Hawthorn Football Club Estimation of the economic contribution of Hawthorn Football Club 2017 www.pwc.com.au games in Launceston to the Tasmanian economy 01 Executive summary

02 Direct impacts

Total impacts 03 (including indirect or flow on impacts)

04 Appendices

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 PwC any such errors on our analysis, suggestions or report. 01 Executive summary

Direct impacts

Total impacts (including indirect or flow on impacts)

Appendices

PwC Executive summary

The Hawthorn Football Club is currently contracted by the Tasmanian Government to play five games per season in Launceston, . $20.4m $28.5m total direct expenditure direct and indirect PwC has been engaged to estimate the total dollar GSP ($2017) 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: 61,301 15,126 interstate and international local, interstate and visitors visiting Tasmania in JLT Community Series: Hawthorn v Geelong international game attendances 2017* (Friday, 17 February 2017) Round 6: Hawthorn v St Kilda (Saturday, 29 April 2017) $816 Round 8: Hawthorn v 137 average spend per direct and indirect (Saturday, 13 May 2017) interstate and additional jobs supported* international visitor Round 16: Hawthorn v Greater Western Sydney (Saturday, 8 July 2017) Round 21: Hawthorn v North (Sunday, 13 August 2017). 25% 77% The evaluation quantifies both the direct impacts of percentage of attendees from proportion of interstate and expenditure by game attendees, as well as the indirect interstate or overseas international visitors spending flow on impacts of that expenditure to the Tasmanian more than 1 night in Tasmania economy.

* Estimate based on extrapolated survey results.

PwC 4 Executive summary (cont’d)

61.3k $323 19.8m

International/ interstate 15k International/ International/ interstate $12.4m interstate $812 Tasmania 27k

Tasmania $6.7m Launceston 19k Tasmania $244 Launceston $41 Launceston $0.8m Game attendances Spend per person Total direct attendee spend

Spend per person depends on the Attendance can be analysed by residential The composition of direct spend is driven residential origin of the spectator, and is origin of the spectator (Launceston, Tasmania by a combination of game attendees by greatest for interstate/international (other) and interstate/international) residential origin and spend per person spectators

PwC 5 Executive summary (cont’d) Expenditure and total impacts

$20.4m $28.5m direct and flow-on m GSP ($2017) across Australia model inputs GSP $19.8m Match attendee spend

$579k 137 Hawthorn Football Club direct and flow-on additional jobs spend Jobs CGE multipliers $74k Assumed opposition club spend $8.5 m household consumption ($2017) Consumption Total direct expenditure

Source: PwC analysis based on HFC financial data, PwC survey and PwC modelling.

PwC 6 Executive summary

02 Direct impacts

Total impacts (including indirect or flow on impacts)

Appendices

PwC We estimated the economic impact of HFC’s Launceston matches by analysing survey results and using a CGE model

Approach 1. Understanding of Hawthorn Football Club’s matches at University of In order to collect expenditure data, email Tasmania Stadium addresses were requested from a sample of match day attendees, who were Developed and launched an online survey to capture activities of attendees at each match. The purpose of the survey was to subsequently sent an electronic survey in determine the proportion of local, interstate and international visitors and their associated average spend profiles. the days following the match. The survey asked questions primarily related to the amount spent by each 2. Attendance spending participant, who they attended the game with, and what else they did in relation to The sample of match attendee information from the online survey was then extrapolated to the total number of match their trip to the football game. attendees. This provided us with an estimate of the total amount of local, interstate and international attendees and the total Expenditure data was collected in relation to spending for each group (including associated tourism-related activities). This formed the basis for the total direct transport, accommodation, food and drinks, expenditures. entertainment and activities, Where the HFC games were indicated ‘part’ of the reason for their travel to Launceston, we only attributed 50 per cent of their and other. expenditure to the HFC game, and where it was a ‘non-primary’ reason for their travel, then only 20 per cent was attributed. In addition to the survey data, information was also sought from the Hawthorn Football 3. Club expenditure Club on their own expenditure that is likely to flow to Tasmania, including the cost of Match attendee expenditure was then combined with spend data from the Hawthorn Football Club and opposition sides. This accommodation, and food and drinks included items such as advertising spend and security and also included travelling team spend (including support staff, during games, as well as for other club trips accommodation and travel allowance). during the year. 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 8 Around of quarter of the 61,000 attendees in 2017 travelled to Tasmania from interstate or overseas

