Young Women Sports Bettors in the United Kingdom: An Overlooked Demographic?
Gambling and Sports in a Global Age
ISBN: 978-1-80117-305-6, eISBN: 978-1-80117-304-9
ISSN: 1476-2854
Publication date: 17 November 2023
Abstract
Despite increasingly persuasive women-focused marketing of gambling products, there has only been limited investigation around women sports betting. Men remain the focus of much of the conversation about sports betting as they have generally been found to be the most active sports bettors and the most at risk of experiencing harms associated with their behaviour. This chapter aims to fill this gap by exploring the characteristics of young women sports bettors in the United Kingdom and the relationship between sports betting and the experience of gambling harms. To do this, we created two models of analysis. Our analysis is based on data from the first wave (2019) of the Emerging Adults Gambling Survey (EAGS) dataset (n = 3,549). The EAGS is a non-probability longitudinal survey that includes individuals between the ages of 16 and 24 who were residents in Britain at the time of data collection. Firstly, we examined the associations between women sports bettors and several factors identified as important predictors of sports betting. Secondly, we sought to understand the relationship between women's sports betting and the harms associated with this activity. From these models, we found that women's sports betting was most reliably predicted according to fandom and peer influence. We also found that women sports bettors were more at risk of experiencing harms associated with difficulties with family and friends than women gamblers using other products.
Keywords
Citation
Biggar, B., Kesaite, V., Ukhova, D. and Wardle, H. (2023), "Young Women Sports Bettors in the United Kingdom: An Overlooked Demographic?", McGee, D. and Bunn, C. (Ed.) Gambling and Sports in a Global Age (Research in the Sociology of Sport, Vol. 18), Emerald Publishing Limited, Leeds, pp. 145-167. https://doi.org/10.1108/S1476-285420230000018010
Publisher
:Emerald Publishing Limited
Copyright © 2024 Blair Biggar, Viktorija Kesaite, Daria Ukhova and Heather Wardle. Published by Emerald Publishing Limited. These works are published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of these works (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode.
License
These works are published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of these works (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
Introduction
Despite increasingly persuasive women-focused marketing of gambling products (McCarthy et al., 2018, p. 3), there has only been limited investigation around women sports betting. Yet there has been little consideration of the profile of these women, their sociodemographic and socioeconomic characteristics, and the impact it may have on them. Men are the focus of much of the conversation about sports betting as they have generally been found to be the most active sports bettors and the most at risk of experiencing harms associated with their behaviour (e.g. see Mercier et al., 2018; Russell, Hing, Li, et al., 2019). This chapter aims to fill this gap, using data from the EAGS to explore the characteristics of young women sports bettors in the United Kingdom and the relationship between sports betting and the experience of gambling harms.
First, we review what is already known about sports betting, its relationship to harm and how sports betting experiences are gendered before outlining our methodological approach to analysis. In our results section, we examine the associations between women sports bettors and several factors identified as important predictors of sports betting. We also present findings on how experiences of harm differ for women and men engaged in sports betting and other forms of gambling. From this, we examine whether the relationship between sports betting and harm varies between men and women. Our discussion will position these findings in the limited literature that already exists on women sports bettors and the more expansive literature on sports bettors at large. Before concluding, we highlight the limitations of our study and make recommendations for future research on the risks of harm, with a particular focus on the role of intersectional inequalities.
Rise, Normalisation and Alleged Feminisation of Sports Betting Globally and in the United Kingdom
Betting, alongside gambling more widely, has deep historic roots in society. Betting on horse racing can be traced back to the Assyrian Kings of 1500 bc and the Roman Emperors of the first millennium (Reith, 1999, p. 72). Reith (1999, p. 73) tracks the ‘explosion’ of betting on an array of activities back to the seventeenth and eighteenth centuries alongside a wider gambling boom. In particular, it was the eighteenth century when betting on horse racing shifted from an ‘elite pastime to a mass spectator sport’ (Reith, 1999, p. 73). Today, sports betting is ‘one of the fastest developing forms of gambling internationally’ and has garnered an increasing amount of attention from academia, media and regulators (Lopez-Gonzalez, Russell, Hing, Estévez, & Griffiths, 2020, p. abstract). A key driver of this rise of sports betting is the many options arising through the expansion of accessible internet (Lopez-Gonzalez et al., 2020, p. 937).
This fits within a broader policy shift which has stimulated the growth of the contemporary gambling landscape in the past few decades. In the UK context, the 2005 Gambling Act dramatically changed the gambling landscape (Reith & Wardle, 2022). The Act was built on neoliberal ideas of personal responsibility and a free economic market. Thus, it sought to liberalise the UK market with a fundamental aim to ‘permit’ gambling (Reith & Wardle, 2022, p. 73). In many ways, the Act has succeeded, with the United Kingdom's (previously highly restricted) commercial gambling market growing to be one of the world's largest (Orford, 2018; cited by Reith & Wardle, 2022, p. 73). The Gambling Commission was created as a regulator for gambling, but it adopted the same fundamental ideas as the Act. As a result, the responsibility for regulation is placed largely on the individuals whose ‘freedom to choose’ is paramount (Reith & Wardle, 2022, p. 74).
Greater proliferation of gambling products in everyday life and the cultural salience of sport have been of significant concern to policymakers and health professionals due to its effects on mental and physical health and the social and economic problems it poses (see Lopez-Gonzalez et al., 2020).
Much work has been done already to account for and understand how targeted marketing has cast sports betting as a desirable and normal activity. Seal et al. (2022) give a detailed overview of the different foci of the rising scholarship on sports betting. Firstly, the impact of normalising gambling through sports betting and the link between sport and gambling, especially among young people, is of increasing concern (e.g. see Purves, Critchlow, Morgan, Stead, & Dobbie, 2020; cited by Seal et al., 2022, p. 1). Secondly, a focus on the ‘symbiotic’ nature of the relationship between gambling and sports has been described by McGee (2020) as the ‘gamblification’ of sports, with a focus on the commercial agreements between football clubs and players and gambling companies (Seal et al., 2022). Anyone consuming sports-related content in the United Kingdom or internationally can easily recognise the increasingly ubiquitous presence of gambling sponsorship and advertisement through sports teams, players, managers, celebrities and broadcasters. This advertising is a public health concern when we recognise its role in normalising and encouraging products that are associated with extensive harms for some (see Ireland, 2021, p. 143). Through the platform of sports, we see how gambling companies can target a young and diverse audience of potential gamblers (Lopez-Gonzalez et al., 2020, pp. 937–938).
We have growing evidence of the way sports betting advertising influences people's attitudes and behaviour (Seal et al., 2022). Key findings relate to the ‘normalising’ effect of sports betting and its ‘acceptance as part of peer-based socialisation and general sports fandom’ (Bunn et al., 2019; Raymen & Smith, 2017; cited by Seal et al., 2022, p. 2). This accompanies a refocusing of the responsibility for gamblers' behaviour on the wider environment and social determinants as opposed to being solely focused on individuals (Johnstone & Regan, 2020). Relatedly, there has also been a large focus on the marketing of sports betting across online platforms and sports broadcasting (Milner, Hing, Vitartas, & Lamont, 2013; Thomas, Lewis, Duong, & McLeod, 2012).
