THE ECONOMIC VALUE OF FRESHWATER IMPOUNDMENT FISHERIES IN :

THE BJELKE-PETERSEN, AND FAIRBAIRN

John Rolfe*, Prabha Prayaga*, Peter Long** and Rod Cheetham**

Report prepared by University

February 2005

* Faculty of Business and Law, Central Queensland University, Rockhampton, ** Department of Primary Industries and Fisheries EXECUTIVE SUMMARY

1. An economic study was conducted in 2002-03, involving staff from Central Queensland University (CQU) and the Queensland Department of Primary Industries and Fisheries (DPI&F). Data was obtained through the collection of surveys from anglers at the Bjelke-Petersen , and over a 12 month period.

2. The total number of surveys collected during the 12 month period was 264 at the Bjelke-Petersen dam, 250 at the Boondooma dam and 182 at the Fairbairn dam.

3. The expected number of groups visiting the dams each year is 2,513 at Bjelke- Petersen, 3,275 at Boondooma, and 1,622 at Fairbairn.

4. Average group size ranged from 2.6 people at Bjelke-Petersen to 3.8 people at Fairbairn.

5. Average distance travelled (one way) to reach the dams ranged from 240 kilometres at both Bjelke-Petersen and Boondooma Dams to 701 kilometres at Fairbairn Dam.

6. The average length of the fishing trip ranged from 6.5 days at Bjelke-Petersen to 8.6 days at Fairbairn. Anglers at Boondooma were making trips solely for fishing; by comparison anglers at Fairbairn tended to make the visit as part of a longer trip.

7. Expenditure per group per trip at the dams has been estimated at $457 for Bjelke- Petersen, $448 at Boondooma, and $1,368 at Fairbairn. After allowances have been made for the non-fishing component of holidays, the total annual expenditure by recreational anglers to the three dams are estimated at $0.95M for Bjelke-Petersen, $1.47M for Boondooma, and $1.07M for Fairbairn.

8. Total spending by anglers in the local impoundment areas has been estimated at $0.78M at Bjelke-Petersen, $1.10M at Boondooma, and $0.97M at Fairbairn.

9. The value of recreational fishing at each dam was assessed with the travel cost method (TCM) – a specialist non-market valuation technique. This used the expenditure incurred by anglers to estimate the additional economic benefits of the visit. This can be thought of as the ‘profit’ that anglers get from their experience at the dam after all expenditure has been considered.

10. The data set had to be split into two groups for each dam to fit a TCM. The economic value of groups only making single visits were assessed with a zonal TCM, while the economic value of groups making multiple visits each year was assessed with an individual TCM.

11. The proportion of anglers making single visits to each dam (the tourism market) were 34% at Bjelke-Petersen, 29% at Boondooma, and 62% at Fairbairn.

12. The estimated consumer surplus for the visitors (making just 1 trip per annum) to each dam per person per trip was $60 at Bjelke-Petersen, $348 at Boondooma, and $904 at Fairbairn. The total annual consumer surplus for visitors with just a single visit per

i year was estimated at $0.16M for Bjelke-Petersen, $0.96M for Boondooma, and $3.43M for Fairbairn.

13. `For the regular anglers (making > 1 trip per annum), the estimated consumer surplus per person per trip was $221 for Bjelke-Petersen, $359 for Boondooma, and $441 for Fairbairn. The total annual consumer surplus for visitor group was $0.91M for Bjelke-Petersen, $2.23M for Boondooma, and $1.11M for Fairbairn.

14. The total economic value of recreational fishing can be estimated by adding the values of the single visit group to the value of the repeat visit group. The total economic value of current recreational activities is estimated at $1.07M for Bjelke-Petersen, $3.2M for Boondooma, and $4.54M for Fairbairn. These estimates represent the net value of the recreational experience once all costs have been considered.

15. A contingent valuation question was included to assess values for improving the fishing experience at each dam. A hypothetical scenario was used, where respondents were asked if they would be willing to pay an increased fee of a set amount to improve their fishing experience by 20%. (The scenario is not government policy and has only been used in the project as a mechanism to assess these values).

16. An analysis of the responses indicated that the average values for improving catch rates by 20% was $19/angler at Bjelke-Petersen, $43/angler at Boondooma, and $36/angler at Fairbairn.

17. The results show that the value of improving catch rates by 20% per annum at each dam are estimated to be $0.12M for Bjelke-Petersen, $0.39M for Boondooma, and $0.22M for Fairbairn.

18. Anglers at the Bjelke-Petersen Dam are spending $0.95M, mostly in the local economy, have a recreational value of $1.07M for fishing at the dam, and value a potential 20% improvement in catch rates at $0.12M.

19. Anglers at the Boondooma Dam are spending $1.43M, mostly in the local economy, have a recreational value of $3.2M for fishing at the dam, and value a potential 20% improvement in catch rates at $0.39M.

20. Anglers at the Fairbairn Dam are spending $1.07M, mostly in the local economy, have a recreational value of $4.54M for fishing at the dam, and value a potential 20% improvement in catch rates at $0.22M.

ii Table of Contents EXECUTIVE SUMMARY ...... i TABLE OF CONTENTS ...... iii LIST OF TABLES ...... iv LIST OF FIGURES ...... v 1 INTRODUCTION ...... 1 1.1 IMPORTANCE OF RECREATIONAL FISHING IN AUSTRALIA ...... 1 1.2 IMPORTANCE OF RECREATIONAL FISHING IN QUEENSLAND ...... 2 1.3 HISTORY OF FISH STOCKING ...... 4 1.4 ASSESSING THE BENEFITS OF RECREATIONAL FISHING ...... 5 1.5 SCOPE OF THE STUDY ...... 6 1.6 AIMS OF THE STUDY ...... 7 1.7 METHODOLOGY ...... 7 1.8 REPORT STRUCTURE ...... 8 2 DATA COLLECTION AND PRELIMINARY RESULTS ...... 9 2.1 DATA COLLECTION ...... 9 2.2 PRELIMINARY RESULTS ...... 10 3 ECONOMIC IMPACT OF FRESHWATER RECREATIONAL FISHING ...... 14 3.1 OVERVIEW OF INPUT-OUTPUT ANALYSIS ...... 14 3.2 CASE STUDY ...... 16 3.4.1 Estimated visitation rates for each dam ...... 16 4 RECREATIONAL USE VALUES OF FRESHWATER FISHING ...... 21 4.1 INTRODUCTION ...... 21 4.1.1 The concept and measurement of consumer surplus ...... 21 4.2 OVERVIEW OF TRAVEL COST METHOD ...... 22 4.2.1 The assumptions of the study ...... 24 4.3 CASE STUDY ...... 26 4.3.1 The ITCM ...... 26 4.3.2 The ZTCM ...... 29 5 ESTIMATING THE BENEFITS OF IMPROVING THE FISHING EXPERIENCE ...... 34 5.1 OVERVIEW OF CONTINGENT VALUATION ...... 34 5.2 CASE STUDY ...... 35 6 RESULTS, CONCLUSIONS & RECOMMENDATIONS ...... 38 REFERENCES ...... 39 APPENDIX ...... 41

iii List of Tables

TABLE 1.1 EXPENDITURE OF QUEENDLAND RECREATIONAL FISHERS ...... 2 TABLE 1.2 MOTIVATION FOR FISHING – QUEENSLAND ANGLERS ...... 3 TABLE 1.3 STOCKED FISH SPECIES ...... 5 TABLE 2.1 AVERAGE ANNUAL FISHING TRIPS ...... 10 TABLE 2.2 SUMMARY STATISTICS FOR THE THREE DAMS ...... 11 TABLE 2.3 METHOD OF FISHING ...... 12 TABLE 2.4 NUMBER OF FISH CAUGHT ...... 13 TABLE 2.5 NUMBER OF FISH KEPT ...... 13 TABLE 3.1 AVERAGE EXPENDITURE OF ANGLERS AT THE DAMS ...... 16 TABLE 3.2 AVERAGE ANGLER PARTICIPATION RATES ...... 17 TABLE 3.3 VEHICLES ENTERING BOONDOOMA DAM AREA ...... 17 TABLE 3.4 AVERAGE NUMBER OF GROUPS VISITING EACH DAM ...... 19 TABLE 3.5 TOTAL NUMBER OF VISITS TO THE DAMS ...... 19 TABLE 3.6 AVERAGE EXPENDITURE PER GROUP ...... 19 TABLE 3.7 RATIO OF TRIP TO HOLIDAY ...... 20 TABLE 3.8 ESTIMATES OF LOCAL SPENDING ...... 20 TABLE 4.1 ANNUAL VISIT RATES ...... 24 TABLE 4.2 ITCM – TGF REGRESSION STATISTICS ...... 27 TABLE 4.3 ITCM – DEMAND SCHEDULES ...... 27 TABLE 4.4 ITCM – DEMAND REGRESSION STATISTICS ...... 28 TABLE 4.5 ITCM - CONSUMER SURPLUS ...... 29 TABLE 4.6 ZTCM – TGF REGRESSION STATISTICS ...... 30 TABLE 4.7 ZTCM – DEMAND SCHEDULES ...... 30 TABLE 4.8 ZTCM – DEMAND REGRESSION STATISTICS ...... 31 TABLE 4.9 ZTCM - CONSUMER SURPLUS ...... 32 TABLE 4.10 CONSUMER SURPLUS WITH PARTITION ...... 32 TABLE 4.11 TOTAL CONSUMER SURPLUS ...... 33 TABLE 5.1 REACTION TO LICENSE FEE INCREASES ...... 36 TABLE 5.2 CHOICE MODELS ...... 36 TABLE 5.3 MEAN AND TOTAL WILLINGNESS TO PAY ...... 37

iv List of Figures

FIGURE 1 MODE OF TRAVEL TO DAM ...... 11 FIGURE 2 IMPORTANCE OF FISHING ...... 12 FIGURE 3 PERCENTAGE OF HOLIDAY SPENT FISHING ...... 12 FIGURE 4 EXPECTED CATCH ...... 13 FIGURE 5 ANGLER PARTICIPATION DETAILS ...... 17 FIGURE 6 SUMMARY OF EXPENDITURE PATTERNS ...... 20 FIGURE 7 CONSUMER SURPLUS ...... 21 FIGURE 8 DEMAND CURVES FROM INDIVIDUAL TRAVEL COST MODELS ...... 28 FIGURE 9 DEMAND CURVES FROM ZONAL TRAVEL COST MODELS ...... 31 FIGURE 10 TOTAL CONSUMER SURPLUS ...... 33

v 1 Introduction

1.1 Importance of recreational fishing in Australia

This report is focused on estimating the economic values associated with recreational fishing at three freshwater dams in Queensland, Australia. It is not possible to directly estimate all of these values from market data, and specialist non-market valuation techniques need to be applied to assess these values. This can be done by interviewing or surveying recreational fishers directly to assess their expenditure patterns and other factors. Before moving to these stages, it is useful to summarise some available data about recreational fishing in Australia and Queensland.

The Fisheries Research and Development Corporation (FRDC) estimated at the national level that 25 – 30% of all Australians (3.36 million people) are involved in recreational fishing1. About 15% of this group (504,000 persons) fish regularly, for more than 20 days per year. Freshwater fishing accounted for approximately 20% of the national recreational fishing effort with about 11% in rivers and 8% in dams or lakes. At the national level, expenditure attributable to recreational fishing between the period, May 2000 to April 2001 was estimated at $1.8 billion (Henry & Lyle 2003). Highest expenditure was on boats ($872 million), followed by travel ($432 million), accommodation ($184 million) and fishing tackle ($146 million). Travel costs were estimated by allowing $0.50 per kilometre travelled, implying that the average travel distance per angler is 164 kilometres per year.

A key benefit of recreational fishing is the economic impact that it has on many local and regional communities.

Significant economic benefits from recreational fishing flow to many regional areas including jobs in the tourism, tackle, boating, and charter industries. Charter boats support game fishing, estuarine and coastal fishing, skin-diving and whale-watching activities, and there is a diverse boat-hire and service industry. These industries support others. For example, of the 4.475 million international tourists visiting in 2001, some 12 per cent (450 000) participated in diving activities, 4 per cent (179 000) participated in fishing activities, and 2 per cent (75 000) in whale-watching. One estimate of annual direct, indirect and capital expenditure on recreational fishing is $1.8 billion. Employment from national recreational fishing expenditure is conservatively estimated at between 27 000 and 54 000 jobs nationally, using expenditure multiplier estimates (FRDC, http://www.frdc.com.au/industry/recreation.htm).

