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Modeling Willingness to Pay for CoastalTourism Resource Protection in Ko ChangMarine National Park, ThailandSunida Piriyapadaa & Erda Wanga
a Department of Human Resource and Tourism Management, School ofBusiness Management, Dalian University of Technology, No. 2 LinggongRoad, Dalian 116024, People's Republic of ChinaPublished online: 29 Apr 2014.
To cite this article: Sunida Piriyapada & Erda Wang (2014): Modeling Willingness to Pay for Coastal TourismResource Protection in Ko Chang Marine National Park, Thailand, Asia Pacific Journal of Tourism Research,DOI: 10.1080/10941665.2014.904806
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Modeling Willingness to Pay for Coastal TourismResource Protection in Ko Chang Marine National
Park, Thailand
Sunida Piriyapada∗
and Erda WangDepartment of Human Resource and Tourism Management, School of Business Management,
Dalian University of Technology, No. 2 Linggong Road, Dalian 116024, People’s Republic of
China
The value of non-market resources is important information for the nature-based parkinvestment and management. In this paper, we estimate visitors’ willingness to pay(WTP) an entrance fee for beach resource protection of the Ko Chang Marine Park inThailand using a standard contingent valuation method of a single-bounded (SB) anddouble-bounded (DB) dichotomous choice format. An on-site stratified sampling surveyof 409 beach visitors was conducted at the park along the White Sand Beach shoreline.By comparing the two survey methods, the average WTP for a Thai beach visitor isabout $12.01 under the SB elicitation survey and $7.27 per adult per visit under theDB elicitation method, respectively. It turns out that the foreign visitors’ WTP is twiceas much as that of Thai visitors’ WTP. These can be translated to the lower and upperbounds of an aggregated value ranging between $10.33 million and $17.41 million perannum. The policy implications for the park management are addressed.
Key words: contingent valuation, dichotomous choice, a logit model, WTP
Introduction
Beaches are not only a major source of attrac-
tions for tourists but also fundamental assets
in the natural balance of coastal ecosystems
(Birdir, Unal, Birdir, & Williams, 2013). In
consideration, Thailand possesses many valu-
able beach resources along 2600 km of the
coastline and offshore islands, which include
well-defined beach areas suitable for tourism.
Unfortunately, beaches around Thailand are
now under threat from human activities such
as solid waste, surface water pollution and
encroachment (Wattayakorn, 2006). The Ko
Chang Marine National Park (KCMNP) is
one of the most valuable coastal resources of
Asia Pacific Journal of Tourism Research, 2014http://dx.doi.org/10.1080/10941665.2014.904806
∗Email: [email protected]
# 2014 Asia Pacific Tourism Association
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all beach recreation sites, where the marine-
based tourism activities are well recognized,
which makes the island to be the key tourism
sites in Thailand. In the KCMNP, White
Sand Beach is the most popular beach visited
and its economic importance grows with a
strong concentration of tourism facilities. At
the same time, this long stretch of seacoast
has been seriously depleted and degraded
(Department of Mineral Resources, 2013),
mainly because competitive uses of natural
resources and many of them are not consistent
with environmental protection. Thus, it is
necessary to justify the various coastal
tourism projects based on a full consideration
of economics, social and environment.
While environmental resource valuation
could link human and natural systems to
ensure ecologically sustainable development
(Chen & Jim, 2008; Howarth & Farber,
2002). Most environmental goods and services,
for example, beach visits, healthy fish and wild-
life species, are not revealed in market prices. A
commonly used method to disclose a non-
market resource valuation is the contingent
valuation which is considered as the stated pre-
ference techniques; in which a market partici-
pant can be asked to state his or her
willingness to pay (WTP) for alternative levels
of the environmental resource amount and
quality improvement or willingness to accept
(WTA) for compensation with various levels
of environmental quality deterioration (Mitch-
ell & Carson, 1989). Although an abundance
of research literature on non-market resource
valuation is available, most research works
were based on the environment and conditions
in developed countries such as the USA and the
UK and seldom research work has been done in
developing countries (Wang, Shi, Kim, &
Kamata, 2013), especially so for relatively
small countries, such as Thailand. This may
be due to inadequate financial support received
from the governments and the shortage of
research personnels as well (Adjaye & Tapsu-
wan, 2008). This work asymmetry restricts
utility of the well-recognized research method
such as contingent valuation method (CVM)
(Whittington, 2002).
The objectives of this study involve the fol-
lowing three aspects: (i) to provide another
case study on non-market resource valuation
based on beach park resources in Thailand, deli-
vering more fresh empirical results to represent
the situation of the Southeast Asian countries;
(ii) to identify factors which contribute to the
level of visitors’ WTP to the park resources
and identify WTP differences between Thai
tourists and foreign visitors and (iii) to address
policy implications under alternative park man-
agement strategies. The results of this study
could be used as references for the park entrance
fees based on the beach users’ WTP.
This paper is organized as follows: second
section gives a literature review on CVM
applications to the beach resource conserva-
tion in the developed and developing
countries; third section presents the character-
istics of the studied location; fourth section
discusses the research methodology and data
collection; fifth section provides the analytical
framework and WTP results; and followed by
a final section of the conclusion.
Literature Review
The CVM is utilized to elicit individuals’ WTP
for non-market benefits or their WTA a com-
pensation for non-market costs (Mitchell &
Carson, 1989). Theoretically, the underlying
CVM is a survey-based economic method-
ology frequently created in a hypothetical
market to assess those individuals’ preferences
from the marginal utility of the environmental
values, which it could not be measured upon
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actual behaviors and therefore does not have a
price in the market. Economists have tra-
ditionally a much-debated issue in the critics
of the CVM studies (Hausman, 2012) since
the CVM produces unreliable estimates due
to the hypothetical CVM data. Therefore, it
can result in economic values that are biased
upwards, affecting the validity and credibility
of the stated preference estimates (Ajzen,
Brown, & Carvajal, 2004; Collins &
Vossler, 2009; Poe & Vossler, 2011).