Generally, this literature shows that gambling and sports are increasingly intertwined. As part of this relationship (and because of it), sports betting is promoted heavily as a normal everyday part of sports fandom. As a result, a large proportion of sports fans see betting as a part of the culture of sports and can recognise gambling brand names whether or not they bet themselves. This includes young people and others who are more at risk from gambling harms. Profiling of sports bettors has also commonly found that the two broad groups of concern due to the heightened risk of harm from sports betting are men and young people (both men and women) (Seal et al., 2022). In their narrative review, Frisone, Settineri, Sicari, and Merlo (2020) found that adolescent women were less at risk than adolescent men because adolescent women are more involved in ‘casual gambling’ than adolescent men. However, gambling harms exacerbate and compound existing inequalities. Therefore, people who already face disproportionate levels of exclusion and stigmatisation in society are more vulnerable to gambling harms. The increased focus on sports betting trends, especially among young people, focuses largely on men and has been viewed through the lens of masculinity and gendered assumptions about the consumption of sports – arguably reinforced by advertising and marketing (Hing, Russell, Tolchard, & Nower, 2016; Mercier et al., 2018; Russell, Hing, & Browne, 2019). However, women are also sports bettors. Despite this, there is very little insight into these groups and their experiences. Focusing on Great Britain, Wardle (2017) argued that gendered patterns of gambling behaviour were a result of conscious decisions taken in the 1960s by politicians wishing to limit women's gambling, and purposively creating men-centric spaces (bookmakers), excluding women from a previous pastime that they engaged in. She argued that the advent of internet gambling might undo some of these trends, by giving women a safe, more gender-neutral space to engage (Wardle, 2017). Again, the advent of internet gambling has intertwined with the changing shape of regulation through the 2005 Gambling Act to make for a much larger commercial gambling market in the United Kingdom.
Althaus, Zendle and Bowden-jones (2021, p. 944) have also noted that ‘the prevalence of gambling has increased among women in recent years and is likely to represent a cyclic resurgence in gambling among women, linked to societal change and targeted advertising, rather than a new phenomenon’. They also suggest that women tend to start gambling later than men, but are at greater risk of progressing more rapidly to problem gambling or ‘gamble for shorter durations before seeking treatment’ (Althaus et al., 2021). Targeted advertising has increased the vulnerability of women to gambling in more recent years (Althaus et al., 2021). A simple Google search of ‘women’ and ‘betting ads’ produces examples of targeted marketing from large gambling companies that have an inescapable presence in sports broadcasting: William Hill, Skybet and Ladbrokes who prominently feature women in their ads. These ads can be seen as intersectionally targeted along lines of race and gender. This is not an entirely new strategy as we can see from studies like Downs (2010), who detailed a case study of the birth of commercial bingo in 1958–1970 to show how celebrities were strategically used to promote gambling among women. Similar strategies were also used by the tobacco industry (Schmidt, 2012, pp. 1–5). Wardle (2017) has argued that the changes that came from the 2005 Gambling Act created space for the growth of the industry. This has come with increases in online gambling and greater gambling advertising which has legitimised gambling as a valid recreational activity and has contributed to a re-feminisation of gambling.
In the twenty-first century, there has also been a rise in girls playing male-dominated sports such as football, rugby and cricket. In recent years, we have seen an increasing focus and viewership for women's sports – the Women's Euros in 2022 were BBC primetime programming and saw record crowds in stadiums. Thus, there has also been an increase in popularity and media coverage of women's sports has grown audiences for sports that were traditionally male dominated. Gambling sponsorship has predictably risen in women's sports as part of this development as the profile of women's sports rising has come with rapid changes to the promotional culture that surrounds its marketisation. Thus, it is likely that gambling may also be becoming more normalised within women's behaviours, and women's sporting fandom.
Determinants of Sports Betting Among Young People and Their Relationship With Gender
Understanding the determinants of youth gambling behaviours requires attention to the broader social contexts in which individuals are embedded, how gambling products are promoted and provided and the connections between individuals and their environments (Wardle, Reith, Langham, & Rogers, 2019). Much attention has been given to the intergenerational transfer of risk, exploring associations between parental gambling behaviours and that of young people. Greater gambling frequency among children of problem gamblers has been reported by a range of studies (Delfabbro, Lahn, & Grabosky, 2005; Delfabbro & Thrupp, 2003; Dowling et al., 2016; Vachon, Vitaro, Wanner, & Tremblay, 2004).
Studies have also found that parental influence can lead to gambling behaviours starting earlier and ‘an elevated incidence of problem gambling’ (Dowling et al., 2016, p. 13; Emond & Griffiths, 2020). Emond and Griffiths (2020) found that the risk of gambling harm among adolescents was greater among those with low self-esteem, a history of hyperactivity and impulsivity, those who consumed more alcohol than their peers, had less parental supervision and had parents who gamble. Thus, they understood parental influence alongside a range of other factors. Whether parental influence is as reliable a predictor of their children's gambling regardless of gender is disputed. For example, Forrest and McHale (2021) found in their UK-based longitudinal study that parental problem gambling increased the likelihood of problem gambling among their children. However, they found that this result was only found across gender. Thus, a father's behaviour influenced their daughters and mothers influenced their sons. Conversely, Vachon et al. (2004) suggested that while parental influence was seen in the frequency of gambling, only paternal problem gambling heightened the risk of adolescents being involved in gambling and developing their own gambling-related problems.
It is not only parental influence on gambling behaviours that have been examined. Being exposed to peers who gamble is a strong predictor of adolescent gambling involvement (cited by Chalmers & Willoughby, 2006, p. 375; Hardoon & Derevensky, 2001; Langhinrichsen-Rohling, Rohde, Seeley, & Rohling, 2004). Being surrounded by peers who gamble normalises this behaviour and can normalise gambling-related harm (Russell, Langham, & Hing, 2018, p. 12). Freund et al. (2022) found that having a parent, sibling, best friend, another relative or someone else within the social network who had gambled in the last month increased the likelihood of participation in gambling among secondary school students. Knowing gamblers in their network also increased the likelihood of engaging in ‘hard gambling activity’ in the past month and heightened their risk of suffering from gambling-related problems (Freund et al., 2022, p. 5). Van Hoorn et al.'s (2017) study of peer influence among adolescents when gambling found that risk-taking was increased by the presence of peers. This was caused primarily by the social norms that they associated with their peers (Van Hoorn, Crone, & Van Leijenhorst, 2017). Chalmers and Willoughby (2006) found when testing for gender differences in this relationship among adolescents that it was more likely for girls that ‘parents and peers may have a greater influence on engagement in gambling behaviour’ compared with men (Chalmers & Willoughby, 2006, p. 389). Hardoon and Derevensky (2001) also found that women were more likely than men to be impacted by gambling in situations where they were playing with other men and women – thus they were found to be more influenced by the group condition than men in the study.
A recent paper, also using the EAGS data, found that advertising and marketing had a direct impact on youth gambling behaviours, with around a third of gamblers stating that this prompted them to gamble when they were not otherwise going to do so (Wardle, Critchlow, Brown, Donnachie, & Hunt, 2022). However, this study did not look at these effects by gender. Some studies argue this increased visibility of gambling is responsible for increases in sports betting among younger adults. Labrador and Vallejo-Achón (2020) argued the rise of betting was influenced by its omnipresent advertising; the targeting of young people; the ease of betting (particularly online); the relationship between consumption promotion across social networks through celebrities and recognisable influencers (Labrador & Vallejo-Achón, 2020, p. 299).
Whilst this brief (and non-exhaustive) review sheds some light on the range of determinants that shape individual behaviours, it is also clear that very few studies have examined how this may (or may not) vary for men and women. Using EAGS dataset, we present preliminary findings to start to address this gap.
