Hervé, Christianne, Juvénale Richard Suffo

Claire Iason

Pablo Antoine Lea

Claudio

Sophia Adewale

Maman Julienne Mfangam

Père Olivier Ndam

Nji Pierre

Oumarou, Micheline Jules Zakari

Keywords: Households' livelihoods strategies, Deforestation, Human-wildlife conflict, Endangered forest elephant conservation, Landscape, Spatial Econometrics

This thesis investigates the key drivers of rural households' choices of livelihoods, and how these choices impact forest clearing and biodiversity conservation under a landscape approach. Using a novel and unique database obtained from a face-to-face survey with a representative sample of 1035 households in the Dja-Odzala-Minkébé trinational transboundary conservation landscape (Tridom-TCL)-Congo basin , this PhD thesis address three main questions investigated in three chapters. Using a spatial probit model, the first chapter investigates "how do local and indigenous households formulate their preferences among livelihoods strategies?" Using a spatial lag model, the second chapter investigates "how and how much do these livelihoods strategies, given wildlife constrains such as human-wildlife conflicts, impact smallscale deforestation?" Using corner solution models, the third chapter investigates "how the nature of the interactions among households and wildlife, the households' main activities as well as their land holdings impact their willingness to pay to prevent endangered forest elephant extinction?" Among other, we find that livelihoods strategies are determined by autochthonous status, financial assets (money transfer and access to loan), distance to market and larger crop losses resulting from human-wildlife conflicts. Further, we show that livelihoods strategies are important for deforestation. Therefore, the commitments to reducing small-scale deforestation may be favored by a good consideration of factors that drive households' livelihoods strategies. We find out that spatial issues seem to be important. Proximity among households yields spatial shift effects and spatial spillover effects that are likely to amplify small-scale deforestation.

We also argue that, cross-cutting solutions towards a sustainable landscape considering these three crucial issues involve optimizing trade-off between households' livelihoods strategies, forest and the natural habitats of fauna. Therefore, the issues of community land security, of where natural habitat is needed and of how it should be managed are at the core of the problem. For example, decision-makers should proceed with the integration of large mammals mobility corridors far away from the community settlements in order to enhance zero-conflict-oriented elephant habitats; corridors should be consistently placed relatively to high elephant-concentration zones without crossing into zones with denser human populations.

Declaration

I hereby declare that except where specific reference is made to the work of others, the contents of this dissertation are original to the best of my knowledge and have not been submitted in whole or in part for consideration for any other degree or qualification in this, or any other university. This dissertation is my own work and contains nothing which is the outcome of work done by others unless referenced or specified in the text or acknowledgements.

Jonas Ngouhouo Poufoun

December 12, 2016 i I express my deepest gratitude to my supervisor M. Philippe Delacote. This thesis would not have been possible unless he had not accepted to host me, mentor me, support me, encourage me, and believe in me during these four years. Thank you very much Philippe.

I am very grateful to M. Serge Garcia, the Director of the Laboratory of Forest Economics. He contributed significantly to my integration at the laboratory. He has been available to discuss my research and had provided me with very usefull advices during my stay in the laboratory.

M. Jens Abiltrup and M. Dénis Jean Sonwa were my co-supervisors. They have significantly contrubuted to the realisation of the Thesis. Jens has been patient with me, discussing and guiding me on my research. Dénis believed in me since the begining, and accepted to embed my survey under the project he was managing at CIFOR. He provided me with significant assistance during the fieldwork. Jens and Dénis, please, accept my gratitude. I also express my gratitude to M. Pfaff and M. Luc Doyen for serving with Jens and Denis as my thesis committee members.

Following many discussions with Pr. Chicot Eboué , I contacted M. Bruno Jeandidier and Mrs. Sabine Chaupain-Guillot who provided me with very good comments on the survey questionnaires. Please, Chicot, Bruno and Sabine, accept my gratitude. I am very grateful to Pr. Samuel Ferey and M. Christian Poirson for their unconditional support.

I thank Sandrine Claudel-Cecchi, the Scretary of the Doctoral School, for her availability and services.

