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Willingness to Pay for Biodiversity ConservationAmit K. Bhandari a & Almas Heshmati ba Indian Institute of Social Welfare and Business Management (IISWBM), Management House,College Square West, Kolkata-700 073, Indiab Department of Food and Resource Economics, College of Life Sciences and Biotechnology,East Building, Room 217, Korea University Anam-dong Seongbuk-gu, Seoul, 136-701, SouthKoreaPublished online: 15 Sep 2010.
To cite this article: Amit K. Bhandari & Almas Heshmati (2010) Willingness to Pay for Biodiversity Conservation, Journal ofTravel & Tourism Marketing, 27:6, 612-623, DOI: 10.1080/10548408.2010.507156
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Journal of Travel & Tourism Marketing, 27:612–623, 2010Copyright © Taylor & Francis Group, LLCISSN: 1054-8408 print / 1540-7306 onlineDOI: 10.1080/10548408.2010.507156
WILLINGNESS TO PAY FOR BIODIVERSITYCONSERVATION
Amit K. BhandariAlmas Heshmati
ABSTRACT. Nature-based tourism is the fastest growing tourism in many parts of the world. Theattitude toward conservation of nature is measured by individuals’ willingness to pay. This study hasmade an attempt to investigate the determinants of tourists’ willingness to pay (WTP) for biodiversityconservation. The determinants include a combination of socio-economic and site-specific character-istics of tourists. The study was conducted in Sikkim, which is India’s prime nature-based tourismdestination. Results show WTP, and effects of education, and income of tourists. Among site-specificcharacteristics, length of stay and number of spots are the significant determinants of WTP. This empiri-cal research is a valuable input to identify market segment among tourists, which might help to generatemore revenues for biodiversity conservation in Sikkim.
KEYWORDS. Willingness to pay, conservation, tourism, logit model, tobit model, Sikkim
INTRODUCTION
The natural environment, as well as wildlife,plays an important role in contemporarytourism. Nature-based tourism has become thefastest growing tourism in the last decadeor so (Mason, 2003). The goals of nature-based tourism are to provide ecologically soundtravel experiences that contribute to the nature,economic, social, and cultural environments(Wearing & Neil, 2001). The protection of theenvironment is an essential part of tourism
Amit K. Bhandari is Assistant Professor at the Indian Institute of Social Welfare and BusinessManagement (IISWBM), Management House, College Square West, Kolkata-700 073, India (E-mail:[email protected]).
Almas Heshmati is Professor of Economics in the Department of Food and Resource Economics Collegeof Life Sciences and Biotechnology, East Building, Room 217, Korea University Anam-dong Seongbuk-gu,Seoul, 136-701, South Korea (E-mail: [email protected]).
The authors acknowledge their indebtedness to three anonymous referees, the Editor of the Journal, andRenee Zehr for their valuable suggestions on the earlier version of the article. The first author is grateful to thefinancial support received from the SANEI-ICRIER-sponsored research project on “Economics of Nature-Based Tourism in South Asia.” He is also grateful to Project Director Professor Rabindra N. Bhattacharyaand Professor Mihir Baidya for their intellectual support. The usual disclaimer applies.
Address correspondence to: Amit K. Bhandari at above address.
development. Tourism destinations receive pub-lic funds for the maintenance of infrastructureand services. However, many tourism destina-tions are struggling with shortage of funds.There must be adequate funds to maintaintourist spots and enhance its natural attractions.Acquiring funds has always been a concern formanaging tourism destinations.
The revenue gap of the government wouldbe reduced if the tourists bear the cost burdenof conserving the natural resources they use.Visitor entry fees are particularly promising in
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areas of high visitation due to their ability togenerate income through market-based demandfor the tourism destination (Baral, Stern, &Bhattarai, 2008). It is argued that nature-basedtourism, if properly managed, can be a sustain-able mechanism for transfer of global environ-mental values to developing countries to protecttheir natural resources (Boo, 1990). Tisdell(1987) and Chase, Lee, Schulze, and Anderson(1998) argued that the levy imposed on tourists,in addition to a source of replacement of pub-lic funds, could also work as a regulatory tool tocontrol the number of tourists.
