1. 1
A test of the contingent travel cost method: Recreation
values ex ante and ex post dredging in Port Phillip Bay,
Victoria.
Jeremy R. Willcox
Bachelor of Applied Science (Marine Environment)
A thesis submitted in partial fulfilment of the requirements for the Degree of Bachelor of
Applied Science (Marine Environment) with Honours
National Centre for Marine Conservation and Resource Sustainability
(NCMSRS)
University of Tasmania
October 2010

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*Picture on title page is of the Portsea pier, showing a recreational fisher with two large
commercial ships and the Queenscliff ferry in the background. Photo is courtesy of J.R.
Willcox, private collection, 2010.

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Declaration and Authority of Access
I hereby declare that the material contained in this thesis is original except where due
reference is made in the text, and the material has not been accepted for the award of any
other degree or diploma at any other university.
This thesis may be made available for loan and limited copying in accordance with the
Copyright Act, 1968.
Jeremy R. Willcox
University of Tasmania
October, 2010

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Abstract
The Port Phillip Bay Channel Deepening Project (CDP) was administered to deepen the
existing shipping channels in Port Phillip Bay, Victoria, Australia. The intent was to
accommodate the expected increase in large container ships entering the bay, in response
to a growth in the shipping trade and the Port of Melbourne being Australia‟s largest port.
Approval of the CDP did not happen immediately, as the Environmental Effects
Statement (EES) was rejected. A Supplementary Environmental Effects Statement (SEES)
was then produced, which led to the approval of the CDP; however the economic
assessment was inadequate.
The aim of this thesis was to assess the costs of damaging or losing the goods and services
freely provided to society by Port Phillip Bay, from environmental degradation from the
CDP. Such as assessment was unavailable in the SEES or any other publication, which
emphasised the need for this study to be undertaken. This project aimed to provide
decision-makers, as well as the public with evidence that supports an economic
assessment of the negative environmental effects of the CDP, which have the potential to
cascade through effects on the local economy and society.
The non-market valuation method, contingent travel cost was used in this evaluation, both
before and after the CDP, using pooled and un-pooled data to test its validity in this
situation. Some regression results were conflicting, both between pooled and un-pooled
analyses and pre and post-dredge analyses. Useful results were found for travel cost,
which was the most important variable, as it assisted in calculating consumer surplus
(CS).

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Two surveys were administered, asking recreational beach users details about their beach
visits, before and after the dredging activities involved with the CDP. Prior to the CDP, it
was found that recreational beach users generally predicted to make less beach visits,
which was a trend that was upheld post-CDP. Most recreational beach users interviewed
at beaches within and close to Port Phillip Bay, did not intend to change their number of
visits to Port Phillip Bay beaches in 2007 and early 2008, before the CDP had
commenced. After the CDP had been completed in 2010, recreational beach users
interviewed between February and May at beaches within and near Port Phillip Bay
continued the trend from the pre-dredge survey, as most stated that they did not change
their beach visits as a result of the CDP.
Beach recreational users of Port Phillip Bay incur a cost in CS in the magnitude of
millions to tens of millions of Australian 2010 dollars per year. Beach users overall, have
decreased the amount of beach visits they make because of the CDP by 0.12% to 0.26%
This finding is significant, as it is provides conclusive evidence that beach users are being
disadvantaged because of the CDP.
People‟s views of ecosystems are not assessed in this study, which is an area of future
research. Another important area of future research is beach visits to Port Phillip Bay
beaches. The output from a study such as this would provide very useful information for
other future studies on Port Phillip Bay. The surveys in this study asked TC and visits
questions, but not questions on willingness to pay, which could be an area of future study
to consider for capturing social or ecological value changes.

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Acknowledgments
I would like to thank my family and friends for supporting me this year with the big move
out of home, especially my incredible girlfriend, Maddy; Mum, Dad, Cama, Grandma,
Nan, Hulsey, Petrovsky, Travo, Emman, Crea, Jonny D, Ashlé, and the Whelan family.
Cheers to Alex “Alpaca” Inwood and Fahud “Hoodla” Shihab for being unreal
housemates, and Tom, Liam and Amy for letting me crash on their couch.
I must attribute my long-distance bands The Wax Vaegas and Dead Pool, as well as all of
the incredible people I have befriended in Tasmania over the last 4 months to helping me
stay sane. I thank my supervisor, Dr. Boyd Blackwell for sharing the vast array of
knowledge that inhabits his brain, for helping with data collection, for coping so well with
the huge obstacle that got thrown our way, and also for his enduring enthusiasm. We got
through it!
I would also like to thank my co-supervisor, Dr. Troy Gaston and Professor John
Freebairn for all of the useful feedback, as well as Simon Perraton, Julian Reid, Jason
Strugarek, Annie, and again, Maddy Whelan for assisting with my data collection.
This thesis would not have been possible without the survey respondents. I thank all of
you who got involved.
Lastly, I would just like to thank everyone again for making what could have been an
overwhelming honours year quite manageable.

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Table of Contents
Declaration and Authority of Access................................................................................................ 3
Abstract............................................................................................................................................. 4
Acknowledgments ............................................................................................................................ 6
Table of Contents.............................................................................................................................. 7
List of Tables.................................................................................................................................... 9
List of Figures................................................................................................................................. 11
Appendices ..................................................................................................................................... 12
List of Acronyms............................................................................................................................ 13
Chapter 1: Introduction................................................................................................................... 14
1.1 Introduction: ......................................................................................................................... 14
1.2 The Port Phillip Bay Channel Deepening Project (CDP)..................................................... 14
1.2.1 Reason for the CDP ....................................................................................................... 14
1.2.2 Negative Effects of the CDP.......................................................................................... 15
1.2.3 Environmental Effects of Dredging............................................................................... 15
1.2.4 Social Effects of Dredging............................................................................................. 19
1.2.5 Economic Effects of Dredging ...................................................................................... 19
1.2.6 Management of Environmental Effects......................................................................... 20
1.2.7 Management of Dredged Material................................................................................. 21
1.3 Aims and Justification: ......................................................................................................... 21
1.4 Research Questions............................................................................................................... 23
1.5 Hypotheses............................................................................................................................ 24
1.6 A Priori Expectations ........................................................................................................... 25
1.7 Outline of Thesis .................................................................................................................. 28
Chapter 2: Methods ........................................................................................................................ 29
2.1 Introduction .......................................................................................................................... 29
2.2 Non-Market Valuation.......................................................................................................... 29
2.2.1 Consumer Surplus.......................................................................................................... 29
2.2.2 Travel Cost Method....................................................................................................... 30
2.2.3 Contingent Behaviour.................................................................................................... 30
2.2.4 Contingent Travel Cost.................................................................................................. 31
2.3 Experimental design ............................................................................................................. 32
2.3.1 Survey............................................................................................................................ 32
2.3.2 Approach to data analysis.............................................................................................. 36
2.3.3 Consumer Surplus Calculation ...................................................................................... 38
2.3.4 Beach Visits Estimation and Change in Consumer Surplus.......................................... 40

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2.4 Conclusion............................................................................................................................ 43
Chapter 3: Results........................................................................................................................... 44
3.1 Introduction .......................................................................................................................... 44
3.2 Descriptive Statistics ............................................................................................................ 44
3.3 Inferential Statistics .............................................................................................................. 48
3.4 Conclusion............................................................................................................................ 63
Chapter 4: Discussion and Conclusion........................................................................................... 66
4.1 Introduction .......................................................................................................................... 66
4.1 Consumer Surplus................................................................................................................. 66
4.2 Visits..................................................................................................................................... 70
4.3 Answering the Hypotheses and Research Questions............................................................ 72
4.4 Limitations............................................................................................................................ 74
4.5 Areas for Future Research .................................................................................................... 75
4.6 Conclusion............................................................................................................................ 76
References ...................................................................................................................................... 79

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List of Tables
Table 1) A Priori Expectations for explanatory variables (dependant variable = visits)...................... 25
Table 2) Survey dates, locations and number of questionnaires administered at each location........... 33
Table 3) The 20 unpooled regressions.................................................................................................. 37
Table 4) The 10 pooled regressions...................................................................................................... 37
Table 5) Key figures used in the estimation of total annual beach visits to the Mornington
Peninsula. .............................................................................................................................. 44
Table 6) Regression results for un-pooled pre-dredge data, dependent variable = current visits, n =
113......................................................................................................................................... 56
Table 7) Regression results for un-pooled pre-dredge data, dependant variable = new visits, n =
113......................................................................................................................................... 57
Table 8) Regression results for pooled pre-dredge data, dependant variable = visits, n = 226. ........... 58
Table 9) Regression results for un-pooled post-dredge data, dependant variable = current visits, n
= 105. .................................................................................................................................... 62
Table 10) Regression results for un-pooled post-dredge data, dependant variable = old visits, n =
105......................................................................................................................................... 63
Table 11) Regression results for pooled post-dredge data, dependant variable = visits, n = 210......... 64
Table 12) Comparison of the independent variables in meeting a priori expectations........................ 66
Table 13) CS measures per person, per visit, for pre-dredge, un-pooled data in 2007 AUS dollars.... 69
Table 14) CS measures per person, per visit, for pre-dredge, un-pooled data in 2010 AUS dollars
(CS measures multiplied twice by 1.04). .............................................................................. 70
Table 15) CS measures per person, per visit for post-dredge, un-pooled data..................................... 70
Table 16) Change in total consumer surplus (CS) for all visits per year at 2 million, 4.7 million
and 6 million visits, for pre-dredge, pooled data in 2007 AUS dollars (collected 2007
and 2008)............................................................................................................................... 71
Table 17) Change in total consumer surplus (CS) for all visits per year at 2 million, 4.7 million
and 6 million visits, for pre-dredge, pooled data in 2010 AUS dollars, collected in 2007
and 2008 (CS measures multiplied twice by 1.04)................................................................ 71

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Table 18) Change in total consumer surplus (CS) for all visits per annum, at 2 million, 4.7 million
and 6 million visits, for post-dredge, pooled data (collected 2010)...................................... 72
Table 19) The overall change in visits per person, per year for pre-dredge data.................................. 73
Table 20) The overall change in visits per person, per year for post-dredge
data….…………………73

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List of Figures
Figure1) Location of dredging activity in Port Phillip Bay, showing the 3 channels to be deepened
and dredged material grounds
(DMGs)……………………………………………………………………16
Figure 2) Consumer surplus , showing the level of demand for recreational beach activity based on
the value of the beach visit to the beach user, and their quantity of beach
visits…………………………30
Figure 3) Sample
sites…………………………………………………………………………………32

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Appendices
Appendix 1) Pre-dredge survey
Appendix 2) Post-dredge survey

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List of Acronyms
DMG Dredged Material Ground
EES Environmental Effects Statement
TC Travel Cost
CDP Channel Deepening Project
OEM Office of the Environmental Monitor
OLS Ordinary Least Squares
PoMC Port of Melbourne Corporation
RP Revealed Preference
SEES Supplementary Environmental Effects Statement
SP Stated Preference
TEU Twenty-foot Equivalent Unit

