The Centre’s Calculation Methodology

Since the Productivity Commission’s survey in 1999, EGM expenditure has continued to grow strongly. Between 1998-99 and 2002-03, South Australian expenditure on EGMs rose by more than twice the rate of gross household disposable income (51 per cent compared to 24 per cent). Given that survey data indicates that participation in EGM gambling has not changed significantly since 1999, this suggests that there has been some change in the pattern of EGM spending, either with those gambling willing to spend a greater proportion of their income on gaming machines, or that there has been an increase in the proportion of individuals experiencing problem gambling, or some combination of the two. For this reason it was decided to repeat the analysis that the researchers first undertook in 2001 which used regional expenditure and income data to try and assess the potential distribution of problem gambling in South Australia.

The original motivation behind the development of this methodology was a desire to try and examine the extent to which problem gambling may vary between regions. This is a difficult issue to examine through the use of surveys, as to determine the level of a relatively rare phenomenon in individual local authorities would require a very large sample size. The advantage of using expenditure data is that they are available at a very disaggregated level, they are even available at SLAs within councils, but of course it is impossible to actually prove that any “excess” expenditure is due to problem gambling and so the results can only ever be indicative.

The model developed by the researchers in 2001 used data from the Productivity Commission on average national net EGM gaming expenditure by problem and non-problem gamblers to calculate the average proportion of after tax income spent by each type of gambler.89 By making the assumption that these averages were constant between regions average net gaming revenue estimates could be calculated for both types of gambler. This data was then combined with information on overall participation in gaming to estimate the number of problem gamblers implied by each of the council’s expenditure levels. The key result was that whilst for the state as a whole these calculations imply a slightly smaller number of problem gamblers than the Productivity Commission’s survey, there were significant regional variations. The methodology for estimating problem gambler numbers is summarised in Box 4.2.

Box 4.2: Methodology for Estimating Problem Gambler Numbers

The first stage in our calculation methodology is to determine the proportions of average income spent nationally by non-problem and problem gamblers.

Let a = (R1/npg)/Y1, where R1 is the net gaming revenue of non problem gamblers, npg is the number of non- problem gamblers (both based on data in the Productivity Commission’s report) and Y1 is average income per non-problem gambler.

Similarly, let b = (R2/pg)/Y2, where R2 is the net gaming revenue of problem gamblers, pg is the number of problem gamblers (with both estimates again coming from the Productivity Commission’s report) and Y2 is average income per problem gambler.

89 _{See Section 4, SACES (2001).}

Assume Y1 = Y2 = Y, where Y is average national disposable income (defined as Total Income minus Net Tax90

divided by the number of adults). This assumption means that we are assuming that problem gambling is broadly even distributed between income levels. Evidence from the Productivity Commission’s report on Gambling suggests this is probably a reasonable assumption, though it may obviously not be true in all regions. Also note that R1 + R2 = R, where R is total net gaming revenue.

We know that total net gaming revenue can be expressed as follows:

Rm = (R1m*npgm) + (R2m + pgm)

In any given region we know the regional disposable income Ym (from TaxStats data) where the subscript ‘m’

refers to a specific region. We can then specify the regional expenditure function in terms of income (which we know) rather than the regional expenditures by problem and non-problem gamblers (which we don’t know).

Rm = (aYm*npgm) + (bYm*pgm)

We also know Rm (total gaming revenue) and gm (the number of gamers). Since npgm = gm - pgm, we can

substitute this into the equation leaving only one unknown - the number of problem gamblers.

Rm = (aYm* (gm - pgm)) + (bYm*pgm)

This equation can then be rearranged and solved for pgm to produce an estimate of the number of problem

gamblers in the region m:

pg = (Rm - (aYm* gm))/( bYm - aYm)

As with any model, the results are only as accurate as the model’s assumptions and the data used in the model. The extent to which these assumptions appear to be reasonable determines whether or not the methodology is appropriate for a particular region. Three key assumptions were made by the researchers in order to implement the methodology. It was assumed that:

• the proportion of persons using electronic gaming machines in regions other than the Provincial Cities reflects the results of the CPSE survey (i.e., 37.5 per cent in Adelaide and 33.2 in rural South Australia). For the Provincial Cities we have applied the Productivity Commission’s participation rate for South Australia (Vol. 3, p. B.2) of 41 per cent to reflect the greater role of hotels in these cities91;

• homogeneous preferences across the state within gambler types, for both problem

and non problem gamblers; and

• the majority of expenditure in each region is due to local residents.

Turning to the implications of the assumptions not being met, if the actual overall proportion of South Australians who gamble was below the estimate used then the model would tend to understate regional problem gambler numbers. Conversely if the estimate understates the number of South Australians participating in gaming then the model would overstate the extent of the problem.

90 _{Both from 2002-03 TaxStats data.}

91 _{Providing that on average the average participation rates for each of the Adelaide metropolitan area; the regional cities; and the}

rest of the state are in line with the assumed average then the estimates will be accurate, although estimates at an individual council level may be inaccurate. Data from the South Australian Department of Health presented in Table 4.4 of the Phase 1 report showed that the participation rate for gaming machines remained stable at 37 per cent in 2004.