Growth in the number of tourists hasa detrimental impact on the environment(Doggart & Doggart, 1996; Drost, 1996;Nelson, 1993). Uncontrolled tourist growth canlead to damaged natural resources and increasedconservation costs. An increase in tourist inflow,without a concomitant increase in facilities andinstitutional mechanism for revenue genera-tion to pay for these facilities, indicates thegrowth of tourism bypassing the local com-munity (Goodbody & Thomas-Hope, 2002). Inanalyzing the impact of tourism on the wel-fare of a small open economy, Copeland (1991)opined that local people would benefit from thetourism if additional rent was extracted fromun-priced natural amenities enjoyed by tourists.Otherwise, local people may not benefit fromthe growth of tourism. Conservationists arguedthat protected species, areas, and ecosystemswould survive only if those local people liv-ing nearest them benefited financially from thetourism (Honey, 1999).
Sikkim is one of the least industrially devel-oped states in India. Tourism forms the back-bone of the state’s economy, generating revenueand offering enormous employment opportu-nities. Tourism in Sikkim is a joint venturetype of activity where the state governmentprovides facilities to different tourist destina-tions, which includes maintenance of existingtourist spots, developing new sites, and provid-ing security to the tourists. Tourists contributeto compensate for parts of the expenditures,either directly or indirectly, through consump-tion taxes and payment of entry fees. Touristrush in Sikkim increased from 9,157 personsper annum between 1990 and 2000 to 28,014
persons per annum between 2000 and 2007(Joshi & Dhyani, 2009). Domestic tourists aredominating the tourism industry while foreigntourists comprise only 5.5% of the total touristinflow per annum. West Bengal, the neighbor-ing state of Sikkim, contributes to a majorityof the tourist inflow. Tourism is a year-roundphenomenon in Sikkim. However, 40% of thetourist inflow occurs during the summer months(April–June) and about 28% of the inflow occursduring October–December.
Charging fees for natural attractions has beenthe subject of debate and controversy. However,the objective of this study is not to explore thisdebate. The primary objective of this study isto investigate the factors that influence tourists’willingness to pay (WTP) for biodiversity con-servation. There are numerous studies determin-ing the non-use value of environmental goodsusing contingent valuation methods. None ofthese studies reflect the target market segmentaccording to the probability of visitor’s WTP.This study enables us to overcome these short-comings by identifying market segments amongnature-based tourists where more revenue mightbe generated for the conservation of nature.Apart from this, an understanding of the behav-ior of WTP for biodiversity conservation helpsin framing policies to increase recreational ben-efits, to balance the flow of tourists, and tominimize tourism congestion.
WHY DO TOURISTS PAY FORNATURAL ATTRACTIONS?
Introducing entrance fees to natural attractionsis a controversial issue, which has significanteconomic, administrative, and political implica-tions (Clawson & Knetsch, 1966). There are twoopposing views regarding this: the “public good”viewand the“userpay”view(Reynisdottir,Song,& Agrusa, 2008). The former is expressed bythe non-use value of natural attractions (More& Stevens, 2000). The non-use value of natu-ral attractions is defined as the value individualsplace on the natural environment irrespective oftheir use (Dharmaratne, Sang, & Walling, 2000).The latter includes access fees or admission feesfor enjoying recreational services.
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The main argument for the “public good”view is that natural beauty belongs to nationalheritage and should be free for the public.Charging visitors entry fees would result in dou-ble taxation for those who already pay taxesunder the national taxation system. However, taxrevenue is not sufficient to cover the expenditurenecessary for natural attraction conservation.Therefore, entrance fees should be introduced inorder to generate adequate revenue to providebetter recreational services (Lindberg, 1998).Charging entrance fees for a natural area cre-ates a barrier to entry, which is beneficial forreducing congestion and enhancing the experi-ence for visitors. The burden of conservationis borne generally by local, state, and centralgovernments. The tourists who depend on thesenatural resources have to pay for the benefitsthat nature provides. Bird (1992) considers fis-cal relation in the form of extracting rents withtourism activities.
According to the “user pay” view, it is appro-priate to bear the cost of maintaining natu-ral resources for those who enjoy it. Only asmall proportion of the population frequentlyvisits natural attractions. It can be argued, there-fore, that without charging any visitors’ fees,nonusers are actually “subsidizing” the userswho visit these attractions (Reynisdottir et al.,2008). Charging an entrance fee is a very goodway to earn revenue for international touristswho pay taxes elsewhere (Reynisdottir et al.).International tourism is growing in nature-basedtourism segments in all parts of the world, whichcould provide a solution for financing naturalattractions.
