Background and importance of forest...
Transcript of Background and importance of forest...
2ème Conférence annuelle de la FAERE – 10-11 Septembre 2015 – Toulouse
The value of forest elephant conservation for indigenous and local communities in the Tridom landscape - Combining Open-Ended and Close-Ended Contingent
Valuation methods (work in progress)
Jonas Ngouhouo1, Jens Abildtrup
2, Dénis Sonwa
3, Philippe Delacote
2
1The Laboratory of Forest Economics – INRA - CIFOR, PhD Candidate, [email protected] 2The Laboratory of Forest Economics – INRA 3CIFOR - Central Africa Regionl Office
Abstract
The purpose of this paper is to determine and characterize the social and cultural preferences for forest
elephant conservation in the Congo Basin’s Tridom landscape. Using data from a contingent valuation
survey with 1035 households conducted in 2014 in 108 villages of landscape, the paper runs five different
models comparing the Open-ended and the Closed-ended elicitation format, and finds that combining both
formats would lead to approximating the true WTP. According to the hypothesis tested, we found the
following outcomes. On average, local and indigenous households will to pay a CFA1139,4 (€1,74) monthly
amount for forest elephant conservation. Unlike the second hypothesis, the estimates shows that the
existence of human/elephant conflict, as well as the distance of the households‘ location to the nearest
protected area do not influence their preferences for elephant conservation. Finally, as expected, the
indigenousness has a positive and significant influence on the household’s preferences. This information
is all the more important to increasing incentives for elephant conservation as the extinction of forest
elephant would lead to a severe threat on spiritual enrichment, cultural identity as well as the way of life of
this ethnic minority group. Furthermore, we found that improving household’s education level as well as an
increase in their income lead to greater preferences for elephant conservation.
Keywords: Forest Elephant Extinction, Local and indigenous people, Combining Open-Ended and Close-Ended formats, Contingent Valuation, Willingness-to-pay
Highlights
Combining Open-Ended and Close-ended elicitation formats to improve the Stated Preference Valuation
Technique
Original data from a recent (January - July 2014) face-to-face interview with 1035 households
Issue of securing the minority indigenous Baka’s spiritual and cultural way of life through Forest
Elephant conservation
This work increases awareness of the forthcoming forest elephant loss as well as the incentives for
conservation JEL Classification: Q 57, 29.
1. Introduction
1.1. Background and importance of forest elephants
Forest Elephant (Loxodonta Africana cyclotis) poaching in Tropical Africa is a big threat for the dynamic of
this iconic species. In 2011, The Congo Basin’s forest elephant population was less than 10% of its potential
size, occupying less than 25% of its potential range (Maisels and Al, 2013; Blake et al., 2007; Martin and
Stiles, 2000). The Tri-national Dja-Odzala-Minkebe’s cross-border landscape (Tridom), spanning Cameroon,
Congo (R) and Gabon, deemed to have ecological and biodiversity uniqueness and hosting the most
important population of elephant in the world, with the highest density in the Minkébé National Park
(MNP)1. Between 2004 and 2012, the MNP lost more than 11,000 individuals, which account for more than
50% of the 2004 population (Maisels et al, 2013). The current growing demand of ivory for jewelry, pleasure
and Asian medicine is the main driver of its overexploitation. It is evident that this iconic species is much
appreciated for its provisioning services. However, it contributes to achieve ecological equilibrium as well as
social and cultural services.
Indeed, In addition to being an iconic species, Loxodonta Africana cyclotis disseminates seeds of important
tropical fleshy fruits trees over unprecedented distances and then contributes to regeneration of these tree
species as well as in maintaining the forest structure in the Congo Basin (Blake et al., 2009; Chi Wang, 2009;
Beaume et al, 2013). For instance, Baillonella toxisperma, a traditional multiple-use species for Bantu and
Baka villagers in the Tridomhas also became an endangered species because of its high commercial value in
the wood market. By disseminating Baillonella toxisperma, elephant contributes to restauration of the forest
as well as strengthening carbone storage. Hence, it is in accordance with the REDD+ policy which emerge as
an important framework for forest conservation. Forest elephant can thus help to improve the forest habitat
and lead to restauration of forest’s ecological services. In this way, it contributes to ecosystem regulating
services.
The forest elephant populations are crucial for the cultural identity of indigenous Baka. Their main rituals are
practiced after the elephant hunt. The most important are the yeli and the jengi ceremonies. Yeli is the female
ritual and “Jengi”, is the male ritual (Kent, 1996). The traditional hunting of elephants is also the most
important ceremonial and religious event of the indigenous populations of the Congo Basin. The hunting
brings together dispersed groups, all having specific responsibilities, e.g. the vital contribution of women to
the mystical preparations for a safe hunting. Only the oldest is permitted to kill elephants and they undergo a
rigorous preparation, learning from experienced hunters over many years before being permitted to kill
elephants. Once killed the elephant is celebrated for many days and nights in a complex series of ritual feasts
and celebrations until all the meat is consumed (Lewis, 2002). Therefore, elephant participates to
maintaining a spiritual enrichment, cultural identity and knowledge of indigenous Baka. Hence, it contributes
to providing cultural service, implying that elephant extinction also has an opportunity cost in terms of loss
of cultural values (Garrod and Willis, 1999). The figure below shows a Synoptic description of ecosystem
services and the Economic Total Value of elephant in the Tridom landscape.
Figure 1 : Ecosystem services and Economic Total Value of Forest elephants; Source: authors, adaptation
from MEA, 2005 and Brahic et Terreaux, 2009
1 There are two subspecies of elephants. Forest elephant ans Savannah elephant. This paper focus on forest elephant.
PROVISIONNING SERVICES
CONSUMPTIVE - Elephant meat (trade and consumption) - Tusk and Trophies -
REGULATING SERVICES - Forest’s ecological services restoration (seeds dissemination Ex.: Baillonellatoxisperma) - Biodiversity conservation
CULTURAL SERVICES
- Traditional knowledge - Ritual component (JENGI, YELI, TUMA, ME, ...) - Cultural identity of indigenous people. - Influencing the social structure of indigenous
ECO
NO
MIC
TOT
AL V
ALU
E
USE V
ALU
E N
ON
USE V
ALU
E
Direct
Use
Value
Indirect
Use
Value
Existence
Value
Option Value
Bequest Value
CONSUMPTIVE - Elephant meat (trade and consumption) - Tusk and Trophies value NON CONSUMPTIVE -Loxodontacyclotis viewing - Loxodontacyclotis riding - Spiritual/ cultural
- Forest’s ecological services restoration (seeds dissemination Ex.: Baillonellatoxisperma) - Biodiversity conservation - REDD+ co-benefit
- Loxodontacyclotis has value for individuals even though not using - Iconic species and unique forest
elephant’s species
- Potential use by future generation
- Possible use in the future
SUP
PO
RTIN
G SER
VIC
ES
Considering the importance for the ecological, socio-cultural and economic equilibria for the local
populations, the extinction of elephants will severely affect human welfare and would lead to irreplaceable
costs to the Tridom society. This makes forest elephants’ preservation a priority for biodiversity conservation
decisions. Thereby, assessing the non-use value and the cultural services taking into account the damage cost
of elephant for the local populations can provide relevant information to highlight the significant costs of
extinction. This could increase incentives for biodiversity conservation. Therefore, to the main objective of
this paper is to reply to the question “what is the willingness to pay of local households to avoid a total loss
of elephant?”
1.2. Objective, interest and hypothesis
This paper aims to determine and characterize the social and cultural preferences for forest elephant
conservation in the Tridom landscape. More precisely, it calculates and compares the willingness-to-pay of
the households to the economic damage caused by elephants and it analyses the factors that influence the
calculated WTP. This will provide important knowledge for conservation managers and design for
conservation policies.
The paper tests the following hypothesis:
1. The extinction of forest elephant can lead to a significant net lost in the household’s wellbeing.
2. The WTP for elephant’s preservation increases with the distance of the household’s location to the nearest
protected areas and decreases with the human-elephant conflicts presence. Indeed, the distance to the
protected area can be considered as an indicator of elephant scarcity. Assuming a decreasing marginal
utility from forest elephant presence leads to the first part of the hypothesis. Hence, households closed to
protected area would be likely to express lower preferences for elephants conservation
3. The WTP is significantly influenced by the indigenousness status of households. Knowledge about the
spatial and ethnic differences in WTP may be used for the design of optimal spatially explicit
conservation policies.