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Chapter 1: Introduction
1.1 Introduction:
Dredging activities involved with the Channel Deepening Project (CDP) in Port Phillip
Bay may contribute to environmental degradation and, in turn, economic and social
dissatisfaction. The environmental, social and economic effects of dredging are known,
however the extent to which these consequences have direct affect on Port Phillip Bay and
its people is a topic of intense scrutiny.
This study used the non-market valuation method, contingent travel cost (Blackwell and
Willcox 2009), to evaluate the environmental effects the CDP had on recreational beach
users. The contingent travel cost method was used both before and after the dredging of
the bay, to test its ability to value changes in environmental (water) quality ex ante – that
is before they have occurred.
This study was timely, as the CDP had just recently been completed.
This chapter provides an insight into the study, proving an in-depth review of the channel
deepening project and the possible effects that it may have.
The project is then justified, providing a brief on the reason why it was undertaken and
why it is important. The research questions and hypotheses, which this study aims to
answer, are also provided in this chapter.
A brief outline of the thesis is then provided to conclude the chapter.
1.2 The Port Phillip Bay Channel Deepening Project (CDP)
1.2.1 Reason for the CDP
The Port of Melbourne Corporation (PoMC) conducted the CDP to deepen the existing
shipping channels in Port Phillip Bay. This was done to accommodate the expected
increase in large container ships entering the bay. Work was done on the great ship
channel at the entrance, the south channel and the Port Melbourne channel, as shown in

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Figure 1. These channels could previously accommodate ships of a maximum draft
(underwater ship depth) of 11m. The channels now can accommodate ships with 14m
draft (Edmunds et al. 2003; Upchurch, 2008).
Rapidly increasing trade is the reason given by the Port of Melbourne to why larger ships
are required to come into Port Phillip Bay (Upchurch, 2008). These larger ships are
expected to help with capturing economies of scale, and will lower the costs of
transportation for both imports and exports. It is also expected that there will be an
increase in the amount of cargo transported, with less ship movements (PoMC, 2009). As
the busiest port in Australia, the Port of Melbourne handles approximately 40% of
Australia‟s shipping containers, employs more than 60,000 people and was reported to
have had a growth in trade by 10.4% during the 2006-2007 financial year (Upchurch,
2008; World Port Source, 2010).
1.2.2 Negative Effects of the CDP
There have been concerns of the CDP negatively impacting the Port Phillip area. There
are concerns for environmental, social and economic values, with some negative effects
having the potential to cascade through more than one.
For example, PoMC has described the four major ways in which the sediment plumes
from the dredging can negatively affect the environment (URS, 2007):
 Clogging of gills and membranes of marine organisms;
 Reduced visibility within the water column (water quality);
 Reduced light within the water column (water quality); and
 Settled sediment smothering marine organisms.
1.2.3 Environmental Effects of Dredging
It is widely reported that dredging can have negative effects on the environment (Walker
and McComb, 1992; Erftemeijer, and Robin-Lewis III, 2006; Sinclair, 2009). Hawk et al.
(2007) has highlighted the following possible environmental impacts:

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 Hydrodynamics.
 Sediment transport and coastal processes.
 Light, productivity, turbidity, sedimentation.
 Nutrient cycling.
 Penguins
 Fish and fisheries
 Listed aquatic species
 Terrestrial ecology
 The entrance
These effects may have implications for the environmental, social and economic values of
Port Phillip Bay. This study‟s main focus was the economic values of water quality
changes

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Figure 1) Location of dredging activity in Port Phillip Bay, showing the 3 channels to be deepened and dredged
material grounds (DMGs). (Port of Melbourne, 2010).

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An example of the magnitude of the possible environmental effects of the CDP is a
decline in seagrass health. Seagrass habitats are vital for the prosperity of marine life in
Port Phillip Bay, as they perform an essential function in coastal zones (Duarte, 2002).
Seagrass beds have also been noted for their importance in regards to the prevention of
coastal erosion (Scoffin, 1979; Fonseca and Fisher, 1986; Fonseca 1989) and for
maintaining production of fisheries (Bell and Pollard, 1989; Jackson et al., 2001). These
habitats are put at risk due to the physical removal and burial of vegetation, as well as
increased sedimentation and turbidity (USACE, 1983; APB Research, 1999; Erftemeijer
and Robin-Lewis III 2006). Globally the primary cause of seagrass depletion is a
reduction in water clarity (Walker and McComb, 1992; Duarte, 2002; Short, 2003), and
there have been concerns that the CDP will impact on water quality in Port Phillip Bay
(Harris, 2004; Blue Wedges, 2010b). This was assessed by PoMC, who stated that
approximately 20% of seagrass habitat in the South of the bay will experience “reduced
leaf density or total loss of leaves” (Edmunds et al., 2006; Upchurch, 2008, p. 63).
As stated by PoMC the dredged material from the North of the bay is contaminated, due
to the Yarra River being highly polluted (Upchurch, 2008; Ren, 2010). In order to manage
this problem, PoMC has utilized the „bunding‟ process, which involves burying
contaminated dredge material in an „underwater clay containment area‟ (Upchurch, 2008;
Ren, 2010). This process will be discussed ahead, in further detail. PoMC has stated that
there is no reason to be concerned of any long term effects (ABC, 2008), which is
debateable and may be tested with time.
During a trial dredge at the bay entrance in 2005, damage was done to the canyon at the
heads. It was stated by Blue Wedges (2007) that the Trial Dredge Deep Reef Impact
Report (Edmunds et al., 2006) found that at 17m depth (a common recreational diving
depth); over 90% of the surveyed environment had been damaged by falling rocks. The
rockfall survey ceased at 57m depth, where damaged was found also. It is predicted by

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Blue Wedges (2007) that rocks fell to the bottom of the 100m canyon, where they still
remain.
Recently post-dredge, there has been growing concern about the effect of the CDP on
hydrodynamics and landform and seabed topography. Erosion has been reported at
Portsea front beach (Blue Wedges, 2010c, d;), as well as unusual tidal events. It is not yet
certain if this has been caused by the CDP, however it has been reported that tides have
become higher in Port Phillip Bay (Hast, 2010a) with stronger swells (Habermann, 2010;
Hast, 2010b) since commencement of the CDP.
Prior to commencement of the CDP however, water quality was the main impact
concerning the CDP.
1.2.4 Social Effects of Dredging
The beaches within Port Phillip Bay are very popular recreational sites. People‟s
enjoyment of these beaches is highly correlated with their visual presentation. People who
enjoy using the beach for recreation will be disadvantaged if the dredging lowers the
quality of the beach visually, as their level of enjoyment of their beach visit will be
negatively affected. Hawk et al. (2007) defines the social impact as:
 Adequacy of social impact research;
 Community fears and perception;
 Employment (and loss of business) opportunities;
 Proposed dredge and schedule changes; and
 Recreation and tourism impacts.
Hawk et al. (2007, p. 18) states that even though the social benefits of the CDP adequately
outweigh the costs, the social impact assessment was inadequate as there was a “deep and
unrelenting concern” shown by community participants.
1.2.5 Economic Effects of Dredging
It is stated in Hawk et al. (2007) that the economic considerations of the CDP are:

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 Appropriateness of economic modelling, including sensitivities and externalities;
 Benefit cost analysis, including project benefits and costs;
 Distribution of effects; and
 Strategic context for the proposal.
If dredging negatively effects the marine environment of Port Phillip Bay, it is quite
plausible that the local economy will suffer due to a reduction in the demand for tourism,
and possibly residency. An example of how the CDP could affect tourism is what may
happen to dive companies. If turbidity is high, visibility in the water column is low. Dive
companies make money from taking people SCUBA diving at visually pleasing sites,
where water clarity plays a vital role. If Port Phillip Bay acquires a reputation for being a
low quality place to dive, dive companies will be severely disadvantaged by a reduction in
demand for their only service. It was reported in Habermann (2010), that stronger swells
in the bay could be attributed to the CDP, and had damaged the vessels of marine
recreation businesses.
1.2.6 Management of Environmental Effects
PoMC was required to produce an Environmental Effects Statement (EES) (Edmunds et
al. 2003) to highlight the potential environmental risks involved with the activities of the
CDP. The environmental risks associated with the CDP can be concluded as being high
and abundant, as the initial EES was deemed inefficient by an independent Panel Inquiry.
The reason for this dismissal was that there were “issues that need further consideration”
(Hulls, 2005, p. 3), such as a lack of consideration for effects on ecosystems and
biodiversity; and inadequate risk management (Harris, 2004). A Supplementary
Environmental Effects Statement (SEES) was required (Edmunds et al., 2006) to address
the issue. It can be noted that this alone highlights the delicate nature of the CDP and the
severity of the negative effects. When the SEES was released the CDP was approved and
PoMC insisted that “all risks could be managed, and social impacts would largely be
minimal” (Hawk et al., 2007, p. 18).

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1.2.7 Management of Dredged Material
There are two dredged material grounds (DMGs) within Port Phillip Bay (Figure 1),
which act as sites for the dredged material. The Port of Melbourne DMG is located in the
North end of the bay, about 4 kilometres South-West of the Port Melbourne channel
(Figure 1). The South East DMG is located approximately 5 kilometres from the shore,
between Mt Martha and Mornington (Figure 1). There is also a bund (underwater clay
containment area) located at the Port of Melbourne DMG.
The bunding process involves lowering the contaminated material to the sea floor by use
of a specialised pipeline. This pipeline is run through a diffuser (like a shower head
underwater) in order to reduce the chance of the contaminated material dispersing through
the water column. This area is then capped with clean dredged material to keep the
contaminants from escaping. This bund is 15 metres under the surface, where tides and
weather are not expected to have much influence (Upchurch, 2008; CDPBP Factsheet,
2009).
It is stated in Ren (2010) that from observing in-situ testing on a trial bund in Port Phillip
Bay, the bund process is a robust one and that the stability of the sediment will increase
over time.
1.3 Aims and Justification:
The issue of the CDP in Port Phillip Bay was a controversial one. The „Blue Wedges
Coalition‟ was “against the deepening and dredging of Port Phillip Bay” (Blue Wedges,
2010a), and gathered 65 organisations who were also against the CDP. People local to the
Port Phillip area expressed their concern about the CDP. An extreme example of this was
when a number of protestors paddled out dangerously close to the Queen of the
Netherlands (the main dredge vessel) on surfboards and kayaks upon its arrival in the bay
(Coster and Wotherspoon, 2008).
To further complicate the issue, information regarding the long-term effects that dredging
will have on the environment is minimal, due to the fact that it may take up to 5 years