According to traditional economic theory, thehigher the price of goods and services, the fewerthe number of people who are willing to pay forit. When the entrance fees on natural attractionsare introduced, or when existing fees are raised,it would result in fewer tourists’ inflow, whichmay reduce future potential revenue. Therefore,understanding the likely effect of entrance feeson the tourist number is extremely important.There is no clear consensus on the demand fornatural attractions with the introduction of entryfees. As far as outdoor recreation is concerned,introduction of entry fees, or modest hikes inentry fees, does not cause a significant reduction
in demand (Eagles, McCool, & Haynes, 2002;Schroeder & Louviere, 1999). However, somestudies argued that demand is relatively sensi-tive to prices (Richer & Christensen, 1999).
STUDY AREA
Sikkim, one of the most fascinating touristdestinations in India, is located under the shad-ows of Mount Kanchendzonga. It is bounded byspectacular valleys, majestic waterfalls, beau-tiful lakes, and a paradise of natural habitats.The state is situated between 27–28◦ N lati-tude and 88–89◦ E longitude, an area nearly7,096 km2, and measures approximately 110km from north to south and about 64 km fromeast to west. Elevation ranges from 224 m toover 8,591 m above sea level. The state is sur-rounded by the mountain kingdom of Bhutan onits east, Nepal on its west, and West Bengal onthe southern boundary (see Figure 1). Sikkimis a culturally diverse state with the inhabitantscomprised of Nepalese, Bhutias, and Lepchas,who all share a common cultural link. Sikkimis divided into four districts—East, West, North,and South. North Sikkim is the largest in termsof geographical area (4,226 km2), consisting of60% of the total area of the state. According tothe State Socio-Economic Survey 2006 report(Government of Sikkim, 2006), East Sikkim hasthe highest population density, nearly 281 per-sons per km2. The literary rate is around 81%.
Gangtok, the state’s capital, is situated inthe eastern part of the state, and is the state’smajor tourist destination (see Ray & Das, 2001).Tsomgo Lake, the most attractive tourist desti-nation around Gangtok, is about 38 km awayfrom Gangtok and at the altitude of 3,780 m.The lake is filled with water from the meltingsnow of the surrounding mountains. The road-side beauty on the way toward Tsomgo Lake isecstatic and one can enjoy the snowfall in thechilling atmosphere. The western part of Sikkimis famous for adventurous travel and trekking. Itis actually the base point for all the treks intothe wilds of the majestic Himalayas. Gyalshingis the head-quarters of the western district ofSikkim. Pelling is among the important touristspots in this district; situated at an altitude
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FIGURE 1. Map of Sikkim.
of 1,859 m, it offers the closest view of Mt.Khangchendzonga. Yuksum, the first capital ofSikkim, is the trekker’s gateway from where onecan reach Samiti Lake and Goechala pass.
The North District of Sikkim provides abreathtaking landscape and huge diversity inflora and fauna. This part exhibits the mostenjoyable scenic beauty and appears to be aparadise for nature lovers. Yumthang (3,627 m)alone is enough to satisfy the most demand-ing tourists with its panoramic valley of flowerssurrounded by snowy mountains, and with theLachung Chhu (Chhu is a river) intersecting thevalley. Yumesandong, 17 km from Yumthang,is the last tourist spot at an elevation of around4,572 m above sea level. It waits for tourists toenjoy its gorgeous hilltop and colorful rhodo-dendron. Another part of the North Sikkim visitis through the Lachen–Thangu–Chopta Valley
belt, where the night stay will be at Lachen(2,743 m). The southern part of the state is sit-uated at an elevation of 1,676 m and coveringan area of 50 km2. Namchi is the headquartersof the South District, which is located at about78 km from Gangtok and 100 km from Siliguri.The South District is also famous for adventuretrekking with a base camp at Ravangla (2,133m). The trekking spots are Meanam Hill (3,048m)—a wildlife sanctuary having a 12-km uphilltrek, and Tenong Hill (2,600 m)—the root ofLepcha folklore.
