Sam Houston State University

Department of Economics and International Business Working Paper Series

_____________________________________________________

Economic Valuation for a better Conservation: A Case Study of Kaziranga National Park, India

Raju Mandal

Assam University

Subrata Barman Nalbari College

M.P. Bezbaruah

Gauhati University

SHSU Economics & Intl. Business Working Paper No. 14-10 September 2014

Abstract: This paper makes an attempt to estimate the public and non-public good component values of Kaziranga National Park (KNP), a World Heritage Site in the northeast part of India, using contingent valuation method and individual travel cost method respectively. Such a decomposition of total value of an environmental amenity into public good and non-public good components can have significant implications for conservation policies. The results of our analysis led us to conclude that the conservation efforts in terms of resource allocation for KNP are by no means excessive as it amounted to only 3.52 % of total willingness to pay that was estimated in a very conservative way. The estimated consumers’ surplus, a proxy for use values of the park, turned out to be 8.86 % of estimated total economic values, and we suggest a same share of conservation outlay be recovered from the users. A relatively smaller proportion of current user charges in total conservation expenditure (5.87 %) provide a justification for an upward revision of the user charges for a better and more effective conservation in view of the ongoing deterioration of the heritage site from various sources.

SHSU ECONOMICS WORKING PAPER

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Economic Valuation for a better Conservation: A Case Study of

Kaziranga National Park, India*

Raju Mandal Assistant Professor, Department of Economics

Assam University, Silchar, Assam, India.

Subrata Barman Associate Professor, Department of Economics

Nalbari College, Nalbari, Assam, India.

M. P. Bezbaruah Professor, Department of Economics

Gauhati University, Guwahati 781014, Assam, India.

Abstract

This paper makes an attempt to estimate the public and non-public good component

values of Kaziranga National Park (KNP), a World Heritage Site in the northeast part of

India, using contingent valuation method and individual travel cost method respectively.

Such a decomposition of total value of an environmental amenity into public good and

non-public good components can have significant implications for conservation policies.

The results of our analysis led us to conclude that the conservation efforts in terms of

resource allocation for KNP are by no means excessive as it amounted to only 3.52 % of

total willingness to pay that was estimated in a very conservative way. The estimated

consumers’ surplus, a proxy for use values of the park, turned out to be 8.86 % of

estimated total economic values, and we suggest a same share of conservation outlay be

recovered from the users. A relatively smaller proportion of current user charges in total

conservation expenditure (5.87 %) provide a justification for an upward revision of the

user charges for a better and more effective conservation in view of the ongoing

deterioration of the heritage site from various sources.

JEL Classification Code: Q5.

Key Words/Phrases: Travel Cost, Willingness to Pay, User Charge.

* This version of the paper was completed when Mandal was a Visiting Post-Doctoral Scholar at the

Department of Economics & International Business, College of Business Administration, Sam Houston

State University, Huntsville, Texas, USA, under the Raman Fellowship Programme of the University

Grants Commission, India. He would like to thank the host institution for providing a conducive

environment for conducting research.

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Economic Valuation for a better Conservation: A Case Study of Kaziranga

National Park, India

1. Introduction

Environmental amenities like wildlife sanctuaries, national parks etc. are valuable

for both their users and non-users.2 Information on economic values of such amenities

have important policy implications as the same can help quantify the trade-off between

gains and losses of land management decisions, efficiently target infrastructure

investments and support budget allocation decisions by public authorities (Heberling and

Templeton, 2008). For a user the amenity is often excludable and can even be rival, and

hence is akin to a private good. The non-use value, on the other hand is non-rival and

non-excludable. Therefore, from the non-users’ perspective the amenity is a public good.

Decomposition of total economic value of an amenity into these two components may

prove to be quite useful from policy perspectives. It may help public authorities in

devising suitable mechanisms for mobilization of resources from different sources. For

the public good component of the total benefit of the amenity, the usual pricing process

does not work, and hence its maintenance and conservation should be provided from the

general revenue of the public exchequer. In contrast, the non-public good component can

not only be priced but should also be priced adequately to ensure sufficient mobilization

of resources and prevent congestion, and thus help sustainable use of the amenity. With

this idea in mind this paper makes an attempt to estimate the public and non-public good

component values of Kaziranga National Park (KNP, hereafter), a World Heritage Site in

Assam, a state in the northeast region part of India. KNP is famous for its rich bio-

diversity, especially the one horned rhinoceros and several endangered species. The

estimated value components so obtained could be useful to answer the following research

2 The benefits or utility derived from environmental goods and services are broadly categorized into use

values and non-use values. Use values arise from the benefits derived by people by participating in the use

of the goods or services. The non-use values, on the contrary, arise when people derive utility without

participating in use of the goods or services concerned. For example, an individual may feel happy merely

from knowing that a particular wildlife sanctuary rich in biodiversity exists even though he/she has never

visited it (existence value). Likewise, he/she may want to keep the option of participating in future (option

value) or may want it to be preserved so that future generation gets to enjoy it (bequest value).

1

questions that have important implications for conservation policies of the park in the

wake of its continuing threats from various sources (discussed later). First, is present

level of conservation efforts in terms of resource allocation economically justified?

Second, are current user charges appropriate or is there any scope to revise them? Third,

how should total conservation and maintenance costs be divided between users of the

park and general tax payers of the state?