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post-dredging before any effects are noticed (Sinclair, 2009). It is therefore important for
evidence to be provided to decision makers and the public that may help capture the likely
environmental costs or benefits of dredging, especially where previous economic analyses
as part of the EES were narrowly undertaken, without considering the monetary values of
environmental degradation or improvement (Hawk et al., 2007, Blue Wedges, 2008).
Allocating dollar amounts to these likely costs and benefits is an objective and impartial
way of assessing the environmental risks or contingencies associated with the CDP.
Combining the travel cost and contingent behaviour methods delivers economic values of
such risks.
The contingent behaviour method and other stated preference (SP) methodologies are
useful in regards to valuation of public goods in environmental economics (Whitehead et
al. 2008). The travel cost method and other revealed preference (RP) methodologies are
also useful in this field, but in different ways. An example of this that is directly relevant
to this project, is how the travel cost method is useful in valuation of the benefits of
outdoor recreation such as visits to the beach (Herriges and Kling, 1999; Parsons, 2003;
Blackwell, 2007; Whitehead et al. 2008;), and contingent behaviour is useful for creating
hypothetical behavioural questions for events that are not known to have occurred yet
(Cameron, 1992; Englin and Shonkwiler, 1995; Whitehead et al. 2008), such as a decrease
in water quality.
There are however, some disadvantages attributable to SP data relative to RP data, but
Whitehead et al. (2008) states that the weaknesses of one approach are generally the
strengths of the other. RP approaches heavily rely on historical data and SP approaches
can lack information and accuracy due to their hypothetical nature (Whitehead et al.
2008). The process of merging the data and estimation processes can successfully combat
this setback (Whitehead et al. 2008).
There are a number of indications for the need for a study such as this one on the
economics of the CDP. The need for future research in this field is discussed in

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Whitehead et al. (2008), where it is stated that data sets with a long-term approach that
“forecast beyond the range of historical experience” and “allow the collection of SP and
RP data with time for respondents to experience gradual and rapid environmental change”
are needed to test the validity of valuation methods and provide information for policy
analysis of important environmental issues (in this case, channel deepening).
Whilst reviewing the literature, no mention of non-market valuation was present in direct
reference to the Port Phillip Bay CDP. Although the social and environmental assessment
of the CDP appears thorough (Hulls, 2005, Hawk et al. 2007; PoMC, n.d.), the economic
assessment does not appear to be as rigorous. It is stated in Blue Wedges (2008) that
PoMC “failed to properly examine the economic case of the CDP”, and that a traditional,
narrow cost benefit approach will not adequately assess the costs of damaging or losing
the goods and services freely provided to society by the bay. Blackwell (2008a, b)
provides criticism and solutions to this traditional cost benefit approach using a social
cost-benefit framework with other Victorian coastal development examples. The work in
this thesis contributes to such a broader approach by assessing lost recreational values for
beach and bay users.
1.4 Research Questions
In this project, the following research questions were addressed;
1) What are the likely costs, if any, to beach and bay recreational users of Port Phillip
Bay as a result of the CDP?
2) What is the variance, if any, between people‟s contingent and actual behaviour
(visits) given an expectation of degraded environmental (water) quality such as in
the case of dredging in Port Phillip Bay?
3) What are the methodological implications for valuing environmental (water
quality) degradation ex ante?

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This study assessed the costs or benefits of a change in environmental quality, before and
after the CDP had taken place in Port Phillip Bay. This was achieved by finding out if
actual visits to the bay post-dredge diverge from expected visits pre-dredge (research
questions 1 and 2).
The effectiveness of the contingent travel cost method in placing monetary values on non-
market services was assessed by measuring these costs or benefits, before and after the
CDP. This approach was then able to contribute to discovering the strengths and
limitations of the contingent travel cost method, providing insight into its refinement not
yet determined in the literature (Whitehead et al. 2008), thus meeting the objectives of
research question 3.
1.5 Hypotheses
From these research questions, three sets of hypotheses were developed.
1) Ho = There were no costs to beach and bay recreational users as a result of the
CDP.
H1 = There were costs to beach and bay recreational users as a result of the CDP.
2) Ho = There was no variance between people‟s contingent and actual behaviour,
given an expectation in degraded environmental quality as a result of the CDP.
H1 = There was variance between people‟s contingent and actual behaviour, given
an expectation in degraded environmental quality as a result of the CDP.
3) H0 = The contingent travel cost method is not a reliable and valid approach for
valuing environmental (water) degradation before the fact.
H1 = The continent travel cost method is a reliable and valid approach to valuing
environmental (water) degradation before the fact.

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1.6 A Priori Expectations
Table 1) A Priori Expectations for explanatory variables (dependant variable = visits).
Variable A Priori
Sign
Description
Aware + Whether the respondent was aware (1) of the CDP or not (0).
Solepurp + Whether the beach was (1) the sole purpose of the respondent‟s trip or not (0).
TC (travel
cost)
- The amount in 2008 Australian dollars that respondents spent during their
return trip to the beach = per person ATO allowable running cost of a standard
size car + 40% of the value of the respondent‟s time spent in travel.
Surfer + Whether the respondent was clearly a surfer (0) or not (1).
Age - The age (in decades) of the respondent.
Age2
- The square root of the age of the respondent.
Fem - Whether the respondent was female (1) or not (0).
Income + The total before tax household income (in tens of thousands of Australian
dollars) of the respondent.
Educ + Amount of years spent in formal education.
Fullemp - Whether the respondent was employed full-time (1) or not (0) (self
employed=0).
Visitor
WtrQual
-
+
Whether the respondent was a visitor (1) or resident (0) of the area.
The water quality rating given by the respondent (1-5). (only in post-dredge
analysis)
Entero - Enterococci levels at survey sites.
Subvisit + or - The amount of visits made by beach users at their other most visited beach.
ABCurvis* + or - The amount of beach visits made by respondents at another beach of their
choice.
ABVis** + or - The amount of beach visits made by respondents at another beach of their
choice.
ABNewvis* + or - The amount of beach visits respondents predicted they made per year, post
CDP commencement.
ABOldvis* + or - The amount of beach visits respondents predicted they made per year, pre-
CDP commencement.
Baybch + or - Whether the respondent was interviewed at a beach in Port Phillip Bay (1) or
not (0).
Gunna + or - Whether the respondent was interviewed at Gunnamatta beach (1) or not (0).
Dredgeaffect** + or - Dummy variable used to separate the 2 datasets in the pooled regression
analyses.
*= only used in unpooled regressions ** = only used in pooled regressions (+) = positive expected
relationship with visits; (-) = negative expected relationship with visits.

26. 26
Table 1 shows the twenty variables that were selected from the research questionnaire a
priori (before regression analysis) to help explain beach visits. (+) indicates a positive
expected relationship with visits and (-) indicates a negative expected relationship with
visits. (0) and (1) refer to the dummy variables used to group the variables during data
entry, providing they were not discrete values.
Descriptions of the questions in which these variables were selected from are provided in
section 2.3.1 of this thesis.
Travel cost (TC) was the variable of primary concern in this study, as it is essential for
calculating changes in CS from the CDP. There was a negative expected relationship
between visits and TC, as beach users with a given level of disposable income are likely
to make fewer visits to the beach as costs to visit the beach increase.
A positive relationship between respondents who are aware of the CDP (Aware) and visits
was expected due to possible greater knowledge and understanding of the issue, and
therefore taking a higher amount of visits than someone who is not aware of the CDP. A
positive relationship between visits and whether the beach was the sole purpose of the
respondent‟s visit (Solepurp) was also expected due to a possibility of increased
compassion for, and enjoyment of the marine environment.
The two age variables (Age and Age2
), were expected to have curvi-linear relationships
with visits, as people generally have more spare time when they are young and when they
are elderly. During this spare time, there is greater opportunity to visit beaches. Generally
people have less spare time as they get older (before retirement) as they are raising a
family or employed full time.
Surfers (Surfer) were expected to make more beach visits than typical members of the
population, as were males, due to fact that males generally participate in more risky
recreational behaviour than females (Fem) (Harris et al., 2006).

27. 27
Income was predicted to have a positive relationship with visits, as it is more likely for
someone with high income to be able to afford to make beach visits than someone with
low income. The respondent‟s highest level of education (Educ) was also predicted to
have a positive relationship with visits, due to the fact that people with a higher level of
education may have a greater appreciation of the health benefits of participating in
outdoor recreation than people of lower income.
An inverse relationship was predicted between visits and whether people were employed
full time (Fullemp). This was because it is likely that someone who is employed full-time
does not have as much leisure time as someone who isn‟t employed full-time. Whether
people were visitors to the survey site (Visitor) was also predicted to have an inverse
relationship with visits, as someone who is a visitor to the survey site is likely to visit less
than someone who is a resident to that site.
The water quality observed by respondents (WtrQual) was expected to have a positive
relationship with visits, as the higher the water quality rating, the more pleased they
should be to visit the beach. Therefore, the happier people are to visit the beach, the
higher the chance was of them visiting more than those who are displeased with the state
of the water quality.
Enterococci levels at the survey sites (Entero) were predicted to have a negative
relationship with visits, as high enterococci readings were expected to be conducive to
beach user dissatisfaction.
None of the four “another beach” variables (ABCurvis, ABVis, ABOldvis, and
ABNewvis) were expected to have a specific relationship with visits, as respondents were
expected to choose a variety of sites for this question of the questionnaire. Sites may
include an unaffected site that they visit less or more because of the dredging, an affected
site they visit less or more because of the dredging, or no site at all.

28. 28
1.7 Outline of Thesis
Chapter 2 of this thesis focuses on the methods used in this study. This chapter gives an
insight into non-market valuation, its importance in this study, and the different
components of it that were used. These components were consumer surplus, the
contingent behaviour method and the travel cost method.
Chapter 2 also provides a summary of the experimental design, which covers the
questionnaire design and implementation of the survey, sample size, possible biases,
survey sites, data entry, data analysis, consumer surplus calculations, and recreational
beach visit calculations.
Chapter 3 of this thesis is where the results are presented for the regression analyses.
Descriptive analyses of the questionnaire answers are provided in chapter 3, along with
inferential statistics, which present the regression results.
Chapter 4 of the thesis is the discussion. This chapter administers the consumer surplus
and visits calculations, and answers the research questions and hypotheses. Limitations
and areas for future research are also provided in chapter 4.