2. Literature review
A large body of research has contributed to enriching the literature on the economics of endangered species
conservation (Bishop, 1978; Barbier et al, 1990; Kremer and Morcom, 2000; Lindsey et al, 2007; Tisdell,
2002; Bulte and Kooten, 2002). However, only a few research papers have been assessing the indirect use
value, the bequest value and the existence value of elephant (Bandara and Tisdel, 2003, 2005), while this
iconic species plays an important role in terms of socio cultural and ecological integrity (Lewis, 2002; Blake
et al, 2009).
Bandara and Tisdell (2002) used data from a face-to-face Contingent Valuation study of a stratified sample
of 300 urban residents in Colombo to assess the willingness to pay for elephant conservation in Sri Lanka.
Their assessment allowed distinguishing between users and non-users values of Asian elephant. The
respondents who have ever used elephant facilities were willing-to-pay Rs.137,38, while the non-user were
willing-to-pay Rs. 82,96 for elephant conservation with an average of pay Rs. 110,17 (€1,65).The results
reveal that urban residents are willing-to-pay for elephant conservation because they want to secure the
existence of elephant (non-use value) and because they have an economic use of the presence of elephants,
i.e. their importance for recreational and tourism. Applying another methodology, using the same data, they
found that the probability of being willing-to-pay an amount among the bids values is significantly and
positively influenced by pro-conservation attitude, higher income and negatively influenced by the scale of
the bid value. They found that the total present discounted annual value (Rs. 40248 million) can yield a
perpetual return of Rs. 2012,43 million if invested, that is sufficient to compensate an annual crop damage
value that is Rs. 1121,42 million if the farmers continue allowing elephants some access to their crops for
food.
Even though a few number of research papers investigate the farmers' values of ecosystem services (Smith
and Sullivan, 2014) as well as the option value and the non-use value of African and Asian elephant as an
incentive to improve its conservation (Vredin, 1997; Bandara and Tisdell, 2003, 2004; Muchapondwa et al,
2009), no research has addressed forest elephants. These are currently severely threatened by poaching and
land acquisition for non-forested activities. Besides being the first paper to point out this non negligible
value of forest elephants, this paper considers the role of landscape factors like the distance between the
households and areas nearest elephant concentrations zones (Protected Areas), the relative density of the
protected areas and the households’ plantation size for WTP. Furthermore, this paper is based on a recent
and not previously exploited rich dataset obtained by face-to-face interviews.
3. Methodology In this paper, we use a Contingent Valuation survey to measure households’ WTP for avoiding reduction and
loss of forest elephant population in the Tridom. The application of CV in developing countries is
increasingly growing; however, the present study is the first to measure the impact the loss of an iconic
biological species in the Tridom landscape.
A main distinction between different CV approaches is OE and closed-ended mechanisms (Cameron and
James, 1987). In OE, or continuous CV questions, the respondents are asked to specify their WTP, while in
close-ended (CE), or discrete CV, respondents are asked to choose whether or not to pay a specified amount
(Kealy and Turner, 1993).
3.1. Valuation Technique As CE and OE mechanism may yield significantly different results for public goods due to the differences in
incentives for strategic behaviour (up., Kealy and Turner, 1993), we use both mechanisms to approximating
the true WTP. Indeed, Kealy and Turner (op. cit.) suggest using either one or the other format. The main
types of CE approaches consist of the single and the double (multiple) bounded dichotomous choice (DBDC)
(Hanemann, 1985 ; Carson, 1985 ; Hanemann and Kanninen, 1998).
We first use a DBDC contingent valuation model proposed by Hanemann (1985) and Carson (1985) to
estimate the WTP of the indigenous and local households in the Tridom. This method reduces the variance of
estimated WTP and therefore, is asymptotically more efficient than the single bounded method developed by
Bishop and Heberlein in 1979 (Hanemann et al., 1991; Bateman et al, 1994; Alberini, 1995; Haab and
McConnel, 2002). Indeed, yet the single bounded approach is quick and easy to administer and analyse, but
it is more likely to result in strategic bias (Harris and Roach, 2013). Nevertheless, the answer to the first
question when using the DBDC may sometimes be inconsistent with the response to second bid and
contribute to lowering the WTP (Hanemann et al, 1991; Herriges and Shogren, 1996).
The DBDC elicitation format can produce more consistent values than the OE format when the anchor is
well defined. Indeed, good starting points have the benefit of preparing and encouraging the respondents to
state their maximum WTP (Frew, 2010-a; Fischhoff and Furby, 1988; Seenprachawong, 2004; Carmona-
Tores and Calatrava-Requena, 2006). Nevertheless, we find that, the real problem when using the DBDC, is
that, as each respondent identifies two amounts that bound their maximum WTP (i.e. one amount greater
than and one less than their maximum WTP (Bateman et al, 2002), the respondents who would have been
willing to pay an amount lower than the lower bid are registered as “no/no” respondents. In the same way,
bidding the WTP of the respondents who would have been willing to pay an amount greater than the higher
bid plays down the importance their true WTP. Therefore, using the DBDC format provides only with partial
information about the WTP of all the respondents, indicating only whether their WTP is below or above a
specified bid, or whether it is within a specified interval. Finally, we propose to combine both closed-ended
DBCD format and OE format. Once the starting points are well defined, the use of the DBDC in a first stage
is expected to yield the best bases for an OE question. Indeed, using the DBDC may reduce unrealistically
large stated WTP. It can also help reducing the non-response rates as the closed-ended questions are
normally considered easier. Hence, combining both closed-ended and OE formats helps to offset the limits
associated with identification of distribution of yes-yes or no-no answers, as well as to reducing the limits
associated with the cognitive limits of OE.
3.2. Survey design The CV questions were imbedded in a questionnaire addressing the characteristics of the households, their
behavior and motivations. The CV first described the socio-economic and ecological attributes of elephant,
as well as the potential change in sociocultural services associated with a possible extinction of elephant. The
following components of the elephant economic value were presented: existence, viewing, disponibility for
future generation, cultural and spiritual enriches, baillonella toxysperma seeds dissemination. Even if we
propose a detailed presentation of the various components of the Non Use Value of elephant to the
households, they were asked to provide only with a unique willingness to pay given the various components.
This is because these components are often overlapping. For instance, the existence value willinsure the
bequest value, as well as the option value (Loomis and Larson, 1994; Bondara and Tisdell, 2003). Next, we
proposed the following hypothetical scenario. “Considering the trend to extinction of Loxodonta cyclotis, if
action is not taken quickly, this multiple use iconic species will disappear in the next few years. To stop this
tendency to extinction and make the species abundant, the Tridom Regional Project Management Unit can
develop a 10 years elephant protections program that aims to seize weapons currently used by poachers, to
effectively fight against cross-border poaching by (1) creating joined checkpoints at the landscape scale, (2)
recruiting more young people in the villages, involving them in a communication network to improve the
anti-poaching control strategy and prevent human-elephant conflicts”. Then each respondent was asked
whether he was willing to contribute to the program by paying some initial CFA monthly bid amount if the
Regional Management Unit demands financial support of all the inhabitants of the village? The payment
vehicle presented was the direct cash payment in secure funds and lodged at the Tridom program. This mean
of payment is the most familiar, credible and feasible according to the economic situation in the landscape It
generated a realistic reaction of the respondents as far as any financial engagement is concerned. To
minimizing the “Yea saying”, the “Nay saying” bias2 and the starting point bias, (1) each respondent was
asked to consider his monthly income, his sources of income, and the usual monthly expenditure; (2) each
respondent was asked to be realistic, making assured that he could actually pay that monthly amount for the
next 10 years before he answered; (3) the respondents were randomly assigned to one of six starting points
developed and validated during two pretests steps with 40 households in four villages (Meyomessi, Oveng,
Kongo and Mbieleme) of two subdivisions of the study area. A lower bid was presented to those who gave a
negative answer, and a higher bid to those who gave a positive answer. The bid cards structure is presented
in Appendix 1. The WTP of each household belongs to one of the four interval formed by his income, the higher bid, the
initial bid, the lower bid and 0 (Haab and McConnel, 2002). To the respondents who gave yes/yes and no/no
answers to bids, an OE question was propose, asking the maximum amount they would be willing to pay for
forest elephant conservation. We finally introduce follow-up questions that examine reasons for zero WTP to
be able to remove protest refusal bias from the data base before estimation (Arrow et al, 1993).
