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Valuing Biodiversity of Hokera Wetland Reserve:
A Contingent Valuation Approach
1. Introduction
Wetlands are most productive ecosystems providing wide range of important ecological
functions, life supporting and other services (Turner et.al. 2000). However, existing
development and environment trade-offs particularly in developing countries have resulted
into wetland loss and degradation. This consequently has led to the loss of associated
functions and undermined their capacity to provide diversified services. The loss of wetland
attributes has serious implications on the welfare of the people whose wellbeing is directly or
indirectly dependent on these attributes. One of the critical non-marketable wetland attribute
is biodiversity which has been significantly lost without much understanding of its economic
value and ecological and socio-cultural benefits (Barbier et.al 1997, Verma, 2001). Economic
value of wetland biodiversity (both use and non use value) is reflected through its linkages
with economic welfare of humans and is retrieved by consumer preferences for biodiversity
benefits. The economic valuation of wetland biodiversity is required to perform
environmental accounting, natural resource damage assessment and to carry out proper
pricing (Nunes et.al. 2000). Biodiversity valuation particularly in developing countries like
India assumes added importance to justify/supplement the conservation measures in the
existing development-environment trade-off scenario.
In the absence of market prices for environmental resources, economists have devised various
techniques to estimate economic ‗value‘ of non-marketable resources (Freeman 1993). The
dominant measure of economic value has been ‗Willingness to Pay‘ (WTP) for a specific
improvement in an environmental resource or service. Pearce and Moran (1997) opined that
―If biodiversity is economically important we would expect this to show up in expressed
willingness to pay for its conservation‖1. Economic valuation of environmental resources is
being considered as an effective operational tool for sustainable use of these resources.
Substantial literature is now available on wetland valuations (Ghermandi, et.al. 2008;
Brander, et.al 2006; Woodward and Wui, 2001). Various economic valuation methods have
been applied to value wetland services which include Contingent Valuation Method (e.g.
1 Pearce and Moran (1997), pp.13
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Wattage and Mardle, 2008; Orapan, et.al. 2008; Kwak, et.al. 2007; Hammitt, et.al. 2001;
Oglethorpe and Miliadou, 2000; etc), Travel Cost Method (e.g. Flaming and Cook, 2008;
Ortacesme, et.al. 2002; Whitten and Bennett, 2002; Curtis, 2002; Feather and Shaw, 1999;
Craig, et.al. 1996; etc), both Contingent Valuation and Travel Cost Methods (Whitehead,
et.al. 2008; Mayor, et.al. 2007; Chopra and Adhikari, 2004; Fix and Loomis, 1998; etc),
Hedonic Pricing Method with Contingent Valuation Method (Verma, 2001; etc), Choice
Experiments (Do and Bennett, 2009; Carlsson, 2003; Kuriyama, 1998; etc), among others.
Many studies have only estimated one out of many possible wetlands attributes (Bell 1997;
Acharya 2000) while others have estimated more than one or the total economic value of
wetland ecosystems(Woodward and Wui 2001; Hammitt et.al. 2001 ). In the recent years
there is surge in studies which deal with bio-economic modelling of wetland ecosystems and
studies related to ecological-economic values of wetlands using computer simulations (e.g.
Chopra and Adhikari, 2004; etc.). In the Indian context, although few important wetland
valuation studies have been conducted on nationally important sites (e.g. Chopra and
Adhikari, 2004; Verma, 2001; Prasher, et.al. 2006; Bandyopadhyay, et.al. 2007; Wattage and
Mardle, 2008; etc.), but many equally important wetlands have remained out of focus from
economists of which one classical example is Hokera Wetland ( a northern Himalayan
wetland in Jammu and Kashmir, India). The northern Himalayan high altitude wetlands are
the natural wetlands and sustain rich biodiversity. Four out of the 25 Ramsar sites and ten out
of 104 nationally important wetlands of India fall in the state of Jammu & Kashmir (India).
For conservation and management of these wetlands, there is need to provide the information
to the policy makers about the potential economic benefits of preservation of these rich
ecosystems. Economic valuation of these wetlands is an obvious requirement for any
pragmatic policy design.
1.1 Hokera Wetland Reserve
India has a wealth of wetland habitats of immense importance in terms of ecology and exhibit
enormous diversity based on origin, geography hydrological regime and substrate (National
Biodiversity Action Plan, 2008; Verma 2001). Kashmir valley is endowed with number of
wetlands of critical importance in terms of their potential economic value and one among
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them is Hokera (Hokar Sar) Wetland Reserve2. Hokera is a natural permanent wetland
located in District Budgam and Srinagar of Kashmir valley (J&K, India). The wetland lies on
the geographical co-ordinates of 34o, 05´ N to 74
o, 42´ E. The Himalayan wetland is elevated
on an average of 1584 meters above sea level with an area of 1375 hectares. The wetland
goods and services benefits are of high socio-economic importance like biodiversity
conservation, pollution abatement, trapping sediments and nutrients, flood mitigating, ground
water recycling, climatic stability etc Apart from this, the wetland reserve supports a rich
biodiversity of flora and fauna (Khan et.al 2004), (Khan and Bashir 2003). The wetland is a
depository of rich biodiversity of birds providing food and breeding sites to the wintering
migratory birds coming from Siberia, China, Central Asia, North Europe and other countries
as well as for summer migrants coming from Indian sub-continent. Sixty eight avian species
have been inventorised from the area including IUCN Red listed (2004) endangered (Aythya
nyroca) white eyed pochard3. The diverse avifauna and macrophytes are of high socio-
economic and ecological importance. The wetland is facing lot of natural and anthropogenic
pressure which has resulted in considerable loss and degradation of the wetland ecosystem
(Joshi et.al. 2002). The wetland area has been reduced from 13.75 km2 to 5 km
2 since last
fifty years4. This had also reduced the associated wetland goods and services delivery
capacity and consequently the associated human welfare benefits. The wetland is under
Government control since 1945. Fifty years under the legal status of ‗reserve‘ could not
preserve this wetland ecosystem as it should have been. Thus the need of the hour is to
reformulate the conservation policies and practices keeping in mind the growing loss of
wetland resources like biodiversity and the benefits of its preservation. But so far, no
attempt has been made to quantify the economic value of the wetland biodiversity for this
site. The present paper is an effort in this direction and attempts to estimate the economic
benefits of bio-diversity preservation for this wetland reserve. The aim is to estimate the
demand function for biodiversity preservation and to calculate the household benefits from
2Declared Ramsar Site on 08/11/2005 bearing No. 1570 and Important Bird Area Lacking (IBA 9). Notified as
reserve in 1945 and presently maintained by Department of Wildlife Protection, Government of Jammu and
Kashmir, India.
