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Hawthorn Football ClubEstimation of the economic contribution of Hawthorn Football Club 2017 games in Launceston to the Tasmanian economy
PwC
01 Executive summary
Direct impacts02
Total impacts(including indirect or flow on impacts)
03
Appendices04
01
Report Disclaimer
This Report has been prepared by PricewaterhouseCoopers Consulting (Australia) Pty Ltd (PwC) at the request of the Hawthorn Football Club (HFC) in our capacity as advisors in accordance with the Terms of Reference and the Terms and Conditions contained in the Consultant Agreement between the HFC and PwC.This document is not intended to be utilised or relied upon by any persons other than the HFC, nor to be used for any purpose other than that articulated above. Accordingly, PwC accept no responsibility in any way whatsoever for the use of this report by any other persons or for any other purpose.The information, statements, statistics and commentary (together the “Information”) contained in this report have been prepared by PwC from publicly available material, from material provided by the HFC and from information provided by match day attendees through a survey. PwC has not sought any independent confirmation of the reliability, accuracy or completeness of this information. It should not be construed that PwC has carried out any form of audit of the information which has been relied upon.Accordingly, whilst the statements made in this report are given in good faith, PwC accept no responsibility for any errors in the information provided by the HFC or other parties nor the effect of any such errors on our analysis, suggestions or report.
PwC
Executive summary
Direct impacts
Total impacts(including indirect or flow on impacts)
Appendices
01
PwC 4
Executive summary
The Hawthorn Football Club is currently contracted by the Tasmanian Government to play five games per season in Launceston, Tasmania.
PwC has been engaged to estimate the total dollar contribution of the four Hawthorn Football Club home and away games played at University of Tasmania Stadium, Launceston to the Tasmanian economy in 2017.
These games were:
JLT Community Series: Hawthorn v Geelong (Friday, 17 February 2017)
Round 6: Hawthorn v St Kilda(Saturday, 29 April 2017)
Round 8: Hawthorn v Brisbane Lions(Saturday, 13 May 2017)
Round 16: Hawthorn v Greater Western Sydney (Saturday, 8 July 2017)
Round 21: Hawthorn v North Melbourne(Sunday, 13 August 2017).
The evaluation quantifies both the direct impacts of expenditure by game attendees, as well as the indirect flow on impacts of that expenditure to the Tasmanian economy.
$20.4mtotal direct expenditure
61,301local, interstate and international game attendances
25%percentage of attendees from interstate or overseas
77%proportion of interstate and international visitors spending more than 1 night in Tasmania
15,126interstate and international visitors visiting Tasmania in 2017*
$28.5mdirect and indirect GSP ($2017)
137direct and indirect additional jobs supported*
* Estimate based on extrapolated survey results.
$816average spend per interstate and international visitor
PwC 5
Executive summary (cont’d)
Game attendances Total direct attendee spendSpend per person
Spend per person depends on the residential origin of the spectator, and is greatest for interstate/international spectators
Attendance can be analysed by residential origin of the spectator (Launceston, Tasmania (other) and interstate/international)
The composition of direct spend is driven by a combination of game attendees by residential origin and spend per person
61.3k $323 19.8m
Tasmania 27k
Launceston 19k
International/interstate 15k
Tasmania $244
Launceston $41
International/interstate $812
Tasmania $6.7m
Launceston $0.8m
International/interstate $12.4m
PwC
Total directexpenditure
6
Executive summary (cont’d)Expenditure and total impacts
$20.4mm
$579kHawthorn Football Club spend
$28.5mdirect and flow-on GSP ($2017) across Australia
$8.5 mhousehold consumption ($2017)
137direct and flow-on additional jobs
CGE multipliers
model inputsGSP
Jobs
Consumption
$74kAssumed opposition club spend
$19.8mMatch attendee spend
Source: PwC analysis based on HFC financial data, PwC survey and PwC modelling.
PwC
Executive summary
Direct impacts02
Total impacts(including indirect or flow on impacts)
Appendices
PwC
1. Understanding of Hawthorn Football Club’s matches at University ofTasmania Stadium
Developed and launched an online survey to capture activities of attendees at each match. The purpose of the survey was to determine the proportion of local, interstate and international visitors and their associated average spend profiles.
8
We estimated the economic impact of HFC’s Launceston matches by analysing survey results and using a CGE model
Approach
In order to collect expenditure data, email addresses were requested from a sample of match day attendees, who were subsequently sent an electronic survey in the days following the match.
The survey asked questions primarily related to the amount spent by each participant, who they attended the game with, and what else they did in relation to their trip to the football game.
Expenditure data was collected in relation to transport, accommodation, food and drinks, entertainment and activities,and other.
In addition to the survey data, information was also sought from the Hawthorn Football Club on their own expenditure that is likely to flow to Tasmania, including the cost of accommodation, and food and drinks during games, as well as for other club trips during the year.
2. Attendance spending
The sample of match attendee information from the online survey was then extrapolated to the total number of match attendees. This provided us with an estimate of the total amount of local, interstate and international attendees and the total spending for each group (including associated tourism-related activities). This formed the basis for the total direct expenditures. Where the HFC games were indicated ‘part’ of the reason for their travel to Launceston, we only attributed 50 per cent of their expenditure to the HFC game, and where it was a ‘non-primary’ reason for their travel, then only 20 per cent was attributed.
3. Club expenditure
Match attendee expenditure was then combined with spend data from the Hawthorn Football Club and opposition sides. This included items such as advertising spend and security and also included travelling team spend (including support staff, accommodation and travel allowance). Along with step 2, this provided total direct impact of HFC’s matches in Launceston.