29. 29
Chapter 2: Methods
2.1 Introduction
This chapter explains non-market valuation, the contingent travel cost and its components.
Other examples of contingent travel cost studies are discussed in this chapter, and
similarities to those studies are made to the approach of this study.
The experimental design is outlined in this chapter, which covers details on the beach
survey, including where, when and how it was administered. A general overview of the
approach to this study is provided in this chapter, which includes details of the 30
different regressions, the biases that come with them, and the types of data used.
Consumer surplus and beach visits calculations are presented in this chapter, along with
reasons why they are being calculated.
2.2 Non-Market Valuation
Assessing the economic implications for environmental risks, like dredging, can be a
difficult task, as typically there is no market for the goods and services provided by the
environment (Haab and McConnell 2002). Where there is no market, there are no dollar
values to assign to these goods and services. This is where non-market valuation becomes
useful.
Non-market valuation can provide hypothetical economic values for resources such as
environmental goods and services. In this study the environmental good or service is
water quality. Deterioration in water quality means a decline in the value of this service.
2.2.1 Consumer Surplus
Consumer surplus (CS) is “the area under an income constant demand curve” (Haab and
Connell 2002, pg.12) as depicted by the triangle area X in Figure 2. CS is the amount that
consumers benefit from paying prices lower than the maximum amounts they would be

30. 30
willing to pay. Changes in CS in this study are used to measure the benefit or detriment
that recreational beach users will experience from a change in environmental quality,
relating to the CDP.
The triangle in Figure 2 represents CS, which decreases in size with environmental
degradation and increases in size with environmental improvement.
2.2.2 Travel Cost Method
The travel cost method estimates the “price” of a recreational site by assessing the time
and travel cost expenses. People‟s willingness to pay to attend a recreational site is
estimated by assessing the amount of trips made along with the different travel costs
incurred (Ecosystem Valuation, 2010a).
2.2.3 Contingent Behaviour
The contingent behaviour method involves directly asking people hypothetical questions
about their behaviour, contingent on a certain scenario. Contingent behaviour is
undertaken by use of a survey (Ecosystem Valuation, 2010b). The aim of the contingent
behaviour method is to directly extract hypothetical statements from the survey
respondents (Whitehead et al. 2008). Typically contingent behaviour is included in a
Figure 2) Consumer surplus, showing the level of demand for recreational beach activity based on the value
of the beach visit to the beach user, and their quantity of beach visits.
Value of beach visit
($/visit)
Quantity of beach visits (person visits)
Demand
0
X
Increase in demand (environmental improvement)
Decrease in demand (environmental degradation)

31. 31
travel cost study in order to assess, ex ante, whether people‟s behaviour would change
given a change in environmental quality as first undertaken by Cameron (1992).
2.2.4 Contingent Travel Cost
In this paper, the simple approach of modifying the travel cost method with a respondent‟s
stated additional visits given a change in environmental quality is taken. This approach
was called the contingent travel cost method.
This approach, however, is not new and Whitehead et al. (2008) provides a review of
combining methods. The first well known contingent behaviour study was undertaken by
Cameron (1992) who supplemented the travel cost method by asking people whether
particular cost rises would drive their fishing trips to zero.
Hanley et al. (2003) estimated the benefits of water quality improvements for beach users
in Scotland as part of the European Union‟s toughening of water quality legislation. Here
the authors combined revealed preference data on actual and expected visits to beaches
when „hypothetical quality improvements‟ were offered to respondents.
An alternative but similar approach was taken by Kragt et al. (2009). They asked people
to estimate their decrease in diver and snorkelling trips to the Great Barrier Reef,
Australia, given a fall in coral and fish diversity. The distinction was valuing
environmental degradation rather than improvement.
The approach taken in this study is most similar to Hanley et al. (2003), as people are
asked whether their visits to a beach site change in response to a change in environmental
quality. However, this study‟s application of the method is unique in Australia involving
an assessment of the recreational costs and benefits to beach users from the possible
negative effects of the CDP.
Also, this assessment is much simpler than that of Hanley et al. (2003). This is because CS
is estimated based on both actual and expected behaviour, which are both then subtracted
to get the change in surplus from the expected dredge effects.

32. 32
2.3 Experimental design
2.3.1 Survey
The survey was administered over several visits to beaches within Port Phillip Bay
(Figure 3) between February and June 2010. Beach users were interviewed face to face
on-site using the survey instrument provided in Appendix 1 and Appendix 2.
Four additional interviewers were included in the data collection. This was to assist in
gaining a reasonable data set in the short period of time available. In this study a
reasonable data set was classified as 100 or more observations. This goal was reached,
as 105 questionnaires were completed, with the pre-dredge study completing 113
questionnaires (Table 2). There was no need to undertake supplementary data collection
in September, as suggested in the proposal, Willcox (2010).
A visitation and environmental data collection form (Appendix 2) was filled out before
every survey session. Information included on this form included the date, beach location,
position on beach, number of questionnaire rejections, time, recorder‟s name, level of
surf, wave height, wind direction, wind speed and tide.
11
2
43
5
6
7
8
9
10
Figure 3) Sample sites 1 = Portsea front beach, 2 = Sorrento front beach, 3 = Rye beach, 4 = Dromana beach, 5 = Mt Martha
beach, 6 = Mothers beach (Mornington pier), 7 = Frankston beach, 8 = St Kilda beach, 9 = Gunnamatta back beach, 10 =
Sorrento Back Beach

33. 33
Table 2) Survey dates, locations and number of questionnaires administered at each location.
Pre-dredge Survey (n=113) Post-dredge Survey (n=105)
Date Site
Number of
Questionnaires
Date Site
Number of
questionnaires
12/11/2007
19/11/2007
20/11/2007
25/04/2008
Portsea Front Beach 32
19/06/2010
21/06/2010
Portsea Front Beach 14
16/11/2007
24/05/2008
Rye Front Beach 16 25/03/2010 Rye Front Beach 17
15/11/2007
17/11/2007
01/12/2007
Dromana Beach 6 26/03/2010 Dromana Beach 17
27/11/2007
28/11/2007
27/12/2007
Sorrento Back Beach 39 21/06/2010 Sorrento Back Beach 5
12/11/2007
13/11/2007
15/11/2007
25/10/2007
26/10/2007
14/08/2009
Gunnamatta Beach 19
09/05/2010
23/06/2010
Gunnamatta Beach 11
03/04/2010 Sorrento Front Beach 17
19/03/2010
20/03/2010
20/06/2010
Mt Martha Beach 7
24/03/2010
18/06/2010
Mothers Beach
(Mornington Pier)
8
12/03/2010 Frankston Beach 3
02/04/2010 St Kilda Beach 6
Total Questionnaires 113 Total Questionnaires 105
The questionnaires from the pre-dredge (Appendix 1) and post-dredge (Appendix 2)
studies were slightly different. In order to make the questionnaire easier to understand, the
layout was altered and some questions were reworded. For example, the question titled
“transport method” was changed to “how did you get to the beach today?” In addition to
this example, the questions were numbered (excluding socio-economic questions) and
some of the sentences were tidied up and spaced out.
The questionnaire asked the respondents about their current annual beach visits. Standard
travel cost questions such as; „how far did you travel to get to the beach today?‟ and „how
did you get to the beach today?‟ were also included.
Respondents were asked if they were a resident and if not, what the length of their stay is.
There was a level of ambiguity regarding residency. Some respondents stated the same
postcode but had different perceptions on whether they were local or not. If the
respondent was at a beach on the Mornington Peninsula, and lived on the Mornington

34. 34
Peninsula, they were considered a resident. For St Kilda beach, respondents who lived in
Melbourne and Melbourne‟s outer suburbs were classified as residents.
Respondents were asked how much money they have spent or intend to spend in regards
to their current beach visit. This included money spent on or around the beach for
example, food or drinks. Money spent away from the coastal strip in preparation for the
beach trip was also included, such as sunscreen or fishing equipment.
Two questions regarding the water quality were included also, asking the respondent to
rate the water quality now and before the dredging, when given five categories; „very
bad‟, „bad‟, „OK‟, „good‟ or „very good‟. These questions are quite general, as the
respondent may refer to clarity (most likely method); substrate integrity, smells or other
methods that they personally may use to assess the quality of the water. Respondents had
to think back two years (before the CDP) and rate the water quality. If they had not visited
the survey site in 2008 or they couldn‟t remember, the question was left blank.
The original questionnaire did not include a water quality question, so an attempt was
made to obtain some water quality data from this time period. Measurements of
enterococci were obtained for Gunnamatta (site 3) (Melbourne Water, 2010a), Sorrento
back beach (beach 6) (Melbourne Water, 2010b) and Port Phillip Bay beaches (EPA
Victoria, 2010) to determine bacteria levels in the water. Enterococci levels were the
measurement of choice due to the World Health Organisation‟s guideline values for
coastal waters (WHO, 2003).
Respondents were previously asked as part of Blackwell & Willcox (2009), if they were
aware of the CDP and whether it would change their number of visits to the beach and by
how much. This included the beach they were being interviewed at as well as another
beach of their choice. This question was asked again in this study, but asking the
respondent if they had changed the number of visits they made to the beach because of the
CDP. Because this question was asked in the post-dredge survey instrument, which was
implemented after dredging had occurred the question checked whether people‟s visits

35. 35
increased or decreased after completion of the CDP. The purpose of this question was to
find out if the CDP had increased or decreased their visits. The results would therefore
find out if recreational beach users had lived up to the expectation that they would
increase or reduce their beach visits as a result of the CDP when compared to the
corresponding pre-dredge question. It was expected however, that recreational beach users
would reduce their number of beach visits as a result of the CDP, due to its negative
impact expected by beach users on the bay and the negative publicity in the media. If the
respondent was not aware of the CDP, a brief explanation was provided in order to
educate them on the issue (Appendix 2).
Standard socio-economic questions were included in the questionnaire as well. This
included highest level of education, employment status, age group and before tax
household income. For each of the socio-economic questions in the post-dredge survey,
respondents were asked to state their current status and their status two years prior (2008).
This was intended to discover patterns in regards to how people might have changed since
commencement of the CDP. Data was collected before commencement of the CDP on
people‟s likely changes in visits prior to the CDP as part of Blackwell and Willcox
(2009).
The pre CDP data was then compared with the post-CDP data in order to find out if
people‟s expectations had been fulfilled. Losses in recreation value were ascertained post
and pre dredging using panel and pooled regression methods (Cameron, 1992; Englin and
Shonkwiler, 1995).
Respondents included recreational beach users of the beach face and foreshore such as
sunbakers, swimmers and walkers. Other users of the bay were included in the survey,
such as divers, fishers, surf life savers, boat operators and surfers (Gunnamatta – who may
also use quarantines surf break, which is close to the great ship channel dredge site at the
entrance).

36. 36
2.3.2 Approach to data analysis
The survey answers were entered into a Microsoft Excel database. This raw data was then
imported into the statistical analysis program, Limdep (NLogit 3.0.6). This program was
used because of its ability to work with limited dependant variables. In this study the
dependent variable (visits) is limited, by being a count variable (only taking on whole
numbers or zero).
A number of different regression analyses were run in Limdep. As shown in Table 3,
Poisson, negative binomial and ordinary least squares (OLS) regressions were run for pre-
dredge survey data (current and future visits) and post-dredge survey data (past and
current visits). Truncated versions of Poisson and negative binomial regressions were also
carried out.
Table3) The 20 un-pooled regressions.
Pre-Dredge Survey Post-Dredge Survey
Dependent
Variable ► CURVIS
(Current
Visits)
SVYSNEWV
(Survey site
new visits)
CURVIS
(Current
visits)
SVYSOLDV
(Survey site old
visits)
Regression
Form ▼
OLS    
Negative
Binomial
   
Poisson
   
Truncated
Negative
Binomial
   
Truncated
Poisson
   

37. 37
Table4) The 10 pooled regressions.
Pre-dredge survey Post-dredge Survey
Dependant
Variable ►
VISITS VISITS
Regression Type
▼
OLS  
Negative Binomial  
Poisson  
Truncated
Negative Binomial
 
Truncated Poisson  
Pooled regressions were also undertaken using the same regression techniques. This was
achieved by pooling together current and future visit data for pre-dredge survey data and
past and current visit data for post-dredge survey data (Table 4). This was intended to
increase the sample size and to reduce the amount of outputs, therefore simplifying the
analysis. In total 30 regressions were run.
The OLS and non-truncated regressions were included for comparative purposes, being
mindful of the possible bias and inconsistency that could be generated for β (travel cost
coefficient) and thus the CS measures estimated from β (Shaw, 1988).
The truncated Poisson and truncated negative binomial regressions are typically preferred,
due to their ability to eliminate the 3 common problems related to bias (Shaw 1988):
 Truncation: where data from non-locals and those who do not use the survey site is
not included in the study;
 Endogenous stratification: where those who regularly use the survey site are most
likely to be interviewed; and