3 Information sheet on ramsar sites (RIS) http://www.wetlands.org/reports/ris/2IN021_RISen05.pdf.
4 Department of Wildlife Protection, Government of Jammu and Kashmir, India.
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estimated demand function parameters, among the inhabitants surrounding the wetland using
Contingent Valuation Method.
2. Materials and Methods
2.1 Contingent Valuation Method (CVM):
Contingent Valuation Method (CVM) is a direct survey based method for eliciting
consumers‘ preferences for a non-marketed goods in the absence of well defined market
mechanism. It uses a hypothetical market situation to seek people‘s preference and their
Willingness to Pay (WTP)/ Willingness to Accept Compensation (WTAC) for enjoying
/sacrifice a particular environmental good or service. Since 1970s, it is widely used to
evaluate values of non-market goods (Liaw and Chen; 2006). Despite its limitations and
suspected reliability CVM has emerged as the only theoretically sound technique for
estimating non-market values. The merit of the CVM lies in its capacity to capture both use
and non-use value of the resource and to measure ex-ante changes. The selected wetland has
potential for non-use values such as option value, passive value or bequest value. There are
hundreds of identified or unidentified species of birds, fishes, plants and trees and micro-
organisms which can have a high non-user value. Without understanding non-use values an
economic valuation of wetland biodiversity cannot be appropriate. On that ground we chose a
contingent valuation survey for the present study to capture the non-use component of the
total values of the Hokera wetland ecosystem biodiversity.
2.2 Survey
Contingent Valuation household survey was designed to collect information on ‗Willingness
to Pay‘ for preservation and management of biodiversity of Hokera wetland from the
identified adult respondents who had attained the age of eighteen years and above. For any
survey-based study identifying the target population is the first task after setting up the basic
goals of the study. The most important thing while deciding a target population for a CV
study should be based on who will be benefited / affected from a change in the environmental
service in question, directly or indirectly. Bateman et.al. (2002) think that the determinants of
user or non-user population depends upon uniqueness of the said good/ service,
substitutability, familiarities of respondents with the good or service, scale of the change in
question and context in which the valuation results will be used. Although many studies
assume that if non-use value exits, it must exists for all non-users. It is more appropriate to
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sample the non-user population by geographical location (Bateman, et.al. 2002). As the
present study aims to estimate non-use value of the selected wetland biodiversity, it would be
more appropriate to set the target population for sampling on geographical basis as suggested
by many experts.
From above considerations we carefully restricted our target population to the area
surrounding the wetland within ambient of 5 Km from outer periphery of wetland. The
selected target population was opted within this limited area to avoid the overvaluation and
isolate the value which people may associate with the other wetlands present in the outer
surroundings.
We applied a ‗stratified multistage random sampling‘ for collecting the household primary
data. The stratification was done on the basis of administrative divisions (Tehsils5) touching
the wetland periphery along different directions. As the wetland falls in two districts
(Srinagar and Budgam of J&K) the surrounding inhabitants/ localities (within 5 Km) were
identified and listed which include part of Srinagar (Municiplity ward. No 25 and 26) and
part of sub-district (Tehsil) Beerwah and Budgam. Then we randomly selected few
colonies/villages as primary sampling units from listed Villages /Colonies. From the primary
sampling units we chose 247 households on random basis and interviewed an adult person
(male or female) form each household.
A structured CV questionnaire was used to collect the primary information from
respondents. The format of the CV questionnaire which was used for the present study had
three sections. The first section of the questionnaire was designed to understand respondent‘s
overall perception and knowledge about wetland biodiversity and their management and
preservation issues. This section was also used as a ‗warm-up‘ section for respondents to
understand and get familiar with the need for the interview.
The second part of the questionnaire was regarding Economic Valuation including WTP and
follow up questions. This section starts with a small introductory policy decision statement
which helped the respondent to understand the aims and objectives of the study in brief. For
the present study we provided a brief introduction of the economic valuation of wetland
5 Three Tehsils (sub-districts) are Srinagar, Beerwah and Budgam.
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biodiversity and a proposed ―Wetland Biodiversity Management and Preservation Fund6 ‖
for the wetland in question. The present study uses a hypothetical ―Government-Local People
Joint Management Initiative‖ as the sole institute for the collection of contributions made by
people and looks after the management and preservation of the wetland with full
transparency. The Joint Management Initiative is used as it was revealed preferred strategy
for sustainable management and preservation of wetland by respondents in the pilot survey
and focus group discussions. The mode and frequency of payment are also explained clearly
to overcome any ambiguity. For the present study we chose a ―yearly donation‖ for each
respondent over tax as a payment vehicle keeping in mind the poor direct tax collection in
rural India7.
After analyzing various types of associated biasness and errors from different CV eliciting
formats it was finally decided that for the present study a ‗Single-Bounded Dichotomous
Choice Model‘ or ‗Referendum Method‘ using ‗binary data‘ in the payment answer would be
best suited to elicit people‘s willingness to pay for ―Preservation and Management of
Wetland Biodiversity‖. A dichotomous choice format minimizes the non-responses and
avoids outliers. The Blue Ribbon NOAA Panel Report (1993) has also recommended using a
dichotomous choice method instead of an open-ended eliciting format in a CV questionnaire.
This facilitates to overcome ‗zero responses‘ and outliers. The questionnaire also includes
some important follow-up questions. Questions like ―why are not willing to pay?‖ or ―why
don‘t you support such plan?‖ or ―If respondent‘s answer was very high then what makes him
to answer such higher value‖, etc. These questions help us to explain people‘s responses more
accurately. Such follow-up questions can also help to verify respondent‘s answers and
identify the scenario protesters.