If preferences (in terms of expenditure shares for problem gamblers and non-problem gamblers) were not homogeneous between regions and within gambler types, then the model would tend to overestimate the number of problem gamblers in high expenditure regions, and underestimate it for low expenditure regions. The most likely cause of preferences not being homogeneous would be in rural councils where the significant distance between many residents and the hotels or clubs of the region means that an average gambler may gamble less often and generally spend less because of the inconvenience of gambling. However this inconvenience factor would at least be partially reflected in the reduced participation rate for rural South Australia and may or may not flow through to lower expenditure once the decision is made to gamble.

If the assumption of local expenditure did not hold then the model would overestimate the number of problem gamblers in regions which cater to gamers from neighbouring councils and under estimate numbers for councils with few gaming facilities which saw their gamblers go to neighbouring regions. This would suggest that whilst aggregate results from the model may be reasonably accurate, it is inappropriate for councils such as the Adelaide City Council (covering the CBD), and certain other metropolitan councils which act as “entertainment hubs” for several councils.

It is worth noting when considering the realism of the key assumptions that the model does not appear to have an inherent propensity to overestimate problem gambler numbers. When it was developed and applied to 1998-99 data the model suggested that the rate of EGM related problem gambling in South Australia was 2.04 per cent; significantly lower than the result of the Productivity Commission’s survey, and slightly lower than the results of the CPSE survey. There is an additional complication in using this approach on recent data. In 2001 it was considered reasonable to use the results from the Productivity Commission’s study without any modification as it was survey results collected in 1999 being applied to data from 1998- 99. However, in the current analysis, given the time that has past since the PC undertook their survey, some modifications were made.

The share of overall expenditure devoted to EGM gambling seems to have been superseded. The most important factor in suggesting this is the very rapid growth in EGM expenditure in South Australia as a proportion of gross household disposable income (rose 22 per cent from 1.5 in 1989-90 to 1.8 per cent in 2002-03) while household final consumption expenditure rose 5.4 per cent from 87.9 per cent to 92.6 per cent.

The impact of the continued growth of EGM expenditure on the results of the model can be significant. For example, if it were assumed that the expenditure shares on EGM gambling for both problem and non-problem gamblers, and the overall proportion of the South Australian population currently experiencing problem gambling were still at the level they were when the Productivity Commission undertook their research, then in order to explain the current levels of expenditure would require 50 per cent of the population to be non-problem gamblers, implying that participation in gambling as a share of the population had increased by over 25 per cent in four years. Given it seems unlikely that half the South Australian adult population are now EGM gamblers (given all of the surveys conducted in South Australia have suggested rates of 41 per cent or less, see Phase 1, Table 4.4) this increase in expenditure suggests that there have been some changes that will need to be accounted for in the model.

The most likely change which needs to be accounted for is some shift in the share of disposable income available to spend on EGM gambling. Over the period in question total consumption as a share of disposable income has increased by 7.3 per cent, and there is no reason to think that this wouldn’t affect EGM expenditure. The harder question is deciding whether it is more likely that this increase in expenditure would affect only non-problem gamblers or whether problem gamblers would also increase the share of their income (from the already high average of 68 per cent). This is likely to depend on the extent to which the fall in savings represents an increased willingness to use financial products to access wealth/smooth income, and the extent to which it represents an increase in wealth because of the housing market. If it is the former then the income share of non-problem gamblers should arguably be up rated, as it is likely that problem gamblers were already using dissaving and debt as much as they could to be able to afford to spend an average of 68 per cent of their income. However, if it is the latter then problem gamblers would have more wealth to access and could spend an even higher share of their income.

Lacking any information to the contrary, it appears reasonable to assume that all EGM gamblers (i.e., problem gamblers and non-problem gamblers) have increased their net gaming expenditure by a proportion equal to the overall increase in consumption as a share of household income in South Australia since the Productivity Commission’s survey. This gives average expenditure shares for EGM gamblers of 4.9 per cent of after tax income for non- problem gamblers and 73.1 per cent for problem gamblers.

4.5 Results

To summarise the discussion from the previous section, there are four key assumptions underlying the calculation approach:

• The proportion of persons using electronic gaming machines in the three

regions are 37.5 per cent in Adelaide; 41 per cent in Regional South Australia and 33.2 in Rural South Australia.92

If the actual participation rates are lower than this then the model will underestimate the scale of problem gambling in the region concerned.

If the actual participation rates are higher than this then the model will over estimate problem gambler numbers in the region.

• Homogeneous (consistent) preferences across the state within gambler types, for

both problem and non-problem gamblers.

The impact if preferences are not homogeneous depends on how widespread it is. If preferences are not homogenous between councils, but the aggregate preferences for each of the 3 high level regions are, then this assumption holds at the level of this analysis. If, however, preferences (as reflected by expenditure shares) are different at the regional level, e.g. if people in rural South Australia who chose to gamble spend a smaller share of their income on EGMs, then the model’s estimates will be incorrect.

If actual expenditure shares are lower than assumed then the model will underestimate the scale of problem gambling in the region concerned.