4. Total economic impacts (direct and flow-on) Flow-on effects
The total direct expenditures were then modelled using multipliers developed from a globally accepted robust computable general equilibrium (CGE) model. This model assesses the flow-on and total economic impacts from Hawthorn Football Club’s matches in Launceston. Using multipliers developed through CGE analysis is superior to simply using input-output analysis and is the preferred approach of many Governments and Treasury Departments in Australia.1
Further detail related to our methodology and approach can be found in Appendix A – Economic modelling approach and Appendix CGE methodology.The key results of the economic impacts are presented in the following pages.
PwC 9
Around of quarter of the 61,000 attendees in 2017travelled to Tasmania from interstate or overseas
Source: PwC survey of game attendees.
Match attendance
The four 2017 home and away season games held by HFC in Launceston attracted 52,789 people, plus an additional 8,512 at the pre-season match, making total attendance 61,301 at an average of 12,260 people per match.
The number of attendances varied by game, with the highest crowd attending the St Kilda game (15,571), and the lowest crowd attending the pre-season game against Geelong (8,512). Attendances are shown in the figure below.
Residences of attendees
While data is publically available on the total attendance of each game, there is no complete dataset available on the exact place of residence of each of the game attendees. However, we can extrapolate the survey results to estimate the proportion (and therefore the number) of people that attended the games from:
• Launceston or nearby suburbs• in Tasmania, but not in Launceston or nearby suburbs• interstate or overseas.
This results in the following estimates shown in the table below.Figure 1: Attendance numbers
0
4,500
9,000
13,500
18,000
Geelong (JLT) St. Kilda Brisbane GWS NorthMelbourne
Survey responses
A total of 1,078 correctly completed responses to the online survey were received and used to estimate the average spend by game attendees. This represented 1.8 per cent of game attendees.
Launceston
or nearby
suburbs
In Tasmania, but
not in
Launceston or
nearby suburbs
Interstate
or
overseas Total
Count of
survey
responses
333 479 266 1,078
Percentage
(from survey
results)
31% 44% 25%
Assumed total
attendances18,936 27,239 15,126* 61,301
*According to the ABS (2015) there is a total of 5,104 guest nights available in Northern Tasmanian hotels, motels and serviced apartments each night, meaning there is sufficient accommodation for the estimated number of interstate and international visitors for any one game.Note totals in the table may not appear to add correctly due to rounding in the presentation of the figures shown.
PwC 10
Interstate attendees spend much more than attendees from Tasmania
Based on the survey results, the average amount spent in Tasmania by survey participants as a consequence of attending one of the five matches in Launceston is estimated to be $323, up from $316 in 2014.
This amount includes the spending of people from Launceston, as well as people from elsewhere in Tasmania and interstate or overseas. Where the HFC games were indicated ‘part’ of the reason for their travel to Launceston, we only attributed 50 per cent of their expenditure to the HFC game, and where it was a ‘non-primary’ reason for their travel, then only 20 per cent was attributed.
A breakdown of spending by expenditure type and place of residence is provided in the table across.
Average
expenditure
Launceston
or nearby
suburbs
In Tasmania, but
not in Launceston
or nearby suburbs
Interstate or
overseasTotal
Transport (for
example: taxis, public
transport, car hire)
$14 $29 $83 $38
Accommodation n/a $47 $250 $83
Food and drinks $27 $67 $220 $92
Entertainment and
activities (excluding
admission to the AFL
game)
n/a $25 $86 $32
Shopping n/a $56 $110 $52
Other n/a $20 $66 $25
Total $41 $244 $816 $323
Source: PwC analysis of survey data.Note that visitors from interstate or overseas spent $265 on average to get to Tasmania (via plane or ferry), however this hasn’t been included in the estimates of the contribution to the Tasmanian economy, as the money goes directly to airlines (none of which are based in Tasmania).Note totals in the table may not appear to add correctly due to rounding in the presentation of the figures shown.
PwC 11
Average spend per person is highest for interstatetravelers, driven by the nights spent away from home
Nights spent away from home
Of the spectators from interstate or overseas, 59 per cent stayed between 1 to 2 nights, with only 3 per cent choosing to arrive and return on the day of the game (see Figure 2). This helps to drive the higher spend per person from interstate attendees (see Figure 4).
On average, attendees who stayed for three nights or longer spent over three times more than those with shorter stays (see Figure 3).
Figure 2: Breakdown of nights spent away
3%
19%
40%
16%
22%
0 nights 1 night 2 nights 3 to 4 nights Over 4 nights
Figure 3: Breakdown of nights spent away
$328
$1,008
$-
$500
$1,000
$1,500
Stays of 0-2 nights Stays of 3+ nights
Sources: PwC analysis of survey data.
Figure 4: Average spend per person (all games)
Sources: PwC analysis of survey data.Note 14 survey responses did not indicate postcode data so have been omitted from the geographical analysis.
Northern Territory
N/A
Western Australia
$226South
Australia$980
Queensland$936
New South Wales$1,062
Victoria$794
Tasmania$159
PwC
Most attendees are Tasmanian, but most of the spending is from interstate especially Victoria
12
Figure 5: Proportion of attendance by location of origin (all games)
Figure 6: Total spend by location of origin (all games)
Northern Territory
N/A Queensland1.4%
New South Wales 1.5%
Western Australia
0.3% South Australia
1.0%
Victoria19.8%
Tasmania74.5%
Northern Territory
N/A Queensland$0.8 m
New South Wales $1.0 m
Western Australia$0.04 m
South Australia
$0.6 m
Victoria$9.7m
Tasmania$7.3m
Sources: PwC analysis of survey data.Note 14 survey responses did not indicate postcode data so have been omitted from the geographical analysis.