38. 38
 Non-negative integers.
When a dataset includes greater variability in its data than expected, the dataset has over-
dispersion. Where over-dispersion is present, the truncated negative-binomial regression
form is typically the preferred regression method to use (Dobbs 1993; Englin and
Shonkwiler 1995; and Offenbach and Goodwin 1994).
There were two types of data used in this study, which were continuous (travel cost of
beach users) and count (number of visits made to study sites). Count data regression
analyses were run as the dependant variable was always a count data variable.
2.3.3 Consumer Surplus Calculation
To find out the CS figures, a number of calculations were administered.
First, CS per person, per visit for non-pooled data was calculated, using the following
equation, where CS/q is the CS per person per visit and β is the travel cost coefficient
(Blackwell, 2007).
1CS
q 

This calculation was administered for all four non-pooled data sets. The calculation for
OLS was different however. Following Cerda Urrutia et al. (1997) and Blackwell (2007),
this equation is as follows, where β = TC coefficient, q = median no. of all visits, which
was 6 for pre-dredge data and 3 for post-dredge data.
CS/q = q/2β x 100
CS/q for CURVIS was subtracted from CS/q for SRVYSNEWV (pre-dredge); and CS/q
for SRVYSOLDV was subtracted from CURVIS (post-dredge). This was done to find the
expected changes in CS/q from before to after the CDP pre-dredge and the actual changes
in CS/q post-dredge which relate to a decrease in environmental quality in Port Phillip
Bay as a result of the CDP.

39. 39
The change in CS was then calculated for pooled data regressions. The coefficients (b)
and means (c) for each independent variable from the regression analyses were multiplied
together, to create (b)x(c) for each variable. The (b)x(c) figures were then summed
together to find the average expected future visits and current visits for pre-dredge data,
and average current visits and expected prior-dredge visits for post-dredge data. The
Dredgeaffect variable was used to separate the two sets of data in each pooled set. There
were two Dredgeaffect variables in each pooled set (Dredgeaffect 0 and Dredgeaffect 1).
To put this process into an example, when expected future visits was calculated for pre-
dredge, (b)x(c) multiplied all independent variables except for Dredgeaffect 0, as this
variable represented the data only used for current visits such as the number of visits to
the sub-site. For calculating current visits pre-dredge, the calculation included all
independent variables but Dredgeaffect 1, as it represented data for future visits such as
the amount of visits respondents intend to take as a result of the CDP.
The change in visits was then found by subtracting current visits from expected visits for
pre-dredge and previous visits from current visits for post-dredge. The change in visits
was then divided by current visits, which produces the figure needed for the next step,
which is calculating the change in total visits for Port Phillip Bay beaches. To calculate
this change in total visits, the figure from the previous step was multiplied by annual Port
Phillip Bay beach visits estimates, using three different visits estimates as a sensitivity
analysis, which is discussed ahead. The change in total CS per annum was then calculated
by multiplying the change in total visits by CS per person, per visits.
As all pre-dredge CS calculations, were originally calculated using 2007 Australian
dollars due to the survey being administered mostly in 2007 and partly in 2008, the results
had to be converted to 2010 Australian dollars. To achieve this, the CS per person, per
visit and change in CS results for pre-dredge, as well as the change in total CS results for

40. 40
post-dredge data were multiplied twice by 1.04 to allow for two years of inflation,
assuming a rate of 4% for Victoria.
2.3.4 Beach Visits Estimation and Change in Consumer Surplus
To calculate the change in total CS for beach visits per annum, an estimation of annual
visits to Port Phillip Bay was required.
A request was made for figures on Port Phillip Bay and Mornington Peninsula beach
visits at the Dromana information centre, which is run by Mornington Peninsula Tourism
incorporated. This request was unsuccessful; as such specific figures apparently do not
exist. A general figure on the amount of visits to the Mornington Peninsula was provided
however, which was 3 million visits per year.
As information on this particular statistic could not be located, some estimations had to be
carried out. Statistics on visits to Mornington Peninsula were obtained from Tourism
Victoria (2008). This is the most recent data available.
Tourism Victoria (2008) stated that on the Mornington Peninsula, in the year ending
December 2008, there were 4.1 million domestic visitor nights, and 54% of these
domestic visitors made trips to the beach. There were 38,000 international overnight
visitors, and 37% of international visitors stayed for 1-3 nights, 19% stayed for 4-7 nights,
19% stayed for 8-14 nights, and 25% of International visitors stayed for over 15 nights.
There were some key figures missing, such as the total amount of international visitors to
the peninsula. In order to obtain the most accurate measure of beach visits possible, these
figures were combined with data from the surveys administered in this study.
Mean beach visits and length of stay of respondents who claimed to be visitors to the
survey site were calculated for both pre-dredge and post dredge data.

41. 41
Visitor beach visits was calculated by summing all of the answers to the “length of stay in
days” question from the questionnaire (Appendix 1, Appendix 2) for respondents who
stated they were visitors in question 7 of the questionnaire, then the means were found. To
be consistent with the conservative nature of this study, the figure from the respondent‟s
annual daily visits to the survey site (question 1 of questionnaire) was used if it was lower
than their intended length of stay (days), as the following example demonstrates. A
respondent who is a visitor makes 6 visits per year to the study site, which is Mt Martha
beach. The respondent states that they intend to stay for 12 days on this particular trip. For
calculating the mean visitor beach visits, 6 would be used instead of 12.
The length of stay of respondents who were visitors to the study site was calculated
identically to visitor beach visits, except the figures used were solely from the question in
the questionnaire, “length of stay in days”.
The following figures are displayed in (Table 5)
The mean number of visitor beach visits (2.6) was divided by the mean number of the
length of stay of visitors to the Mornington Peninsula (3.6). This figure (0.72) was then
converted to a percentage (72%), which is the proportion of beach visits to length of stay
for visitors.
To estimate annual domestic visitor beach visits, 72% of the 4.1 million domestic visitor
nights (Tourism Victoria, 2008) was found, which was 2.95 million visits. This was
because it was predicted that not all visitors to the Mornington Peninsula would visit the
beach every day of their stay.
To find the amount of domestic beach visits, 54% (beach trips) of the 2.9 million
domestic visits (Tourism Victoria, 2008) was found, which was 1.6 million visits.
Visit made internationally to Mornington Peninsula beaches was found by using the
midpoint of the length of stay, in nights categories used in Tourism Victoria (2008) (e.g.

42. 42
4-7 nights was changed to 5.5 nights) and dividing this by the percentage of international
visitors that stayed for that period. For example, if 37% of international visitors stayed for
1-3 nights; the calculation is 37% of 2.
The sum of figures for all 4 categories was found, and this figure (7.63) was multiplied by
the 38,600 international overnight visitors identified by Tourism Victoria (2008), to find
international visitor nights (294,325). This figure proportioned to 72% (beach visits to
length of stay for visitors), as used previously, then divided by 1 million so it is consistent
with the other figures to get 0.2.
Finally, the sum of 2.95, 1.6 and 0.2 was found, to calculate total beach visits to
Mornington Peninsula beaches, which was 4.7 million visits.
This process was repeated for post-dredge data and total visits to Mornington Peninsula
beaches were found to be 4.5 million visits.
Two of the survey sites (Frankston and St Kilda) used in the post-dredge data collection
of this study were not located on the Mornington Peninsula. It was decided however, the
above method was still the most accurate measure of total annual beach visits. This is
because only 9% of the post-dredge survey was administered in these locations,
comprising of only 4% of the total sample. Also, Frankston is located close to the
Mornington Peninsula border.

43. 43
Table 5) Key figures used in the estimation of total annual beach visits to the Mornington Peninsula.
2.4 Conclusion
The next chapter uses the data collected in the survey, and presents the regression
analyses that are important for calculating CS measures and visits to Port Phillip Bay
beaches. This chapter is where the contingent travel cost method with comes into effect.
The calculations presented in this chapter are not administered until chapter 4.
Pre-dredge study Post-dredge study
Mean of visitor annual beach
visits
2.6 2.6
Mean of visitor length of stay
on MP (days)
3.6 3.9
Proportion of domestic
visitors beach visits to length
of stay
72.1% 67.4%
Annual domestic visitor beach
visits (millions)
2.95 2.75
Domestic beach days
(millions)
1.6 1.6
International nights (millions) 0.29 0.29
Total beach visits (millions) 4.73 4.51

44. 44
Chapter 3: Results
3.1 Introduction
The regressions of various explanatory variables outlined in Chapter 2 are provided in this
chapter. This is a critical part of the thesis, as it provides the information needed for
calculating CS, testing regression validity and assessing the relationships between the
explanatory variables and visits and thus, finding out if they met the a priori expectations.
Without a statistically significant relationship between the travel cost coefficients and
visits (the dependent variable), estimates of consumer surplus using the TC coefficients,
are not reliable and for ethical reasons should not be estimated. Part of this chapter‟s
contribution is to test this critical relationship.
3.2 Descriptive Statistics
The following provides a summary of answers that were given to the questions within the
questionnaires. The questions in the pre-dredge questionnaire are not numbered, but are
the same questions as the post-dredge questionnaire, with the exception of new questions
12 and 14, and altered questions, 15 and 16. Texts of the questionnaires are provided in
Appendix 1 and Appendix 2. The descriptive statistics presented here are over both
surveys unless stated otherwise.
Respondents visited the survey site from 1 (if it was their first ever visit or first visit in the
year the interview took place) to 365 days per year (question 1), and they spent between 5
and 24 hours per visit (question 2).
Most respondents stated that their other most visited beach (question 2a) was relatively
close to the site, which they were being interviewed. Interstate sub-sites were stated often
and there were 2 international sub-sites listed, The Netherlands and Fiji. An example of
this statement can be taken from interviews at Sorrento. Out of the 17 respondents, 7
stated their sub-sites as beaches on the Mornington Peninsula such as, Dromana, Rosebud,

45. 45
McCrae, Mt Martha, Frankston and Portsea back beach; 4 stated beaches within Port
Phillip Bay such as St Kilda, Chelsea and Elwood; 5 stated other Victorian sites such as,
Lorne, Murray River and Western Port and 1 listed an interstate site, Clifton, Queensland.
These results were somewhat site-specific, for example people at Dromana often stated no
sub-site, as Dromana beach is the only beach they visit. The average amount of visits to
the sub-site was 43 days per year (question 2b).
Respondents mostly travelled to the beach by automobile (question 4), with the average
distance travelled being 31.5 kilometres and the average trip length was 35.7 minutes.
The most common party size was 2 and the average party size was 3. 58% of interviewees
were visitors and 42% were residents. The average length of stay for visitors was 3 days.
The most common postcode was 3941, which covers South Mornington Peninsula
locations; Rye, St Andrews Beach and Tootgarook.
60% of respondents stated that the beach environment was the main reason why they were
at the beach, with 40% saying that the beach was not the main motive of the beach visit.
The average amount of beach trip enjoyment that respondents attributed to the beach was
about 70%.
On average, respondents spent about $35 per beach visit along the beach and coastal strip.
Money spent away from the coastal strip in preparation for the beach trip was on average,
about $23.
For the post-dredge survey, most people stated that the water quality was good in 2008
and very good in 2010, which shows that respondents generally believe water quality has
improved in the last two years. Most people were aware of the CDP, with more people
becoming aware over time. 88% of respondents were aware pre-dredge, where 90.5% of
respondents were aware post-dredge.