This thesis was partially supported by the CIFOR under the GCS-REDD Project with funding provided by the Norwegian Agency for Development Cooperation (NORAD). I received a partial scholarship from the French Government. The BETA also contributed to my financial standing with a two years contract as assistant lecturer. M. Jules Kunghetyou Poufoun also contributed significantly to my fiancial standing during the Thesis. I would like to thank Professors Aril Anglesen, Pascale Combes-Motel, Olivier Damette and Doctor Julie Subervie for being in my thesis examination committee.

iii

Résumé

Cette thèse examine les principaux déterminants des stratégies de subsistance des ménages ruraux. Elle cherche à comprendre comment ces stratégies impactent la déforestation à petite échelle et la conservation de la biodiversité suivant une approche paysagère. A partir d'une base de données uniques obtenues grâce à une enquête en face-à-face auprès d'un échantillon représentatif de 1035 ménages dans le paysage transfrontalier de conservation du Trinational Dja-Odzala-Minkébé (Tridom-TCL)-Bassin du Congo, cette thèse vise à répondre à trois questions et s'organise en trois chapitres. Le premier chapitre analyse "comment les ménages autochtones et locaux formulent leurs préférences parmi les stratégies et moyens d'existence" à l'aide d'un model Probit autorégressif spatial. Le deuxième chapitre examine "comment et à quelle amplitude ces stratégies impactent la déforestation à petite échelle" à l'aide d'un modèle de décalage spatial. Compte tenu de la nature des interactions entre les ménages et de la faune, de leurs principales activités, le troisième chapitre examine "les préférences des ménages pour la conservation des éléphants de forêt" à l'aide des modèles à variable qualitatives limitées.

Les actifs financiers (transfert d'argent et emprunts), la distance au marché, les dommages résultant des conflits homme-éléphant et l'ethnicité, plus précisément, l'autochtonie comptent parmi les facteurs déterminants des choix de stratégies de subsistance des ménages ruraux dans le Tridom-TCL. Nous montrons en outre que l'intensité de déforestation des ménages varie significativement en fonction de ces stratégies de subsistance. Par conséquent, les engagements en faveur de la réduction de la déforestation peuvent être favorisés par une bonne prise en compte des facteurs qui gouvernent les choix des modes de subsistance opérés par les ménages. Ils peuvent également être favorisés par la prise en compte les interactions entre ménages ainsi que leur localisation dans le paysage. En effet, nous trouvons qu'il existe des effets d'imitation, dans la décision de déforestation, entre les ménages d'un même voisinage, avec des effets spatiaux indirects susceptibles d'amplifier la déforestation à petite échelle.

Les solutions transversales, aux trois questions abordées dans cette thèse, en faveur d'un paysage durable devraient viser l'optimisation des compromis entre les stratégies de subsistance des ménages, les forêts et/ou les habitats naturels de la faune. Les décideurs devraient, par exemple, procéder à l'intégration des corridors de mobilité de grands mammifères dont les éléphants de forêt, dans des zones à forte concentration de la faune et loin des espaces communautaires afin de réduire le risque de conflits hommes-faune.

Mots Clés

Background and issues

In tropical forested landscapes, agricultural expansion for livelihoods (Deakin et al., 2016;Laurance et al., 2014) and trade (Leblois et al., 2016) have transformed and fragmented forest habitats at alarming rates. It has proximately driven around 80 percent of deforestation worldwide (Gibbs et al., 2010;KissinGer et Herold, 2012), contributing for 10 to 12 percent of the total global annual anthropogenic emissions of GHGs (Verchot, 2014;Pachauri et Reisinger, 2008). As a result, the destruction of large-scale natural habitats has led to the endangering of multiple biodiversity species, while the ability of ecosystems to provide some services also depends both on the number and type of species (Tilman et al., 2002(Tilman et al., , 2001)). There is a significant regional variation among the responsibility of commercial agriculture and subsistence farming. Commercial agriculture accounts for almost 70 percent of deforestation in Latin America, while, in tropical Africa, forest land conversion for small-scale agriculture is known as one of the main drivers (FAO, 2016;Hosonuma et al., 2012).