15%
27%
1% 5%2% 6%
1%
0.5%
17%
PwC 13
Almost half of the attendees were Hawthorn Football Club members
Figure 7: Ticket types
4%
10%
49%
31%
4%
2%
0% 20% 40% 60%
Other club member
Other
HFC member
General admission
Corporate ticket
AFL memberFigure 8: Social media
33%
19%
48%
No Yes Yes, multiple times
Sources: PwC analysis of survey data. Sources: PwC analysis of survey data.
Ticket types
Almost half of all attendees were HFC members and over 30 per cent used general admission (see figure below).
Social media
Just over 80 per cent of visitors who attended the match from interstate shared their experience on social media. Almost 60 per cent of these people shared their experience on multiple occasions (48 per cent in total).
PwC 14
There was overwhelmingly positive feedback fromspectators in regard to HFC’s matches in Launceston
Figure 9: Survey feedback
87%
2%11%
Good Bad Neutral
Positive feedback
Almost 87 per cent of the feedback provided on the online survey with respect to the arrangement between Hawthorn and Tasmania, as well as the spectacle in general, was positive (see figure below).
The vast majority of survey responses (and anecdotal evidence captured during surveying) were in favour of having Hawthorn play in Tasmania due to the tourism and other benefits to the state.
“I firmly believe that Hawthorn are not only beneficial to Tasmania in an Economic sense, but the Social benefit to the region is immeasurable. Not only the joy they bring children that support Hawthorn, they also are role models and the elderly from a personal experience look forward to them being in Launceston. Some things are unable to be quantified.”
“Hawthorn, their Tasmanian supporters and the Tasmania Government have established a unique football experience in northern Tasmania. Please do not take it away from future fans of the game.”
“The game was epic, the atmosphere was intense, loved everything about all of it. Definitely coming back again.”
“Overall, the experience was terrific, thoroughly enjoyed Tasmania and the Launceston footy ground was great to attend.”
Sources: PwC analysis of survey data.
PwC
The primary reason for the vast majority of attendees to travel to Launceston was due to the game
15
Figure 10: Reason for travel - Tasmanians Figure 11: Reason for travel - Interstate or overseas
1%
8%
91%
0% 20% 40% 60% 80% 100%
Non-primary
Part
Primary
5%
23%
71%
0% 20% 40% 60% 80%
Non-primary
Part
Primary
Reason for travel
The reasons for travel vary depending on whether the attendee is from Tasmania or otherwise; over 91 per cent of locals who visited Launceston did so to attend the game, whereas this proportion was only approximately 71 per cent for people from interstate or overseas.
Impact on modelling
Where the HFC games were indicated ‘part’ of the reason for their travel to Launceston, we only attributed 50 per cent of their expenditure to the HFC game, and where it was a ‘non-primary’ reason for their travel, then only 20 per cent was attributed.
Sources: PwC analysis of survey data. Sources: PwC analysis of survey data.
PwC
Direct attendee spend was almost $20m, driven predominantly by interstate or overseas attendees spending on ‘accommodation’ and ‘food/drink’
16
The direct expenditure flowing to Tasmania as a result of the five Hawthorn Football Club in Launceston in 2017 is estimated at around $19.8 million.
A breakdown of spending by expenditure type and place of residence is provided in the following table.
Total expenditure
Launceston
or nearby
suburbs
In Tasmania, but not
in Launceston or
nearby suburbs
Interstate
or
overseas
Total
Transport (for example:
taxis, public transport,
car hire)
$0.3m $0.8m $1.3m $2.3m
Accommodation n/a $1.3m $3.8m $5.1m
Food and drinks $0.5m $1.8m $3.3m $5.7m
Entertainment and
activities (excluding
admission to the AFL
game)
n/a $0.7m $1.3m $2.0m
Shopping n/a $1.5m $1.7m $3.2m
Other n/a $0.5m $1.0m $1.5m
Total $0.8m $6.6m $12.3m $19.8m
Proportion of direct spend from interstate/overseas match attendees
62%
Source: PwC analysis of survey data.Note totals in the table may not appear to add correctly due to rounding in the presentation of the figures shown.
PwC
Direct spend was 13 per cent higher in 2017 than in 2014
17
Spend profile
Direct spend by match attendees in 2017 is 13 per cent ($2.3 million) higher than in 2014. Spectators increased spending in all categories except for transport (which was 3 per cent/$0.08 million lower in 2017).
The categories on which spectators spent significantly more money are ‘shopping’ and ‘other’ (such as amounts spend on the survey respondent by others), experiencing a 33 and 207 per cent increase respectively –this change in spending patterns drove the higher overall spend in 2017. The other categories which people spent more money on experienced increases of between 2 and 10 per cent. The overall spend profile differential between 2014 and 2017 is summarised in the table and figure below.
Note spending was categorised into different regions in 2014, removing the possibility of a regional analysis.
Source: PwC analysis of survey data.Note attendance from the pre season match was not included in the 2014 analysis.