Match attendance Residences of attendees

The four 2017 home and away season games held by HFC in Launceston While data is publically available on the total attendance of each game, there attracted 52,789 people, plus an additional 8,512 at the pre-season match, is no complete dataset available on the exact place of residence of each of the making total attendance 61,301 at an average of 12,260 people per game attendees. However, we can extrapolate the survey results to estimate match. the proportion (and therefore the number) of people that attended the games from: 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 • Launceston or nearby suburbs game against Geelong (8,512). Attendances are shown in the figure below. • in Tasmania, but not in Launceston or nearby suburbs • interstate or overseas. Figure 1: Attendance numbers This results in the following estimates shown in the table below. 18,000 In Tasmania, but 13,500 Launceston not in Interstate or nearby Launceston or or 9,000 suburbs nearby suburbs overseas Total Count of 4,500 survey 333 479 266 1,078 responses 0 Geelong (JLT) St. Kilda Brisbane GWS North Percentage Melbourne (from survey 31% 44% 25% results)

Assumed total 18,936 27,239 15,126* 61,301 Survey responses attendances

A total of 1,078 correctly completed responses to the online survey were *According to the ABS (2015) there is a total of 5,104 guest nights available in Northern Tasmanian received and used to estimate the average spend by game attendees. This hotels, motels and serviced apartments each night, meaning there is sufficient accommodation for the represented 1.8 per cent of game attendees. 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. Source: PwC survey of game attendees.

PwC 9 Interstate attendees spend much more than attendees from Tasmania

Based on the survey results, the average Launceston In Tasmania, but Average Interstate or amount spent in Tasmania by survey or nearby not in Launceston Total expenditure overseas participants as a consequence of attending suburbs or nearby suburbs one of the five matches in Launceston is estimated to be $323, up from $316 in 2014. Transport (for example: taxis, public $14 $29 $83 $38 This amount includes the spending of people transport, car hire) from Launceston, as well as people from elsewhere in Tasmania and interstate or Accommodation n/a $47 $250 $83 overseas. Where the HFC games were Food and drinks $27 $67 $220 $92 indicated ‘part’ of the reason for their travel to Launceston, we only attributed 50 per Entertainment and activities (excluding cent of their expenditure to the HFC game, n/a $25 $86 $32 admission to the AFL and where it was a ‘non-primary’ reason for game) their travel, then only 20 per cent was attributed. Shopping n/a $56 $110 $52 A breakdown of spending by expenditure Other n/a $20 $66 $25 type and place of residence is provided in Total $41 $244 $816 $323 the table across.

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 10 Average spend per person is highest for interstate travelers, 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 Figure 4: Average spend per person (all games) 3%

22% 19%

16% Northern 40% Territory N/A Queensland $936 Western 0 nights 1 night 2 nights 3 to 4 nights Over 4 nights Australia South $226 Figure 3: Breakdown of nights spent away Australia $980 New South $1,500 Wales $1,062 $1,008 $1,000 $328 $500 $794 Tasmania $- $159 Stays of 0-2 nights Stays of 3+ nights Sources: PwC analysis of survey data. Note 14 survey responses did not indicate postcode data so have been omitted from the Sources: PwC analysis of survey data. geographical analysis.

PwC 11 Most attendees are Tasmanian, but most of the spending is from interstate especially Victoria

Figure 5: Proportion of attendance by location of origin Figure 6: Total spend by location of origin (all games) (all games)

Northern Territory Northern N/A Territory Queensland N/A 1.4% Queensland Western $0.8 m Australia Western South Australia 0.3% South Australia $0.04 m 1.0% Australia New South $0.6 m New South Wales 1.5% Wales $1.0 m

Victoria 19.8% Victoria $9.7m Tasmania Tasmania 74.5% $7.3m 27%

15% 2% 6% 1% 5%

17% 1%

0.5% Sources: PwC analysis of survey data. Note 14 survey responses did not indicate postcode data so have been omitted from the geographical analysis.

PwC 12 Almost half of the attendees were Hawthorn Football Club members

Ticket types Social media

Almost half of all attendees were HFC members and over 30 Just over 80 per cent of visitors who attended the match from per cent used general admission (see figure below). 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). Figure 7: Ticket types

Figure 8: Social media AFL member 2%

Corporate ticket 4%

33% General admission 31% 48%

HFC member 49% 19% Other 10%

Other club member 4% No Yes Yes, multiple times 0% 20% 40% 60%

Sources: PwC analysis of survey data. Sources: PwC analysis of survey data.