However, economics make efforts to eliminate
hypothetical bias in eliciting accurate econ-
omic values through a well-designed question-
naire that such bias and inconsistency has been
successfully minimized (Carson, 2012).
Many CVM studies reported economic
benefits associated with coastal and marine
resources in the developed and developing
countries (Birdir et al., 2013), the CVM has
been commonly applied to measure direct use
values in various types of marine recreational
activities such as snorkeling (Park, Bowker,
& Leeworthy, 2002), diving (Adjaye & Tapsu-
wan, 2008; Parsons & Thur, 2008) and fishing
(Yamazaki, Rust, Jennings, Lyle, & Frijlink,
2011); indirect use values include improved
surface water quality (Borg & Scarpa, 2010;
Wang et al., 2013), coastal defense (Koutrakisa
et al., 2011),beach erosion protection (Logar
& Van den Bergh, 2012) and improved beach
quality (Birdir et al., 2013). A valuation of
beach protection benefit in Thailand which is
closely related to this paper is that of Saengsu-
pavanich, Seenprachawong, Gallardoa, and
Shivakoti (2008). They use the single-
bounded (SB) elicitation CVM to estimate the
WTP for protecting a public recreational
beach at Nam Rin beach. In their study, they
attempted to integrate environmental econ-
omics and coastal engineering in managing
port-induced coastal erosion occurring at the
study beach by using Map Ta Phut port as a
case study. The result indicated that the WTP
estimates of 280 beach users for beach resource
protection were $24.8 per year. The total use
value of Nam Rin beach became approxi-
mately $2.11 million annually.
Based on the tourists’ WTP for coastal
resource protection, we found that some
studies focused on the impact of the candidate
entrance price and recommended methods for
imposing an appropriate entrance price. This
pricing policy could be used as an economic
instrument to achieve the dual goals of
revenue generation and conservation (Wang
& Jia, 2012). A case study was designed to esti-
mate the coastal resource benefits arising from
proposing biodiversity conservation and
environmental protection at Dalai Lake Pro-
tected Area in northeast China. Wang and Jia
(2012) used the CVM to assess a tourist’s
WTP an entrance fee to support park funding
and local development in order to sustain the
protected area. The results revealed that
73.6% of the 1618 respondents were willing
to accept a higher entrance fee which was
71.08 RMB ($10.72) per visit, which was
higher than the current entrance fee (20 RMB,
$3.02). Likewise, Togridou, Hovardas, and
Pantis (2006) addressed the issue of determin-
ing national park fees for the National
Marine Park in Greece. They also examined
visitors’ actual and estimated consensus regard-
ing WTP. According to their findings, approxi-
mately 81% of 484 visitors agreed to pay a fee
ranging from E1 to E100, the WTP estimates
from the minimum (E1) to median (E5)
amounts would yield the lower and upper
limits of a source of revenue, these were
E300,000 and E1,400,000, the lower limit of
the aggregated value could cover maintenance
costs of the Protected Area Management
Body. While a study has focused on estimating
the value of public beach access and also com-
bined it with other attributes, e.g. water
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quality, beach nourishment or beach erosion
protection. Shivlani, Letson, and Theis (2003)
use the CVM to estimate visitor preferences
for public beach amenities and beach restor-
ation in South Florida, the US Beach visitors
were willing to pay higher to support beach
nourishment ($1.69 per visit) when enhancing
nesting habitat for turtles ($2.12 per visit) was
combined as an attribute of beach nourishment.
Study Area
The KCMNP is the second largest island after
Phuket, and geographically situated at 11856′–
12816′N and 102825′ –102861′E in the Trat pro-
vince of Eastern Thailand, covering an area of
650 km2, of which 458 km2 consists of surface
water. The KCMNP is made up of more than
40 islands with approximately 5 km2 of coral
reef areas (Figure 1). It features fine beaches
with an abundance of natural resources and
plentiful marine life. There are many hills,
forests, waterfalls and streams, and the sur-
rounding ground water serves as an important
sourceof freshwater to thenation’s consumption
(Mu Ko Chang National Park, 2013). With its
long stretches of sandy beaches and bay areas,
the western coast side of Ko Chang has been
planned for the coast park tourism development
zone by the Thailand government. All the sur-
rounding islands of the KCMNP belong to tropi-
cal climatic destinations, which are dominated
by the southwestern monsoon: wet from May
to October, cool and dry in winter from Novem-
ber to February and hot and dry in summer from
March to April. The long and hot summer in Ko
Chang has an average temperature of 278C. The
KCMNP was designated as the National Marine
Park in 1982 by the Thailand government, and it
was assigned to the local government councils
for playing a role in administration (Mu Ko
Chang National Park, 2013).
In this study, we concentrate on the most
popular beach area which was selected for the
study, namely Haad Sai Khao (White Sand
Beach). At present, it can be accessed as a free
beach, of course, this beach park exhibits a dis-
tinctive style of tourism attraction to satisfy
various tourists’ preferences. Since 2002,
along with the increase in tourism demand,
White Sand Beach has generated a significant
amount of economic benefits each year for the
local economy. There are many small and
medium enterprises related to coastal rec-
reational businesses located on this land area
which can attract more than 2000 visitors
daily during the peak tourism season (Novem-
ber–April). According to the government stat-
istics in 2012, over 900,000 tourists visited the
KCMNP, which generated some $255 million
(Department of Tourism, Thailand, 2013).
There are three broad groups of beach visitors:
local residents, domestic visitors and foreign
tourists. However, tourism development in tro-
pical coastal areas frequently results in signifi-
cant environmental degradation over the
years, as a result of a rapid increase in tourism
demand and inadequate input to the park man-
agement (United Nations Environment Pro-
gramme, 2007).
A park entrance fee upon arrival has been
levied in most Thailand-protected areas. At the
time of this study (2013), tourists were regularly
charged to use public park amenities, varying
from 10 to 400 Thai Baht ($0.33–$13.34) per
person per visit (Department of National Parks,
Wildlife and Plant Conservation, 2012).