Total expenditure 2014 2017 Difference
Transport $2.4m $2.3m -$0.1m
Accommodation $5.0m $5.1m $0.1m
Food and drinks $5.4m $5.7m $0.3m
Entertainment and
activities$1.8m $2.0m $0.2m
Shopping $2.4m $3.2m $0.8m
Other $0.5m $1.5m $1.0m
Total $17.5m $19.8m $2.3m
Figure 13: Spend profile comparison
Source: PwC analysis of survey data.Note totals in the table may not appear to add correctly due to rounding in the presentation of the figures shown.
$0
$2
$4
$6
Mill
ions
2014
2017
PwC
Club expenditure
18
Expenditure in Tasmania by the Hawthorn Football Club and opposition sides also makes a significant contribution to the Tasmanian economy.
Information was sought from Hawthorn as to their expenditure throughout the year, and it was assumed that 67 per cent of the Hawthorn Football Club expenditure for each game was also spent by the opposition team for each match.
The following table shows that club expenditure accounted for $653k of expenditure that contributed to the Tasmanian economy.
ClubGame related -
Accommodation
Game related -
Catering
Game related -
Transport
Game related -
Other
Non-game
related spendTotal
Hawthorn $47k $38k $6k $19k $469k $579k
Visiting clubs* $31k $25k $4k $13k - $74k
Total $78k $63k $10k $32k $469k $653k
Source: PwC analysis of HFC data.Note totals in the table may not appear to add correctly due to rounding in the presentation of the figures shown.
* Visiting club spend assumed to be two thirds that of the Hawthorn Football Club related to games. No visiting club spend was assumed for the Tasmanian operations component for the Hawthorn Football Club spend.
PwC 19
Summary of results
Breakdown of the direct contribution to Tasmania’s economy from the Hawthorn Football Club matches in Launceston, 2017
Item Description Per person impact Total impact
Transport Expenditure by spectators on transport in Tasmania. For example: taxis, public
transport, car hire etc.$38 $2.3m
AccommodationExpenditure by spectators on accommodation in Tasmania. For example:
hotel, AirBnB$83 $5.1m
Food and drinks Expenditure by spectators on food and drink in Tasmania. $92 $5.7m
Entertainment and
activities
Expenditure by spectators on entertainment in Tasmania (excluding admission
to the AFL game). For example, movies, live music etc.$32 $2.0m
Shopping Expenditure by spectators on shopping in Tasmania. $52 $3.2m
Other Expenditure by spectators in categories other than transport, accommodation,
food and drinks, entertainment and shopping in Tasmania.$25 $1.5m
Club expenditure Expenditure in Tasmania by the Hawthorn Football Club and opposition sides N/A $0.7m
Total $323 $20.4m
Source: PwC analysis of survey results.Note totals in the table may not appear to add correctly due to rounding in the presentation of the figures shown.
PwC
Executive summary
Direct impacts
Appendices
Total impacts(including indirect or flow on impacts)
03
PwC
Total impacts
Breakdown of the flow-on contribution to Tasmania’s economy from the Hawthorn Football Club matches in Launceston, 2017
Source: PwC CGE modelling.
Note: Each results are presented as nominal in year they are calculated and are not directly comparable.
Note 2: The modelling shows gains across the Tasmania economy as a flow on impact from HFC games. CGE modelling can typically show some industries decreasing in output as others grow, due to the constraints on inputs that CGE places on the model economy. However, the results presented here are only for Tasmania and some of these negative effects would be felt outside Tasmania as visitor spending is attracted to Tasmania and away from other states. Although the impact on other states are not presented here, there may be some losses in employment or output in other jurisdictions as constrained inputs, such as labour, move to the Tasmanian economy (or stay in Tasmania where they might have otherwise left).
Measure Change (amount)
Gross State Product $28.5 million
Household consumption $8.5 million
Employment 137 people
Method
In addition to the direct expenditure by people who attended Hawthorn Football Club’s 2017 home and away season games in Launceston, there are also a range of flow on impacts for the Tasmanian economy.
To estimate these flow-on economic benefits of hosting HFC games in Launceston, we have utilised a Computable General Equilibrium (CGE) model. CGE modelling is a widely used economic impact analysis tool for simulating the economy-wide effects of projects or policies, which represent ‘shocks’ to the economy. CGE models replicate how the economy will adjust to ‘shocks’ from significant projects and policies.
Appendices A - D describes the methodology of our CGE modelling in further detail.
Results
We estimate that the $19.8 million direct expenditure from people that attended one of the games (discussed in the previous chapter) together with $0.7 million direct expenditure by the HFC and opposition teams in Tasmania, generates a total contribution to the Tasmanian gross state product (GSP) (in value add terms) of $28.5 million or 0.11 per cent of GSP.
Household consumption (a proxy for consumer welfare) and employment are also enhanced by the flow on effects of the additional spending in the economy with household consumption increasing by $8.5 million and employment increasing by 137 persons. These are summarised in the headline results shown in the table across.
21
PwC
Impact on GSP
Component Amount
Compensation of employees $13.5 million
Gross operating surplus $11.9 million
Taxes less subsidies $3.0 million
Total GSP increase $28.5 million
Figure 14: Modelling results, percentage change in industry value add
Impact on GSP
Figure 14 shows contribution to GSP distributed across industries in percentage change terms.
GSP composition
GSP (in industry value add terms) is made of three main components; compensation of employees (wages), gross operating surplus (profit) and taxes less subsidies on production and imports (net government position). To give a high level estimate of how the $28.5 million increase is split across these components, we have combined the modelling results with information from ABS State Accounts (seetable below).
Source: PwC modelling
Source: PwC modelling and ABS cat. no. 5220.0, table 7.Note totals in the table may not appear to add correctly due to rounding in the presentation of the figures shown.