Gambling Harms Among Women
The analysis in this chapter presents findings on the relationships between these bettors and the associated harms they face. There is a growing international recognition of the types and scale of harm caused by gambling (see Public Health England, 2019). Aligned with this literature we consider gambling to be a public health issue, whereby gambling harms are faced by gamblers and wider society. Types of harms are commonly grouped in relation to financial, emotional and psychological (mental health), relationships, education, work and other harms (see Public Health England, 2019). For every person dealing with problems associated with gambling harms, it is estimated that up to six others can be adversely affected (Goodwin, Browne, Rockloff, & Rose, 2017). Thus, the economic and social costs of gambling are often underestimated (Wardle et al., 2019). Different products may come with different levels of risk of harms according to context and some groups such as younger individuals and individuals from ethnic minority groups are more at risk of being harmed by gambling. When considering the levels of risk faced by gamblers, we must consider the intersectional risks faced alongside other inequalities that may make people more at risk due to wider societal inequalities. For example, gender, race, sexuality and class must be viewed alongside and in relation to factors such as age. Any groups who are broadly disadvantaged in society have the potential to be more at risk of gambling-related harm (McCarthy, Pitt, Bellringer, & Thomas, 2021, p. 1).
This chapter does not dispute the commonly respected idea that young men are more likely to be sports bettors than women. However, our findings fit with some other studies in suggesting that a significant number of women are sports bettors. McCarthy et al. (2018) found that younger women were more likely to be betting on sports and gamble on other products than older women. As women and young people, they are more at risk of experiencing gambling harms and therefore it is important to learn more about the gendered experiences of sports bettors. Consequently, women-specific gambling harms have been under-researched; a better understanding of these harms would allow for a more tailored approach to reducing gambling harms among women.
Data and Methodology
Our analysis is based on data from the first wave (2019) of the Emerging Adults Gambling Survey (EAGS) dataset (n = 3,549). The term ‘emerging adult’ was coined by Arnett (2000) and is used to refer to an age group characterised by a high propensity for impulsivity and risk-taking behaviour before settling into adult roles and responsibilities. These characteristics of their behaviour make emerging adults more vulnerable to problem gambling. Thus, our sample is of a group of young people who are particularly at risk of facing gambling harms. The EAGS is a non-probability longitudinal survey that includes individuals between the ages of 16 and 24 who were residents across Britain at the time of data collection. Respondents were interviewed on a range of topics including their gambling activity, health, socio-economic status and their parental background (Wardle, 2020). Non-probability sampling is commonly used for research in which people are included in the study when they have met certain criteria (Acharya, Prakash, Saxena, & Nigam, 2013, p. 332). This means that the sampling is not random and potential bias is not measured or controlled (Acharya et al., 2013, p. 332). For this reason, the findings we discuss throughout this chapter relate our specific sample. One useful feature of the EAGS dataset is that it provides data on online and in-person sports betting as well as a wide range of sociodemographic and socio-economic variables. This enables us to examine the profile of young women and young men who bet on sports.
Data was collected from respondents from across the United Kingdom. As seen in Tables 1 and 2, our results from the first model considered various sociodemographic and socioeconomic variables. These included the age range of respondents, ethnicity, educational attainment, whether they were employed or studying and where their area level of deprivation according to the Indices of Multiple Deprivation (IMD).
Our sports betting measure was created by combining two categorical measures: frequency of betting on sports in licenced betting offices and frequency of betting online on sports events. We ran summary statistics that showed that a third of sports bettors online were women in our sample. We also found that a third of sports bettors in betting shops were women. Thus, we opted to combine these to make a sports bettor measure. The two variables combined to make our sports bettor measure asked respondents to answer whether they had bet online or in-person in the past 12 months. Thus, the sports bettor measure captures all sports bettors in our sample who were active in the 12 months beforehand.
Our results section can be divided into three main stages. Firstly, summary statistics present both unweighted and weighted summaries. The latter allows us to attenuate bias due to the non-random non-response of the sample (Heeringa, West, & Berglund, 2010). Secondly, we examine the profile of sports bettors and factors associated with sports betting, by gender. This includes examining the role of parental and peer gambling as well as education, income and region on the likelihood of being involved in sports betting.
Finally, we examine the extent to which women and men sports bettors are more or less likely to experience a range of gambling harms compared with other types of gamblers, i.e. those who only gambled on lotteries and any other form of gambling (ranging from online casinos, slots, to bingo) but who do not bet on sports. In our exploration of harm, we used variables which captured whether respondents had experienced harms related to finances, productivity and relationships often/sometimes or never/rarely. Our financial harm measure was based on whether respondents had less money to buy things (including food and drink). Our productivity harm measure asked whether gambling had negatively impacted performance in school or work. Finally, relationship harm was measured by difficulties with family or friends caused by gambling.
Results and Analysis
Over one-third (33%) of the sports bettors (online and offline) in our sample were women. Thus, we agree with the common finding that men are more likely to be sports bettors than women. However, this proportion was higher than we expected, suggesting that the number of young women engaged in sports betting in Britain may be greater than previously thought. As Table 1 shows, there were some key differences in the profile of men and women sports bettors. Firstly, men sports bettors were more likely to report betting once a month or more regularly than women sports bettors (45.8% vs. 37.7%) and they were more likely to bet in play (57% vs. 34%). However, this still means that around one-third (33%) of women sports bettors were betting on sports regularly (that is once a month or more) and were engaging in betting practices (in-play betting) which have been highlighted as being riskier (Gainsbury, Abarbanel, & Blaszczynski, 2020). Notably, among both men and women sports bettors, around 20% were under the legal age to gamble the first time they placed a sports bet online. This suggests that some young people can still navigate around age verification rules to access gambling online before being of legal age. Among the emerging adult age cohort, women sports bettors were a similar age as men sports bettors, with 50.8% being aged 22–24 compared to 46.2% of males. Notably, despite the similar age profile of sports bettors, women had higher levels of educational attainment with over 50% being educated to degree level. For other characteristics, the patterns between men and women sports bettors were similar: around 1-in-10 (10%) were not in formal education, employment or training; between 8.7% and 13.2% were from non-white ethnic groups and similar proportion numbers lived in the most deprived area quintile. 1
Sample Characteristics of Current Gamblers Among Online or Offline Sports Bettors.
| Socio-economic/Demographic and Gambling Characteristics | Women | Men | ||||||
|---|---|---|---|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | |||||
| % | N | % | N | % | N | % | N | |
| Age | ||||||||
| 16–18 | 10.7 | 15 | 14.5 | 17 | 21.0 | 59 | 16.1 | 52 |
| 19–21 | 34.3 | 48 | 34.8 | 41 | 42.0 | 118 | 37.7 | 121 |
| 22–24 | 55.0 | 77 | 50.8 | 60 | 37.0 | 104 | 46.2 | 148 |
| Ethnicity | ||||||||
| White | 92.0 | 126 | 91.3 | 106 | 85.7 | 234 | 86.8 | 269 |
| Non-white | 8.0 | 11 | 8.7 | 10 | 14.3 | 39 | 13.2 | 41 |
| Educational Attainment | ||||||||
| Degree or other professional | 58.5 | 79 | 55.0 | 63 | 39.0 | 105 | 43.9 | 134 |
| A level of equiv. | 28.9 | 39 | 29.3 | 34 | 49.4 | 133 | 44.6 | 136 |
| GCSE or other qual. | 12.6 | 17 | 15.7 | 18 | 11.5 | 31 | 11.5 | 35 |
| Not Working or Studying (NEET) | ||||||||
| Yes | 10.0 | 14 | 9.3 | 11 | 11.4 | 32 | 11.8 | 282 |
| No | 90.0 | 126 | 90.7 | 108 | 88.6 | 249 | 88.2 | 38 |
| Indices of Multiple Deprivation (IMD) | ||||||||
| 5th most deprived | 28.2 | 37 | 28.1 | 31 | 22.5 | 53 | 24.8 | 69 |
| 1st to 4th most deprived | 71.8 | 94 | 71.9 | 79 | 77.5 | 183 | 75.2 | 211 |
| Whether Was Under Legal Age to Gamble on Online Sports First Time | ||||||||
| Yes | 16.8 | 20 | 22.2 | 23 | 19.6 | 46 | 19.4 | 51 |
| No | 83.2 | 99 | 77.8 | 79 | 80.4 | 189 | 80.6 | 214 |
| How Often Bet In-Play | ||||||||
| At least once a month | 15.7 | 22 | 15.7 | 19 | 26.0 | 73 | 25.2 | 81 |
| A few times a year | 18.6 | 26 | 17.1 | 20 | 31.3 | 88 | 30.8 | 99 |
| Never | 65.7 | 92 | 67.2 | 80 | 42.7 | 120 | 44.0 | 141 |
| Frequency of Betting Online on Sports | ||||||||
| At least once a month | 30.7 | 43 | 30.5 | 36 | 45.9 | 129 | 45.3 | 145 |
| A few times a year | 56.4 | 79 | 56.6 | 67 | 46.6 | 131 | 47.3 | 152 |
| Never | 12.9 | 18 | 12.9 | 15 | 7.5 | 21 | 7.4 | 24 |
| Frequency of Betting in Betting Shops on Sports | ||||||||
| At least once a month | 5.0 | 7 | 5.3 | 6 | 8.2 | 23 | 8.3 | 27 |
| A few times a year | 12.1 | 17 | 12.4 | 15 | 12.5 | 35 | 12.0 | 38 |
| Never | 82.9 | 116 | 82.4 | 98 | 79.4 | 223 | 79.7 | 255 |
Note: Current gamblers include those individuals who engaged in gambling in the last 12 months.