3.3. Study Area Sampling and Data
The Tridom is a cross-border conservation landscape covering a geographical area of 191 541 km2,
representing 7,5% of the total area of the Congo Basin Tropical Forests in central Africa. It was created in
2005 by an agreement between Cameroon, Gabon and Congo governments, as one of The 12 Congo Basin
Forest Partnership (CBFP) priority landscapes, targeting to promote long-term biodiversity and protected
area conservation, a rational use of natural resources and a sustainable development, as well as to contribute
to the poverty reduction. It encompasses of 9 protected area representing 37 498 km2, including four into the
Cameroon segment (BoumbaBek, Kom-Mengame, Nki and Dja Biosphere reserve), three in the Gabonese
segment (Minkébé, Mwagne and Ivindo National Parks) and two in the Congolese segment (Odzala and
Lossi National Parks). Between the protected area, there is a livable inter zone representing 111000 km2.
It includes a rich and diversified flora and fauna. It abounds commercial high value timber species and
houses the largest population of forest elephants in the world with a high concentration in the Minkébé
National Park, as well as other large mammals such as buffalo, bongos, giant pangolin and gorillas. The map
(Appendix 2) shows some of the glades visited by elephant populations in the landscape. Currently, there is a
high inflow movement of human population within the landscape for resource exploitation. With an average
density comprise between 1-4 inhabitants / km², the Tridom buffer zone is subject of numerous economic
stakes, including forest management, rural agriculture, hunting, traditional and industrial mining as well as
fishing and gathering non timber forest products. The field work was held in the Cameroon and Gabon
segment. Both segments are inhabited by more than 43 tribes, dominated by Bantu, while there is a
minority group of indigenous Baka (see Appendix 3.)
The paper uses data from a representative face-to-face survey by questionnaires with a random and stratified
sample of 10353 from approximately 65141 households, conducted between December 2013 and July 2014,
in 108 villages chosen in all the 26 administrative units of the Cameroonian and the Gabonese part of
landscape (Appendix 4). The villages visited are spread over nearly 27,000 km2, which is 2/3 of the
landscape livable inter-zone.
2 The Yea-sayers and Nay-sayers are the respondents who trie to please or to counter the interviewer without
considering the specific amount they are asked about (Carson and Hanemann, 2005 ; Frew, 2010-b). 3 The sample size required at a confidence level of 95% (typical value of 1.96) is 384.
The interviews lasted between 1 to 3 h. In addition, there were evening visits in the various households
surveyed to quantifying and measuring daily production. The survey was supervised by the first author. Ten
Masters Students selected after 5 training seminars participated as surveyors. Every village provided us with
at least two local translators for the situation the head of household doesn’t communication in French. Every
household was geo-localized with a GPS
3.4. Theoretical model specification Household preferences for forest elephant conservation in the Tridom can be described by a random utility
model developed by McFadden's (1973) and formalized by (Manski, 1977) and Hanemann et al (1991).
Therefore, the utility function is given by :
𝑈 𝑖𝑗 = 𝑉𝑖𝑗(Y𝑖 − 𝑎, 𝑋𝑖 ) + 𝜀𝑖𝑗
Where 𝑗 = 1 𝑎𝑛𝑑 𝑎 > 0 if the household 𝑖 accept to pay 𝑎 amount or , 𝑗 = 0 𝑎𝑛𝑑 𝑎 = 0 otherwise.
The household 𝑖 will then accept to pay if:
𝑈 𝑖1 > 𝑈 𝑖0 That’s 𝑉𝑖1(Y𝑖 − 𝑎, 𝑋𝑖) + 𝜀𝑖1 > 𝑉𝑖0(Y𝑖, 𝑋𝑖) + 𝜀𝑖0
Where 𝑉𝑖𝑗 is the deterministic component of the utility function, measuring the indirect utility function for
the respondent 𝑖, in the state j ; 𝑋𝑖 is vector of socioeconomic and geographical characteristics influencing
households preferences, 𝜀𝑖𝑗 is the unobserved random component of the utility function.
A household would be willing to contribute to loxondota cyclotis conservation if doing so provide him with
greater utility than not. In other words, he will accept to pay if the utility difference is positive as following:
𝑈 𝑖1−𝑈 𝑖0 > 0 That’s (𝑉𝑖1(Y𝑖 − 𝑎, 𝑋𝑖) + 𝜀𝑖1) − (𝑉𝑖0(𝑌𝑖, 𝑋𝑖) + 𝜀𝑖0) > 0
Or 𝑉𝑖1(Y𝑖 − 𝑎, 𝑋𝑖) − 𝑉𝑖0(Y𝑖, 𝑋𝑖) + (𝜀𝑖1 − 𝜀𝑖0) > 0
3.5. Econometric model specification Following Lopez-Feldman (2013) and Barrena et al (2014), the WTP can be modelled as the following
function:
𝑊𝑇𝑃𝑖(𝑋𝑖, 𝜇𝑖) = 𝑋′𝑖β + 𝜇𝑖
Where 𝑊𝑇𝑃𝑖 represents the willingness to pay vector of the ith respondent, 𝑋𝑖 is a vector of explanatory
variables, β is a parameter vector and 𝜇𝑖 a normally distributed error terms.
Taking into account the valuation technique developed above, we specify five econometric models, using the
two elicitation procedures (the DBDC and open ended) to.
The first two models to be estimated are the DBDC with and without protesters. Moving from the first
to the second using the motives behind the respondent answers helps improving the results with respect
to the protest refusal bias (Arrow et al, 1993).
Afterwards, we estimate a Tobit model based on the OE elicitation procedure. Even if DBDC is
supposed to yield systematically higher stated preferences than the CE (Brown et al. 1996; Boyle et al.
1996, Kealy and Turner 1993; Halvorsen and Soelensminde, 1998), both technics reduces the average
WTP. Indeed, the DBDC left-censors all the no/no respondents at the lower bid, while the Tobit model
left-censors from 0, considering the entire “zero” respondent as those who faced negative preferences.
The design used allows us combining both closed-ended and OE format as the third elicitation procedure
applying the interval censored regression models (ICRM). Indeed, we hypothesize that, combining the
DBDC with a good starting point to the OE yields result closer to the true WTP. The ICRM takes into
account the point data and offers the greatest increase in efficiency with the least ambiguity compared to the
DBDC (Haab and Mcconnel, 2000). In addition, it helps account for negative, zero and positive preferences.
The ICRM (1) left-censors from zero (as upper limit), only the households who expressed a loss in utility
caused by elephants and who effectively faced a human/elephant conflict. These individuals were
identified by using the motives behind the answers captured with the follow-up questions given
theirprevious experiences with elephants.
The ICRM (2) lef-censors the households who expressed a possible lost in utility with the presence of
elephants.
3.5.1. DBDC model and DBDC model without protesters Under a closed ended format, an individual 𝑖 will either be willing to pay a given bid amount 𝑎 for elephant
conservation if 𝑊𝑇𝑃 𝑖 ≥ 𝑎 or not be willing to pay if 𝑊𝑇𝑃 𝑖 < 𝑎 with the following probabilities.
𝑃𝑟𝑜𝑏(𝑦𝑒𝑠 𝑡𝑜 𝑎) = 𝑃𝑦 = 𝑝𝑟𝑜𝑏(𝑊𝑇𝑃 𝑖 ≥ 𝑎) = 𝑃(𝑋′𝑖β + 𝜇𝑖 ≥ 𝑎) = 𝑃( 𝜇𝑖 ≥ 𝑎−𝑋′𝑖β) ; 𝜇𝑖 ≈ 𝑁(0, 𝜎)
𝑃𝑟𝑜𝑏(𝑛𝑜 𝑡𝑜 𝑎) = 𝑃𝑛 = 𝑝𝑟𝑜𝑏(𝑊𝑇𝑃 𝑖 < 𝑎) = 𝑃(𝑋′𝑖β + 𝜇𝑖 < 𝑎) = 𝑃( 𝜇𝑖 ≥ 𝑎−𝑋′𝑖β)
The DBDC model described above yields four possible outcomes with respect to the Yes/Yes, Yes/No,
No/Yes and No/No answers.