22
0.0% 0.1% 0.2% 0.3% 0.4% 0.5%
Total
Agriculture, forestry and fishing
Mining
Food manufacturing
Other manufacturing
Utilities
Construction
Trade
Hotels and cafes
Transport
Communications
Banking, finance and insurance
Property services
Computer services
Government services
Education
Health services
Other services
PwC
Impact on GSP (cont’d)
Figure 15: Modelling results, dollar change in industryvalue add ($ millions)GSP
Higher demand for inputs into industry supply chains raises activity in industries that support the directly affected industries, such as the food manufacturing industry.
Some industries may be negatively affected however through price changes. Higher demand for inputs into these industries will raise prices in the source industries of these inputs. Some industries not directly affected by increased visitor expenditure may compete for these inputs. The greater an industries similarity of inputs with the directly affected industries, the greater the negative impact through higher input prices.
In this case, potentially higher prices across some sectors only partially offset the higher to value added through the visitor spending, and not industry is negatively affected. For example, industries such as health services and education receive no direct benefit from visitor expenditure, however they must still compete for input (namely labour) with industries that do directly benefit. So while health services and education are benefitting through higher demand for their products, they must also pay more for their inputs, mitigating the overall impact on these industries.
Figure 15 shows the dollar change in industry value add, split between wages, gross operating surplus and taxes.
Source: PwC modelling and ABS cat. no. 5220.0, table 7.
23
-1.0 0.0 1.0 2.0 3.0 4.0 5.0
Agriculture, forestry and fishing
Mining
Food manufacturing
Other manufacturing
Utilities
Construction
Trade
Hotels and cafes
Transport
Communications
Banking, finance and insurance
Property services
Computer services
Government defence services
Education
Health services
Other services
Wages Gross operating surplus Taxes
PwC
Impact on employment
Impact on employment
The impacts on employment differ slightly from the value add results as it depends on the labour intensity of the impact industry. For example, industries like hotels and cafes are relatively labour intensive so change in output flows through to employment as it is a key input into the sector. The impact also depends on which industries compete for the same labour, and which act as key inputs to each other.
These impacts are shown in the figures below.
Figure 16: Modelling results, percentage change in employment by industry
Figure 17: Modelling results, change in employment, persons
Source: PwC modelling Source: PwC modelling
24
0.00% 0.05% 0.10% 0.15% 0.20%
Total
Agriculture, forestry and fishing
Mining
Food manufacturing
Other manufacturing
Utilities
Construction
Trade
Hotels and cafes
Transport
Communications
Banking, finance and insurance
Property services
Computer services
Government services
Education
Health services
Other services
0 50 100 150
Total
Agriculture, forestry and fishing
Mining
Food manufacturing
Other manufacturing
Utilities
Construction
Trade
Hotels and cafes
Transport
Communications
Banking, finance and insurance
Property services
Computer services
Government services
Education
Health services
Other services
PwC
Executive summary
Direct impacts
Total impacts(including indirect or flow on impacts)
Appendices04
PwC
01 Economic Modelling Approach
CGE MethodologyB
CGE Modelling InputsC
Detailed CGE modelling resultsD
A
Appendices
PwC
CGE Methodology
CGE Modelling Inputs
Detailed CGE modelling results
01 Economic Modelling ApproachA
PwC
Understanding attendance and tourism
To analyse the economic impacts of Hawthorn Football Club’s matches in Launceston in 2017, PwC conducted an online survey of ticket holders. The purpose of the online survey was to develop visitor spending profiles by residential location. The data sets from the online and ticketing data from the AFL official website were combined to determine total expenditure as a result of the 2017 matches. Further details of the online survey is provided below.
About the online survey
In consultation with the Hawthorn Football Club, an online questionnaire was developed and launched using a secure web-based survey tool. At a high level, information collected in the survey included demographics, place of origin (country, state/territory, region), matches attending, nights away from home, primary reason for travelling, and type of ticket purchased habits (eg Hawthorn Football Club member, General Admission etc). This allowed us to identify, for example, that 15,126 visitors to the Hawthorn Football Club matches in Tasmania were from interstate or overseas, 71 per cent of which identified the football match as their primary purpose of visiting Launceston. The survey collected information from those who purchased or received tickets to one of the four Hawthorn Football Club home and away matches in Launceston.
28
PwC
Flow-on and total economic impact
In our CGE analysis, we have estimated the impacts of the HFC matches in Launceston on key macroeconomic variables. Each of these measures is described below.
• Gross state product (GSP) – this represents the “value added” to the economy through spending patterns. Since the GSP figure captures the difference between the value of output and the value of intermediate inputs, it represents the unduplicated total value of economic activity that has taken place. The GSP impacts in this report represent the value added to the economy as a result of the spending made in Tasmania in connection with the HFC matches in Launceston.
• Employment – represents the number of additional full time equivalent jobs created as a result of the spending made in Tasmania in connection with the HFC matches in Launceston.
• Household consumption – measures household economic wellbeing through the acquisition of goods and services. To the extent that consumption can be considered as a proxy for living standards, an increase in consumption implies the Australian population is better off.
Indirect or flow on impacts and total economic impacts are described below.
• Indirect impacts or flow on impacts – The direct spending, will flow through the Australian economy to stimulate other industries. These flow on impacts arise from changes in activity for suppliers through the various industry’s supply chain. For example, these impacts include companies that provide goods or services in connection with consumer spending resulting from the test match. An example of an indirect impact related to consumer spending would include additional demand for a food product supplier.
