted with text used to summarise and explain findings [ 23 ]. Studies will be summarised according to themes. An appraisal of the quality of the literature will be included. Differences by sub-group will be examined where this is reported in the literature to integrate a focus on equity, using the Cochrane PROGRESS-Plus tool [ 22 ]. The body of evidence will be assessed according to the four principles laid out in the CERQual approach which are (1) the methodological limitations of the studies which make up the evidence, (2) the relevance of findings to the review question, (3) the coherence of the findings and (4) the adequacy of data supporting the findings [ 24 ].

This rapid umbrella review will identify and examine the breadth of risk factors associated with gambling and problem gambling. The findings of this review will be utilised as part of a broader review of evidence conducted by Public Health England on gambling-related harms. A full report of this work will be shared and discussed with government departments and published on our government website GOV.UK. The results of this review will also be submitted for publication in a peer review journal.

Any deviations to the protocol considered necessary will be discussed by the Project Team prior to being implemented and documented in a decision log (stored in Excel) for later reporting.

A number of limitations are anticipated. The reliance on existing systematic reviews and meta-analyses is impacted by the quality of their methods and reporting—whilst we are assessing this, if the quality is poor, our ability to fully utilise their results will be limited. In addition, there may be a large number of systematic reviews and meta-analyses, and if they are focused on different risk factors, the results may be difficult to synthesise.

Availability of data and materials

Not applicable.

Abbreviations

Assessing the Methodological Quality of Systematic Reviews

Confidence in the Evidence from Reviews of Qualitative Research

Diagnostic and Statistical Manual

National Institute for Health and Care Excellence

Organisation for Economic Co-operation and Development

Problem Gambling Severity Index

Public Health England

Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols

South Oaks Gambling Screen

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Acknowledgements

The authors would like to thank the following people who either supported the development of the methods or provided feedback on the protocol:

Jenny Mason, Mary Gatineau, Fionnuala O’Toole, Alyson Jones, Dr Robyn Burton, Marguerite Regan, Clive Henn, Dr Felix Greaves, and Professor John Marsden.

This review will be funded by Public Health England.

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CB and RC developed the methods. NPS developed the search strategy. All participated in drafting the manuscript. RC will be the guarantor of the review. The author(s) read and approved the final manuscript.

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Correspondence to Rachel Clark .

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Supplementary information

Additional file 1..

PRISMA Checklist

Additional file 2.

MEDLINE search. Full search conducted in MEDLINE, enabling replication of review

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Beynon, C., Pearce-Smith, N. & Clark, R. Risk factors for gambling and problem gambling: a protocol for a rapid umbrella review of systematic reviews and meta-analyses. Syst Rev 9 , 198 (2020). https://doi.org/10.1186/s13643-020-01455-x

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• How does gambling affects the effectiveness of sports?
• Is gambling a national evil?

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Best Gambling Essay Topic Ideas

• Gambling from different perspectives.
• College students and sports gambling.
• Predictors of gambling behavior among college students.
• The role of the media in promoting betting.
• Family factors contributing to gambling addiction
• Motivation factors for consumer decision-making to participate in soccer lotteries.
• Gambling in California.
• Gambling proclivities of the clergy.
• The stance of the Bible and Quran on gambling.
• The stress coping mechanisms for youths who gamble.
• The link between betting companies and money laundering.
• The link between gambling and prostitution.
• Gambling project: effect of cultural change in America.
• Gambling in America: The economic reasons verse moral obligations.
• Strategies to prevent gambling among adolescents.
• Positive and negative effects of gambling.
• Cognitive distortions of addicted gamblers.
• Gender difference in gambling addiction.
• Gambling proclivities of senior citizens.
• The gambling behaviors of casino employees.
• The link between pathological gambling and deviance.
• Legalization of Gambling in Ohio.
• Gambling issues in society today.
• Online gaming and gambling legalization.
• Prevention of high-risk gambling.
• Gambling Tips.
• Cognitive strengths of pathological gamblers.
• Strategies to recover from gambling addiction without treatment.
• The lived experiences of women who gamble.
• The link between betting and homelessness.
• Depression among chronic gamblers.
• Link between gambling and narcissism.
• Steps for stopping compulsive and impulsive gambling.
• Link between anticipated risk-seeking and regret in gambling behavior.
• Policy implementation in gambling.
• Gambling in the United States.
• Economic significance of the gambling industry.
• Impacts of casinos on the community.
• Gambling and taking risks.
• The negative impacts of casinos
• Pros and cons of gambling.
• Government-operated gambling.
• Legal issues in gambling.
• Public policies and gambling.
• The social construction of gambling addiction treatment.
• The governance of gambling in China.
• The impact of culture on gambling habits.
• Should gambling be legalized?
• The concept of charitable gambling in American culture.
• Positive impacts of Gambling.
• Revised challenges of gambling.
• Benefits of gambling on the country's economy.
• Why should gambling be banned?
• Gambling addiction.
• The effects of gambling on the gaming industry.
• Research approaches to gambling addiction.
• The popularity of the casino gambling industry.
• Economic concerns of casino gambling.
• Legalization of casino gambling in Texas.
• Legalization of online gambling.
• Disadvantages and benefits of gambling.
• Effects of gambling addiction on neuroplasticity.
• The effects of gambling on families.
• The impact of internet gambling on the youth.
• Gambling regulation.
• Benefits of gambling to the banking sector.
• Impact of gambling revenue legislation
• The psychology of gambling.
• Gambling companies and American Laws.
• The challenges of gambling in the current society.
• Issues of internet gambling.
• Hong Kong casino gambling legalization.
• Gambling as a form of leisure.
• The legalized gambling era.
• The impact of gambling on individual happiness.
• The gambling history and its relationship with the modern era.
• Effects of internet application, gambling, and online gaming among high school students.
• The debate against the restriction of gambling.
• Combating casino addiction.
• The costs and benefits of gambling

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Good Gambling Research Topics

• Causes and effects of gambling in society.
• Bearing the risk: luck, gambling, and love in literature.
• Financial fraud and gambling in the modern world.
• Management of information issues in casino gambling.
• Internet gambling victims and regulation.
• Youth gambling effects: challenges and issues to counseling.
• The distinction between gambling and investment speculation.
• Human resources in the gambling industry.
• The influence of gambling on society and an individual.
• Evaluating the differential effects of mixed, offline, and online gambling.
• The effects of gambling taxation in bringing public equity.
• Behavioral treatments and accounts of gambling.
• Protective and risk factors in gambling: An analysis of psychological resilience.
• Cognitive interventions for gambling addiction.
• Gambling: the challenges and the history of tragedy, addiction, and helpfulness.
• The facts and myths of casino gambling: when leisure becomes a problem.
• Relationship between mental health aspects, problem gambling, Socio-Demographics, and the type of gambling.
• The effects of making the risky choice in gambling.
• Gambling addiction: hidden facts of online gambling.
• Effects of functional brain connectivity on victims of gambling disorder.
• Relationship between gambling and the increasing rate of suicide.
• The influence of gambling on mental health disorders.
• Human perception of investing and betting.

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Simple and Unique Gambling Essay Topics

• The pleasure theory: gambling and brain association.
• Diagnosis and treatment of compulsive gambling addiction.
• Gambling deprivation, geographical variation.
• Behavioral change techniques for managing gambling addiction.
• How economic recession affects gambling recession.
• The sociological outcomes of gambling in western countries.
• The effects of gambling on culturally diverse adults.
• Gambling: leisure full of losses.
• The challenges and concerns about legal online gambling.
• Gambling beliefs and attitudes that promote addiction.
• Financial and accounting reports in gambling monopoly.
• Relationship between gender gambling behaviors and gambling settings.
• An exploratory study of gambling among Croatian youth.
• Risk aspects of gambling.
• Ethical evaluation of gambling.
• Online gambling: taking society to death bed.
• Gambling history in the United States.
• Casino business and its economic influence
• Gambling in football
• Is gambling fun or a threat?
• Gambling: an expanding addiction.
• Immorality issues in casino gambling.
• The expanding gambling among the youth is a critical concern.
• How to avoid gambling.
• Online gambling.
• Participation of LGBTQ people in gambling.
• Effects of gambling on world economies.
• Gambling should be illegalized forever.
• Gambling: An ever-increasing addiction.
• Stop gambling.
• Evaluation of the aspects that lure people to gambling.
• Compulsive gambling.
• Psychological gambling.
• Causes of gambling.
• Impacts of gambling on the economy.
• An analysis of personal perspective concerning gambling.
• Gambling: An expanding form of leisure in the modern world.
• Is gambling a source of motivation?
• The effect of gambling on family financial management.
• Gambling beyond entertainment.
• Gambling odds.
• Gambling: a victimless fraud.
• Gambling: the addiction.
• Cyberspace gambling.
• Gambling bioethics.
• Gambling and the Asian culture.
• Internet gambling and regulation.
• Social problems of gambling.
• Association between gambling and game theory.
• Legalizing gambling in Ohio.
• The role of ethics in gambling.
• How gambling affects the American culture.
• Assessment of gambling among women.
• How casinos lure customers.
• Online casinos.
• Gambling: the dangerous leisure.
• Gambling analysis.
• Regulation of casinos.
• A history of gambling.
• The effects of taxation on gambling
• Benefits of casinos.
• Off-track gambling.
• Gambling in contemporary society.
• Advantages of gambling.
• Social impact of gambling.
• The casino industry.
• A comprehensive study on unfavorable aspects of gambling in India.
• The benefits and disadvantages of increasing casinos.
• Reservation gambling.
• Situational analysis of gambling.
• Religious perspective of gambling.
• The effects of gambling on individual life.
• Psychological effects of gambling.
• Treatment approaches for gambling addiction.
• How to manage gambling addiction?
• Gambling prevention awareness.
• The contradiction between gambling and ethics.
• Personality type, motivation, and the consideration between luck and skill gambling products

Gambling Research Questions

• What are the challenges associated with gambling?
• Should the government ban gambling?
• How has gambling affected American society?
• Who is affected by gambling?
• What is the link between gambling and money laundering?
• What is the link between gambling and terrorism?
• Why should people transform their regressive perception of gambling?
• What are the effects of gambling on popular culture?
• How does gambling addiction affect individuals and families?
• How has gambling affected Americans?
• To what extent has gambling contributed to mental illness?
• Should the gambling age restriction be decreased?
• Should gambling be regulated?
• How does taxation affect the gambling industry?
• How has gambling affected the youth in the modern world?
• Should gambling in sports be allowed?
• Is gambling and drug use associated?
• Does the bible and Quran support gambling?
• Do gambling institutions promote environmental conservation efforts?
• What are the attractions of gambling?
• How does the stigma of gambling affect treatment, recovery, and health-seeking?
• How does the expected utility theory discuss gambling?
• Does casino gambling increase government revenues?
• How does gambling impact society?
• Does gambling comply with societal values?
• What are the effects of gambling on the economy?
• What promotes gambling behavior?
• Why did the internet gambling ban fail?
• What should the state do to curb the expansion of gambling among the youth?
• Does gambling promote laziness among consumers?
• What is the biblical perspective concerning gambling?
• What are the benefits of gambling?
• Does gambling contribute to the rise of the lazy generation?
• How does gambling affect individual satisfaction?
• What are the social benefits of online gambling?
• What is the relationship between gambling and passion for sports?
• Does gambling increase crimes?
• How do gambling victims cope with addiction?
• What are the possible benefits of banning gambling?
• What are the benefits of regulating gambling?
• Does gambling have an economic impact?
• How does gambling affect students?
• Should the state sponsor gambling?
• Should the government ban gambling in colleges?
• Should the taxation on gambling increase?
• Should the government impose strict rules to curb the expansion of gambling?
• Does charitable gambling affect charitable donations?
• What is the state's policy on gambling?
• What are financial markets structured differently from gambling markets?
• Why should gambling be policed or prohibited?
• How to write a Research Proposal .
• Interesting topics for psychology papers.

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• Research article
• Open access
• Published: 22 August 2017

Why do young adults gamble online? A qualitative study of motivations to transition from social casino games to online gambling

• Hyoun S. Kim 1 ,
• Michael J. A. Wohl 2 ,
• Rina Gupta 3 &
• Jeffrey L. Derevensky 4  

Asian Journal of Gambling Issues and Public Health volume  7 , Article number:  6 ( 2017 ) Cite this article

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The present research examined the mechanisms of initiating online gambling among young adults. Of particular interest was whether social casino gaming was noted as part of young adults’ experience with online gambling. This is because there is growing concern that social casino gaming may be a ‘gateway’ to online gambling. Three focus groups ( N  = 21) were conducted with young adult online gamblers from two large Canadian Universities. Participants noted the role of peer influence as well as incentives (e.g., sign up bonuses) as important factors that motivated them to start engaging in online gambling. Participants also noted a link between social casino games and online gambling. Specifically, several young adults reported migrating to online gambling within a relatively short period after engaging with social casino games. Potential mechanisms that may lead to the migration from social casino games to online gambling included the role of advertisements and the inflated pay out rates on these free to play gambling like games. The results suggest initiatives to prevent the development of disordered gambling should understand the potential of social casino gaming to act as a gateway to online gambling, especially amongst this vulnerable population.

Over the past decade, the use of computers and the Internet has significantly altered the gambling landscape. The gambling industry is no longer bound by brick and mortar gambling venues (e.g., casinos, racetracks). Today, access to gambling activities can be achieved with a few keystrokes on a computer. One point of access that has gained increased attention from researchers in the field of gambling studies is social media sites such as Facebook (Wohl et al. 2017 ). In part, this increased attention is because social media sites have become a popular platform for people to access online gambling venues via hyperlinks embedded in advertisements (Abarbanel et al. 2016 ). Social media sites also allow users to engage in free-to-play simulated gambling games through applications. These free-to-play simulated gambling games have become referred to as social casino games (Gainsbury et al. 2014 ). There is evidence to suggest, however, that social casino game play may act as a ‘gateway’ to gambling for real money (for a review see Wohl et al. 2017 ).

The current research took a qualitative approach to assess young adult online gamblers experiences with online gambling to determine the process and mechanisms that may lead young adults to gamble online, including the role of social casino games. In other words, the present research aimed to examine the motivations for gambling online, including transitioning from social casino games to online gambling. A focus was placed on young adults’ experience with online gambling due to their propensity to gamble online (McBride and Derevensky 2009 ), play social casino games (Derevensky and Gainsbury 2016 ), as well as their elevated rates of disordered gambling (Welte et al. 2011 ). Further, social casino games were the focus as there is a current need to understand the issues regarding the gaming-social media crossover.

Online gambling and social casino gambling among young adults

The Internet has changed the way people engage in many activities, including gambling. Online gambling (compared with land-based gambling) provides players with ease of access, 24/7 accessibility, and confidentiality—all within the comfort of a person’s home. This ease of access has been flagged as a potential concern among researchers, regulators, and policy makers alike (Gainsbury 2015 ; Gainsbury and Wood 2011 ; Räsänen et al. 2013 ). Specifically, online gambling is often framed as a ‘risky’ form of gambling that may heighten the risk of developing a gambling disorder (Gainsbury et al. 2015b ; Griffiths et al. 2009 ; McBride and Derevensky 2009 ; Olason et al. 2011 ; Wood et al. 2007 ). In this light, it may be informative to examine factors that propel young adults to gamble online, including the link between social casino gaming and online gambling. This is because there is increasing evidence of the role played by social casino games in precipitating online gambling (Wohl et al. 2017 ) and young adults are increasingly exposed to social casino games (Kim et al. 2016 ).

