Tax Revenue Gambling Australia

The statistics were obtained from cross-sectional analysis of Household, Income and Labour Dynamics in Australia (HILDA) Survey data, wave 15, which is the first wave to include gambling questions. The HILDA Survey was designed so that participants' responses (17,606 participants in wave 15) could be generalised to the Australian adult population. South Australia’s BOT rate is set at 15 per cent of net wagering revenue subject to a $150,000 threshold. SACES modelled the impacts on Government tax collections from WSPs other than the SA TAB if the BOT rate was reduced from 15 per cent to 10 per cent and from 15 per cent to 8 per cent. We're responsible for administering, collecting and managing debts of gaming and wagering tax payable in NSW, on behalf of Liquor and Gaming NSW. Find out more about COVID-19 (coronavirus) and gaming machine tax. The taxes applied to gaming and wagering in NSW are based on: what kind of gambling is being offered; who's providing the gambling. Discount valid only for tax prep fees for an original 2017 personal income tax return prepared in a participating office. May not be combined with any other promotion including Free 1040EZ. Void if sold, purchased or transferred, and where prohibited. In the fourth installment of our “Sin Tax” series, we explore the financial implications of gambling on Uncle Sam’s bottom line. It’s no secret that the government makes some income from gambling. But, many would be surprised to find that total gaming brings in nearly $100 billion in tax revenue each year.

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Department of Treasury and Finance has released a report prepared by SACES regarding the revenue impacts of the Betting Operations Tax (BOT).

The BOT was introduced in South Australia in 2017 and most Australian States have since introduced BOT’s of their own or plan to do so. A key feature of the BOT is that it collects taxes from wagering service providers (WSPs) wherever they are located, with the tax liability in South Australia based on all wagering by South Australian residents wherever the WSP and betting event is located.

The SACES report was commissioned by the Department in response to representations from the South Australian racing codes that they have been adversely affected by the introduction of the BOT. The codes presented the Government with a study prepared by the wagering services industry body Responsible Wagering Australia (RWA) which argued that a cut in the BOT rate would increase SA Government tax collections.

South Australia’s BOT rate is set at 15 per cent of net wagering revenue subject to a $150,000 threshold. SACES modelled the impacts on Government tax collections from WSPs other than the SA TAB if the BOT rate was reduced from 15 per cent to 10 per cent and from 15 per cent to 8 per cent.

SACES’ modelling results are in stark contrast to RWA’s results. SACES finds that the most likely consequence of reducing the BOT rate would be to reduce South Australia’s BOT collections from non-SA TAB WSPs quite substantially.

SACES concludes that “The RWA argument that a BOT rate reduction would boost South Australian Government BOT revenues implies that everybody can win from a BOT rate reduction. But on the available evidence RWA’s position is simply not plausible”.

The report was released by the Department of Treasury and Finance on 7 June 2019.

It was co-authored by Jim Hancock, Suraya Abdul Halim and Owen Covick.

Executive Summary

This report provides an overview of gambling activity in Australia in 2015, with respect to participation, expenditure, and problems among regular gamblers. The report follows a format and style common to gambling prevalence studies conducted in Australia and elsewhere.

As with those studies, the report is intended as a reference document. It is written primarily for researchers and government officials who have an interest in Australian gambling statistics. This report makes a unique contribution to knowledge of gambling in Australia, since Australia has no prior history of surveying and reporting on gambling activity among regular gamblers at the national level.

The content consists primarily of descriptive statistics with a focus on population estimates. The statistics were obtained from cross-sectional analysis of Household, Income and Labour Dynamics in Australia (HILDA) Survey data, wave 15, which is the first wave to include gambling questions. The HILDA Survey was designed so that participants' responses (17,606 participants in wave 15) could be generalised to the Australian adult population.

The participation statistics include population-representative estimates of the proportion and number of Australians who spent money on up to ten common gambling activities (lotteries, instant scratch tickets, electronic gaming machines, race betting, sports betting, keno, casino table games, bingo, private betting and poker) in a typical month of 2015. The report refers almost entirely to these gamblers, which we refer to as regular gamblers.

Chapter 1 of this report provides the background to the study and details regarding study design and methodology. Chapters 2 and 3 respectively provide statistics regarding typical gambling participation and expenditure.Chapters 4 and 5 address participation and expenditure among adults who experienced gambling-related problems. In Chapter 6 gambling expenditure is positioned within the household budgets of low, middle and high-income households. As well, rates of financial stress are compared between households that contain members with and without gambling problems. Additional tables, including a comparison of the HILDA Survey gambling statistics with recent state/territory and national prevalence data and industry revenue data, can be found in the Appendices.