• Total impacts – represent the sum of the gross direct and indirect economic impacts.
Flow on and total economic impacts
When considering flow on and total economic impacts, the direct economic impacts as a result of HFC matches in Launceston were modelled using CGE, a globally accepted and robust approach. This technique overcomes the limitations of simpler input-output models, providing a more accurate estimate of the impacts and is the preferred approach of many Governments and Treasury Departments in Australia.
Comprehensive economic impact analysis generally makes use of sophisticated economic modelling to represent an economy and simulates the effect a change has on the economy. It incorporates detailed representations of industry production, consumption, government, trade, prices and the behaviours that link the economy together.
For example, an additional $1 spent directly in the Australian economy in the restaurant industry may stimulate a further 50 cents of spending by that sector in the Australian food processing industry, which would then lead to 25 cents of spending in the industrial equipment industry. In this simple example, we would say that the indirect effect was equal to 75 cents for every $1 spent, for a total expenditure effect of 1.75. From these expenditure impacts, the most commonly used of these measures are, gross state product (GSP), employment and household consumption.
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PwC
CGE modelling
A CGE model attempts to ‘push forward’ the base Input-Output table through time by utilising a set of equations that capture neoclassical microeconomic theory to determine behaviour of economic agents (such as households, governments, industries) when they are faced with changes in key economic variables, especially relative prices. The equations are solved simultaneously, where some variables are determined by the model (endogenous variables) and some are determined outside the model (exogenous variables). The classification of endogenous and exogenous variables is determined by the user based on the set of assumptions derived for the specific modelling exercise. CGE modelling is a widely used economic impact analysis tool for simulating the economy-wide effects of projects or policies, often involving large expenditures and revenues, which represent ‘shocks’ to the economy. CGE models recognise that complex macroeconomic mechanisms and inter-industry interactions exist in the economy and, in light of this, replicate how the economy will adjust to ‘shocks’ from significant projects and policies.
What is a CGE model?
A CGE model is a mathematical model of an economy that is capable of capturing economy-wide impacts and inter-sectoral reallocation of resources that may result from a ‘shock’ (that is, change in the status quo) to the economy. CGE models are widely used in economic analysis of policies and projects around the world including in Australia by both government and the private sector.
Both input-output (I-O) modelling and CGE modelling have been used previously in the preparation of economic impact assessment for sporting events. However, we prefer to use CGE analysis as it provides a more robust assessment that is used and accepted by government departments, particularly Treasury departments in Australia. Using CGE modelling to measure economy-wide impacts is superior to simply using input-output analysis. For example, I-O models can only scale up or down industries, with no regard for economic interactions and constraints, while CGE models include these features. This means CGE models are able to more realistically capture indirect impacts such as the impact on prices through increased demand for a finite products, or the impact on cost structures of additional demand for finite labour and capital.
The core data of a CGE model is an input-output table. An input-output table is a system of accounts which shows, in value terms, the supply and disposal of goods and services within the economy in a particular year. An input-output table captures sales of products to other industries for further processing (intermediate usage) or to the various categories of final demand. It also captures the inputs used in an industry’s production, whether they be intermediate or primary inputs (such as labour and capital). The table is balanced such that total inputs to each industry are equal to total outputs from each industry. Essentially, an input-output table is a snapshot of an economy (whether it is a region, state or country) in a particular year.
Assumptions
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• Our analysis of total attendance and visitor numbers are driven by the attendance data on the official AFL website.
• We applied average spend profiles by postcode of origin collected in our PwC online survey to our analysis of total attendance and visitor numbers to provide us with the direct economic impacts resulting from attendance and tourism.
• Match attendee expenditure was combined with club expenditure to provide us with our total direct expenditures. This included items spent by clubs such as travelling spend (including support staff, accommodation and travel allowance). PwC did not verify or audit this data.
• Total direct expenditures represent the gross spending associated with the HFC matches in Launceston.
PwC
Economic Modelling Approach
CGE Methodology
CGE Modelling Inputs
Detailed CGE modelling results
B
PwC
The Monash TERM model
TERM is capable of modelling a region-specific, demand or supply-side shocks (that is, change in the status quo) and its effect on region-specific prices and quantities.
TERM’s responsiveness to exogenous shocks is dependent upon the three key elements:
• the database (input-output tables for each region)
• choice of behavioural parameters (how demanders of commodities minimise costs)
• choice of closure (combination of exogenous and endogenous variables in the model).
The Monash TERM model
The Centre of Policy Studies (CoPS), at Monash University, has developed a number of CGE models of the national economy. The Monash suite of models is widely used in Australia by both government and the private sector. The models have been peer reviewed and are regarded by all State and Commonwealth Treasuries as being capable of producing credible results when applied appropriately.
For this study, PwC used the TERM model (or The Enormous Regional Model). TERM is a regional CGE model that provides a highly disaggregated representation of the Australian economy. It uses a ‘bottom up’ approach that explicitly represents the economy of each region. However, it has the advantage over other regional models of being specifically created to allow regional CGE analysis without being overly burdensome computationally.
Using TERM, an analyst is able to assess a large number of regions or sectors. TERM’s database has 57 regions (statistical division) and 144 sectors, and can be aggregated depending upon the focus of the analysis. Each region can be defined either as an individual statistical division or a summation of statistical divisions. This version of TERM has 80 key industries in Australia’s economy.