The last and final section of the CV questionnaire was devoted to understand respondent‘s
socio-economic and demographic information which help a researcher to analyze the results
more appropriately. According to Hammit et. al. (2001), an economic value of a wetland is
6 Contingent valuation is contingent to a hypothetical market scenario as in maximum cases there is a market
failure for the good/ service being valued. In that case there is a need to construct a well-defined hypothetical
market mechanism under which the said resource would be valued. Valuation for an environmental resource
using a CV questionnaire can significantly be different if the hypothetical market is different. So it is very
important and crucial to design a very unambiguous and straightforward market mechanism under which the
economic valuation would be carried out.
7 As most of the rural people with low income level are exempted from income tax. Besides tax is a compulsory
payment while donations are voluntary.
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the function of the system‘s ecological characteristics and their socio-economic environment.
This also depends on location and other attributes. For a CV study variables such as age, sex,
level of education, occupation of the respondent, size of the respondent‘s family, level of
family income, total earning members in the family, etc. play very crucial roles in
determining people‘s WTP or WTA (compensation) for a particular environmental resource.
This section of the questionnaire was thus designed to understand various socio-economic
and demographic characteristics of the respondents which could help us to analyze their
responses regarding their WTP for management and preservation of wetland biodiversity
more accurately.
Focus group discussion is very important for finalizing a CV questionnaire (Bateman, et.al.
2002; the NOAA Panel Report, 1993; Hanemann, 1994) and many of the CV studies have
conducted focus group discussion for developing an informed and relevant CV questionnaire
for valuing wetland resources (see for example, Kwak, et.al. 2007; Orapan, et.al. 2008).
Before finalizing the CV questionnaire various focus group discussions and a pilot survey
was conducted to understand the appropriateness and reliability of the designed CV
questionnaire and identify their weaknesses so that a CV questionnaire can be modified
conveniently before the final data collection. Certain questions were modified after the focus
group discussions keeping in view the regional socio-economic dimensions of study area, e.g.
‗bid value‘ for the single-bounded dichotomous choice format, hypothetical scenario and
socio-economic variables etc.
CV questionnaire requires very careful and systematic data collection procedure otherwise
there can be many errors and biasness in the collected data. Most of the weaknesses of CV
study can be traced back to designing questionnaire and survey administration problems
(Stoll; 1983). We conducted primary survey using face-to-face interviews. The CV data were
collected during the month of May, 2010 from selected respondents of Srinagar and Budgam
Districts falling within neighbouring areas. The enumerators were very careful about the
place of interview. According to Hanemann (1994), CV interview should take place, such as
home, in a setting that permits respondents to reflect and give a considered opinion. The data
was collected mainly from off-field household surveys. We collected more data in the week-
end days when generally heads of the houses are at home. The enumerators needed to explain
certain questions which the respondents felt difficult to give a justifiable answer. The
enumerators also facilitated the respondents in ‗do not know‘ type of questions and allowed
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them for reconsideration of earlier answers at the end of interview, as suggested by experts
like Hanemann (1994). All the enumerators were research students knowing the regional
language who, easily translated and communicated the questionnaire to respondents to ensure
the accuracy of responses. For each and every respondent the data collectors made it very
clear that collected information would only be used for academic purpose and full
confidentiality would be maintained.
2.3 Methods Used for Analyzing CV Data:
As the value eliciting technique for the CVM used in this study is a single-bounded
dichotomous choice method, originally developed by Bishop and Heberlein (1979), it uses
discrete responses or qualitative values of ‗yes‘ or ‗no‘ type of answers. The suggested
econometric methods for the analysis of these data are regression techniques such as logit or
probit methods (Loomis, 1987; Fix and Loomis, 1998). The logit model can be used when the
dependent variable in a regression equation can take a qualitative discrete choice among a set
of alternatives. It is typically used to explain a binary dependent variable like the single-
bounded format (Kaval, et.al. 2007). Thus we chose a ‗binary logistic regression model‘ with
linear bids for the analysis of the CVM variables for eliciting people‘s WTP for the
management and preservation of the selected wetland. To explain the behaviour of a
dichotomous dependent variable we chose a suitable cumulative distribution function (CDF).
The probability that a respondent would say ‗yes‘ (Pi) to contribute a specific amount (in
Rupees ) for the plan to establish a management initiative to look after management and
preservation of the specific wetland in question can be explained by:
Pi = Fη (ΔV) = 1/ {1 + exp (-ΔV)} = 1/ {1 + exp (-(α + βD + πS + ∂K+e))}
Where, Pi is the Probability of answer ‗yes‘ from a respondent, Fη(.) is the Cumulative
distribution function (CDF) of a standard logistic variate, D Represents the ‗bid‘ Amount, S
is the Socio-economic and Demographic Variables, K =Knowledge and Attitudes of
Respondent on Environment, α, β, ∂ and π are Unknown Parameters to be Estimated. It is
expected that with the increase in bid amount, D, the probability of saying yes (Pi) will be
less which means the co-efficient of D variable β will be negative or simply β≤0. It means
higher the bid value lesser is the probability of saying ‗yes‘ by the respondent. The expected
sign of the co-efficient of ‗K‘ variable is positive as higher the level of knowledge and
positive attitudes of a respondent will enhance his/her willingness to pay for the proposal. But
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the co-efficient of ‗S‘ or socio-economic and demographic vector will depend on exact
variable like for ‗Y‘ or income it is expected to be positive; for ‗A‘ or age, it is negative; ‗E‘
or level of education may exhibit positive; but ‗N‘ (number of family members) can be either
positive or negative.
The binary logistic regression model represented by the above model is estimated using a
‗Maximum Likelihood Method‘ due to the difficulties of using an OLS model. We used
SPSS-18.0 for the analysis of the CV data and run the logit regression. To calculate the mean
WTP, we used the formula suggested by Hanemann (1989):
MWTP = {ln(1+ exp(α))}/ -(β)
Where, α represents estimated logit regression constant; β represents the estimated co-
efficient of bid amount.
3. Results and Discussion
The response of the WTP i.e. yes or no is taken as the dependent variable. This is generally
regressed on a set of independent variables to check its validity. The variables used in the
model along with their description are given in Table 1.
For estimating the mean WTP we collected information on selected socio-economic,
demographic, knowledge and understanding of environmental problems from the
respondents. After screening the partially filled/incomplete questionnaire and scenario
protesting respondents 234 out of 247 questionnaires were used for analysis. The protesting
respondents were identified as those who were not WTP the offered bids only because they
deemed wetlands as public goods and Government responsible for proper management and
preservation of its bio-diversity. In table No. 2 descriptive statistics are presented as a
prelude to and background information to the main discussions and inferences therefrom.