To extend this, using logistic regression models (separately for men and women sports bettors), we examine the factors associated with being a sports bettor. These models include socioeconomic and demographic features, and also measures of broader gambling behaviours – such as the influence of parents and peers, the influence of advertising and marketing and sports fandom.
Who Are Women Sports Bettors?
The total number of observations in our sample was 683 women and 554 men. Table 2 shows the results from our logistic regression from which three key findings are clear: the impact of your peer network, the frequency of watching sports (whether online or in person) and age, are associated with women sports betting.
For women sports bettors, the odds ratio of being a sports bettor was 3.62 times higher among those whose closest friend also gambled once a week (among males, the odds were 2.32). Sports betting was 6.58 times more likely among women who watched sports on TV or online more than once a week and 3.7 times if they had watched a few times in the past 12 months. For men, these odds were further increased with men watching more than once a week being 10.58 times more likely to be sports bettors. Men who had watched live sports on TV or online a few times in the last 12 months were 4.26 times more likely to be sports bettors.
Watching sports in person also had a significant impact. Women who watched live sports in person more than once a week were 2.17 times more likely to be sports bettors whilst watching a few times in the last 12 months increased their chances by 1.68. For men, there was no evidence of a relationship between watching sports in person and sports betting.
Among men, age was associated with sports betting, with the odds of sports betting being higher as age increased. A similar pattern was observed for women among 22–24-year-olds, though the age group of those aged 19–21 did not differ from the reference group of 16–18-year-olds, likely because the sample is somewhat underpowered to detect these differences. For the adolescent boys and men up to 24 in our sample, we found that the likelihood of being a sports bettor was increased by having a mother who gambles. However, for women, we did not find evidence to confidently suggest that there was any parental influence in predicting the likelihood of sports betting among our sample. We believe that further research is needed to examine the general parental effects on both sons and daughters.
Binary Logistic Regression of Predictors of Online or Offline Sports Bettors.
| Women, N = 693 | Men, N = 554 | |||||||
|---|---|---|---|---|---|---|---|---|
| Explanatory variables | ORadj | 95% CI | p Value | Wald test, Prob > F |
ORadj | 95% CI | p Value | Wald test, Prob > F |
| Age groups | 0.0409 | p < 0.001 | ||||||
| 16-18 (ref.) | 1 | 1 | ||||||
| 19-21 | 2.16 | 0.96 to 4.83 | P = 0.061 | 3.09 | 1.74 to 5.49 | p < 0.001 | ||
| 22-24 | 2.72 | 1.24 to 5.95 | P = 0.013 | 3.24 | 1.80 to 5.82 | p < 0.001 | ||
| Deprivation level | 0.1414 | 0.2352 | ||||||
| 5th most deprived | 1.44 | 0.86 to 2.34 | P = 0.141 | 0.74 | 0.45 to 1.22 | P = 0.235 | ||
| 1st to 4th most deprived (ref.) | 1 | 1 | ||||||
| Not in employment, education or training | 0.4868 | 0.4654 | ||||||
| Yes | 1.30 | 0.62 to 2.71 | P = 0.487 | 1.28 | 0.66 to 2.49 | P = 0.465 | ||
| No (ref.) | 1 | 1 | ||||||
| Ethnicity | 0.4697 | 0.0581 | ||||||
| White (ref) | 1 | 1 | ||||||
| Non-white | 0.73 | 0.31 to 1.71 | P = 0.470 | 2.03 | 0.98 to 4.22 | P = 0.058 | ||
| Parental education | 0.4235 | 0.1162 | ||||||
| University level (ref.) | 1 | 1 | ||||||
| Secondary or lower | 1.40 | 0.54 to 1.30 | P = 0.424 | 1.40 | 0.92 to 2.13 | P = 0.116 | ||
| Whether mother gambles at least once a week | 0.7983 | p < 0.001 | ||||||
| Yes | 0.92 | 0.48 to 1.75 | P = 0.798 | 0.24 | 0.11 to 0.53 | P < 0.001 | ||
| No (ref.) | 1 | 1 | ||||||
| Whether father gambles at least once a week | 0.8181 | 0.3572 | ||||||
| Yes | 0.93 | 0.48 to 1.78 | P = 0.818 | 1.35 | 0.71 to 2.53 | P = 0.357 | ||
| No (ref.) | 1 | 1 | ||||||
| Whether closest friend gambles at least once a week | P < 0.01 | P < 0.01 | ||||||
| Yes | 3.62 | 1.66 to 7.89 | P < 0.001 | 2.32 | 1.27 to 4.25 | P < 0.01 | ||
| No (ref.) | 1 | 1 | ||||||
| Whether received any direct marketing (tv, radio, social media, web and apps) | 0.6133 | 0.3059 | ||||||
| Yes | 0.59 | 0.07 to 4.62 | P = 0.613 | 0.43 | 0.09 to 2.15 | P = 0.306 | ||
| No (ref.) | ||||||||
| Whether seen any gambling advertising in the past month | 0.5297 | 0.8431 | ||||||
| Yes | 1.48 | 0.44 to 5.01 | P = 0.5297 | 0.90 | 0.32 to 2.51 | p = 0.843 | ||
| No (ref.) | 1 | 1 | ||||||
| Frequency of watching sports on TV or online | p < 0.001 | p < 0.001 | ||||||
| More than once a week | 6.58 | 2.88 to 15.05 | p < 0.001 | 10.58 | 4.75 to 23.53 | p < 0.001 | ||
| A few times (in the last 12 months) | 3.70 | 1.66 to 8.24 | P < 0.001 | 4.26 | 1.83 to 9.89 | P < 0.05 | ||
| Not at all in the last 12 months (ref.) | 1 | 1 | ||||||
| Frequency of watching sports in person | 0.1453 | |||||||
| More than once a week | 2.17 | 1.10 to 4.26 | P < 0.05 | P < 0.05 | 1.73 | 0.92 to 3.25 | P = 0.088 | |
| A few times (in the last 12 months) | 1.68 | 1.00 to 2.84 | P = 0.052 | 1.61 | 0.95 to 2.75 | P = 0.079 | ||
| Not at all in the last 12 months (ref.) | 1 | |||||||
| Impulsivity | P = 0.8676 | 0.1697 | ||||||
| Yes | 1.04 | 0.63 to 1.72 | P = 0.868 | 0.70 | 0.42 to 1.16 | P = 0.170 | ||
| No (ref.) | 1 | |||||||
Note: The sample includes current gamblers who engaged in gambling in the last 12 months.