Following Hanemann et al (1991), the log-likelihood function, considering the starting point (𝑎𝑠), the lower
(𝑎𝑙) and the upper (𝑎𝑢) bids with the given sample of N respondents, takes the following form:
ln 𝐿(β) = ∑ {b𝑦𝑦𝑖 ln(1 − Ψ(𝑎𝑢, β) + b𝑦𝑛
𝑖 ln(Ψ(𝑎𝑢, β) − Ψ(𝑎𝑠, β) + b𝑛𝑦𝑖 ln(Ψ(as, β) − Ψ(al, β) +𝑁
𝑖=1
bnni ln Ψ(al, β)}
Where (1 − Ψ(𝑎𝑢, β), (Ψ(𝑎𝑢 , β) − Ψ(𝑎𝑠, β), (Ψ(𝑎𝑠, β) − Ψ(𝑎𝑙 , β) and Ψ(𝑎𝑙 , β)are respectively the
probability associated with the “”yes/yes”, “yes/no”, “no/yes” and “no/no” answers, and Ψ(∗, β) a normal
cumulative distribution function
Considering the six starting points or bid cards randomly assigned to respondents, the above log-likelihood
function becomes:
ln 𝐿(β) = ∑ ∑ {b𝑦𝑦𝑖𝑗 ln(1 − Ψ(𝑎𝑢, β) + b𝑦𝑛
𝑖𝑗 ln(Ψ(𝑎𝑢 , β) − Ψ(𝑎𝑠, β) + b𝑛𝑦𝑖𝑗 ln(Ψ(𝑎𝑠, β) −6
𝑗=1𝑁𝑖=1
Ψ(𝑎𝑙 , β) + b𝑛𝑛𝑖𝑗 ln Ψ(𝑎𝑙 , β)}
In this equation, b𝑦𝑦𝑖𝑗, b𝑦𝑛
𝑖𝑗, b𝑛𝑦𝑖𝑗 and b𝑛𝑛
𝑖𝑗 are dummy variables taking as value, one or zero given any
of the 𝑗𝑡ℎ bid card, with respect to the individual situation among the four outcomes stated above.
3.5.2. Interval Censored Regression Model The fundamental difference between the DBDC model and the ICRM lays in the consideration of
the observed point data gathered using the OE format on the “no/no” respondent. The OE WTP helps indeed
account for 169 more households.
Stewart (1983) suggests using the maximum likelihood technic when estimating the ICRM. Hence, following
Wooldridge (2013), the log likelihood function can be given by:
ln L(β) =
∑ {birc ln (1 − Ψ (
SBi−X′iβ
σ)) + bi
id ln (Ψ (SBi−X′
iβ
σ) − Ψ (
FBi−X′iβ
σ)) + bi
lc ln (Ψ (FBi−X′
iβ
σ)) −N
i=1
1
2bi
OE ((ai
OE−X′iβ
σ) + ln2πσ2)}
In this equation, 𝑏𝑖𝑟𝑐, 𝑏𝑖
𝑖𝑑 and 𝑏𝑖𝑙𝑐 are dummy variables taking as value, one or zero with respect to the
individual situation among the right censored interval, the interval data and the left censored interval. 𝑏𝑖𝑂𝐸 is
a dummy variable taking as value, one if a “No/No” respondent wills to pay a 𝑎𝑖𝑂𝐸 amount after the OE
question or zero otherwise, and Ψ(∗, β) is the normal distribution function.
3.5.3. The Tobit model Yet the OE format reduces the proportion of the “no/no” respondent, accounting for 169 households willing
to pay a positive amount lower than the lower bid, it still remains a nontrivial proportion of population that
don’t have any preferences for forest elephants conservation but recorded as “zero” WTP.
Let 𝑊 be the observed preference associeted with the latent variable (𝑊 ∗) that is the maximum WTP. The
Tobit model expresses the observed household preferencesw, in terms of the latent variable is given by:
𝑊 = {𝑊 ∗ 𝑖𝑓 𝑊 ∗> 0
0 𝑖𝑓𝑊 ∗≤ 0
Where 𝑊 ∗= 𝑋′𝛽 + 𝜇; 𝑋′ is a vector of observable variables, β is the vector of unknown parameters
and μ is the errors which are independent and identically distributed (Tobin, 1958)
4. Results This section presents the descriptive Statistics, the answers-to-bids and outcomes of the various models and
the Econometric results
4.1. Variable Description and Descriptive Statistics The preferences of the households for forest elephant conservation may vary with respect to the
socioeconomic characteristics and the geographical characteristics. The table below describes the
independent variables used in the various econometrics models, as well as descriptive statistics. The 1035
head of household surveyed were considered for the DBDC without follow-up, while, 936 among the 1035
were considered for the DBDC with follow-up, the ICRM and the Tobit model. Based on the follow-up
questions 99 respondents were identified as protesters and were removed from the sample before estimation
(Arrow et al, 1993).
In the Tridom landscape, most of the households are managed by men. Indeed, a proportion of 76% of the
head of surveyed households are male. The youngest respondent is 16 years old, while the oldest is 90. Only
27 (2,88%) of the households are under twenty-five years of age, 383 (41%) are between 25 and 45 years
old, 143 (15,28%) are between 45 and 55 and 16,35% are over 65 years old. A relatively small number of
households, that is 69 (7,37%) are illiterate, most heads of household surveyed that is 867 (88,14%) have
been at least in the primary school, and 55% reached the secondary school among which less than 5% have
been at the University. very few households (17%) have a secondary education diploma, while 75% have at
most a first-certificate primary diploma among which 29% have no diploma.
The indigenous Baka, known as an ethnic minority group are represented in the proportion of 5%, which are
47 households. Their way of life is highly linked to elephant existence. Therefore, elephant conservation is
very important for the preservation of their cultural and spiritual assets. The main activity or the type of land-
use may also influence the household’s preferences for forest elephant conservation. Among the 936
respondents, 19% make cash crop (Cocoa) their main use of land, 41% are small scale farmers, producing
crop for subsistence and small scale trading. A proportion of 3% of the households surveyed use forest land
for traditional gold mining). The respondent using forest for hunting and gathering represent 15% of the
sample, 3% work either in a biodiversity conservation organisation, either in the forest administration, or in a
forest concession management. Among the remaining respondents, 9% work for other administrations, and
10% practice animal husbandry, fisheries, and trade. Human-elephant conflicts remain a disturbing reality in
the landscape that may have a negative impact on the wellbeing of the households and thus on their
preferences for elephants conservation. Indeed, some conflicts have been reported by 259 households (28%)
with about CFA28140 that’s €43 damage cost per household.
Table 1 : Variable and Descriptive Statistics
Variable Description Min Max Mean Std. Dev.
Obs Sign
sex 1 if the respondent is male and 0 otherwise 0 1 0,76 0,42 936 +
age The respondent’s age in years 16 90 48,29 14,68 936 +
hsize The respondent’s household size 0 20 6,43 4,04 936 -
schlcycl The respondent’s school cycle 0 3 1,52 0,69 936 +
expensemth The respondent monthly expense 0 500000 46604 59463 936 +
autochbaka The respondent is an indigenous BAKA (Pygmies) 0 1 0,05 0,22 936 +
smalfarm The respondent is a small scale farmer 0 1 0,40 0,49 936 -
tgoldmin The respondent is a traditional gold miner 0 1 0,03 0,16 936 +
hunt_gath The respondent is a hunter-gatherer 0 1 0,15 0,36 936 -
fmu_foad The respondent works in the forest adm. or a FMU 0 1 0,03 0,17 936 +
othadmin The respondent works in other administration 0 1 0,08 0,28 936 +
Hum_el 1 if the respondent faced a human/elephant conflict 0 1 0,27 0,44 936 -
ldarea The respondent’s land area (ha) 0 56.25 4,32 5,32 936 +
dist_narea The distance between to the nearest protected area 0 94.55 28,97 22,25 936 +
protarea Inceasing elephant density protected area 1 10 5,74 2,33 936 -
Source: Authors
The customary land tenure, the distance of the respondent to the nearest protected area and the proximity to a
relatively high elephant density protected area are also considered as variables that can influence the
preferences of the respondent for elephant conservation. The two first of these three variables were
determined using a GPS and ArcMap sofware. Indeed, the customary land tenure consists of the area of land
owned by a household. This variable was generated using a tracking with GPS to capture the exact area.