Six ‗bid values‘ were chosen for the single bounded dichotomous choice CV model which
required only either ‗yes‘ or ‗no‘ answers. The bids randomly offered were ` 10, 20, 100,
200, 500, and 1000. The six bids and probability of their acceptance of are presented in the
table No. 3 along with the bar diagram.
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Table- 1: Definition of the Selected Variables Used in the Regression
S.No. Variable Definition & Unit
1 BIDVALUE Six bid prices offered for accepting the hypothetical improved wetland
service (in Rupees)
2 WTP If WTP for the offered bid is yes=1, otherwise =0
3 MAXWTP Open ended question for Max WTP (in Rupees)
4 AGECAT If age of respondent is greater than 50= 1, otherwise =0
5 SEX 1=Male, 0=Female
6 FMLYSZ Number of family members of the respondents‘ families
7 MARSTS Marital Status, married =1 otherwise =0
8 INCOME Per household monthly income (in Rupees)
9 EDU No. of Schooling Years
10 BIODKNOW Biodiversity Knowhow index. Respondents were asked six questions
regarding biodiversity and its preservation, if answer is yes to all six
questions the value of index is 6 with each having a score of one.
11 URBAN If respondent resides in Urban Area=1, otherwise =0
Table-2: Descriptive Statistics
Variable N Minimum Maximum Mean Std. Deviation
WTP 234 0 1 .84 .366
MAXWTP 234 0 10000 621.75 1265.582
BIDVALUE (Y) 234 10 1000 337.65 362.149
BIODKNOW 234 3.00 6.00 5.8846 .45334
INCOME 234 1200 75000 8736.97 8874.190
SEX 234 0 1 .88 .320
FMLYSZ 234 3 22 8.30 3.568
EDU 234 0 18 7.34 6.478
AGECAT 234 0 1 .2179 .41374
MARSTS 234 0 1 .67 .471
URBAN 234 0 1 .2308 .42223
Valid N (listwise) 234
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Theoretically increase in the bid amounts should lead to decrease in their probability of
acceptance. The results of our survey also supported such claims, except for the figure of `
1000 which was found to have larger probability than the preceding bid amount of ` 500.
This can be due to the reason that some bids offered to respondents might have strong effect
of other socio-economic determinants. For example 33% of the randomly offered ‗bid‘ of `
500 was answered by (or unevenly fall on) dependents whose budget constraint does not
allow them to accept the offered bid. But overall the probability of accepting the offered bids
was quiet high indicating that people enthusiastically supported the programme. The
respondent‘s knowledge about biodiversity and its preservation was assessed on the basis of
arbitrary biodiversity awareness index. As mentioned above the respondents were asked six
yes/no questions regarding knowledge of wetland biodiversity and its preservation. Each
question was assigned a value of 1 for ‗yes‘ and zero for ‗no‘. The index attains a value of
six if all questions were answered as ‗yes‘ and zero if all questions were answered with ‗no‘.
The mean index value worked out to be 5.88 revealing that respondents are highly aware
about biodiversity. This index was used for assessing the respondent‘s knowledge and
awareness of biodiversity keeping in view the complexity of good to be valued. Biodiversity
index based on multiple questions was thought to represent the variable (biodiversity
knowhow) in a better way than single question and also avoids the multicollinearity among
variables. Average age of the respondents was found to be around 38.25 years. However, the
age was used as dummy equal to ‗1‘ representing elderly people as those having age greater
than or equal to 50 years and otherwise zero. We assume that older people majority of whom
are by occupation farmers or dependents have strong preference for agriculture/farming on
wetland reclaimed landmasses instead of wetland biodiversity than young people. The
proportion of elderly people (i.e. respondents with greater than fifty years) turned out to be
21.7%. The average monthly family income of the respondents was appraised to be ` 8737.
10 50 100 200 500 1000 Total
P(Y) 1 0.919 0.919 0.868 0.61 0.778 0.842
0
0.2
0.4
0.6
0.8
1
1.2Table-3: Probability of Accepting Bids
Bids
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This implies that the respondents on an average had income lower than the state level8. The
average family size of the respondents‘ of the study area was estimated to be 8.30 which are
slightly higher than state average (6.5 members/ family). It means the respondent families
were larger than nuclear families. To facilitate comparison with the indirect WTP calculated
from the Logit regression, we also put a supplementary question and tried to estimate
maximum willingness to pay for preservation of the lake by asking a direct question on ‗how
much maximum amount would you like to pay for the said improvement of the lake
environment?‘ The open-ended MWTP turned out to be ` 671.75 per respondent per year.
Among the respondents 88% were reported to be male. The reasons can be attributed to the
fact the Sub-continent is mainly male-dominated due to socio-cultural reasons and female
generally do not enjoy a favourable status in decision making (Singh et. al 1993). Among the
respondents 67% were assessed to be married. The level of education of the respondents was
found to be quite low. The average number of schooling years of respondents was found to be
7.34 years. This implies that majority of the respondents were not educationally well-
advanced provided years of schooling is trusted as an indicator of knowledge. The low
educational profile might be because of two reasons: i) as one of the districts (i.e. Budgam)
has literacy rate (50.05%) which is lower than state average (65.67%)9, it might have
influenced the figure downwards; ii) as the target respondents are adults who have attained
the age of eighteen years or more, the literacy level among higher age groups is low. To
capture urban impact on WTP we used urban respondents as dummy variable and 23% of
respondents‘ turns out to be from urban/semiurban areas.
3.1 Results from Binary Logistic Regression of CVM
CV data should always be carefully tested using appropriate techniques for their validity. The
parameters estimated should be examined to establish that they are correctly signed and their
statistical significance is reported (Bateman, et.al. 2002). As explained earlier the single
bounded dichotomous choice CV format is supposed to be best suited in dummy dependent
regression models like Logit, Probit or Tobit. On that ground we used a binary logistic
regression model for estimating the mean WTP from the model. The findings of the
8 Survey based Per capita income calculated at current prices turns out to be 12631 ( during 2010) for
respondents while state average per capita income (even in 2006-07) at current prices was Rs. 22506 (Digest of
Statistics, Government of J&K, 2006-07).