Recreational fishing is important because it generates both long term and short term benefits. The commercial benefits of recreational fishing are more apparent and hence easier to quantify. Commercial benefits in the short term include jobs and increased turnover for businesses. In the long-term they include the attraction of new businesses to regions and increases in the profitability of existing ones. In this way recreational fishing can have significant direct and indirect effects on industries and business that rely on it. Recreational fishing has a direct effect on industries such as boat building and recreational magazines, and on local businesses such as charter vessel and tour guide operators, fishing tackle shops, and

1 Results are available at http://www.frdc.com.au/industry/recreation.htm (accessed 2.Sept.2004).

1 commercial bait collectors. It may also have a direct effect on the hospitality and transport service industries.

There are also non-commercial benefits of recreational fishing. These can be thought of as the benefits that people gain from the recreation activity. Given the number of people involved in fishing, it is likely that these benefits are also very substantial. A focus on only the commercial benefits would underestimate the contribution that a recreational fishing facility might make to a community. Constructed impoundments provide inland residents with water based recreational opportunities that were previously unavailable, or required much more travel.

1.2 Importance of recreational fishing in Queensland

Recreational fishing is the fifth most important recreational activity in Queensland, after golf, swimming, tennis, and fitness activities like aerobics. Recreational fishing occurs in marine, estuarine and freshwater habitats (Williams, 2002). About 28% of the Queensland population aged over five years has fished at least once during in the year 2000-01, while expenditure on recreational fishing and associated recreational activities was around $300 million in 2000-01 (Henry and Lyle 2003). It is also an important factor in attracting tourists to Queensland. Henry and Lyle (2003) report that 11% of fishing activities occur outside of the state of residence of anglers, and that Queensland was one of the states that achieves a net import of fishing effort.

Henry and Lyle (2003) report that from May 2000 to April 2001, 785,000 recreational fishers in Queensland spent an estimated 5.8 million fisher days of effort (approximately 7.4 days per angler). A break-up of expenditure by Queensland anglers estimated by Henry and Lyle (2003) is shown in Table 1.1.

Table 1.1: Expenditure of Queensland Recreational Fishers Total Average Item category expenditure expenditure per ($ million) angler ($) Accommodation 6.26 7.97 Camping gear 12.38 15.77 Bait 3.77 4.80 Boat/Trailer 160.58 204.56 Clothing 1.78 2.27 Dive gear 0.80 1.02 Fees and licences 2.74 3.49 Fishing gear 39.88 50.80 Travel 64.54 82.22 Other 10.60 13.50 Total 303.33 386.40 Source: Henry & Lyle (2003: pg.??)

Henry and Lyle (2003) also reported on the factors that motivated people to go fishing for recreation. The data for Queensland anglers is reported in Table 1.2, where the percentage of respondents indicating that the motivation was ‘very important’ or ‘quite important’ is shown. The results show that people not only fish for sport or food, but also participate in recreational fishing activities for psychological, environmental and social reasons. This may also indicate

2 that fishing might not be the sole reason for a trip. Trips could be to multiple sites and could be for multiple reasons.

Table 1.2: Motivation for Fishing - Queensland Anglers % indicating ‘very Motivation factor important’ or ‘quite important’ Relax and unwind 84 To be outdoors 85 For solitude 34 To be with family 64 To be with friends 63 Competition 4 Fish for sport 80 Fish for food 61 Source: Henry & Lyle (2003)

In 1995, the Queensland Fisheries Management Authority (QFMA) developed an integrated program (RFISH) to collect information about the State’s recreational fisheries. (In 2000 the QFMA was incorporated into Department of Primary Industries and Fisheries (DPI&F)). Since that time, information has been collected about fishing activities through a variety of means, including telephone surveys, angler diary surveys, mail surveys and boat ramp surveys. The results of this data collection process provide a general overview of recreational fishing activities in the state, and are summarized below2. Other data collected in the process has formed an input into the results reported by Henry and Lyle (2003).

About one in three households in Queensland (30.4% in 2001) had at least one household member aged 15 or over who had been fishing in some way over the previous 12 month period. It was estimated that 851,000 Queensland residents aged 5 years or older (24.6% of the Queensland population) were involved in fishing. Only 25% of the population has never fished before. Households in the coastal strip from the Sunshine Coast to Far were more likely to be fishing households. The ‘typical’ angler in the state is either a male aged 14 – 49 years, or a young person of either sex aged 5 – 14 years.

The majority of fishing effort in Queensland is focused on saltwater and marine fishing. In 2001, 66% of anglers reported fishing only in saltwater, 8% only in freshwater and 25% in both. Residents in inland areas were much more likely to fish in freshwater than those living in the coastal zone. The most popular freshwater fishing locations in Queensland in 2001 were: • (15.6% of freshwater anglers) • Brisbane (13.4%) • (13.3%) • Fitzroy (9.6%) • Wide Bay – Burnett (8.8%)

2 The results are available at http://www.dpi.qld.gov.au/fishweb/2897.html (accessed 2.Sept.2004).

3 • Sunshine Coast (8.5%) • South West/Central West/North West (8%)

The most popular freshwater species that were targeted for fishing in Queensland included Golden Perch, Australian Bass, Murray Cod and Silver Perch in southern locations, and Barramundi, Sooty Grunter and Crayfish in northern locations. The proportion of freshwater anglers fishing only in dams was 39% in 2001, compared to 42% fishing only in streams or rivers, and 18% in both. Anglers who fished in both freshwater and saltwater locations tended to be either more frequent anglers, or males under 40 years of age, or targeting specialist species, or be a member of a fishing club, or be in a household that owns a boat.

1.3 History of Fish Stocking

The stocking of fish for recreation anglers to target has been a significant part of fisheries management throughout Australia, for a number of years. Brown trout and rainbow trout, both exotic species, have been released throughout the southern states since the late 1800s. Significant recreational trout fisheries have been established in Victoria, NSW and Tasmania, which now support a thriving freshwater recreational fishing industry.

Australian native fish have only been available in stocked programs comparatively recently, and many anglers in different parts of the nation are taking advantage of the variety of native sportfish now available with stocking programs to provide continuous supplies. In Queensland, the Apex Club in began to stock silver perch in the then newly constructed Boondooma dam in 1983.

Fish stocking to create freshwater recreational fisheries in Queensland began in earnest 1986, when the Queensland Government initiated the Recreational Fishing Enhancement Program. Funds were made available for an initial three-year period to stock artificial impoundments to create recreational fisheries. From the beginning the program was a partnership between the Government and local communities with Fish Stocking Associations being formed for each impoundment.

The stocking program in general has been successful, with 70 active Fish Stocking Associations throughout Queensland in 2004. A large proportion of these groups assist with the management of their respective dams and areas by helping Fisheries staff with netting and electrofishing surveys, recording data on the Department’s behalf, and in a few cases, interviewing anglers for the freshwater Creel Survey Program

The majority of the fisheries are following a ‘put, grow and take’ fisheries policy with no natural recruitment and annual stockings required to maintain the fishery. There are also stockings into some river systems with recruitment. Stocked systems tend to have higher output of desirable fish species, and allow increases in the number of anglers who can fish at the impoundments.

The aims of the fish stocking program initially were to stock inland storages with native fish, create a recreational fishing resource, attract tourism and reduce the pressure on the saltwater estuary fisheries. Today it is seen as a program that has achieved significant economic development in some rural communities. There are a number of dams that now have successful stocked fisheries, which in turn has led to the development of supporting tourist infrastructure and provided economic benefits to the local economies.

4 Table (1.3) below illustrates the significant investment over the years at the three case study dams that are the focus of this report. The investment has been made by local communities and groups, in partnership with the State Government, and more recently the Stocked Impoundment Permit (SIP) Scheme. The total number of fish stocked over the past 20 years represents efforts on behalf of groups and communities to create significant fisheries in their local areas.

Table 1.3 - Stocked Fish Species Stocked Species Bjelke-Petersen Boondooma Fairbairn Australian bass 580 000 628 058 ––– Golden perch 1 600 000 1 425 972 ––– (yellowbelly) Silver perch 180 000 134 620 196000 Murray cod ––– 2 670 80500 Barramundi ––– 60 000 4270 Saratoga 450 ––– 100

The Boondooma and Bjelke-Petersen dams in the have established a reputation as attractive fisheries. These fisheries are primarily based on the return of Australian bass and Yellowbelly. The dams have very active and successful stocking groups that manage the fisheries with DPI&F support and guidance. The purchase of fingerlings is supported by funds from the SIP Scheme.

In 2004, annual fingerling purchases for both Boondooma and Bjelke-Petersen dams each totalled approximately $35,000, with about two-thirds supplied with funds via the SIP Scheme. These two dams are within about four hours of travelling time from Brisbane. Many visitors from other parts of Queensland and Australia also visit these two dams. Both the local authorities and private business have invested in tourist facilities (cabins and camping sites) at the two dams.

At Fairbairn the fishery is primarily based on the illegal introduction of redclaw crayfish, which has thrived in the 15,000ha impoundment, although most anglers target barramundi and yellowbelly. Fish stocking commenced in 1987 at Fairbairn with the now considered unsuccessful stocking of silver perch. Limited numbers of barramundi have been stocked in recent years whilst yellowbelly naturally recruit above the dam. A popular caravan park has been privately developed adjacent to the dam to service anglers and other recreational dam users.

1.4 Assessing the benefits of recreational fishing

A full appraisal of the recreation benefits derived from fishing has to consider more than the increased expenditure that might be associated with the activity. It is important to consider the level of benefits associated with recreational fishing because it is often not costless to provide fishing opportunities. There may be substantial costs involved in fish stocking programs, providing facilities and access and in managing and monitoring fishing activities. As well as, recreational activities there may be very important side-benefits associated with major water storages, and so should be included in decisions about management and evaluation of those storages.

5 The economic benefits of recreational fishing can be assessed in several ways. The economic impact of recreational fishing in fresh water could be measured with techniques like input- output analysis, which focuses on factors like the additional spending or employment generated. This approach is about showing what the expenditure impacts of recreational fishing are on local, regional and state economies. In contrast, a measure of economic value focuses on the estimation of consumer surpluses associated with recreation activities, essentially calculating the net benefits of recreation activities (in contrast to simple measures of expenditure). These estimates of net benefits require specialized valuation techniques to be applied, but are the more appropriate measures of economic value than a simple expenditure assessment approach. (Suggestion: Perhaps an example to demonstrate the difference)

Estimating the economic value of freshwater recreational fishing will provide local communities, government and other stake holders with valuable information. An indication of the value of freshwater recreational fishing to Queensland can help in the planning and decision making process of the governments and other stake holders. It may also help in identifying where public funding is most efficiently allocated and whether public funds should be allocated to such activities. Information about values may also help in balancing the benefits from recreational fishing against its impacts on fish stocks and the environment to ensure it is conducted in a sustainable and equitable fashion.

The data that is available on recreational fishing (eg. Henry and Lyle 2003) gives a good overview of activities at a state and population level. However, there is little detail available about the economic values of fishing activity at a site level. In many cases, details are needed at a site level to be able to properly evaluate fish stocking, infrastructure and maintenance proposals. For these reasons, the focus of this study is on the economic analysis of recreational fishing activities on a site basis in Queensland, where three freshwater impoundments have been used for the case study locations.

1.5 Scope of the study

The three dams chosen for this study are the Boondooma dam and the Bjelke-Petersen dam in , and the Fairbairn dam in central Queensland. The Boondooma dam and the Bjelke-Petersen dam are in the (Wondai and Murgon shires respectively), while the Fairbairn dam is in the (Emerald shire).

All of the dams are on or near major highways, meaning that access is not a limiting factor. Boondooma dam is near the Bunya Highway about 20kms to the west of . The Bjelke- Petersen dam is located on the Burnett Highway about 10kms south of Murgon. The Fairbairn dam is on the Capricorn Highway about 11kms south of Emerald. The impoundment of the Fairbairn Dam is Lake Maraboon which covers about 15,000ha. The dam was constructed in the early 1970s, and water is used for irrigation (cotton, citrus and grapes), coal mines and urban supplies. Both the Boondooma and the Bjelke-Petersen dams were built in the early 1980’s, Boondooma dam to provide water for the Tarong Power Station and Bjelke-Petersen dam to provide water supply to the upper areas of the South Burnett.

The three dams are useful case studies because each of them is associated with high levels of recreational fishing3. The dams are located away from major population centres, making it easier to identify visitor numbers. There are accommodation, service or tourist facilities at

3 There is no commercial fishing at the dams, although eels have been commercially fished at the Bjelke- Petersen and Boondooma Dams in the past.

