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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.

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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

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