However, the KCMNP does not charge an
entrance fee on visitation except for entering
into the waterfall areas. International tourists
are usually charged 200 Thai Baht ($6.67) per
visitor, exchange rates of $1.00 ¼ 29.985 Thai
Baht at the time of the study (Bank of Thailand,
2013), while a local tourist pays one-fourth of
this price (Mu Ko Chang National Park, 2013).
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The waterfall entry fees collected from tourists
are an important source of funds for environ-
mental protection. Annual revenue generated
by each park is returned to the government’s
coffers, which in turn provide an annual operat-
ing budget to the protected area based on land
area and local jurisdictional responsibility
rather than the park visitation rate. This results
in the problem whereby the KCMNP with the
heavy use of tourism resources does not obtain
sufficient public funds for park management
and investment (Adjaye & Tapsuwan, 2008).
Methodology
WTP Elicitation Methods
In order to elicit the mean WTP, we conducted
a dichotomous choice (DC) CVM or referen-
Figure 1 Map of the KCNMP.
(Source: Tourism Authority of Thailand, 2013).
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dum questionnaire format to explore respon-
dents’ WTP based on the recommendations
of the National Oceanic and Atmospheric
Administration for the CVM (Arrow et al.,
1993). In their simplest form, bid amounts
are proposed to a respondent who either
accepts or rejects the amount. One advantage
of this elicitation method is a price taking
approach quite similar to a take-it-or-leave-it
market transaction (Xu, Loomis, Zhang, &
Hamamura, 2006). Compared with the
open-ended WTP questions, studies by
Hoehn and Randall (1987) and Carson,
Groves, and Machina (2000) indicated that
the DC approach is realistic and easier to
assess public preferences to the provision of
non-market goods. However, like other WTP
elicitation methods, the approach may be sen-
sitive to yea-saying problem in the double-
bounded dichotomous choice contingent
valuation method (DB DCCVM) (Kanninen
& Khawaja, 1995), whereby respondents
simply accept to pay a given monetary
amount, it has been subjected to a critical
drawback in bid thresholds, due to some evi-
dences that the responses to the initial bid
may be inconsistent with the responses to the
second bid, as a result, the latter reveals the
lower WTP (Hanemann, Loomis, & Kanni-
nen, 1991; Xu et al., 2006).
Adjaye and Tapsuwan (2008) argued that
the dichotomous choice contingent valuation
method (DCCVM) suffers from a number of
biases due to: (i) this method is based on
hypothetical rather than real choices and there-
fore subject to hypothetical bias from the over-
estimation; (ii) biases in the double-bounded
dichotomous choice method (the starting
point bias, shift effects, anchoring effects and
framing effects) are inherent and (iii) the “yea
saying” in the DB DCCVM may be motivated
by the social pressure faced by respondents
during the survey. CV researchers make
efforts to minimize sources of bias in the
method and to ensure that respondents take
the question seriously. Arrow et al. (1993) rec-
ommended that a CV instrument should
contain questions designed to detect the pres-
ence of the various biases. They also suggested
that the survey must include other questions to
verify whether respondents have done so. Even
though the starting bid was not correctly
specific, the higher bid provides effective insur-
ance against too low a choice of the initial price,
and the lower bid provides insurance against
too high a choice (Cooper, Hanemann, & Sign-
orello, 2002). This helps to considerably solve
the criticism in CVM studies, namely starting
point bias as well as anchoring effects and
yea-saying (Adjaye & Tapsuwan, 2008).
The SB format involves offering respondents
a single payment amount which they can
respond with either a “yes” or a “no” to the
offered bid. The probability (Pi) that a respon-
dent will answer a “yes” (Pyi ) or a “no” (Pn
i ) is
Pyi = 1 − G(BID; u), (1)
Pni = G(BID; u), (2)
where G(BID; u) is a statistical distribution
function of a parameter vector u and BID is a
bid offer, which can be estimated using the
logit regression model (Adjaye & Tapsuwan,
2008). The logit model can be expressed as
two forms, the log-logistic cumulative density
function
G(BID; u) = 1
[1 + expa−b(ln Bid)], (3)
and the logistic cumulative density function,
G(BID; u) = 1
[1 + expa−b(Bid)], (4)
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where u ; (a,b), a and b are the intercepts and
the slope coefficients being valued, respectively.
G(BID;u) expresses the cumulative density func-
tion of the individual’s true maximum WTP.
Hanemann (1984) concluded that a utility max-
imization reflects the probability of a “yes”
response to the BID, when the BID is less than
or equal to maximum WTP or the probability
of a “no,” if the BID is greater than the
maximum WTP. According to Hanemann
et al. (1991), the log-likelihood function for all
respondents is
ln Ls(u) =∑N
i=1
{dyi ln Py
i (BIDi) + dni ln Pn
i (BIDi)},
=∑N
i=1
{dyi ln[1 − G(BIDi; u)]
+ dni ln G(BIDi; u)}, (5)
where dyi is unity if the ith response is “yes” and
zero otherwise, whereas, dni is unity if the ith
response is “no” and zero otherwise (Adjaye
& Tapsuwan, 2008).
Therefore, there are four possible outcomes:
(i) both responses are “yes” (Pyyi ); (ii) both
responses are “no” (Pnni ); (iii) a “yes” followed
by a “no” (Pyni ) and (iv) a “no” followed by a
“yes” (Pnyi ). For any given underlying WTP
distribution Gc(.), the likelihood of the prob-
ability that a respondent will respond these
outcomes is
Piyes
yes
( )= Pyy
i = 1 − Gc(BIDU), (6)
Pino
no
( )= Pnn
i = Gc(BIDL), (7)
Piyes
no
( )= Pyn
i =Gc(BIDU)−Gc − (BID), (8)
Pino
yes
( )= Pny
i =Gc(BID)−Gc − (BIDL). (9)
In this sense, the log-likelihood function for
the DB model is referred to by Hanemann
et al. (1991) as follows:
ln Ls(u) =∑N
i=1
[dyyi ln Pyy
i + dnni ln Pnn
i
+ dyni ln P
yn+dnyi
ln Pnyi
]
i ,
(10)
where dyyi , dnn
i , dyni and dny
i are binary-valued
indicator variables that are equal to one
when the two responses are one of the four
possible outcomes (Pyyi , Pnn
i , Pyni and Pny
i ), and
zero otherwise.