6 each dam, which help to service the recreational fishing industry, as well as other recreation activities.

There are also some differences between the dams, which may impact on recreational fishing levels. The Boondooma and Bjelke-Petersen Dams are closer to the major population centres of south-east Queensland, while the Fairbairn Dam is close to Emerald – an affluent regional centre. The Fairbairn Dam is also close to major inland highways (Gregory and Capricorn highways), and may be more accessible to passing visitors and tourists. Redclaw are a key fishing target at Fairbairn, while the other two dams are stocked almost exclusively with popular native fish. There was little data available for the three dams about the economic impacts of recreational fishing on the local regions, or the economic values associated with recreational fishing at the different sites.

1.6 Aims of the study

The aim of this study is to measure the recreational use values and the additional benefits of fresh water fishing in three dams in regional Queensland. The three dams used for this study are the Bjelke-Petersen, Boondooma and Fairbairn dams. A study was conducted in 2002-03, involving staff from Central Queensland University (CQU) and the Queensland Department of Primary Industries and Fisheries (DPI&F). Data was obtained through the collection of surveys from anglers at the dam sites over a 12 month period.

The major aims of this study are to:

1. Ascertain the economic impacts of recreational fishing. This can be obtained by identifying the expenditure of recreational anglers in the regional areas across different spending categories.

2. Identify the economic benefits of recreational fishing. These are different to the economic impacts, because they need to take account of the private satisfaction that people will have derived from fishing after their costs have been accounted for.

3. Identify the additional benefits that might be forthcoming if the fishing experience (catch rate) could be improved at the three dams.

1.7 Methodology

Each of these three aims involves the application of different economic techniques. The first aim is focused on the analysis of expenditure flows within a region. The full analysis would involve the assessment of both direct and indirect impacts of additional spending, with the latter estimated through the application of multipliers or input-output analysis. The second aim can be achieved with the application of the Travel Cost Method, a specialist non-market valuation technique. This uses information about the amount of money (and time) that people have expended to reach a site to assess how valuable it is. The third aim can be achieved with the application of the Contingent Valuation Technique, another specialist non-market valuation technique. It can be used to evaluate data collected about whether people are prepared to pay higher fishing license fees if the catch rates were to improve through hypothetically improved fish stocking programs. (The contingent valuation survey employed a hypothetical increase in permit fees and stocking programs to assess angler tradeoffs. There was no intention by DPI&F to increase the costs of Stocked Impoundment Permit, or that

7 stocking would be allowed to increase above permitted management levels as a means of improving a particular fishery).

The data required for the study were collected by surveys over a one year period. The surveys were designed to provide input data for the three separate aims of the economic assessment.

1.8 Report Structure

This report is structured in the following way. A brief overview of the data collection process and preliminary results is provided in section 2. In section 3 the economic impact on regional areas is discussed. The travel cost analysis is presented in section 4, followed by the contingent valuation analysis in section 5. Conclusions are presented in the final section.

8 2 Data Collection and Preliminary Results 2.1 Data collection

The data required for the study were collected by surveys over a one year period from November 2002 to November 2003, on behalf of Central Queensland University (CQU) and Department of Primary Industries Queensland Fisheries (DPI&F) Service. The surveys were designed by John Rolfe (CQU), Peter Long (DPI) and Rod Cheetham (DPI). The collection for Boondooma and Bjelke-Petersen Dams was organised and administered on site by the DPI&F, while the collection for the Fairbairn Dam was organised by CQU. The sample of respondents for the surveys was selected from the actual anglers to the dams at random.

Data was collected on two days each week or fortnight, a weekday and a weekend day, in two-hour blocks. The time blocks used were early morning, late morning or evening. The day and time were both selected randomly. Surveys were collected by Les Kowitz at the Bjelke- Petersen Dam and by Lance Frahm at the Boondooma Dam. The majority of surveys at the Fairbairn Dam were collected by Daniel Teghe.

At each sample time the collector would also count information on the number of vehicles with trailers, the number of shore-based anglers, and the number of fishing boats visible on the water at each dam. In the remainder of the two-hour time period, the collector would survey available anglers, usually as they were either leaving for or returning from fishing. The survey form was designed to fit on one double-sided sheet of paper, and was quick to complete. The collector either asked the questions verbally and completed the form this way, or gave the anglers a survey form and a self-addressed envelope.

The survey forms received ethical clearance from CQU’s ethical committee. Participants were also given a separate information sheet about the project, including contacts for the CQU and DPI&F researchers, and a tear-off section where people could ask for a summary report to be sent to them. The tear-off section was put on the information sheet rather than the survey form so that the survey responses remained anonymous.

Data were collected from each visitor group on a number of variables including: a. the number of people in the group, b. the number of children 15 or younger, c. number of family members in the group, d. hours spent fishing on that day, e. the usual place of residence: city and postcode, f. approximate distance travelled one-way, g. travel time in hours, h. trip cost for the entire group, i. fishing costs for this trip, j. annual boat expenses, k. mode of travel,

9 l. number of days planning to fish this trip, m. length of the total trip, n. importance of fishing relative to other things this trip, o. method of catching fish, p. number and variety of fish caught and kept this trip, q. expected catch levels, r. number of fishing trips to the dam and other places in a year, s. a hypothetical question on what price the anglers are willing to pay for improved catch.

2.2 Preliminary results

The total number of surveys collected during the 12 month period was 264 at the Bjelke- Petersen dam, 250 at the Boondooma dam and 182 at the Fairbairn dam. Summary results from the surveys collected at the three dams are presented below. Table 2.1: Average Annual Fishing Trips Name of Dam Statistics Bjelke-Petersen Boondooma Fairbairn Total number of surveys 264 250 182 Average number of visits to dam / Year 5.23 6.51 3.14 Average number of visits to other fishing places / year 21.73 9.1 21.37 Average number of total fishing trips / Year 26.88 15.4 23.92

For anglers at the Boondooma dam, there was very little difference between days of holiday (7.4 days) and days fishing (7.03), suggesting that most trips are solely for the purpose of fishing. For anglers at the Bjelke-Petersen dam there was some difference between days of holiday (10.06) and days fishing (6.52), suggesting that some multipurpose trips were occurring. For the Fairbairn dam, there was a major difference between days of holiday (53.22) and days fishing (8.55), indicating that multipurpose trips were the norm (or that most anglers were tourists on extended holidays). This is confirmed by the ratings for the importance of fishing (Table 2.3) where 81% of Boondooma anglers and 84% of Bjelke- Petersen anglers said the trip was ‘only for fishing’ or ‘fishing very important’, compared to 57% of Fairbairn anglers giving the same responses.

The results show that a large proportion of anglers at the Fairbairn Dam were targeting the redclaw there. This is shown by the 91.8% of anglers using crayfish traps (Table 2.3) and the high catch rates (Tables 2.2 and 2.4). Results show that catch rates are relatively low (Table 2.5). For example, if redclaw are excluded from the Fairbairn Dam results, the average catch of finned fish was only 0.6 fish/group. At both the Bjelke-Petersen and Boondooma Dams, the average catch rate per person was approximately 1.7 fish. However, the majority of anglers appeared satisfied with their fishing experience, with only a small proportion saying that they caught fewer than expected (Figure 4). Statistical analysis revealed that there was a significant relationship between this group and groups catching fewer fish at each dam.

10 Table 2.2: Summary Statistics for the Three Dams Name of Dam Statistics Bjelke-Petersen Boondooma Fairbairn Average group size 2.56 2.78 3.8 Average number of children 15& under in group 0.39 0.34 0.57 Average number of family members in group 1.73 1.78 2.24 Average number of hours spent fishing (hrs) 4.32 4.37 3.14 Average one-way distance travelled to reach dam (km) 239.56 239.55 701.28 Average time spent travelling (hrs) 3.05 4.16 10.09 Average trip cost / group ($) 390.23 397.63 1252.8 Average fishing costs / group ($) 60.52 32.77 102.87 Average spending on boat / year ($) 201.43 278.27 301.97 Averave number of days spent fishing at dam this trip (days) 6.52 7.03 8.55 Average number of days spent fishing at other sites 21.73 9.1 21.37 Average length of the entire holiday (days) 10.06 7.4 53.22 Average number of fish caught on this trip 9.48 7.83 83.88

More detailed information about the statistics, together with confidence intervals, is provided in the appendix. Some of the data is displayed in the figures below as well.

Figure 1: Mode of Travel to Dam

90 82.4 80

70 68.4

60 54.6

50 B-P 42.4 Boondooma 40 Fairbairn %of Respondents 30 27.2

20 16

10 3.6 1.5 0 0 Car 4WD Other Mode of Travel

11 Figure 2: Importance of Fishing

70

59.2 60

50 44.7

39.3 40 B-P 34.6 33.5 Boondooma Fairbairn 30

%of Respondents 23.7 22

20 16.8 15.2

10 8.2

2 0.8 0 0 0 0 Trip only for fishing Very important Moderately important Slightly important Not at all important Importance of fishing

Figure 3: Percentage of Holiday Spent Fishing

100 92 91.6 90

80

70

60 53.3 B-P 50 Boondooma Fairbairn

40 36.3 %of Respondents

30

20

10.4 10 5.7 5.6 2.8 2.3 0 Less than 50% of holiday 50% to 75% of holiday 76% to entire holiday % of Holiday spent fishing

Table 2.3: Method of Fishing (% of respondents) Name of dam Method Bjelke-Petersen Boondooma Fairbairn Boat 99.2 98.8 89 Line with bait 62.9 55.6 47.8 Line with lure 89 84 25.8 Baited crayfish trap 21.2 54 91.8

12 Figure 4: Expected Catch

60 56.6

52

50 48.5

40.2 40

30.8 B-P 30 28.8 Boondooma Fairbairn %Respondents

20 18.6 17.2

10

2.7 1.6 1.4 1.6 0 0 0 0 A lot more About the same Fewer Not Applicable Missing Expected Catch

Table 2.4: Number of Fish Caught Name of dam Bjelke-Petersen Boondooma Fairbairn Variety Total Ave. / group Total Ave. / group Total Ave. / group Redclaw / crayfish 18 1.5 297 4.43 18059 111.48 Yellowbelly 804 5.62 544 3.28 24 1.71 Australian Bass 1269 8.24 845 4.69 0 Jewfish 311 4.38 152 2.27 0 Barramundi 0 0 0 0 20 1.82 Other 102 2.55 119 2.53 111 3 Total 2504 9.48 1957 7.83 18214 100.08

Table 2.5: Number of Fish Kept Name of dam Variety Bjelke-Petersen Boondooma Fairbairn Total Ave. / group Total Ave. / group Total Ave. / group Redclaw / crayfish 18 6 288 5.76 11052 68.65 Yellowbelly 585 4.37 430 2.89 17 1.42 Australian Bass 402 2.79 302 1.85 0 0 Jewfish 112 1.81 106 2.12 0 0 Barramundi 0 0 0 0 13 1.44 Other 37 1.19 59 1.97 80 2.29 Total 1154 4.37 1185 4.74 11162 61.33 Note: The number of fish kept did not exceed the bag limit.

13 3 Economic Impact of Freshwater Recreational Fishing 3.1 Overview of input-output analysis An economic impact assessment process is usually focused at the local or regional level to identify how expenditure creates direct and ripple effects in an economy. The assessment process normally follows several steps. These include: • Description of the regional or local area of interest (demographics, industry base, employment levels, etc) • Description of the industry where proposed changes are occurring, • Description of the project or changed activity, • Identification of primary impacts on employment, spending and income, • Identification of secondary impacts on employment, spending and income, • Summing of impacts (positive and negative) to identify net impacts. The key impacts of recreational fishing activities on a local or regional area can be summarised in the following way. The key concepts of interest (Jensen and West 2002) are: • The extent to which recreational anglers purchase inputs from the local or regional economy. Examples include accommodation, fishing gear, boat hire, travel expenses and boat purchases. The more that is sourced from the local or regional economy, the more money that is directly injected into the economy. • The extent to which money spent in a local or regional economy is retained within that economy. If there is not much opportunity for people receiving income to spend it on goods and services in their local or regional area, then not as much money will be kept in the local or regional area. Larger and more diverse regional economies tend to be better at keeping expenditure in their economy and not ‘losing’ it to other regions. The first concept can be thought of as the amount of direct injection of money into the local or regional economy that can be sourced from a particular project or activity. The second concept can be thought of as the extent to which that initial injection is multiplied through the economy by secondary expenditure. Initial expenditure flows become revenue and income to the people and firms providing labour, goods and services to the project operator. Those people and firms can then spend that revenue and income, creating secondary economic impacts. In this way, an initial injection of expenditure can be multiplied into a larger economic effect on a region, while an initial decrease in expenditure can multiply into a larger regional downturn. The multiplier effect is limited, because at each round of expenditure some money is lost to pay for goods and services that come from outside the region. As well, some money will be allocated to pay for taxes, and for savings purposes. Only a proportion of money that is spent in a region becomes available for expenditure in that region in a subsequent expenditure round. A similar process drives the demand for employment in a region. A simplistic approach to estimating the total economic impacts of expenditure changes in a regional economy can be generated by estimating (a) the size of the direct impacts (the amount of expenditure change) and (b) the multiplier effect.

