sam houston state university department of economics and ...€¦ · gauhati university, guwahati...
TRANSCRIPT
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
0
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.
0
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
3
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
References:
Adamowicz, W., Louviere, J., Williams, M., 1994. Combining Revealed and Stated
Preference Methods for Valuing Environmental Amenities. Journal of
Environmental Economics and Management 26, 271-292.
Barman, S., 2012. Valuation of Environmental Amenity: A Case Study of Kaziranga
National Park. Unpublished PhD Thesis submitted to the Department of
Economics, Gauhati University.
Bharali, A., Mazumder, R., 2012. Application of Travel Cost Method to Assess the
Pricing Policy of Public Parks: The Case of Kaziranga National Park. Journal of
Regional Development and Planning 1(1), 41-50.
Bhat, G., Bergstrom, J,. Teasley, R.F., 1998. An Ecoregional Approach to the Economic
Valuation of Land- and Water-Based Recreation in the United States.
Environmental Management 22(1), 69-77.
Bowker, J.M., English, D.B. K., Donovan, J. A., 1996. Toward a Value for Guided
Rafting on Southern Rivers. Journal of Agricultural and Applied Economics
28(2), 423-432.
Blackwell, B., 2007. The Value of a Recreational Beach Visit: An Application to
Mooloolaba Beach and Comparisons with other Outdoor Recreation Sites.
Economic Analysis & Policy 37(1), 77-98.
Cameron, T.A., 1992. Combining Contingent Valuation and Travel Cost Data for the
Valuation of Nonmarket Goods. Land Economics 68(3), 302-317.
Cameron, A.C., Trivedi, P.K., 1986. Econometric models based on count data:
comparisons and applications of some estimators and tests. Journal of Applied
Econometrics 1(1), 29–53.
Cesario, F.J., Knetsch, J.L., 1976. A recreation site demand and benefit estimation model.
Regional Studies 10(1), 97-104.
Chae, D., Wattage, P., Pascoe, S., 2012. Recreational benefits from a marine protected
area: A travel cost analysis of Lundy. Tourism Management 33, 971-977.
Creel, M., Loomis, J., 1990. Theoretical and empirical advantages of truncated count data
estimators for analysis of deer hunting in California. American Journal of
Agricultural Economics 72 (2), 434–441.
Dehlavi, A., Adil, I. H., 2010. Valuing the Recreational Uses of Pakistan’s Wetlands: An
Application of the Travel Cost Method. SANDEE Working Paper 58 – 11.
Eom, Y., Larson, D.M., 2003. Improving environmental valuation estimates through
consistent use of revealed and stated preference information. Journal of
Environmental Economics and Management 52, 501-516.
Freeman, A.M., 1993. The Measurement of Environmental and Resource Values.
Resources for the Future (RFF) Press, Washington, DC.
20
Grogger, J. T., Carson, R. T., 1991. Models for Truncated Counts. Journal of Applied
Econometrics 6(3), 225-238.
Haab, T., McConnell, K.E., 2002. Valuing Environmental and Natural Resources: the
Econometrics of Non-market Valuation. Edward Elgar.
Hanley, N., Spash, C.L., 1993. Cost-benefit Analysis and the Environment. Edward
Elgar.
Heberling, M. T., Templeton, J. J., 2009. Estimating the Economic Value of National
Parks with Count Data Models Using On-Site, Secondary Data: The Case of the
Great Sand Dunes National Park and Preserve. Environmental Management
43(4), 619–627.
Hotelling, H., 1947. Letter to the National Park Service, reprinted in: An Economic Study
of the Monetary Evaluation of Recreation in the National Parks, 1949. U.S.
Department of the Interior, National Park Service and Recreational Planning
Division, Washington, D.C.
Khan, H., 2004. Demad for Eco-toursim: Estimating Recreational Benefits from the
Margalla Hills National Park in Northern Pakistan. Working Paper 5-04,
SANDEE.
Knestch, J. L., 1963. Outdoor Recreation Demands and Benefits. Land Economics 39(4),
387-396.
Lopes, A. A., 2014. Civil unrest and the poaching of rhinos in the Kaziranga National
Park, India. Ecological Economics 103, 20-28.
Martı´n-Lo´pez, B., Go ´mez-Baggethun, E., Lomas, P. L., Montes, C., 2009. Effects of
Spatial and Temporal Scales on Cultural Services Valuation. Journal of
Environmental Management 90(2), 1050–1059.
Martı´nez-Espin˜eira, R., Amoako-Tuffour, J., 2008. Recreation demand analysis under
truncation, overdispersion, and endogenous stratification: an application to Gros
Morne National Park. Journal of Environmental Management 88(4), 1320-1332.
Mendelsohn, R., Olmstead, S., 2009. The Economic Valuation of Environmental
Amenities: Methods and Applications. Annual Review of Environment and
Resources 34, 325-347.
Ovaskainen,V., Mikkola, J., Pouta, E., 2001. Estimating Recreation Demand with On-site
Data: an Application of Truncated and Endogenously Stratified Count Data
Models. Journal of Forest Economics 7(2), 125–144.
Park, T., Bowker, J. M., Leeworthy, V.R., 2002. Valuing snorkeling visits to the Florida
Keys with stated and revealed preference models. Journal of Environmental
Management 65, 301-312.
Parsons, G.R., 2013. Travel Cost Methods, in: Shogren, J.F., (Ed.), Encyclopedia of
Energy, Natural Resource, and Environmental Economics 3, pp. 349-358.
21
Saikia, A., 2011. Kaziranga National Park: History, Landscape and Conservation
Practices. Economic and Political Weekly XLVI(32), 12 – 13.
Shaw, D., 1988. On-site Samples Regression: Problems of Non Negative Integers,
Truncation and Endogenous Stratification. Journal of Econometrics 37(2), 211–
223.
Talukdar, S., 2012. The twin threat to Kaziranga rhinos. The Hindu, October 7. Guwahati
edition.
Whitehead, J.C., Haab, T.C., Huang, J., 2000. Measuring recreation benefits of quality
improvements with revealed and stated behavior data. Resource and Energy
Economics 22, 339-354.
Yen, S.T., Adamowicz, W.L., 1993. Statistical Properties of Welfare Measures from
Count-Data Models of Recreation Demand. Review of Agricultural Economics
15(2), 203-215.
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.