In 2005, Africa showed a continued net loss of forest area with 21.4 percent, compared to 23.6 percent in 1990 and small farming has been responsible for more than 50 percent of this forest loss (Delacote, 2012). Particularly, in Sub-Saharan Africa, where many people are poor, rural households adopt low-risk, immediate and low-return agricultural and other income-generating strategies [START_REF] Fao | The State of Food and Agriculture; Social protection and agriculture: breaking the cycle of rural poverty. FOOD AND AGRICULTURE ORGANIZA-TION OF THE UNITED NATIONS Rome[END_REF]. In the same vein, when property rights are insecure, deforestation can be seen as a risk management strategy (Araujo et al., 2009). Small-scale agriculture is relatively inefficient, since it is based on obsolete agricultural technologies resulting to large yield gaps compared to potential agricultural production (Masters et al., 2013;Laurance et al., 2014).

Environmental resources are important to millions of poor households in developing countries (Angelsen, 2011). As pointed out by Ellis (2000b); Scoones (1998), rural households develop further livelihoods strategies, beside agriculture, including forest resource extraction, hunting bushmeat and/or mixing among them. In many places in the developing world, rural households adopt various sets of livelihood strategies (Delacote, 2009). Some of them specialize in one activity, while others tend to diversify their activities portfolios. Motivations behind these choices can depend on households' characteristics (education, productivity, etc.), on the economic and environmental context (access to markets, access to land, quality of environmental resources, etc.) and on preferences (profit-maximizing or safety-first approaches, etc.). Unsustainable harvesting of non-timber forest products (NTFPs) observed in these countries can lead to forest degradation (Belcher et Schreckenberg, 2007;Hosonuma et al., 2012;KissinGer et Herold, 2012). Together with agriculture, this can reduce the ability of ecosystems to provide goods and services (Tilman et al., 2002).

In these conditions, there are strong interconnections among, (1) socioeconomic issues including households' livelihoods as well as rural development, (2) forest cover and (3) biodiversity and environmental issues into a whole system.

A broad range of literature has investigated the issue of livelihoods and deforestation (Babigumira et al., 2014;Belcher et al., 2015;Vedeld et al., 2007;Wunder et al., 2014), the issue of small scale farming and deforestation (Angelsen, 1995;Angelsen et Wunder, 2003) and the issue of deforestation and biodiversity conservation (Amin et al., 2014;Pfaff et al., 2015), targeting poverty reduction mainly at the national and global level. These studies did not explicitly address these issues as interconnected, while issue-specific solutions would lead to adverse effects among these components of the landscape and contribute to worsen rather than to improve the system (Holmgren, 2013). This is especially true in situation with competing and often contradictory uses among forest, biodiversity and productive land-uses.

For instance ignoring human-wildlife conflicts when pursuing compatibility between households' livelihood strategies and less deforestation would undoubtedly worsen the perception of wildlife conservation by households. In the meantime, some wild species have became endangered species. For instance, loxodonta africana cyclotis (forest elephants) has been subject to a devastating decline during the last two decades (Maisels et al., 2013;Blake et al., 2007). Also, disregarding the issue of biodiversity conservation would contribute to a weakening of the rural livelihoods system and the forest ecosystem as well. As an example, the forest elephant, as a flagship species, contributes to ecological, cultural and socio-economic equilibria (Blake et al., 2009). It contributes to indigenous people's cultural and spiritual enrichment in tropical Africa (Lewis, 2002;Kent, 1996). (2) It is considered as a tree planter, disseminating seeds of some species over long distances (Blake et al., 2009;Wang, 2008;Beaune et al., 2013).

(3) It also make some fleshy fruit like Baillonella toxisperma (moabi) available for rural communities' livelihoods.