DETERMINANTS OF WILLINGNESSTO PAY FOR CONSERVATION
Previous literature on consumption in gen-eral, and tourism services in particular, have
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identified several key determinants of WTP.Tourists’ WTP is influenced by certain socio-economic characteristics, perception towardconservation, and visit characteristics. It isimportant to consider these characteristicsbefore implementing a possible fee structure. Aperson’s WTP for something depends largelyon their income level, regardless of the pur-pose. Likewise, a tourists’ WTP for biodiver-sity conservation depends on their capacity topay. Problems arise, however, when people withlow income are not willing to pay higher entryfees. The effect of income on WTP is a widelydebated issue, and the solution is unclear. Somestudies have found that charging an entry feefor natural attractions has small distributionalimpact on different income groups (Vogt &Williams, 1999; Williams, Vogt, & Vittersø,1999). Studies on outdoor recreational activitieshave found that low-income users are more sen-sitive to price changes than high-income users(More & Stevens, 2000). Since environmentalquality is classified as luxury goods, the incomeelasticity of demand must be increased propor-tionately greater than income increase (Kriström& Riera, 1996). Hence, it is hypothesized thatthe income level of a tourist has a positiveimpact on the contribution for biodiversity con-servation.
Among the demographic variables, age isan important determinant of tourists’ holidaybehavior and their choice of a tourist spot(Cottrell, 2003; Weaver, McCleary, Lepisto,& Damonte, 1994). In general, older touristsspend more time and money in order to learn,beyond just enjoying the adventure of tourism(Odunga & Folmer, 2004). Younger tourists tendto be attracted more to nature and wildlife,while elderly people prefer to search for placesthat provide more understanding and knowl-edge about a particular place. The older touristsare particularly attracted to the cultural activi-ties of tourist spots. Hence, it is hypothesizedthat age is positively related to the WTP forconservation.
Education creates awareness and allows anindividual to know more about the environmentin which they are living (Mok & Armstrong,1995). A superior education level increasesthe possibility that a person will engage in
environmental conservation activities due togreater experience, awareness, and access toinformation. The general public lacks educationrelating to conservation of natural resources.Hence, it is anticipated that higher educationwould imply higher awareness and appreciationfor natural resources, which should result in ahigher WTP (Tapsuwan, Brennan, & Ingram,2007).
Traveling with family members or friendsis likely to diversify their activity more thanthat of a single traveler. Group size is likely tohave a positive impact on the heterogeneity ofinterests as every individual traveler has his/herown expectation for the holiday. Agarwal andYochum (1999) found that couples generallymake decisions jointly and cooperate on vari-ous options. Large groups can visit more placesthan small groups because of their diversity ofchoices (Leu, Crompton, & Fesenmaier, 1993).The average travel cost per tourist for a largergroup is less compared to a small group or sin-gle traveler because of the economies of scale.Hence, it is hypothesized that a traveling groupwill spend less compared to a single traveler.
Length of stay is also a crucial variable affect-ing tourists’ WTP for conservation. Time limita-tions may restrict tourists to visiting fewer spots.Tourists with sufficient time at hand are likely tovisit more spots and undertake other activitiesbesides their primary motivation (McKecher,1998). However, expenditure increases withlength of stay yet it helps tourists to be involvedin a number of activities. Likewise, tourists areready to pay more if they get to enjoy a rangeof sites at a particular destination. Hence, theimpact of “length of stay” on “WTP” is ambigu-ous, yet it is hypothesized that the number ofspots visited by a tourist is positively related totheir WTP.
DATA
The data used in this study was collectedin 2004 from a field survey in Sikkim. Thedata was collected with the help of a pre-tested structured questionnaire. Tourist arrivalin Sikkim follows a seasonal pattern, where thepeak seasons are March to May and October
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TABLE 1. Summary Statistics of the Variables
Variable name Description Percentage Mean Standard deviation
Age (years) 18–29 17.3 39.37 10.1030–39 34.140–49 32.6≥ 50 16.0
Education ≤ Higher secondary 15.5 14.93 1.89Graduate 61.9Postgraduate & above 22.4
Household income (Rs./month) ≤ 10,000 41.1 16927.20 14761.0810,001–20,000 37.1≥ 20,001 21.8
Travel cost (per day per person) ≤ 500 21.1 1088.52 1045.81501−1,000 48.8≥ 1001 30.1
Group member 1–2 33.3 3.72 3.723–4 47.0≥ 5 19.7
Length of stay (days) 2–3 20.5 5.11 1.904–5 41.6≥ 6 37.9
Number of spots visited 1 20.3 2.16 0.782 45.9≥ 3 33.8
Conservation practice Adequate 70.4 0.70 0.45Inadequate 29.6
Willingness to pay entry fee Yes 67.7 0.67 0.46No 32.3
Note. Exchange rate at the time of survey: 1 US$ = 45 Indian Rupee.
to December in a year. The survey was carriedout in both seasons. The data was collected ran-domly through personal interviews at a varietyof interview sites which includes all major natu-ral attractions. The survey contains informationon 375 domestic tourists. Foreign tourists werealso interviewed but their number is too small toconsider for analysis. Interviewers approachedthe respondents outside the tourist spots. Thisapart, interviews were conducted at differenttypes of hotels (luxury to economy). The sam-ple includes only adult members with a definedsource of personal income or family income.Only one member from each traveling groupwas selected for an interview.