WTP Econometric Model
As the manner given by Hanemann (1984), we
assume that there exists a market participant
who has an indirect utility function V(Y,
BID, Q, S), in which it has some unobservable
components of the utility. The level of the indi-
vidual’s WTP depends on personal income
(Y ), the bid offer (BID), the quality of
natural sites (Q) and a vector of socioeco-
nomic characteristics (S). When offered a
given amount for a change in the quality of
natural sites (Q0 � Q1), the probability of
the respondent saying yes is
v(Y − BID, Q1, S) + 11
≥ v(Y − 0, Q0, S) + 10, (11)
where e0 and e1 are identically, independently
distributed (i.i.d.) random variables with zero
means. Assuming the individual’s response to
a binary question is a random variable with
some cumulative distribution function (c.d.f)
in the declared WTP. Therefore, the prob-
ability of a yes (Pyi ) that the individual will
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accept the proposed bid can be written as
DV = [v(Y − BID, Q1, S) + 11
≥ v(Y − 0, Q0, S) + 10]. (12)
When faced with a binary dependent vari-
able and that variable is a qualitative choice
behavior, the logit model is then estimated
the probability function by maximum likeli-
hood (ML). Bengochea-Morancho, Fuertes-
Eugenio, and Saz-Salazar (2005) indicated
that the ML estimator has better properties
than others when the dependent variable is
categorical. The probability that an individual
will say a “yes” for a bid offer in the SB elicita-
tion format can be modeled in log-logistic
form as
Pyi =FhDv= (1+exp−Dv)−1
Pyi
1
1+exp{− (a−b1, lnBID+b2 lnQ+b3 lnS)}
(13)
and the double-bounded (DB) elicitation
format can be formulated in the logistic form
as
Pyi =FhDv= (1+exp−Dv)−1
Pyi =
1
1+exp{(−a−b1,BID+b2Q+b3S)}
(14)
where Fh is a cumulative distribution func-
tion, a is a constant and b is the coefficient
of a bid offer (BID), respectively.
Following Hanemann (1984), the con-
strained mean WTP (WTPmean) for the SB
and DB DCCVM were estimated in the next
expressions:
The single − bounded log − logistic model:
WTPmean = exp−a∗/b x(p/b)
( sin (p/b)),
(15)
The double−bounded logistic model: WTPmean
= ln(1+expa∗)
b,
(16)
where a∗ belongs to the adjusted intercept and
b denotes the slope regression coefficient value
for the proposed bid amount (Adjaye & Tap-
suwan, 2008).
Model Specification
As usual, the WTP amounts offered are
hypothesized as dependent variables, which
are influenced by a number of independent
variables, including socioeconomic character-
istics, individuals’ preferences and knowledge
about environmental issues. The WTP func-
tion can then be derived as the following:
Yi = f (BID, GEN, AGE, HH, EDU, INCOME,
CROWDED, REEFS, BEACHQ,
WATERQ, OVERALLQ, STILL),
where Yi represents the respondent’s WTP in a
binary number, BID is a bid offer. The socioe-
conomic variables include the visitor’s gender
(GEN), age (AGE), household size (HH), edu-
cational attainment (EDU) and personal
income (INCOME). Furthermore, the model
also incorporates variables which can reflect
the site quality characteristics. Those variables
are perceive crowding (CROWDED), abun-
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dant of coral reefs (REEFS), beach quality
(BEACHQ), bathing water quality
(WATERQ), the surrounding environment
(OVERALLQ) and visitor’s intention toward
his or her future visit to the site (STILL).
Data Collection
Beach visitors along the White Sand beach
shoreline in the KCNMP were interviewed in
person during the tourism season from
January to March in 2013. The in-person
interviews are strongly recommended by
Mitchell and Carson (1995) and Xu et al.
(2006) because this method could ensure the
quality and accuracy from a survey of the
respondents. To decrease the sample error, a
stratified random sampling was adopted in
the formal survey (Wang & Jia, 2012).
According to the park visitation statistics
from 2003 to 2012 (Department of Tourism,
Thailand, 2013), the ratio of domestic visitors
(including the number of local visitors) to
foreign visitors was approximately 63%:
37%. Every 10th visitor entering the survey
locations was intercepted for interview. A
total of 409 questionnaires were selected for
the interviewing process by trained interview
specialists. Therefore, the sample comprised
256 Thai visitors and 153 foreign visitors.
The questionnaire was designed with
support from the park management personnel
who are familiar with the beach uses and man-
agement. A focus group discussion was held
with a total of 60 individuals’ participation
for pretest conducted in December 2012 in
order to detect sources of potential bias and
identify misunderstand wording in the ques-
tionnaire (Arrow et al., 1993; Huhtala, 2004;
Nunes, 2002). Four interviewers were
involved, three of them college degree
holders, and one researcher participated in
the full CV survey. For this purpose, a two-
day training was held to ensure that the inter-
viewers could operate live interviews and fill in
the survey form correctly. Thai and English
used in the survey were administered in the
original version. To minimize significant
changes in the meaning, the Thai version was
independently translated into English by a
bilingual translator whose native tongue was
Thai, and a native English speaker who was
also fluent in Thai then retranslated the Thai
version into English. After comparing the
two versions, the different points were ident-
ified and resolved by consensus (Leelapattana,
Keorochana, Johnson, Wajanavisit, & Laoha-
charoensombat, 2011). During the survey
period, only the foreign visitors who could
speak English were interviewed by an English
degree holder.