PwC 13 There was overwhelmingly positive feedback from spectators in regard to HFC’s matches in Launceston

“I firmly believe that Hawthorn are not only beneficial to Positive feedback Tasmania in an Economic sense, but the Social benefit to Almost 87 per cent of the feedback provided on the online the region is immeasurable. Not only the joy they bring survey with respect to the arrangement between Hawthorn children that support Hawthorn, they also are role models and Tasmania, as well as the spectacle in general, was positive and the elderly from a personal experience look forward to (see figure below). them being in Launceston. Some things are unable to be quantified.” 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. “Hawthorn, their Tasmanian supporters and the Tasmania Government have established a unique football experience Figure 9: Survey feedback in northern Tasmania. Please do not take it away from future fans of the game.”

2% 11% “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 87% Tasmania and the Launceston footy ground was great to attend.”

Good Bad Neutral

Sources: PwC analysis of survey data.

PwC 14 The primary reason for the vast majority of attendees to travel to Launceston was due to the game

Figure 10: Reason for travel - Tasmanians Figure 11: Reason for travel - Interstate or overseas

Primary 91% Primary 71%

Part 8% Part 23%

Non-primary 1% Non-primary 5%

0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80%

Reason for travel Impact on modelling

The reasons for travel vary depending on whether the Where the HFC games were indicated ‘part’ of the reason for attendee is from Tasmania or otherwise; over 91 per cent of their travel to Launceston, we only attributed 50 per cent of locals who visited Launceston did so to attend the game, their expenditure to the HFC game, and where it was a ‘non- whereas this proportion was only approximately 71 per cent primary’ reason for their travel, then only 20 per cent was for people from interstate or overseas. attributed.

Sources: PwC analysis of survey data. Sources: PwC analysis of survey data.

PwC 15 Direct attendee spend was almost $20m, driven predominantly by interstate or overseas attendees spending on ‘accommodation’ and ‘food/drink’

The direct expenditure flowing to Tasmania as a Launceston In Tasmania, but not Interstate result of the five Hawthorn Football Club in Total expenditure or nearby in Launceston or or Total Launceston in 2017 is estimated at around suburbs nearby suburbs overseas $19.8 million.

A breakdown of spending by expenditure type Transport (for example: and place of residence is provided in the taxis, public transport, $0.3m $0.8m $1.3m $2.3m following table. car hire)

Accommodation n/a $1.3m $3.8m $5.1m

Food and drinks $0.5m $1.8m $3.3m $5.7m

Entertainment and 62% activities (excluding n/a $0.7m $1.3m $2.0m admission to the AFL Proportion of direct game) spend from interstate/overseas Shopping n/a $1.5m $1.7m $3.2m match attendees

Other n/a $0.5m $1.0m $1.5m

Total $0.8m $6.6m $12.3m $19.8m

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 16 Direct spend was 13 per cent higher in 2017 than in 2014

Spend profile

Direct spend by match attendees in 2017 is 13 per cent ($2.3 million) Total expenditure 2014 2017 Difference higher than in 2014. Spectators increased spending in all categories Transport $2.4m $2.3m -$0.1m except for transport (which was 3 per cent/$0.08 million lower in 2017). Accommodation $5.0m $5.1m $0.1m The categories on which spectators spent significantly more money are Food and drinks $5.4m $5.7m $0.3m ‘shopping’ and ‘other’ (such as amounts spend on the survey respondent Entertainment and $1.8m $2.0m $0.2m by others), experiencing a 33 and 207 per cent increase respectively – activities this change in spending patterns drove the higher overall spend in 2017. The other categories which people spent more money on experienced Shopping $2.4m $3.2m $0.8m increases of between 2 and 10 per cent. The overall spend profile Other $0.5m $1.5m $1.0m differential between 2014 and 2017 is summarised in the table and Total $17.5m $19.8m $2.3m 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 totals in the table may not appear to add correctly due to rounding in the Figure 13: Spend profile comparison presentation of the figures shown.

$6 2014

$4 2017

Millions $2

$0

Source: PwC analysis of survey data. Note attendance from the pre season match was not included in the 2014 analysis. PwC 17 Club expenditure

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.