The questionnaire consisted of two parts.The first part inquired about socio-economiccharacteristics of the respondents. In the sec-ond part, respondents travel- and trip-relatedcharacteristics were collected. Tourists werealso asked about their willingness to contribute
more than the existing fees if it would lead toimproved environmental quality and better facil-ities. Tourists were asked the additional amountover the existing fees that they want to pay forSikkim as a whole. The socio-economic char-acteristics included tourists’ age, education, andhousehold income. Trip-related questions wereper day travel cost, number of group members,length of stay, and number of spots visited.Summary statistics of the variables, includingthe percentage distribution of the respondents,is presented in Table 1.
METHODOLOGY AND APPROACH
The decision to pay or not to pay for conser-vation takes the form of dichotomous variables.A cumulative probability distribution functionis used to solve the problem. Both logit andprobit models can be used in dealing with
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qualitative variable cases. However, the logitmodel is more suitable for the survey data,while the probit model fit better in experimen-tal data. The present study uses the logit modelfor regression (Alberini, 1995). In the followingbinomial logistic regression model, the proba-bility of WTP is estimated with respect to theexplanatory variables described above, whichcontain both qualitative, as well as quantitative,variables (Hamilton, 1992). Logit models areestimated in the following form:
Li = ln
(Pi
1 − Pi
)= α + βiXi, (1)
where L is the log of the odds ratio called thelogit or log-odds which is a linear function ofthe explanatory variables. Pi is the probabilityof WTP of ith individuals, the vector Xi con-tains attributes of tourists’ characteristics, and β
is the corresponding unknown regression coeffi-cients to be estimated. The probability Pi rangesbetween 0 and 1 and is nonlinearly related to theXi attributes (Pindyck & Rubinfeld, 1976).
The cumulative logistic distribution functionin (1) can be represented as:
Pi = E (Y = 1|Xi) = 1
1 + e−(α+βiXi)· (2)
If Pi is the probability of WTP for conser-vation, then (1 − Pi) is the probability of notwilling to pay:
1 − Pi = 1
1 + e(β1+βiXi)· (3)
The odds ratio is defined as:
Pi
1 − Pi= 1 + e(β1+βiXi)
1 + e−(β1+βiXi)= e(β1+βiXi)· (4)
The odds ratio is the ratio of favorable tounfavorable cases of WTP in the current con-text. This preference depends on the values ofthe explanatory variables. Taking the natural logof equation 4, we get equation 1.
This model is estimated using the log likeli-hood estimation method. From the coefficientsof logistic regression, it is possible to calculate
the predicted probability of individual tourists topay for environmental conservation. This helpsin segmentation according to their WTP andtheir respective characteristics.
The information regarding amount of WTP isavailable only for tourists who have expressedtheir willingness to contribute. Thus, in the caseof the amount of WTP, these values are censoredat zero. The most widely accepted solutionto regression involving censored variables wasproposed by Tobin (1958) and is known as theTobit model. In order to perform the analysis,the following generalized Tobit model can bedefined as:
yi = α + βXi + εi, i = 1, 2, ...., n, (5)
where yi is a latent response variable, Xi is anobserved 1 × k vector of explanatory variables,and εi normally distributed and is independentof Xi .
yi = y∗i if y∗
i > π ,(6)
= 0 if y∗i ≤ π ,
where γ is a non-stochastic constant. In otherwords, the value of y∗
i is missing when it isless than or equal to π . The problem with thestandard Tobit model is that π is often notobserved in economic data and is often assumedto be zero in empirical applications.