The two broad categories of valuation techniques, namely revealed preference and

stated preference, have been used quite extensively for valuation of environmental

amenities, each with its own strengths and limitations. While the revealed preference

methods have been used for estimating use values, the stated preference method has

mostly been used to estimate non-use values although it is capable of capturing both

types of values. There is an emerging literature on combining both the methods

(Cameron, 1992; Adamowicz et al, 1994; Cameron et al, 1996, Whitehead et al, 2000;

Park et el, 2002; Eom and Larson, 2006). In this paper we use both types of methods. The

novelty of the current study lies in the fact that we try to decompose total economic value

into public good and non-public good components with a direct policy focus.3 From

policy perspectives, such decomposition may help public authorities in dividing the cost

of maintenance and conservation among visitors and general tax payers in the ratio of the

two value components. This apart, this paper improves upon the previously available

value estimates of KNP. 4

The results of the analysis reveal that the conservation efforts in terms of resource

allocation for KNP are only 3.52 % of total willingness to pay estimated in a very

conservative way, and hence are not economically excessive. The estimated consumers’

surplus turns out to be 8.86 % of estimated total economic value. This leads us to suggest

8.86 % of conservation outlay should be recovered from the users. A relatively smaller

3 Most previous studies in this regard (Cameron, 1992; Adamowicz et al, 1994; Cameron et al, 1996; Park

et el, 2002; Eom and Larson, 2006) have used revealed preference and stated preference information of the

same respondents who actually participated in the enjoyment/use of the environmental quality. However,

our focus is neither to compare estimates from the two models nor to test their internal consistency. 4 For example, Bharali and Mazumder (2012), and Barman (2012) use zonal travel cost method and

individual travel cost method respectively to estimate the use values of KNP. But although based on on-site

sampling none of their estimates correct for endogenous stratification bias.

2

share of current user charges (5.87 %) in actual total conservation expenditure provides a

justification for an upward revision of the user charges.

The rest of the paper is organized as follows. Section II gives a brief background

of KNP. Section III deals with the methods, data and models used while section IV

reports the value estimates of the amenities of KNP. The conclusion and policy

recommendations are covered in section V.

2. The Kaziranga National Park: A brief profile

The Kaziranga National Park (KNP) is located in the state of Assam in the northeast part

of India between latitude 26°30 N to 26°45 N and longitude 93°08 E to 93°36 E. It covers

an area of 430 sq km. along the river Brahmaputra on the north and Karbi Anglong Hills

on the south. It is the oldest park in Assam. The importance of KNP has been recognized

from time to time since 1905 with creation of Kaziranga Proposed Reserve Forest and its

subsequent designation as a reserve forest in 1908. In 1916, it was converted to a game

sanctuary and remained so till 1938, when hunting was prohibited and visitors were

permitted to enter the park. The Kaziranga Game Sanctuary was renamed as Kaziranga

Wildlife Sanctuary in 1950 in order to rid the name of hunting connotations. In 1954, the

government of Assam passed the Assam (Rhinoceros) Bill, which imposed heavy

penalties for rhinoceros poaching. In 1968, the state government passed ‘The Assam

National Park Act of 1968’, declaring Kaziranga a designated national park. The park

was given official status by the central government of India on 11th February 1974. In

1985, it was declared a World Heritage Site by UNESCO for its unique natural

environment. KNP was recognized as a Tiger Reserve in 2007.

[Insert Fig. 1]

[Insert Table 1]

The moderate climatic conditions and availability of food resources support the

growth and survival of an exceptional and diverse wildlife in KNP. Apart from being

home to the Indian one-horned Rhinoceros (Rhinoceros unicornis), it has a sizable

population of the wild buffalo, tiger and Indian elephants (Elephus maximus). The

number and growth of important wildlife of the park can be seen in Table 1. The park has

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the rare distinction of being one of the very few places in the world which has breeding

populations of three different species of tiger outside Africa, namely the Royal Bengal

Tiger (Panthera Tigris), the Indian Leopard (Panthera pardus fusca), and the Clouded

Leopard (Neofelis nebulosa). KNP harbors 479 birds, 42 fish and 35 mammal species out

of which at least 17 species of mammals, 23 species of birds and 10 species of reptiles are

in endangered list (Barman, 2012). KNP is open for the visitors from January to April,

and November to December every year. The importance of the rich amenity services of

the park can be visualized from an increasing number of tourists from different parts of

India and the world as shown in Table 2. A glimpse of the fauna of KNP can be found in

Fig. 2 – Fig 6.

[Insert Table 2]

[Insert Fig. 2 – Fig 10]

The KNP has been constantly facing threats induced by both natural and

anthropogenic factors (Fig. 7 – Fig. 10). As it is on the bank of the mighty river

Brahmaputra (see Fig. 1), every year large areas of the park get inundated with flood

water, thereby causing death of several animals very often (see Fig. 8).5 This apart, floods

have adversely impacted upon the habitat of the park by way of erosion and siltation.

There are reports of encroachments in and around the park by suspected illegal

immigrants that has stirred tensions among the people of that area. Another major

anthropogenic factor causing degradation of the heritage site are encroachment and

poaching of animals, especially one-horned rhino which is the identity of KNP (Fig. 10).

During 1981 and 2012, the park lost around 20 rhinos per annum to the poachers.6

Because of surging prices of rhino horn in the international market they have been regular

targets of the poachers. During floods when the rhinoceros move out of their usual habitat

in search of shelter in the highlands of Karbi Anglong foothills along the southern

boundary of the park, poachers find it easier to kill and dehorn them as these areas fall

5 This is not to deny the fact that alluvial depositions from seasonal floods do help in maintaining diversity

of flora and fauna in the park. 6 Calculated from data used in Lopes (2014).