In view of the foregoing considerations, there is a need to build diagnostics and research in order to guide decision-makers to adopt cross-cutting solutions towards sustainable development combining different transmission channels such as sustainable livelihoods, biodiversity conservation issues and forest restoration. This thesis thus seeks to answer the following question : what are the key drivers of rural house-holds livelihoods, and how do those choices impact forest clearing and biodiversity conservation?

This question can give rise to several side issues. Yet, this thesis considers three crucial questions that comply with rural households' behavior in a landscape: (1) How do local and indigenous households formulate their preferences among livelihoods strategies? (2) How and how much do the different livelihoods strategies developed by households, given wildlife constrains such as human-wildlife conflict impact smallscale deforestation? And finally, (3) given the nature of interaction among households and wildlife, given their main activities and their land holding, how much are local households willing to pay to prevent endangered forest elephant extinction?

The aim of this work is to investigate interactions among households' livelihoods strategies, their ecological footprint in terms of deforestation and their sensitivity to biodiversity conservation using our recent household's survey in the Dja-Odzala-Minkébé trinational conservation landscape (Tridom-TCL) -Congo Basin. The main interest is to improve an integrative perspective of the governance of this multifunctional landscape.

Conceptual and Theoretical Approaches

This thesis adopts as main point of view, the landscape approach. It is a framework for cross-cutting and combined solutions that help integrate agriculture and forestry issues and reduces or even remove externalities among components (Holmgren, 2013). The landscape approach helps to better understand and recognize the interconnections between different land uses and different stakeholders by integrating them into a joint management process (GLF, 2015). Following (Sayer et al., 2013), the landscape approach is viewed in this thesis as a framework for convenient management of forest land by local households to achieve (1) sustainable forest and non-forest-based livelihoods, (2) spiritual and cultural enrichment of local and indigenous populations, (3) sustainable forest-related businesses and (4) biodiversity conservation, inclusive of the issue of endangered forest elephants. The landscape approach is consistent with the framework proposed in the literature for analyzing households' strategies, deforestation and ecosystem services.

Our starting point to analyze livelihoods strategies is the sustainable livelihoods framework. "A livelihood comprises the capabilities, assets and activities required for means of living. A livelihood is sustainable when it can cope with and recover from stresses and shocks and maintain or enhance its capabilities and assets both now and in the future, while not undermining the natural resource base" (DfID, 1999;Chambers et Conway, 1992). According to Scoones (1998); Ellis (2000a); Jansen et al. (2006); Soltani et al. (2012); DfID (1999), the ability to sustainably pursue different livelihoods strategies relies on households specific assets, such as social, tangible, intangible assets and geographical assets that people have in their possession.

Investigating the link between different livelihoods strategies developed by households and small-scale deforestation requires considering the framework for analyzing deforestation developed by Angelsen et Kaimowitz (1999); Geist et Lambin (2002); Combes et al. (2015). According to Angelsen et Kaimowitz (1999), when analyzing households' deforestation inputs, microeconomic models have to be considered with a focus on immediate causes such as socioeconomic characteristics and decisions parameters including income, given the livelihoods strategy, financial asset, to name a few.

In order to investigate the sensitivity of rural households to the endangered forest elephant conservation given the nature of interaction with wildlife, we use the ecosystem services framework developed by MEA (2005) . This framework helped to provide a synoptic description of the ecosystem services and the total economic value of an environmental asset.

Finally, spatial interactions are among the crucial components of the landscape approach. Neighborhood interactions should be considered when analyzing landscape. Indeed, green spaces can contribute to the strengthening of the local social cohesion (Felbinger et Jonuschat, 2006) which require considering the location of households. Proximity among households may lead to social interactions and strengthen or worsen their social cohesion. We rely on the strategic-interaction models and more precisely on the spillover model and the resource flow model developed by Brueckner (2003) to explicitly account for the effect of neighborhood interactions. According to Anselin (2002), both models are suitable when a farmer would determine the amount of farmland devoted to a crop by taking into account the amounts allocated by the other farmers in the system. From the conceptual and the theoretical approches presented above, we built a tentative framework, displayed in Figure 1, that should be applied when investigating issues related to a multifunctional landscape.

