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, jngouhouopo@nancy.inra.fr 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