Game related - Game related - Game related - Game related - Non-game Club Total Accommodation Catering Transport Other related spend

Hawthorn $47k $38k $6k $19k $469k $579k

Visiting clubs* $31k $25k $4k $13k - $74k

Total $78k $63k $10k $32k $469k $653k

* 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.

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.

PwC 18 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 Expenditure by spectators on transport in Tasmania. For example: taxis, public Transport $38 $2.3m transport, car hire etc. Expenditure by spectators on accommodation in Tasmania. For example: Accommodation $83 $5.1m hotel, AirBnB Food and drinks Expenditure by spectators on food and drink in Tasmania. $92 $5.7m Entertainment and Expenditure by spectators on entertainment in Tasmania (excluding admission $32 $2.0m activities to the AFL game). For example, movies, live music etc. Shopping Expenditure by spectators on shopping in Tasmania. $52 $3.2m Expenditure by spectators in categories other than transport, accommodation, Other $25 $1.5m food and drinks, entertainment and shopping in Tasmania. 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 19 Executive summary

Direct impacts

Total impacts 03 (including indirect or flow on impacts)

Appendices

PwC Total impacts

Breakdown of the flow-on contribution to Method Tasmania’s economy from the Hawthorn Football Club matches in Launceston, 2017 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. Measure Change (amount) To estimate these flow-on economic benefits of hosting HFC games in Launceston, we have utilised a Computable General Equilibrium (CGE) Gross State Product $28.5 million model. CGE modelling is a widely used economic impact analysis tool for Household consumption $8.5 million simulating the economy-wide effects of projects or policies, which represent ‘shocks’ to the economy. CGE models replicate how the economy will adjust Employment 137 people to ‘shocks’ from significant projects and policies. Appendices A - D describes the methodology of our CGE modelling in further Source: PwC CGE modelling. detail. Note: Each results are presented as nominal in year they are calculated and are not directly comparable. Results 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 We estimate that the $19.8 million direct expenditure from people that others grow, due to the constraints on inputs that CGE places on the model economy. attended one of the games (discussed in the previous chapter) together with However, the results presented here are only for Tasmania and some of these negative $0.7 million direct expenditure by the HFC and opposition teams in effects would be felt outside Tasmania as visitor spending is attracted to Tasmania and Tasmania, generates a total contribution to the Tasmanian gross state away from other states. Although the impact on other states are not presented here, there product (GSP) (in value add terms) of $28.5 million or 0.11 per cent of may be some losses in employment or output in other jurisdictions as constrained inputs, GSP. such as labour, move to the Tasmanian economy (or stay in Tasmania where they might Household consumption (a proxy for consumer welfare) and employment are have otherwise left). 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.

PwC 21 Impact on GSP

Impact on GSP GSP composition

Figure 14 shows contribution to GSP distributed across GSP (in industry value add terms) is made of three main industries in percentage change terms. components; compensation of employees (wages), gross operating surplus (profit) and taxes less subsidies on Figure 14: Modelling results, percentage change in industry production and imports (net government position). To give a value add high level estimate of how the $28.5 million increase is split across these components, we have combined the modelling Other services results with information from ABS State Accounts (see Health services table below). Education Government services Computer services Property services Component Amount Banking, finance and insurance Communications Compensation of employees $13.5 million Transport Hotels and cafes Gross operating surplus $11.9 million Trade Taxes less subsidies $3.0 million Construction Utilities Total GSP increase $28.5 million Other manufacturing Food manufacturing Mining Agriculture, forestry and fishing

Total 0.0% 0.1% 0.2% 0.3% 0.4% 0.5% 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 Source: PwC modelling presentation of the figures shown.

PwC 22 Impact on GSP (cont’d)

Figure 15: Modelling results, dollar change in industry GSP value add ($ millions)

Higher demand for inputs into industry supply chains raises Other services activity in industries that support the directly affected Health services industries, such as the food manufacturing industry. Education Some industries may be negatively affected however through price changes. Higher demand for inputs into these industries Government defence services will raise prices in the source industries of these inputs. Some Computer services industries not directly affected by increased visitor Property services expenditure may compete for these inputs. The greater an industries similarity of inputs with the directly affected Banking, finance and insurance industries, the greater the negative impact through higher Communications input prices. Transport In this case, potentially higher prices across some sectors only Hotels and cafes partially offset the higher to value added through the visitor Trade spending, and not industry is negatively affected. For example, industries such as health services and education Construction receive no direct benefit from visitor expenditure, however Utilities they must still compete for input (namely labour) with Other manufacturing industries that do directly benefit. So while health services and education are benefitting through higher demand for Food manufacturing their products, they must also pay more for their inputs, Mining mitigating the overall impact on these industries. Agriculture, forestry and fishing Figure 15 shows the dollar change in industry value add, split -1.0 0.0 1.0 2.0 3.0 4.0 5.0 between wages, gross operating surplus and taxes. Wages Gross operating surplus Taxes

Source: PwC modelling and ABS cat. no. 5220.0, table 7.