14 While this approach is useful for demonstration purposes, it is not the most accurate way of estimating economic impacts. For that purpose, mathematical models termed input-output models are used. These model a regional economy in terms of a number of sectors, and allow for differential impacts between sectors, depending on the extent to which sectors supply inputs to each other. Input-output models have been developed for regions of the Queensland economy. Because the models are complex to build and operate, separate models are not available at a Shire level. However, estimates about economic impacts at a Shire level can be made from an input-output model. A simplistic approach to assessing economic impacts can be achieved by the application of what are known as Keynesian multipliers. This relates the change in economic activity from an initial spending change to both the propensity of spenders to shop locally, and the proportion of expenditure that becomes income to local residents (Jensen and West 2002). A simple formulation of the Keynesian income multiplier is as follows (where k stands for the multiplier):

k = 1/(1 - MPCL x PSY) Where:

MPCL is the marginal (or average) propensity to consume locally, or the proportion of income (or income change) which is spent locally and PSY is the proportion of local consumption expenditures that eventually becomes local income, i.e. local salaries and wages, profits and interest payments. It is an expression of the proportion of each dollar spent locally which ends up in the pockets of the local community rather than paying for imported goods and services (Jensen and West 2002, pp.13-16). Jensen and West (2002) suggest that for small communities (less than 5000 people), the MPCL will range from 0.2 – 0.4. For larger communities, the MPCL will be more like 0.6 - 0.7. The PSY can be expected to range between 0.25 and 0.75. Higher proportions are expected in service industries where labour is a high proportion of total costs. Lower proportions are expected where most of the business turnover reflects goods and services purchased in from outside a local area. In small communities the PSY can often take the value of about 0.4 – 0.5. The range of values for an income multiplier can be demonstrated by using the low, medium and high values for the MPCL and PSY (Jensen & West 2002, pp. 13-16). Taking the highest values in the range:

k = 1/(1 – 0.8 x 0.75) = 2.5 Taking the middle-range values:

k = 1/(1 – 0.5 x 0.25) = 1.33 Taking the lowest values in the range:

k = 1/(1 – 0.2 x 0.25) = 1.05 These examples demonstrate the range of values that a multiplier might take. The examples show that if $1 of additional income is added to a regional economy, the resulting indirect or flow-on impact on incomes can be expected to range from $1.50 down to $0.05. Jensen and West (2002) suggest that the multipliers for small regions would be approximately 1.15 to 1.2. These exercises demonstrate that the flow-on effects of increased or diminished spending in small communities tends to be limited. This is because: • it is difficult for small communities to capture a high proportion of spending, and

15 • a large proportion of business inputs (goods and services) have to be sourced from outside the small community.

3.2 Case study The expenditure levels of recreational anglers to the three dams are summarised in Table 3.1. These are the total costs for a sample of anglers to those dams, where many anglers have travelled long distances to reach the sites. To identify expenditure levels on the local area, there are two key steps to perform, which are carried out in the sections below: (a) estimate the total number of anglers for each dam in the 12 month period, and (b) estimate the proportion of expenditure likely to apply at the local level.

Table 3.1: Average Expenditure of Anglers at the Dams Statistics Name of Dam Bjelke-Petersen Boondooma Fairbairn Total number of surveys 264 250 182 Average trip cost / group ($) 390.23 397.63 1252.8 Average fishing costs / group ($) 60.52 32.77 102.87 Average spending on boat / year ($) 201.43 278.27 301.97

3.4.1 Estimated visitation rates for each dam

It is possible to estimate visitation rates from the survey data collected. The survey collectors followed a collection pattern over the 12 month period to maximize the likelihood that a representative sample of anglers on each dam was collected. Over the 12 month period, half of the surveys were collected on weekends and public holidays, and the other half were collected on weekdays. The two hour time period for collection was assigned randomly amongst three time slots, chosen to coincide with maximum departure or return times for anglers: • 7 am to 9 am • 10 am to 12 am • 4 pm to 6 pm (3 pm to 5 pm in winter).

As well as interviewing anglers, the collectors noted a number of other details relevant to fishing activities in each two hour visit. Details are summarised in figure 5 and table 3.2, and show that the average number of groups surveyed were much smaller than the number of parked vehicles (Bjelke-Petersen and Fairbairn), and the number of boat trailers (all dams). There were very few people fishing from the shore at Bjelke-Petersen and Fairbairn, indicating that possession of a boat was an integral part of fishing activities. Fairbairn had lowest average number of surveys per 2 hour block, while Bjelke-Petersen had the highest.

A simple way of estimating annual visitation numbers is to multiply the average number of groups surveyed by 4 (to estimate an average 8 hour visitation rate) and then by 365 (to estimate an annual visitation rate. This exercise allows the following visitation rates to be estimated: • Fairbairn = 3851 groups visiting annually • Boondooma = 7156 groups visiting annually • Bjelke-Petersen = 7557 groups visiting annually

16 Table 3.2: Average Angler Participation Rates Groups Bjelke-Petersen Boondooma Fairbairn Groups surveyed 5.17 4.9 2.64 Parked vehicles 9.65 5.04 8.65 Boat trailers 9.18 10.36 6.45 Moored boats 0.76 6.44 1.84 Boats on water 4.47 3.23 1.36 People on shore 0.06 1.7 0.15 Average days fishing 6.52 7.03 8.55

Figure 5: Angler Participation Details

Some support for these rates can be gained from an analysis of the vehicle movements at the dams. To further compound accurate data collection a significant number of anglers at Boondooma have developed the habit of launching their boat and leaving it moored for several days (giving higher trailer and moored boat numbers but lower parked vehicles) which in the main does not occur at the two other dams (refer Figure 5). For example, the number of vehicles entering the camping and boat ramp area at Boondooma Dam over the 12 month survey period is shown in table 3.3. Table 3.3: Vehicles Entering Boondooma Dam Area Month Vehicle movements Nov-02 2044 Dec-02 2483 Jan-03 2736 Feb-03 2109 Mar-03 1393 Apr-03 2653 May-03 1425 Jun-03 1798

4 The large difference in moored boats at Boondooma dam can be explained by the fact that camping at the waters edge is allowed.

17 Jul-03 1094 Aug-03 1018 Sep-03 1564 Oct-03 1324 12 month total 21641

However, these visitation rates may be overstated because most groups tended to stay at the dams for several days (see Table 3.2). There is evidence that not all groups fishing at the dams in a two hour block have been surveyed, as the number of parked vehicles/boat trailers is much higher than the number of groups surveyed. This was expected, as the collectors were only able to survey anglers as they departed from, or returned to the boat ramps, or if they were fishing from the shore.

A more conservative estimate of total fishing visits may be obtained by estimating the total number of groups fishing on the dams in a single day, and then adjusting by the average trip length. To this needs to be added attendances at fishing competitions. There are a number of fishing competitions held at Bjelke-Petersen and Boondooma dams each year, which include annual fundraising and promotional events held by the fish stocking groups, Bass Fishing competitions, and other specialised group events. Total estimated angler numbers for fishing competitions or special events are: • Bjelke-Petersen: 1200 anglers • Boondooma: 1300 anglers • Fairbairn: 100 anglers

For each dam, the estimates are made by calculating: • Highest parked vehicles/boat trailers x 12/(average hours spent fishing) = groups / day • x 365 = group days per annum • / average days fishing per group (= groups per annum) • + number in fishing competition

The results of this calculation are shown in the table below in table 3.4. There is a substantial difference in the number of groups estimated with the different approaches, and it is difficult to reconcile the difference. The number of vehicle movements at Boondooma Dam suggests that the first approach is more accurate, while the more detailed considerations undertaken in the second approach suggest it would be more accurate. The difference may be partly explained by the mean of ‘days fishing per group’ being influenced by some longer staying groups. After extreme values for ‘days fishing per group’ have been removed, the estimated number of groups at each dam is as follows: • Bjelke-Petersen: 2452 groups • Boondooma: 3195 groups • Fairbairn: 1581 groups

There may have also been some selection bias, where groups on a return trip may have declined to complete a second survey, leading to a slight under-estimation in participation rates. Evidence from the Fairbairn Dam suggests that about 2.5% of recreational anglers may have declined to complete a second survey. To compensate for this, the estimates of visiting groups at the three dams are increased by a further 2.5%, as follows: • Bjelke-Petersen: 2513 groups

18 • Boondooma: 3275 groups • Fairbairn: 1622 groups

Table 3.4: Average Number of Groups Visiting Each Dam Groups Bjelke-Petersen Boondooma Fairbairn Average groups surveyed/ 2 5.17 4.9 2.64 hour block Maximum groups per annum 7557 7156 3851 Highest of vehicles/trailers 9.65 10.36 8.65 Estimated daily groups 29 31 26 Estimated annual group days 10567 11344 9472 Average days fishing per group 6.52 7.03 8.55 Estimated groups per annum 1621 1614 1108 Groups involved in fishing 469 468 53 competitions Minimum groups per annum 2090 2082 1161

These approaches are summarized in the following table, with the estimated people visiting shown for the different approaches: Table 3.5: Total Number of Visits to the Dams Total Bjelke-Petersen Boondooma Fairbairn Maximum groups per annum 7,557 7,156 3,851 People per annum 19,346 19,894 14,634 Minimum groups per annum 2,090 2,082 1,161 People per annum 5,350 5,787 4,412 Expected groups per annum 2,513 3,275 1,622 People per annum 6,433 9,105 6,164

The expected levels of annual expenditure by groups to fish at the dams are shown in the table 3.6 below. An allowance has been made for boat costs by dividing the average annual reported costs by the total number of fishing trips that respondents indicated that they were doing. Table 3.6: Average Expenditure per Group Expenditure Bjelke-Petersen Boondooma Fairbairn Expected groups per 2,513 3,275 1,622 annum Travel costs $390 $397 $1252 Fish costs $60 $32 $102 Boat costs per trip $7 $18 $13 Expenditure per group $457 $448 $1,368 Total expenditure $1,148,441 $1,467,200 $2,218,896

It may not be fully appropriate to attribute all of the travel spending to recreational fishing, as some people may have been fishing as part of a longer holiday. This particularly appears to the case with anglers at the Fairbairn Dam. In these cases it is more appropriate to allocate expenditure according to the proportion of the holiday covered by the fishing trip, with a further 33% of the remainder also allocated to the fishing trip (to take account of responses where the remainder of the holiday and expenditure was still to take place). Allowing for a day of travel with each trip with Bjelke-Petersen and Boondooma, and three days for Fairbairn, the calculations for the appropriate proportions are outlined in Table 3.7, together with revised estimates of expenditure.

19 Table 3.7: Ratio of Trip to Holiday Bjelke-Petersen Boondooma Fairbairn Days fishing at dam 6.52 7.03 8.55 Estimated fishing trip 7.52 8.03 11.55 Days in holiday 10.06 7.4 53.22 Proportion of holiday fishing 0.83 1 0.48 Expenditure attributable to fishing trips $953,206 $1,467,200 $1,065,070

Some estimates of the proportion of expenditure that would occur in the local region can be made by estimating travel and accommodation costs. Local accommodation costs can be estimated for the length of stay at the site, while local travel spending can be estimated as the proportion of 100 kilometres out of the total (one-way) trip length. Accommodation at Bjelke-Petersen ranges from approximately $6.70 - $45 per person to night, while accommodation at Boondooma Dam ranges from approximately $6 - $25 per adult per night. Setting an average rate of $15 per night per person allows the following estimates to be made.