RESULTS AND DISCUSSION
Sample Profile
The personal information of the touristincludes age, education, and income. Around64.3% of the tourists belong to the 31–50age group, while 22.4% of the respondentsare below 30 years and 13.3% are morethan 50. As far as the respondents’ educa-tion level, the majority are graduate (62.1%),followed by post-graduate (22.4%), higher sec-ondary (10.4%), and secondary (5%). Regardingincome, the tourists’ household income is con-sidered, rather than their personal income,because a visitor’s WTP depends not only
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Amit K. Bhandari and Almas Heshmati 619
his own income but also on the incomeof other family members. In the sample,41.1% of respondents’ household income wasbelow Rs. 10,000, while 37.1% fell into theRs. 10,001–20,000 income bracket, and theremaining 21.8% had an income level more thanRs. 20,000.
The majority of the tourists coming as agroup to Sikkim consisted of 1–3 persons, while33.9% of respondents belonged to the groupsize of 4–6 persons, and only 9.6% belongedto the bigger group size of more than 7 per-sons. The majority of tourists visiting Sikkimare first-time visitors, while 25.6% of touristshad visited earlier. As far as length of stay isconcerned, 41.5% of the respondents spent 4–5days in Sikkim, followed by 6–7 days (30.1%),2–3 days (20.5%), and 7 and above days (7.7%).The majority of the tourists visited 2–3 placesin Sikkim, 20.3% of the tourists visited only asingle place (Gangtok), and only 2.9% of thetourists visited more than 4 places in Sikkim.As far as tourists’ origins are concerned, themajority belonged to the neighboring state WestBengal (61.4%), followed by Maharastra (11%),and Delhi (10%).
On-site travel cost included food and lodging,travel between lodging and the areas of vis-its, cost of movement from one site to another,and incidental expenses related to visits. Thedistribution of tourists on the basis of travelcost indicates that for 48.8% of respondents,the per day travel cost was between Rs. 501and Rs. 1000, 30.1% of tourists mentioned atravel cost of more than Rs. 1000, while 21.1%of respondents had a per day cost below Rs.500. Regarding the conservation practices oftourist spots, a majority of the tourists (70.4%)expressed that the current conservation practicewas adequate and 68.5% were willing to paymore for better environmental practices.
Factors Affecting WTP for BiodiversityConservation
The estimated results are almost in line withgeneral expectation (Table 2). The likelihoodratio tests of regressions of WTP show that theexplanatory variables are mostly significant and
TABLE 2. Results of Logit Model of theDeterminants of Tourists’ WTP Responses
Variables Coefficient Odds ratio
Age [18–29]30–39 .169 1.18440–49 .658∗∗ 1.932≥ 50 .371 1.449
Education [≤ higher secondary]Graduate .657∗ 1.923Postgraduate and above .732∗∗ 2.079
Household income [≤ 10,000]10,001–20,000 .377∗∗ 1.458≥ 20,001 .587∗∗ 1.798
Travel cost [≤ 500]501–1,000 −.309∗ 0.734≥ 1,001 .013 1.013
Length of stay [2–3]4–5 −.058 0.943≥ 6 .381∗ 1.464
Number of spots visited [1]2 −.241 0.786≥ 3 .349∗∗ 1.418
Constant −.133 0.876-2 log likelihood 439.64Percentage correct 69.1Number of observations 375
Note. ∗∗ and ∗ show significance level of 5 and 10%, respec-tively. The constant term represents tourists at the ageinterval 18–29 years, with higher secondary as the highestlevel of education, with income less than Rs. 10,000, witha travel cost less than Rs. 500, traveling in a group of 1–2persons, staying 2–3 days, and visiting only 1 spot.
relevant and they should be incorporated in thespecification of the model.
The socio-economic variables like age, edu-cation, and income of the respondents havesignificant influence on tourists’ WTP. As ageincreases, the “odds of WTP” also increases.This implies that awareness about conserva-tion increases with age. Older tourists are moreaware of the environment and are more willingto contribute to conservation than their youngercounterparts. However, odds are lower for thehigher age group tourists (above 50 years),although the coefficient is not statistically sig-nificant.
Education level of the respondents seemsto have a dominant and significant impact onthe WTP response. Education plays an impor-tant role in generating awareness levels towardthe environment. Compulsory environmentalawareness programs among the people should
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raise their awareness and help in conservationof natural resources. The odds of WTP are sig-nificantly higher with successive higher levels ofeducation.
Similarly, successive higher monthly house-hold income leads to higher odds of WTP.Higher income leads to higher probability tovisit more sites and demand for better services.Among site-specific characteristics, WTP is sig-nificantly lower for medium-budget tourists (Rs.501–1000) than for low-budget tourists (≤ 500).No distinction can be made between high-budget tourists and low-budget tourists. Anothercharacteristic, the size of group members, alsohas a negative effect on WTP. Visiting morespots enhances the probability of WTP. In thepresent study, tourists visiting 3 or more siteshad a significant influence on the probability ofWTP.