Social casino games are an immensely popular form of entertainment, with millions of users playing in any given day (Derevensky and Gainsbury 2016 ; Martin 2014 ). One reason for their popularity may be their ubiquity on social network sites like Facebook , which provide ample opportunities to play social casino games via embedded apps (Gainsbury et al. 2014 ). Moreover, social casino games are among the most heavily advertised products on social network sites and convey the activity (i.e., gambling) as positive and glamorous (Gainsbury et al. 2015a ). These advertisements appear to have a significant influence on engagement with social casino games (SuperData 2016 ). It should be noted that some social casino games are now owned by online gambling operators who advertise their online gambling site within the social casino game, thus easing migration from social casino gaming to online gambling (Schneider 2012 ).

There is now converging evidence that suggests social casino gamers migrate to online gambling (Gainsbury et al. 2016 ; Kim et al. 2015 ). Furthermore, amongst people who engage in both gambling and social casino gaming, social casino games directly increase future gambling behaviors (Gainsbury et al. 2016 , 2017 ; Hollingshead et al. 2016 ). Social casino games are also popular among adolescents and young adults. In a large Canadian survey of over 10,000 students, roughly 9% reported having played social casino games (Elton-Marshall et al. 2016 ). In addition, a recent longitudinal study in a large sample of adolescents found that social casino games significantly predicted the transition to real money gambling (Dussault et al., in press). Providing further support for the popularity of social casino games, in focus groups with university students who were social media users, all participants reported being aware of the ample opportunities to play social casino games on Facebook, thus speaking to the increased exposure of these games on social networking sites (Kim et al. 2016 ).

Motivations for transitioning to online gambling from social casino gaming

Social casino games are popular among adolescents and young adults and may influence the transition to online gambling. Yet, researchers have paid little attention to potential processes or mechanisms that influences the transition to online gambling amongst this cohort, including the role played by social casino games. With that said, Hollingshead et al. ( 2016 ) argued that the motivations for playing social casino games likely mimic those of online gambling, including for excitement, to relieve boredom, and social motivations. In addition, they reported that some social casino gamers are motivated to engage in these games to hone their skills before playing for real money on online gambling sites. In line with Hollingshead et al. ( 2016 ) and King and Delfabbro ( 2016 ) proposed a framework for understanding factors that may increase or decrease the link between social casino gaming and online gambling among adolescents. Specifically, in their two pathways model, they identify both protective (e.g., early losses, awareness of risks, boredom) and risk factors (e.g., peer pressure, early big wins, greater confidence of winning) that may lead adolescents who are exposed to social casino games to either be disinterested in gambling or to increase future gambling behaviors.

The present research sought to add to the growing literature on the potential link between social casino gaming and online gambling. To do so, focus groups with young adult online gamblers were conducted to explore their motivations for gambling online, including the potential role social casino games played in initiating or facilitating online gambling behaviors. Focus groups provide a compromise between obtaining personal experiences without having to interview people individually, while also having a group environment where other people’s experience stimulate the recall and views of others. In this light, focus groups are an effective method of obtaining a variety of detailed information in an exploratory way.

Participants

Twenty-one young adults (18 males, 3 females) were recruited from two large Canadian Universities to participate in one of three focus groups described as being about young adults’ experience with online gambling. Specifically, the study was advertised as a focus group for people who gambling online. It was explained that we were interested in online gamblers’ “opinions and experiences regarding online gambling”.

The inclusion criteria were as follows: college students aged 18–24 years who reported gambling online at least twice per month. The method of recruitment occurred in two ways. First, all incoming first year students at one of the large Canadian universities complete a short survey screening for disordered gambling. Embedded in that questionnaire were items that assessed online gambling. This allowed us to recruit participants who met the inclusion criteria for the focus groups. Only those who consented to be recruited for future studies were contacted. The second method of recruitment consisted of visiting large classrooms and advertising the study at both universities.

While every effort was made to recruit an equal number of male and female online gamblers we were unable to do so despite our best efforts. Moreover, seven individuals who had initially agreed to participate in the study subsequently notified the research team before the group meeting that they could not participate for logistical reasons (i.e., work and school commitments, unexpected appointments). Participants in the first group were compensated $20 for their time and those in the remaining two groups were provided with $40 (the increased compensation was used as an incentive to attract a greater number of participants and was cleared by the authors’ Research Ethics Board). Additionally, participants were provided food and beverages throughout the course of the discussions that ensued.

Procedure and materials

All participants were provided with a description of the study objectives and were asked to read and sign an informed consent prior to participating in the current research. Participants were informed they were free to terminate participation at any time without penalty. Thereafter, participants were asked to complete a short background questionnaire, which included demographic information (gender, age), frequency of gambling, and how knowledgeable they believe themselves to be on the topic of online gambling.

A series of open-ended questions were asked of the group as part of a larger project assessing online gambling among young adults. For the present research, two open-ended questions were of importance. The first examined general factors that lead young adults to gamble online, “I’d like to gain a better understanding of the things that lead to online gambling in the first place. Based on what you know, what are the factors, the events, or the influences that result in a young person deciding to bet money on gambling activities online?” The second assessed the social casino game-online gambling link including the potential mechanisms, “You know that social media sites have gambling-type games such as Texas-Hold’em or Sloto-mania. In your opinion, do you think experience with these games leads a person to seek online gambling sites? In other words, do these types of games serve as a form of initiation to gambling online with real money?”

A licensed clinical psychologist trained in conducting focus groups led the discussions accompanied by two note-takers. Each group was approximately 60–75 min in duration and discussions were conducted at two Canadian universities. Two recording devices recorded the focus group to ensure no loss of data. Upon the completion of the focus groups, the discussions were subsequently transcribed by a professional coder and coded by two independent reviewers. The initial categories generated by the data were highly consistent between the two raters with regards to general themes and number of categories. The data was reviewed two additional times to arrive at a consensus when disagreements between raters were noted. Categorical names were arrived through consensus after discussion between raters. NVivo 10 qualitative research software for qualitative analyses was used to organize and quantify the data.

With respect to frequency of online gambling, 52% of individuals indicated gambling less than once per week, while 48% indicated gambling at least once per week or several times per week. Seventy-six percent of individuals indicated gambling more frequently and/or for longer periods of time than intended (61.9% occasionally; 14.4% often). Participants were asked to indicate on a 7-point Likert scale how knowledgeable they perceived themselves to be on the topic of online gambling. The overall mean score was 4.38. The majority of the sample (85.7%) indicated that they tend to play on one or two online gambling sites, whereas 14.3% stated they like to experiment with different sites. Importantly, more than half (62%) of the participants revealed playing social casino games (e.g., Texas Hold’em ) on Facebook or on other platforms. Of the participants ( n  = 3) who spontaneously reported having transitioned from playing for fun to online gambling, they did so in relatively short period of time. One participant reported transitioning after only two weeks, while another stated having moved to real money gambling after a couple of months.

General factors leading to online gambling

Several themes emerged in regards to the factors that led the emerging adults to online gambling. For example, some of the emerging adults in the focus groups stated that friends played an important role in their initial participation to online gambling. Specifically, several participants reported having first learned to gamble with friends and thereafter transitioning to online gambling as their friends were not always available.

From my personal experience for example, I started gambling online with poker because I started playing poker with friends, and that is how I got to gambling online… with friends they did not always have the time [to play poker]. Gambling online was just easier – with friends they did not always have time.

Another theme that was noted in the precipitation of online gambling was the incentives (e.g., sign up bonuses) offered by online gambling. The young adult online gamblers noted that the first time they gambled online was when they were offered bonuses and free credits. Indeed, the participants agreed that the bonuses were an important incentive in moving to online gambling.

The bonuses actually attract us to them. You don’t get that at the casino.

For me the first incentive was they offered us 10 lb… so I got the 10 lb and then started betting real money

Motivations from transitioning from social casino games to online gambling

Texas Hold’em with free chips, that’s how I started. A general progression starts with these Facebook entertainment games which are purely for fun and some people take it to the next level where it’s for fun and money, that’s where we are now - most of us and then some people will take it eventually to the next level where the fun has disappeared and they are just doing it for the money.

Social casino games were noted as a potential factor that influenced the initiation of online gambling among young adults. In fact, whilst the moderator had intentions to bring up social casino games as a topic, in all three focus groups, the young adult online gamblers spontaneously brought up social casino games. These results indicate that social casino games are a salient aspect of young adult online gamblers’ experiences. Not surprisingly, the young adult online gamblers mentioned the constant advertisements as a potential factor that may lead social casino gamers to online gambling. Specifically, the frequent nature of the advertisements that provided social media users with an opportunity was brought up by several focus group members, with few young adult online gamblers mentioned the role of advertisement in the transition to online gambling.

I’d argue that you are just sort of lured into playing more through back link advertising where you will have all these ads like partypoker.com keep coming back at you even when you are on other sites…

… and obviously the companies [social media] give out the information on things that you are doing like all the games and poker, even though it’s not for money. Your side bar has all advertisements that are personalized to you so for me I see a lot of gambling, sports, apparel stuff and stuff like that is all on my side bar.

When I started, it was Facebook. Randomly the opportunity comes up with ads. I was stressed so I went to the online casino from Facebook. Every day, every day, the online casino sends you notifications…

The young adult online gamblers also noted a link between social casino games and online gambling, with several participants stating they transitioned to online gambling after playing for free on Facebook. One potential implication is the inflated payout rate offered by social casino games. The focus group members noted they win more frequently on social casino games, which provides them a sense of hope that they would be winning money had they been gambling for real. There was a general sense of needing to be “smart” and “savvy” to not fall prey to the tactics of online casinos and social media sites.

Once you play for fun, they sort of get people into the gambling, you think ok, this would be great if it were real money, so you try. That’s the way the websites make you go through that road.

They want you to win… if you are winning on Facebook and then you see [an advertisement] on the side to go online to play at party poker you will think if I can do this for free I can do this for real and then you go to do it for real and the next thing you know you are down $150 when you were getting Blackjack with the other one [social casino site].

There was a consensus that social casino games provided an excellent learning opportunity. Specifically, social casino games allow people to learn rules, procedures, and strategies to gamble.

So regardless of whether it is Facebook or just the practice sites on the online casinos, it’s a natural progression to start from social casino games: train, learn… then you realize you are not learning enough because people are not taking the game seriously, and then you move onto paying.

I don’t know those procedures so I don’t play (in casinos). But online who is going to yell at you online? So like you can just practice online and you can play lower [limit] tables. Basically you can practice online without other people yelling at you.

Participants also noted that after playing for free, they transitioned to online in part as most players who play for free do not play the game the ‘right way’

The difference between a table with real money and a table with fake money, the people with fake money, they don’t do the moves they usually do with their real money. You just mess around, you don’t really care “Oh I’m all in” – it’s like you don’t care. But at the real tables everyone plays the way they want to play. You get to learn a lot when you play.

I started playing online and when I played online without money I realized this was not really like anywhere close to the situation you would be in at a real table cause you don’t have any money on it, so I decided to start gambling with money.

However, not everyone perceived a link between social casino games and online gambling. These individuals explained that the interfaces of the games were so different (social media being much less sophisticated) that people who are attracted to one would likely not be attracted to the other.

I don’t think it’s as dangerous as people make it to be. If I want to switch from gambling on Facebook to a real site I just go to Google and type in poker and have it [online site].

You start playing poker with your friends and like you move from that step onto other things. I don’t think you go from Facebook to gambling. I don’t see that as a gateway at all.

In today’s technological world, young adults are exposed to a plethora of opportunities to engage in gambling activities, including simulated gambling games on social media sites. For some young adults, exposure to gambling and gambling-like activities may result in the over-involvement of gambling. In three focus groups, motivations that influenced young adults to engage in online gambling were explored. The participants noted several factors that motivated them to engage in online gambling: including suggestions from friends, the ease and accessibility of online gambling (compared to land-based venues), and incentives offered by the online gambling operators (e.g., $10 in free play).

The results of our present research may have important implications for the progression and maintenance of online gambling among young adults. First, several participants reported having been drawn to online gambling by bonuses offered by the gambling operators. Whilst incentives may help attract new customers, it should be noted that they may not be creating frequent customers. Indeed, free-play offers (e.g., bonus offers) bring customers into a gambling venue, but fail to generate significant increases in volume of play (Lucas et al. 2005 ). Having said that, given that online gambling is often framed as a risky form of gambling, in part due to the increased accessibility, whether operators should be allowed to offer incentives, especially amongst vulnerable population may be an important question which policy makers should address.

In addition to general factors that may motivate young adults to engage in online gambling, potential mechanisms for the social casino games-online gambling link were explored. One potential mechanism noted by the participants that may lead to the migration of online gambling from social casino games involves the use of advertisements by the online gambling operators. Specifically, it was noted that gambling operators sometimes use social casino games to advertise gambling activities without legal restrictions because it is a game. Indeed, as social casino games are not technically gambling activities, there is no regulation in regards to advertisement, prompting some to suggest that advertisements for social casino games be held to the same standard as gambling (Gainsbury et al. 2014 ). It has been suggested that these advertisements are more likely to appear to young adults and adolescents (Abarbanel et al. 2016 ). Further, advertisements for gambling (including social casino games) are frequent on social media sites and portray the positive aspects of gambling without any of the potential dangers (Gainsbury et al. 2016 ). Some of the participants in the focus groups reported moving from social casino games to real money gambling due to the constant advertisements of online casinos. As young adults may be more likely to be influenced by advertisements (Derevensky et al. 2010 ), some researchers have suggested that advertisements for social casino games be held to the same standard as gambling (Derevensky and Gainsbury 2016 ). Our results seem to provide support for this suggestion.

A second mechanism by which players migrated from social casino games to online gambling was via the inflated payout rates on social casino games. Note this mechanism was also identified in the two pathways model proposed by King and Delfabbro ( 2016 ). Specifically, participants felt an increased confidence in winning should they have engaged in real-money gambling. Further, several participants stated that their frequent wins on social casino games propelled them to try engaging in online gambling. This is in line with previous research, which found that a portion of casino gamers play these games to build up their ‘skill’ before migrating to gambling in land-based or online gambling venues (see Kim et al. 2016 ). However, the inflated payout rates may give players an inflated belief in the skill, and, of course, there is no skill if the game of choice is one of pure-chance, like a slot machine. In fact, social casino game outcomes are not based on random odds and mathematics, but are rather designed to enhance player enjoyment (Wohl et al. 2017 ). Because of this, the social casino gamer wins more than he loses (Sévigny et al. 2005 ), which in turn, may falsely increase their confidence in winning, as proposed by King and Delfabbro ( 2016 ). Providing further support that frequent wins and perception of skills as a process by which social casino games to lead to online gambling, Hollingshead et al. ( 2016 ) showed that playing social casino games for skill purposes have been linked to problematic gambling behaviors. In this light, it would behoove regulators to enforce payout rates that are similar to gambling activities, or at very least mandate social casino gaming operators to inform players of that social casino games are not based on random odds as their gambling counterparts.