4

outside the notified areas of the park and lack effective anti-poaching cover (Talukdar,

2012). As various animals attempt to cross National Highway 37 on the southern border

of the park looking for elevated shelters, many a times they get hit and run by vehicles

(Fig. 9). There have also been land use changes in the surroundings of KNP that include

conversion to tea gardens, human settlement, logging, Jhum cultivation etc. thereby

leading to habitat destruction for its animals.7 The tea gardens close to the park

boundaries also pose a threat through pesticide run-off and increasing the potential for

invasive species. Further, there is shortage of sanctioned staff for managing the KNP.

With more areas added to the park, additional staff and infrastructure is needed for

effective control over the additional areas (Barman, 2012).

Although the state government has taken some steps in recent years for

protection of animals they do not seem to have enough impacts. The initiatives of the

public authorities with regard to land acquisition for expansion of area of the park are

often faced with resistance from the land holders (Saikia, 2011). Attractive

compensations might be helpful in this regard. There is need to recruit more guards and

equip them with modern tools and training. The discussion above make it clear that

conservation policy of KNP needs a holistic approach and that it is not possible without

an adequate resource mobilization. This, coupled with growing demand for amenity

services of the park (as evident from Table 2) call for quantifying their economic value

and ensuring enough conservation efforts in terms of resource mobilization. 8

3. Methods, Materials and Models

3.1. Valuing non-public good component of the amenity of KNP

3.1.1. Method:

Although access to many environmental amenities like KNP requires an entry fee to be

paid, it is often meagre compared to total expenses incurred by a visitor on traveling to

the site. Thus, entry fee does not reflect the amount that the visitors are actually willing to

7 Jhum cultivation is also known as shifting cultivation or slash-and-burn cultivation whereby the forest

cover of land is cut and burnt before sowing seeds. 8 In recent times, the issue of conservation of KNP, especially eviction of alleged encroachers from the park

and protection of its animals from poaching, has aroused lots of enthusiasm among people of the state

cutting across different walks of life.

5

pay to enjoy the amenities and hence cannot be taken as a measure of value of such

benefits. Hotelling (1947) suggested travel cost method (TCM) of valuation in this regard

in his now famous letter to the director of the National Park Services of US and it has

been in use since then (Parsons, 2013). As a revealed preference method, the TCM is one

of the most widely used methods of estimating recreational demand for the environment

(Mendelsohn and Olmstead, 2009). The variants of TCM include zonal travel cost

method, individual travel cost method, random utility model and hedonic travel cost

method. They have been used in different contexts, each with its own merits and

limitations. In the present empirical context of valuation of non-public good component

of the amenity services of KNP, individual travel cost method (ITCM) has been used.

ITCM has become more popular in the last two decades as it has the added advantage of

being able to include a number of individual specific socio-economic characteristics such

as age, income, education etc. to help capture heterogeneity among individual as opposed

to zonal visitations (Blackwell, 2007; Khan, 2004; Bowker et al, 1996; Haab and

McConnell, 2002).

The first step towards valuation of amenity services is estimation of the demand

function for them. The theory of demand suggests that quantity demanded of a

commodity falls with increase in its price and vice versa, ceteris paribus. Likewise if cost

of travelling to a site is more people tend to visit it less. The TCM uses amount spent by

visitors to travel to a particular site as a proxy for the market price of amenity services of

that site (Hanley and Spash, 1993; Freeman, 1993) and seeks to examine how variations

in travel cost affect the frequency of visits to the site. The number of visits, expressed as

a visitation rate, is used to illustrate the amount of amenity services purchased at those

prices. Exploiting the empirical relationship between travel costs and visitation rates is a

crucial step that permits estimation of a demand function for such services (Knetsch,

1963, Mendelsohn and Olmstead, 2009). From the demand curve thus obtained,

consumer surplus is calculated as value of non-public good component of amenity

services of KNP.

6

3.1.2. The sample:

Valuation of non-public good component of KNP is based on primary data collected

through face-to-face interview with the help of structured schedules. Data were collected

in several rounds during November 2008 to April 2009 keeping in view the fact that the

park remains open to the visitors for these six months every year. For collection of

primary data twenty four different hotels, lodges, and resorts from in and around the park

and also some resorts located close to the core area of the park have been selected. The

reason for selecting different resorts from varied ranges is to capture tourists with

different socio-economic backgrounds. A total of 233 visitors to the park constituted the

sample of respondents covered for this component study. Here the unit of observation is

the visitor concerned, or the head of household in case of more than one member of

household visiting together.

3.1.3. The model and its estimation:

Dependent variable

The ITCM assumes that each visitor chooses the number of trips he will take in a given

period of time, and also that the visitor’s marginal utility decreases with the number of

trips (Martin-Lopez et al, 2009). Hence, as costs of travel increase the frequency of visits

declines, given other socio-economic-demographic characteristics. Thus visitation rate,

i.e., number of visits during a given period of time, usually a year, is taken as the

dependent variable. In the present empirical context, trips to KNP are a seasonal activity

as the park is open for the visitors for six months only and hence, a visitor is very

unlikely to visit more than once a year. This phenomenon is common to other parks as

well, and in order to incorporate variations in the dependent variable, an alternative

measurement of it is often used. Some researchers have measured visitation rate as the

product of size of the visiting group and number of trips taken by a person with reference

to a year (Heberling and Templeton, 2009; Bowker et al, 1996), while some others have

done the same for a relatively extended period of five years (Martı ´nez-Espin˜eira and

Amoako-Tuffour, 2007; Bhat, 2003). In the present study to capture variations in

7

visitation rate, number of visits by a respondent during last five years is taken as the

dependent variable.9

Explanatory variables

In the TCM total expenditure incurred to make visits to the site concerned is taken as a

proxy for the price of its recreational benefits paid by the visitors. Hence, the key variable

of concern is the amount of travel cost (TC) incurred by visitors. The TC has been

computed as the sum of round trip travel expenses to and from KNP; the expenses on

food and beverages and lodging; expenses on gypsy and animal ride, if any; and entry fee

of the park. The measurement of opportunity cost of travel time can be problematic.