The tourism market is generally segmentedon the basis of various criteria like demogra-phy, geography, society, economy, etc. Havingidentified the market, all activities are planned,keeping in mind the target market. Similarly,the pricing policies have to be consistent withthe preference and behavioral pattern of the tar-get market consumers. From the coefficients oflogistic regression, we have calculated the pre-dicted probability of individual tourist’s WTPand categorize them according to their charac-teristics (Table 3).
The majority, around 70% of tourists, arewilling to contribute for conservation. The prob-ability of willingness to pay (Pi) lies between 0and 1 in that it varies non-linearly with the char-acteristic variables (Xi). The total numbers oftourists are divided into three categories on thebasis of their predicted probability: high (> .80),moderate (.60–.80), and low (< .60) willingnessto pay. The classification is done on the basis ofan arbitrarily selected threshold level, which hasa probability value below 60%. Of all tourists,46.9% belongs to the moderate WTP category,30.7% belongs to the low WTP category, andthe remaining 22.4% belongs to the high WTPcategory (Table 4).
The predicted probability from the logisticmodel by various characteristics of visitors ispresented in Table 4. Young tourists showed lit-tle interest to spend for conservation. Tourists in
TABLE 3. Average Probability ofWTP According to Tourists’
Characteristics
Variables Predicted WTP
Age:18–29 .6030–39 .6340–49 .75≥ 50 .70
Education:≤ Higher secondary .53Graduate .69Postgraduate and above .74
Household income:≤ 10,000 .5710,001–20,000 .73≥ 20,001 .77
Travel cost:≤ 500 .70501–1,000 .64≥ 1,001 .71
Group member:1–2 .633–4 .735–6 .56≥ 7 .67
Length of stay:2–3 .664–5 .61≥ 6 .75
Number of spots visited:1 .672 .60≥ 3 .77
TABLE 4. Percentage Distributionof Tourists According to TheirPredicted Probability of WTP
Probability of WTP Percentage of tourists
High 22.40Moderate 46.90Low 30.70
the 40–49 age bracket had the highest predictedprobability of WTP, followed by the more-than-50 age group tourists. Thus, aged touristsmight constitute an important group of revenue-generating biodiversity conservation. Educationis one of the key independent variables for pre-dicting tourists’ WTP. The predicted probabilityof WTP is highest for tourists with postgraduate
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Amit K. Bhandari and Almas Heshmati 621
or above educational background. Thus, higherlevel of education plays an important role inconservation, which might represent an attrac-tive market segment. Income level represents thecapacity to pay of an individual.
The predicted probability is higher fortourists who belong to higher income groups.No systematic patterns are observed with regardto travel cost per day. Predicted probability ofWTP is higher for both low-budget and high-budget tourists compared to medium-budgettourists (Rs. ≥ 1001). Traveling with smallergroups has a higher predicted probability ofWTP. Thus, targeting smaller groups representsan attractive segment for both revenue genera-tion and conservation of nature. The value ofpredicted probability increases as tourists visitmore sites. The predicted probability of touristsvisiting more than 3 sites is .77. This impliesthat discovering new sites should be given pri-ority in order to generate more revenue andsustainable conservation of nature. The esti-mated predicted probabilities reported in Table 4suggest that average tourists in the age inter-val 40–49, with a post-graduate degree andabove, income above Rs. 30,000, who travel ina group of 3–4 members, a stay more than 6days, and visit more than 3 spots, are the targettourists with the highest WTP for biodiversityconservation.