Before the interview, the respondents were
clearly explained the CV survey’s content
and each specific question. In case that the
respondents misunderstood some questions,
the interviewees made clarification at the
scene. Only adults were required (aged 18 or
above), in the case of family groups, the head
of the family was invited for the interview.
During the sampling period, the interviewer
surveyed three times a day, namely at 10:00,
14:00 and 16:00, when visitor numbers
reached the site maximum in order to catch
more beach visitors. Each interview took
about 30 minutes, once the visitors were
recruited into the study, a small gift as a
token of appreciation was presented.
Survey Instrument
The survey questionnaire comprised four sec-
tions. The first section dealt with problem
statements and the purpose of the research,
the respondents were explained by talking
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about the status of the beach park environ-
ment and ecological system and emphasized
the importance of the environmental protec-
tion in the KCMPN. To reduce rejection
rates and obtain accurate information, the
respondents were informed that the data
from the survey would be used to estimate
economic value of recreational resources for
academic research (Lee & Han, 2002).
The second section consisted of visitor
activities, travel expenses, time spent at the
park and visitors’ preferences over the site
attributes. In a measurement of visitors’ pre-
ferences, the respondents were asked to scale
a score for five variables, those variables
included crowding condition, coral reef,
beach quality, bathing water quality and the
surrounding environment using the nine-
point Likert scale method. The third part con-
tained the questions on the WTP estimates and
the reasons for a negative WTP.
Based on the pretest, we adopted both
payment card and open-ended questions to
test the focus group conducted in order to
make the questionnaire effective and to
verify the starting bid used in the formal
survey (Cooper, 1993). After revising the ques-
tionnaire, the question sequence was restruc-
tured, the wording of the questionnaire was
refined to properly improve clarity between
the respondents and the interviewers; and the
payment vehicle was chosen to be an entrance
fee. This is due to the fact that tourists in the
protected areas are familiar with park
entrance fees (Lee, 1997). As noted by
Barral, Stern, and Bhattarai (2008), the
entrance fee is the realistic and acceptable
mechanism for users of non-market resource
services. Mitchell and Carson (1989) stated
that CV questions related to personal status
should be placed at the end of the survey. In
the final section, socioeconomic data about
respondents, including gender, age, monthly
income, level of education, family size, as
well as country of residence were collected.
In practice, a CVM hypothetical scenario
and the SB and DB DCCVM questions were
used to elicit the WTP amount by asking
respondents to state the maximum amount
they would be willing to pay as an entrance
fee for beach resource protection. To this
respect, the hypothetical market scenario was
designed to show the status quo (current
beach resource conditions) and after charging
visitors for conservation (improved coastal
resource quality), the hypothetical scenario
was used to familiarize the respondents.
Since the on-site survey was conducted where
the respondents were sampled on the beach,
they were able to notice the current state of
beach resource degradation. Therefore, the
hypothetical scenario asked in the WTP ques-
tion was easily understood. Concerning ques-
tions in the DB approach, the respondent
was asked whether he would be willing to
pay an initial bid at random, if he said “yes”
to the first bid amount (e.g. $10), he was
then offered the second bid at the next higher
amount (the second bid equaled to $20); if
the initial response was ‘no’ to $10, he was
proposed a lower amount (i.e. $5). However,
if he said ‘no’ to both the initial and the
lower bids, the reasons of a negative WTP
were enquired. With regard to a list of bid
prices, the initial bid levels used in the SB ques-
tions were randomly chosen with one of the
five bid amounts ($3, $5, $10, $20 and $30),
followed by the second bid offered in the DB
format, that was half or double of the initial
bid, accordingly. As a result, five sets of the
DB questions were ($3, $1.5 and $7), ($5,
$2.5 and $10), ($10, $5 and $20), ($20, $10
and $40) and ($30, $15 and $60), respectively,
(Barral et al., 2008; Eagles, McCool, &
Haynes, 2002). The full CV questionnaire is
presented in the appendix.
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Data Description
As regards the definition of visitors, domestic
visitors mean the national visitors who tra-
veled and stayed at the site at least one night
and less than one year, including the day
visitor who spent less than one day at the
park and went home the same day. A foreign
tourist is a person visiting the KCMNP on a
foreign passport for leisure, entertainment
and other purposes and staying at least one
day. Descriptive statistics for those variables
used in the regression analysis are summarized
in Table 1.
Most long-stay foreign visitors came from
European countries, which account for 85%.
A long-stay visitor means that a visitor will
stay at the destination area for at least one
month of time. The respondents’ sex ratio is
about 65:35 between male and female; Thai
and foreign visitors are, respectively,
accounted for 67% and 33%. The dominant
age group of Thai visitors is about 21–30
years (48.05%), followed by 31–40 years
(35.55%). Most foreign respondents are in
the age of 40–50 years (57.52%). The
majority of Thai respondents had a high
school education (42.19%), followed by a
college degree (34.77%), with an average
household size of 3.97. Of the foreign respon-
dents, 32.03% had completed high school and
24.18% had bachelor degrees as their highest
level of educational attainment. The personal
annual income of Thai visitors is relatively
lower with the average being $11,633, about
one-third of the income earned by foreign visi-
tors. Domestic visitors of 69.14% earn less
than $10,000 in income per year, followed
by $10,000–$19,999 (27.34%). The lower
level of annual income is simply due to a
high proportion of visitors who are students
who obviously make less money or no
income. By contrast, foreign respondents
earn more than $60,000 per year of income
(50.33%), followed by $30,000–$39,999
income groups (19.61%). About 32% of the
foreigners are employed in private sectors (fol-
lowed by government officials (22.22%) and
self-employed (18.30%). Among Thai visitors,
46.09% classify themselves as firm employees,
and 22.27% of them are university students.