9 Economic Survey 2008-09, Directorate Of Economics & Statistics, Government of J&K.
13
regression model along with its test statistics are presented in the tables 4 and 5 and discussed
below.
Bid Values (BIDVALUE): It was hypothesized that ‗Bid Amount‖ would have an inverse
relationship with the probability of accepting them implying thereby that higher the bid
amount lesser the probability of accepting it. The coefficient shows negative sign and is
significant at 1% level. The proposition is also supported by bid amounts and their rate of
acceptance.
Table-4: Variables in the Binary Logistic Regression
B S.E. Wald df Sig. Exp(B)
BIDVALUE -.002 .001 11.964 1 .001* .998
BIODKNOW .220 .415 .282 1 .596 1.246
INCOME .000 .000 7.651 1 .006* 1.000
SEX 1.314 .538 5.976 1 .015** 3.721
FMLYSZ -.034 .058 .349 1 .555 .966
EDU -.046 .041 1.238 1 .266 .955
AGECAT -1.292 .537 5.788 1 .016** .275
MARSTS .434 .533 .664 1 .415 1.544
URBAN .641 .558 1.321 1 .250 1.899
CONSTANT -.564 2.688 .044 1 .834 .569
*1% level of significance, ** 5% level of significance
Knowledge of Biodiversity (BIODKNOW): The knowledge about biodiversity was found to
be directly related with probability of accepting the bid values. It implies that people having
more knowledge about ‗biodiversity‘ were ready to accept the offers than the others who did
not have any such knowledge. The findings seemed to be expected but the variable does not
exhibit signification association.
Level of Income (INCOME): Income is expected to have a positive relation with WTP. The
findings from our study also showed similar results and is significant (with a 5% level of
significance).
Sex of the Respondents (SEX): Gender is considered as an important variable which may
influence the WTP of the respondents. The findings suggest that males are ready to accept the
offered bids (WTP) more than their counterparts (female). In a male dominant society men
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have more decision making and socio-economic powers than women. Hence the variable has
positive sign and the relation is significant.
Number of Family Members (FMLYSZ): The expected relationship between WTP and
number of family member is sometimes very difficult to predict in advance as literature on
CVM gives a mixed picture. Although we expected that NFM would have a negative relation
with WTP assuming that large families would be willing to pay less because of high
household expenditure. This was supported by results but relation is insignificant.
Educational Qualification (EDU): Impact of education on WTP was found not to be in line
with established theory and evidences which suggest that education is positively linked with
people‘s WTP. It is expected that people who are higher educated can understand the need for
managing environmental resources better than other who are not higher educated. The present
study variable shows negative sign which is quite unexpected but insignificant. One of the
reasons may be the low educational level in the region (7.3 years) where additional years of
schooling up to certain level may not have any impact on WTP.
Age of the Respondent (AGECAT): Older people may not be able to contribute much due to
many reasons like lesser control over family‘s budget, dependence on children after their
retirements, more expenditure on health, and strong preference for alternative use of wetlands
like agriculture etc. The results from the regression show a negative relation between the
acceptances of bids with the respondent‘s age. It is also in line with many of other CV studies
(like Jeanty and Hitzhusen, 2007; Landry, et.al 2003).
Marital Status (MARSTS): The variable coefficient has positive sign which implies married
respondent‘s probability of accepting offered bids is higher than non-married respondents.
The reason could be that married people are considered better managers of household affairs
and they want to conserve the wetland biodiversity for future generation (Samdin et.al. 2010).
Urban-Rural status (URBAN): It is expected that urban people have strong and well
developed preferences for environment and natural resources due to scarcity of these
resources in urban areas. The positive sign of the variable explains the same but the relation
is not significant.
Test Statistics of the Binary Logistic Regression Model: The test statistics from the proposed
binary logistic regression model showed that the model was quite robust with a Chi-square of
15
over 35 with over 99% level of confidence. It is hypothesized that lower the log-likelihood
statistic for a binary logistic regression better is the model. The log-likelihood statistic of
168.429 was quite comparable with other similar studies like Loomis (2008) found 155.19;
Kwang and Gil (2007) estimated a 277.00; Landry, et.al. (2003) got 725.85; Brower, et.al
(2006) study showed as higher as 500.1 and 454.1 for two different models on estimating
WTP for flood control in Bangladesh, among others. Although there is no close analogous
statistic in logistic regression to the coefficient of determination R2 however some
approximations are used. The Cox & Snell Square10
(a measure of R-square) was found to be
over 0.142 with a Nagelkerke R-Square, which is equivalent to adjusted version of the Cox &
Snell R-square and the maximum value can reach upto one, 0.241. It implies that over 24% of
the variation in the dependent variable was explained by the independent variables included
in the model. It means in predicting the respondents‘ WTP for the proposed management and
conservation programme for the wetland the model proved to be quite robust. The
McFadden‘s Pseudo R-Square11
test statistic takes values 'between 0 to 1'. As the pseudo R-
square takes a value almost 0.258 it means that about 26% of the variation in the dependent
variable is explained by the covariates as per the McFadden‘s R-square is concerned. The
findings were quite in line with studies like Loomis (2008); Brower, et.al. (2006); Landry.
et.al. (2003), among others.
Another very important indicator of the model is the percentage correct or the ‗sensitivity of
prediction‘. The CVM showed that about 85.5 % of the occurrences were correctly predicted
by the model. This is again in line with Landry et.al (2003) who found only 32.09 and 39.86
percentages for two different models; Brower et.al. (2006) got 74.9 and 79.0 percentages
correct for two different models used to estimate people‘s WTP for flood control in
Bangladesh. An alternative to the model chi square is Hosmer ans Lemeshow test to test
goodness of fit. The significance value greater than 0.05 (in our case 0.608) implies that the
model is good fit. The overall test-statistics from the CVM were quite robust and similar in
many such studies as mentioned above.
10
It gives similar results like an R-square in a multiple regression analysis but the upper limit of the value
cannot reach upto one i.e. less than one.
11 Although there is no commonly accepted threshold value for the pseudo R-square that denotes a satisfactory
or well-specified model yet in general larger the value of the pseudo R-square greater the explanatory power of
the model. Certainly one must be worried if the value of the statistic comes lesser than 0.10 (Bateman, et.al.
2002).