PwC 23 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 Figure 17: Modelling results, change in employment, persons employment by industry Other services Other services Health services Health services Education Education Government services Government services Computer services Computer services Property services Property services Banking, finance and insurance Banking, finance and insurance Communications Communications Transport Transport Hotels and cafes Hotels and cafes Trade Trade Construction Construction Utilities Utilities Other manufacturing Other manufacturing Food manufacturing Food manufacturing Mining Mining Agriculture, forestry and fishing Agriculture, forestry and fishing

Total Total 0.00% 0.05% 0.10% 0.15% 0.20% 0 50 100 150 Source: PwC modelling Source: PwC modelling

PwC 24 Executive summary

Direct impacts

Total impacts (including indirect or flow on impacts)

04 Appendices

PwC Appendices

01A Economic Modelling Approach

B CGE Methodology

C CGE Modelling Inputs

D Detailed CGE modelling results

PwC 01A Economic Modelling Approach

CGE Methodology

CGE Modelling Inputs

Detailed CGE modelling results

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.

PwC 28 Flow-on and total economic impact

In our CGE analysis, we have estimated the impacts of the HFC matches in Launceston on Flow on and total economic impacts key macroeconomic variables. Each of these measures is described below. • Gross state product (GSP) – this represents the “value added” to the economy When considering flow on and total economic impacts, the direct economic through spending patterns. Since the GSP figure captures the difference between the impacts as a result of HFC matches in Launceston were modelled using CGE, value of output and the value of intermediate inputs, it represents the unduplicated a globally accepted and robust approach. This technique overcomes the total value of economic activity that has taken place. The GSP impacts in this report limitations of simpler input-output models, providing a more accurate represent the value added to the economy as a result of the spending made in estimate of the impacts and is the preferred approach of many Governments Tasmania in connection with the HFC matches in Launceston. and Treasury Departments in Australia. • Employment – represents the number of additional full time equivalent jobs created Comprehensive economic impact analysis generally makes use of as a result of the spending made in Tasmania in connection with the HFC matches in sophisticated economic modelling to represent an economy and simulates the Launceston. effect a change has on the economy. It incorporates detailed representations of industry production, consumption, government, trade, prices and the • Household consumption – measures household economic wellbeing through the behaviours that link the economy together. 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 For example, an additional $1 spent directly in the Australian economy in the population is better off. 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 Indirect or flow on impacts and total economic impacts are described below. 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 • Indirect impacts or flow on impacts – The direct spending, will flow through the $1 spent, for a total expenditure effect of 1.75. From these expenditure Australian economy to stimulate other industries. These flow on impacts arise from impacts, the most commonly used of these measures are, gross state product changes in activity for suppliers through the various industry’s supply chain. For (GSP), employment and household consumption. 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.