According to Blaszczynski and Nower’s ( 2002 ) pathways model of problem and pathological gambling, there are three distinct subgroups of gamblers, each with different pathways that manifest in problem gambling behaviors. In the model, the starting point is ecological factors, which include increased availability and accessibility. In this way, social casino games may influence the development of problem gambling among young adults by providing ease of access and increased availability. Indeed, one of the concerns of social casino games is that although they purport to have age verifications, a UK study found that 300,000 youths aged 11–16 reported having engaged in free online gambling games in the past week (Parke et al. 2013 ). Furthermore, it is plausible that if social casino games lead to the development of problem gambling, it does through Pathway 1, the behaviourally conditioned gambler. This pathway includes cognitive mechanisms such as irrational beliefs and illusion of control, which may manifest due to the inflated payout rates on social casino games. That said, this is an assertion and would be in need of empirical support.

Limitations

Some limitations of the current study should be noted. First, we did not recruit a sufficient number of female online gamblers to ascertain different trends and cognitions that may be gender-specific. That said, studies have consistently found that online gamblers tend to be young males (Griffiths et al. 2009 ; for a review see Gainsbury 2015 ). Thus, we have confidence that the observed results maintain ecological validity. Secondly, the findings of the current project are not intended to be reflective of the college population as a whole. Rather, the findings are qualitative in nature and should be used to guide future research initiatives. Lastly, we recruited online gamblers to participant in the focus groups, rather than social casino gamers. Thus, the current study cannot speak to social casino games being a deterrent to online gambling (e.g., knowing you can’t win).

The Internet has drastically shaped the way in which people engage with the world, including with gambling activities. Furthermore, social networking sites have become a fabric of the modern day world. While the Internet and specifically social networking sites are a great medium to stay connected with loved ones, they have increasingly become an avenue to engage in gambling activities, including simulated forms of gambling (i.e., social casino games). The present research explored the motivations that push young adults to engage in online gambling, including the role of social casino games. Further research and attention is needed in this domain to mitigate the potential migration from gaming to gambling, specifically amongst those most vulnerable.

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MW, JD, and RG, conceptualized the research project. RG, conducted the focus groups. HK wrote the first draft of the manuscript and MW, JD, and RG, edited subsequent versions. All authors read and approved the final manuscript.

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This research was funded by a grant from the Ontario Problem Gambling Research Centre (#3400).

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Kim, H.S., Wohl, M.J.A., Gupta, R. et al. Why do young adults gamble online? A qualitative study of motivations to transition from social casino games to online gambling. Asian J of Gambling Issues and Public Health 7 , 6 (2017). https://doi.org/10.1186/s40405-017-0025-4

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The Gambling Behaviour and Attitudes to Sports Betting of Sports Fans

1 Social and Global Studies Centre, RMIT University, 360 Swanston Street, Melbourne, VIC 3000 Australia

Buly A. Cardak

2 School of Business, La Trobe University, Melbourne, VIC Australia

Matthew Nicholson

3 Monash University Malaysia, Kuala Lumpur, Malaysia

4 Centre for Sport and Social Impact, La Trobe University, Melbourne, VIC Australia

Alex Donaldson

Paul o’halloran, erica randle, kiera staley, associated data.

Survey responses from a sample of nearly 15,000 Australian sports fans were used to study the determinants of: (i) gambling behaviour, including if a person does gamble and the type of gambling engaged with; (ii) the number of sports and non-sports bets made over a 12-month period; and (iii) attitudes towards betting on sports. The probability of betting on sports decreased with increasing age and was lower for women and people with a university education. This gender difference varied with age, with the greatest difference found among the young. Similar effects were observed for the number of sports bets made, which declined with age. The gender difference in the number of sports bets also varied with age with the greatest difference found among the young arising from the high propensity of young men to bet on sports. Attitudes to sports betting were also analysed, with a key finding that, within friendship circles, the views that sports betting is perceived as harmless, common and very much a part of enjoying sports were stronger among young men. These permissive attitudes were stronger among people who bet on sports and those who bet on sports more frequently. The analysis of sports fans provides insights into the characteristics of the target market most likely to bet on sports, which can be used to inform public health initiatives and harm reduction campaigns.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10899-021-10101-7.

Introduction

Harm from gambling is a significant global public health issue, with negative impacts on the health and wellbeing of individuals, families and communities (Gainsbury et al., 2014 ). Researchers have argued the harm to health and wellbeing caused by gambling is equivalent to that associated with major depressive disorders, and substance misuse and dependence (Browne et al., 2016 ). There is an array of research linking harmful gambling to health and social issues, including an individual’s health and wellbeing (Rockloff et al., 2020 ; Suomi et al., 2014 ), impacts on families and relationships (Dowling, 2014 ), and an association with intimate partner and family violence (Dowling et al., 2019 ). Generally, harms related to gambling reflect social and health inequalities, with negative effects unequally skewed towards economically and socially disadvantaged groups (Cowlishaw et al., 2016 ; Raybould et al., 2021 ; Wardle et al., 2018 ). Further, Deans et al., ( 2017a , 2017b ) argued that older adults, young men, and children are most vulnerable to harm from gambling.

In this paper, we explore the gambling choices of a diverse group of sports fans from Victoria, Australia, aged 18 and over, based on data from a survey of almost 15,000 members and fans of elite sporting clubs. In doing so, we investigate the relationship between individual demographic characteristics, the gambling behaviour of these sports fans and differences in attitudes to sports betting. Australia is recognised globally as having one of the most accessible and liberalised gambling environments, with policy and regulation, online platforms and the diversification of gambling products all increasing the availability and uptake of different gambling opportunities (Deans et al., 2016a ; Hing et al., 2017 ; Pitt et al., 2017a ). However, this trend is reflected elsewhere, with similar issues reported in the United Kingdom (McGee, 2020 ), Spain (Lopez-Gonzalez, et al., 2020 ) and Ireland (Fulton, 2017 ), implying our analysis is of international importance for those seeking to understand gambling choices and attitudes, and mitigate harm through appropriate policies and programs.

Recent evidence from the Household, Income and Labour Dynamics in Australia survey (a nationally representative longitudinal survey) demonstrated that there were 6.8 million regular gamblers in 2015, of whom an estimated 1.1 million were at risk of harm from gambling-related problems (Armstrong & Carroll, 2017 ). The National Australian Gambling Statistics Report highlighted that total gambling losses rose 5% between 2017 and 2018 to $24.89 billion. These statistics demonstrate gambling is an ongoing and increasing threat to individual and public health. Not only are individuals at risk of harm from gambling, for one person with problematic behaviour, an estimated five to ten people are adversely affected (Productivity Commission, 1999 ), implying widespread economic and social costs of gambling (Wardle et al., 2018 ).

Rise and Normalisation of Sports Betting

This research focuses on sports betting, a rapidly emerging sector of the gambling industry. Its impact on normalising gambling, especially among the young, has been of increasing concern over the last decade in countries like Australia and the United Kingdom (Purves et al., 2020 ). Sports betting is one of the few forms of gambling that has shown a substantial increase in participation in recent years (Hare, 2015 ). In Australia, sports betting resulted in the largest year-on-year percentage increase (16.3%) in gambling losses during 2017–2018. The relationship between sports and gambling is increasingly symbiotic, with teams from Australia’s two major professional sports, the Australian Football League (AFL) and National Rugby League (NRL), significantly involved in the ownership and promotion of gambling products and services. Activities include formal sports partnerships, uniform naming rights, stadium signage and the promotion of odds during televised broadcasts. This general trend has been termed the ‘gamblification’ of sports by McGee ( 2020 ) and has become ubiquitous across a variety of sports settings, from elite to community level.

As a consequence of the pervasiveness of sports betting, researchers have increasingly sought to identify and describe the ‘normalisation’ effect of sports betting and its acceptance as part of peer-based socialisation and general sports fandom (Bunn et al., 2019 ; Raymen and Smith, 2017 ). A growing body of evidence has started to address the factors that lead to sports betting being perceived as an everyday part of sports, fostering its uptake. This is aligned with an increasing focus within broader gambling research on the influence of the environment and social determinants on people’s behaviour, as opposed to concentrating on a problem or pathology within the individual (Johnston and Regan, 2020 ).

There has been a strong research focus on the rise and prominence of sports betting marketing, quantifying how prevalent gambling promotions are during sports broadcasting (Milner et al., 2013 ), on social media platforms (Thomas et al., 2018 ), in live events within stadia (Thomas et al., 2012 ), and exploring how online platforms have been harnessed by wagering companies to encourage consumption (Deans et al., 2016a ). Thomas et al. ( 2012 ) highlighted there were very few visible or audible messages to counter overwhelmingly positive messages about sports betting during matches. Their research also addressed how sports betting advertising and associated strategies affect the attitudes of specific community sub-groups, including young people, parents, and young males. Pitt et al. ( 2016a ) found children could recall sports betting brand names, places they had seen betting advertising and associated plot details of advertisements. Deans et al. ( 2017b ) conducted similar work with young men and demonstrated sports betting marketing influences their betting behaviour.

Research has also focused on people’s attitudes to sports betting advertising, to improve understanding of community sentiment. Generally, this has shown that both parents and young people disagree with the increase in sports betting advertising and have concerns about how these messages promote a seemingly natural affinity between gambling and sports (Nyemcsok et al., 2021 ; Pitt et al., 2016b ). However, Pitt et al. ( 2016b ) reported that young people’s discourses about sports increasingly involve discussions about gambling ‘odds’ and that some young people believe that gambling is a usual and valued consumption activity during sports. Alternative evidence suggests young men feel particularly overwhelmed and bombarded by sports betting advertising (Thomas et al., 2012 ). This is unsurprising because this group is the target market for most Australian wagering operators. Hing et al. ( 2016 ) argued that such operators deliberately position sports betting as an activity engaged in by single, professional, upwardly mobile young men.

Other environmental or ‘normalisation’ issues investigated in the research literature include the availability and convenience of sports betting on mobile phone apps or online, with ease of access facilitating gambling (McGee, 2020 ), the socio-cultural alignment between sports betting and sports (Deans et al., 2017a ; Thomas, 2014 ), and how physical and online environmental factors influence the gambling risk behaviours of young men (Deans et al., 2016b ). For example, Deans et al. ( 2017a ) conducted semi-structured interviews with a convenience sample of 50 Australian men, aged 20–37, who were fans of and had bet on either NRL or AFL matches (games). These young male sports bettors reported their betting was normal and socially accepted, especially among sports fans, and ‘gambling-related language had become embedded in peer discussions about sport’ (p. 112). As such, Deans et al. concluded an exaggerated normalisation of wagering might exist in male sports fans’ peer groups. The young men in their study had established rituals (e.g. punters’ clubs) that reinforced their social connection to sports betting, but also enhanced the peer pressure to bet—an outcome that is perhaps inevitable given the role of social interaction in normalising behaviour (Russell et al., 2018 ).

Understanding ‘Sports Bettors’

A separate but connected branch of research literature has concentrated on profiling groups most at risk of experiencing harm from sports betting, particularly describing their attitudes and characteristics. As indicated previously, two groups of major concern are men and youth in general (both male and increasingly female). Studies and reviews have consistently found that young adult males are at greater risk of problem gambling (Hing et al., 2015 ; Williams et al., 2012 ). Recently, such research has also explored sports betting specifically. For example, Hing et al. ( 2016 ) in a quantitative study with a purposive sample of 639 Australian adults, identified key demographic risk factors for problem sports bettors included being male, younger, never married, and living either alone, in a one-parent family with children, or in a group household. Other risk factors included having a higher level of education and working or studying full-time. Numerous, frequent, and larger bets appeared to characterise high-risk sports bettors, as opposed to those deemed at low risk of experiencing harm. This is supported by recent research by Ayandele, Popoola and Obos ( 2019 ), who surveyed 749 Nigerian tertiary students aged 16–30 years to explore how socio-demographic factors, peer-based gambling and sports betting knowledge interact to shape young adults’ attitudes to sports betting. They found a favourable attitude towards sports betting was associated with being older and male, having a knowledge of sports betting and was positively related to the betting attitudes and behaviours of friends.

Whilst understanding normalisation factors and processes is important, alongside the attitudes and characteristics of different sub-groups, what is missing from current research is a large-scale examination of the attitudes of sports fans specifically, and the key demographic factors that are associated with their sports betting behaviour. This study addresses the gap. To the best of our knowledge, this research, conducted with almost 15,000 unique respondents, is the largest quantitative study operating at the nexus of sports fans and gambling behaviour. Arguably, the issues described above are more impactful in this cohort because they are highly engaged with sports and very exposed to marketing and the gambling economy. It is imperative to understand how such environmental and socio-cultural processes influence sports fans’ betting behaviour and to identify the sub-groups where these behaviours are most apparent.

Using quantitative research with a broad demographic group of sports fans (aged 18 and over), we aimed to compare attitudes between sports bettor, non-sports bettor and non-bettor cohorts, and examine the factors that would make it more likely for a sports fan to be a sports bettor. Broadly, we focused on attitudes to betting in sports, the risks associated with sports betting and perceptions about how much of a social norm this activity is. The research was guided by the following questions:

• What demographic factors make it more or less likely for a sports fan to bet on sports? How does this correlate with the number of sports bets a person makes?
• What is the impact of described normalisation processes on the attitudes of sports fans that bet on sports, compared with those that do not bet at all, and those that bet but not on sports?

Understanding the demographic profiles and risk factors for sports bettors and their attitudes is an increasingly important area of research. The results from this research could inform public health interventions and policy to help ensure they appropriately address areas of concern.

The research questions above are addressed as part of a broader project undertaken in partnership with the Victorian Responsible Gambling Foundation (VRGF). The project involved a survey that was distributed in collaboration with 17 professional sporting clubs from Australian Football, Basketball, Cricket, Soccer, Netball and Rugby Union in the state of Victoria, Australia. The survey was targeted at members, fans, and supporters of these clubs. In each instance, the survey was shared via social media channels (including Facebook and Twitter) and electronic direct marketing using email to each club’s membership base. Depending on the sporting code and relevant season (summer or winter), the survey was shared either between 30th October–3rd December 2020 or 25th February–18th March 2021. Data were collected in the context of the COVID-19 pandemic and research has demonstrated an increase in sports betting and a decrease in other types of gambling (e.g., casino, horse racing, pokies, etc.) during this period (Jenkinson et al., 2020 ). Whilst this could have impacted the behaviour and attitudes of respondents in this survey, the results still highlight the groups most at risk from engaging in sports betting and their associated attitudes. The survey took on average 15 min to complete and elicited a total of 17,228 responses. However, due to incomplete survey responses, the estimating sample is restricted to at most 14,950 observations in the analysis below. Three components of the survey were used in this study. First was data on gambling behaviour comprising responses about (i) whether an individual gambles or not and whether any gambling is sports betting, non-sports betting or both; and (ii) the number of bets in a given period.

Participants were asked about gambling activity in general first. They were advised: “Gambling includes activities in venues such as casino table games, pokies, TAB, Keno etc ., as well as raffles, lotteries and scratchies. It also includes gambling online or via apps such as sports betting, race betting and online pokies and casino games, where you bet with money.”