What Is the Harm By Gambling Activity Among Women
To explore the extent to which sports betting is associated with the experience of gambling harms, we analysed different measures of harm (financial, productivity and relationship) by gambler type (sports bettors, lottery or other gambling). First, we created three mutually exclusive types of gamblers: those who only played lotteries; those who had bet on sports (as well as other things) in the past year and those who gambled on other activities but not sports betting or lotteries alone. This latter group included those who may have played slot machines, online casino games, online bingo or engaged in private betting. Overall, 17.5% of women gamblers were sports bettors, 36.71% were lottery-only gamblers and 45.79% were other gamblers. Equivalent estimates for men were 38.6%, 23.7% and 37.68%, respectively.
The harms we measured related to participants' experiences of financial harm, their work/education and productivity at work or school and their relationships with friends and family. Financial harms included anyone who stated that they at least sometimes have less money to spend on food or drink because of gambling or that gambling had stopped them from buying other things they wanted. Productivity included individuals who stated that at least sometimes gambling had made it difficult for them to attend work/education; made it difficult for them to concentrate in work/education; or made them very tired when attending work/education. Finally, relationship harms consist of anyone saying that gambling at least sometimes caused them to argue with friends or family; made them feel less close to friends and family; or made them lie to family members or guardians.
Overall, 4.5% of women gamblers reported experiencing financial harms sometimes or often, 4.7% experienced productivity harms and 11.7% experience relationship harms. Equivalent estimates for men were 7.3%, 7.9% and 22.9%.
Among women, there appeared to be a pattern by which the experience of each of these harms was greatest among those who were sports bettors, followed by those who were other gamblers and lowest among those who were lottery-only gamblers. Notably, 22% of women sports bettors stated that they had at least sometimes experienced one of the relationship harms asked about. Comparable estimates among other bettors were 12.6% and among lottery-only gamblers were 5.3%.
For relationship harms, this pattern was confirmed in logistic regression models, whereby among women the odds of experiencing gambling-related harm were significantly higher among sports bettors (1.97) than those who were engaged in other gambling activities. The odds of experiencing relationship harms were lower among those who were lottery-only gamblers compared with other gamblers. For men, sports betting was statistically insignificant, while lottery results were similar in magnitude to women's findings, and it was also statistically significant.
For financial and productivity harms, there were no differences between sports bettors and those engaged in other forms of gambling, whilst lottery-only gamblers were less likely to experience these things. This pattern was the same for men and women. Thus, we can suggest that men and women gamblers in our sample had comparable levels of risk from these harms (Tables 3 and 4).
Measures of Harm by Gambling Activity.
| Measures of Harm | Women, N = 766 | Men, N = 730 | ||||
|---|---|---|---|---|---|---|
| Gambler Type | Gambler Type | |||||
| Sports Bettors | Lottery | Other Gambling (e.g. Casino) | Sports Bettors | Lottery | Other Gambling (e.g. Casino) | |
| % (n) | % (n) | % (n) | % (n) | % (n) | % (n) | |
| Financial Harm: Had Less Money to Buy Things, Including Food/Drink | ||||||
| Sometimes | 9.3 (13) | 1.6 (5) | 5.1 (19) | 8.3 (24) | 4.0 (8) | 8.34 (23) |
| Never/rarely | 90.7 (127) | 98.4 (282) | 94.9 (320) | 91.7 (257) | 96.0 (165) | 91.66 (253) |
| Productivity Harm: Poorer Performance at School/Work | ||||||
| Sometimes | 9.7 (13) | 1.8 (6) | 5.0 (17) | 7.8 (25) | 4.7 (9) | 10.13 (26) |
| Never/rarely | 90.3 (127) | 98.2 (281) | 95.0 (322) | 92.2 (256) | 95.3 (164) | 89.87 (250) |
| Relationship Harm: Difficulties With Family/Friends | ||||||
| Sometimes | 22.3 (31) | 5.3 (17) | 12.7 (46) | 25.8 (74) | 12.3 (22) | 26.64 (73) |
| Never/rarely | 77.7 (109) | 94.7 (270) | 87.3 (293) | 74.2 (207) | 87.7 (151) | 73.36 (203) |
Impact of Gambling Activity on Gambling Related Harm.
| Women, N = 766 | Men, N = 730 | |||||||
|---|---|---|---|---|---|---|---|---|
| Financial Harm: Had Less Money to Buy Things, Including Food/Drink | ||||||||
| Odds Ratio | 95% CI | p Value |
Wald Test,
Prob > F |
Odds Ratio | 95% CI | p Value | Wald Test, Prob > F | |
| Gambler type | 0.003 | 0.172 | ||||||
| Sports bettor | 1.93 | 0.90 to 4.11 | P < 0.1 | 0.99 | 0.53 to 1.84 | P = 0.973 | ||
| Lottery | 0.30 | 0.11 to 0.84 | P < 0.05 | 0.46 | 0.19 to 1.09 | P < 0.1 | ||
| Other gambling, e.g. casino (ref.) | 1 | 1 | ||||||
| Productivity Harm: Poorer Performance at School/Work | ||||||||
| Gambler type | 0.003 | 0.134 | ||||||
| Sports bettor | 2.03 | 0.93 to 4.43 | P < 0.1 | 0.75 | 0.41 to 1.38 | P = 0.356 | ||
| Lottery | 0.35 | 0.13 to 0.92 | P < 0.05 | 0.43 | 0.19 to 0.99 | P < 0.05 | ||
| Other gambling, e.g. casino (ref.) | 1 | 1 | ||||||
| Relationship Harm: Difficulties With Family/Friends | ||||||||
| Gambler type | 0.000 | 0.002 | ||||||
| Sports bettor | 1.97 | 1.17 to 3.34 | P < 0.05 | 0.96 | 0.65 to 1.42 | P = 0.835 | ||
| Lottery | 0.38 | 0.21 to 0.70 | P < 0.01 | 0.38 | 0.22 to 0.66 | P < 0.01 | ||
| Other gambling, e.g. casino (ref.) | 1 | 1 | ||||||
Discussion
The results of this study lend further evidence regarding young women's increasing engagement with sports betting (McCarthy et al., 2018). While our results indicate that sports betting among young people in the United Kingdom remains an activity dominated by men, we found that a substantial proportion of sports bettors were women among our British sample of emerging adults. Importantly, we find a comparatively higher share of women among young sports bettors than similar studies recently carried out in Italy, Spain and the United States, where women comprise no more than a quarter of young sports bettors (Bozzato, Longobardi, & Fabris, 2020; Labrador & Vallejo-Achón, 2020; Noel, Rosenthal, & Sammartino, 2022; Weidberg et al., 2018) 2 .
As discussed in our results section, we found that there was a significant overlap in the predictors for sports betting among our sample of men and women. Thus, we suggest that women should not be considered safe from sports betting and its associated harms. We found that watching sports and peer influence are important predictors of young women's sports betting. According to this, women were more likely to bet on sports when they were fans of the sports or when gambling was an activity that their close peer network was involved with. We also found through our question on harms that women sports bettors in our sample were disproportionally at risk of facing harms compared to women who gamble on other products.
