Problem Gambling and the Circumstances Facing Older People

Abstract

Local gambling venues are an important contemporary context for older people's gambling in many parts of the world typically being more accessible to this segment of the population than traditional, centralised gambling venues, such as casinos. This study, undertaken in South East Queensland, analyses older people's electronic gaming machine (EGM) behaviour and motivations, specifically in the context of licensed social and recreational clubs-a popular local gambling venue in many parts of Australia. The study gathered data via a postal survey of 80 managers of licensed clubs, interviews with Gambling Help services and a survey of 414 people aged 60+ who regularly play EGMs, self-administered on site at local clubs. The analysis undertaken suggests that certain age-related circumstances of older people-such as being without a partner, having a disability that impacts on everyday activities, having a low annual income, and no longer participating in the workforce-are associated with higher overall levels of motivation for playing EGMs and greater reliance on EGMs to meet social, recreational and mental health needs. Over a quarter of the older people surveyed (27%) reported drawing on their savings to fund their EGM gambling. Certain categories of older people, including those who were without a partner and those with a disability, were more likely to report drawing on their savings to fund EGM play and betting more than they could afford to lose, pointing to age-related vulnerabilities older people may experience to the negative impacts of gambling given the greater likelihood of their dependency on smaller, fixed incomes. The explanatory contribution of a range of demographic and motivational variables on problem/moderate risk gambling status was computed via a logistic regression model. Younger age (60-69), male gender, single marital status and being motivated to play EGMs to experience excitement and to win money all emerged as significant predictors in the model.