This was followed with the questions:

• Thinking of all these types of gambling, in the past 12 months, have you spent any money on these gambling activities?
• In the past 12 months, how often have you gambled? (with a number of times per week, month or year options available)

Participants were then asked about sports betting activity. They were advised: “ Sports betting refers to legal wagering with bookmakers on approved types of local, national or international sporting activities, (other than horse or greyhound racing both on or off the course) in person, via the telephone or via an app or online.”

• Thinking of all these types of sports betting, in the past 12 months, have you spent any money on these sports betting activities?
• In the past 12 months, how often have you taken part in sports betting? (with a number of times per week, month or year options available)

This data are summarised in the first six rows of Table ​ Table1 1 under the sub-headings ‘betting category’ and ‘number of bets’. For clarity, when referring to the specific participant groups involved in the research, we will use the terms sports bettors, non-sports bettors, or non-bettors to avoid referring to gambling and betting interchangeably. Table ​ Table1 1 shows that about 35% of the sample are non-bettors while another 35% are non-sports bettors (i.e. they bet on lotteries, raffles, poker or slot machines and casino gambling). The remaining 30% of the sample are sports bettors divided evenly between those that engage in sports betting only and those that engage in both sports and non-sports betting. The average number of bets in a year for the full sample is 19.6 for non-sports and 14.5 for sports bettors. 1 This data are also presented by betting category, showing that sports bettors, on average bet many more times per year than non-sports bettors. However, those that bet on both have a much higher betting frequency again, betting nearly twice as often as those who bet only on sports. The much higher standard deviation among sports bettors is also notable, suggesting there is much greater variation in betting frequency among sports bettors than non-sports bettors.

Descriptive statistics of survey respondents for the full sample and by the different types of bettors. Full sample includes people who responded that they did not gamble at all. Standard deviations provided in parentheses only for variables that are not dichotomous

Observations for Non-Sports Bets and Sports Bets variable are 14,293 and 14,686 respectively for the full sample. For Non-Sports bettors only, observations for Non-Sports Bets are 5102. For Sports bettors only, observations for Sports Bets are 2285. For Sports + Non-Sports bettors, observations for Non-Sports Bets and Sports Bets are 1658 and 2051 respectively

The second component of the data used in this study comprises demographic characteristics. This includes gender, age, location (metropolitan or regional), marital status, education, employment status, income categories, country of birth, parents’ country of birth, Aboriginal Torres Strait islander origin and health status. The data are summarised for the full sample and the different betting categories in Table ​ Table1. 1 . Some key features of the data are that about 70% of the sample is male but nearly 80% of sports bettors are male and the average age of the sample is 50 years, but sports bettors have an average age of 43 years for sports bettors and 46 years for those who both sports and non-sports bet. Two other notable features are the much higher proportion of non-sports bettors who are retirees (0.24) compared to the proportion of sports bettors (0.07) and the high proportion of responders who did not report their income (0.20).

The third component of the data used here comprises responses to questions about attitudes to gambling. The questions are grouped into two categories including (i) general attitudes to gambling and sports betting; and (ii) perceptions of the attitudes and behaviours of others. Responses were elicited on a scale from 0 (totally disagree) to 10 (totally agree). The questions are presented in Table ​ Table2, 2 , where sample means and standard deviations are presented for the full sample and the different betting categories.

Summary statistics of responses to questions about gambling for the full sample and by the different types of bettors. Standard deviations in parentheses

Means of each gambling group are compared to non-gamblers using a t -test with a Sidak correction to account for repeated testing, with the level of statistical significance denoted by * (10%), ** (5%) and *** (1%). P -values of an F -test of whether mean responses of each group are statistically significantly different from each other presented in Full Sample column in braces

An important research question here is whether people in different betting categories respond to each question differently. We conducted a one-way analysis of variance (ANOVA) for each question to test whether the mean responses of each group are statistically significantly different. The p -value of this joint F -test for each question is presented in the ‘Full Sample’ column in braces; all tests have p -value of [0.000] implying we reject the null hypothesis of equality between the mean response of each group to each question. We also test the differences between means for each group using t -tests with a Sidak correction to account for the possibility of a false positive finding given the large number of t- tests. The table reports results of tests of differences between the mean response of non-bettors and each type of betting group with statistically significant differences at the 10%, 5% and 1% levels denoted by *, ** and *** respectively. The results show average responses of almost all betting groups differ from those of non-betters at the 1% level of significance. Differences are most stark in relation to the notion that ‘sports betting should not be part of experiencing sport’ and items related to the social aspects of sports betting, particularly the place of sports betting in the person’s family and friendship groups. However, responses to (i) ‘most people in society think betting on sport is harmless’ were not statistically different between sports bettors and non-bettors; and (ii) ‘odds talk is common in discussions about sport with my friends and peers’ were not statistically different between non-bettors and non-sports bettors.

An important feature of the survey is that it reached beyond people who identify as gamblers, providing insights into a more diverse sample than many previous studies. However, a limitation is that the design is focused on members, fans or supporters of a group of elite clubs or teams, implying to some degree that respondents are likely to be more engaged with sport than the average member of the Victorian population. Therefore, the differences between bettors and non-bettors identified here are potentially lower bounds and analysis of a more representative sample might uncover even greater differences. Another important benefit of the sampling frame is that it is likely the target audience for sports betting advertisers. Therefore, the analysis offers insights into a group that is likely most targeted and affected by sports betting advertising and understanding this group provides valuable insights to harm minimization policies with respect to sports betting.

Empirical Methods

The empirical analysis can be divided into two broad approaches. The first is to analyse the determinants of the betting choices of survey respondents. The second is to analyse the responses to gambling attitude questions. The approaches to these analyses are described in turn below.

Who Bets and How Often?

Each survey respondent is assumed to choose between four types of betting activity: no betting, non-sports betting, sports betting or both sports and non-sports betting. We define the gambling choice of each survey respondent i as G i = j ∈ 1 , 2 , 3 , 4 . We want to understand the relationships between different demographic characteristics’ and gambling choices. Given these four possible gambling choices or outcomes are unordered, we used a multinomial Probit specification to estimate the relationships. The probability that individual i makes gambling choice G i = j is given by

where X i is a vector of personal characteristics (including gender, age, location, marital status, education, employment status, income categories, country of birth, parents’ country of birth, Aboriginal Torres Strait islander origin and health status, as listed in Table ​ Table1), 1 ), Φ . is the cumulative density function of the Standard Normal distribution and β j provides the parameter estimates on X i for gambling choice j ; see Greene ( 2018 ) Chapter 18 for more details. This model allows us to understand the influence of each characteristic on the four possible gambling choices from a model that jointly estimates the probabilities of each alternative gambling choice.

As parameter estimates do not have a clear intuitive interpretation in such models, we compute marginal effects for each X i which are given by

where β ¯ is the probability weighted average of the parameter estimate across the four different possible gambling choices. The multinomial Probit results reported in Table ​ Table3 3 below are average marginal effects which are computed as the average of for δ ij across all i individuals. The interpretation of these marginal effects is that they tell us the impact of a unit increase in X i (for example female versus male or a one-year increase in age) on the probability of making gambling choice j ; that is, four marginal effects will be reported for each variable, one for each of no betting, non-sports betting, sports betting or both sports and non-sports betting.

Results of estimation of multinomial Probit model of betting behaviour. Average marginal effects on each betting behaviour are presented

Model includes linear, second and third order age terms. All age terms interacted with gender indicator. Standard errors in parentheses. Level of statistical significance denoted by * (10%), ** (5%) and *** (1%). Sample size N  = 14,813

Along with the choice of gambling type, individuals choose how many bets to place, and the factors that influence this number of bets are also of interest. Since 35% of survey respondents are non-bettors, the data on the number of bets comprises a large number of zeros. To accommodate this feature of the data, a Cragg hurdle model is adopted (Cragg, 1971 ). This model involves two parts: the first is a model of the decision to gamble (selection model), while the second is a model of the number of bets. As we have data on the number of sports bets and non-sports bets, we estimate this two-part model separately for each of these choices. The first part of the model is the selection decision, given by

where C i is individual i ’s choice of whether to bet on sports (1) or not (0) or alternatively to make non-sports bets (1) or not (0). The control variables X i are as defined above for the multinomial Probit model in Eq. ( 1 ), while α is a vector of parameters capturing the influence of each control variable on the decision to place bets or not and ϵ i is a mean zero, constant variance normally distributed disturbance term. The second part of the model estimates the number of observed bets. This continuous variable is given by

an exponential specification of the Cragg hurdle model where B i is the number of bets made per year by individual i , θ is the set of parameters reflecting the impact of variables X i , again as defined above and u i is a mean zero, constant variance disturbance term.

The key idea of this model is that the decision to gamble is modelled separately from the decision of how many bets to place. Estimates of α are important determinants of the decision not to gamble and therefore choose zero bets, whereas α and θ together determine the number of bets if a person chooses to gamble. The overall marginal effect of control variables X i , which in our specification are common to the selection and number of bets equations, are computed using the margins command in STATA and presented in Table ​ Table4 4 below. The detailed expressions for these marginal effects can be found in Burke ( 2009 ). The interpretation of these marginal effects is that they reflect the impact of a unit change in the value of a control variable X i (i.e. gender or age) on the average number of bets in a given period of time (a year in this instance).

Models of number of bets (intensity of gambling). Estimates are based on Cragg Hurdle regression. Average marginal effects are presented

Standard errors in parentheses. Level of statistical significance denoted by * (10%), ** (5%) and *** (1%)

Factors Affecting Attitudes Towards Sport Betting

Our analysis of responses to the 12 different questions about attitudes to sports betting listed in Table ​ Table2 2 comprises two key objectives. First, we are interested in the relationship between individual gambling choices and attitudes to sports betting—this involves the type of betting and the number of bets. It is anticipated that sports bettors will hold more positive attitudes towards sports betting than non-bettors or non-sports bettors. Second, we are also interested in the relationship between demographic characteristics and attitudes to sports betting. Responses to each question range on a scale from 0 to 10 but are standardized to have mean zero and standard deviation of one, to enable comparisons of effects of different variables across survey questions. These standardized responses are modelled using OLS. The model estimated is given by

where A i is the response of individual i to one of the 12 questions on their attitude to gambling listed in Table ​ Table2. 2 . For each question, two versions of the model are estimated where Z i is a vector of control variables, including all variables in X i which is as defined above, along with either (i) gambling choice, G i , (no betting, non-sports betting, sports betting or both sports and non-sports betting); or (ii) the number of sports and non-sports bets, B i , included. Model parameters are given by γ while ε i is a disturbance term with zero mean and constant variance. As the dependant variable, A i , is a standardized measure of responses to attitude questions, γ should be interpreted as the average number of standard deviations of change in A i per unit change in Z i .

In all models, we allow for non-linear age effects by including quadratic and higher order age terms, along with a linear age term, testing their significance using a likelihood ratio (LR) test. We also allow for gender effects to vary with age by including age and gender interactions and testing for their significance, again using a LR test.

Factors Affecting Gambling Behaviour

The results of estimation of Eq. ( 1 ) are presented in Table ​ Table3. 3 . The average marginal effects of the listed control variables on different gambling choices of no betting, non-sports betting, sports betting and both sports and non-sports betting are presented in first, second, third and fourth columns respectively. The model includes linear, quadratic and third order age terms. 2 Focusing on the cases of sports betting only (third column) and both types of betting (fourth column) and on results that are significant at the 1% level (denoted by ***), we found that relative to males, females are 9.6% less likely to bet on sport and 6.2% less likely to bet on both. To test the hypothesis that gender effects vary with age, all the included age terms are interacted with the gender indictor, with these interactions supported by a LR test = 62.63 ( p value = 0.00). The marginal effect for females relative to males is plotted for each betting category in panels (a)–(d) of Fig.  1 . The difference between men and women sports betting is greatest among the youngest in the sample and the difference decreases with age. The result suggests that young men are up to 25% more likely than women of the same age to bet on sports; the difference is less than 10% for people over 50 years. In addition, the average marginal effect of a 1-year increase in age is reported in Table ​ Table3. 3 . Comparing otherwise identical individuals with a 10-year age difference, the older person is 5.0% less likely to sports bet only and 2.0% less likely to make both sports and non-sports bets relative to the younger person.

Marginal effect of gender on the probability of each betting category plotted over age. Panels ( a )–( d ) are based on estimates presented in columns (1)–(4) of Table ​ Table3 3 respectively. Point estimates are denoted by dots with 95% confidence intervals for each point estimate included

Relative to self-employed, which is the base category, students are 6.7% less likely to bet on sports, while those on home duties are 7.0% less likely to gamble on both sports and non-sports. Income results are all relative to the base range of “less than $10,000”, with little difference between different income ranges in the probability of sports betting only. However, respondents above $60,000 are between 5.6 and 8.0% more likely to bet on both sports and non-sports than those in the base income range. This suggests there is little effect on gambling probability of additional income as the marginal effects are similar for each category above $60,000. Finally, respondents whose parents were both born overseas were 2.7% less likely than the base category (both parents born in Australia) to bet on both sports and non-sports.

In Table ​ Table4, 4 , we present estimates of the model of the number of bets specified in Eqs. ( 3 ) and ( 4 ). The marginal effects of the full set of control variables on the number of non-sports bets and sports bets are presented in first and second columns of Table ​ Table4 4 respectively. Age is included in these models through linear, quadratic and cubic terms. 3 As the number of bets is a continuous variable, the marginal effects are interpreted as the impact of a unit change in the control variable on the number of bets per year.

Focusing first on results significant at the 1% level (denoted by ***) for the number of non-sports bets, females make on average 9 fewer bets per year than males. Betting increases with age, with a 10-year older person making on average 5.29 more bets. The model also includes an interaction between all age terms and gender to test the hypothesis that gender effects vary with age — this interaction is supported relative to the model without the interactions, LR test = 28.41 ( p value = 0.00). The marginal effect of gender on the number of non-sports bets increases with age with women over 58 placing around 20 fewer bets than men of the same age: panel (a), Fig.  2 . Further results include that someone who is married makes 4.5 fewer bets than a single person and parents place 5.0 fewer bets than people with no children. Education reduces betting with those holding trade qualifications betting 6.3 times less than someone who did not complete high school while those with university qualifications betting 15.3 fewer times than someone who did not complete high school. Employment status and income are uncorrelated with the number of non-sports bets. Respondents whose parents were both born overseas place on average 4.4 fewer bets than people whose parents were both born in Australia.

Marginal effect of gender on the ( a ) number of non-sports bets and ( b ) number of sports bets, plotted over age. Panels ( a ) and ( b ) are based on estimates presented in columns (1) and (2) of Table ​ Table4 4 respectively. Point estimates are denoted by dots with 95% confidence intervals for each point estimate included

Again, focusing on results significant at the 1% level, results for the number of sports bets show that women place on average 18.2 fewer bets than men per year. In contrast to non-sports bets, the number of sports bets placed decreases with age; a person 10-years older bets on average 5.0 fewer times per year. The interaction between age and gender confirms that the gender effect on the number of sports bets does vary with age—supported relative to the model without the interactions, LR test = 37.52 ( p value = 0.00). However, the marginal effect of gender on the number of sports bets decreases with age, with 18-year-old women placing 50 fewer sports bets than 18-year-old men. This difference is as low as 10 fewer sports bets per year when comparing women and men aged over 60 years: panel (b), Fig.  2 . Education effects are similar to those for non-sports bets. People with trade qualifications bet on average 6.7 (10.0) times less than someone who did not complete high school. The only employment status category that is related to the number of sports bets is being a student, who place on average 10.3 fewer bets than the self-employed. The number of sports bets is related to income with most annual income categories above $40,000 betting on average between 8 and 11 more times per year, though some differences are significant only at the 5% level and others are smaller with 5 more bets per year and significant at the 10% level. Finally, respondents whose parents were both born overseas place 4.2 fewer sport bets on average than people whose parents were both born in Australia.