The case study and households survey

The Dja-Odzala-Minkébé Trinational Landscap

The household's survey was held in the Dja-Odzala-Minkébé transboundary conservation landscape (Tridom-TCL). It is a cross-border conservation landscape covering a geographical area of 191,541 km 2 , representing 7.5 percent of the total area of the tropical forests in the Congo Basin of Central Africa. It was created in 2005 by an agreement between Cameroon, Gabon and Congo governments called "Tridom Agreement", as one of the twelve "priority landscapes" of the Congo Basin Forest Partnership (CBFP) 1 . The management of the Tridom-TCL is entrusted to a Re-gional Management Unit (RMU) with representative members in each of the three countries. Each representative member manages the corresponding segment of the landscape according to the national law. The RMU is prepared to act beyond national borders with the following missions: (1) sustainable management of transboundary biodiversity in the inter-zone, (2) maintaining the ecological, economic and social connectivity of the complex, (3) ensuring long term conservation of its protected area system and (4) fostering local development with reduced impact on environment.

The Tridom-TCL is located very close to the Equator, the climate is bimodal. There are two seasons with less rainfall, around January and July, and two rainy seasons, around October and April-May. It lies on a plateau at an altitude of 300 to 1,000 m above the see. In many places, especially in the regions of Minkébé in Gabon and the Dja in Cameroon, the plateau is punctuated with inselbergs. Regarding the hydrology, The Gabonese section is drained by the Ivindo, the Djidji and the Langoué that are tributaries of the Ogooué; and by the Ntem. The Cameroonian section is drained by the Ntem and more significantly the Dja and the Boumba, tributaries of the Congo River. The Congolese section is drained by the Mambili and pertains entirely to the basin of the Congo River (De Wachter et al., 2009).

The majority of the Landscape is covered with forests. The Tridom-TCL consists of a network of 10 protected areas, namely, the Dja biosphere reserve, the Boumba-Bek NP, the Nki and the Mengamé gorilla sanctuary (Republic of Cameroon), the Odzala-Kokoua NP and the Lossi NP (Republic of Congo), the Minkébé NP, the Ivindo NP, the Lopé NP and the Mwagne NP (Gabonese Republic). These protected areas spread over 37,498 km 2 and are connected by a wide inter-zone of more than 111, 000km 2 with 40,000 km 2 of thelivable area. The Tridom-TCL includes rich and diversified flora and fauna and is abundant in highly commercial valuable timber species. It houses the largest population of loxodonta africana cyclotis in the world, with the highest density in the Minkébé National Park. It also has a relatively high density of other large mammals, such as Gorilla gorilla gorilla (western lowland gorilla), Syncerus caffer (buffaloes), Tragelaphus eurycerus (bongo) Manis gigantea (giant pangolins) and Cephalophus (duikers), to name a few.

Households in the landscape

The inhabited inter-zone of the Tridom-TCL is home to indigenous and local people who depend on slash and burn agriculture and forest-based activities. The human population density is between 1-7.9 inhabitants per km 2 and is currently growing due to resource exploitation Ngoufo et al. (2012). The Tridom-TCL hosts industrial activities such as forest management units; industrial agriculture, safari hunting zones and industrial mining. The landscape also include illegal logging of timber with high commercial values, illegal mining and intensive poaching. From a quick look into our data set, about 78 percent of the indigenous and local communities declare relying on small-scale farming (40.9 percent), Cash-cropping (19.6 percent), Hunting-Gathering bushmeat and NTFP (14.8 percent) as well as Traditional gold-mining (3 percent) as predominant livelihoods activities with full-time employment.

In the Tridom-TCL, there is a competition for land among households. Land holding is almost considered as an indicator of renown and honor. Thus, economic rationality is not always the motive behind land clearing. After removal or selling the softwood lumber in surrounding forests, all the remaining plants and material in the forest are burnt and the land is used for extensive agricultural and cash-crop production with very poor yield.