As indicated in Table 1, a large percentageof tourists reported a WTP of zero. Regressionanalysis using ordinary least square (OLS) forsuch data is known to lead to biased and incon-sistent parameter estimates (Greene, 2008). Thezero response is very common for open-endedpayment-related questions. Under such condi-tions, the Tobit censored model would be appro-priate. The results are indicated in Table 5.Tourists who belong to higher age groups (morethan 50 years) have significant influence onWTP amounts compared to tourists who belongto other age groups. Education of tourists has asignificant impact on WTP amounts. However,the impact is higher for graduates comparedto postgraduate and higher levels of education.Income of tourists also has significant impacton WTP amounts. Likewise, a tourist stayingmore than 6 days has a significant impact onWTP amounts. More recreational facilities are
TABLE 5. Results of Tobit Analysis on theAmount of WTP for Biodiversity Conservation
Variables Coefficient Marginal effect
Age [18–29]30–39 −5.886 −4.08440–49 0.336 0.233≥ 50 2.870∗ 1.915
Education [≤ higher secondary]Graduate 11.326∗ 7.859Postgraduate & above 9.996 6.936
Household income [≤ 10,000]10,001–20,000 12.632∗ 8.766≥ 20,001 23.732∗∗ 16.468
Travel cost [≤ 500]501–1,000 −4.092 −2.840≥ 1,001 7.014∗ 4.867
Length of stay [2–3]4–5 0.365 0.440≥ 6 19.223∗∗ 13.339
Number of spots visited [1]2 5.448∗ 3.808≥ 3 27.620∗∗ 19.166
Constant −8.797∗ −6.104Standard error 55.379
Note. ∗∗ and ∗ show significance level of 5 and 10%, respec-tively. Reference category represents tourists at the ageinterval 18–29 years, with higher secondary as the highestlevel of education, with income less than Rs. 10,000, with atravel cost less than Rs. 500, staying 2–3 days, and visitingonly 1 spot.
required to hold the tourists who spend moredays in a particular site. In line with this expec-tation, a higher number of spots increases theimpact of WTP amounts. Tourists visiting morethan 3 spots have a substantial influence onWTP amounts, indicating that more sites shouldbe developed to enhance revenue as well asbetter conservation practices.
SUMMARY AND CONCLUSIONS
The present study is an attempt to estimatethe impact of various determinants of tourists’WTP for biodiversity conservation. The studywas conducted in India’s premier nature-basedtourist destination: Sikkim. Tourists were askedif they were willing to pay more than existingfees for better conservation practices. The anal-ysis clearly demonstrates that the majority ofthe tourists are ready to pay additional fees forbetter conservation practices in Sikkim. Raising
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622 JOURNAL OF TRAVEL & TOURISM MARKETING
tourist inflow in Sikkim will, in turn, drive therequirement of more funds for conserving itsnatural attractions. Hence, an entry fee programcan be implemented in order to provide addi-tional revenue to meet the financial shortagefaced by the government.
Regarding the determinants of tourists’WTP, higher socio-economic status is posi-tively related with WTP responses. Tourists whobelong to higher age brackets might constitutean important revenue generator for biodiversityconservation. Tourists with higher educationhave a higher probability of WTP, because theymight be aware of the benefits of biodiversityconservation. Tourists’ income also has a sig-nificant impact on WTP amounts. Trip-relatedcharacteristics failed to provide a systematicbehavioral pattern regarding WTP responses.Tourists traveling in smaller groups have a highpreference to contribute. Thus, targeting smallergroups represents an attractive segment, bothfor revenue generation and for biodiversity con-servation. The value of predicted probability ofWTP increases as tourists visit more sites. Thisimplies that discovering new sites should begiven priority in order to generate more revenueand sustain conservation of nature.
In Sikkim, the burden of nature conservationis borne generally by local, state, and centralgovernments. Under such circumstances, sincetourism depends on these natural resources, itis expected to be compensated for the ben-efits it provides. However, in addition, thereare other reasons for extracting rent from thetourists. Thus, it is important to consider fis-cal relations with tourism activities. In orderto ensure this relation, appropriate fees arecharged for the use of existing tourism infras-tructures. A determination of WTP and fees tobe charged requires more careful and detailedelaboration.
From the policy perspective, this empiricalresearch is an important input into the regula-tory process to frame tariffs and to find effectiveways to deliver services at prices worth thevalue of money for the tourists. The combina-tion of public funds and user fees could be amore reasonable and effective way to sustain thetourism market. The higher entry fees may alsohelp in reducing and regulating congestion by
tourists who use environmentally sensitive sites,because congestion not only impacts the touristsnegatively, it may have significantly negativeimpacts on natural resources as well. Lastly,some limitations of the study on its applicationside are worth mentioning. Some of the impor-tant explanatory variables may have been leftout from the analysis. The current data does notcontain such information. Some psychologicalfactors, such as membership in environmen-tal organizations and attitude toward environ-mental protection, could be included in futureresearch.
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SUBMITTED: December 28, 2009FINAL REVISION SUBMITTED:
March 31, 2010ACCEPTED: April 9, 2010
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