Attitudes to Sports Betting

Selected results of estimating the model presented in Eq. ( 5 ) using the responses to the first set of questions on general attitudes about sports betting summarized in Table ​ Table2 2 are presented in Tables ​ Tables5 5 and ​ and6. 6 . Full results of these models are available in Online Resource 1 and Online Resource 2 of the Supplementary Materials, where estimates for all variables included in the models are presented. In all these models, an interaction between gender and all age terms (up to third order age terms are included in all models) is considered with the result of a LR test of the restriction that the coefficients on the interactions are zero presented in the last row of each column. In cases where the restriction is rejected and the interaction is non-zero ( p value < 0.05), the model presented includes the interactions.

Selected results of general attitudes about gambling and sports betting with focus on type of gambling behaviour included as explanatory variables

Dependant variable modelled in each column is response to the respectively labelled statement below. Responses to each question are standardised to have a mean of zero and standard deviation of one. Age is included as a third order polynomial with average marginal effects presented. P value of likelihood ratio ( LR ) test of interaction between age and gender presented in last row with interaction included if p value < 0.05. Standard errors in parentheses. Level of statistical significance denoted by *(10%), **(5%) and ***(1%). All models also include controls for marital status, being a parent, employment status, income bands, country of birth, parent’s country of birth, ATSI and health condition or disability over past 6 months – full results available in Supplementary Materials

(1): Sports betting should not be part of experiencing sport

(2): People who bet regularly on sport are at risk of harm from gambling

(3): Sports betting can place people at higher risk of relationship problems, mental health and wellbeing issues and money worries

(4): Regular discussion of the ‘odds’ when talking about sport can lead to gambling problems in individuals

(5): It’s easy for people with sports betting issues to stop gambling

Selected results of general attitudes about gambling and sports betting with focus on the number of non-sport and sport bets per year as explanatory variables

Dependant variable modelled in each column is response to the respectively labelled statement below. Responses to each question are standardised to have a mean of zero and standard deviation of one. Age is included as a third order polynomial with average marginal effects presented. P -value of likelihood ratio ( LR ) test of interaction between age and gender presented in last row with interaction included if p -value < 0.05. Standard errors in parentheses. Level of statistical significance denoted by *(10%), **(5%) and ***(1%). All models also include controls for marital status, being a parent, employment status, income bands, country of birth, parent’s country of birth, ATSI and health condition or disability over past 6 months—full results available in Supplementary Materials

Results in Table ​ Table5 5 are for the model estimated with all the demographic control variables in X i together with a set of indicators for each type of betting behaviour, G i , including non-sports betting, sports betting and both sports and non-sports betting, with no betting the omitted base category. Each column presents estimates for a model of standardized responses (mean zero and standard deviation of one) to a separate statement about sports betting. The statements upon which each dependent variable is based are listed in the table notes. Focusing on results significant at the 1% level (denoted by ***), the results show that after controlling for a large set of individual demographic characteristics, people who bet are on average less concerned about sport betting issues than non-bettors. This is evident for all 5 statements modelled. We can see in column (1), for example, responses to the statement ‘sports betting should not be part of experiencing sport’, relative to non-bettors, the average response of people who bet on non-sport only is 0.20 standard deviations lower, while people who bet on sport (only or both sport and non-sport) have an average response that is 0.71 standard deviations lower. Other key results from these models are that females are more concerned about sports betting than males, except for in their responses to the statement ‘people who bet regularly on sport are at risk of harm from gambling’, where there is no difference between men and women. The relationship with age is statistically significant for the statements ‘sports betting should not be part of experiencing sport’ and ‘people who bet regularly on sport are at risk of harm from gambling’ but the effects are small, with a 10-year older person having on average a 0.06 standard deviation higher response to the former question and a 0.03 standard deviation lower response to the latter. People from regional locations are on average more concerned about sports betting than people from metropolitan locations; however, this concern is not evident in response to ‘sports betting should not be part of experiencing sport’. The results on education show that relative to the base case of ‘did not complete high school’, those with trade qualifications are on average more concerned about sports betting and in turn, people with university education are even more concerned with even greater differences evident than for those with trade qualifications.

The results presented in Table ​ Table6 6 are for models of the same questions with the same demographic controls included but with betting behaviour replaced by the number of sports and non-sports bets, B i / 100 . Once again, each column presents estimates for a model of standardized responses (mean zero and standard deviation of one) to a separate statement about sports betting. The statements upon which each dependent variable is based are listed in the notes to the table. The impacts of the demographic characteristics in Table ​ Table6 6 are qualitatively similar to those found in Table ​ Table5. 5 . The key difference between Tables ​ Tables5 5 and ​ and6 6 is that gambling categories are replaced with the number of sports bets and the number of non-sports bets. On average, people who bet more often are less concerned about sports betting. However, 100 more sports bets per year (approximately 2 bets per week) has nearly double the impact on responses of 100 more non-sports bets. For example, responses to ‘sports betting should not be part of experiencing sport’ are on average 0.14 standard deviations lower for every additional 100 non-sports bets but are 0.30 standard deviations lower for every additional 100 sports bets.

The above analysis is repeated for the second set of statements summarized in Table ​ Table2 2 which focus on perceptions of the sports betting attitudes and behaviours of others. Selected results of this analysis with betting categories included are presented in Table ​ Table7 7 and with the number of bets included are presented in Table ​ Table8. 8 . Each column presents estimates for a model of responses to a separate statement about sports betting. Survey responses used to estimate each model have been standardized to have mean zero and standard deviation of one. The statements upon which each dependent variable is based are listed in the notes to each table. Full results of these models are available in Online Resource 3 and Online Resource 4 of the Supplementary Materials.

Selected results of perceptions of other’s attitudes and behaviours with respect to sports betting with focus on type of gambling behaviour included as explanatory variables

Dependant variable modelled in each column is response to the respectively labelled statement below. Responses to each question are standardised to have a mean of zero and standard deviation of one. Age is included as a third order polynomial with average marginal effects presented. P value of likelihood ratio ( LR ) test of interaction between age and gender presented in last row with interaction included if p value < 0.05. Standard errors in parentheses. Level of statistical significance denoted by *(10%), **(5%) and ***(1%). All models also include controls for marital status, being a parent, employment status, income bands, country of birth, parent’s country of birth, ATSI and health condition or disability over past 6 months—full results available in Supplementary Materials

(1): Most people in society think betting on sport is harmless

(2): Most people in society bet on sport

(3): Most people in my family think betting on sport is harmless

(4): Most people in my family bet on sport

(5): Most people in my friendship group think betting on sport is harmless

(6): Most people in my friendship group bet on sport

(7): Odds talk is common in discussions about sport with my friends and peers

Selected results of models of perceptions of other’s attitudes and behaviours with respect to sports betting with focus on the number of non-sport and sport bets per year as explanatory variables

Dependant variable modelled in each column is response to the respectively labelled statement below. Responses to each question are standardised to have a mean of zero and standard deviation of one. P value of likelihood ratio ( LR ) test of interaction between age and gender presented in last row with interaction included if p value < 0.05. Standard errors in parentheses. Level of statistical significance denoted by *(10%), **(5%) and ***(1%). All models also include controls for marital status, being a parent, employment status, income bands, Country of birth, parent’s country of birth, ATSI and health condition or disability over past 6 months—full results available in Supplementary Materials

Focusing on significance at the 1% level (denoted by ***), Table ​ Table7 7 shows, except for the first two statements that focus on attitudes in society, bettors have families and friendship groups where gambling is common and perceived as harmless. However, the difference between sports bettors and non-sports bettors is stark. For example, in response to the question ‘most people in my friendship group bet on sport’, the average response of non-sports bettors is 0.16 standard deviations higher than non-bettors whereas the average response of sports bettors is up to 0.50 standard deviations higher, with both significant at 1%. The difference between non-bettors and sports bettors (0.49 standard deviations) is nearly 10 times as large as the difference between non-bettors and non-sports bettors (0.05 standard deviations) in response to the question ‘odds talk is common in discussions about sport with my friends and peers’. The largest impact of age is on the peer and friendship group statements, columns (5)–(7), with a response of a person 10-years younger on average 0.15 standard deviations higher. The interaction between gender and age is illustrated for the results reported in column (6) which is based on the statement “most people in my friendship group bet on sport” in panel (a) of Fig.  3 . The difference between men and women is greatest at younger ages with women in the 18–45 year range responding on average 0.5 standard deviations lower than men, suggesting young men have a much stronger belief than young women that their friends are involved in sports betting.

Marginal effect of gender on response to the question “Most people in my friendship group bet on sport”, plotted over age. Panel ( a ) is based on model in column (6) from Table ​ Table7 7 which includes betting categories and panel ( b ) is based on model in column (6) from Table ​ Table8 8 which includes number of bets. Point estimates are denoted by dots with 95% confidence intervals for each point estimate included

The results presented in Table ​ Table8 8 are for models of the same questions analyzed in Table ​ Table7, 7 , with the same demographic controls included but with betting behaviour replaced by the number of sports and non-sports bets, B i / 100 . The relationships with the demographic characteristics are qualitatively similar to the results presented in Table ​ Table7 7 and discussed above. The more a respondent bets, the greater their agreement with all but the first two statements that focus on attitudes in society. The relationship between responses and number of sports bets is up to 4 times as large as the relationship with the number of non-sports bets. For example, responses to the question ‘most people in my friendship group bet on sport’ on average increase by 0.10 standard deviations for people who place 100 more non-sports bets per year (2 more bets per week) but for an otherwise identical person who places an additional 100 sports bets, their response is on average 0.38 standard deviations higher. These sorts of differences are evident across all questions about family and friendship groups, with a greater number of bets associated with responses that show sports betting is believed to be more common and perceived as less harmful in these circles. It is also found the more sports bets a person places, the stronger their agreement with the statement ‘most people in society bet on sport’, though the relationship with the number of non-sports bets is statistically insignificant. The gender and age interaction for the model in column (6) in Table ​ Table8 8 is presented in panel (b) of Fig.  3 . The figure shows the difference between men and women is greatest among 18–28-year-olds, with the responses of women in this age range on average 0.70 standard deviations lower. This compares with differences of less than 0.30 standard deviations for those over 60 years. The results are consistent with those in panel (a) of Fig.  3 , suggesting that gender age differences are robust whether we control for the betting category or the number of bets per year.

This study builds on existing literature at the intersection of sports betting and sports, providing a comprehensive analysis of the sports betting behaviour of sports fans, including many people who choose not to gamble at all. Survey respondents’ attitudes to sports betting were analysed using betting behaviour and a wide range of demographic characteristics. The approach differs from many previous studies as we targeted a broader demographic of sports fans, rather than focusing only on those engaged in gambling (sports or non-sports), which is a strength. We did not measure whether a person’s gambling behaviour is deemed ‘problematic’, but previous research has demonstrated a connection between frequency of sports betting and problematic gambling behaviour (Hing et al., 2016 ). Therefore, our analysis of the number of sports bets provides a useful proxy to identify those most at risk of experiencing gambling harm. The research was guided by two overarching questions addressed in turn through the following discussion.

Demographic Profile of Sports Bettors

The dominant theme emerging from our analysis is the importance of gender, age and their interaction. The gender difference in the probability of sports betting is wider among the youngest in the sample; 18-year-old men are about 25 percentage points more likely than their female counterparts to bet on sports, whereas this difference is less than 5 percentage points for those over 60 years. Similar patterns are evident for the number of sports bets placed, with younger men placing more bets than similar aged women and fewer bets being placed with each additional year of age. Consequently, young men are most at risk based on their sports betting engagement and number of bets placed. This aligns with previous studies (Hing et al., 2016 ; Williams et al., 2012 ), but widens our understanding of the sports betting behaviour of sports fans. Moreover, even though this has been described in smaller-scale qualitative research studies (Deans et al., 2017b ; Waitt et al., 2020 ) our results are based on empirical analytic techniques applied to a larger and more diverse sample. The results comprehensively demonstrate sports betting is predominantly pursued by young men, in sharp contrast to other forms of gambling. Given the recent growth of sports betting, its marketing, and increasing contribution to problem gambling (Hing et al., 2019 ), as well as the need for appropriately tailored prevention and early intervention public health initiatives, this finding is significant for highlighting the distinctive sports betting behaviour of young men aged 18–35. Recent research has started to examine young women aged 18–35 as an emerging gambling cohort (see McCarthy et al., 2020 ), but our results demonstrate no significant gender or age effects for women’s sports and non-sports betting behaviour.

Other important demographic factors included education level, relationship status, and employment status. People who are widowed or separated were less likely to bet on sports, but no other relationship types were significant at the 1% level. University educated individuals were less likely to bet on sports than those who did not complete high school. Employment status did not exhibit a strong relationship with sports betting, except students and unemployed were less likely than self-employed to bet on sports. Surprisingly, income did seemingly not influence whether people engaged in sports betting only. This was more important in the context of making both sports and non-sports bets—those reporting higher levels of income were more likely to engage in these gambling types.

Whilst other studies have reported various demographic risk factors for sports betting and gambling, our results contribute by clearly demonstrating the significant interaction between age and gender. The importance of our study for public health policy and harm reduction campaign strategies is twofold. First, our sampling frame is likely the target audience of sports betting marketers, providing strong evidence upon which to base public health policy and harm reduction campaigns. Second, such campaigns should be aimed specifically at young men to help counteract the increasing environmental and social normalisation of sports betting. The next section focuses on the key attitudinal differences that emerged from the results to answer our second research question.

Attitudes Associated with Sports Betting

Our results demonstrate there are significant differences between the attitudes of sports bettors (either sports betting or sports betting combined with non-sports betting), non-sport bettors and non-bettors. Not only do sports bettors feel more strongly that sports betting has a place in sport, they are also less concerned about the risks and harms of sports betting. These results help to demonstrate the effects of the normalisation processes outlined in previous studies. Whilst existing literature has documented how sports and sports betting have become synonymous (Milner et al., 2013 ; Nyemcosk et al., 2021 ; Pitt et al., 2016a ; Thomas, 2018 ), the attitudinal differences we identified highlight how the ‘gamblification’ (McGee, 2020 ) of sports has penetrated individual perceptions about sports betting as an activity and influenced behaviour. Moreover, the differences between sports bettors and non-sports bettors suggest something unique is happening for this group; it is not necessarily related to the act of gambling, but potentially broader environmental and socio-cultural influences. Gender and age effects are also apparent, with women less likely to agree that sports betting should be part of experiencing sports and more likely to agree that sports betting can place people at higher risk of other harms. A similar pattern is evident for age, with younger people generally being more permissive of sports betting.