46. 46
Prior to the CDP, most people stated that they would not change the amount of visits they
make to the survey site. This trend was upheld after the CDP had been completed. From
the pre-dredge survey, those that said they would change their visits stated that they would
embark on an average of 19 less trips a year if the CDP was to commence. None of these
respondents stated that they would take more visits.
From the post-dredge survey, there were only 5 respondents who stated that they had
changed their visits to the survey site. 2 respondents stated that they had made less visits.
Their reasons for this were that they had changed where they visit due to the damage that
has been inflicted at the entrance of the bay and because they dislike the changes that have
occurred to the beach in recent times. 3 respondents stated they had made more visits to
the survey site. Their reasons for this were that they had more free time, or they were
checking up on the beach, due to media reports and personal observations of erosion and
changes in beach topography.
For the pre-dredge survey, most people stated that they would not change the number of
visits they make to another beach because of the CDP. 6 people however, said that they
would make more visits to another beach because of the CDP. These sites tended to be
outside of Port Phillip Bay such as Apollo Bay, Pt Leo and Sorrento back beach. Fifteen
respondents stated that they would make less beach visits to sites within and near Port
Phillip Bay such as Mt Martha, Frankston, Brighton and Sorrento back beach.
For the post-dredge survey, a similar trend was observed. People generally stated that they
had not changed the amount of visits they had made to another beach because of the CDP.
The amount of respondents who had changed their visits was very low, at only 3. 2
respondents decreased their beach visits, both at bay beaches. Reasons were that dredging
had affected scallop numbers at Rosebud and that the environment has become nicer at
Rye than McRae. 1 respondent increased their visits, at the rip, which is at the entrance of

47. 47
the Bay. The reason given for this increase was that the CDP had affected certain diving
locations.
The majority of respondents for both surveys were male. The most common level of
education was bachelor degree for the pre-dredge survey and high school for the post-
dredge survey. For both surveys, most people were employed full-time. The most
common household wage was greater than 151,000 for the pre-dredge survey and 35,000
for the post-dredge survey. For the pre-dredge survey, most people were aged between 18
and 30 years old and over 60 for the post-dredge survey.
The question that follows the socio-economic inquiries is the last on the questionnaire. An
open-ended question, it asks if the respondent has any other concerns or issues that they
would like to raise. Answers varied from neutral, for example “all is good”, “do not have
an issue with dredging, (and) have not seen any problems” or no comment, to concerned,
such as “worried of the risks”. Answers were often brief but were sometimes quite in-
depth. One particular respondent offered a strong opinion against dredging, saying that
there is an “amazing amount of silt all the way to Mornington” that there is “less sea life”
and that the “resident ray has gone” from Mornington. This respondent was a diver and
stated that they now “dive at Flinders instead”. There were also some answers, where
respondents expressed their support for the CDP such as, “(I) was approached to sign anti-
dredge partition but refused because of (the) progress – it (CDP) creates jobs” and that
fishing had become better within the last year. Not all answers were about dredging
however. There was a range of answers covering issues such as professional fishing in the
bay, the state of the beaches, pollution, the wastewater outfall at Gunnamatta, dune
reclamation and recreational boating.
A number of respondents who participated in the post-dredge survey voiced their concern
for the coastal erosion and unusual tide variations that have been reported in the bay, as
discussed previously. One respondent at Sorrento front beach stated that they had

48. 48
observed the sand bank recede of the last two years, as his children now have to climb up
it. Another respondent from Mothers beach declared that “water quality is not the issue;
erosion is (as) there is more water in the bay because of channel deepening”.
3.3 Inferential Statistics
Table 6 and table 7 provide un-pooled regression results for the pre-dredge survey with
dependent variables CURVIS and SRVYSNEWV respectively. Table 8 provides pooled
regressions for the pre-dredge survey with dependent variable, VISITS. Table 9 and table
10 provide un-pooled regression results for the post-dredge survey with dependent
variables CURVIS and SRVYSOLDV respectively. Table 11 provides pooled regression
results for the post-dredge survey, with dependent variable, VISITS. All statistically
significant findings are highlighted. The TC variable is shaded dark grey as it is the most
important independent variable in this study for calculating CS changes from the CDP.
Each of the regression models were found to be statistically significant at the 1% level, as
shown by the F statistic for OLS and the chi squared test for the Poisson and negative
binomial models.
It should be noted that Age, Age2
, Income, Educ, Fullemp and WtrQual were measured
both before and after the CDP for the post-dredge regression analyses, which is why they
are labelled as such, for example Age 2008 (pre-dredge) and Age 2010 (post-dredge).
The regression results for un-pooled data for current visits pre-dredge (CURVIS) as
depicted in Table, generally reflected expectations a priori.
TC coefficients were all negative, which reflects expectations, and all were significant at
the 1% level, except in the OLS.
The coefficients for all regressions were positive for Aware, as expected, and all except
for OLS (which was insignificant) were statistically significant at the 1% level.

49. 49
With Solepurp, only in the Poisson regressions were they significant, with the non-
truncated coefficient being positive as expected, and the truncated being negative.
Regression results for Surfer were all significant, mostly at the 1% level and the
coefficients were all positive, as expected.
Age was significant in the truncated Poisson, and had a positive sign, which did not
reflect expectations. Age2
contained no significant results. Fem was statistically
significant, and negative in all models, which reflects what was predicted a priori, as well
as Harris et al. (2006). Income was significant for Poisson and truncated Poisson at the
1% level. There was a positive relationship between Income and visits, which also
confirms expectations. Educ did not meet expectations however, as the two regressions
(Poisson and truncated Poisson) where it was significant where both negative.
Fullemp contained no significant results, but Visitor did with all regression results except
for negative binomial being significant at the 1% level. The coefficient signs for Visitor
were all negative, as expected.
Poisson, negative binomial and truncated Poisson regressions found significant results for
ABCurvis, and the coefficient signs were positive.
Subvis was insignificant in all regressions, while Entero was significant in all except the
truncated negative binomial. The coefficients for Entero were positive, which reflects that
beach visits increase with a higher enterococci reading, which was not expected. Baybch
was insignificant, whereas Gunna was significant in all but OLS. The significant
coefficients for Gunna were all negative, mostly at the 1% level.
Regression results for un-pooled data, for expected visits to the survey site post-CDP
(SRVYSNEWV) assessed pre-dredge (Table 7), were generally indicative of the a priori
expectations.

50. 50
All regressions for TC were significant except for OLS. The coefficients for TC were all
negative, which reflects expectations. Aware was significant in all regressions (except
OLS) at the 1% level. The coefficients for Aware were positive, as predicted.
None of the results were significant for Solepurp, unlike Surfer, which was significant in
all regressions at the 1% and 5% levels, and the coefficients had positive signs. This also
reflects expectations.
Age and Age2
were only significant in the Poisson regressions, with the coefficients for
Age and positive for Age2
both having negative signs.
Fem was significant in the Poisson, truncated Poisson and OLS regressions, at the 1% and
5% levels, and the coefficients had negative signs. This is consistent with the predictions
and Harris et al. (2006). No significant results were found for Income; however both of
the Poisson regressions found significant results at the 1% level for Educ, and the
coefficients had negative signs. This does not support the predictions a priori.
Fullemp was insignificant except in the truncated Poisson regression, at the 5% level. The
coefficient had a negative sign, which does not support expectations.
Visitor was significant in all regressions, mostly at the 1% level. Coefficients were all
negative, as expected.
ABNewvis regressions found significant results for Poisson and truncated Poisson, at the
1% and 5% levels respectively. Coefficients were both positive.
All regressions except for truncated negative binomial found significant results for Entero,
at the 1% and 5% levels. Coefficients were all positive, which does not reflect what was
expected.

51. 51
Baybch was significant in the Poisson regression, at the 1% level. The coefficient had a
negative sign, which was not what was expected before the regression analysis. No other
regression results were significant.
Both of the non-truncated regressions, as well as Truncated Poisson found significant
results for Gunna. These results were all significant at the 1% level. The coefficients all
had negative signs, as expected.
The signs of coefficients in the regression results for pooled visits (VISITS) assessed pre-
dredge as depicted in Table 8 were generally indicative of the a priori expectations.
Aware and TC were all significant (excluding OLS) at the 1% level. The coefficients were
all positive for Aware and all negative for TC, as expected a priori. No significant results
were found for Solepurp.
Surfer regressions all found significant results at the 1% level, and the coefficients were
positive, as expected.
Only the Poisson regression was found to have significant results for Age, and the
coefficient had a negative sign, which is not consistent with expectations. Age2
found
significant results in OLS (10% level), Poisson (1% level) and truncated Poisson
regressions (5% level). The coefficients mostly had a positive sign, which does not reflect
expectations.
Regression results for Fem were all significant, mostly at the 1% level and the coefficient
signs were all negative. This is consistent with the expectations and Harris et al. (2006).
Regression results for Income and Educ were significant for the Poisson regressions only,
all at the 1% level. Income coefficient signs were positive as expected, however Educ
coefficient signs were negative, which was not expected.
Fullemp was significant in the truncated Poisson regression at the 5% level. The expected
result was not found, as the coefficient sign was negative.

52. 52
Regression results for Visitor were all significant at the 1% level and coefficients all had
negative signs, as expected a priori. Poisson regression results for ABVis (both at 1%)
and Subvis (at 1% and 5%) were significant. The coefficient signs were positive for
ABVis and negative for Subvis.
All regression results for Entero were significant, mostly at the 1% level. The coefficients
for all but truncated negative were positive, which defies a priori expectations. The
truncated binomial coefficient was negative, which reflects expectations. The truncated
Poisson regression result (negative relationship with visits) was preferred as it was
significant at the 1% level, as the truncated negative binomial was significant at the 10%
level.
For Baybch, the Poisson regression results were significant at the 5% level. The
coefficient sign was negative. The regression results for Gunna were statistically
significant for all regressions, mostly at the 1% level. The coefficient signs were all
negative.
Dredgeaffect had one significant regression result, which was Poisson at the 1% level.
The coefficient sign was negative.
Regression results for un-pooled data for visits previously made to the survey site
(SRVYSOLDV) assessed post-CDP, somewhat reflected a priori expectations.
TC was significant (at the 1% level) in all regressions but OLS, with all negative
coefficient signs, being similar to the other analyses.
In a similar fashion to Table 9, a number of variables were only significant for Poisson
and truncated Poisson. These variables were; Aware, Surfer, Age 2008, Age2
2008, Fem,
Income 2008, Educ 2008, Fullemp 2008, ABOldvis, Baybch and Gunna.
Coefficient signs that did not reflect expectations a priori were Aware, Surfer, Income
2008, Educ 2008, which were negative, and Age 2008, which was positive. Coefficient

53. 53
signs which did reflect expectations a priori were Age2
2008, Fem and Fullemp 2008,
which were negative.
Visitor was significant at the 1% level, with negative coefficient signs in all regressions.
This accurately reflects expectations. Wtrqual 2008 was significant in Poisson, negative
binomial and truncated Poisson regressions. The coefficient signs were negative, which
does not reflect expectations.
Regression results for pooled data for visits made to the survey site (VISITS) assessed
post-CDP (Table 11) generally reflected a priori expectations.
Similar to the other analyses TC was significant with negative coefficient signs in all
regressions except for OLS. These results matched up with expectations. The same
regressions were significant for Aware, Solepurp and Visitor. Aware coefficient signs
were negative, which does not reflect expectations. Solepurp and Visitor coefficient signs
matched up with expectations, as they were positive and negative respectively.
Similar to other analyses, a number of variables were significant in Poisson and truncated
Poisson regressions, which were Surfer, Age, Age2
, Fem, Income, Educ, Fullemp,
Subvisits, ABVis, WtrQual and Gunna. The significance levels were mostly at the 1%
level. Results from these variables that did not reflect a priori expectations were Surfer,
Income and Educ with negative coefficient signs and Age, with positive coefficient signs.
Results that that did reflect expectations were Age2
, Fem, and Fullemp with negative
coefficient signs and WtrQual with positive coefficient signs.