PwC 29 CGE modelling

A CGE model attempts to ‘push forward’ the base Input-Output table through time by What is a CGE model? 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 A CGE model is a mathematical model of an economy that is capable of are solved simultaneously, where some variables are determined by the model capturing economy-wide impacts and inter-sectoral reallocation of resources (endogenous variables) and some are determined outside the model (exogenous that may result from a ‘shock’ (that is, change in the status quo) to the variables). The classification of endogenous and exogenous variables is determined by the economy. CGE models are widely used in economic analysis of policies and user based on the set of assumptions derived for the specific modelling exercise. CGE projects around the world including in Australia by both government and the modelling is a widely used economic impact analysis tool for simulating the economy-wide private sector. effects of projects or policies, often involving large expenditures and revenues, which Both input-output (I-O) modelling and CGE modelling have been used represent ‘shocks’ to the economy. CGE models recognise that complex macroeconomic previously in the preparation of economic impact assessment for sporting mechanisms and inter-industry interactions exist in the economy and, in light of this, events. However, we prefer to use CGE analysis as it provides a more robust replicate how the economy will adjust to ‘shocks’ from significant projects and policies. 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 Assumptions 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. • Our analysis of total attendance and visitor numbers are driven by the attendance data The core data of a CGE model is an input-output table. An input-output table on the official AFL website. 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- • We applied average spend profiles by postcode of origin collected in our PwC online output table captures sales of products to other industries for further survey to our analysis of total attendance and visitor numbers to provide us with the processing (intermediate usage) or to the various categories of final demand. direct economic impacts resulting from attendance and tourism. It also captures the inputs used in an industry’s production, whether they be • Match attendee expenditure was combined with club expenditure to provide us with intermediate or primary inputs (such as labour and capital). The table is our total direct expenditures. This included items spent by clubs such as travelling balanced such that total inputs to each industry are equal to total outputs spend (including support staff, accommodation and travel allowance). PwC did not from each industry. Essentially, an input-output table is a snapshot of an verify or audit this data. economy (whether it is a region, state or country) in a particular year. • Total direct expenditures represent the gross spending associated with the HFC matches in Launceston.

PwC 30 Economic Modelling Approach

B CGE Methodology

CGE Modelling Inputs

Detailed CGE modelling results

PwC The Monash TERM model

TERM is capable of modelling a region-specific, demand or supply-side shocks (that is, The Monash TERM model 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 Centre of Policy Studies (CoPS), at Monash University, has developed a • the database (input-output tables for each region) 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 • choice of behavioural parameters (how demanders of commodities minimise costs) models have been peer reviewed and are regarded by all State and Commonwealth Treasuries as being capable of producing credible results • choice of closure (combination of exogenous and endogenous variables in the model). 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.

PwC 32 The TERM Database

• International exporters are assumed to adjust behaviour depending on movements in The TERM Database the terms of trade and exchange rate. • Interstate exporters are assumed to adjust behaviour depending on changes in state TERM is based on Input-Output tables prepared by the CoPS, drawing upon price relativities. Australian Bureau of Statistics (ABS) input-output tables and on various A key feature of a CGE model like TERM is its ability to capture the substitution effects supplementary ABS data. The input-output table used for this study is a and supply-side constraints that exist in the economy. database produced by CoPS for 2009/10. The supply-side constraints of the economy must be recognised when assessing the The input-output tables may be considered a “snap-shot” of the economy in economic impacts of projects and policies. That is, the supply of inputs (land, labour, 2009/10, providing a detailed description of the structure of production and capital and intermediate factors of production) is limited. There is a finite quantity of these demand at the regional, state and national level. These tables form the inputs from which to drawn upon to increase economic activity. Constraints on the database which shows for each regional economy the flow of industry outputs availability of inputs require prices to act as a rationing device. to other industries, together with the flow of industry output to final users (households, government, investors and foreign markets). 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 The input-output tables also contain the cost structure of each industry utilising resources. If the economy is utilising close to all of its resources then the addition including cost of taxes, intermediate inputs and primary factors of production of, for example, a construction project, is likely to have a significant crowding out effect (labour, capital and agricultural land). since there are few spare resources available. As such, the flow-on economic activity and Economic theory captured in TERM 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 Like other CGE models, TERM captures standard neoclassical economic utilisation of resources, the flow-on economic activity and employment may be quite high. theory to determine the behaviour of economic agents when they are faced with changes in key economic variables (especially relative prices). For The CGE modelling framework works to address these complex economic mechanisms example: and, as such, produces conservative measures of economic benefits. • 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.

PwC 33 Comparative static model and its application

Application of TERM to model the flow-on impact of Accounting for distributional effects HFC games in Tasmania

The sectoral structure of TERM allows identification of the distribution of While the TERM model is an ‘off the shelf’ modelling product, a number of impacts, between industry sectors and regions. Economy-wide constraints in modifications to the model were required before it could be applied to TERM, including those for the labour market and the balance of trade, mean modelling the flow-on impact of HFC games. This primarily involves that for most simulations the distribution of impacts will differ by sector. modelling the investment facilitation activities as a long run simulation, which involves: • Employment is considered fixed in the long run. This assumption is A comparative static model 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. CGE models can be developed as either ‘comparative static’ or ‘recursive • Investment and capital are linked together. That is, investment in one dynamic’, depending on the treatment of time in the modelling exercise. year, will lead to a proportional increase in capital in the following year. While recursive dynamic modelling can account for how the economy changes over time to move from one equilibrium position to another, • Public (government) consumption spending follows movements in the comparative static modelling presents a static viewpoint, comparing the long-run regional distribution of economic activity. This is done through economy with and without the impact of the shock at a particular point region specific ratios of real public consumption spending to real private in time. consumption spending. 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.