The influence of the social aspects of sports betting, namely the characteristics of sports bettors’ social networks, is also a strong emerging theme, underpinned by several attitudinal measures. Sports bettors are more likely to have family and friendship groups where gambling is common and perceived as relatively harmless. Additionally, they are more likely to agree that discussions about odds and the placing of bets is the norm amongst peers. In this context, there are significant differences observed between both sports bettors and non-bettors, and sports bettors and non-sports bettors. This again highlights that there are potentially distinct socialisation processes specifically influencing the attitudes and behaviours of sports bettors. Whilst this has previously been described on a relatively smaller scale (Thomas, 2017a ), our research demonstrates that these interactional and socialisation factors are highly meaningful in an extensive cohort of sports fans.

Age and gender are the key demographic factors related to responses in a similar way to that described in the previous section. Whilst women are more likely to agree that sports betting is common in broader society and amongst family members, men are more likely to indicate it is common within their peer groups. Men are also more likely to state that ‘odds talk’ is prevalent when socialising with their peers. In combination with the demographic risk factors outlined in the previous section, it is apparent that men also have different interactions with sports betting. They are more likely to agree it has a place in sports, less likely to think it is risky and can lead to other harms, more likely to have friends and peers who bet on sports, and more likely to have dialogue that supports and endorses the normalisation of sports betting. These attitudes combined suggest it is imperative public health prevention measures and harm reduction interventions target young men. The impact of peer socialisation processes and hegemonic masculine norms around sports betting have been described in previous studies (Ayandele et al., 2019 ; Bunn et al., 2019 ; Deans et al., 2017a ). Sports betting has also been related to the development of socially valorised identities for young men (Lamont and Hing, 2021 ). Our research supports and builds on these previous findings by demonstrating in a large sample that it has become a more prevalent part of sports fandom for younger adult men.

On a large and unique scale, we have demonstrated fundamental attitudinal and behavioural differences, and distinct and concerning trends, among those who engage in sports betting, thereby offering important insights about those most at risk. Most previous research has been qualitative or focused on those identified as having problematic gambling behaviours. By contrast, the scale and type of results generated from this study have afforded the ability to compare differences between non-bettors and bettors, providing compelling evidence of current issues amongst a general cohort of sports fans. Importantly, this study provides data about an emerging public health crisis in which younger men are most at risk because they are more exposed to sports betting normalisation processes, show greater engagement with sports betting and express more permissive attitudes. As such, the results of this study provide a foundation for public health interventions and programs.

Below is the link to the electronic supplementary material.

Acknowledgements

We would like to acknowledge the Victorian Responsible Gambling Foundation for supporting and funding this research. We would also like to acknowledge and thank Joe Vecci and Roger Wilkins for helpful comments and suggestions.

Author Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Emma Seal, Buly Cardak and Matthew Nicholson. The first draft of the manuscript was written by Emma Seal and Buly Cardak with guidance from Matthew Nicholson and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Open Access funding enabled and organized by CAUL and its Member Institutions. This research received funding from the Victorian Responsible Gambling Foundation.

Declarations

The authors have no competing interests to declare that are relevant to the content of this article.

1 One participant reported 52,000 bets per year while some others reported 5,200 and 2,600 bets per year. Such observations were treated as outliers and excluded from the analysis. This amounted to 14 observations being excluded from the analysis.

2 Higher order age terms are not supported by a likelihood ratio (LR) test of a model with a fourth order age term with a test statistic of 4.63 ( p -value = 0.20).

3 Higher order age terms are not supported by a likelihood ratio (LR) test of a model with a fourth order age term with a test statistic of 3.33 ( p -value = 0.19) for the model of the number of non-sports bets and 2.74 ( p -value = 0.25) for the model of the number of sports bets.

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Gambling Research Paper

This sample gambling research paper features: 4800 words (approx. 16 pages), an outline, and a bibliography with 44 sources. Browse other research paper examples for more inspiration. If you need a thorough research paper written according to all the academic standards, you can always turn to our experienced writers for help. This is how your paper can get an A! Feel free to contact our custom writing service for professional assistance. We offer high-quality assignments for reasonable rates.

Introduction

The development of sociological perspectives on gambling, current sociological perspectives on gambling, researching gambling in the 21st century, gambling and public policy, linkages with other sociological perspectives, conclusion: technology, change, and the future.

• Bibliography

The size and scope of legalized gambling—to put aside its illegal manifestations for a moment—are simply mind-boggling. In America, for instance, more money is legally spent on gambling than is spent on movie tickets, theme parks, sports events, and music events combined (Morais 2002). Of course, sociologists have spent a substantial amount of productive research time examining the vast sociocultural impacts of Hollywood’s movies, and the field has developed an impressively broad literature on the sociology of leisure and sport. Furthermore, sociologists of popular culture have studied the sociological reach of a music culture that today encompasses everything from Mozart to MTV.

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Meanwhile, the gambling industry now dwarfs these more familiar sociological subjects, at least in the economic sense. Gambling also constitutes a formidable political entity: As of this writing, 48 of the 50 U.S. states offer some form of legalized gambling (Utah and Hawaii stand as the lone holdouts). Just as strikingly, a somewhat similar proportion of international jurisdictions are also embracing legalized gambling (or considering doing so).

Of course, gambling activity has probably been around as long as human groups have been around (the phrase “rolling the bones” harkens back to an era when playing dice games meant exactly that). Nor are the activity’s intimate linkages with government new: In the United States, for instance, lotteries were legalized in the colonies by 1750. City governments, churches, jails, public utilities, road repair, and institutions of higher education, including many Ivy League schools, were financed by these lotteries (Rosecrance 1988).

However, at no time in human history have more types of gambling been more widely available to more human beings than they are today. In light of these observations, it would seem that sociologists everywhere might devote their tools to help advance our understanding of those of us who wager money on events whose outcomes are in doubt.

Some of the earliest writings on gambling were not specifically sociological, but they certainly invoked themes familiar to today’s sociologists. For instance, because gambling was seen as undermining the very foundations of the Protestant ethic, it threatened those who were passionately protective of the latter in predictable ways. In 1883, Anthony Comstock warned that “the promise of getting something for nothing, of making a fortune without the slow plodding of daily toil, is one of Satan’s most fascinating snares” (p. 56). For many, gambling’s insidiousness offended social and moral sensibilities more than other scourges of the day such as alcohol.

In his pioneering study, Edward Devereux (1949) lamented that sociologists had neglected the study of gambling, given its ubiquity and institutionalization. Devereux viewed gambling within the context of functionalist theory, suggesting that wagering behavior had societal implications beyond the individualistic and pathological approaches that seemed to dominate then—and indeed, continue to dominate studies of gambling behavior today. Given the sociological frameworks popular in his time, it was perhaps predictable that Devereux explored the act as a safety valve that relieved stress and strain generally emanating from the restraints and rationality of a capitalistic system. In addition, Devereux also felt that dominant values were reinforced with admonitions against gambling and other deviant behaviors (p. 946).

Of course, it was recognized that gambling can also be dysfunctional, as Bloch (1951) pointed out, creating problems for family, work, and personal life. Much of the early nonsociological work on gambling behavior focused on the dysfunctional effects that gambling has on both the gambler as well as those close to him or her. This perspective coalesced in the field of psychology into a vast literature exploring treatments for gambling pathologies. Even sociologists were not immune to this impulse: Herman’s (1967) study of racetrack betting used this more or less pathological framework for his analysis, as did Zola’s (1963) research on offtrack betting.

In the early days of the field, legalized gambling was rare, and illegal gambling was widespread. As such, many of the first studies of the gambling act tended to employ a more or less criminological framework to interpret these behaviors. For instance, Tec’s (1964) study of football betting in Sweden found that bettors were more likely to be employed, upwardly mobile, and motivated to achieve. They did not appear to be alienated or detached—contrary to what anomie theorists would predict. Other analysts presented evidence to support opportunity theory and anomie, demonstrating that those with available avenues of advancement and lower levels of status frustration were less likely to gamble (Li and Smith 1976). Studies by Light (1977) and Newman (1968) did not find that relative deprivation motivated gambling activity, particularly within the lower class. Instead, gambling was interpreted as a communal or shared activity with important cultural meanings. Downes et al. (1976) found that gambling was not peculiar to the lower class but was found across all categories of the social structure—that is, across racial, class, and occupational divides.

Not all studies of gambling focused on the financial “losers” who constitute the majority of gamblers. One interesting sociological research piece explored the familial, social, and professional changes confronted by lottery winners—a scenario that many of us have no doubt contemplated (Kaplan 1978). This was the first systematic study of gambling’s “winners”—all of whom had come away with a prize of one million dollars or more. In his work, Kaplan found that relationships transformed in significant and unforeseen ways, and that many winners found that they could not maintain their prior institutional or organizational affiliations.

Against this background, the sociologist Henry Lesieur’s work emerged as a pioneering contribution to our understanding of the ways in which social networks and communities affect gamblers’ lives. Lesieur (1977) sought sociological explanations for problem gambling in his groundbreaking study of the career of racetrack and sports bettors—a work that established him as a pioneer of this emerging area of sociological inquiry.

In fact, Lesieur’s work was so influential that despite his background as a sociologist, he was asked to play a central role in defining the American Psychiatric Association’s criteria for pathological gambling (see American Psychiatric Association 1994). Lesieur observed that many problem gamblers found themselves entangled in an effort to try to win back losses—or “chasing”—a characteristic that has since served as a central feature of the diagnostic literature (Lesieur and Custer 1984:149–50; American Psychiatric Association 1994). Later, Lesieur served as the founding editor of the first specialty journal in the field, the Journal of Gambling Behavior, later renamed the Journal of Gambling Studies. Today, he is widely recognized in mental health circles as one of the founding figures in the field of pathological gambling studies.

The sociologists Smith and Abt (1984) argued for a shift from concern with the problematic aspects of gambling to a focus on understanding the activity as “play.” In their view, gambling reinforces capitalistic and materialistic American values of self-reliance, risk taking, decision making, and skill enhancement. Furthermore, much like other games, gambling provides an outlet for socialization and cultural learning: From marbles to baseball card flipping, games of chance prepare children for games at a higher level and for participation in American life.

As Goffman (1967) noted before Smith and Abt, character is demonstrated through rituals—including gambling rituals. Thus, gambling might be seen as functional for social order by providing an escape from everyday life while reinforcing existing cultural norms (Smith and Abt 1984; Abt, Smith, and McGurrin 1985:64.)

Today, gambling has “normalized” and may be understood via lenses currently used to study other late-emerging capitalist industries. Reith (2003) points out that

the gambling industry itself is increasingly owned by a limited number of multinational corporations, concentrated in an oligopolistic market. It is organized in a similar way to other major industries, with market research and advertising strategies designed to identify and target niche groups . . . Modern consumers have a variety of products and experiences to choose from, and an ever-larger and more powerful industry to supply them. (Pp. 19–20)

This “new” gaming industry has attracted a growing number of professional observers, primarily in the United States, Canada, Australia, New Zealand, and Europe. Most sociological perspectives have employed familiar tools of the field and applied them to our understanding of the spread of gambling. As the Australian sociologist Jan McMillen attests, gambling has not been exempt from trends commonly associated with the spread of globalization. McMillen (2003) points out that gambling has succumbed to

the homogenizing forces of globalization: economic dominance of transnational corporations, often American; the acceptance of certain governing rules and economic tendencies; and standardization of products and consumer behavior . . . this new globalization is a largely cultural phenomenon, whatever its economic base. Nowhere is this better seen than in the transformation of gambling into one of the world’s most rapidly expanding consumer activities. (P. 51)

More generally, it would seem that the development of the global tourism industry—one that, by many accounts, has evolved into the world’s largest—has provided the macro-economic backdrop for the development of a global gaming industry. And as this industry becomes a truly global one, scholars of the globalizing gaming industry— like scholars of globalization as a whole—might begin to focus on homogenizing forces as well as those forces that highlight local differences.

For instance, gambling locations in North America, Australia, and Europe are largely dominated by a relatively similar collection of machine games developed by a handful of multinational corporations. In a postindustrial “deforestation effect,” the overall portrait on the casino floor is one of machines replacing wooden tables, which are being hauled off to storage.

In Asia, meanwhile, these machine games have not proven as popular. In Asian casinos, these games are usually relegated to peripheral spaces within the gambling environment, as table-oriented games of chance predominate. Gamblers, for their part, “play” in an environment that is notably more serious than settings in Western societies. Were we to insist on a one-size-fits-all theoretical or methodological model for understanding such disparate sociocultural locales, our approaches might well prove to be deficient.

In some more-developed regions of the globalizing world, we are observing signs of a shift from social problems of deficiency to social problems of excess: for instance, starvation becomes less of a problem, only to be replaced by obesity. Both starvation and obesity, of course, are shaped by sociological as well as psychological and biological factors—as is certainly the case with gambling problems as well. In this context, it would seem that the “individual” problem we now call problem gambling may in fact be characterized as a quintessentially twenty-firstcentury “social problem”—one that is profoundly affected by macrolevel factors and that predictably involves an overindulgence (rather than an underindulgence).

Of course, professional views of those with gambling problems have not always been nuanced or multidimensional. For years, the experts who tackled the task of understanding and explaining the lives of those who “gambled too much” spoke from church pulpits (rather than academic podiums) and located the problem in morality. In a sermon delivered on April 19, 1835, Samuel Hopkins tells us exactly how we are to “treat” the problem gambler:

Let the gambler know that he is watched, and marked; and that, as a gambler, he is loathed. Let the man who dares to furnish a resort for the gambler know that he is counted a traitor to his duty, a murderer of all that is fair, and precious, and beloved among us. Let the voice of united, incensed remonstrance be heard— heard till the ears of the guilty tingle. (Pp. 17–18)

Unfortunately for problem gamblers, these kinds of historical perspectives have not been forgotten. This is why problem gamblers—who now are labeled by psychological institutions as “sick”—still self-diagnose as “evil” many years after these kinds of sermons were delivered.

This is a classic illustration of how a sociological imagination can help people understand the nuances of what “ails” them. A problem gambler might wonder, “If pathological gambling is a medical problem, then why is it that my friends treat me like a moral one?” The sociological answer is this: because the older religious interpretations of problem gamblers have generated far more momentum and power than the relatively youthful medical interpretations have. In the social battlefields of public discourse, 20 years or so of medical interpretations do not somehow magically eliminate the inertia of hundreds of years of influential religious interpretations. No matter how much we hail the recent advances of problem gambling science and medicine, they have not yet captured the public’s intellectual and emotional imagination in the way that earlier moral-religious understandings have (Bernhard, forthcoming-b).

In his book Pathological Gambling: The Making of a Medical Problem, Brian Castellani (2000) concludes that the field is careening carelessly down a decidedly medical pathway at the expense of more multidimensional perspectives. Drawing on the insights of Foucault, Castellani argues that medical experts have dominated the problem gambling discourse for too long and that it is time for those representing a wider range of discourses to be included in the construction of knowledge about this social problem. Castellani argues for an approach that explores how medical discourses collide with those emanating from moral or policy quarters. What remains to be seen is whether these kinds of sociological perspectives can contribute to the popular view of gambling in the way that moral and then psychological ones have.

The British sociologist Gerda Reith recently developed a thoughtful critique of the ways in which sociology should engage gambling as a subject of sociological inquiry. Noting that sociologists have long focused on the immorality or the sickness of those who gamble too much, Reith (2003) seeks instead to focus on the vast majority of gamblers who engage in gambling for recreation and fun.