Some researchers have attempted to capture is arbitrarily in varied ways. For example,

while Cesario and Knetsch (1976) suggested and used 60% of wage rate as a proxy for

opportunity cost of travel time, the corresponding figures as used by Blackwell (2007)

and Chae et al (2012) are 40% and 30% of wage rate respectively. In this paper, however,

the opportunity cost of travel time is not taken into account primarily because of two

reasons. First, there is no strong consensus on its appropriate measure. Second, the actual

figures of wage, or daily income for that matter, are difficult to be found because of

reasons mentioned later.

Another unsettled issue concerning calculation of travel cost relates to visits of

multiple sites. In case of visits to multiple sites, particularly in case of package tours,

priority to visit KNP has been ranked in 10 point scale. Depending upon the rank given

by the visitors the cost of visit to KNP is calculated out of the total cost of the package

tour. Finally if the travel cost reported by the respondent relates to more than one person

then the per head travel cost is taken into account.

9 In the ITCM the unit of observation is an individual visitor and his demand for the environmental

amenities is estimated as a function of his socio-economic attributes, in addition to his cost of visitation.

Let us suppose that the individual visitor concerned is making a first time visit to a site in a group of three.

It may not be appropriate to take visitation rate of the concerned individual as three (i.e., his visits during

the period times size of the group) since his demand is unlikely to be representative of all three because of

obvious differences in their socio-economic characteristics. Moreover, three times visits to the site might be

beyond his reach, given his personal characteristics.

8

In addition to travel cost, the number of visits by individuals to a recreational site

also depends on factors such as economic status; age, gender and educational attainment

of the visitors; size of their households; location of the visitors and perceptions regarding

quality of environmental amenity of the site concerned. Hence these factors are used as

control variables that may affect the visitation rate to the park.

The most widely recommended measure of economic status of an individual is

her/his income. However getting true household income data in a survey is difficult –

more so in developing countries like India where a good part of a household’s income

come from informal and even non-monetized sources. Therefore, as a proxy for economic

status of visitors, an index of consumption standard has been constructed on the basis of

consumer durables possessed by them. First of all six items of consumer durables have

been selected and assigned a score from 1 to 6 starting from less expensive to most

expensive ones. The respective scores of the consumer durables possessed by an

individual visitor are added and divided it by the grand total of scores of all consumer

durables (i.e., 21). If a visitor possesses all the six consumer durables then his index of

consumption standard will be equal to 1, and if he possesses nothing then it will be equal

to 0. It is reasonable to assume that the index has strong positive correlation with the

household economic status, or more specifically household per capita income. It is

expected that economic status of the visitors (ES) and number of visit to the park are

positively related. The non-economic factors that are taken into account as possible

determinants of visitation rate are briefly outlined below.

Age of an individual may affect his frequency of visits to a site. Hence, age of the

visitor in years (AG) is also taken as an explanatory variable. AG may affect the visitation

rate both positively and negatively, depending on the nature of the site concerned. In case

of pilgrimage sites, aged people tend to visit more whereas the reverse may be true for

recreation sites like KNP. In order to capture the possible differential impact of gender of

the visitors on visitation rate a dummy variable SX has been used, where SX = 1 for male

visitors, and 0 otherwise. It is expected that because of their greater mobility, in general

in a society like India, males visit KNP more than females.

9

Education makes an individual aware of the existence of environmental amenities

and their importance as a source of recreation and other uses. Hence, level of educational

attainment (in years) of the visitors has been taken as an explanatory variable. It is

expected that education (ED) has a positive impact on visitation rate.

The quality of the park as perceived by visitors may also affect visitation rate. It is

assumed that the visitors have some prior knowledge about the quality of KNP. Their

perception about the quality of KNP is captured by a dummy variable PQ, where PQ = 1

if perception about quality of the park is good, and 0 otherwise. It is to be noted that the

visitors were asked about their perception before their entry into the park.10

The place of residence or locality of the visitors is another factor that may affect

visitation rate, and hence considered as another explanatory variable. In this regard a

dummy variable L is used, where L = 1 for visitors from urban areas and 0 otherwise. It is

expected that the sign of this explanatory variable is positive, that is the urban dwellers

tend to visit more to the park than their rural counterparts. Finally, household size (HS) of

the visitors is taken as another explanatory variable. The individuals belonging to larger

households are likely to visit less.

Dependence of visitation rate on the factors mentioned above can be written as

follows:

),,,,,,,( iiiiiiiii HSLPQEDSXAGESTCfV ------ (1)

Where, Vi represents visitation rate for the ith individual. Definitions of the

explanatory variables are mentioned in Table 3.

[Insert Table 3]

There are a few technical issues involved while estimating economic values

applying ITCM (Martı´nez-Espin˜eira and Amoako-Tuffour, 2008). This is mainly

because of nature of the dependent variable visitation rate. First, it takes non-negative

10 As Whitehead et al (2000) notes that the objective measures of park quality, e.g., oxygen, nitrogen,

phosphorous loadings or other environmental variables, suffers from the limitation that such quality

measures do not vary across individuals at the same recreation site which makes its valuation a difficult

task. Hence, the subjective measure of perceived quality of the site is suggested as an alternative.