PwC 34 Limitations of the CGE model

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, and model closure.

PwC 35 Economic Modelling Approach

CGE Methodology

C CGE Modelling Inputs

Detailed CGE modelling results

PwC CGE modelling inputs

Baseline output from Tasmanian industries was estimated using industry value add ABS Current Tasmanian economy 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 Our approach estimates the economic impacts of an initiative as a percentage Accounts: Input-Output Tables, 2013-14. change. As such, a baseline scenario is required to measure the impact against. The baseline represents the current Tasmanian economy and key Tasmanian Expenditure from Expenditure as % 2017 industry economic indicators are shown in the table below. These indicators are drawn Measure survey and club of industry (shock output ($’million) from the latest Australian Bureau of Statistics (ABS) data. The baseline is ($’million) for model) presented in 2017 dollars to align with the survey data. Where 2017 data was not available, CPI for was used to inflate 2016 data. Transport 2,953 2.4 0.08% Hotel and cafes 1,636 10.8 0.66% Measure Base case value Trade 6,040 4.8 0.08% Gross State Product $26,804 million Other services 1,408 2.3 0.17% Household consumption $18,346 million

Employment (total persons) 243,309 Results Source: ABS cat no 5220.0 Australian National Accounts: State Accounts, table 1 and table 17, ABS cat This results in the following headline modelling results. 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. Base case Change Measure Change New value value (amount) Gross State Product $26,804m 0.11% $28.4m $26,832m Shocks used for flow on modelling Household consumption $18,346m 0.05% $8.5m $18,355m Employment (total persons) 243,309 0.06% 136 243,445 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 Source: Base case values as above, PwC modelling. 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.

PwC 37 Economic Modelling Approach

CGE Methodology

CGE Modelling Inputs

D Detailed CGE modelling results

PwC Value add and employment

Value add by industry results Employment by industry results

Base case New value Base case value Change Change New value Measure Change (%) Change ($m) Measure value ($m) ($m) (persons) (%) (persons) (persons) Agriculture, forestry Agriculture, forestry 1,613 0.07 1.07 1,614 13,695 0.05 7 13,702 and fishing and fishing Mining 458 0.14 0.64 458 Mining 3,956 0.06 2 3,958 Food manufacturing 1,032 0.23 2.34 1,035 Food manufacturing 4,463 0.10 5 4,468 Other manufacturing 2,930 0.15 4.48 2,934 Other manufacturing 13,360 0.07 9 13,369 Utilities 864 0.12 1.00 865 Utilities 3,528 0.05 2 3,529 Construction 1,172 0.15 1.74 1,174 Construction 22,493 0.07 15 22,508 Trade 3,247 0.01 0.27 3,247 Trade 32,633 0.00 0 32,633 Hotels and cafes 758 0.39 2.95 761 Hotels and cafes 19,279 0.18 34 19,313 Transport 1,322 0.14 1.89 1,324 Transport 11,636 0.06 8 11,644 Communications 1,709 0.21 3.63 1,713 Communications 3,735 0.10 4 3,738 Banking, finance and Banking, finance and 1,878 0.10 1.85 1,880 5,368 0.04 2 5,371 insurance insurance Property services 2,209 0.13 2.90 2,212 Property services 2,991 0.06 2 2,992 Computer services 66 0.18 0.12 66 Computer services 11,822 0.08 10 11,832 Government services 2,142 0.04 0.88 2,143 Government services 16,666 0.02 3 16,669 Education 1,284 0.03 0.37 1,285 Education 19,479 0.01 3 19,481 Health services 1,636 0.02 0.41 1,636 Health services 37,114 0.01 4 37,118 Other services 658 0.29 1.92 660 Other services 21,092 0.13 28 21,120

PwC 39 Total wage bill

Total wage bill by industry results

Base case New value Measure Change (%) Change ($m) value ($m) ($m) Agriculture, forestry 470 0.05 0.22 471 and fishing 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 704 0.04 0.29 704 insurance 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

PwC 40 www.pwc.com.au

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