In her work, Reith (2003) skillfully contemplates how an “age of chance” has emerged and engaged an age of reason. Long ago, of course, very little that occurred was attributed to mere chance—the gods, after all, controlled virtually every imaginable outcome. In the current context, chance has become accepted—and even commodified—by capitalist economies in the Western world. Looking to the future, Reith senses that a peculiar affection for chance will continue to develop, noting that “at the start of the twenty-first century, life does seem to be increasingly insecure,” citing market fluctuations, transformations in work life, environmental doomsday scenarios, and the postmodern grappling with truth and certitude as evidence (pp. 182–83). Against these sociological backdrops, Reith astutely notes that gambling serves as “a conduit for chance: an arena in which (chance) appears in an intensified and, more importantly, controlled form” (p. 183). Hence, gambling provides a unique outlet for the impulses that accompany this era. From this perspective, gambling seems less a deviant act than a distilled one: It serves as a microcosm for much that is characteristic of our times.

Methodologically, the field continues to grapple with a variety of issues that are common in many relatively young areas of inquiry. Summarizing the methodological state of the field, Eadington (2003) notes that “it remains difficult to fully comprehend what the evidence is telling us” (p. 32) and later argues that “benefit/cost analysis applied to . . . gaming activities is still a relatively primitive science, primarily because of the difficulties in conceptualizing, observing, and measuring social costs” (p. 46).

Notably, it is a sociologist, Rachel Volberg, who has served as the problem gambling field’s leading prevalence methodologist and researcher. Volberg (1996), whose tool of choice for determining problem gambling rates has been the telephone survey, nevertheless insists that multiple methods are preferable to any single one:

Many of the questions now being asked about gambling and problem gambling cannot be answered by single surveys . . . As we move forward, it will be important to use a variety of methods to provide insights that no single approach can yield. Since all scientific methods contain biases, multiple research techniques (including experimental, clinical, historical, ethnographic and survey approaches) are needed to resolve puzzles and discrepancies as well as to provide a much-needed depth of perception to the field of gambling studies. (P. 126)

Today, it appears that even the medically and psychologically oriented researchers in the field of gambling studies are embracing these broader approaches to theory and method. For instance, a group of influential scholars—all trained in psychology—recently put forth a call to embrace a more macrolevel “public health” approach to the study of gambling behavior. Interestingly, this public health approach strikes a chord familiar to sociologists, because it advocates multiple levels of analysis, including those that focus on the individual, group, organizational, and institutional levels (Blaszczynski, Ladouceur, and Shaffer 2004).

From a policy perspective, what makes gambling different from more conventional industries is the peculiar relationship between government entities and gambling businesses. As Eadington (2003) notes, “Gambling is one of the largest industries whose fundamental economic characteristics are substantially determined by political decisions” (p. 45). To this, we might add that state lotteries exist in a way that allows the government to sell products to its constituency directly and not via a generous tax break or other subsidy. Because of these relationships, government bodies may well find themselves with conflicting interests: On the one hand, they have an interest in maximizing gambling revenues (to sponsor government programs); on the other, they have an obligation to protect the public (some of whom may consume excessive amounts of lottery tickets).

In the United States, the government has been largely content to allow individual states to enforce and regulate gambling within their borders (Frey 1998). In other jurisdictions, national and provincial governments have entered into unique agreements with gaming business operators to offer gambling to native and tourist populations. In some cases, as with Canada, the government serves as a sort of “owner-operator” of casinos. As Rosecrance (1988) envisioned, gambling’s widespread acceptance and its partnership with public entities has resulted in its mainstreaming and legitimization—and also its decriminalization.

Recently, gambling has enjoyed unprecedented support from a wide variety of public figures. Especially in more conservative political environments, where uttering the “t word” (taxation) is a sure way to get voted out of office, gambling is often seen as a “voluntary tax” willingly donated to state coffers by participants, who in exchange for their donation receive an entertainment benefit.

At the same time, in jurisdictions across Canada and Australia, for instance, public clamor has resulted in growing efforts among government entities to mitigate the costs associated with this “entertainment.” Social movement organizations—most of which are affiliated with religious organizations in some manner—have once again emphasized the downside of gambling, and some jurisdictions have moved to address these critiques. To wit, the Canadian jurisdiction of Nova Scotia recently unveiled a test study of “responsible gaming devices” that have been attached to all gambling machines provincewide. These devices allow gamblers to check the amount of money they have won or lost over given periods of time (a sort of gambling “bank statement”) as well as set monetary limits and/or time limits for their play.

With each technological leap forward, however, we must also be on guard against falling into traps that some of sociology’s most famous voices have articulated. Bernhard and Preston (2004) point out that these policy interventions have a way of backfiring, as Robert Merton famously warned. As it turns out, several of the policies implemented in an effort to mitigate problem gambling have had unintended consequences, and a few have actually harmed those whom the policies ostensibly target. For instance, some jurisdictions have slowed down the speed of machine gambling games (thinking that this would help slow down the progression of gambling addicts), but research emerged that suggested that addicts actually played for longer periods of time when this policy was implemented. It would seem that in the twenty-first century, sociologists may well continue to rest their analyses on the able shoulders of the field’s twentieth-century giants.

Some of sociology’s favorite tools and perspectives can help illuminate a variety of aspects of gambling behavior. Robert Putnam’s (2000) popular work Bowling Alone, for instance, argues that many of our recreational activities have become decidedly less social over the past few generations. Putnam’s fundamental argument is that Americans are engaging in fewer social activities than in the past, and that this reduction in “social capital” can have potentially deleterious—even disastrous—consequences. Most germane to our discussion, in developing his argument, Putnam laments the decline of traditional game playing (such as bridge games) and the expansion of machine-based gambling:

Substitutes for card playing have emerged, of course, everything from computer and video games to casino gambling. Like cards, these pastimes provide the spice of chance. Unlike card playing, however, these successors are distinguished by their solitary nature . My informal observation of Internetbased bridge games suggests that electronic players are focused entirely on the game itself, with very little social small talk, unlike traditional card games. Even fanatics of Microsoft Solitaire rarely play in a group, and any visitor to the new megacasinos that dot the land has chilling memories of acres of lonely “players” hunched in silence over onearmed bandits. Bridge, poker, gin rummy, and canasta are not being replaced by some equally “schmoozable” leisuretime activity. (Pp. 104–105)

For Putnam, the recent gambling mania is symptomatic of far larger social ills. Beyond Putnam’s perspectives, there is much that is sociologically rich about the “social capital” (or lack thereof) of problem gamblers. For many problem gamblers, earlier in their “career,” gambling activity was decidedly unproblematic and heavily social—a way to socialize and enjoy and evening with friends. Later in their career, many problem gamblers find themselves gambling in social situations less often and gambling alone more often. By the time they reach what the 12-step groups call “bottom,” very few are gambling with anyone else— their social capital, it seems, has been reduced to nearly zero. Interestingly, their “recovery” embraces a dramatic reversal of this trend: Many turn to organized groups (such as professional therapy groups or Gamblers Anonymous) and reconnect with social worlds that they had abandoned. Whereas once they gambled alone, they eventually heal together (Bernhard, forthcoming-a).

For sociologists, obviously, group life has long been accepted as a foundational element of sociological inquiry. For gamblers with problems, an enhanced understanding of the nature and power of group life can perhaps deliver what Mills’s (1959) sociological imagination once promised. As Bernhard (forthcoming-b) argues, gamblers with problems can recognize, as all of us can, that “individual” problems can in fact be better understood by exploring the sociological backdrops on which they are projected.

Other books that are familiar to sociologists touch on gambling as well. The cultural critic Neil Postman (1985) begins his seminal work Amusing Ourselves to Death by talking about the American “city symbols” that captured the essence of a variety of periods—Boston in the colonial days, for instance, or New York in the Ellis Island immigration era. In this work, which is remarkable for how well it continues to resonate 20 years later, Postman suggests that in our current age, the ultimate city-symbol of the times may well be found in Las Vegas. For Postman, this is not necessarily a positive development:

For Las Vegas is a city entirely devoted to the idea of entertainment, and as such proclaims the spirit of a culture in which all public discourse increasingly takes the form of entertainment. Out politics, religion, news, athletics, education and commerce have been transformed into congenial adjuncts on show business, largely without protest or even much popular notice. The result is that we are a people on the verge of amusing ourselves to death. (Pp. 3–4)

Following Postman’s lead, other analysts engaged in research that might be called a “sociology of Las Vegas.” The work of Gottdiener, Collins, and Dickens (1999) presents an urban sociological perspective on Las Vegas—one that emphasizes the need to understand what might be called the first postindustrial city. After all, unlike Detroit or Pittsburgh, Las Vegas produces little that is physical in nature. Instead, the city “produces” experience for the nearly 40 million people who visit it annually.

Drawing on the sociospatial approach, these authors continue by arguing that we have seen a “Las Vegasization” of the rest of America and a simultaneous “Americanization” or “normalization” of Las Vegas. This convergence effect means that Las Vegas is no longer the deviant case study it once was—in fact, quite the contrary, it may well be a model laboratory for urban sociological inquiry.

The sociological study of gambling also shares affinities with the sociological study of “risk” (see, e.g., Frey 1991). Risk is a cultural construct that is shaped by the perceptions and evaluations of risk that individuals and societies assign to certain activities (including, presumably, gambling). More recently, some scholars have used this framework to better understand gambling’s effects on societies. Invoking the term “risk society,” Kingma (2004) observes that the liberalization of gambling laws, the growing perception of gambling as a legitimate economic and recreational pursuit, and the subsequent development of mechanisms to deal with gambling addiction are natural outcomes of the modernization process.

Risk analysis may well provide us with a framework for understanding the pros and cons of a variety of social influences. Social processes, as Short (1984) notes, have benefits as well as negative impacts on the “social fabric” (Giddens 1990, meanwhile, focuses on the term social order in his analysis). Wildavsky (1988) claims that risk is inherent in all activities, and seeking a “zero-risk” society—where all is safe and secure—leads only to stagnation.

Thus, as with all development, opportunities arise in some sectors, but costs too rear their heads. In policy and social research contexts, this means that gambling’s potential benefits as a recreational, employment, and economic resource must be considered against the potential costs of addiction, crime, and personal/familial disruption. Risk management is the exercise of alerting individuals and societies to these adverse conditions (Short 1984).

Finally, gambling as risk was also examined by Erving Goffman (1967), who actually assumed the role of a dealer for several months in a Las Vegas casino. He was not studying gambling per se but rather used the gambling setting to study patterns of social interaction. Goffman was interested in the choice individuals made to place themselves in settings where there was personal or property risk at stake. In these settings, individuals seek “action,” pursuing risky activities even when the risk is avoidable. Later, Frey (1984) applied this concept to gambling: “Action activities are consequential and fateful in that something of value can be won or lost on the outcome, and, by committing something of value, players indicate their seriousness. The greater the consequences, the more fateful the enterprise becomes” (p. 113).

Like so many forces that ensnare our sociological attention, technology will certainly continue to shape gambling activities in the twenty-first century. Already, a variety of newer technologies can double as a gambling device, including cell phones and computers. In the United States, attorneys for the Bush administration have decided that Internet wagering is illegal under the 1961 Wire Act, which prohibited phone bets that took place across state lines. Of course, the Internet is more amorphous than the phone lines of the 1960s were, revealing the complexities inherent in regulating and monitoring acts that take place in virtual rather than brick-andmortar worlds. Despite this presidential interpretation, millions of Americans (and many more bettors internationally) wager billions of dollars on sites that operate in jurisdictions that allow operators to flourish. Few Internet gamblers, it is safe to say, are fully aware of the legal status of the act—an oversight that is understandable, perhaps, given the widespread and increasing acceptance of gambling in general.

Other familiar technologies will also continue to shape the gambling landscape. Television has recently wielded its powerful cultural force, contributing significantly to the gambling boom by televising events such as celebrity poker and an ever-growing number of fictional and reality tales set in Las Vegas. With all this sociological momentum, it is difficult to envision a twenty-first century in which gambling becomes increasingly less important as a sociological force.

In closing, however, we should note that the history of gambling is hardly a tale of linear expansion: The activity has experienced spikes in popularity as well as occasional bouts with prohibition. To wit, even the state of Nevada has legalized gambling on three separate occasions (and banned it twice). When it comes to gambling, if we take the long sociological view, it seems prudent to bet on both growth and backlash as we look ahead to the twenty-first century.

Bibliography:

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• Castellani, Brian. 2000. Pathological Gambling: The Making of a Medical Problem. Albany: State University of New York.
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• Comstock, Anthony. 1883. Traps for the Young. New York: Funk & Wagnalls.
• Conrad, Peter and Joseph W. Schneider. 1992. Deviance and Medicalization: From Badness to Sickness. Philadelphia, PA: Temple University Press.
• Devereux, Edward C. 1949. “Gambling and the Social Structure: A Sociological Study of Lotteries and Horse Racing in America.” Ph.D. dissertation, Harvard University, Cambridge, MA.
• Downes, David M., B. P. Davies, M. E. David, and P. Stone. 1976. Gambling, Work and Leisure. London, England: Routledge & Kegan Paul.
• Eadington, William R. 2003. “Values and Choices: The Struggle to Find Balance with Permitted Gambling in Modern Society.” Pp. 31–48 in Gambling: Who Wins? Who Loses? edited by G. Reith. Amherst, NY: Prometheus.
• Frey, James H. 1984. “Gambling: A Sociological Review.” Annals of the American Academy of Political and Social Science 474:107–21.
• Frey, James H. 1991. “Social Risk and the Meaning of Sport.” Sociology of Sport Journal 8:136–45.
• Frey, James H. 1998. “Federal Involvement in U.S. Gaming Regulation.” Annals of the American Academy of Political and Social Science 556:138–52.
• Giddens, Anthony. 1990. The Consequences of Modernity. Palo Alto, CA: Stanford University Press.
• Goffman, Erving. 1967. Interaction Ritual: Essays on Face-toFace Behavior. New York: Anchor.
• Gottdiener, Marc, Claudia C. Collins, and David R. Dickens. 1999. Las Vegas: The Social Production of an All-American City. Malden, MA: Blackwell.
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• Li, Wen Lang and Martin Smith. 1976. “The Propensity to Gamble: Some Structural Determinants.” Pp. 189–206 in Gambling and Society, edited by W. R. Eadington. Springfield, IL: Charles C Thomas.
• Light, Ivan. 1977. “Numbers Gambling among Blacks: A Financial Institution.” American Sociological Review 42:892–904.
• McMillen, Jan, ed. 1999. Gambling Cultures: Studies in History and Interpretation. London, England: Routledge.
• McMillen, Jan. 2003. “From Local to Global Gambling Cultures.” Pp. 49–63 in Gambling: Who Wins? Who Loses, edited by G. Reith. Amherst, NY: Prometheus.
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• Rosecrance, John. 1988. Gambling without Guilt: The Legitimation of an American Pastime. Pacific Grove, CA: Brooks/Cole.
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• HISTORY & CULTURE

Gambling is everywhere now. When does that become a problem?

A new age of gambling means sports betting is more accessible than ever—and there's little incentive to prevent problem gambling.

Gambling trends have exploded periodically throughout history, but its latest peak may be now, in the period following the 2018 Supreme Court decision to overturn the Professional and Amateur Sports Protection Act (PASPA) . The federal law barred most states from authorizing gambling on competitive sporting events.  