About 70% of the households own between 0,1 and 5 ha, 8% do not have access to land, 29 % own between
5 and 15 ha, 3% own between 15 and 25 ha. There are a few household owning between 25 and 57 ha. With
regard to the proximity to a relatively high elephant density protected area, the ten protected areas are
classified from least to highest elephant density. The least been the Gorilla Sanctuary of Megame and the
highest been the Minkebé National Park. Among the less dense is the Nkom National Park and among the
densest are Mwagna, Lobeké , Ivondo, Nki National Parks and the Dja Biosphere Reserves.
The table below gives the structure of 936 answers to bids after removing protesters using the motives
behind the answers captured with the follow-up questions (Arrow et al, 1993). In this table, we observe a
sensitivity of respondents to the starting bids. Indeed, the frequency of “yes” and “yes/yes” respondents
decreases with the starting bid. However, there is an ultra-weak intensity of dependence between the
respondent’s stated preferences and the starting points, the lower bid and the upper bid. The corresponding
correlation coefficients are 0,11 ; 0,085 and 0,11.
The “no/no” respondents oscillate between 7,6 and 11,2%. About 56% were “no/no”. Among the remaining,
the WTP stated by 18,4% were somewhere in the interval between the lower bid and the starting bid (6,5%)
and between the starting bid and the upper bid (11,9%). About 25,4% were willing to pay more that the
higher bid.
Table 2 : answers to bids
Bid cards 𝒂𝒔/𝒂𝒍/𝒂𝒖
Stat of bid cards Yes
to 𝒂𝒔
No to 𝒂𝒔
Ansews to bids
Percentage
Freq Perc. YY NY YY NY
YN NN YN NN
1000/500/1500 191 20% 46% 54% 56 15 5,98% 1,60%
31 89 3,31% 9,51%
1500/1000/2000 161 17% 47% 53% 53 5 5,66% 0,53%
22 81 2,35% 8,65%
2000/1500/2500 148 16% 34% 66% 33 14 3,53% 1,50%
18 83 1,92% 8,87%
2500/1000/3000 163 17% 33% 67% 40 5 4,27% 0,53%
13 105 1,39% 11,22%
3000/1500/3500 115 12% 38% 70% 31 10 3,31% 1,07%
3 71 0,32% 7,59%
3500/2000/4000 158 17% 31% 56% 25 12 2,67% 1,28%
24 77 2,56% 10,36%
Total 936 100% 238 61 25,43% 6,52%
111 526 11,86% 56,20%
Souces : Authors (𝒂𝒔 = 𝒔𝒕𝒂𝒓𝒕𝒊𝒏𝒈 𝒃𝒊𝒅; 𝒂𝒍 = 𝒍𝒐𝒘𝒆𝒓 𝒃𝒊𝒅; 𝒂𝒖 = 𝒖𝒑𝒑𝒆𝒓 𝒃𝒊𝒅)
The table below show the outcome summary with respect to the 5 econometric models estimated. The DBDC
model without protesters left-censors all the 56,2% of the “no/no” observation . The OE format assigned to
the respondent after the DBDC yielded 578 positive stated WTP, that’s 61,75% of the respondents. As
corollary, the information on the forest community preferences has improved in two ways. Indeed, (1)
among the “no/no” observation (56,2%) of the overall sample, the OE question helped to capture a positive
WTP from 32% more observation (169 households) ; (2) there is a gain in precision for the 25,4% who were
willing to pay more that the higher bid.
Table 3 : Outcomes of the various models
DBDC
DBDC
without
protesters
INTREG with
negative Preferences
INTREG with
expected
negative Preferences
Tobit
Freq Perc. Freq Perc. Freq Perc. Freq Perc. Freq Perc.
Left Censored data 625 60,4% 526 56,2% 27 2,9% 104 11,1% 358 38,2%
Right Censored Data 238 23,0% 238 25,4% 0 0,0% 0 0,0% 0 0,0%
Interval Data 172 16,6% 172 18,4% 170 18,2% 170 18,2% 0 0,0%
Point data 0 0,0% 0 0,0% 739 79,0% 662 70,7% 578 61,8%
Total 1035 100% 936 100% 936 100% 936 100% 936 100% Source: authors
The Tobit model with zero as lower limit, accounted for the 578 positive stated WTP, while left-censoring
the remaining 358 households who were not willing to pay a greater that zero amount. The Tobit model
considers these remaining household as those who stated negative WTP, while some of them may be
indifferent to the elephants issues. The follow-up question allowed identifying 254 indifferent households
and 104 cost potential households who expressed negative preferences among with 27 cost effective
experienced human/elephant conflict with crop damage. The following paragraph presents and discusses the
econometric results.
4.2. Econometric results and discussion
The estimates using the maximum likelihood function of five econometric models are shown in the table
below. It shows the coefficients and their standard errors. All the five regression models fit significantly
better than models with no predictors. Indeed, the Wald statistics are above the 99% quantile of the χ2 (15
dof) ie 37,70 and the p-values are less than 0,01. The Mean WTP (MWTP) shown in the
last row according to the different model is directly estimated as Xβ̂ (Lopez-Feldman, 2010, 2013 and
Wooldridge, 2013). Moving from the DBDC model to the DBDC model with follow-up helped controlling
for protest refusal bais and leaded to an increase in the monthly MWTP from CFA338,532 (€0,52) to
CFA722,349 (€1,10) that is an annual equivalent increase from CFA4062,4 (€6,2) to CFA8668,2(€ 13,2) per
household. The Tobit model used for the OE format generate a monthly MWTP of CFA543,03 (€0,83) that
is annually equal to CFA6516,4 (€9,9). In compliance with several empirical studies (Brown et al. 1996;
Boyle et al. 1996, Kealy and Turner 1993; Halvorsen and Soelensminde, 1998), we find that CE elicitation
format yields higher WTP estimates than OE format. The low MWTP from the OEformat result from the fact
that the Tobit model left-censors a non-trivial proportion of all the respondents recorded as “zero” WTP.
Combining both OE and CE formats under the ICRM produced a monthly MWTP of CFA1246,3 (€1,89) in
the fourth model. This model left-censored only the 27 cost-effective households who experienced both
human-elephant conflict with crop damage and expressed negative preferences.
The fifth model (ICRM) left-censored the 104 cost-potential households who do not all face crop damage
and who expressed negative preferences for elephants conservation. This model generated a MWTP of
CFA1139,4 (€1,74).
Table 4 : Coefficients Estimates
1. DBDC With protesters
2. DBDC Without protesters
3. Tobit model 4. ICRM Censoring effectif
negative Preferences
5. ICRM Censoring expected
negative Preferences
Number of obs 1035 936 936 936 936 Wald chi2(15) 75.56 87.66 F( 15, 921) = 6.12 113.55 106.65 Prob > chi2 0.0000 0.0000 Prob > F=0.0000 0.0000 0.0000 Pseudo R2 = 0.0095 Log likelihood -1067.7052 -1005.331 -5622.701 -7007.5991 -6439.2456
Coef, Std, Err,
Coef, Std, Err,
Robust Coef. Std. Err. Robust Coef.
Std. Err. Robust Coef. Std. Err.