TERM’s extensive disaggregation of the Australian economy allows each region to be independently modelled via the regional input-output tables. The linkages between regions are established through trade and primary factor flows. Each region trades commodities with other regions and with the world market. Importantly, TERM captures the demand for and supply of commodities, as well as their movement from producer to purchaser via various transport modes and wholesale and retail trade.
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PwC
The TERM Database
• International exporters are assumed to adjust behaviour depending on movements in the terms of trade and exchange rate.
• Interstate exporters are assumed to adjust behaviour depending on changes in state price relativities.
A key feature of a CGE model like TERM is its ability to capture the substitution effects and supply-side constraints that exist in the economy.
The supply-side constraints of the economy must be recognised when assessing the economic impacts of projects and policies. That is, the supply of inputs (land, labour, capital and intermediate factors of production) is limited. There is a finite quantity of these inputs from which to drawn upon to increase economic activity. Constraints on the availability of inputs require prices to act as a rationing device.
The extent to which additional expenditure in the economy instigates growth in economic activity largely depends on the way in which, and the extent to which, the economy is utilising resources. If the economy is utilising close to all of its resources then the addition of, for example, a construction project, is likely to have a significant crowding out effect since there are few spare resources available. As such, the flow-on economic activity and employment would be negligible as the new activity would simply be redirecting resources that are currently in use on other projects. On the other hand, if there is an under utilisation of resources, the flow-on economic activity and employment may be quite high.
The CGE modelling framework works to address these complex economic mechanisms and, as such, produces conservative measures of economic benefits.
The TERM Database
TERM is based on Input-Output tables prepared by the CoPS, drawing upon Australian Bureau of Statistics (ABS) input-output tables and on various supplementary ABS data. The input-output table used for this study is a database produced by CoPS for 2009/10.
The input-output tables may be considered a “snap-shot” of the economy in 2009/10, providing a detailed description of the structure of production and demand at the regional, state and national level. These tables form the database which shows for each regional economy the flow of industry outputs to other industries, together with the flow of industry output to final users (households, government, investors and foreign markets).
The input-output tables also contain the cost structure of each industry including cost of taxes, intermediate inputs and primary factors of production (labour, capital and agricultural land).
Economic theory captured in TERM
Like other CGE models, TERM captures standard neoclassical economic theory to determine the behaviour of economic agents when they are faced with changes in key economic variables (especially relative prices). For example:
• Households are assumed to maximise utility subject to a budget constraint, with changes in household income and relative prices of household goods affecting household consumption.
• Industries are assumed to minimise costs subject to production functions, with the use of labour, capital and agricultural land changing depending on the relative cost of these factors.
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Comparative static model and its application
Accounting for distributional effects
The sectoral structure of TERM allows identification of the distribution of impacts, between industry sectors and regions. Economy-wide constraints in TERM, including those for the labour market and the balance of trade, mean that for most simulations the distribution of impacts will differ by sector.
A comparative static model
CGE models can be developed as either ‘comparative static’ or ‘recursive dynamic’, depending on the treatment of time in the modelling exercise. While recursive dynamic modelling can account for how the economy changes over time to move from one equilibrium position to another, comparative static modelling presents a static viewpoint, comparing the economy with and without the impact of the shock at a particular pointin time.
The TERM model is a comparative static model due to the difficulty of simulating changes in the economy over multiple time periods when there is a large number of industry sectors and regions. While this does present some limitations to modelling temporal impacts, the TERM model was selected for this project because of its ability to provide insights to the geographic and sectoral distribution of impacts – which was viewed to be of greater importance in this study than modelling the dynamics.
In order to generate a time series of impacts using a comparative static model, it is necessary to ‘shock’ the TERM model with average measures of construction and operating expenditure over the project timeframe. The results of this shock are then apportioned to each year of the project in proportion to the level of expenditure incurred in each year. This procedure was used in this study to produce annual estimates of GSP change and the other economic measures.
Application of TERM to model the flow-on impact of HFC games in Tasmania
While the TERM model is an ‘off the shelf’ modelling product, a number of modifications to the model were required before it could be applied to modelling the flow-on impact of HFC games. This primarily involves modelling the investment facilitation activities as a long run simulation, which involves:
• Employment is considered fixed in the long run. This assumption is appropriate as it is assumed that long-run employment will be determined by demographic, policy and sociological factors which are independent of increased tourism or associated activities.
• Investment and capital are linked together. That is, investment in one year, will lead to a proportional increase in capital in the following year.
• Public (government) consumption spending follows movements in the long-run regional distribution of economic activity. This is done through region specific ratios of real public consumption spending to real private consumption spending.
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Limitations of the CGE model
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The economy is large and complex. It cannot be known or described with certainty, and it is constantly subject to unpredictable external forces, whether economic, political or geographic.
Economic forces include changes in foreign trading environments, technologies, preferences, and matters such as the willingness of people to move to new places of employment. The model makes the following simplifying assumptions:
• Consumer preferences, industry technologies and productivity are fixed at 2009/10 levels.
• The willingness of labour to move to regions is based on wage differentials, plus an adjustment factor to allow for the possibility that households may have a preference for particular geographic locations.
• Political factors include changes in government policy. For example, changes in regulations impacting on a particular industry would not have been factored into the model.
Any economic model must necessarily adopt simplifying assumptions to abstract from the overwhelming detail of the real economy, and these abstractions may affect the results of any given application of the economic model. Further simplifying assumptions must be adopted in translating the specific details of any particular economic issue into a set of tractable model shocks. The economic modelling undertaken in this study is no different in this regard, and the model results might be different under alternative model assumptions governing simulation design, economic theory, economic structure, values for parameters governing behavioural responses, public policy responses, andmodel closure.