10

integer values. Second, the sample is truncated at zero because the sample includes all

visitors who have visited at least once. This may cause biased and inconsistent estimates

and overstate consumer surplus estimates (Shaw 1988; Creel and Loomis 1990; Grogger

and Carson 1991). Third, on-site sampling makes the frequent visitors more likely to be

sampled compared to occasional visitors that often lead to a bias known as endogenous

stratification. This if uncorrected, would create inference problems and lead to overstated

welfare estimates (Heberling and Templeton, 2008; Ovaskainen et al 2001; Haab and

McConnell 2002; Martı´nez-Espin˜eira and Amoako-Tuffour 2007). Finally, the data

usually exhibit an over-dispersion problem (Cameron and Trivedi, 1986; Grogger and

Carson, 1991), meaning that variance of the dependent variable is greater than its mean

because a few make many trips while most make only a few (Martı´nez-Espin˜eira and

Amoako-Tuffour, 2008).

In view of the issues mentioned above a linear regression model and the standard

ordinary least square estimators will not be appropriate. In single-site valuation studies

such as the present one, truncated count data models like Poisson and negative binomial

models have been widely applied.

The density of a Poisson distribution for the count y is given by:

!

)exp()Pr(

yyY

y

iiii

----- (2)

(y = 0,1,2,….. i = 1,2,…, n)

A simple count data model that satisfies the issues of non-negative integers and

truncation is truncated Poisson distribution that can be written as follows.

)exp(1

1.

!

)exp()0|Pr(

i

y

iiii

yYyY

----- (3)

(y = 1,2,3,… i = 1,2,…, n)

Where, iY is a discrete random variable for the number of trips taken by individual

i and iy is the realized integer value. Conventionally the mean i is parameterized for

estimation in a regression framework as follows (Bowker et al, 1996; Bhat et al, 1998;

Martı´n-Lo´pez et al, 2009).

11

iii X ln ----- (4)

Where iX is a vector of explanatory variables, is a vector of parameters and i is

a vector of random disturbance term. It is to be noted that Poisson distribution is

appropriate when mean and variance of the dependent variable are equal. Otherwise, to

take care of the problems of over-dispersion or under-dispersion negative binomial

distribution is more appropriate. The density of truncated negative binomial distribution

is given as:

)/1(

)/1(

)1(1

1.)1().(

)1()/1(

)/1()0|(

i

y

i

y

i

i

iiii

ii

y

yYyYP ----- (5)

Where (.) represents the gamma function. The parameter determines the degree of

dispersion relative to mean. The mean of the random variable Y is and variance

is )( 2 . When 0 , 0 and 0 there exists over-dispersion, under-dispersion

and no over-dispersion/under-dispersion respectively.

Finally, since the data have been collected on-site the problem of endogenous

stratification cannot be ignored. Several authors have proposed to address this problem

under the assumption of equi-dispersion (Martı´n-Lo´pez et al, 2009). In this regard Shaw

(1988) showed that

)!1(

)exp()0|(

)1(

i

y

iiiii

yYyYP

i ----- (6)

Thus, in the presence of equi-dispersion, the issues of truncation and endogenous

stratification can be addressed with a Poisson distribution by modeling visitation rate

minus one as the dependent variable (Martı´n-Lo´pez et al, 2009; Haab and McConnell,

2002; Heberling and Templeton, 2009).

In the present empirical context truncated Poisson (TP) and truncated negative

binomial (TNB) models have been estimated first to account for truncation and over-

dispersion/under-dispersion. The regression results show absence of over-dispersion or

under-dispersion. Therefore, the problems of truncation and endogenous stratification

have been addressed with a Poisson distribution by modeling visitation rate minus one as

12

the dependent variable. After estimating the demand function consumers’ surplus figures

have been calculated and used as a proxy for economic value of non-public good

attributes of KNP.

3.2. Valuing public good component of the amenity of KNP

3.2.1. Method:

For estimating the value of public good component of KNP contingent valuation method

(CVM) has been used. Despite its limitations CVM has widely been used for measuring

the value of public goods in different countries and contexts. Contingent valuation is

capable of capturing both use and non-use values of any amenity for which market may

be not yet existent or too imperfect to reflect the use values. However, in the present

empirical context the focus of this contingent valuation is the public good component of

the amenity, and hence willingness to pay (WTP) of people is elicited for the non-

excludable and non-rival services of the amenity only. The sample respondents for WTP

survey also consisted of almost entirely non-visitors to the park. Hence the WTP revealed

by the respondents can be interpreted as their valuation of the pure public good

component of the amenity.

Following the standard procedures of a CVM study, the contingent valuation

exercise is divided into following stages. The first step in a CVM study is to define a

hypothetical market. Towards this end, information of KNP regarding its flora, fauna and

rich biodiversity were provided to the respondents along with photographs. The

respondents were also informed that the KNP is now facing some serious problems such

as man-animal conflict, flood, soil erosion, encroachment, poaching, insufficient number

of well equipped forest guard, increasing number of hotels and lodges in the nearby area

of the park, and also about the National Highway 37 which divides the park using

photographs. Later on they were told that there is a proposed plan for KNP which can be

helpful for conservation and protection of the park and this proposed plan cannot be

undertaken unless raising some funds from the public. Having defined the hypothetical

market this way, the respondents were asked to state their maximum WTP per month for

the proposed plan for a period of next five years. In the next step, to estimate the bid

13

curve WTP is regressed on index of economic status, socio-demographic characteristics

and environmental awareness of the respondents. The final step in CVM is to aggregate

data for society’s WTP. Replacing the explanatory variables by their respective average

values, the average WTP for KNP has been calculated. The total WTP for the society as a

whole has been calculated by multiplying the average WTP by total population of

households.