The report identifies an estimated 6.8 million regular gamblers in 2015, among whom lottery participation was very common (76%). Instant scratch tickets (22%) and electronic gaming machines (EGMs; 21%) followed, attracting 1.4 to 1.5 million gamblers. Less than a million gambled regularly on anything else, including racing (14%), sports betting (8%), keno (8%), casino table games (3%), bingo (3%), private betting (2%) and poker (2%). It was common for people to participate either solely in lotteries (59%), or a combination of lotteries and up to two additional activities.

While lotteries and instant scratch tickets were the most popular activities, individual gamblers spent comparatively little on these activities in a typical month, and therefore over the entirety of the year ($695 and $248 per year on average). Those who gambled on Electronic Gaming Machines spent a great deal more per year ($1,292 on average). So too did those who regularly gambled on races ($1,308), sports ($1,032), casino table games ($1,369), and particularly poker ($1,758).

Regular gamblers, viewed by activity, have quite different profiles. For example, compared to the Australian population:

• lottery participants were over-represented among older couples living without children;
• EGM participants were over-represented among people for whom welfare payments formed their main source of income;
• bingo participants were over-represented among retired women living alone;
• regular race or sports bettors were over-represented among men on higher incomes, yet the race bettors were more likely to be older and live in outer regional/remote areas; and
• sports bettors were more likely to be younger and live in an inner-regional area or major city.

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Gambling problems are indicated in the HILDA Survey by endorsing one or more items on the Problem Gambling Severity Index (PGSI). According to the standard use of the PGSI, 1.1 million regular gamblers were estimated to have behaved in ways that caused or put them at risk of gambling-related problems.

Among this subset of regular gamblers, there were more sociodemographic similarities than differences. Those who experienced problems were generally more likely to be young, single, unemployed or not employed (excluding retirees and full-time students), Indigenous, men, living in rental accommodation, in a low socioeconomic area, and were more likely to draw their income from welfare payments than those who had no problems.

Those with problems were also more likely to participate regularly in certain activities. This led to rates of problems being particularly high among participants in six activities (EGMs, race betting, sports betting, casino table games, private betting, and poker) with almost 1-in-2 gamblers on any of these activities experiencing one or more issues.

Another thing those with problems had in common was higher than average spending on gambling. This was particularly so among EGM, race and sports betting participants. Those experiencing the greatest problems spent more than four times as much on these activities, and on gambling overall, as those without problems. Well over half of all expenditure by regular gamblers on these activities came from people who had problems.

Overall, more than forty percent of gambling expenditure by regular gamblers, aggregated across all activities, was accounted for by the 17% who experienced problems.

Tax Revenue Gambling Australia 2019

Gambling expenditure has significant financial ramifications for low-income households, particularly among households where gamblers experienced problems. Gamblers living in low-income households spent a much greater proportion of their household's total disposable income on gambling than high-income households (10% vs 1% on average) - this despite spending less in actual dollar terms ($1,662 vs $2,387).

Gamblers who had problems spent much more of their households' income on gambling than other regular gamblers, with those experiencing severe problems in low-income households spending an average 27% of their disposable household income on gambling - equivalent to four times their yearly household utility bills, or more than half the grocery bills for that income group.

Consistent with these patterns of expenditure, the households of those with gambling problems had a much greater proportion of stressful financial events. Inability to pay electricity, gas or telephone bills on time, and needing to ask friends or family for financial help, were common occurrences.

Future waves of the HILDA Survey will provide nationally representative longitudinal data with which to measure changes in gambling activity and effects on individuals and their households.

The authors would like to thank all those colleagues who contributed to creating gambling questions for the HILDA survey and for their input into this report. In particular, we would like to thank:

• Doctor Anna Thomas, Australian Gambling Research Centre, Australian Institute of Family Studies
• Doctor Jennifer Baxter, Australian Institute of Family Studies
• Assistant Professor Nicki Dowling, Deakin University
• Professor Bryan Rodgers, Australian National University
• Acting Director Rachel Henry, Welfare Quarantining and Gambling Branch, Department of Social Services
• Professor Mark Wooden, Melbourne Institute of Applied Economic and Social Research, University of Melbourne
• Doctor Diana Warren, Australian Institute of Family Studies
• Director Anne Hollonds, Australian Institute of Family Studies
• And all of the participants who took part in the HILDA Survey and made this report possible.

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Disclaimer

This report uses unit record data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey. The HILDA Project was initiated and is funded by the Australian Government Department of Social Services (DSS) and is managed by the Melbourne Institute of Applied Economic and Social Research (Melbourne Institute). The findings and views reported in this paper, however, are those of the authors and should not be attributed to either DSS or the Melbourne Institute. As well, the views expressed may not reflect those of the Australian Institute of Family Studies or the Australian Government.

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