Very few studies have considered patterns and associations of women sports betting in depth, and our study lends support to emerging evidence on the relationships between sports fandom and the importance of social networks in shaping women's behaviour. Our findings highlight, for women as well as men, there is likely a reciprocal relationship between watching sports and sports betting. Whilst much research literature to date has focused on men's experiences, whereby gambling has become a normalised aspect of sports fandom for young men (McGee, 2020), it appears similar processes may be at play for women. McCarthy et al., have examined this among young women, noting that motivation for betting whilst watching sports was a way to enhance the excitement and experience. They also noted that some participants engaged in sports betting as a way to bond with their partners or friends who were interested in sports (McCarthy et al., 2022, p. 12). Their findings suggested overall that sports betting was often socially motivated and connected to ease of access.
Notably, our study found that having a close friend who gambled increased the odds ratio of being a sports bettor by 3.62 for women. As a result, our findings suggest that there is a significant power held by peer influences over gambling behaviour. Having peers who gamble can normalise gambling behaviour and experiences of gambling-related harm (Russell et al., 2018, p. 12). The presence of peers may also heighten the likelihood of risk-taking for gamblers (Van Hoorn et al., 2017). These influences are just as apparent in our sample for women sports bettors as for men.
In addition, we sought to examine the extent to which parental influences may shape gambling behaviours. As noted earlier, the evidence tends to show intergenerational correlations between parental and child gambling behaviours. A wealth of prior research has found that family members are often key facilitators in gambling initiation. Less is known about the role of family networks in the continuation and trajectory of gambling careers across the life course. McCarthy, Thomas, Pitt, Daube and Cassidy (2020) found that family members were the key facilitators of gambling for young women in Australia but were replaced by peers and partners as most instrumental after they reached the legal gambling age. Our study tentatively supports these findings because we did not find evidence of a relationship between parental gambling behaviour and women's sports betting but did between peer gambling and women's sports betting. However, there are some limitations to the analysis performed here – we only measured how often each parent gambled (which itself may be subject to measurement error) and not what forms of gambling each parent participated in. In future studies, capturing the format of parental gambling is likely to be important to tease out these relationships further.
There are multiple interesting findings from our results on the relationship between harms and sports betting. We looked at data on three types of harm: (1) Gambling meant less money to buy things including food and drink; (2) gambling meant poorer performance at work or school; (3) gambling caused difficulties with family and friends. We found that young women sports bettors experience harms at rates similar to men. This finding is in contrast with an argument tentatively put forward by Russell, Hing and Browne (2019) that being a woman might be viewed as a protective factor against harms from sports betting. Women engaged in sports betting experienced much higher levels of harms than women gamblers using other products. For men, there was no such noticeable ‘harms gap’ between those engaged in sports betting and ‘other types’ of gambling (except lottery). Thus, the risk of harm for sports betting men was the same across products.
When we consider our findings on the demographics of women sports bettors together with the risk of harm, we find a potentially paradoxical situation. As discussed, women are more likely to be sports bettors due to peer influence, social bonding and social motivations. However, we also found that women sports bettors were more at risk of experiencing harms associated with difficulties with family and friends than women gamblers using other products. Thus, the same relationships that motivate sports betting among women may be at risk of deterioration due to the activity and its associated harms. This chapter tentatively suggests that these processes may be more apparent for women than men, which requires further investigation. It is further notable that women sports bettors tended to gamble less regularly and were less engaged in risky sports betting practices (in-play betting) than men. Given these behaviours are highly associated with an increased risk of harm, it is concerning to see this elevated level of relationship harms reported among women sports bettors. Whilst further investigation is needed into this and understanding of how women integrate sports betting into wider gambling repertoires, at minimum our data suggests that women should be considered equally as vulnerable to gambling harms as men. Women's vulnerabilities may manifest in particular ways which is important to recognise and further investigate.
There are some limits to the findings presented here that must be acknowledged. Primarily, the sample size means that our base sample was relatively small. As a result, we want this chapter to serve as a call for further research and attention to be done to test our findings and push them forward to more fully understand the demographics of women sports bettors and their gendered experiences of harms. Our findings are also based on a non-probability sample which influences what we can say from our data. There are some themes which we expected to find and did not which may be because of the peculiarities of this particular sample. For example, we did not find a significant relationship between parental gambling behaviours and the likelihood of their children being sports bettors. However, we approach this with caution as we have already highlighted in our background section that there have been significant studies looking at this, albeit with inconclusive results. Thus, again, we need further studies to explore the relationships discussed here which would avoid the risk of sample selection bias.
Furthermore, we would normally expect to see a relationship between certain demographic or socio-economic characteristics and gambling propensity which was not evident in this study (for example, relationships with income, ethnicity, employment, deprivation, etc.). Whilst sampling bias may be at play here, also it may be possible that these patterns are less relevant to this age cohort. According to this idea, socioeconomic factors may be less relevant for younger generations who are exposed to gambling from a wide array of directions (particularly through the internet), which both encourages and normalises gambling. This needs further investigation. In writing this chapter, we were surprised by the number of young women sports bettors we identified in our sample. We should not have been. Prior theories about how the increasing availability of gambling through more gender-neutral channels (like online), changing social processes promoting sports and sports fandom as both gender-neutral and family-orientated activities and women-centric gambling promotion would suggest an inevitability of such observations (e.g. see McCarthy et al., 2018; Wardle, 2017). Yet, this has not translated into a women-focused research agenda, and too often women's experiences are explored by continued comparisons to men. This has a notable impact, as Nadine, a contributor to the Tackling Gambling Stigma project outlines:
I know the majority who come forward for support are males……like 75% of the people that come forward (for support) are males and then nothing is said about the 255 that are women. You’re just making women feel like they have no place and I just always feel like we’re pushed off the table.
We believe our findings and the focus of this chapter have sought to solidify the need for women-focused experiences of gambling harms going forward so as not to push women off the table in future research. The invisibility of women in health research has a long history (Langer et al., 2015). With this chapter, we call for this history not to be repeated.
Conclusion
This chapter has sought to answer two questions. Firstly, we have given new insights into the demographics of women sports bettors in the United Kingdom. An under-researched sub-group of sports bettors, we have argued that there is a need to understand these characteristics to aid policy that will be sensitive to gendered experiences. From our models, we found that women’s sports betting was most reliably predicted by fandom and peer influence. With the increasing profile of women in sport (e.g. through the growing reach and investment in women's football), we believe it is essential that further studies draw out the scale of women sports betting and their profile.
Secondly, we sought to understand the relationship between women sports betting and the associated harms faced. We found that women sports bettors were more at risk of experiencing harms associated with difficulties with family and friends than women gamblers using other products. As a result, we are suggesting that our findings show that women sports bettors are a particularly vulnerable group that requires further attention. Whilst further investigation is needed into this and understanding of how women integrate sports betting into wider gambling repertoires, at minimum our data suggests that women should be considered equally as vulnerable to gambling harms as men. Women's vulnerabilities may manifest in particular ways which are important to recognise and further investigate. It is necessary to place women's experience at the centre of research endeavour to help understand, explain and mitigate experiences of gambling harms among this underserved group.
Other ethnic group includes Irish, Gypsy or Irish Traveller, Any other White background, White and Black Caribbean, White and Black African, White, and Asian, Any other Mixed/Multiple ethnic backgrounds, Indian, Pakistani, Bangladeshi, Chinese, Any other Asian background, African, Caribbean, Any other Black/African/Caribbean, Arab, and any other ethnic group. The Index of Multiple Deprivation (IMD) is based on seven domains: Income, Employment, Health Deprivation and Disability, Education, Skills Training, Crime, Barriers to Housing and Services, and Living Environment (https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/835115/IoD2019_Statistical_Release.pdf).