Table 3.8. Estimates of Local Spending Bjelke-Petersen Boondooma Fairbairn Days fishing at site 6.52 7.03 8.55 People per group 2.56 2.78 3.8 Average one-way distance 239.56 239.55 701.28 Average accommodation cost (in $) 15 15 15 Accommodation cost per group(in $) 250.37 293.15 487.35 Reported travel cost (in $) 390.23 397.63 1,252.80 Allowance for local travel (in $) 58.38 43.61 109.15 Total local spending per group (in $) 308.75 336.77 596.50 Total local spending per dam (in $) 775,890 1,102,907 967,523

This information is summarised in figure 6. This shows that visitors to Fairbairn have the highest total expenditure, but slightly less than half of this is related to recreational fishing. The Boondooma Dam is associated with the highest level of recreational fishing expenditure, as well as the highest estimated level of local expenditure.

Figure 6: Summary of Expenditure Patterns

2,500,000 2,000,000 1,500,000 1,000,000 500,000 0 BP Boondooma Fairbairn

Fishing expenditure in local area Other fish related expenditure Other travel expenditure

20 4 Recreational Use Values of Freshwater Fishing

4.1 Introduction In this section, the use of the travel cost method to estimate economic values for recreational fishing activities at the three dams is outlined. The travel cost method needs to be employed because recreational fishing is an activity that provides direct use values to anglers, but these are not directly reflected in markets. Before the application of the travel cost method is outlined, some underlying economic concepts are presented.

Use values are benefits that accrue to individuals who use the good and use values often form the major part of the non-market value of a good (Hodge, 1995; Turner, Pearce & Bateman, 1993). Direct use values are those that flow directly from the good to its users. The measurement of recreational use value for the three dams can be done using the concept of consumer surplus. 4.1.1 The concept and measurement of consumer surplus Economists assess the desirability of different options by measuring their net value or surplus that accrues to society. Social welfare is the sum of the individual welfare functions, and welfare is assessed by estimating the surplus associated with the different options or choices available to individuals. Surplus measures rest on the net change in welfare (Winch, 1973:136). Consumer surplus is the difference between what the consumer is willing to pay and what he/she actually pays for a good or service. Graphically consumer surplus is the area under the market demand curve and above the price line. Assume that the consumer’s demand for good X is a straight line AB as shown in Figure 7.

Figure 7: Consumer Surplus

P x

A

P1

P2

P* C

0 Q1 Q2 Q* B Q x

Source: Modified from Koutsoyiannis (1975: 33)

21 The demand curve AB for the good represents how much an individual is willing to pay for each successive unit. At the market price P* the consumer buys Q* of X. However the consumer would be willing to pay P1 for Q1 and P2 for Q2 units of X. The price the consumer is willing to pay is higher than the market price P*. This implies that this actual expenditure is less than what the consumer would be willing to spend in order to enjoy Q* units of X. The difference in actual expenditure and what the consumer is willing to pay is consumer surplus. This is the area of the triangle P*AC in Figure 4.1. In the real world the individual just pays the market price, and those individuals willing to pay more retain a surplus. In a competitive market the area under the demand curve and above the price line gives an estimate of consumer surplus, while in an imperfect market the area under the demand curve generally understates the surplus (Layard & Walters, 1978). Consumer surplus is very sensitive to the specification of the demand function and to the estimation methodology. Adamowicz et al. (1989) found that the choice of the functional form affected the size and accuracy of consumer surplus estimates. They argue that the variability of welfare measures could be altered to fit different specifications of the functional form, which best fits the sample data. Their arguments are driven by the fact that the coefficient of the price variable appears in the denominator of the consumer surplus equation. They argue that if the coefficient of the price variable is not significantly different from zero then welfare measures will be unstable. 4.2 Overview of travel cost method

Traditionally the value of services provided by outdoor recreational activities like recreational fishing have been calculated using non-market valuation techniques like revealed preference and stated preference techniques. Stated preference techniques are generally used to measure non-use values, while revealed preference techniques are used to measure direct use values. Since the bulk of the benefits from freshwater recreational fishing arise from use values, revealed preference techniques are considered superior for this study.

Revealed preference models use the observed behaviour of individuals to estimate the value of providing a public good (Garrod & Willis, 1999; Haab & McConnell, 2002). They are also sometimes known as behavioural models because revealed preference models are built on the hypothesis that it is possible to estimate the demand of a good by observing actual individual behaviour in related markets (Kahn, 1995). The revealed preference models assume a behavioural function like a demand function or a cost function and estimate benefit measures like consumer surplus. Methods include Travel Cost Method (TCM), Hedonic Price Method, Replacement Cost Method and Market Value Method.

The TCM is typically used to estimate the use values of various outdoor recreational activities. It uses the actual costs of consumption incurred by the respondents to derive a demand curve for the recreational good, from which the benefit estimates (consumer surplus) are derived. The costs of consumption include travel costs, entry fees, on-site expenses, capital equipment costs etc. (Garrod & Willis, 1999, Haab & McConnell, 2002; Hanley & Spash, 1993).

The TCM involves three key steps (Read et al., 1999). The first is to derive the “trip generation function” (TGF) which relates some estimate of the visit rate to travel costs and other variables associated with the visits such as income, occupation, age, education and attractiveness of substitute events. The second step is to use this trip generation function to derive the demand function for entry to the site or event with a hypothetical set of entry fees

22 over and above the existing level of payments. The demand function that is derived typically displays an inverse relationship between the number of potential visits and the hypothetical entry fees. Finally the consumers’ surplus is estimated by integrating the relevant area under the demand curve. This provides an estimate of value for the issue of interest.

The demand for freshwater recreational fishing can be estimated from the costs incurred while travelling to and from the dam, as well as for the actual fishing activities. The costs of travel vary from visitor to visitor depending on the visitor’s point of origin. The varying travel costs make it possible to estimate the demand for recreational fishing and then its value. There are a number of other advantages for choosing the travel cost method for this study. First, the method is based on actual observed behaviour of individuals rather than on hypothetical situations. This means that observed costs and other data can be collected. Second, the method is relatively simple and inexpensive to use and the results are also relatively easy to interpret. It has a strong theoretical and economic base. Finally the Travel Cost Method uses demand theory to estimate and explain the value of a recreation activity. It is based on a simple assumption that the value of freshwater recreational fishing depends on, and is inversely related to, the travel costs.

However, there are a number of issues associated with using the Travel Cost Method for estimating the value of freshwater recreational fishing. The general disadvantages of the TCM as enumerated by Bateman (1993) also apply in this case. Some of the disadvantages of the TCM are:

(i) The TCM assumes an inverse relationship between travel costs and the number of visits. This assumption implies that individuals react to increases in entry fees the same way as they would to increases in prices in the organised markets.

(ii) The model may have a sampling bias because anglers at the dams were interviewed on site rather than prior to their visit and the decision to incur their travel costs. Only visitors are sampled (Bowker & Leeworthy, 1998; Ward & Beal, 2000: 157).

(iii) The travel cost method cannot measure the entire regional impact of recreation, but it is only a part of impact analysis by providing use values (Garrod & Willis, 1999). Since it does not estimate non-use values, it may provide an under estimated true value of the site.

(iv) A number of factors affect the values that are estimated. The choice of the functional form and the method of estimation, the treatment of multipurpose trips, treatment of multiple and substitute sites, and treatment of opportunity cost of time will all affect the value estimated with a TCM model.

(v) It is not possible to estimate travel cost to a particular site when visitors are on multi- purpose and multi-destination trips. The travel cost method would be fully valid only for visitors whose sole purpose for travelling is for recreational fishing (Navrud & Ready: 2002, 18).

(vi) Another common problem in TCM is endogenous stratification. This occurs because the chance of being selected in a sample increases with the number of visits. That is a visitor who visits a site more often is more likely to be selected than one who visits rarely (Bowker & Leeworthy, 1998; Ward & Beal, 2000: 157).

23 While the travel cost method has restrictive assumptions, it is not data intensive and involves the simple estimation of a single demand equation for the recreational activity with the least amount of basic data and is generally straightforward to apply (Hueth & Strong, 1984). 4.2.1 The assumptions of the study

A number of assumptions were made for the study and these are listed in this section.

Assumption 1: Choice of the model. There are two basic variants of TCM depending on the definition of the dependent variable. These are the zonal travel cost model (ZTCM) and the individual travel cost model (ITCM). In the ZTCM, the dependent variable is the number of visits made from a particular zone, over a specific period of time, divided by the population of that zone. In the ITCM, the dependent variable is simply the number of visits to a site made by each visitor over a specific period of time. The ITCM is appropriate for sites that have high individual visitation rates and the ZTCM is appropriate for sites that have different visitation patterns.

Initial attempts to fit either an ITCM or a ZTCM model to the three data sets were unsuccessful, as robust models could not be developed The poor model fits that resulted suggested that some confounding effects might be being caused by underlying variations in angler behaviour. To address this, it was decided to partition the data set for each dam into two groups.

The data from the three dams was divided into two groups according to the number of the number of annual visits made by the visitors to each of the three dams – those who visited the dams only once and those who visited more than once in a year. The ITCM was applied to the group with more than one visit to the dam in a year and the ZTCM was applied to the group who visited the dam only once during the year (table 4.1).

Table 4.1: Annual Visit Rates Dam Annual visit rate Bjelke-Petersen Boondooma Fairbairn One visit 89 72 112 More that one visit 175 178 70 Total 264 250 182 Assumption 2: Identification of zones. The use of ZTCM leads to an issue that needs to be resolved, namely the identification of zones. It is common for the zones to be based on population groupings like postcode areas or statistical divisions (Stoeckl, 1994 quoted in Driml, 2002). Lockwood & Tracy (1995) identified zones on the basis of postcode clusters which contained approximately equal populations. Beal (1995) based the identification of zones on statistical divisions which were aggregated according to their approximate distance from the site (Carnarvon Gorge).

The zones were identified on the basis of statistical divisions given by Australian Bureau of Statistics. The population for each zone was calculated from the ABS census data, 2001. The zones identified for the two southern dams Bjelke-Petersen dam and Boondooma dam are the same.

The zones identified for the two southern dams are: Zone 1: Core shires around the two dams which include Wondai, Kilkivan, Murgon and Kingaroy shires.

24 Zone 2: Rest of Wide Bay-Burnett Zone 3: Darling Downs which includes the towns of Dalby and Toowoomba Zone 4: Brisbane and Moreton shires including the towns of Gold Coast and Sunshine Coast Zone 5: Rest of Queensland Zone 6: New South Wales Zone 7: Victoria Zone 8: Northern Territory and Zone 9: South Australia. Zones 1, 7, 8 and 9 were not considered for the ZTCM because of the low visitation rates5.

The zones identified for Fairbairn dam are: Zone 1: Emerald Shire Zone 2: Rest of Central Queensland which includes the Statistical Divisions of Fitzroy, Central West and Mackay. Zone 3: Rest of Queensland Zone 4: New South Wales Zone 5: Victoria Zone 6: South Australia Zone 7: Western Australia.

Assumption 3: Definition & treatment of costs. Bateman (1993: 206) gives three options for the estimation of travel costs. Option 1 is to consider only petrol costs. Option 2 is to consider full car costs that include petrol, insurance, maintenance etc. Option 3 is to consider the perceived costs as estimated by the respondents. According to Bateman (1993) and Bennett (1996), the correct cost measure is Option 3. They say this is so because visitors are not able to perceive daily insurance and maintenance costs and even if they did they might consider them as sunk costs. This study therefore uses the costs estimated by the respondents (Option 3) for both the ITCM and the ZTCM.

The opportunity cost of time is the benefit which would be derived by undertaking the next best alternative activity during the time spent travelling to and at a recreation site (Ward & Beal, 2000). However, in the modern era where the number of work hours are fixed and there are weekends, public holidays and paid vacations the traditional definition of opportunity cost of time is sometimes not relevant because individuals travel for leisure and recreation during their holidays when there is no loss of income (Ward & Beal, 2000). Following this view the opportunity cost of time is considered to be zero in this study because the visitors travel during their holidays. Whitten & Bennett (2002) consider that time spent onsite is exogenously determined and that the marginal utility derived from time spent on site would be equal to that derived from alternate activities. Hence time spent onsite was treated as having no impact on the consumer surplus estimates.

Assumption 4: Choice of functional form. The choice of a functional form has a significant effect on the size of the consumer surplus estimates (Crooker & Kling, 2000). There are a number of functional forms under which the trip generation function and the demand function could be specified, namely linear, quadratic, semi-log and double log forms. It is standard practice to test a number of functional forms on the data and select those that yield best fitting

5 The ZTCM includes only visitors who visited the dam only once in the last year, hence the low visitation rate in zone 1. Most local visitors to the dam were included in the ITCM.