In the CVM questions, respondents were
asked to scale a variable from 1 to 9 about
their perception of the beach’s overall
current conditions. About 76% of the beach
visitors felt somewhat crowded at White
Sand beach (scores 4–5). Visitors also
expressed that the coastal beach quality has a
strong effect on their decision of visiting the
beach although in general the beach visitors
are satisfied with the beach conditions (with
the average score of 7–8).
Table 2 reports the percentage of responses
to the CVM questions on WTP for the beach
resource protection; as one would expect, the
probability of a “yes” to the initial bid
decreased when the bid level increased, and
the reverse was true for the probability of a
“no,” which is supportive by the economic
theory of demand (Chen & Jim, 2008;
Kotchen, Kallaos, Wheeler, Wong, &
Zahller, 2009; Wang & Jia, 2012).
In the survey response rate, 78.5% of
respondents would accept an entrance fee for
the beach park preservation, whereas the
“zero” WTP was chosen by 88 respondents
(21.5%). The reasons behind a negative WTP
were given by the protestor: (i) 37 of the pro-
testors were unwilling to pay because they
could not afford to pay more travel expenses;
(ii) 31 believed that it was the government’s
responsibility and; (iii) 20 stated that they
were satisfied with the current beach con-
ditions. Following standard practice in the
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Table 1 Variable Definitions and Descriptive Statistics
Variables Label
Thai visitors Foreign visitors
Mean (std. dev) Mean (std. dev)
WTP Dependent variable, takes the value 1, if the respondent accepts
the offered bid amount, 0 if they refuse to pay
lnBID/BID Hypothetical amounts of the offered bid
Socioeconomic variable
GEN Gender, 1 if the respondent is male, 0 otherwise 0.65 (0.479) 0.67 (0.474)
AGE Age in year 34.46 (9.788) 43.46 (14.990)
HH Household size 3.97 (1.942) 2.64 (1.206)
EDU Level of education (scale variable: 1–5) 1.60 (1.199) 1.69 (1.622)
INCOME Average annual income before tax in ’000 ($) 11.633 (12.996) 36.346 (23.778)
Perception about quality of the current beach conditions
CROWDED Crowding condition (scale variable: 1–9, from low to high satisfaction) 4.85 (1.975) 5.17 (1.937)
REEFS Coral reef condition (scale variable: 1–9, from low to high satisfaction) 1.67 (2.890) 2.23 (3.121)
BEACHQ Beach quality (scale variable: 1–9, from low to high satisfaction) 7.31 (1.307) 7.31 (1.351)
WATERQ Bathing water quality (scale variable: 1–9, from low to high satisfaction) 7.47 (1.308) 7.04 (1.869)
OVERALLQ The surrounding environment (scale variable: 1–9, from low to high
satisfaction)
7.02 (1.496) 7.04 (1.566)
STILL The intention of visiting the site in future, 1 if the respondent will revisit
the site, 0 otherwise
0.89 (0.319) 0.87 (0.336)
Notes: (i) Numbers in parentheses refer to standard errors; (ii) in this CV study, it is important to note that ln BiD is the bid offer in the SB questions and BID variablerepresents the bid offer in the DB format.
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CVM analyses, the respondents were asked to
screen the protest zero bidder; this was to
ensure that the respondents who rejected the
proposed entrance fee were not influenced by
a result of protest beliefs (Cho, Newman, &
Bowker, 2005). Thus, these protest responses
were deleted from the CVM sample because
it was assumed that the protest responses can
introduce bias for the valuation (Garcıa-Llor-
ente, Martın-Lopez, & Montes, 2011). The
respondents who answered reason (ii) were
classified as the protest bidders who objected
to an aspect of the CV survey or the WTP
(Adjaye & Tapsuwan, 2008; Cho et al.,
2005). The final sample used in the model
was 378 respondents, which consisted of 236
Thai visitors and 142 foreign visitors.
Empirical Results
In this study, we were concerned with measur-
ing the WTP estimates of Thai and foreign visi-
tors, as well as comparing the results of the
logit models in the SB and DB approaches.
The ML estimates in the log-logistic of the
SB model and the linear DB functional forms
of WTP values are shown in Table 3. The
correlation test was conducted by using the
stepwise regression procedure in order to
select variables into the model. From the
analysis, all socioeconomic factors used of
the respondents were not highly correlated
with the one another.
The coefficients of the bid offer variable
(lnBID and BID) are negative and statistically
significant (p , 0.01) across all models,
which are consistent with the previous CVM
literatures studied by Saengsupavanich et al.
(2008) and Kotchen et al. (2009). This indi-
cates that the probability of a “yes” response
decreases as the bid price offered increases
and vice versa. Similarly, the coefficient of
the gender variable is negative and significant
(p , 0.05) across all models, which means
females are more likely to pay an entrance
fee for beach resource conservation (Bord &
O’Connor, 1997; Brown & Taylor, 2000).
The age variable has positive coefficients that
are statistically significant across Thai
samples, which means that in this particular
sample the older visitors are relatively more
acceptable to pay the bid offered than the
younger ones. By contrast, the household size
variable is estimated negative and significant
(p , 0.05) in all models, this suggests that
Table 2 Distribution of WTP response in the DB DCCVM
Initial bid Yes/yes (%) Yes/no (%) No/yes (%) No/no (%)
$3 9.02 11.65 3.88 2.36
$5 4.38 8.53 4.83 5.37
$10 3.53 5.21 3.25 8.69
$20 1.94 2.13 1.63 7.55
$30 0.89 1.54 0.65 12.97
Total 19.76 29.06 14.24 36.94
Notes: (i) The five entrance fees were distributed randomly among respondents; (ii) the responses to the initial and thefollow-up bids are recorded as Y for a “yes” and N for a “no,” the proposed bid levels are listed above.