All studies except the one done by Weidberg et al. (2018) had non-probability sampling. As is standard, differences in sampling procedures and study designs suggest that these cross-country differences should be interpreted with caution.
References
Acharya et al., 2013 Acharya, A. S. , Prakash, A. , Saxena, P. , & Nigam, A. (2013). Sampling: Why and how of it? Indian Journal of Medical Specialities, 4(2), 330–333. doi:10.7713/ijms.2013.0032
Althaus et al., 2021 Althaus, J. , Zendle, D. , & Bowden-jones, H. (2021). Textbook of addiction treatment. Textbook of Addiction Treatment, 943–953. doi:10.1007/978-3-030-36391-8
Arnett, 2000 Arnett, J. J. (2000). Emerging adulthood: A theory of development from the late teens through the twenties. American Psychologist, 55(5), 469–480. doi:10.1037/0003-066X.55.5.469
Bozzato et al., 2020 Bozzato, P. , Longobardi, C. , & Fabris, M. A. (2020). Problematic gambling behaviour in adolescents: Prevalence and its relation to social, self-regulatory, and academic self-efficacy. International Journal of Adolescence and Youth, 25(1), 907–919. doi:10.1080/02673843.2020.1772842
Bunn et al., 2019 Bunn, C. , Ireland, R. , Minton, J. , Holman, D. , Philpott, M. , & Chambers, S. (2019). Shirt sponsorship by gambling companies in the English and Scottish Premier Leagues: Global reach and public health concerns. Soccer and Society, 20(6), 824–835. doi:10.1080/14660970.2018.1425682
Chalmers and Willoughby, 2006 Chalmers, H. , & Willoughby, T. (2006). Do predictors of gambling involvement differ across male and female adolescents? Journal of Gambling Studies, 22(4), 373–392. doi:10.1007/s10899-006-9024-6
Delfabbro et al., 2005 Delfabbro, P. , Lahn, J. , & Grabosky, P. (2005). Further evidence concerning the prevalence of adolescent gambling and problem gambling in Australia: A study of the ACT. International Gambling Studies, 5, 209–228.
Delfabbro and Thrupp, 2003 Delfabbro, P. , & Thrupp, L. (2003). The social determinants of youth gambling in South Australian adolescents. Journal of Adolescence, 26, 313–330.
Dowling et al., 2016 Dowling, N. A. , Shandley, K. , Oldenhof, E. , Youssef, G. J. , Thomas, S. A. , Frydenberg, E. , & Jackson, A. C. (2016). The intergenerational transmission of problem gambling: The mediating role of parental psychopathology. Addictive Behaviors, 59, 12–17. doi:10.1016/j.addbeh.2016.03.002
Downs, 2010 Downs, C. (2010). Mecca and the birth of commercial bingo 1958–70: A case study. Business History, 52(7), 1086–1106. doi:10.1080/00076791.2010.523460
Emond and Griffiths, 2020 Emond, A. M. , & Griffiths, M. D. (2020). Gambling in children and adolescents. British Medical Bulletin, 136(1), 21–29. doi:10.1093/bmb/ldaa027
Forrest and McHale, 2021 Forrest, D. , & McHale, I. G. (2021). Transmission of problem gambling between adjacent generations. Journal of Gambling Studies, 37(2), 711–722. doi:10.1007/s10899-020-09977-8
Freund et al., 2022 Freund, M. , Noble, N. , Hill, D. , White, V. , Leigh, L. , Scully, M. , … Lambkin, D. (2022). Exposure to other people's gambling and gambling behaviors in Australian secondary school students. Psychology of Addictive Behaviors. doi:10.1037/adb0000854
Frisone et al., 2020 Frisone, F. , Settineri, S. , Sicari, F. , & Merlo, E. M. (2020). Gambling in adolescence: A narrative review of the last 20 years. Journal of Addictive Diseases, 38(4), 438–457. doi:10.1080/10550887.2020.1782557
Gainsbury et al., 2020 Gainsbury, S. M. , Abarbanel, B. , & Blaszczynski, A. (2020). The relationship between in-play betting and gambling problems in an Australian context of prohibited online in-play betting. Frontiers in Psychiatry, 11, 1–9. doi:10.3389/fpsyt.2020.574884. Retrieved from https://www.frontiersin.org
Goodwin et al., 2017 Goodwin, B. C. , Browne, M. , Rockloff, M. , & Rose, J. (2017). A typical problem gambler affects six others. International Gambling Studies, 17(2), 276–289. doi:10.1080/14459795.2017.1331252
Hardoon and Derevensky, 2001 Hardoon, K. K. , & Derevensky, J. L. (2001). Social influences involved in children's gambling behavior. Journal of Gambling Studies, 17(3).
Heeringa et al., 2010 Heeringa, S. , West, B. , & Berglund, P. (2010). Applied Survey Data Analysis (1st ed.). New York: Chapman and Hall/CRC.
Hing et al., 2016 Hing, N. , Russell, A. , Tolchard, B. , & Nower, L. (2016). Risk factors for gambling problems: An analysis by gender. Journal of Gambling Studies, 32(2), 511–534. doi:10.1007/s10899-015-9548-8
Ireland, 2021 Ireland, R. (2021). Commercial determinants of health in sport. The example of the English Premier League. [University of Glasgow]. Retrieved from http://theses.gla.ac.uk/82394/
Johnstone and Regan, 2020 Johnstone, P. , & Regan, M. (2020). Gambling harm is everybody's business: A public health approach and call to action. Public Health, 184, 63–66. doi:10.1016/j.puhe.2020.06.010
Labrador and Vallejo-Achón, 2020 Labrador, F. J. , & Vallejo-Achón, M. (2020). Prevalence and characteristics of sports betting in a population of young students in Madrid. Journal of Gambling Studies, 36(1), 297–318. doi:10.1007/s10899-019-09863-y
Langer et al., 2015 Langer, A. , Meleis, A. , Knaul, F. M. , Atun, R. , Aran, M. , Arreola-Ornelas, H. , … Frenk, J. (2015). The Lancet Commissions Women and Health: The key for sustainable development. The Lancet, 386, 1165–1210. doi:10.1016/S0140-6736(15)60497-4
Langhinrichsen-Rohling et al., 2004 Langhinrichsen-Rohling, J. , Rohde, P. , Seeley, J. , & Rohling, M. (2004). Individual, family, and peer correlates of adolescent gambling. Journal of Gambling Studies, 20(1), 23–46.
Lopez-Gonzalez et al., 2020 Lopez-Gonzalez, H. , Russell, A. M. T. , Hing, N. , Estévez, A. , & Griffiths, M. D. (2020). A cross-cultural study of weekly sports bettors in Australia and Spain. Journal of Gambling Studies, 36(3), 937–955. doi:10.1007/S10899-019-09898-1/TABLES/3
McCarthy et al., 2021 McCarthy, S. , Pitt, H. , Bellringer, M. E. , & Thomas, S. L. (2021). Strategies to prevent and reduce gambling harm in Australian women. Drugs: Education, Prevention & Policy. doi:10.1080/09687637.2021.1973963
McCarthy et al., 2020 McCarthy, S. , Thomas, S. , Pitt, H. , Daube, M. , & Cassidy, R. (2020). ‘It's a tradition to go down to the pokies on your 18th birthday’ – The normalisation of gambling for young women in Australia. Australian & New Zealand Journal of Public Health, 44(5), 376–381.