25 models. In this study, the fictional forms with the highest R-Square were chosen for the analysis.

Assumption 5: Multi-purpose and multi-destination trips. It is clear from the preliminary results that there is a problem of both multi-purpose and multi-destination trips (table 1.). The problem with the multi-destination trips is eliminated to a certain extent by dividing the samples into two groups – those with only one visit to the dam sites per year and those who visited the dam sites more that once. It is assumed that the visitors who visited the dam more than once in a year, made these trips with a single purpose and only to the dam sites. While it is difficult to make the same assumption for the second group of visitors, namely those who made only one visit during the year, the estimated CS could be adjusted to reflect the multi destination and multi purpose trips by dividing the estimated CS figures by the ratio of the days spent fishing at the dam to the total number of days spent on holiday.

4.3 Case Study

The travel cost models for each of the three dams are developed in three steps. In the first step the trip generation function (TGF) is estimated. In the second step the TGF is used to estimate the demand for visits at additional travel costs. The estimated demand curve is then used in the third step to estimate the consumer surplus. The TGF could be specified in a number of different functional forms, with the key one being: linear, semi-log and double log, with two kinds of semi-log models, the semi-log dependent and the semi-log independent (Bateman, 1993:223; Hanley & Spash, 1993:91). In this study the double log functional form is used in the first stage for estimating the TGF, as this form appears to fit the data better than the others. The travel cost analysis is presented in two parts – the ITCM analysis and the ZTCM analysis. The value of the travel cost (TC) variable for both the ITCM and the ZTCM models was calculated by using the following formula in equation (4.1).

TC = trip cost + fishing cost + (annual boat expenses/number of annual fishing trips) — (4.1)

Each of the cost variables used data estimated by the anglers. There was no attempt to include travel time as part of the travel cost because this may have also provided recreational benefits for many of the anglers. 4.3.1 The ITCM The TGF used for this study is given below in equation (4.2). log (Visit rate) = a + b log (travel cost) — (4.2)

The ITCM TGF regression statistics for the three dams are given below in Table 4.2. The F test statistic indicates that the model is significant for all the three dams. The R2 indicates the variation in log of visits explained by the other variables. That is about 46% of the variation in the log of visits to the Bjelke-Petersen dam is explained by the variations in travel costs. Similarly about 26% and 39% of the variation in the log of visits to the Boondooma and Fairbairn dams respectively are explained by the travel cost variable. None of the other variables were significant in the three models and including them improved the R2 only slightly. Hence the other variables were not included in the model.

26 Table 4.2: ITCM - TGF Regression Statistics Coefficients Test Statistics Dam Constant Travel Costs F (t statistic) (t statistic) R2 (p-value) 4.471 -0.531 149.529 0.46 Bjelke-Petersen (18.417) (-12.228) (0.00) 4.329 -0.477 62.805 0.26 Boondooma (12.775) (-7.925) (0.00) 3.578 -0.392 43.833 0.39 Fairbairn (10.966) (-6.621) (0.00) The equations used to generate the data for the demand curve for the three dams are therefore: Bjelke-Petersen: log (Visit rate) = 4.471 - 0.531 log (travel cost) — (4.3) Boondooma: log (Visit rate) = 4.4329 – 0.477 log (travel cost) — (4.4) Fairbairn: log (Visit rate) = 3.578 – 0.392 log (travel cost) — (4.5)

To estimate the demand functions, travel costs were increased and sequentially added to the average cost for each group6. Estimates were made of the visitation rates under these additional cost circumstances and the total expected number of visits at each travel cost computed. Table 4.2 provides a summary of the total predicted visits for each level of additional travel costs. This comprises the data for estimation of the demand equation for visits to the three dams (table 4.3).

Table 4.3: ITCM - Demand Schedules Increases in No. of Visits to Dams (Q) TC per group Bjelke- in $ (P) Petersen Boondooma Fairbairn 0 997 1072 345 50 791 895 289 100 695 812 261 150 632 755 242 200 586 710 228 300 520 643 208 500 438 555 182 1000 335 438 149 1500 282 374 131 2000 248 334 119 3000 205 282 103 5000 160 225 86 Actual 1291 1157 459

The model with the highest R2 was chosen as the preferred functional form for demand analysis. The functional form was chosen for demand analysis was the semi log independent functional form. The demand regression statistics for the three dams are given in Table 4.4.

6 The average cost was calculated based on equation 1.

27 Table 4.4: ITCM – Demand Regression Statistics Coefficients Test Statistics Dam Constant Q F R2 (t statistic) (t statistic) (p-value) 20,064 -3,179.16 363.18 0.86 Bjelke-Petersen (941.87) (166.82) (0.00) 23,766.15 -3,653.82 410.26 0.87 Boondooma (1068.56) (180.41) (0.00) 22,662.86 -4,209.38 339.97 0.85 Fairbairn (1,113.99) (228.3) (0.00)

All the models are highly significant as indicated by the F test statistic and their p-values. The R2 values are also high indicating that about 86% of the variations in visitation patterns (Q) are explained by travel costs (P). The demand equations for the dams are given in equations 4.6, 4.7 and 4.8. Bjelke-Petersen: Price = 20,064 – 3,179.16 Log (Q) — (4.6) Boondooma: Price = 23,766 – 3,653.82 Log (Q) — (4.7) Fairbairn: Price = 22,662 – 4,209.38 Log (Q) — (4.8)

Figure 8: Demand Curves from Individual Travel Cost Models

25000 20000 15000 10000

Value of visit of Value 5000 0 1 2 3 4 5 6 7 8 9 10 Number of annual visits

BP Boon Fair

The consumer surplus can be estimated as the area under the demand curve (see Figure 8) up to some limit. To find the consumer surplus for each dam, the demand curve was integrated from zero to the average number of visits per dam (Garrod & Willis, 1999: p. 60)7. The total consumer surplus figures were calculated for the respective sample sizes of all the three dams, and a convolutions approach was used to estimate 95% confidence intervals for those consumer surplus amounts. The total consumer surpluses of the sample, per group and per individual, together with confidence intervals, are given in Table 4.5.

7 The formula used for calculating the integral was: Integral = a.Q + b (Q.logQ – Q) + C, where a and b are the coefficients of the demand function and Q is the average number of visits per dam.

28

Table 4.5: ITCM8 - Consumer Surplus9 (95% confidence intervals shown in brackets) Bjelke-Petersen Boondooma Fairbairn CS for total sample $95,088 $170,578 $124,341 ($86,444 - $103,596) ($156,555 - $184,759) ($112,511 - $135,314) # of groups 175 178 70 CS per Group $543.36 $958.30 $1,776.30 ($493.97 - $591.98) ($879.52 - $1,037.98) ($1,607.30 - $1,945.30) Average group size 2.46 2.67 4.03 CS per Person $220.88 $358.92 $440.77 ($200.8 - $240.64) ($329.41 - $388.75) ($398.83 - $479.67) Expected groups 1,666 2,332 624 per annum Total expected $905,237 $2,234,756 $1,108,411 CS10 ($822,954 - $986,238) ($2,051,040 - $2,420,569) ($1,002,955 - $1,213,867)

The expected number of groups visiting each dam on multiple trips each year can be derived from the sample data and is also shown in Table 4.5. This has allowed estimates to be made of the total consumer surplus available from the groups of recreational anglers making multiple visits to the dams each year. 4.3.2 The ZTCM The ZTCM model was applied to all the survey responses where participants indicated that they were only making a single trip to the dam within the 12 month period. In this case the key relationship is expected to be between the travel costs incurred and the proportion of the zonal population that is visiting the dam. It is expected that as travel costs increase, the proportion of visitors will decrease. Some single visitor responses from remote zones were excluded from the analysis, so the number of responses available in the dataset for each dam is as follows: Bjelke-Petersen 81 Boondooma 71 Fairbairn 112 The travel costs were calculated using the formula in equation 4.1 and ABS data were used to estimate visitation rates from the relevant zones. The only other variable used in the zonal TGF is the average zonal weekly income, also drawn from ABS data. A number of other variables were tested but did not emerge as significant in the models. The TGF for the zonal model was calculated using the formula in equation 4.9. log V = a + b log(TC) + c income (4.9) The ZTCM TGF regression statistics, using Ordinary Least Squares (OLS), are given in table 4.6.

8 The consumer surplus figures were calculated based on the semi log independent demand function. 9 Consumer surplus was calculated for the average number of visits to the dam made by a family or group in a year. 10 Note that the confidence intervals do not include any variance for expected visitor numbers.

29 Table 4.6: ZTCM - TGF Regression Statistics Coefficients Test Statistics Dam Constant TC Income F R2 (t statistic) (t statistic) (t statistic) (p-value) 11.391 -2.086 -0.013 120.774 0.99 Bjelke-Petersen (7.832) (-8.036) (-11.057) (0.008) 8.135 -1.670 -0.011 163.601 0.99 Boondooma (8.000) (-8.788) (-10.808) (0.006) -4.479 -1.227 0.002 58.488 a 0.98 Fairbairn (-1.724) (-10.169) (0.482) (0.001) a Income is not significant in this model.

The F test statistic indicates that each model is highly significant for all the three dams. The R2 statistic indicates the proportion of variation in the log of visits explained by the other variables. That is about 99% of the variation in the log of visits to the Bjelke-Petersen dam is explained by the variations in travel costs and average zonal income. Similarly about 98% of the variations in the log of visits to the Boondooma and Fairbairn dams are explained by the two variables.

The equations used to generate the data for the demand curve for the three dams are therefore: Bjelke-Petersen: log (Visit rate) = 11.391 – 2.086 log (travel cost) – 0.013 income — (4.10) Boondooma: log (Visit rate) = 8.135 – 1.670 log (travel cost) – 0.011 income — (4.11) Fairbairn: log (Visit rate) = -4.479– 1.227 log (travel cost) + 0.002 income — (4.12)

To estimate the demand function, the travel costs were increased by a hypothetical fee and sequentially added to the average cost for each person. Estimates were then made of the visitation rates under these additional cost circumstances and the total expected number of visits at each additional travel cost computed. Table 4.6 provides a summary of the total predicted visits for each level of additional travel costs. This comprises the data for estimation of the demand equation for visits to the three dams. Table 4.7 gives a condensed demand schedule for the three dams.

Table 4.7: ZTCM - Demand Schedules Increases in No. of Visits (group) to Dams (Q) TC person in Bjelke- $ (P) Petersen Boondooma Fairbairn 0 177 266 509 50 107 183 435 100 73 137 386 150 53 108 349 200 40 88 319 300 26 63 273 500 13 38 211 1000 4 16 133 2000 1 6 74 3500 0 3 43 Actual 229 218 409

30 The model with the highest R2 was chosen as the preferred functional form for demand analysis. The functional form was chosen for demand analysis was the semi log independent functional form. The demand regression statistics for the three dams are given in Table 4.8.

Table 4.8: ZTCM - Demand Regression Statistics Coefficients Test Statistics Dam Constant Q (t statistic) (t statistic) R2 F (p-value) 1755.55 -439.075 67.809 0.67 Bjelke-Petersen (14.319) (-8.235) (0.00) 5110.57 -1246.49 (75.542) 0.66 Boondooma (11.080) (-8.517) (0.00) 79762.51 -15905.17 51.319 0.54 Fairbairn (7.787) (-7.164) (0.00)

All the models are highly significant as indicated by the F test statistic and their p-values. The R2 values are also high indicating that more than 60% of the variations in visitation patterns (Q) are explained by travel costs (P). The demand equations for the dams are given in equations 4.13, 4.14 and 4.15, and demonstrated graphically in Figure 9. Bjelke-Petersen: P = 1755.55 – 439.075 log (Q) — (4.13) Boondooma: P = 5110.57 – 1246.49 log (Q) — (4.14) Fairbairn: P = 79762.51 – 15905.17 log (Q) — (4.15)

Figure 9: Demand Curves from Zonal Travel Cost Models

1500

1400

1300

1200

1100

1000

900

800

700

600

500

Increases in Travel Costs Travel in Increases 400

300

200

100

0 Bjelke-Petersen Boondooma Fairbairn

0 100 200 300 400 500

Number of Visits The appropriate consumer surplus amounts for the zonal travel cost model is the area under the demand function. This has been calculated for each of the dams using a simulation

31 approach. The total consumer surplus of the sample and CS per group and individual CS are given in Table 4.9.