Beta sex 491,220* 295,810 497,407* 284,983 432,800** 211,658 238,645** 118,743 249,082* 131,488 age -29,331*** 8,919 - 27,959*** 8,498 - 23,890*** 7,429 - 14,456*** 4,730 - 17,103*** 5,289 hsize 27,769 30,115 29,766 29,142 - 12,978 22,059 - 22,878 14,512 - 26,440 16,250 schlcycl 584,495*** 196,693 661,844*** 188,414 445,501*** 139,870 208,873** 81,929 247,462*** 92,187 expensemth 0,004** 0,002 0,007*** 0,002 0,007*** 0,002 0,006*** 0,002 0,006*** 0,002 autochbaka 1629,425*** 541,001 1 680,557*** 518,150 824,327** 341,501 518,515** 207,833 559,506** 224,124 smalfarm 39,951 291,911 68,255 283,718 177,065 225,469 51,431 152,722 76,627 162,091 tgoldmin 2441,012*** 774,991 1 990,380*** 721,248 1 629,836** 697,465 1 172,941* 599,737 1 223,234** 615,577 hunt_gath 283,950 381,427 274,325 364,665 227,672 290,832 85,329 195,159 84,606 209,344 fmu_foad 1917,690*** 660,370 1 595,936** 634,823 1 240,173*** 449,642 664,046* 380,290 724,859* 380,670 othadmin 995,920** 448,627 811,934* 436,410 686,954* 375,618 373,956 294,090 405,658 310,968 hum_el -97,875 264,282 - 108,028 253,412 - 38,443 203,442 - 190,393 143,385 - 64,845 141,066 ldarea 18,997 24,206 25,366 22,925 27,562* 16,127 18,076* 10,463 16,496 11,435 dist_narea -1,042 2,617 7,371 5,527 3,977 4,285 1,767 2,961 1,996 3,122 protarea 106,669*** 41,061 - 221,884*** 56,684 - 120,657*** 43,714 - 53,314* 27,569 - 57,331* 30,685 _cons -649,110 705,790 835,753 716,622 642,231 525,877 1 390,918
*** 392,383 1 347,309*** 412,399
Sigma _cons 3011,279 197,847 2 795,707*** 181,752 /lnsigma 7,517 0,135 7,579*** 0,134 /sigma 2579,683 320,016 1839.486 248.1248 1957.309 261.4254 Obs.summary: left-censored 358 (at maxwtp<=0) 27 104 uncensored 578 739 662 right-censored 0 0 0 interval 0 170 170
MEAN WTP 338,532 722,349 543,033 1 246,332 1 139,402
Legend : * p<0,1 ; ** p<0,05 ; *** p<0,01
The log likelihood can help comparing the models, but, according to the variability of the types of
data, we cannot directly compare the log likelihoods for the DBDC, the ICRM Model. Indeed, the
DBDC use only interval data, while the ICRM uses a mixture of discrete and interval data. We can
see that the ICRM that left-censors expected negative preference fits better than the first (lower
loglikelihood).
All the models present almost similar significant variables, we consider the ICRM that left-Censors
expected negative Preference. This model shows that, improving household’s education level
increases their preferences for elephant conservation by CFA247. A CFA1000 increase in individual
income would increase the individual WTP by CFA6. The WTP for forest elephant conservation
decreases with the head of household age. Among the heads of household, females are more likely to
state a greater preference. Indeed, an additional female will be willing to pay CFA17 more than an
additional male. The coefficient associated to this variable “autochbaka” is positive and significant.
Indeed, an indigenous baka would be likely to pay CFA559 more than the non-indigenous group. The
variable “proteria” has a negative and significant influence on the household’s preferences for
elephant conservation. Indeed, the households who are closed to the protected areas with less elephant
density are willing to pay more than those closed to higher density protected areas. Among the various
land use and activities, one more traditional gold-miner as well as one more forest management or
conservation related worker are likely to increase the WTP by CFA1223 and CFA 725 respectively.
The existence of conflict as well as the distance between the household and the nearest protected area
(dist_narea) don’t significantly drive the household preferences for elephant conservation.
4.3. Aggregate and Net Benefits To estimate the expected preferences for the population living in both Cameroonian and Gabonese
segment of the Tridom for elephant conservation, the simple transferring point estimate produces
robust aggregate with fewer bias (Bandara and Tisdell, 2004; Loomis et al 2000; Rodriguez et al,
2011). The aggregate population size in both segments of the Tridom is 418855 inhabitants
(Cameroonian Central Bureau of Census and Population Studies, 2010; Population Census Report –
Gabon, 2010). Considering the mean household size of the sample (6,43), the number of households is
around 65141. The protest refusal observation weighed 9,57% in the initial sample, the paper finally
considers 58910 households willing to pay CFA1139,4 (€1,74) per month for forest elephants
conservation. the monthly willingness of the overall population is CFA62,8 million (€95778), that is
annually equal to CFA753,9 million (€1,15 million). Considering the current 2.4% population growth
rate (World Bank) and a 3% discount rate, the Tridom local and indigenous households will to pay
CFA7, 4 billion (€11,3 million) for the 10 years elephant protections’ program to avoid losing the
services provided by elephant in cas of extinction. According to the high density population and the
increasing urban sprawl, the Haut-Nyong, the “Dja et Lobo” and the “Boumba et Ngoko” subdivision’s households in Cameroon expressed a greater aggregrae WTP for elephant conservation. among the gabonese subdivions, the “Ivindo”, the “Woleu” and the “Haut-Ntem” divisions expressed greater WTPs (see the figure below).
Figure 2 : Agregate WTP of Tridom local population for elephant conservation
Considering the CFA 28140 (€43) annual mean damage incurred by the 27,7% of the sampled
households, the net preferences of the overall population in both the Tridom segment is CFA3,383
billion (€5,165 millions). Indeed, the households stated value of the services provided by forest
elephants exceeds the aggregate economic cost incurred by the farmers in terms of crops and property
damage caused by elephants. This result states that forest elephant conservation is socially beneficial.
It may also state the tolerance by the farmers of the presence of elephants in their agricultural fields.
5. Discussion
The above estimates and aggregate provide four major outcomes with respect to the hypothesis stated
above.
1. The extinction of Loxodonta cyclotis can lead to a significant net lost in the household’s wellbeing,
indeed, the per month MWTP stated by local population is CFA1139,4 (€1,74). This value is close
to the results found by Bandara and Tisdell, (2005). Indeed, they found that the respondents in
general were willing to pay Rs. 110,17 (€1,65) per month for elephant conservation.
2. The estimates shows that the existence of human/elephant conflict, as well as the distance of the
location of the households to the nearest protected area do not influence the preferences of the
households for elephants conservation. This result rejects the hypothesis that the WTP for
elephant’s preservation increases with the distance of the location of the households to the nearest
protected areas and decreases with the human-elephant conflicts presence. In fact, as the scenario
presented to the respondents indicated that the program for protection also includes measures to
reducing human elephant conflicts. This result shows that local communities are receptive to such
a program. Furthermore, as the households surrounding the low density protected area are willing
to pay more than the one surrounding high density protected areas, it is clear that, no matter far or
closer is the household to the nearest protected area, the scarcity of elephant increases the
preferences of households for conservation.
3. The indigenousness has a positive and significant influence on the household’s preferences. This
information is all the more important to increasing incentives for elephant conservation as the
extinction of forest elephant would lead to a severe threat on spiritual enrichment, cultural identity
as well as the way of life of this ethnic minority group.
4. As methodological and technical outcome, this paper proposes combining both DBDC and Closed-
Ended elicitation formats to investigate de true WTP when doing a CV. We propose the following
steps: (1) after defining good starting points during pre-test (see Frew, 2010-a), (2) one should first
assign the DBDC, varying randomly the starting points among respondents. (3) as a good starting
points prepares and encourages the respondents to state their true (Seenprachawong, 2004;
Carmona-Tores and Calatrava-Requena, 2006, Fischhoff and Furby, 1988) one should then assign
the OE format to improve the level of information about the “no/no” respondents who would be
1 959,20
158,64
2 038,52
975,63
325,21
658,35
245,89
150,71
452,12
277,62
182,44
7 424,33
892,75
72,29
928,90
444,57
148,19
299,99
112,05
68,67
206,02
126,50
83,13
3 383,07
Dja et Lobo
la Mvila
Haut-Nyong
la Boumba et…
la Zadié
l’Ivindo
la Lopé
la Mvoung
le Woleu
le Haut-Ntem
l’Okano
agregate
Per subdivision agregare WTP and Benefit (million of CFA)
Net Benefit (millions of CFA) agregate ICRM (millions of CFA)
willing to pay a positive amount lower than the lower bid, as well as those willing-to-pay above
the upper bid. (4) Afterwards, one should ask the motives behind the answers. The motive should
be defined a way to help removing protesters, as well as distinguishing between negative, zero and
positive preferences (Arrow et al, 1993). Finally (5) one should run an Interval Regression Model
as it takes into account the point data and offers the greatest increase in efficiency with the least
ambiguity compared to the DBDC (Haab and Mcconnel, 2000).