Seemingly overnight, advertisements for sports betting companies became unavoidable. Viewers can now expect to see them not only in sports broadcasts, but also other programming, and any place ads are found online. By the five year anniversary of the decision, Americans had bet over $220 billion on sports, and 2023 was also the third-straight year commercial betting revenue broke records. Today, 38 states, including D.C., allow sports betting in some shape or form.

With an explosion in sports betting, high profile cases have hit the headlines. Last week, the NBA banned Toronto Raptors player Jontay Porter for life after an internal investigation found he had bet on basketball games. Last month, the MLB's Los Angeles Dodgers abruptly fired the star player Shohei Ohtani’s translator for gambling; U.S. officials have accused the translator of stealing more than $16 million to fund an illegal sports gambling habit. Celebrated broadcaster Craig Carton returned to the air after jail time—and has started a show discussing the realities of problem gambling.

Once considered to be a symbol of immorality, gambling has become significantly less stigmatized over the last decades, says Jeff Derevensky, director of McGill University’s International Centre for Youth Gambling Problems and High-risk Behaviours.   “They turned gambling from sin and vice into a socially acceptable recreational form of entertainment,” he says. “As a result of that social acceptability, you don't have to hide.”  

Gambling is more accessible than ever. This makes it easier for individuals, including youth who have difficulty setting and maintaining limits, to fall into severe addiction. Since 2018, there have been more high-profile reports of teens falling into severe gambling addiction , and experts have reportedly noticed a number of adolescents migrating to gambling through the medium of video games, likely because they satisfy similar psychological needs.  

Between 2018 and 2021, the risk of problem gambling grew by 30 percent, according to the National Council on Problem Gaming , a non-profit that aims to minimize the economic and social costs associated with gambling addiction.

The industry is only poised to grow from here, not only in revenue, but also likely expanding legal gambling legislation to still-untouched areas of the country as well.  

“When people ask me who's most addicted to gambling, I usually say it's the government,“ says Derevensky. “They're addicted to the revenues that are being brought in by the gambling industry.”

Gambling, anywhere and anytime

  Casinos have long been the face of the gambling industry, but when it comes to sports betting, today’s high-rollers aren’t necessarily resigned to having to trek to the nearest gaming hub or rustle up a trustworthy bookie to play the odds—they just need a smartphone.  

“It used to be that you had to actually transport yourself to a gambling venue,” says Lia Nower , a professor and director of the Center for Gambling Studies at Rutgers University . “Now you have gambling 24/7 on your cell phone. You have a sports book or a casino in your pocket, and you can be sitting there eating dinner with your family, gambling away the house.”  

Still, while some individuals may choose to never roll the proverbial dice, others may only need to be persuaded to give it a try. Simple online sportsbook services like BetMGM or DraftKings make it easy to sign up and start betting, going so far as to offer an array of payment options through various banking platforms like PayPal to ensure both easy payments and quick withdrawals.

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But those unfamiliar with the lay of the land could find themselves losing more than expected. Features like microbetting, which involves playing the odds on specific or smaller aspects of a game, or same game parlays, bets on multiple events within a single game, are attractive incentives to both newcomers and seasoned gamblers, though each risky in their own ways. Same game parlays, for instance, tend to rely heavily on your powers of prediction, and if a single leg in the bet is wrong, the entire wager is lost.  

“The overall majority of [the] sports betting handle is taking place online,” says Joe Maloney , the senior vice president of the American Gaming Association (AGA) , a national trade group that represents the U.S. casino industry. “Obviously that's reflective of operators in the legal and regulated market meeting consumers where they are increasingly in today's society.”  

The ubiquity of virtual betting doesn’t mean in-person gambling has gone away—in fact, more than ever, opportunities can be found almost anywhere you spend free time—including bars, bowling alleys, and sports venues. Even restaurants have since opened their doors to popular kiosk-style gambling machines, all hoping to get in on the action.  

Who is gambling—and when does it become a problem?

The majority of sports bettors tend to be young male adults, but according to the AGA, the market has been quickly diversifying since the repeal of PASPA.  

In 2023, according to the association, 6 percent of sports bettors were between 21-24 years old, while 34 percent were between 35-44 years old. The same data suggests that 64 percent of sports bettors in the same year were male.

Gambling has long been considered a male-dominated hobby, and has historically fostered social connection. Today, modern gambling is about entertainment and bringing people together through competition, says Timothy Fong , a clinical professor of psychiatry and the co-director of the UCLA Gambling Studies Program. The uptick in social betting may also pave the way for males to be more likely to develop severe gambling addictions.  

“You don't gamble once and become addicted or hooked,” says Derevensky. “It's a progressive disorder. It takes time.“

Now categorized as a chronic mental health condition in the DSM-5 , problem gambling can be hard to diagnose because of how easy it is to hide. It’s estimated that it affects about   1 percent of Americans , but just like any addiction, long-term gambling can alter the way your brain works, and many problem gamblers have reported feeling stress, anxiety, and depression at the height of it.  

“A lot of times people don't want to admit that they have a gambling problem,“ says Fong.

Oftentimes, he says, patients have no idea they have an addiction and boil down their losing streaks to merely bad luck. Even if they acknowledge a problem, problem gamblers often contend with shame and avoid asking for help. A lack of research funding makes data on gambling addiction even more hazy.

As the industry continues to skyrocket, experts suggest gamblers be responsible with their decisions and regardless of the outcome, to try learning from the experience.  

“Losing is part of the gambling experience, part of life, and figuring out how you respond to losses on things that matter to you are really, really critical,” Fong says.

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Researchers detect a new molecule in space

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New research from the group of MIT Professor Brett McGuire has revealed the presence of a previously unknown molecule in space. The team's open-access paper, “ Rotational Spectrum and First Interstellar Detection of 2-Methoxyethanol Using ALMA Observations of NGC 6334I ,” appears in April 12 issue of The Astrophysical Journal Letters .

Zachary T.P. Fried , a graduate student in the McGuire group and the lead author of the publication, worked to assemble a puzzle comprised of pieces collected from across the globe, extending beyond MIT to France, Florida, Virginia, and Copenhagen, to achieve this exciting discovery. 

“Our group tries to understand what molecules are present in regions of space where stars and solar systems will eventually take shape,” explains Fried. “This allows us to piece together how chemistry evolves alongside the process of star and planet formation. We do this by looking at the rotational spectra of molecules, the unique patterns of light they give off as they tumble end-over-end in space. These patterns are fingerprints (barcodes) for molecules. To detect new molecules in space, we first must have an idea of what molecule we want to look for, then we can record its spectrum in the lab here on Earth, and then finally we look for that spectrum in space using telescopes.”

Searching for molecules in space

The McGuire Group has recently begun to utilize machine learning to suggest good target molecules to search for. In 2023, one of these machine learning models suggested the researchers target a molecule known as 2-methoxyethanol. 

“There are a number of 'methoxy' molecules in space, like dimethyl ether, methoxymethanol, ethyl methyl ether, and methyl formate, but 2-methoxyethanol would be the largest and most complex ever seen,” says Fried. To detect this molecule using radiotelescope observations, the group first needed to measure and analyze its rotational spectrum on Earth. The researchers combined experiments from the University of Lille (Lille, France), the New College of Florida (Sarasota, Florida), and the McGuire lab at MIT to measure this spectrum over a broadband region of frequencies ranging from the microwave to sub-millimeter wave regimes (approximately 8 to 500 gigahertz). 

The data gleaned from these measurements permitted a search for the molecule using Atacama Large Millimeter/submillimeter Array (ALMA) observations toward two separate star-forming regions: NGC 6334I and IRAS 16293-2422B. Members of the McGuire group analyzed these telescope observations alongside researchers at the National Radio Astronomy Observatory (Charlottesville, Virginia) and the University of Copenhagen, Denmark. 

“Ultimately, we observed 25 rotational lines of 2-methoxyethanol that lined up with the molecular signal observed toward NGC 6334I (the barcode matched!), thus resulting in a secure detection of 2-methoxyethanol in this source,” says Fried. “This allowed us to then derive physical parameters of the molecule toward NGC 6334I, such as its abundance and excitation temperature. It also enabled an investigation of the possible chemical formation pathways from known interstellar precursors.”

Looking forward

Molecular discoveries like this one help the researchers to better understand the development of molecular complexity in space during the star formation process. 2-methoxyethanol, which contains 13 atoms, is quite large for interstellar standards — as of 2021, only six species larger than 13 atoms were detected outside the solar system , many by McGuire’s group, and all of them existing as ringed structures.  

“Continued observations of large molecules and subsequent derivations of their abundances allows us to advance our knowledge of how efficiently large molecules can form and by which specific reactions they may be produced,” says Fried. “Additionally, since we detected this molecule in NGC 6334I but not in IRAS 16293-2422B, we were presented with a unique opportunity to look into how the differing physical conditions of these two sources may be affecting the chemistry that can occur.”

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Partisan divides over K-12 education in 8 charts

K-12 education is shaping up to be a key issue in the 2024 election cycle. Several prominent Republican leaders, including GOP presidential candidates, have sought to limit discussion of gender identity and race in schools , while the Biden administration has called for expanded protections for transgender students . The coronavirus pandemic also brought out partisan divides on many issues related to K-12 schools .

Today, the public is sharply divided along partisan lines on topics ranging from what should be taught in schools to how much influence parents should have over the curriculum. Here are eight charts that highlight partisan differences over K-12 education, based on recent surveys by Pew Research Center and external data.

Pew Research Center conducted this analysis to provide a snapshot of partisan divides in K-12 education in the run-up to the 2024 election. The analysis is based on data from various Center surveys and analyses conducted from 2021 to 2023, as well as survey data from Education Next, a research journal about education policy. Links to the methodology and questions for each survey or analysis can be found in the text of this analysis.

Most Democrats say K-12 schools are having a positive effect on the country , but a majority of Republicans say schools are having a negative effect, according to a Pew Research Center survey from October 2022. About seven-in-ten Democrats and Democratic-leaning independents (72%) said K-12 public schools were having a positive effect on the way things were going in the United States. About six-in-ten Republicans and GOP leaners (61%) said K-12 schools were having a negative effect.

About six-in-ten Democrats (62%) have a favorable opinion of the U.S. Department of Education , while a similar share of Republicans (65%) see it negatively, according to a March 2023 survey by the Center. Democrats and Republicans were more divided over the Department of Education than most of the other 15 federal departments and agencies the Center asked about.

In May 2023, after the survey was conducted, Republican lawmakers scrutinized the Department of Education’s priorities during a House Committee on Education and the Workforce hearing. The lawmakers pressed U.S. Secretary of Education Miguel Cardona on topics including transgender students’ participation in sports and how race-related concepts are taught in schools, while Democratic lawmakers focused on school shootings.

Partisan opinions of K-12 principals have become more divided. In a December 2021 Center survey, about three-quarters of Democrats (76%) expressed a great deal or fair amount of confidence in K-12 principals to act in the best interests of the public. A much smaller share of Republicans (52%) said the same. And nearly half of Republicans (47%) had not too much or no confidence at all in principals, compared with about a quarter of Democrats (24%).

This divide grew between April 2020 and December 2021. While confidence in K-12 principals declined significantly among people in both parties during that span, it fell by 27 percentage points among Republicans, compared with an 11-point decline among Democrats.

Democrats are much more likely than Republicans to say teachers’ unions are having a positive effect on schools. In a May 2022 survey by Education Next , 60% of Democrats said this, compared with 22% of Republicans. Meanwhile, 53% of Republicans and 17% of Democrats said that teachers’ unions were having a negative effect on schools. (In this survey, too, Democrats and Republicans include independents who lean toward each party.)

The 38-point difference between Democrats and Republicans on this question was the widest since Education Next first asked it in 2013. However, the gap has exceeded 30 points in four of the last five years for which data is available.

Republican and Democratic parents differ over how much influence they think governments, school boards and others should have on what K-12 schools teach. About half of Republican parents of K-12 students (52%) said in a fall 2022 Center survey that the federal government has too much influence on what their local public schools are teaching, compared with two-in-ten Democratic parents. Republican K-12 parents were also significantly more likely than their Democratic counterparts to say their state government (41% vs. 28%) and their local school board (30% vs. 17%) have too much influence.

On the other hand, more than four-in-ten Republican parents (44%) said parents themselves don’t have enough influence on what their local K-12 schools teach, compared with roughly a quarter of Democratic parents (23%). A larger share of Democratic parents – about a third (35%) – said teachers don’t have enough influence on what their local schools teach, compared with a quarter of Republican parents who held this view.

Republican and Democratic parents don’t agree on what their children should learn in school about certain topics. Take slavery, for example: While about nine-in-ten parents of K-12 students overall agreed in the fall 2022 survey that their children should learn about it in school, they differed by party over the specifics. About two-thirds of Republican K-12 parents said they would prefer that their children learn that slavery is part of American history but does not affect the position of Black people in American society today. On the other hand, 70% of Democratic parents said they would prefer for their children to learn that the legacy of slavery still affects the position of Black people in American society today.

Parents are also divided along partisan lines on the topics of gender identity, sex education and America’s position relative to other countries. Notably, 46% of Republican K-12 parents said their children should not learn about gender identity at all in school, compared with 28% of Democratic parents. Those shares were much larger than the shares of Republican and Democratic parents who said that their children should not learn about the other two topics in school.

Many Republican parents see a place for religion in public schools , whereas a majority of Democratic parents do not. About six-in-ten Republican parents of K-12 students (59%) said in the same survey that public school teachers should be allowed to lead students in Christian prayers, including 29% who said this should be the case even if prayers from other religions are not offered. In contrast, 63% of Democratic parents said that public school teachers should not be allowed to lead students in any type of prayers.

In June 2022, before the Center conducted the survey, the Supreme Court ruled in favor of a football coach at a public high school who had prayed with players at midfield after games. More recently, Texas lawmakers introduced several bills in the 2023 legislative session that would expand the role of religion in K-12 public schools in the state. Those proposals included a bill that would require the Ten Commandments to be displayed in every classroom, a bill that would allow schools to replace guidance counselors with chaplains, and a bill that would allow districts to mandate time during the school day for staff and students to pray and study religious materials.

Mentions of diversity, social-emotional learning and related topics in school mission statements are more common in Democratic areas than in Republican areas. K-12 mission statements from public schools in areas where the majority of residents voted Democratic in the 2020 general election are at least twice as likely as those in Republican-voting areas to include the words “diversity,” “equity” or “inclusion,” according to an April 2023 Pew Research Center analysis .

Also, about a third of mission statements in Democratic-voting areas (34%) use the word “social,” compared with a quarter of those in Republican-voting areas, and a similar gap exists for the word “emotional.” Like diversity, equity and inclusion, social-emotional learning is a contentious issue between Democrats and Republicans, even though most K-12 parents think it’s important for their children’s schools to teach these skills . Supporters argue that social-emotional learning helps address mental health needs and student well-being, but some critics consider it emotional manipulation and want it banned.

In contrast, there are broad similarities in school mission statements outside of these hot-button topics. Similar shares of mission statements in Democratic and Republican areas mention students’ future readiness, parent and community involvement, and providing a safe and healthy educational environment for students.

• Education & Politics
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Jenn Hatfield is a writer/editor at Pew Research Center

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