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Table 3 Maximum-Likelihood Estimates from the SB and DB DCCVM
Variable
SB model DB model
Thai visitors Foreign visitors Thai visitors Foreign visitors
CONS 0.1848∗ (3.37) 21.2666∗∗ (22.16) 0.2357∗ (2.89) 0.8269∗ (2.93)
ln BID 20.9781∗ (24.12) 20.9466∗ (23.18) – –
BID – – 20.3353∗ (23.86) 20.1284∗ (22.93)
GEN 20.8328∗ (23.78) 20.0994∗ (22.53) 20.0683∗∗ (22.09) 20.2145∗∗ (22.12)
AGE 0.0094∗ (4.58) 0.0363 (1.45) 0.0087∗ (4.41) 0.0496 (1.53)
HH 20.0238∗∗ (22.36) 20.1836∗ (25.19) 20.022∗∗ (22.36) 20.1991∗ (25.04)
EDU 0.0058 (1.08) 0.3232∗ (3.33) 0.1668 (1.17) 0.2998∗ (3.14)
INCOME 0.0683∗∗ (2.56) 0.0835 (0.73) 0.0756∗∗ (2.39) 0.0965 (1.18)
CROWDED 20.0075 (21.36) 20.0286 (20.93) 20.0093 (21.46) 20.0202 (21.09)
REEFS 20.0489 (21.37) 20.1225∗ (25.49) 20.0454 (21.48) 20.1014∗ (25.33)
BEACHQ 0.1359∗∗ (2.11) 0.1368 (1.26) 0.0899∗∗ (2.54) 0.1426 (1.34)
WATERQ 0.1594 (1.56) 0.1161∗ (3.34) 0.1212 (1.42) 0.1295∗ (3.29)
OVERALLQ 0.0236 (0.96) 0.1767 (1.59) 0.0199 (1.19) 0.2855 (1.36)
STILL 0.4895∗ (4.45) 0.5232∗ (3.34) 0.4553∗ (4.35) 0.3896∗ (3.16)
N 236 142 236 142
McFadden R2 0.457 0.483 – –
FCCC – – 0.492 0.563
X2 215.79∗ 185.902∗ 212.95∗ 181.853∗
Log likelihood 2584.543 2274.235 2986.22 2576.61
Notes: T-ratios are in parentheses.∗Statistical significance at the 1% level.∗∗Statistical significance at the 5% level.
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the respondents who have a larger family size
are less likely to pay the entrance fee. The edu-
cational level is another variable found to be
positively influenced by the WTP amount
pledged across all Thai visitor models, this
may imply that those well-educated respon-
dents are more likely to pay an increased
WTP amount (Hanley, Colombo, Kristrom,
& Watson, 2009). Similarly, the income vari-
able shows a significant positive influence on
the possibility to pay across foreign visitor
models, indicating that richer respondents
would notably raise WTP.
The SB and DB logit models also accom-
pany environmental variables which can
reflect the site quality attributes, those
environmental factors such as crowding con-
dition and coral reefs have negative effects
on the WTP across all models, implying that
the respondents who felt crowded by the
number of tourists encountered at the study
site and the coral reefs are in danger are
willing to pay for improving natural con-
ditions. However, when the dissatisfaction
reached a certain level, the respondents are
no longer willing to afford an entrance fee
for protecting beach park environment
(Wang and Jia, 2012). On the contrary, we
found the positive effects in beach quality,
bathing water quality and the surrounding
environment coefficients across the SB and
DB models, this may be explained that the
respondents who perceived beach park degra-
dation. They are more likely to pay for the
beach conservation values. The sign of the
mixed effects may depend on the site charac-
teristics and the physically degraded period.
Perhaps more surprising is the positive and
statistically significant coefficient (p , 0.01)
on the intention of visiting the site in future
variable, which implies that the visitors who
have an intention to visit the beach park in
the next time are more likely to respond “a
yes” for the proposed bid if they feel satisfied
with the current beach conditions.
A goodness-of-fit measure of McFadden’s
pseudo R2 was tested, the larger McFadden’s
pseudo R2 values of the log-logistic SB
models demonstrate the explanatory power
of the model (between 46% and 48%),
which is relatively high for cross-sectional
data (Christie et al., 2004). In addition, the
chi-squared tests indicated that all the SB and
DB logit models are statistically significant (p
, 0.01). Kanninen and Khawaja (1995) noted
that a standard goodness-of-fit measure of
McFadden’s pseudo R2 could not be calcu-
lated for the DB logit model. This arises
because the restricted log of the likelihood
function is undefined. As a result, they rec-
Table 4 Estimates of Mean WTP from the SB and DB DCCVM
Mean WTP (US$) Thai visitors (N ¼ 236) Foreign visitors (N ¼ 142)
SB $12.01 ($7.84, $33.85) $25.33 ($14.66, $54.37)
Lower and upper 95% confidence interval
DB $7.27 ($2.68, $14.56) $14.47 ($5.18, $30.92)
Lower and upper 95% confidence interval
Note: Bootstrap method is reported in the lower and upper limits of the 95% confidence intervals around mean WTPestimates in parentheses.
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ommended an alternative measure which is the
fully correctly classified cases (FCCC) method
to evaluate the goodness-of-fit for the DB
model. McCluskey, Durham, and Horn
(2009) defined that the FCCC approach is
used to calculate the percentage of respon-
dents that the model correctly classified, each
observation into the four categories (yes/yes,
no/no, yes/no and no/yes).
Table 4 presents the estimation results of the
mean WTP for both SB and DB elicitation
methods that were calculated by the formulas
illustrated in eqs. (15) and (16). The mean
WTP for a Thai visitor using the SB method
is $12.01 per adult per visit, while that for
the mean WTP of a foreign visitor is twice as
high as the Thai visitor’s WTP as well as the
estimated WTP results in the DB model. More-
over, we derived the 95% confidence intervals
using a bootstrapping approach for calculat-
ing confidence intervals of elasticities. Using
a t-test of the differences between the lower
and upper limits of the 95% confidence inter-
vals suggests that there is no statistically sig-
nificant difference between Thai and foreign
visitors (calculated t-test is 7.195).
Compared with the previous CVM studies,
these WTP values lie within the range of valua-
tions estimated from coastal beach resources.
