Gambling Participation, Expenditure and Risk of Harm in Australia, 1997–1998 and 2010–2011

By Armstrong, A. R., Thomas, A., & Abbott, M.

Abstract: Gambling-related harm results primarily from financial losses. Internationally Australia continues to rank as the largest spending nation per capita on gambling products. This would suggest that Australian gamblers are at disproportionately high risk of harm despite almost two decades of industry scrutiny and regulation, and investment in research, treatment and education programs. However, declines in participation rates, per capita expenditure, household expenditure, national disposable income spent on gambling and problem gambling rates have been cited as evidence that fewer people are gambling, that gamblers are spending less, and that gambling safety in Australia has improved. The current study investigated these propositions using national population and accounts data, and statistics from Australia’s two population-representative gambling surveys conducted in 1997–1998 and 2010–2011. Despite a falling participation rate the study found no real change in the number of people gambling overall, and increasing numbers consuming casino table games, race wagering and sports betting. Further found were increases rather than decreases in average gambler expenditure, overall, and across most products, particularly electronic gaming machines (EGMs). Potentially risky levels of average expenditure were observed in both periods, overall and for race wagering, casino table gaming, and EGMs. Changes in the proportion of income spent on gambling suggest risks declined overall and for race wagering and casino table gaming, but increased for EGMs. Finally, while problem gambling statistics were not comparable between periods, the study found double the number of moderate risk gamblers previously estimated for 2010–2011 amongst the 2 million Australians found to have experienced one or more gambling-related problems. The findings have implications for public health policy and resourcing, and the way in which prevalence and expenditure statistics have been interpreted by researchers, government and industry in Australia and elsewhere.

Armstrong, A. R., Thomas, A., & Abbott, M. (2017). Gambling Participation, Expenditure and Risk of Harm in Australia, 1997–1998 and 2010–2011. Journal of Gambling Studies, 1–20.



A meta-regression analysis of 41 Australian problem gambling prevalence estimates and their relationship to total spending on electronic gaming machines (open access)

By Francis Markham, Martin Young, Bruce Doran, and Mark Sugden.

Background: Many jurisdictions regularly conduct surveys to estimate the prevalence of problem gambling in their adult populations. However, the comparison of such estimates is problematic due to methodological variations between studies. Total consumption theory suggests that an association between mean electronic gaming machine (EGM) and casino gambling losses and problem gambling prevalence estimates may exist. If this is the case, then changes in EGM losses may be used as a proxy indicator for changes in problem gambling prevalence. To test for this association this study examines the relationship between aggregated losses on electronic gaming machines (EGMs) and problem gambling prevalence estimates for Australian states and territories between 1994 and 2016.

Methods: A Bayesian meta-regression analysis of 41 cross-sectional problem gambling prevalence estimates was undertaken using EGM gambling losses, year of survey and methodological variations as predictor variables. General population studies of adults in Australian states and territory published before 1 July 2016 were considered in scope. 41 studies were identified, with a total of 267,367 participants. Problem gambling prevalence, moderate-risk problem gambling prevalence, problem gambling screen, administration mode and frequency threshold were extracted from surveys. Administrative data on EGM and casino gambling loss data were extracted from government reports and expressed as the proportion of household disposable income lost.

Results: Money lost on EGMs is correlated with problem gambling prevalence. An increase of 1% of household disposable income lost on EGMs and in casinos was associated with problem gambling prevalence estimates that were 1.33 times higher [95% credible interval 1.04, 1.71]. There was no clear association between EGM losses and moderate-risk problem gambling prevalence estimates. Moderate-risk problem gambling prevalence estimates were not explained by the models (I 2 ≥ 0.97; R 2 ≤ 0.01).

Conclusions: The present study adds to the weight of evidence that EGM losses are associated with the prevalence of problem gambling. No patterns were evident among moderate-risk problem gambling prevalence estimates, suggesting that this measure is either subject to pronounced measurement error or lacks construct validity. The high degree of residual heterogeneity raises questions about the validity of comparing problem gambling prevalence estimates, even after adjusting for methodological variations between studies.

Self-Reported Losses Versus Actual Losses in Online Gambling: An Empirical Study (full text)

Auer, M., and Griffiths, M.

Many research findings in the gambling studies field rely on self-report data. A very small body of empirical research also suggests that when using self-report, players report their gambling losses inaccurately. The aim of the present study was to evaluate the differences between objective and subjective gambling spent data by comparing gambler’s actual behavioral tracking data with their self-report data over a 1-month period. A total of 17,742 Norwegian online gamblers were asked to participate in an online survey. Of those surveyed, 1335 gamblers answered questions relating to gambling expenditure that could be compared with their actual gambling behavior. The study found that the estimated loss self-reported by gamblers was correlated with the actual objective loss and that players with higher losses tended to have more difficulty estimating their gambling expenditure (i.e., players who spent more money gambling also appeared to have more trouble estimating their expenses accurately). Overall, the findings demonstrate that caution is warranted when using self-report data relating to amount of money spent gambling in any studies that are totally reliant on self-report data.

The relationship between player losses and gambling-related harm: evidence from nationally representative cross-sectional surveys in four countries

Background and Aims: Flaws in previous studies mean that findings of J-shaped risk curves for gambling should be disregarded. The current study aims to estimate the shape of risk curves for gambling losses and risk of gambling-related harm (a) for total gambling losses and (b) disaggregated by gambling activity.

Design: Four cross-sectional surveys.

Setting: Nationally representative surveys of adults in Australia (1999), Canada (2000), Finland (2011) and Norway (2002).

Participants: A total of 10 632 Australian adults, 3120 Canadian adults, 4484 people aged 15–74 years in Finland and 5235 people aged 15–74 years in Norway.

Measurements: Problem gambling risk was measured using the modified South Oaks Gambling Screen, the NORC DSM Screen for Gambling Problems and the Problem Gambling Severity Index…

Source: Markham, F., Young, M., & Doran, B. (2015). The relationship between player losses and gambling-related harm: evidence from nationally representative cross-sectional surveys in four countries: Player loss risk curves for gambling harms. Addiction, n/a–n/a.