3.2.2. The sample:

The contingent valuation (CV) survey was administered through face-to-face interview.

The interview schedule was divided into three sections - (i) information regarding

environmental awareness of the respondents, (ii) information regarding their maximum

willingness to pay and finally (iii) personal profiles of the respondents to capture various

socio-economic characteristics. Notwithstanding the fact that public good component of

the environmental amenities of KNP are not limited to the inhabitants of the state of

Assam alone, the willingness to pay survey had to be confined to the Brahmaputra valley

of state.11 Though the geographical limit had to be set due to logistical compulsions, there

is a sound intuitive rationale beneath setting the limit. The people of Assam, especially

those from the Brahmaputra Valley, are greatly proud of their World Heritage Site of the

KNP and are deeply concerned with its conservation and its continued existence in its

glorious natural ambiance with the riches of its flora and fauna. It is, therefore,

reasonable to assume that the residents of the Brahmaputra Valley predominantly

constitute the population that will be willing to pay for conservation of the Park by

contributing to the state exchequer, though many of them may not have ever extracted its

recreational and other use values. Indeed, the sample of respondents consisted

predominantly of non-visitors to the park. The sample comprises 160 respondents.

11 The state of Assam is comprised of three broad natural divisions, viz., Brahmaputra Valley, Barak Valley

and Hill Zone. They accommodate 85%, 12% and 3% of total population of the state respectively.

14

3.2.3. The model:

The main focus of this CVM exercise is to estimate willingness to pay (WTP) of people

for conservation of KNP. Hence maximum WTP is taken as the dependent variable and

regressed on economic status index (as a proxy of income), socio-demographic

characteristics and environmental awareness of the respondents that are likely to

influence people’s WTP. The multiple linear regression model to be estimated is shown

by equation (7).

jjjjjjjj uDSEAHSSXAGESWTP 6543210 ---- (7)

Where WTPj is the willingness to pay of the jth individual. Economic status

(ES), age (AG), gender (SX), household size (HS) of the respondents have been defined

and measured the way as discussed in section III.1.3 (see Table 3). Environmental

awareness of an individual may also influence his WTP. To capture environmental

awareness of individuals an index of environmental awareness (EA) has been constructed.

The value of the index ranges from 0 to 1. Generally higher the awareness regarding

environment higher will be the WTP. Moreover, distance (DS) from place of residence to

KNP may also influence the WTP of an individual. It is expected that there is a negative

relationship between WTP and DS. The final estimates have been obtained using OLS

method after affecting White’s heteroscedasticity correction procedure.

4. The Estimates of the components and total value of KNP

4.1. The demand function for amenity services of KNP has been first estimated using

truncated Poisson (TP) and truncated negative binomial (TNB) specifications. The results

are shown in Table 4. The over-dispersion parameter alpha (α) is not significantly

different from zero which implies absence of over-dispersion or under-dispersion. This is

confirmed by identical results of TP and TNB models since as per theory when

0 negative binomial model converges to Poisson model (Yen and Adamowicz, 1993;

Dehlavi and Adil, 2011).

15

Once equi-dispersion is confirmed, the problem of endogenous stratification has

been dealt with by running a usual Poisson regression after modeling visitation rate

minus one )1( iV as the dependent variable, as suggested by Haab and McConnell

(2002). The estimated results of this Poisson (POIS) regression are shown in the last

column of Table 4. The interesting point is that the coefficient of the variable of prime

concern TC is found to be significant and negative which implies that higher the travel

cost less will be the number of visits to KNP. This conforms to the usual theory of

consumer’s behavior as far as the demand for any good or service is concerned. A

positive and statistically significant coefficient of ES implies individuals with a better

economic status tend to visit KNP more. Likewise, the young people tend to visit KNP

more than the aged. The positive coefficient of L and its statistical significance means

people from urban areas tend to visit KNP more than their rural counterparts. This might

be because of the fact that urban areas are characterized by polluted environment and are

deficient of the environmental amenities compared to rural areas.

[Insert Table 4]

Taking negative inverse of TC coefficient (Creel and Loomis, 1990; Yen and

Adamowicz, 1993; Martı´n-Lo´pez et al, 2009) the estimated consumer’s surplus per

visitor during last five years was found to be INR 4210.53. The total consumers’ surplus,

which also represents the total use value of the park, is obtained after multiplying the per

visitor consumer’s surplus by number of visitors in last five years.12 Thus, total

consumer’s surplus, or total value of non-public good component of the park is found to

be INR 1546.9403 billion, and its annual equivalent turned out to be INR 309.39 billion.

12 Total number of visitors to the KNP during five years (2004-05 to 2008-09) preceding the survey was

367,398.

16

4.2. The regression results of WTP for conservation of KNP are shown in Table 5. The

prime variable for estimation of the bid curve or the WTP function is economic status of

the individuals (ES). It is seen from Table 5 that the co-efficient of ES has turned out to

be significant and positive. This implies that better the economic status of an individual

higher will be his WTP for conservation of KNP.

[Insert Table 5]

The other explanatory variables have also turned out to be significant. The

estimated coefficients of SX and EA were found to be positive whereas the coefficients of

AG, HS and DS turned out to be negative. This implies that male respondents are willing

to pay more than females for conservation of KNP. Similarly if an individual is more

aware about the environment and environmental issues he will be willing to pay more.