16
Table-5: Summary Statistics of the Logistic Regression
Statistic Result df Sig.
Model Chi-Square 35.871 10 0.000
-2LogLikelihood 168.902 - -
Cox & Snell Square 0.140 - -
Nagelkerke R Square 0.241 - -
Overall % of the Probability Correct 85.5 - -
McFadden’s Pseudo R-Square
Hosmer Lemeshow Test (Chi Square) 6.352 8 .608
Welfare Estimation from CVM: As stated in the methodology we calculated Mean
Willingness to Pay (MWTP) suing the formula provided by Hanemann (1989):
MWTP = ` 507 or US$ 10.14 (considering ` 50 = US$ 1)
The respondents mean of WTP for the preservation and maintenance of biodiversity of
Hokera is Rs.507 per annum which amounts to be the 5.8 % of their monthly family income.
Summation of MWTP for the target population will generate huge welfare estimates. The
value of WTP from the closed-ended value eliciting format was less than open-ended
question which asked the respondent ―Anyway what is the maximum amount of money you
would like to contribute for the said improvement?‖ The average amount of WTP was
estimated to be ` 621.75 which is greater than the Mean Willingness to Pay estimated using
single bound dichotomous eliciting format. This difference can be attributed to the
differences in the responses using single bound and open-ended format.
4. Conclusions
Wetland valuation is still at its infancy in India. Most of the wetlands particularly Himalayan
wetlands of Kashmir valley have remained out of focus from environmental economists.
17
Against this background it was a modest effort from our side to understand the economic
importance of the selected wetlands keeping in mind its larger policy perspective. The
findings from the study indicate quite high conservation values of the wetland biodiversity.
The caveats of the present study lie in being location specific and contextual and the
inferences need not to be generalized. Be that as it may studies like the present one may
change traditional thinking about wetlands as unproductive wastelands. It is also evident from
the study that factors like various socio-economic, demographic, environmental awareness
and attitudes can also considerably alter the values of wetlands. Any wetland policy should
carefully consider such factors for larger societal benefits. Besides estimating economic
benefits (values) of the wetlands for practical policy purposes our other major goal was to use
the information for larger societal awareness and educate people about the need for the proper
management and utilization of wetland resources. Economic valuation demands sophisticated
mathematical modelling and mature understanding of the issues involved. Despite many
limitations we believe that the findings from this study would have a bearing not only on
rethinking about our total approach towards the uses (misuses) of wetland resources but also
may encourage further studies to explore the area of wetland economics in the Sub-
continental context in general and Kashmir valley in particular for ensuring their sustainable
uses. Given the fragile eco-systems (which are key to the world renowned scenic beauty
Kashmir) and the pattern of economic development the economic valuation of wetlands
assumes added importance.
18
References
1. Acharya, Gayatri (1998), ―Capturing the Hidden Values of Wetland Ecosystems as a
Mechanism for Financing the Wise Use of Wetlands‖, Paper Presented at a Workshop on
Mechanisms for Financing Wise Use of Wetlands, Dakar, Senegal, 13th November
2. Acharya, G. (2000), ― Approaches to Valuing the Hidden Hydrological Services of Wetland
Ecosystems‖, Ecological Economics, 35(1), pp. 63-74
3. Adamowicz, W.L., J.J. Louviere and M. Williams (1994), ―Combining Revealed and Stated
Preference Methods for Valuing Environmental Amenities‖, Journal of Environmental
Economics and Management, 26, pp. 271-92
4. Arrow, Kenneth; Robert Solow; Paul R. Portney; Edward E. Leamer; Roy Radner and
Howard Schuman (1993), ―Report of the NOAA Panel on Contingent Valuation‖, Federal
Registrar, 58(10), pp. 4016-614
5. Bandyopadhyay, Sumana; Ramanathan, A. and Narayanan, K. (2007), ―Social Perceptions
and Valuation of Wetland Use: A Study of Kolkata Wetlands‖, in Pushpam Kumar and
Sudhakar B. Reddy (Eds.) Ecology and Human Well-being, Sage Publication, New Delhi
6. Barbier, Edward B., Mike Acreman and Duncan Knowler (1997), ―Economic Valuation of
Wetlands—A Guide for Policy Makers and Planners‖, Ramsar Convention Bureau, Gland,
Switzerland
7. Bateman, Ian J.; Richard T. Carson; Brett Day; Michael Hanemann; Nick Hanley; Tannis
Hett; Michael Jones-Lee; Graham Loomes; Susana Mourato; Ece Ozdemiroglu; David W.
Perce; Robert Sugden and John Swanson (2002), ―Economic Valuation with Stated
Preference Techniques—A Manual‖, Edward Elgar, UK and USA
8. Bell, Frederick W.; Leeworthy, Vernon R. (1990), "Recreational Demand by Tourists for
Saltwater Beach Days," Journal of Environmental Economics and Management, 18(3), pp.
189-205
9. Bell, F. W. (1997), ―The Economic Valuation of Saltwater Marsh Supporting Marine
Recreational Fishing in the Southeastern United States‖, Ecological Economics, 21(3), pp.
243-254
10. Bishop, Richard C. and Thomas A. Heberlein (1979), ―Measuring Values of Extra Market
Goods: Are Indirect Method Biased?‖ American Journal of Agricultural Economics, 61, pp.
926-930
11. Brander, Luke M., Florax, Raymond J.G.M. and Vermaat, Jan E. (2006), ―The Empirics of
Wetland Valuation: A Comprehensive Summary and a Meta-Analysis of the Literature‖,
Environment and Resource Economics, 33(2), pp. 223-250
19
12. Brower, Roy; Aftab, Sonia; Brander, Luke and Haque, Enamul (2006), ―Economic valuation
of flood risk exposure and flood control in a severely flood prone developing country‖,
PREM Working Paper Series 06/02, Institute for Environmental Studies, Vrije Univesiteit,
Amsterdam, The Netherlands
13. Carlsson, Fredrik; Frykblom, Peter and Liljenstolpe (2003), ―Valuing Wetland Attributes: An
Application of Choice Experiments‖, Ecological Economics, 47(1), pp. 95-103
14. Chopra, Kanchan and Saroj Kumar Adhikari (2004), ―Environment Development Linkages:
Modelling a Wetland System for Ecological and Economic Value‖, Environment and
Development Economics, Cambridge University Press, 9(1), pp. 19-45
15. Craig, Layman R; Boyche, John R. and Criddle, Keith R. (1996), ―Economic Valuation of the
Chinook Salmon Sport Fishery of the Gulkana River, Alaska, Under Current and Alternate
Management Plans‖, Land Economics, 72(1), pp.