Regarding the interaction with wildlife, local households undergo many conflicts with wildlife resulting in damage to their livelihoods. For instance, evidence from our survey reports that about 28 percent of local households have been victim of crop damage by forest elephants. These conficts may lead to a poor perception of forest elephant conservation. Also, this may drive the involvement of local households to the slaughtering of this iconic species.

Considering the above, the "Tridom agreement" is still far from meeting the intended objectives stated in sub-section , contrariwise, the ongoing practices often contribute to environmental degradation, increased deforestation, fragmentation and disintegration of the protected area network within the landscape. Hence the Tridom-TCL is a prime example of a landscape that urgently needs research and evaluations for cross-cutting solutions.

Households survey

There is a common understanding among scientists with regards to the lack of reliable subnational and households-level data relating to households livelihoods, deforestation and biodiversity conservation in Sub-Saharan Africa (Ferretti-Gallon et Busch, 2014;Palo, 1999;Geist et Lambin, 2002). This thesis was made possible thanks to a novel and unique database that we gathered from the field to debar and

Sampling, survey design and implementation

The organisation and the realization of the survey were realised in a way to minimize the survey implementation problems when measuring households livelihoods and deforestation as advised by Angelsen (2011). (a) After defining the sample and designing the questionnaires, (b) we recruited and adequately trained the enumerators. (c) We did the questionnaire pre-test on the field, (d) we proceeded further to the main survey. (e) We proceeded to data entry and finally we checked and used them for analysis. This thesis uses data obtained from a representative face-to-face survey of a random, stratified sample of 1035 households. The total number of households in the study area is approximately 65,140 (Gabon, 2010;Bucrep, 2010). The statistical sample size required at a confidence level of 95 percent with the typical value of 1.96 was 384. We expanded the sample size to 1035 for two reasons. First, it was important to have a statistically valid sub-sample's size per sub-division to improve the accuracy of analysis considering social interaction among households. Secondly, it is statistically true that, the larger the sample size, the more representative is the sample. The questionnaire was designed based on a literature review on the research issues. The questionnaire was organized into sixteen topics, including, among others, the drivers of households choices concerning land use, households use right and livelihoods production, land use conflicto; the multi-functionality of the landscape, the contingent valuation design as well as the cultural value and biodiversity conservation. The questionnaire was checked by experts and researchers before pre-test and survey. The questionnaires were prepared at the Laboratory of Forest Economics in Nancy, and the field work preparation was done at the Central African hub of the Center for International Forestry Research (CIFOR) in Yaounde, Cameroon.

To ensure high data quality, we organized five training seminars in Cameroon and five training seminars in Gabon with 20 post-graduate students covering all the topics addressed by the questionnaire. The training seminars included the use of GPS by a GIS expert for successful geo-localization of all the households surveyed. At the end, 10 participants were selected to perform as enumerators under the supervision of the PhD Candidate.

The survey started by two questionnaires pre-test with 40 households in four villages from two different sub-divisions in Cameroun. The first pre-test was held in Eboman and Endon villages in Oveng sub-division from December 20 to 24, 2013. This step helped in redefining and controlling some indicators embedded in the questionnaire. The second pre-test took place in the Kongo and Mbieleme villages in the Meyomessi sub-division from January 26 to 29, 2014. This step helped further to check the questionnaires' validation.

The geographical coverage of the survey included 108 villages from all the 26 administratives sub-divisions and districts of the Cameroonian and the Gabonese part of the landscape (Figure 2). The villages were chosen so as to represent the sub-division. In each sub-division, we chose one to three groups of villages geographically far apart from each other. Each group was inclusive of two to four consecutive villages. All the villages were displayed in 42 geographical maps using ArcGIS before the beginning of the field work to draw the route map of the whole survey. Figure 3 presents an example of the a group of villages located in the Ngoyla-Mintom forest (in Mintom sub-division, Cameroun). The survey was fielded over 8 months between December 2013 and July 2014. The visited villages are spread over nearly 27,000 km 2 , which represent two third of the landscape's livable inter-zone. The random sampling of the households in the villages was based on the inhabitants' registry held by the village chiefs. The interviews lasted between 1 and 3 hours. The survey was facilitated by introductory letters provided by the ministers of forest and fauna of both countries. These letters helped legitimating access to reliable data. Each village provided us with at least two local translators in case the household heads could not communicate in French. Beside Household's survey, numerous interviews were held with the sub-prefects and the mayors of the 26 sub-divisions.