54. 54
Table 6) Regression results for un-pooled pre-dredge data, dependent variable = current visits, n =
113.
CURVIS
Without truncation Truncation (Y=0)
OLS Poisson Neg Binomial Poisson Neg Binomial
Constant
86.71**
(41.1)
3.7*
(0.21)
2.21**
(1.012)
3.65*
(0.21)
1.635
(1.743)
Aware
8.8
(9.33)
0.7*
(0.1)
1.142*
(0.3)
0.68*
(0.71)
1.57*
(0.4)
Solepurp
-3.9
(9)
0.13*
(0.1)
-0.24
0.241
-0.14*
(0.1)
-0.3
(0.31)
TC
-0. 13056100
(0.13)
-0. 02486624*
(0.0023)
-0. 01530629*
(0.005)
-0. 02801440*
(0.003)
-0. 02851529*
(0.01)
Surfer
60.8**
(23.3)
1.98*
(0.2)
2.515*
(0.644)
2.01*
(0.2)
2.96*
(1.03)
Age
-8.4
(15.1)
0.14
(0.1)
0.303
(0.4)
0.14***
(0.1)
0.4
(0.7125)
Age2 1.2
(1.7)
-0.01
(0.01)
-0.021
(0.044)
-0.01
(0.01)
-0.32
(0.1)
Fem
-18.6**
(8.23)
-0.85*
(0.1)
-0.521*
(0.221)
-0.9*
(0.1)
-0.7***
(0.35)
Income
0.1
(0.9)
0.02*
(0.004)
0.03
(0.03)
0.02*
(0.004)
0.03
(0.0401)
Educ
-1.6
(1.54)
-0.1*
(0.01)
-0.015
(0.05)
0.1*
(0.01)
-0.002
(0.1)
Fullemp
-4.53
(9.44)
-0.1
(0.1)
0.0023
(0.3)
-0.04
(0.1)
0.2
(0.42)
Visitor
-39.8*
(12.34)
-1.312*
(0.06)
-1.31
(0.332)
-1.3*
(0.1)
-1.6*
(0.6)
ABCurvis
-0.04
(0.144)
0.002*
(0.001)
0.002*
0.004
0.002*
(0.001)
0.002
(0.01)
Subvisit
0.22
(0.2)
-0.0002
(0.001)
0.0013
(0.01)
-0.0002
(0.001)
-0.0003
(0.011)
Entero
1.05*
(0.33)
0.03*
(0.0014)
0.022**
(0.01)
0.03*
(0.001)
0.021
(0.015)
Baybch
-4.95
(9.86)
0.12
(0.1)
-0.102
(0.3)
0.13
(0.01)
0.04
(0.5)
Gunna
-30.6
(20.84)
-1.32*
(0.2)
-1.71*
(0.62)
-1.32*
(0.203)
-2.135**
(0.91)
Alpha (Dispersion
parameter)
0.9*
(0.123)
1.61**
(0.44)
R2
0.52
Adj. R2
0.44
F 6.41*
Log likelihood (Lg l) -566.022 -1203.484 -414.2659 -1195.147 -393.7780
Restricted Lg l -607.0800 -3612.348 -1203.484 -3612.348 -1195.147
Chi squared 82.12 (0.0000) 2482.42815* 1578.435* 4834.403* 1602.737*
Notes: * = significant at 1% level; ** = significant at 5% level; *** = significant at 10%. (-) sign = negative
relationship with CURVIS, no sign = positive relationship with CURVIS.

55. 55
Table 7) Regression results for un-pooled pre-dredge data, dependant variable = new visits, n = 113.
SVYSNEWV
Without truncation Truncation (Y=0)
OLS Poisson Negative Binomial Poisson Negative Binomial
Constant 111.95*
(41.34)
5.05*
(0.22)
3**
(1.21)
3.83*
(0.23)
1.05
(2.133)
Aware 5.3
(9.4)
0.3*
(0.1)
0.9*
0.32
0.5*
(0.1)
1.6*
(0.42)
Solepurp -0.2
(9.1)
0.1
(0.1)
-0.02
(0.3)
0.1
(0.1)
0.1
(0.4)
TC -0. 09988454
(0.13)
-0. 01981642*
(0.0023)
-0. 01151617***
(0.005)
-0. 02586128*
(0.003)
-0. 02701527**
(0.013)
Surfer 57.4**
(23.23)
2.24*
(0.2003)
3*
(0.8)
2.02*
(0.21)
3.02**
(1.4)
Age -22.002
(15.2)
-0.62*
(0.1)
-0.1
(0.44)
-0.3*
(0.1)
-0.01
(0.8)
Age2
2.8
(1.7)
0.1*
(0.01)
0.035
(0.1)
0.05*
(0.01)
0.014
(0.1)
Fem -17.6**
(8.3)
-0.8*
(0.1)
-0.42
(0.3)
-0.9*
(0.1)
-0.6
(0.4)
Income -0.05
(0.96)
0.004
(0.01)
-0.014
(0.034)
0.01
(0.01)
0.02
(0.1)
Educ -1.5
(1.6)
-0.1*
0.01
-0.01
(0.1)
-0.023*
(0.01)
0.1
(0.1)
Fullemp -1.54
(9.5)
0.1
(0.1)
0.104
(0.32)
-0.13**
(0.1)
0.1
(0.5)
Visitor -38.5*
(11.7)
-1.31*
(0.1)
-1.31*
(0.4)
-1.1*
(0.1)
-1.31**
(0.6)
ABNewvis -0.003
(0.11)
0.001*
(0.0005)
0.0002
(0.004)
0.001**
(0.0005)
0.7
(0.01)
Entero 1.1*
(0.33)
0.035*
(0.002)
0.025**
(0.012)
0.034*
(0.002)
0.02
(0.02)
Baybch -12.1
(9.9)
-0.4*
(0.1)
-0.5
(0.4)
-0.12
(0.1)
0.1
(0.6)
Gunna -29.8
(21.13)
-1.51*
(0.21)
-2.013*
(0.8)
-0.97*
(0.215)
-1.8
(1.3)
Alpha (Dispersion
parameter)
1.4*
(0.2)
1.72*
(0.53)
R2
0.5
Adj. R2
0.4
F 5.39*
Log likelihood (Lg l) -567.61 -1265.404 -393.4603 -945.0628 -342.2687
Restricted Lg l -601.9 -3497.237 -1265.404 -3151.850 -945.0628
Chi squared 68.5 (0.0000) 4463.665* 1743.888* 4413.574* 1205.588*
Notes: * = significant at 1% level; ** = significant at 5% level; *** = significant at 10% level. (-) sign = negative
relationship with CURVIS, no sign = positive relationship with SRVYSNEWV.

56. 56
Table 8) Regression results for pooled pre-dredge data, dependant variable = visits, n = 226.
VISITS
Without truncation Truncation (Y=0)
OLS Poisson Negative Binomial Poisson Negative Binomial
Constant
101.2*
(28.33)
4.44*
(0.15)
2.64*
(0.8)
3.8*
(0.2)
1.4
(1.4)
Aware
6.9
(6.42)
0.51*
(0.1)
1.1*
(0.2)
0.61*
(0.1)
1.6*
(0.3)
Solepurp
-2.04
(6.2)
-0.1
(0.04)
-0.2
(0.2)
-0.05
(0.04)
-0.2
(0.24)
TC
-0. 11342509
(0.1)
-0. 02288321*
(0.002)
-0. 01396055*
(0.003)
-0.02720506*
(0.002)
-0. 02872443*
(0.01)
Surfer
58.3*
(16.03)
2.1*
(0.14)
2.73*
(0.52)
2.02*
(0.142)
2.99*
(0.83)
Age
-15.4
(10.4)
-0.2*
(0.1)
0.2
(0.3)
-0.04
(0.1)
0.3
(0.5)
Age2 2.04***
(1.2)
0.03*
(0.01)
-0.001
(0.03)
-0.02**
(0.01)
-0.02
(0.1)
Fem
-18.01*
(5.7)
-0.9*
(0.04)
-0.5*
(0.2)
-0.9*
(0.04)
-0.65**
(0.3)
Income
0.02
(0.7)
0.01*
(0.003)
0.012
(0.02)
0.02*
(0.0034)
0.03
(0.03)
Educ
-1.6
(1.1)
-0.1*
(0.01)
-0.015
(0.04)
-0.04*
(0.01)
0.03
(0.1)
Fullemp
-3.1
(6.5)
-0.01
(0.04)
0.03
(0.21)
-0.1**
(0.04)
0.12
(0.3)
Visitor
-38.21*
(8.5)
-1.4*
(0.04)
-1.34*
(0.3)
-1.2*
(0.5)
-1.5*
(0.42)
ABVisits
-0.04
(0.1)
0.003*
(0.0004)
0.003
(0.004)
0.002*
(0.0004)
0.0025
(0.01)
Subvisit
0.15
(0.13)
-0.002*
(0.001)
-0.002
(0.004)
-0.0011**
(0.001)
-0.002
(0.01)
Entero
1.1*
(0.23)
0.03*
(0.001)
0.02*
(0.01)
0.03*
(0.0011)
-0.02***
(0.01)
Baybch
-8.24
(6.8)
-0.13**
(0.1)
-0.3
(0.23)
0.02
(0.1)
0.02
(0.4)
Gunna
-29.99**
(14.4)
-1.44*
(0.14)
-1.8*
(0.5)
-1.2*
(0.15)
-1.94*
(0.7)
Dredgeaffect
-4.9
(5.1)
-0.2*
(0.03)
-0.2
(0.2)
-0.04
(0.03)
-0.01
(0.2)
Alpha (Dispersion
parameter)
1.15*
(0.11)
1.7*
(0.4)
R2
0.45
Adj. R2
0.44
F 11.41*
Log likelihood (Lg l) -1134.85 -2517.51 -812.35 -2179.2 -736.93
Restricted Lg l -1209.31 -7133.58 -2517.51 -6766.45 -2179.2
Chi squared (prob) 148.93 (0.0000) 5341.9* 3410.312* 9174.6* 2884.5*
Notes: * = significant at 1% level; ** = significant at 5% level; *** = significant at 10% level. (-) sign = negative
relationship with CURVIS, no sign = positive relationship with VISITS.