McCarthy et al., 2022 McCarthy, S. , Thomas, S. L. , Pitt, H. , Warner, E. , Roderique-Davies, G. , Rintoul, A. , & John, B. (2022). “They loved gambling more than me.” Women's experiences of gambling-related harm as an affected other. Health Promotion Journal of Australia. doi:10.1002/hpja.608
McCarthy et al., 2018 McCarthy, S. , Thomas, S. L. , Randle, M. , Bestman, A. , Pitt, H. , Cowlishaw, S. , & Daube, M. (2018). Women's gambling behaviour, product preferences, and perceptions of product harm: Differences by age and gambling risk status. Harm Reduction Journal, 15(1), 1–12. doi:10.1186/s12954-018-0227-9
McGee, 2020 McGee, D. (2020). On the normalisation of online sports gambling among young adult men in the UK: A public health perspective. Public Health, 184, 89–94. doi:10.1016/j.puhe.2020.04.018
Mercier et al., 2018 Mercier, J. , Sévigny, S. , Jacques, C. , Goulet, A. , Cantinotti, M. , & Giroux, I. (2018). Sports bettors: A systematic review. Journal of Gambling Issues, 38, 203–236.
Milner et al., 2013 Milner, L. , Hing, N. , Vitartas, P. , & Lamont, M. J. (2013). Embedded gambling promotion in Australian football broadcasts: An exploratory study. Communication, Politics & Culture, 46(2), 177–198.
Nadine, 2022 Nadine . (2022). Contributions of Nadine. Tackling Gambling Stigma. Retrieved from https://tacklinggamblingstigma.com/contributor/nadine/
Noel et al., 2022 Noel, J. K. , Rosenthal, S. R. , & Sammartino, C. J. (2022). Correlates of gambling and gambling problems among Rhode Island young adults: A cross-sectional study. Journal of Public Health, 1–7. doi:10.1093/pubmed/fdac023
Orford, 2018 Orford, J. (2018). The regulation of gambling in early twenty-first century Britain: liberalisation and its consequences. In M. Egerer , V. Marionneau , & J. Nikkinen (Eds.), Gambling policies in European welfare states current challenges and future prospects (pp. 241–257). London: Palgrave Macmillan.
Public Health England, 2019 Public Health England . (2019). Harms associated with gambling: An abbreviated systematic review. Retrieved from publishing.service.gov.uk
Purves et al., 2020 Purves, R. I. , Critchlow, N. , Morgan, A. , Stead, M. , & Dobbie, F. (2020). Examining the frequency and nature of gambling marketing in televised broadcasts of professional sporting events in the United Kingdom. Public Health, 184(April), 71–78. doi:10.1016/j.puhe.2020.02.012
Raymen and Smith, 2017 Raymen, T. , & Smith, O. (2017). Lifestyle gambling, indebtedness and anxiety: A deviant leisure perspective. Journal of Consumer Culture. doi:10.1177/1469540517736559
Reith, 1999 Reith, G. (1999). The age of chance. London: Routledge.
Reith and Wardle, 2022 Reith, G. , & Wardle, H. (2022). The framing of gambling and the commercial determinants of harm: Challenges for regulation in the UK. In J. Nikkinen , V. Marionneau , & M. Egerer (Eds.), The global gambling industry (pp. 71–86). Springer Fachmedien Wiesbaden. doi:10.1007/978-3-658-35635-4_6
Russell et al., 2019 Russell, A. M. T. , Hing, N. , & Browne, M. (2019). Risk factors for gambling problems specifically associated with sports betting. Journal of Gambling Studies, 35(4), 1211–1228. doi:10.1007/s10899-019-09848-x
Russell et al., 2019 Russell, A. M. T. , Hing, N. , Li, En , Vitartas, P. , Li, E. , & Vitartas, P. (2019). Gambling risk groups are not all the same: Risk factors amongst sports bettors. Journal of Gambling Studies, 35, 225–246. doi:10.1007/s10899-018-9765-z
Russell et al., 2018 Russell, A. M. T. , Langham, E. , & Hing, N. (2018). Social influences normalize gambling-related harm among higher risk gamblers. doi:10.1556/2006.7.2018.139
Schmidt, 2012 Schmidt, L. (2012). Tobacco industry targeting of women and girls (Vol. 1). Campaign for Tobacco-Free Kids. Retrieved from 0138.pdf (tobaccofreekids.org)
Seal et al., 2022 Seal, E. , Cardak, B. A. , Nicholson, M. , Donaldson, A. , O'Halloran, P. , Randle, E. , & Staley, K. (2022). The gambling behaviour and attitudes to sports betting of sports fans. Journal of Gambling Studies (0123456789). doi:10.1007/s10899-021-10101-7
Thomas et al., 2012 Thomas, S. L. , Lewis, S. , Duong, J. , & McLeod, C. (2012). Sports betting marketing during sporting events: A stadium and broadcast census of Australian Football League matches. Australian and New Zealand. Journal of Public Health, 36(2), 145–152.
Vachon et al., 2004 Vachon, J. , Vitaro, F. , Wanner, B. , & Tremblay, R. E. (2004). Adolescent gambling: Relationships with parent gambling and parenting practices. Psychology of Addictive Behaviors, 18(4), 398–401. doi:10.1037/0893-164X.18.4.398
Van Hoorn et al., 2017 Van Hoorn, J. , Crone, E. A. , & Van Leijenhorst, L. (2017). Hanging out with the right crowd: Peer influence on risk-taking behavior in adolescence. Journal of Research on Adolescence, 27(1), 189–200. doi:10.1111/jora.12265
Wardle, 2017 Wardle, H. (2017). The ‘re-feminisation’ of gambling: Social, cultural and historical insights into female gambling behaviour in Great Britain. In H. Bowden-Jones & F. Prever (Eds.), Gambling disorders in women: An international female perspective on treatment and research (p. 14). Cham: Routledge.
Wardle, 2020 Wardle, H. (2020). The emerging adults gambling survey: Study protocol. Wellcome Open Research, 5(May), 1–10. doi:10.12688/wellcomeopenres.15969.1
Wardle et al., 2022 Wardle, H. , Critchlow, N. , Brown, A. , Donnachie, C. , & Hunt, K. (2022). The association between gambling marketing and unplanned gambling spend: Synthesised findings from two online cross-sectional surveys. Addictive Behaviors. doi:10.1016/j.addbeh.2022.107440
Wardle et al., 2019 Wardle, H. , Reith, G. , Langham, E. , & Rogers, R. D. (2019). Gambling and public health: We need policy action to prevent harm. The BMJ, 365(May), 1–5. doi:10.1136/bmj.l1807
Weidberg et al., 2018 Weidberg, S. , González-Roz, A. , Fernández-Hermida, J. R. , Martínez-Loredo, V. , Grande-Gosende, A. , García-Pérez, Á. , & Secades-Villa, R. (2018). Gender differences among adolescent gamblers. Personality and Individual Differences, 125(December 2017), 38–43. doi:10.1016/j.paid.2017.12.031
- Prelims
- Introduction: Towards a Sociology of Sports Gambling
- Chapter 1 Historicising Sports Gambling
- Chapter 2 Brand Engagement in a Digital Age: Marketing Gambling to Newcastle United Fans
- Chapter 3 Data Ownership, Athlete Rights and the Global Sports Gambling Industry
- Chapter 4 The Production of Horse Racing in the Nordics
- Chapter 5 Integrity Matters: Denormalising Gambling in Belgian and Dutch Sports Clubs
- Chapter 6 Football Betting Among University Students in Ghana
- Chapter 7 Conceptualising the Normalisation of Folk Religion in the Sports Betting Practices of Young Nigerians
- Chapter 8 Where You Stand: Trust and Fixing in the Asian Sports Gambling Market
- Chapter 9 Young Women Sports Bettors in the United Kingdom: An Overlooked Demographic?
- Chapter 10 The Impact of Marketing on the Normalisation of Gambling and Sport for Children and Young People
- Afterword: Sociological Reflections on Gambling, Sport and Power
- Index