For example, Walpole, Goodwin, and Ward
(2001) employed an upper and lower
bounded DCCVM to examine the effect of
hypothetical rises in the entrance fee on visita-
tion at Komodo National Park, Indonesia. The
mean WTP estimate was $11.70, which is a
more closely the result of the mean WTP of a
Thai visitor’s WTP ($12.01) in the SB
approach, as well as the CVM study by
Wang and Jia (2012), they used the multiple
bounded discrete choice elicitation method to
estimate the WTP a higher entrance fee for
biodiversity conservation and environmental
protection at the Dalai Lake Protected Area
in China. The results found that the tourists’
mean WTP ranged between 75.05 RMB and
85.17 RMB (approximately $10.32–$12.35)
per adult per visit.
A simple way to calculate the aggregate
benefit of beach resource protection is to mul-
tiply the estimated WTP per trip resulted from
the DB model as a lower bound estimate, and
that of the SB model as an upper bound esti-
mate by the average number of park visitors
over 10 years. Based on 2003–2012, park vis-
itation figures of Thai and foreign visitors were
356,789 and 177,360, respectively (Depart-
ment of Tourism, Thailand, 2013) and the cor-
responded sample average number of trips
were 3.33 and 1.76, respectively. Thus, the
total annual trips are estimated to be
1,188,107 of Thai visitors and 312,154 of
foreigners, respectively. These respective
tourism statistics are then multiplied by the
WTP per-visitor-trip of $7.27 and $14.47,
respectively, for Thai and foreign visitors
under the DB method, which results in a
lower bound estimate of $8.64 million and
$4.52 million per annum. Similarly, using the
SB model results of the mean WTP to get an
upper bound estimate of $12.01 and $25.33
of per-visitor-trip, we obtain an upper bound
estimate of $14.27 million and $7.91 million
per annum, respectively, in aggregate. These
can be converted to the lower and upper
bounds of an aggregate WTP ranging from
$13.16 million to $22.18 million. Assuming
that 78.5% of the respondents would be
willing to pay an entrance fee for coastal
resource preservation, the aggregate WTP
benefits are worth between $10.33 million
and $17.41 million per year. These substantial
benefits suggest that preserving beach
resources can generate an extraordinary
amount of economic benefits.
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Summary and Conclusions
This study values the WTP an entrance fee for
beach resource protection along the White
Sand Beach coastline in the KCMNP. In the
process, the CVM method is utilized to
measure the WTP estimates in terms of per-
visitor-trip. Specifically, both SB and DB DC
CVMs are used in estimating the WTP based
on the field survey data. The results show
that the mean WTP per beach visitor trip is
$12.01 and $7.27 for Thai visitors under the
SB and the DB models, respectively; $25.33
and $14.47 for foreign visitors, respectively,
under the same models.
The results also reveal that the variables of a
bid offer, gender, household size and the inten-
tion of visiting the site in future are the most sig-
nificant predictors of the beach visitors’ WTP
(p ≤ 0.05), regardless of which model is used
in the estimation. Based on the SB and DB esti-
mation results, 73.5% of the respondents
accepted WTP an entrance fee for coastal
beach protection, we found that the lower
and upper bounds of annual aggregate WTP
range from $10.33 million to $17.41 million
per year. According to the park management
official, the park’s operating cost in 2012 was
$287,500 (Mu Ko Chang National Park,
2013), which is much smaller than the value
of estimated beach park conservation. Based
on our findings, the majority of the respondents
(87%) have intention to visit the KCMNP
again in the future. The park management
can certainly raise the park entrance fees at
both park gate spots and waterfall areas. If
the entrance fee was raised to $7.27 for Thai
visitors and $14.47 for foreign visitors, as the
mean WTP obtained in the DB model (Adjaye
& Tapsuwan, 2008), the KCMNP would
have possible revenues of $5.16 million per
year. Because of the park authorities’ limited
financial resources, entrance fees are an impor-
tant vehicle for natural site protection, even
though collecting entrance fees may reduce
the number of tourists visiting the park,
which could mitigate overcrowding and
beach resource disturbance (Davis & Tisdell,
1995), while preserving positive WTP and
enhancing the tourists’ experience. However,
if the visitors pay an entrance fee to the site,
given that domestic visitors have a lower
WTP, they might reduce their visits in the
future, inducing a larger foreign consumption.
This might create social problems since a
national resource would be mainly consumed
by international visitors.
Visitors’ survey responses and WTP value
estimation of the beach resource protection
provide important information for improving
the park management. With regard to the visi-
tors’ attitudes toward park management, two
points deserve to be mentioned. One is crowd-
ing problem on the site, which perhaps reflects
the over capacity use of the beach resources.
Therefore, the park management has to pay
attention to the number of visitors arrived,
especially during the peak tourism seasons.
Some actions must be taken to cut the
number of visitors to enter the sites per day
or even in a specific time period during the
day. Several measures could be adopted to
limit the beaches in their carrying capacity
use, including visitor diversion scheme,
raising entrance price command control, etc.
The other one is that based on the survey
responses, most visitors have shown their con-
cerns over the beaches’ environmental pol-
lution, which may create serious negative
effect on the travelers’ repeated visit(s), the
level of their WTP, and certainly their length
of stay, thus reducing the park’s revenue
potential in the future. Therefore, both Thai
central and local governments should input
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more resources to environmental protection
for the beaches to ensure no further deterio-
ration of beach amenity. On the other hand,
it is recommended that the park authority
should take some proactive measures in
dealing with environmental protection. In an
operational level, this may include taking
care of the public toilets, showers, water
sources, trash cans, parking lots, walk path-
ways, information signs and local facilities
for convenience. Understand that doing an
adequate management work requires a signifi-
cant amount of financial support.
Funding
This work was supported by the National
Natural Science Foundation of China Grant
No. [70871014] and No. [71271040], and
the Institution of High Education Doctor
Subject Special Research Fund, Ministry of
Education, the People’s Republic of China.
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