PwC
Economic Modelling Approach
CGE Methodology
Detailed CGE modelling results
CGE Modelling InputsC
PwC
CGE modelling inputs
Baseline output from Tasmanian industries was estimated using industry value add ABS cat no 5220.0 Australian National Accounts: State Accounts 2015-16, which was inflated to 2017 dollars and converted to industry output using the relationships between value add and output for each industry from ABS cat no 5209.0.55.001 Australian National Accounts: Input-Output Tables, 2013-14.
Results
This results in the following headline modelling results.Source: ABS cat no 5220.0 Australian National Accounts: State Accounts, table 1 and table 17, ABS cat no 6202.0 Labour Force, Australia, table 9, ABS cat no 6401.0 Consumer Price Index, table 5, and PwC calculations. Note: Employment is taken as the average of the last four quarters of seasonally adjusted numbers.
Measure2017 industry
output ($’million)
Expenditure from
survey and club
($’million)
Expenditure as %
of industry (shock
for model)
Transport 2,953 2.4 0.08%
Hotel and cafes 1,636 10.8 0.66%
Trade 6,040 4.8 0.08%
Other services 1,408 2.3 0.17%
MeasureBase case
valueChange
Change
(amount)New value
Gross State Product $26,804m 0.11% $28.4m $26,832m
Household consumption $18,346m 0.05% $8.5m $18,355m
Employment (total persons) 243,309 0.06% 136 243,445
Current Tasmanian economy
Our approach estimates the economic impacts of an initiative as a percentage change. As such, a baseline scenario is required to measure the impact against. The baseline represents the current Tasmanian economy and key Tasmanian economic indicators are shown in the table below. These indicators are drawn from the latest Australian Bureau of Statistics (ABS) data. The baseline is presented in 2017 dollars to align with the survey data. Where 2017 data was not available, CPI for Hobart was used to inflate 2016 data.
Shocks used for flow on modelling
The expenditure estimated in the previous chapter then needs to be put in the form of a shock to the Tasmanian baseline economy. Shocks are entered in to the flow on impact model as a percentage change in one of the model variables. The added expenditure in each industry as a result of the HFC games have been taken to be an expansion in industry output, as this is the closest proxy for increased activity.
Measure Base case value
Gross State Product $26,804 million
Household consumption $18,346 million
Employment (total persons) 243,309
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Source: Base case values as above, PwC modelling.
PwC
Economic Modelling Approach
CGE Methodology
CGE Modelling Inputs
Detailed CGE modelling resultsD
PwC
Value add and employment
Value add by industry results Employment by industry results
MeasureBase case
value ($m)Change (%) Change ($m)
New value
($m)
Agriculture, forestry
and fishing1,613 0.07 1.07 1,614
Mining 458 0.14 0.64 458
Food manufacturing 1,032 0.23 2.34 1,035
Other manufacturing 2,930 0.15 4.48 2,934
Utilities 864 0.12 1.00 865
Construction 1,172 0.15 1.74 1,174
Trade 3,247 0.01 0.27 3,247
Hotels and cafes 758 0.39 2.95 761
Transport 1,322 0.14 1.89 1,324
Communications 1,709 0.21 3.63 1,713
Banking, finance and
insurance1,878 0.10 1.85 1,880
Property services 2,209 0.13 2.90 2,212
Computer services 66 0.18 0.12 66
Government services 2,142 0.04 0.88 2,143
Education 1,284 0.03 0.37 1,285
Health services 1,636 0.02 0.41 1,636
Other services 658 0.29 1.92 660
MeasureBase case value
(persons)
Change
(%)
Change
(persons)
New value
(persons)
Agriculture, forestry
and fishing13,695 0.05 7 13,702
Mining 3,956 0.06 2 3,958
Food manufacturing 4,463 0.10 5 4,468
Other manufacturing 13,360 0.07 9 13,369
Utilities 3,528 0.05 2 3,529
Construction 22,493 0.07 15 22,508
Trade 32,633 0.00 0 32,633
Hotels and cafes 19,279 0.18 34 19,313
Transport 11,636 0.06 8 11,644
Communications 3,735 0.10 4 3,738
Banking, finance and
insurance5,368 0.04 2 5,371
Property services 2,991 0.06 2 2,992
Computer services 11,822 0.08 10 11,832
Government services 16,666 0.02 3 16,669
Education 19,479 0.01 3 19,481
Health services 37,114 0.01 4 37,118
Other services 21,092 0.13 28 21,120
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PwC
Total wage bill
Total wage bill by industry results
MeasureBase case
value ($m)Change (%) Change ($m)
New value
($m)
Agriculture, forestry
and fishing470 0.05 0.22 471
Mining 113 0.06 0.07 113
Food manufacturing 449 0.10 0.45 449
Other manufacturing 1,343 0.07 0.89 1,343
Utilities 342 0.05 0.17 342
Construction 611 0.06 0.39 612
Trade 1,765 0.00 -0.05 1,765
Hotels and cafes 391 0.18 0.69 392
Transport 620 0.06 0.39 620
Communications 840 0.09 0.79 841
Banking, finance and
insurance704 0.04 0.29 704
Property services 152 0.06 0.09 153
Computer services 62 0.08 0.05 62
Government services 1,737 0.02 0.27 1,737
Education 1,228 0.01 0.13 1,228
Health services 1,623 0.01 0.14 1,623
Other services 303 0.13 0.40 304
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