Acknowledgements
This research is part of the CIFOR-GCS [Center for International Forestry Research’s (CIFOR) global
comparative study on REDD+ (GCS)} project with funds provided by the Norwegian Agency for
Development Cooperation (NORAD)
References
Alberini, A., (1995), "Efficiency vs Bias of Willingness-to-Pay Estimates: Bivariate and Interval-
Data Models," Journal of Environmental Economics and Management, 29, 169-180; Url:
http://ac.els-cdn.com/S009506968571039X/1-s2.0-S009506968571039X-
main.pdf?_tid=dc7b08e2-7647-11e4-9d04-
00000aacb35f&acdnat=1417101355_5c316ffe231b999f30f3a2a074529581
Arrow, K., R. Solow, P. R. Portney, E. E. Leamer, R. Radner and H. Schuman, (1993), “Report
of the NOAA Panel on Contingent Valuation”, Federal Register. V.58:4601-4614
Bandara, R. and C.Tisdell, (2004), The net benefit of saving the Asian elephant: a policy and
contingent valuation study. Ecological Economics,N°48, p. 93–107.
Bandara, R. and C. Tisdell, (2003),Use and non-use values of wild Asian elephants: a total
economic valuation approach. Sri Lanka Journal of EconomicsN°4, p. 3–30.
Bandara, R. and C.Tisdell, (2002), Conserving Asian Elephants: Economic Issues Illustrated by Sri
Lankan Concerns in The Economics of Conserving of Wildlife and Natural Areas C A Tisdell.
Edward Elgar, Cheltenham, UK, p.193–211
Bandara, R. And C.Tisdell, (2005), Changing abundance of elephants and willingness to pay for
their conservation, Journal of Environmental Management N°76, p.47–59
Bandara, R., C. Tisdell, (2003), Comparison of rural and urban attitudes to the conservation of Asian
elephants in Sri Lanka.Biological Conservation,N°110, p. 327–342.
Bateman, I. J., R. T. Carson, B. Day, M. Haneman, N. Hanley, T. Hett, M. Jones-Lee, G.
Loomes, S. Mourato, E. Ozderimoglu, D. W. Pearce, L. Sugden and J. Swanson, (2002),
”Economic Valuation with Stated Preference Techniques: a Manual, UK Department of
Transport,” Edward Elgar Publishing, Cheltenham, UK. ISBN 1840649194
Bateman, I J, Hugh D. Langford and Ian H. Langford, (1994), Multi-level modelling and
contingent valuation : part 1 a triple bounded Dichotomous Choice Analysis [workingpaper]. East
Anglia: Centre for Social and Economic Research on the Global Environment
Beaume, D., F. Bretagnole, L. Bollache, G. Hohmann, A. Surbeck and B. Fruth, (2013), Seed
dispersal strategies and the theat of defaunation in congo Forest, Biodiversity Conservation, N°22,
p. 225 – 238
Bishop, R.C., (1978), Endangered species and uncertainty: the economics of a safe minimum
standard. American Journal of Agricultural Economics57, 10–18.
Blake, S., S. L. Deem, E. Mossimbo, F. Maisels and P. Walsh, (2009), Forest Elephants; Tree
Planters of Congo, Biotropica, Vol. 41 Issue 4 P. 459 – 468
Blake, S., Strindberg, S. Boudjan, P. Makombo, C. Bila-Isia, I. Ilambu, O. Grossmann, F. Bene-
Bene, L. de Semboli, B. Mbenzo, V. S'hwa, D. Bayogo, R. Williamson, L. Fay, M. Hart, J.
and F. Maisels, (2007), Forest Elephant Crisis in the Congo Basin PLoSBiol, Public Library of
Science, 5, Url:
http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.0050111
Boyle, K. J., F. R. Johnson, D. W. McCollum, W. H. Desvousges, R. W. Dunford, and S. P.
Hudson, (1996), "Valuing Public Goods: Dis-crete versus ContinuousC ontingent-Valuation
Responses." Land Economics vol : 72 P : 381- 96.
Brahic, E. et J. P. Terreaux, (2009), Evaluation Economique de la biodiversité : Méthode et
exemples pour les forêts tempérée, editionQuae, (livre)
Brown, T. C., P. A. Champ, R. C. Bishop, and D. W. McCollum, (1996), "Which Response Format
Reveals the Truth about Donations to a Public Good?" Land Economics Vol 72 P: 152-66.
BUCREP, (2010), Rapports de présentation des résultats définitifs du 3e Recensement Général de la
Population et de l’Habitat. 67 p.
Cameron, T. A. and M. D. James, (1987) "Efficient Estimation Methods For 'Close-Ended'
Contingent Valuation Surveys." Rev. Econ. and Statist. P. 269 – 276
Carmona-Torres, C. and J. Calatrava-Requena, (2006), "Bid Design and its Influence on the
Stated Willingness to Pay in a Contingent Valuation Study," 2006 Annual Meeting, August 12-18,
2006, Queensland, Australia 25367, International Association of Agricultural Economists
Carson, R. T. and M. Hamenann, (2005), Contingent Valuation in Mäler, K.-G. and J. R. Vincent,
(2005), Handbook of environmental Economics: valuing environment changes, Vol. 2, Elsevier, p.
822 – 936
Carson, R. T., W. M. Hanemann, and R. C. Mitchell, (1986), "Determining the Demand for Public
Goods by Simulating Referendums at Different Tax Prices." Department of Economics, University
of California, San Diego.
Carson, R., (1985), Three essays on contingent valuation. Ph. D. thesis, University of California,
Berkeley.
Chi Wang, B., (2009), Impact of Hunting on seed dispersal in a Central African Tropical Forest,
ProQuest, LLC (book)
Fischhoff B. and L. Furby, (1988), Measuring Values: A Conceptual Framework for Interpreting
Transactions with Special Reference to Contingent Valuation of Visibility; Journal of Risk and
Uncertainty, June 1988, Volume 1, Issue 2, p. 147-184
Frew, E., (2010-a), Benefit assessment for CBA studies in healthcare using CV methods, in
McIntosh, E., P. Clarke, E. Frew and J. Louviere, (2010), Applied Methods of Cost-Benefit
Analysis in Health Care, Handbooks in Health Economic Evaluation, volume 4, Oxford University
Press
ISBN 978-0-19-923712-8 (pbk)
Frew, E., (2010-b), Benefit assessment for CBA studies in healthcare: A guide to carrying out a
stated preference willingness to pay survey in healthcare, in McIntosh, E., P. Clarke, E. Frew and
J. Louviere, (2010), Applied Methods of Cost-Benefit Analysis in Health Care, Handbooks in
Health Economic Evaluation, volume 4, Oxford University Press
ISBN 978-0-19-923712-8 (pbk)
Garrod, G. D. and Willis, K. G. (1999), Economic Valuation of the Environment: Methods and Case
Studies, Edward Elgar, Cheltenham, 384 p
Haab, T.C., and K.E. McConnell, (2002), Valuing Environmental and Natural Resources: The
Econometrics of Non-market Valuation. Edward Elgar, 326 p.