If the actual expenditure shares are higher than assumed the model will over estimate problem gambler numbers in the region.

92 _{Adelaide represents Adelaide metropolitan area, Regional SA represents the Provincial Cities, while rural South Australia}

• The majority of expenditure in each region is due to local residents.

Obviously this assumption will not hold universally at a council level however providing it holds at the aggregate level then the model’s results are robust.

If it does not hold, then the model will underestimate the number of problem gamblers resident in a region that is a net exporter of gaming expenditure, while the reverse holds if the region is a net importer of gaming expenditure.

• The share of their after tax income that EGM gamblers (both problem and non-

problem) spend on gaming has increased since the Productivity Commission’s survey by 7.2 per cent, the same proportion as overall consumption spending (e.g. that EGM expenditure has remained constant as a share of total consumption expenditure).

If actual expenditure shares are lower than assumed then the model will underestimate the scale of problem gambling in the region concerned.

If the actual expenditure shares are higher than assumed the model will over estimate problem gambler numbers in the region.

Table 4.1 sets out the results of these calculations for South Australia and for each of the three broad ‘regions’. As can be seen, the model suggests that problem gambling numbers have increased significantly. If these results are correct they would imply that 52 per cent of all net gaming expenditure in South Australia comes from the 2.80 per cent of the adult population who are problem gamblers. This is a significant increase since the researchers’ previous study, which estimated that South Australia had 23,196 problem gamblers (2.04 per cent of the adult population) with an average annual spend of $9,733). As was the case in the analysis of the 1998-99 data, South Australia’s regional cities appear to experience significantly higher rates of problem gambling than the rest of the State.

Table 4.1

Prevalence of Electronic Gaming Machine Related Problem Gambling South Australia: 2002-03 Adult Pop. After tax income Gamers Non- Problem Gamers

Problem Gamers Ave. loss per NPG

Ave. loss per PG (No.) ($) (No.) (No.) (No.) (% of adults) ($) ($)

Adelaide Metro 901,662 16,620 338,123 312,322 25,802 2.86 806 10,567 Regional SA 110,947 15,336 45,488 41,405 4,083 3.68 748 10,983 Rural SA 165,311 15,965 54,883 51,804 3,080 1.86 775 9,336 Total SA 1,177,921 16,407 438,495 405,531 32,964 2.80 796 10,504 Source: Productivity Commission, Office of Economic and Statistical Research, Queensland Treasury, and ATO, calculations by the

researchers.

Obviously these results are indicative. However, in examining the data, the significant increase in expenditure does suggest that something significant has changed.

It is not possible to definitively assert that these calculations are correct and that the increase in EGM expenditure has been driven primarily by increased numbers of problem gamblers. However, there is a very plausible reason why it is not unreasonable that the number of problem gamblers would have increased over the past few years.

Over time it is likely that the inflow and outflow of the pool of persons experiencing problem gambling will stabilise (as the population of those gambling seems to remain relatively constant suggesting that new people take up the activity at about the same rate as some existing users decide to stop). However, as problems only emerge with time, and there was a sudden one-off boost in the proportion first exposed to regular and accessible EGM gambling with the legalisation of EGMs in hotels and clubs, there is likely to be a one off “wave” of problem gamblers which will take some time to work through the system. Indeed evidence suggests that problem gamblers who seek treatment will, on average, have been experiencing problem gambling for 9 to 10 years (Blaszczynski, 2002). This suggests that many of those who developed gambling problems in the first few years of legalisation of EGM gambling have as yet not even sought treatment, let alone have been able to stop; whilst a more ‘normally’ sized cohort of problem gamblers has developed in each year since then, creating a demographic ‘bulge’.

It is important to note that our estimate of a significant increase in the number of problem gamblers is consistent with other recent research conducted in respect of population gambling trends in South Australia. Delfabbro (2005), in an analysis of data from a 2001 large-scale prevalence study by the Centre for Population Studies in Epidemiology (2001) and Health Monitor Surveys for 2002 to 2004, found that participation rates for problem and non- problem gamblers in EGM gambling in South Australia remained steady between 2001 and 2004.93 This suggests that growth in per-capita EGM expenditure has been driven by changes in individuals’ gambling behaviour, such as their frequency of gambling, the amount they spend, and/or the types of gambling activities they participate in (i.e., the results suggest that problem gamblers have decreased their participation in lottery products, which implies that expenditure on lottery products may have been diverted to EGMs, though there was no data available from the surveys to confirm the latter). More importantly though:

“Analysis of the limited problem gambling data indicated that there has been an

increase in the percentage of the population concerned about their own gambling as well as the gambling of others close to them. The percentage of the sample reporting at least some difficulty with gambling (i.e., who were not willing to give themselves the minimum rating on a 10-point scale) had increased significantly from 2001 by 50%…”. [p.21]

While the percentage of the sample indicating respondents were concerned about their own gambling does not represent an actual problem gambling prevalence rate,94 the results nevertheless suggest that there has been an increase in problem gambling. As well, analysis of BES client data shows an average of 1,600 new clients each year from 2001 to 2005. Ultimately the robustness of SACES estimate of total problem gamblers will need to be tested