16. Curtis, John A. (2002), ―Estimating the Demand for Salmon Angling in Ireland‖, The
Economic and Social Review, 33(3), pp. 319-332
17. De Groot, R.S.; M.A.M Stuip; C.M. Finlayson and N. Davidson (2006), ―Valuing Wetlands:
Guidance for Valuing the Benefits Derived from Wetland Ecosystem Services‖, Ramsar
Technical Report No. 3/ CBD Technical Series No. 27, Ramsar Convention Bureau, Gland,
Switzerland
18. Do, Thang Nam and Bennett, Jeff (In Press; 2009), ―Estimating Wetland Biodiversity Values:
A Choice Modelling Application in Vietnam's Mekong River Delta‖, Environment and
Development Economics, online ‗First View‘ at:
http://journals.cambridge.org/action/displayJournal?jid=ede
19. Feather, Peter and Shaw, W. Douglass (1999), ―Estimating the Cost of Leisure Time for
Recreation Demand Models‖, Journal of Environmental Economics and Management; 38(1),
pp. 49-65
20. Fix, Peter; Loomis, John (1998), "Comparing the Economic Value of Mountain Biking
Estimated Using Revealed and Stated Preference," Journal of Environmental Planning and
Management; 41(2), pp. 227-236
21. Flaming, Christopher M. and Cook, Averil (2008), ―The Recreational Value of Lake
Mckenzie, Fraser Island: An Application of the Travel Cost Method‖, Tourism Management,
29(6), pp. 1197-1205
22. Freeman, AM III (1993), ―The Measurement of Environmental and Resource Values‖,
Resources for the Future, Washington DC
23. Garrod, Guy and Kenneth G. Willis (1999), ―Economic Valuation of the Environment:
Methods and Case Studies‖, Edward Elgar, UK and USA
20
24. Ghermandi, Andrea; van den Bergh, Jeroen C.J.M.; Brander, Luke M.; De Groot, Henri L.F
and Nunes, Paulo A.L.D. (2008), ―The Economic Valuation of Wetland Conservation and
Creation: A Meta -Analysis‖, FEEM Working Paper No-79/2008; online available at: SSRN:
http://ssrn.com/abstract=1273002
25. Hammack, J. and Brown G.M. (1974), ―Waterfowl and Wetlands: Toward a Bio-economic
Analysis‖, Resource for the Future, John Hopkins University Press, Baltimore, Washington
D.C.
26. Hammitt, James K.; Liu, Jin-Tan and Liu, Jin-Long (2001), ―Contingent Valuation of a
Taiwanese Wetland‖, Environment and Development Economics, 6(2), pp. 259-268
27. Hanemann, W.M. (1984), ―Welfare Evaluations in Contingent Valuation Experiments with
Discrete Responses‖, American Journal of Agricultural Economics, Vol. 66, pp. 332-341
28. Hanemann, W.M. (1989), ―Welfare Evaluations in Contingent Valuation Experiments with
Discrete Response Data: Reply‖, American Journal of Agricultural Economics, Vol.71, pp.
1057-1061
29. Hanemann, W.M. (1994), ―Valuing the Environment through Contingent Valuation‖, Journal
of Economic Perspectives, 8(4), pp. 19-43
30. Heal, G. (2004), ―Economics of Biodiversity: An Introduction‖, Resource and Energy
Economics, 26, pp. 105-114
31. IUCN Redlist (2008), International Union for the Conservation of Nature (IUCN), Gland,
Switzerland; online available at: www.iucnredlist.org
32. Jeanty, P. Wilner and Hitzhusen, Fred (2007), ―Using Stated Preferences to Estimate the
Environmental Benefits of Using Biodiesel Fuel in Diesel Engines‖, Paper Presented in Bio-
fuels, Food and Feed Tradeoffs Conference, St. Louis, Missouri, April 12-13
33. Joshi P. K. , Humayun Rashid and P. S. Roy (2002), ―Landscape Dynamics In Hokersar
Wetland, Jammu & Kashmir- An Application Of Geospatial Approach‖ Journal Of The
Indian Society Of Remote Sensing, 30(1&2), pp. 1-5
34. Kaval, Pamela (2007), ―Recreation Benefits of US Parks‖, Working Paper in Economics
12/07, University of Waikato, New Zealand; online available at:
ftp://mngt.waikato.ac.nz/RePEc/wai/econwp/0712.pdf
35. Kaval, Pamela; Yao, Richard and Parminter, Terry (2007), ―The Value of Native Biodiversity
Enhancement in New Zealand: A Case Study of the Greater Wellington Area‖, Working
Paper in Economics No. 7/22, Department of Economics, University of Waikato, New
Zeeland; online available at:
http://waikato.researchgateway.ac.nz/bitstream/10289/1603/1/Economics_wp_0722.pdf
21
36. Khan, M. A.; S. Bashir (2003), ―Habitat Complexity and Avifaunal diversity of Hokersar
Wetland in the Kashmir Himalaya‖, Indian Journal of Environmental Science, 7, pp. 85-88
37. Khan, M. A.; M. A. Shah; S. S. Mir; S. Bashir (2004), ―The Environmental Status of a
Kashmiri Himalayan Wetland Game Reserve: Aquatic Plant Communities and Eco-
restoration Measures‖, Lakes and Reservoirs: Research and Management, 9, pp. 125-132
38. Klaphake, A., Scheumann and R. Schliep (2001), ―Biodiversity and International Water
Policy: International Agreements and Experiences Related to the Protection of Freshwater
Ecosystems, online available on http://www.water-2001.de
39. Kuriyama, Koichi (1998), ―Measuring the Value of the Ecosystem in the Kushiro Wetlands:
An Empirical Study of Choice Experiments‖, Forest Economics and Policy Working Paper-
9802, Hokkaido University, Japan
40. Kwang, Shin-Yong and Gil, Kim Chang (2007), ―Economic Valuation of Environmentally
Friendly Agriculture for Improving Environmental Quality‖, Journal of Rural Development,
Vol. 29, Issue 4, pp. 73-86
41. Kwak, Seung-Jun; Seung- Hoon Yoo and Chung-Ki Lee (2007), ―Valuation of the Woopo