The CS figures in table 4.9 are based on the travel costs for the entire trip. In cases where trips involve multiple destinations, these estimates may potentially over-estimate the value of recreational fishing at each of these three dams. To obtain a more realistic estimate of the value of recreational fishing the CS figures have to be partitioned. It would be possible to partition the values of recreational fishing in two different ways. The first is to partition values according to the purpose of the trip; this could be done with reference to answers about the importance of fishing to the visitors. The second is to partition according to the length of the fishing trip or using the ratio of the number of days spent fishing to the total number of days of the entire trip (table 3.7). The second option takes into account both multi-purpose and multi-destination trips and has been selected as the more appropriate method. It allows for recreational values of people on longer holidays to be apportioned in some way between the visit to the dam in question and the remainder of the holiday.

Table 4.9: ZTCM - Consumer Surplus (95% confidence intervals in brackets) Bjelke-Petersen Boondooma Fairbairn Total $23,932 $75,211 $2,395,923 (Sample) ($12,888 - $34,083) ($53,748- $96,886) ($1,400,258 – 2,805,482) $295.46 $1,059.31 $21,392.17 Group ($159.11 - $420.78) ($757.01 - $1364.59) $12,502.30 - $25,048.95) $92.04 $366.54 $5,629.52 Person ($49.57 - $131.08) ($261.94– $472.18) ($3,290.08 - $6,591.83)

The consumer surplus estimates calculated using these ratios are given in table 4.10. These estimates are much lower than those presented in table 4.9. These figures indicate that individual consumer surplus ranges from approximately $92 per angler at the Bjelke-Petersen Dam to $5,629 per angler at the Fairbairn Dam.

Table 4.10: ZTCM - Consumer Surplus with Partition

Bjelke-Petersen Boondooma Fairbairn CS for Total $15,510.60 $71,450.45 $384,914.35 sample ($8,352.86 - $22,089.58) ($51,060.6 - $92,041.7) ($224,956.89- $450,711.59) $191.49 $1,006.34 $3,436.74 CS per group ($103.12 - $272.71) ($719.16 - $1296.36) ($2,008.54 - $4,024.21) $59.65 $348.22 $904.40 CS per person ($32.13- $84.96) ($248.85 – $448.57) ($528.56 - $1,059) Expected Groups per annum 847 949 998 $162,191.09 $955,020.80 $3,429,861.79 Total CS11 ($87,344.09 - $230,986.09) ($682,486.05 - $1,230,247.51) ($2,004,526.61 - $4,016,162.25)

These consumer surplus estimates can be extrapolated over the estimated number of groups making single visits to the dams each year to calculate total consumer surplus over a one year period. These estimates are also reported in Table 4.11 (figure 10). Total estimates of consumer surplus were estimated by adding the estimates for the repeat anglers to the estimates of the single trip anglers.

11 Note that the confidence intervals do not include any variance for expected visitor numbers.

32

Table 4.11: Total Consumer Surplus (confidence intervals) Bjelke-Petersen Boondooma Fairbairn Expected CS from $905,237 $2,234,756 $1,108,411 repeat visits group ($822,954 - $986,238) ($2,051,040 - $2,420,569) ($1,002,955 - $1,213,867) Expected CS from $162,191 $955,020 $3,429,861 single visits group ($87,344 - $230,986) ($682,486 - $1,230,247) ($2,004,526 - $4,016,162) $1,067,428 $3,189,777 $4,538,273.00 Total CS ($910,298 - $1,217,224) ($2,733,526 - $3,650,817) ($3,007,482 - $5,230,029)

Figure 10: Total Consumer Surplus

$5,000,000 $4,500,000 $4,000,000 $3,500,000 $3,000,000 $2,500,000 $2,000,000 $1,500,000

Consumer Surplus ($) Consumer $1,000,000 $500,000 $0 Bjelke-Petersen Boondooma Fairbairn Dams

Repeat Visits (ITCM) Single Visits (ZTCM)

33 5 Estimating the Benefits of Improving the Fishing Experience

5.1 Overview of contingent valuation

The previous sections have reported the levels of expenditure and the value of the recreation experience associated with fishing at the three dams. An additional issue centres on the value that could be placed on potential improvements to the fishing experience. The context chosen to assess improvements in the fishing experience was additional fish that might be potentially captured during the period of angling. Fish catch rates could be improved in several ways, particularly by the stocking of additional fingerlings at each dam. Both the biological capacity of the dam to support additional introduced fish stocks and fish species behaviour (that is, can they be captured by anglers?) are limits on potential desired improvements.

Stated preference valuation techniques are suitable for the purpose of assessing responses to contingent scenarios. In these techniques, the researchers use hypothetical questions and scenarios to elicit individuals’ preferences, which are then used to estimate the value of changes to the good in question. Techniques include Contingent Valuation, Choice Modelling and Conjoint Analysis. The Contingent Valuation (CV) method can be used to estimate both use and non-use values. It is a survey-based technique where the respondents are presented with a hypothetical scenario and asked to state their willingness to pay for a good. This technique is commonly used to value changes in quality of a good (Garrod & Willis, 1999; Tietenberg, 2000; Sinden & Worrell, 1979). This technique has been used in the survey to elicit willingness to pay to improve catch rates.

In designing a CV experiment, it is important that the tradeoffs and scenarios being presented to people are realistic, that a suitable payment vehicle is used, that the survey instrument and collection method do not cause biases, and that a representative sample is taken from the relevant population (Mitchell and Carson 1989, Hanemann 1994, Rolfe and Griener 2004). The contingent valuation section of the survey was designed with these goals in mind. Respondents were familiar with the scenarios being presented (they were fishing on the site in question), and were familiar with the payment vehicle. It was unlikely that the questions used or the scenario presented would have caused particular biases, and a random sampling approach was used.

Typically, CV methods employ either dichotomous choice (referendum) or open ended approaches (Mitchell and Carson 1989). The former works by offering respondents one of several price tradeoffs at random, and asking them to indicate (Yes or No) if they would be willing to pay that amount for the trade off. The researcher then ascertains the relationship between the price level and the proportion of “Yes” responses. In the open ended approach, respondents nominate their maximum willingness to pay directly. Dichotomous choice CV approaches to elicit willingness to pay have the advantage of being simple for respondents and reduce incentives to provide strategic responses (Hoehn and Randall, 1987). These were key reasons for selecting this format for use in the case study.

34 5.2 Case study12 In the last section of the survey, respondents were asked a CVM question to ascertain their value for an improved fishing experience. The trade-off was framed in terms of whether they would be prepared to pay a licence fee (Fairbairn) or increased fees (Boondooma and Bjelke- Petersen) to increase their catch rate by 20%. Throughout Queensland there are 29 dams where anglers require a permit (stocked impoundment permit of $35/annually or $7/weekly) to fish (excluding redclaw). Anglers at both Boondooma and Bjelke-Petersen dams require the licence whilst anglers at Fairbairn dam do not. The permit program is administered by DPI&F with the majority of funds collected being returned to the associated fish stocking groups.

The scenario used in the survey was appropriate to the anglers, because many of them indicated in responses or comments to the survey collectors that they would prefer higher catch rates. The licence fee program provided an ideal payment vehicle for use in the survey, because it was already in existence, and anglers could see a clear linkage between the payments and potential management actions such as fish stocking programs.

There were two potential weaknesses with the survey format when compared to the widely accepted standards for implementation (Arrow et al. 1993, Portney 1994). First, there were no reminders of substitute goods and budget constraints because of limitations on space. However, because respondents had already detailed in the survey their travel and fishing costs, and annual pattern of fishing, these reminders should not have been necessary. Second, a statement was added to the CV question to make it clear that the scenario was a hypothetical one so that the nominated payment levels would not be confused with actual government policy. This is at odds with recommended CV design where the focus is on making tradeoffs as believable as possible. Given the relevance of the issue to anglers, and their location at the fishing site, it is unlikely that this clarifying statement would have induced substantial amounts of hypothetical bias. However, there remains the possibility that the combined effects of these weaknesses may have led respondents to overstate their true willingness to pay.

In the CVM dichotomous choice format, responses are ascertained for different price tradeoffs. Five different fee levels were used at random in each survey. An example of the question used from the Fairbairn survey is shown below:

Q 20: A fish stocking program and better monitoring could improve the amount of fish that people could catch at the dam by about 20%. The program could be paid for by charging people for weekly fishing permits. (The next question is hypothetical – there is no current intention to impose weekly permits).

If the price for a weekly permit was $5, and your catch rate improved by 20%, would you still come fishing to Fairbairn Dam?

YES  NO 

A summary of the data received from the question is outlined in Table 5.1.

12 There is no proposal to raise the current annual or weekly cost of a permit and it is not Government policy.

35

Table 5.1: Reaction to License Fee Increases (number of responses) Name of dam Increases in $ B-P Boondooma Fairbairn Permit only for Yes No Yes No Yes No Fairbairn dam $1 weekly or $5 annual 53 1 48 2 33 2 $5 weekly permit $2 weekly or $10 annual 52 2 49 2 23 8 $10 weekly permit $3 weekly or $15 annual 47 3 45 0 21 12 $15 weekly permit $4 weekly or $20 annual 44 6 49 1 21 25 $20 weekly permit $5 weekly or $25 annual 40 11 50 3 14 19 $25 weekly permit sub-total 236 23 241 8 112 66 Maybe 2 0 1 Missing 3 1 3 Total 264 250 182

To produce a useful model, the data set was pooled, and a logistic regression equation estimated. These are reported in Table 5.2. The model fit is strong (the rho-square value is above 0.2) and several variables apart from price are significant explanators of choice.

Table 5.2. Choice Models Variables B S.E. Wald df Sig. Distance in one- .002 .001 5.864 1 .015 way trip Total fish kept -.010 .004 6.547 1 .011 Bid level -.148 .032 20.934 1 .000 Boondooma 3.559 .599 35.283 1 .000 Fairbairn 2.223 .423 27.653 1 .000 Constant 2.560 .661 15.015 1 .000 Model statistics Chi Square (5 d.of.f) 110.023 -2 Log Likelihood 97.102 Rho - square .230

The model results indicate that the willingness of anglers to pay higher fees was lower if they had caught and kept more fish and lower if the nominated price was higher. They were more inclined to pay the higher fees if they had travelled further to reach the dam. The model also indicates that anglers at Boondooma had higher values for the fishing experience than at the other two sites, and anglers at Fairbairn had higher values than those at Bjelke-Petersen. The model allows the following equation to be generated.

Log [(prob of Yes)/(1-prob of Yes)] = 2.56 + .002*(Distance) – 0.01*(# of Fish Kept) - 0.148*(Bid level) + 3.559*(1 if Boondooma) + 2.223*(1 if Fairbairn) - (5.1)

The average figures for distance and fish kept for each dam were substituted into the regression equation to generate estimates of the mean willingness to pay for a 20%

36 improvement in catch. These were then multiplied by the estimated number of people fishing at each dam on an annual basis to generate the appropriate value estimates. These are reported in Table 5.3.

Table 5.3. Mean and Total Willingness to Pay Bjelke-Petersen Boondooma Fairbairn Mean willingness to $19.02 $43.03 $36.45 pay Groups per year 2513 3275 1622 Average Group size 2.56 2.78 3.8 Total value per year $122,360.99 $391,766.64 $224,663.22

The results show that the value of improving catch rates by 20% per annum at each dam are estimated to be $0.12M for Bjelke-Petersen, $0.39M for Boondooma, and $0.22M for Fairbairn.

37 6 Results, Conclusions & Recommendations

The results of the study allow a number of conclusions to be drawn. As expected, recreational fishing activities are important at each of the three dams that were reviewed, with a large number of anglers estimated to visit each year. The average visit was reasonably long (6 – 8 days), giving anglers opportunities to inject spending into the local economies. Estimates of the levels of spending made by anglers at each group have been made.

There were two very separate groups identified in the anglers. One group were the ‘tourist’ who were only visiting the dams on a once-off basis. This group was very important for the Fairbairn Dam (62% of anglers), and less important for the other two sites. However, it was the group of repeat visitors (the regular anglers) that contributed the major portion of economic values at each dam when these were assessed with the travel cost method.

The values for improving catch rates at each dam were also assessed with the use of the contingent valuation method. Most value was recorded at the Boondooma Dam, which has the highest use values for recreational fishing, and tends to be visited by repeat anglers.

A summary of the estimated values for each dam is as follows:

• Anglers at the Bjelke-Petersen Dam are spending $0.95M, mostly in the local economy, have a recreational value of $1.07M for fishing at the dam, and value a potential 20% improvement in catch rates at $0.12M.

• Anglers at the Boondooma Dam are spending $1.43M, mostly in the local economy, have a recreational value of $3.2M for fishing at the dam, and value a potential 20% improvement in catch rates at $0.39M.