On the other hand, age of respondent (AG) has negative impact on the WTP for KNP

which implies that younger people are willing to pay more than elderly people. Likewise,

household size of the respondents is found to have negative impact on the WTP. It means

that higher the size of the household of a respondent lower will be his/her WTP for

protection and conservation for KNP. Finally, distance from the place of residence to

KNP is found to have negative impact on WTP of people which means that if an

individual resides far away from the KNP his WTP will be less than the individuals who

resides nearer to it.

Replacing the explanatory variables other than ES by their respective sample

mean values a relationship between WTP and economic status (ES), i.e., a proxy of per

capita income, can be established and the bid curve can be estimated as follows.

ESWTP 74.25346.25 ----- (8)

After estimating the bid function the average WTP can be obtained either by

taking mean or median of the variable ES. It is to be noted that as far as non-use values

(i.e., public good component) in case of environmental amenity are concerned lower bids

are more likely than higher bids. This is a reflection of income/wealth distributions which

17

are generally positively skewed. In such situation median is more representative than

arithmetic mean. Hence the variable index of consumption standard (ES) is replaced by

its median which yields the estimated average WTP at INR 97.95 per month. For

calculating the total willingness to pay, this average willingness to pay has been

multiplied by total number of ‘above poverty line’13 households in the geographical limit

of the WTP survey, i.e., the Brahmaputra Valley of Assam. The ‘below poverty line’

households have not been left out in view of the limitation of their ability to pay. The

total WTP thus calculated and deflated for price parity with the Travel Cost survey

estimates came to INR. 3181.95 billion per year.14

5. Conclusion and policy implications

The main objectives of the paper are to examine if the existing conservation efforts for

KNP in terms of resource allocation is economically justified and how to apportion the

total cost of conservation among users of the park and general public of the state of

Assam. Our results reveal that the amount of resources mobilized for conservation and

management of KNP amounts to only 3.52 % of total WTP, i.e., total value attached by

people, estimated in a very conservative way. Hence, the level of conservation effort is

economically definitely not excessive.15 Indeed, in view of the ongoing deterioration of

the heritage site from various sources on the one hand and the substantial aggregate WTP

for conservation on the other, stepping up of conservation efforts through allocations of

more funds is well justified.

The non-public good component of the total estimated value of KNP turned out to

be 8.86%. This gives a basis to suggest that an equivalent share of total conservation

expenditure be recovered through user charges and the rest be financed from the public

13 For targeting delivery of subsidized government provided goods and services, households in India are

divided into BPL (below poverty line) and APL (above poverty line) categories. BPL households are

entitled for greater amount of subsidies. For instance, such households are supposed to receive larger

quantity of subsidized foodgrains at lower prices from the Public Distribution System.

14 It is arguable that residents outside the Brahmaputra Valley of Assam may also be willing to pay for

conservation of KNP. Thus, limiting the calculation to APL households of the Brahmaputra Valley results

in a rather conservative estimate of the total WTP.

15 The annual average conservation efforts in terms of resource allocation during 2006-07 to 2010-11 stood

at INR 112.17 billion (Source: Director, Kaziranga National Park).

18

exchequer. The actual user charges amounted to only 5.87% of total conservation

expenditure in the year 2010-11. This implies that the excludable and semi-rival

amenities of KNP are grossly underpriced. Moreover, in light of the substantial estimated

average consumer’s surplus there is obviously a scope for levying higher user charges

from visitors of KNP. Higher user charges can be recovered in a number of possible ways

such as by enhancing entry fee, charging higher rates for amateur and professional

cameras and levy of cess on luxury resorts around the park.16 A combination of these

sources should be tapped towards this end. As the first component is likely to be uniform,

it will not be equitable to enhance this by a very high rate. On the other hand, the last

component can be adjusted on the basis of standard of the resorts and can, hence, have a

progressive structure.

16The Union Environment and Forests Ministry, Government of India has cleared ecotourism guidelines

containing the provisions and submitted them to the Supreme Court in an ongoing case that- all tourist

operations within 5km of all 600 plus tiger reserves, national parks, sanctuaries and wildlife corridors in the

country will soon have to fork out a minimum of 10% of their turnover as " local conservation fee", which

will be used not only to protect wildlife areas but also provide financial assistance to communities and

people living around these green patches (The Times of India, Guwahati Edition, 13thJuly, 2012). This is in

conformity with the kind of institutional arrangement we are suggesting here for the KNP.

.

19

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22

Table 1

Animal stocks as per different census of KNP

Year Rhinoceros Tiger Swamp

deer

Elephant Wild buffalo

1978 938 40 697 773 610

1984 1080 52 756 523 677

1991 1129 - - - -

1993 1164 72 - 1094 -

1997 - 80 - 945 -

1999 1552 - 389 - 1192

2000 - 86 486 - -

2001 - - - - 1431

2002 - - - 1048 -

2005 - - - 1246 -

2006 1855 - - 1293 -

2007 - - 681 - 1048

2008 - - - 1293 1937

2009 2048 - - - -

2010 - 106 - - -

2011 - - 1165 1163 - Source: Director, KNP & Environment and Forest Department, Govt. of Assam.

Table 2

Number of visitors and collection of revenue of KNP

Year Number of visitors Total

visitors

Total revenue (INR)

Indian Foreigner

1990-1991 22704 463 23167 310298

1995-1996 24897 3199 28088 880951

2000-2001 50498 1838 52336 3038258

2005-2006 49116 5210 54326 7615169

2010-2011 112392 7447 119839 13673482

Source: Director, KNP.