57. 57
Regression results for un-pooled data obtained post-dredge for current visits (CURVIS),
as represented in Table 9 were generally indicative of expectations a priori.
TC regression results were significant in all but the OLS regression, at the 1% level and
the coefficient signs were negative, as expected. These TC results are similar to those
from the pre-dredge analyses in Table 6, Table 7 and Table 8.
Aware was significant in Poisson, negative binomial and truncated Poisson at the 1%,
10% and 1% levels respectively. The coefficient signs were all negative, which did not
reflect expectations of an increase in visits for people who were aware of the CDP.
Solepurp was found to be significant in all regressions, with the coefficient signs all being
positive. This reflects a priori expectations, and differs significantly from results from the
pre-dredge regression analyses.
The Poisson and Truncated Poisson regressions found significant results for Surfer, Age
2010, Age2
2010, Fem, Income 2010, Educ 2010, Fullemp 2010, Subvisit, ABCurvis,
WtrQual 2010, and Gunna; all at the 1% level.
Surfer coefficient signs were negative, which does not accurately reflect expectations and
varies from the pre-dredge analyses.
Expectations were upheld for Age, as the coefficient signs were both positive, although
the opposite can be said about Age2
.
Fem lived up to the expectations and reflected similar results to the pre-dredge analyses,
with a negative relationship with visits.
Income and Educ did not live up to expectations, as they both had a negative relationship
with visits. Fullemp was also found to have a negative relationship with visits, and this
reflects expectations a priori.
Coefficient signs for Subvis and Gunna were positive, whereas for ABCurvis they were
negative. WaterQual 2010 had positive coefficient signs, which reflects expectations.

58. 58
Visitor was significant at the 1% level in all regressions. Coefficient signs were all
negative, which reflects expectations and conforms to the pre-dredge regression analyses.
Regression analyses for pre-dredge survey data (dependent variable SRVYSOLDV), as
depicted in Table 10, met expectations somewhat.
In a similar fashion to Table 9, TC was significant at the 1% level for all regressions but
OLS and the coefficient signs were negative, which reflects expectations.
A number of variables were significant for only the Poisson regressions. These variables
were Aware, Surfer, Age 2008, Age2
2008, Fem, Income 2008, Educ 2008, Fullemp 2008,
ABOldvis, Baybch and Gunna. Some of the relationships did not reflect expectations,
such as Aware, Surfer, Income 2008 and Educ 2008 with negative coefficient signs, and
Age 2008 with positive coefficient signs. The variables that had relationships, which
accurately reflected expectations, were Age2
2008, Fem and Fullemp 2008, with negative
coefficient signs.
Solepurp and Visitor were significant in all regressions, mostly at the 1% level. The
coefficient signs were positive for Solepurp and negative for Visitor, as expected a priori.
WtrQual was significant for both Poisson regressions and negative binomial. The
coefficient signs were negative, which doesn‟t reflect expectations as it depicts a negative
relationship with visits.
Regression results for pooled, post-dredge survey data generally met expectations a
priori.
In a similar pattern to regression analyses discussed previously, TC was significant at the
1% level in all regressions but OLS and the coefficient signs were negative. As this infers
a negative relationship with visits, expectations are met for TC.
Aware, Solepurp and Visitor were significant in all regressions except for OLS, mainly at
the 1% level. Solepurp and visitor reflected expectations, with positive and negative

59. 59
coefficient signs respectively, while Aware did not reflect expectations with negative
coefficient signs.
In a pattern that was also similar to the other analyses, many variables were significant for
the Poisson regressions only. The variables were Surfer, Age, Age2
, Fem, Income, Educ,
Fullemp, Subvisits, ABVisits, WtrQual and Gunna. From these variables, Surfer, Age,
Income and Educ were found to have relationships with visits that did not reflect
expectations. Age2
, Fem, Fullemp and WtrQual were found to have matching expected
relationships with visits.
Baybch was significant in the Poisson, truncated Poisson and negative binomial
regressions. The coefficients for Baybch were positive, which implies that people who
visit beaches within Port Phillip Bay visit the beach often.
No significant results were found for Dredgeaffect.

60. 60
Table 9) Regression results for un-pooled post-dredge data, dependant variable = current visits, n = 105.
CURVIS
OLS Without truncation Truncation (Y=0)
Poisson Negative Binomial Poisson Negative Binomial
Constant
20.5
(118.9)
1.2*
(0.24)
2.6
(1.7)
1.12*
(0.24)
1.9
(3.2)
Aware
-45.1
(31.5)
-0.42*
(0.03)
-0.9***
(0.5)
-0.42*
(0.03)
-1.13
(0.8)
Solepurp
38.73***
(21.6)
0.8*
(0.04)
0.8**
(0.34)
0.8*
(0.04)
1.12***
(0.7)
TC
-0. 26883697
(0.7)
-0. 02537005*
(0.002)
-0. 04360334*
(0.01)
-0. 02570029*
(0.002)
-0. 06161787*
(0.02)
Surfer
-5.3
(37.41)
-0.3*
(0.1)
-0.3
(0.82)
-0.3*
(0.1)
-0.53
(1.02)
Age 2010
0.4
(36.93)
0.31*
(0.05)
0.03
(0.5)
0.31*
(0.05)
-0.1
(0.95)
Age2
2010
1.3
(4.42)
-0.03*
(0.01)
-0.01
(0.1)
-0.03*
(0.01)
-0.01
(0.11)
Fem
1.98
(4.42)
-0.13*
(0.03)
0.2
(0.3)
-0.13*
(0.03)
0.2
(0.43)
Income 2010
-3.85
(2.9)
-0.1*
(0.004)
-0.01
(0.04)
-0.1*
(0.004)
0.004
(0.1)
Educ 2010
-1.2
(7.12)
-0.1*
(0.01)
-0.12
(0.11)
-0.1*
(0.01)
-0.13
(0.2)
Fullemp 2010
-4.002
(5.65)
-0.05*
(0.01)
0.04
(0.1)
-0.05*
(0.01)
0.1
(0.13)
Visitor
-88.4*
(21.05)
-1.6*
(0.04)
-1.65*
(0.34)
-1.6*
(0.04)
-1.9*
(0.5)
Subvisit
0.1
(0.12)
0.001*
(0.0001)
0.001
(0.002)
0.001*
(0.0001)
0.001
(0.003)
ABCurvis
-0.31
(0.34)
-0.004*
(0.0005)
-0.003
(0.004)
-0.004*
(0.0005)
-0.003
(0.01)
WtrQual 2010
14.72
(13.34)
0.23*
(0.02)
0.3
(0.21)
0.23*
(0.02)
0.42
(0.4)
Baybch
62.5
(42.95)
2.4*
(0.2)
1.43**
(0.7)
2.45*
(0.2)
1.8***
(0.97)
Gunna
36.02
(55.4)
2.2*
(0.2)
1.21
(1.1)
2.3*
(0.2)
1.6
(1.41)
Alpha (Dispersion
parameter)
1.4*
(0.2)
2.5*
(0.7)
R2
0.4
Adj. R2
0.3
F 3.3*
Log likelihood (Lg l) -612.64 -3399.95 -481.82 -3398.14 -463.9
Restricted Lg l -637.2 -6867.7 -3399.95 -6867.7 -3398.14
Chi squared (prob)
49.04
(0.0000)
6935.42* 5836.3* 6939.03* 5868.5*
Notes: * = significant at 1% level; ** = significant at 5% level; *** = significant at 10% level. (-) sign = negative
relationship with CURVIS, no sign = positive relationship with CURVIS.

61. 61
Table 10) Regression results for un-pooled post-dredge data, dependant variable = old visits, n = 105.
SVYSOLDV
OLS
Without
truncation
Truncation
(Y=0)
Poisson Negative Binomial Poisson Negative Binomial
Constant 137.4
(119.8)
2.4*
(0.24)
5.5*
(1.9)
2.3*
(0.2)
5.93***
(3.1)
Aware -39.2
(32.4)
-0.4*
(0.04)
-0.6
(0.5)
-0.4*
(0.04)
-0.7
(0.84)
Solepurp 38.9***
(21.99)
0.7*
(0.04)
0.9*
(0.3)
0.7*
(0.04)
1.24***
(0.65)
TC -0. 42194324
(0.7)
-0. 02767443*
(0.002)
-0. 04836788*
(0.01)
-0. 02514076*
(0.002)
-0. 06289716*
(0.02)
Surfer -8.8
(34.5)
-0.21*
(0.05)
-0.2
(0.73)
-0.22*
(0.05)
-0.6
(1.03)
Age 2008 -0.5
(38.3)
0.4*
(0.1)
-0.04
(0.52)
0.4*
(0.1)
-0.1
(0.9)
Age2
2008 1.33
(4.8)
-0.03*
(0.01)
-0.0001
(0.1)
-0.03*
(0.01)
-0.005
(0.12)
Fem 6.4
(20.04)
-0.1*
(0.03)
0.3
(0.3)
-0.11*
(0.03)
0.3
(0.45)
Income 2008 -2.95
(2.96)
-0.05*
(0.004)
0.02
(0.04)
-0.1*
(0.004)
0.04
(0.1)
Educ 2008 -2.9
(7.4)
-0.1*
(0.01)
-0.13
(0.11)
-0.1*
(0.01)
-0.2
(0.2)
Fullemp 2008 -2.6
(5.7)
-0.02**
(0.01)
0.04
(0.1)
-0.02*
(0.01)
0.1
(0.15)
Visitor -86.4*
(21.44)
-1.5*
(0.04)
-1.8*
(0.34)
-1.53*
(0.04)
-2.1*
(0.53)
ABOldvis 0.1
(0.4)
0.001***
(0.0004)
0.001
(0.005)
0.001***
(0.0004)
0.001
(0.01)
WtrQual 2008 -9.6
(15.3)
-0.04**
(0.02)
-0.4***
(0.2)
-0.04**
(0.02)
-0.5
(0.35)
Baybch 41.91
(44.1)
2.13*
(0.2)
1.03
(0.7)
2.2*
(0.2)
1.2
(0.95)
Gunna 22.9
(56.01)
1.97*
(0.2)
0.8
(1.02)
2.02*
(0.2)
0.96
(1.4)
Alpha (Dispersion
parameter)
1.4*
(0.2)
2.6*
(0.73)
R2
0.35
Adj. R2
0.24
F 3.2*
Log likelihood (Lg l) -615.9 -3711.7 -480.64 -3700.7 -462.3
Restricted Lg l -638.34 -6984.43 -3711.7 -6911.9 -3700.7
Chi squared 44.87 6545.4* 6462.2* 6422.45* 6476.3*
Notes: * = significant at 1% level; ** = significant at 5% level; *** = significant at 10% level. (-) sign = negative
relationship with CURVIS, no sign = positive relationship with SRVYSOLDV.