• Anglers at the Fairbairn Dam are spending $1.07M, mostly in the local economy, have a recreational value of $4.54M for fishing at the dam, and value a potential 20% improvement in catch rates at $0.22M.

38 References ABS. 2001. "2001 Census of Population and Housing." Australian Bureau of Statistics: Australia. Adamowicz, W. L., Fletcher, J. J. and Graham-Tomasi, T. 1989. "Functional Form and the Statistical Properties of Welfare Measures." American Journal of Agricultural Economics, 71:2, pp. 414-21. Arrow, K., Solow, R., Portney, P.R., Leamer, N.E., Radner, R. and Schuman, H., 1993. Report of the NOAA Panel on Contingent Valuation, Federal Registry 58(10):4601-4614. Bateman, I. J. 1993. "Valuation of the Environment Methods and Techniques: Revealed Preference Methods.," in Sustainable Environmental Economics and Management - Principles and Practice. R. Kerry Turner ed. London: Belhaven Press, pp. 192-233. Beal, D. J. 1995. "A Travel Cost Analysis of the Value of Carnarvon Gorge National Park for Recreational Use." Review of Marketing and Agricultural Economics, 63:2, pp. 292-303. Bennett, J. 1996. "Estimating the recreational use values of national parks." Tourism Economics, 2:4, pp. 303-20. Bowker, J. M. and Leeworthy, V. R. 1998. "Accounting for ethnicity in recreation demand: a flexible count data approach." Journal of Leisure Research, 30:1, pp. 64. Crooker, J. & Kling, C.L. 2000. "Nonparametric Bounds on Welfare Measures: A New Tool for Nonmarket Valuation." Journal of Environmental Economics and Management, 39, pp. 145-61. Driml, S. 2002. "Travel Cost Analysis of Recreational value in the Wet Tropics World Heritage Area." Economic Analysis and Policy, 32:2, pp. 11-26. Garrod, G. and Willis, K. G. 1999. Economic Valuation of the Environment. UK: Edward Elgar. Griener, R. and Rolfe, J. 2004 “Estimating consumer surplus and elasticity of demand of tourist visitation to a region in North Queensland using contingent valuation”, Tourism Economics, 10(3):317 – 328. Haab, T. C. and McConnell, K. E. 2002. Valuing Environmental and Natural Resources The Econometrics of Non-Market Valuation. Cheltenham, UK: Edward Elgar. Hanley, N. and Spash, C. L. 1993. Cost-Benefit Analysis and the Environment. UK: Edward Elgar. Hanemann, W.M. 1994 “Valuing the Environment Through Contingent Valuation”, Journal of Economic Perspectives, 8(4):19-43. Henry, G.W. & Lyle, J.M. 2003. "The National Recreational and Indigenous Fishing Survey." Australian Government Department of Agriculture, Fisheries and Forestry: Canberra. Hodge, I. 1995. Environmental Economics. London: MacMillan Press Ltd. Hoehn, J.P. and Randall, A., 1987. S satisfactory benefit cost indicator from contingent valuation. Journal of Environmental Economics and Management 14(3): 226-247. Hueth, D. and Strong, J. E. 1984. "A Critical Review of the Travel Cost, Hedonic Travel Cost and Household Production Models for Measurement of Quality Changes in Recreational Experiences." Northeastern Journal of Agricultural and Resource Economics, 13:2, pp.187.

39 Jensen, RC and West, GR 2002, Community economic analysis, Queensland Department of Primary Industries, Brisbane. Kahn, J. R. 1995. "Square pegs and round holes: can the economic paradigm be used to value wildreness." Growth and Change, 26:4, pp. 591. Koutsoyiannis, A. 1975. Modern Microeconomics. London: Macmillan Press Ltd. Layard, P. R. G. and Walters, A. A. 1978. Microeconomic Theory. New York: McGraw-Hill. Lockwood, M. and Tracy, K. 1995. "Nonmarket economic valuation of an urban recreation park." Journal of Leisure Research, 27:2, pp. 155. Mitchell, R.C. and Carson, R.T. 1989 Using Surveys to Value Public Goods: The Contingent Valuation Method, Resources for the Future, Washington. Navrud, S. and Ready, R. C. 2002. "Methods for Valuing Cultural Heritage," in Valuing Cultural Heritage. S. Navrud and R. C. Ready ed. UK: Edward Elgar, pp. 15-19. Portney, P.R. 1994. “The Contingent Valuation Debate: Why Economists Should Care”, Journal of Economic Perspectives, 8(4):3-17. Read, M., Sinden, J. A., Branson, J. and Sturgess, N. 1999. "Recreational use values for Victoria's Parks." 43rd Annual Conference of Australian Agricultural and Resource Economics Society: Christchurch, New Zealand. Sinden, J. A. and Worrell, A. C. 1979. Unpriced Values Decisions without Market Prices. Brisbane: John Wiley & Sons. Stoeckl, N. 1994. "A Travel Cost Analysis of Hinchinbrook Island National Park." Tourism Research National Conference: Gold Coast, Australia. Tietenberg, T. 2000. Environmental and Natural Resource Economics. New York: Addison- Wesley. Turner, R. K., Pearce, D. and Bateman, I. 1993. Environmental Economics An elementary introduction. Baltimore: The Johns Hopkins University Press. Ward, F. A. and Beal, D. 2000. Valuing Nature with Travel Cost Models A Manual. Cheltenham, UK: Edward Elgar. Whitten, S. M. and Bennett, J. W. 2002. "A Travel Cost Study of Duck Hunting in the Upper South East of South Australia." Australian Geographer, 33:2, pp. 207-21. Williams, L.E. ed. 2002. Queensland's fisheries resources - Current condition and recent trends 1988-2000. Brisbane: Department of Primary Industries. Winch, D. M. 1973. Analytical Welfare Economics. England: Penguin Education.

40 Appendix

Table A1: Importance of fishing (% of respondents) Name of dam Importance Bjelke-Petersen Boondooma Fairbairn Trip only for fishing 44.7 59.2 23.7 Very important 39.3 22 33.5 Moderately important 15.2 16.8 34.6 Slightly important 0.8 2 8.2 Not at all important 0 0 0

Table A2: Mode of travel to dam (% of respondents) Name of dam Vehicle Bjelke-Petersen Boondooma Fairbairn

Car 42.4 27.2 16

4WD 54.6 68.4 82.4 Other 1.5 3.6 0 Missing 1.5 0.8 1.6

Table A3: Percentage of holiday spent fishing (% respondents) Name of dam Days spent fishing Bjelke-Petersen Boondooma Fairbairn Less than 50% of holiday 5.7 2.8 53.3 50% to 75% of holiday 2.3 5.6 10.4 76% to entire holiday 92 91.6 36.3

Table A4: Expected catch (% respondents) Name of dam Bjelke- Answer Petersen Boondooma Fairbairn A lot more 28.8 30.8 56.6 About the same 48.5 52 40.2 Fewer 2.7 17.2 1.6 Not Applicable 18.6 0 0 Missing 1.4 0 1.6

41

Bjelke-Petersen Statistics Adjusted Deleted Cases Mean 95% Confidence interval Mean 95% Confidence interval values Deleted Lower bound Upper bound Lower bound Upper bound Total number of surveys 264 – – – – – – – Average group size 2.56 2.41 2.72 2.32 2.22 2.43 5 & over 16 Average number of children 15& under in group 0.39 0.29 0.49 0.31 0.23 0.39 3 & over 6 Average number of family members in group 1.73 1.61 1.86 1.5 1.42 1.58 4 & over 20 Average number of hours spent fishing (hrs) 4.32 4.19 4.46 – – – – – Average one-way distance travelled to reach dam (km) 239.56 212.62 266.5 188.13 177.67 199.79 Over 400 23 Average time spent travelling (hrs) 3.05 2.4 3.3 2.07 1.59 2.14 Over 3.5 48 1000 & Average trip cost / group ($) 390.23 337.39 443.06 304.51 276.41 332.62 over 18 Average fishing costs / group ($) 60.52 53.07 67.98 41.88 38.26 45.5 Over 120 31 Average spending on boat / year ($) 201.43 178.04 224.83 160.76 150.61 170.91 Over 300 27(13) Average number of days spent fishing at dam this trip (days) 6.52 5.2 7.84 2.94 2.7 3.18 8 & over 49 Average length of the entire holiday (days) 10.06 6.47 13.65 6.08 5.18 6.98 Over 7 56 Average number of fish caught on this trip 9.48 7.62 11.35 6.08 5.18 6.98 Over 27 20 Average number of fish caught & kept on this trip 4.37 3.59 5.16 3.22 2.72 3.73 Over 15 14 Average number of visits to dam / Year 5.23 4.3 6.15 2.37 2.16 2.58 7 & over 46 Average number of visits to other fishing places / year 21.73 18.71 24.75 17.33 15.49 19.16 Over 50 15

42

Boondooma Statistics 95% Confidence interval 95% Confidence interval Adjusted Cases Mean Upper Mean Lower Upper Deleted Deleted Lower bound bound bound bound values over Total number of surveys 250 – – – – – – – Average group size 2.78 2.58 2.99 2.34 2.23 2.44 Over 4 25 Average number of children 15& under in group 0.34 0.24 0.44 0.27 0.19 0.34 Over 2 6 Average number of family members in group 1.78 1.67 1.88 1.66 1.58 1.74 Over 3 11 Average number of hours spent fishing (hrs) 4.37 4.22 4.51 – – – – – Average one-way distance travelled to reach dam (km) 239.55 207.16 271.95 190.28 172.95 207.61 Over 500 15 Average time spent travelling (hrs) 4.16 3.03 5.29 2.2 2.07 2.32 Over 6 16 Average trip cost / group ($) 397.63 335.2 460.05 262.17 242.05 282.29 Over 700 29 Average fishing costs / group ($) 32.77 24.26 41.27 7.24 6.66 7.82 Over 20 80 Average spending on boat / year ($) 278.27 239.42 317.13 196.67 176.24 217.09 Over 600 31 Average number of days spent fishing at dam this trip (days) 6.98 5.93 8.04 5.29 4.73 5.85 Over 14 19 Average length of the entire holiday (days) 7.42 6.34 8.5 5.5 4.92 6.07 Over 14 23 Average number of fish caught on this trip 7.83 6.63 9.03 5.29 4.7 5.89 Over 17 25 Average number of fish caught & kept on this trip 4.74 4.03 5.45 3.46 3.02 3.9 Over 13 20 Average number of visits to dam / Year 6.52 5.03 8 2.84 2.58 3.09 Over 8 46 Average number of visits to other fishing places / year 9.1 7.08 11.11 2.9 2.46 3.34 Over 12 53

43

Fairbairn 95% Confidence 95% Confidence Statistics interval Adjusted interval Cases Mean Lower Upper Mean Lower Upper Deleted Deleted bound bound bound bound values over Total number of surveys 182 – – – – – – – Average group size 3.8 3.25 4.35 2.9 2.7 3.1 Over 7 14 Average number of children 15& under in group 0.57 0.35 0.78 0.37 0.24 0.5 Over 4 7 Average number of family members in group 2.24 2.11 2.38 2.14 2.03 2.25 Over 4 8 Average number of hours spent fishing (hrs) 3.14 2.37 3.51 2.22 2.13 2.31 Over 4 23 Average one-way distance travelled to reach dam (km) 701.28 586.47 816.08 559.59 479.64 639.54 Over 2000 12 Average time spent travelling (hrs) 10.43 8.41 12.45 – – – – – Average trip cost / group ($) 1252.8 856.41 1649.19 391.21 334.01 448.4 Over 1250 46 Average fishing costs / group ($) 102.87 83.69 122.04 70.51 60.9 80.12 Over 200 20 Average spending on boat / year ($) 301.97 185.2 418.74 206.3 180.27 232.32 Over 600 16 Average number of days spent fishing at dam this trip (days) 8.55 7.2 9.89 6.47 5.59 7.35 Over 21 22 Average length of the entire holiday (days) 53.22 39.75 66.68 32.26 26.8 37.72 Over 120 26 Average number of fish caught on this trip 83.88 63.7 104.06 18.53 15.62 21.44 Over 61 55 Average number of fish caught & kept on this trip 61.33 45.61 77.05 12.79 10.72 14.85 Over 41 56 Average number of visits to dam / Year 3.14 2.26 4.01 – – – – – Average number of visits to other fishing places / year 21.37 16.26 26.48 7.49 6.56 8.42 Over 24 49

44