23

Table 3

Definitions of the explanatory variables

Variables Definition

Travel cost (TC) Round trip travel expenses + gypsy and

animal ride + expenses on food, lodging

etc.+ entry fee

Economic status (ES) Index of Consumption Standard

Age (AG) Age of the respondents in years

Gender (SX) = 1 for male respondents, 0 otherwise

Education (ED) Level of educational attainments of the

respondents in years

Park quality (PQ) = 1 if perception about quality of the park

is good and 0 otherwise

Location (L) = 1 if the respondent is from urban area and

0 otherwise

Household size (HS) No. of members in the household of the

respondents

Environmental

awareness (EA)

Index of Environmental Awareness

Distance (DS) Distance from place of residence to KNP in

km.

Note: EA and DS appear in the second model (equation 7).

24

Table 4

Regression results of demand for environmental amenities of KNP

Variables TP TNB POIS

Travel cost (TC) -.00021***

(.00007)

-.00021***

(.00007)

-.00024***

(.00007)

Economic status (ES) 1.414***

(.425)

1.414***

(.425)

1.616 ***

(.457)

Age (AG) -.025***

(.009)

-.025***

(.009)

-.028***

(.009)

Gender (SX) .410

(.318)

.410

(.318)

.471

(.339)

Education (ED) .183

(.265)

.183

(.265)

.189

(.279)

Park quality (PQ) .221

(.319)

.221

(.319)

.244

(.342)

Location (L) 1.283**

(.509)

1.283**

(.509)

1.384***

(.519)

Household size (HS) -.114

(.079)

-.114

(.079)

-.129

(.085)

Constant -1.304

(1.093)

-1.304

(1.093)

-1.891*

(1.148)

LR chi2(8) 112.89 54.37 64.03

Prob > chi2 0.000 0.000 0.000

Pseudo R2 0.238 0.131 0.150

Log likelihood -180.94 -180.94 -181.0008

Alpha (α) ----------- 5.27e-06

(.003)

---------

Notes: a) Figures in the parentheses represent standard errors of the respective coefficients.

b) ***,** and * represent significance of the coefficients at 0.01, 0.05 and 0.10 levels respectively.

25

Table 5

Regression results of WTP for KNP

Variables/Particulars Estimated

co-efficients/values

Economic status (ES) 253.744***

(33.690)

Age (AG) -2.015**

(0.803)

Gender (SX) 76.221***

(19.898)

Household size (HS) -7.089**

(2.824)

Environmental awareness (EA) 427.416***

(116.922)

Distance from KNP (DS) -0.119**

(0.059)

Constant -304.888***

(104.733)

R2 0.4273

Root MSE 94.642 Notes: a) Figures in the parentheses represent robust standard errors.

b) *, ** and *** represent significant at 10%, 5% and 1% levels

respectively.

26

Fig. 1. Map of Kaziranga National Park, Assam, India.

27

Fig. 2. KNP is world famous for Indian one-horned rhinoceros (Rhinoceros unicornis).

Fig. 3. KNP has largest population of Asiatic wild buffalo (Babalus bubalis).

28

Fig.4. KNP has highest ecological density of tiger (Panthera Tigris).

Fig. 5. KNP has significant population of Asian Elephant (Elephus maximus).

29

Fig. 6. KNP is an important bird area in India.

30

Fig. 7. Flood is a natural threat to animals of KNP.

Fig. 8. Recurring floods cause death of several animals in KNP.

31

Fig. 9. The National Highway 37 is another threat to animals of KNP.

Fig. 10. Rhinoceros of KNP have been regular targets of poachers.

32

Appendix

Table A1

Area under different land cover types in KNP

Land Cover Type Area (sq. km) % Area

Woodland 114.01 27.95

Short grass 12.30 3.01

Tall grass 248.85 61.01

Beels 24.32 5.96

Jiya Daphlu 3.96 0.97

Mora Daphlu 2.84 0.70

Sand 1.62 0.40

Total 407.90a 100.00 Note: aEroded area excluded.

Source: Environment and Forest Department, Government of Assam.

Table A2

The area of vegetation cover under different types

Vegetation cover % Area

Moist mixed deciduous forest 29.13

Grass land 51.91

Water logged/ Beels 6.62

Swampy/ Marshy area 5.21

Sand 7.13

Total 100.00 Source: Environment and Forest Department, Government of Assam

Table A 3

Distribution of visitors by purpose of visits to KNP

Purpose Visitors (in %)

To see great Indian one-horned rhinoceros 48

Enjoy natural beauty 32

Recreation 15

Educational value 3

To know about local people and their culture 2 Source: Field survey 2008-09.

33

Table A4

Descriptive statistics of non-categorical variables (Travel cost survey)

Variables Unit Mean Standard

deviation

Minimum Maximum

Visitation rate (VR) Number 1.48 0.79 1 6

Travel cost (TC) INR 3417.79 5304.79 148 37750

Economic status (ES) Index 0.46 0.26 0.05 1

Age (AG) Years 40.64 11.82 17 75

Education (ED) Years 14.68 2.78 2 19

Household size (HS) Number 4.09 1.25 1 8 Source: Field survey 2008-09.

Table A5

Descriptive statistics of non-categorical variables (Contingent valuation survey)

Variables Unit Mean Standard deviation Minimum Maximum

Maximum willingness

to pay per month (WTP)

INR 121.25 122.68 10 600

Economic status (ES) Index 0.379 0.251 0 1

Age (AG) Years 42.43 10.63 19 71

Household size (HH) No. 4.75 2.19 1 18

Environmental

awareness (EA)

Index 0.931 0.052 0.722 1

Distance (DS) Km. 139.53 106.79 1 315 Source: Field survey 2008-09.