Wetland in Korea: A Contingent Study‖, Environment and Development Economics, 12(2),
pp. 323-328
42. Landry, Craig E; Keeler, Andrew G. and Kriesel, Warren (2003), ―An Economic Valuation of
Beach Erosion Management Alternatives‖, Marine Resource Economics, Vol. 18, pp. 105-
127
43. Liaw, Shyue-cherng and Chen, Wan-jiun (2006), ―Public Opinions For The Upstream
Lanyang Watershed Management in Taiwan‖, AWRA Summer Specialty Conference,
Missoula, Montana, USA; June 26-28
44. Loomis, J.B. (1987), ―Balancing Public Trust Resources of Mono Lake and Los Angeles‘
Water Right: An Economic Approach‖, Water Resources Research, 23(8), pp. 1449-1456
45. Loomis, John (2008), ―Estimating the Economic Benefits of Maintaining Peak In-stream
Flows in the Poudre River through Fort Collins, Colorado‖, Final Report Submitted to the
Department of Agricultural and Resource Economics, Colorado State University, Fort
Collins, USA
46. Mayor, Karen; Scott, Sue and Tol, Richard S.J. (2007), ―Comparing the Travel Cost Method
and the Contingent Valuation Method– An Application of Convergent Validity Theory to the
Recreational Value of Irish Forests”, Working Paper No. 190, The Economic and Social
Research Institute (ESRI), Ireland
47. Mitchell, R.C. and R.T. Carson (1989), ―Using Surveys to Value Public Goods: The
Contingent Valuation Method‖, Resources for the Future, Washington DC
22
48. National Biodiversity Action Plan (2008), Ministry of Environment & Forest, Government of
India, New Delhi
49. Nunes, Paulo A. L. D.; Jeroen C. J. M. van den Bergh (2001), ―Economic Valuation of
Biodiversity: Sense or Nonsense‖, Ecological Economics, 39(2001), pp. 203-222
50. O‘Connell, M.J. (2003), ―Detecting, Measuring and Reversing Changes to Wetlands‖,
Wetlands Ecology and Management, 11, pp. 397-401, Kluwer Academic Publishers,
Netherlands
51. Oglethorpe, David R. and Miliadou, Despina (2000), ―Economic Valuation of the Non-use
Attributes of a Wetland: A Case-study for Lake Kerkini‖, Journal of Environmental Planning
and Management, 43(6), pp. 755-767
52. Orapan, Nabangchang; Jin Jianjun; Anabeth Indab; Truong Dang Thuy; Dieldre Harder and
Rodelio F. Subade (2008), ―Mobilizing Resources for Marine Turtle Conservation in Asia: A
Cross-Country Perspective‖, ASEAN Economic Bulletin; 25(1), pp. 60-69
53. Ortacesme, V., B. Ozkan and O. Karaguzel (2002), ―The Use of Travel Cost Method in
Economic Valuation of Recreation Sites‖, Journal of the Faculty of Agriculture, Akdeniz
University, Turkey, 12 (1), pp. 107-120
54. Pearce, David and Anil Markandya (1989), ―Environmental Policy Benefits: Monetary
valuation‖, OECD, Paris
55. Pearce, David and Dominick Moran (1997), ―The Economic Valuation of Biodiversity‖,
Earthscan Publications Ltd., London
56. Prasher, R.S.; Y.S. Negi and Vijay Kumar (2006), ―Valuation and Management of Wetland
Ecosystem—A Case Study of Pong Dam in Himachal Pradesh‖, Man & Development, India
57. Samdin, Z.; Y. A. Aziz; A. Radam and M.R. Yacob (2010), ―Factors Influencing the
Willingness to Pay for Entrance Permit: The Evidence from Taman Negara National Park‖,
Journal of Sustainable Development, 3(3), pp. 212-220
58. Singh, B.; R. Ramasubban; R. Bhatia; J. Briscoe; C. Griffin and C. Kim (1993), ― Rural
Water Supply in Kerela, India: How to Emerge from a Low-level Equilibrium Trap‖, Water
Resources Research, 29(7), pp. 1931-1942
59. Stoll, John R. (1983), ―Recreational Activities and Non-Market Valuation: The
Conceptualization Issue‖, Southern Journal of Agricultural Economics, 15(2), pp. 119-125
60. Turner, R. Kerry; Jeroen C.J.M. van den Bergh; Tore Soderqvist; Aat Barendregt; Jan van der
Straaten; Edward Maltby and Ekko C. van Ierland (2000), ―Ecological-Economic Analysis of
Wetlands: Scientific Integration for Management and Policy‖, Ecological Economics, 35, pp.
7-23
23
61. Verma, Madhu; Nishita Bakshi and Ramest P.K. Nair (2001), ―Economic Valuation of Bhoj
Wetland for Sustainable Use‖, online available on http://earthmind.net/values/docs/valuation-
wtland-bhoj.PDF
62. Wattage, Premachnadra and Mardle, Simon (2008), ―Total Economic Value of Wetland
Conservation in Sri Lanka: Identifying Use and Non-Use Values‖, Wetland Ecology and
Management, Vol.16, pp. 359-69
63. Whitehead, John C., Pattanayak, Subhrendu K., Houtven, George L. Van, Gelso, Brett R.
(2008), ―Combining Revealed and Stated Preference Data to Estimate the Non-market Value
of Ecological Services: An Assessment of the State of the Science‖, Journal of Economic
Surveys, 22(5), pp. 872-908
64. Whitten, Stuart M. and Bennett, Jeff W. (2002), ―A Travel Cost Study of Duck Hunting in the
Upper South East of South Australia‖, Australian Geographer, 33(2), pp. 207-221
65. Whitten, Stuart M. and Bennett, Jeff W. (2004), ―A Bio-Economic Model of Wetland
Protection on Private Lands‖, Annual Meeting of the American Agricultural Economics
Association, August 1-4, Denver
66. Woodward, Richard T. and Wui, Yong-Suhk (2001), ―The Economic Value of Wetland
Services: A Meta Analysis‖, Ecological Economics, Vol. 37, pp. 257-70
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