Halvorsen B. and K. Sœlensminde, (1998), Differences between Willingness-to-Pay Estimates from
Open-Ended and Discrete-Choice Contingent Valuation Methods: The Effects of
Heteroscedasticity, Land Economics, Vol. 74, No. 2, pp. 262-282 : University of Wisconsin Press
Stable URL: http://www.jstor.org/stable/3147055
Hanemann, W. M. and B. Kanninen (1998), The Statistical Analysis of Discrete-Response CV Data
in Bateman I. J. and K. G. (éd.), Willis Valuing Environmental Preferences: Theory and Practice
of the Contingent Valuation, Method in the US, EC, and Developing Countries. Oxford University
Press, P 302 – 441 Url: http://are.berkeley.edu/~gh082644/wp798.pdf
Hanemann, W. M., J. B. Loomis and B. J. Kanninen, (1991), Statistical Efficiency of Double-
Bounded Dichotomous Choice Contingent Valuation, American Journal of Agricultural
Economics 73, 1255-1263. Url:
http://www.jstor.org/stable/pdfplus/1242453.pdf?&acceptTC=true&jpdConfirm=true.
Hanemann, W., (1985),“Some issues in continuous and discrete response contingent valuation
studies”. Northeast Journal of Agricultural Economics Vol:14, P. 5–13.
Harpman, D. A. and M. P. Welsh, (1999), Measuring Goodness of Fit for the Double Bounded
Logit Model: Comment, American Journal of Agricultural Economics, Vol. 81, No. 1, Feb., 1999
Harris J.M. and B. Roach, (2013), Environmental and Natural Resource Economics: A
Contemporary Approach, cM.E.Sharpe Armonk, New York London, England P. 584 pages (Book)
Herriges, J., and J. Shogren, (1996), "Starting Point Bias in Dichotomous Choice Valuation with
Follow-up Questioning," Journal of Environmental Economics and Management N°30 P. 112-131.
Kealy, M. J. and R. W. Turner , (1993), A Test of the Equality of Closed-Ended and Open-Ended
Contingent Valuations, American Journal of Agricultural Economics, Vol. 75, No. 2, p. 321-331
URL: http://www.jstor.org/stable/1242916
Kent, S., (1996), Cultural DiversityAmongTwentieth-Century Foragers: An African Perspective,
Cambridge universitypress, cambridge, ISBN 0-521-48237-2
Kremer, M. and C. Morcom, (2000), Elephants, The American Economics Review, Val 90, N°1, p
212 - 234
Lewis, J., (2002), Forest Hunter-Gatherersand their world: A study of the MbedjeleYaka Pygmies of
Congo Brazzaville and their Secular and Religious Activities and Representations, Department of
Social Anthropology, LondonSchool of Economics and Political Science, PhD Thesis
Loomis, J. and Larson, D., (1994), Total economic values of increasing gray whale populations:
results from a contingent valuation survey of visitors and households. Mar. Resource Econ., Vol 9,
P : 275-286.
Loomis, J., P. Kent, L. Strange, K. Fausch and A. Covich, (2000), Measuring the total economic
value of restoring ecosystem services in an impaired river basin: results from a contingent
valuation survey. Ecological economics vol 33, P : 103-117.
Lopez-Feldman A., (2013), Introduction to contingent valuation using Stata MPRA paper 41018.
Url: http://ideas.repec.org/p/pra/mprapa/41018.html
Lopez-Feldman, A., (2010), doubleb: Stata module to estimate contingent valuation using Double-
Bounded Dichotomous Choice Model, Available at
http://ideas.repec.org/c/boc/bocode/s457168.html
Maisels F, Strindberg S, Blake S, Wittemyer G, Hart J, et al. (2013) Devastating Decline of Forest
Elephants in Central Africa. PLoS ONE 8(3):e59469. doi:10.1371/journal.pone.0059469.Url:
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0059469
Martin, E. and D. Stiles, (2000),The Ivory Markets of Africa. Save the Elephants, Nairobi, Kenya
and London, UK
Mitchell, R. C. and R. T. Carson, (1989), Using survey to value public goods. The Contingent
Valuation Method. Resources for the Future, Washington, DC
Pearce, D., G. Atkinson et S. Mourato, (2006), Analyses coûts-bénéfices et environnement:
développement récents, OCDE.
Smith, H. F. and C. A. Sullivan, (2014), 'Ecosystem services within agricultural landscapes: farmers'
perceptions', Ecological Economics, vol. 98, p. 72-80, URL:
http://www.sciencedirect.com/science/article/pii/S0921800913003637
Stewart, M. B., (1983), On least squares estimation when the dependent variable is grouped. Review
of Economic Studies Vol 50, Issue 4, P 737–753, URL:
http://darp.lse.ac.uk/PapersDB/Stewart_%28REStud_83%29.pdf
Tobin, J., (1958), Estimation of relationships for limited dependent variables. Econometrica 26: 24–
36.
Seenprachawong U, (2004), An Economic Analysis of Coral Reefs in the Andaman Sea of Thailand,
in Ahmed, M., C.K. Chong and H. Cesar, (2004) Economicb ,kb Valuation and Policy Priorities
for Sustainable Management of Coral Reefs, World Fish Center, Penang, Malaysia (2004)
Vredin, M., (1997), The African Elephant – Existence Value and Determinants if Willingness to Pay.
Umea Economic Studies N°441.
Wooldridge, J. M., (2013), Introductory Econometrics: A Modern Approach. 5th ed. Mason, OH:
South-Western, URL: http://pdf-library.ir/upload/Jeffrey%20M.%20Wooldridge-
Introductory%20Econometrics_%20A%20Modern%20Approach-South-
Western%20College%20Pub%20(2012).pdf
Appendix 1. : Bids-structures for the 4th Bid card;
Source: authors
Appendix 2. : The Tridom Landscape;
Introducing an Open Ended
Question
Answer to follow up and stop
Second Bid
Answer to first Bid
and Follow up
First Bound
Initial Bid
Wilingness to pay the initial Bid (CFA2000 -
€3.05)
Yes
Willingness to pay CFA 2500 -
€3.81
yes
WTP >higher bid max WTP
no
WTP ∈[higher bid, Initial bid[ max WTP
No
Willingness to pay CFA1500 -
€2.29
yes
WTP∈[the lower bid, the initial bid[
max WTP
no
WTP∈ [0, 𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝐵𝑖𝑑[ max WTP
Appendix 3. : Ethnic representativity of the surveyed sample
Freq. Percent Cum.
Baka 47 5.02 5.02 Fang-Beti-Bulu 369 39.42 44.44 Bangando 34 3.63 48.08 Kota et Bakota 64 6.84 54.91 Mahongwe 40 4.27 59.19 Kounabembe 20 2.14 61.32 Mvong Mvong et Mpumpong 22 2.35 63.68 Djem 65 6.94 70.62 Badjoué 66 7.05 77.67 Migration, Yambassa, Bamoun... 59 6.30 83.97 Nzime 61 6.52 90.49 20 other etchnics 89 9.51 100.0 Total 936 100.00
Source: authors
Appendix 4. Stratified sample
Region Divisions Subdivision Freq. Percent Cum.
CAM
ERO
ON
IAN
SEG
MEN
T
South
DJA ET LOBO
Sangmelima 47 5,02 5,02
Meyomessala 53 5,66 10,68
Bengbis 22 2,35 13,03
Meyomessi 27 2,88 15,91
Djoum 48 5,13 21,04
Oveng 33 3,53 24,57
Mintom 17 1,82 26,39
MVILLA Mvangan 20 2,14 28,53
Est
HAUT NYONG
Ngoyla 58 6,2 34,73
Lomié 52 5,56 40,29
Messamena 32 3,42 43,71
Somalomo 43 4,59 48,3
Dja 32 3,42 51,72
Messock 43 4,59 56,31
BOUMBA ET NGOKO
Mouloundou 46 4,91 61,22
Yokadouma 50 5,34 66,56
Salapoumbé 25 2,67 69,23
GABO
NN
ESE S
EG
MEN
T
Ougoué I
vondo IVINDO
Makokou 29 3,1 72,33
Batouala 20 2,14 74,47
Mvadhi 14 1,5 75,97
Makébé Bakouaka 20 2,14 78,11
LA ZADIE Mekambo 42 4,49 82,6
LA LOPE Booué 30 3,21 85,81
LA MVOUNG Ovan 19 2,03 87,84
Wole
u
Nte
m HAUT-NTEM Minvoul 35 3,74 91,58
WOLEU Oyem 56 5,98 97,56
OKANO Mitzic 23 2,46 100,0
Total 936 100
Source: Authors