As regard the post-field work, 50 percent of the data entry was made in Cameroon by six bachelor students and three master students. The remaining data entry was done by the PhD Student. The whole dataset was verified questionnaire-by-questionnaire and a part of the dataset was cleaned for the PhD dissertation. This final step from data entry to data clearing lasted from September 2014 to April 2015. In the next subsections, we present the measurement of the households' livelihoods and deforestation, and the measurement of their references for endangered forest elephant.

Households' Livelihoods measurement

In addition to the face to face survey, evening visits to various surveyed households were conducted to quantify and measure daily, weekly and per annum livelihoods production. The per annum value of livelihoods were derived in two ways. Some well organized household's heads have the habit of recording their daily, weekly and per annum livelihoods statistics. We trusted and collected these statistics. This information was cross-checked using our daily measurement. For the remaining households, we considered the daily and weekly measurement which we realised using contextual material. We extrapolated to obtain the per-annum livelihoods' production according to the recall data they provided and the seasonality of each species or product. The seasonality and the production irregularity of each species was defined during the design of the questionnaire and was validated during the pre-test steps. For instance, among the forest-based livelihoods, (1) Ricinodendron Heudelotti is harvested every year for a two-month period, it is measured with a five liters pail that weight 7.5kg, and a bag of 90kg is made of 12 pails. The total value for two months were considered as the per annum value. (2) Likewise, Irvingia gabonensis is harvested every year for a three-months period. Some products are harvested during 12 months (Gnetum africanum, Piper nigrum, Afromomum dalzeillii etc.) and other are harvested every year for two to three months periods (Fungi, Corylus avellana, Garcinia cola, Monodora myristica etc.). Table A .2.1 in appendix shows the measurement indicators for the 20 NTFP, including fuel wood extraction that account among forest-based livelihoods. We also used a mechanical dynamometer to weight bushmeat, crop (banana, casava, potato, vegetables etc.) and cash crop (including rubber and cocoa) daily and weekly production. We used the average local sales price to estimate the value for such livelihoods resulting from each strategy. Indeed, price variation within rural area is normally too small (Angelsen et Kaimowitz, 1999).

Deforestation Measurement

Regarding deforestation measurement, households were asked to fill the information about their land-holding in a table. After that, we randomly chose one plot among the total plots declared by the household to visit. The visited plot was tracked with a GPS to obtain the real area. A total of 3338 plots of land were declared by overall sample, that is on average 3.2 plots held by each household. A total of 526 plots were tracked with a GPS. The data declared were adjusted using the tracked data to obtain the value used in this thesis. On average, the household's head declared holding 4.75 ha of forest. We found after correction a land holding of 4.41 ha.

Preferences for endangered forest elephant measurement

We used the contingent valuation (CV) technique to assess the preferences of households for endangered forest elephant conservation. We first described the socioeconomic and ecological attributes of the elephant, as well as the potential change in socio-cultural services associated with the possible extinctions, given that forest elephants are currently only present in the Congo Basin. Respondents were asked to state their preferences for the entire bundle of services provided by forest elephants because addressing services separately leads to double counting (Loomis et Larson, 1994;Bandara et Tisdell, 2003).

Next, we proposed the following hypothetical scenario, given that we are valuing a non-market good without an implicit market. "Considering the trend towards the extinction of forest elephants, if action is not taken quickly, this multiple-used iconic species will disappear in the next few years. To stop this tendency towards extinction and make the species more abundant, the Tridom-TCL Regional Project Management Unit may develop a 10-year elephant conservation program that aims to seize weapons currently used by poachers and to effectively fight cross-border poaching by (1) creating joint checkpoints at the landscape scale and ( 2) recruiting more young people from villages and involving them in a communication network to improve anti-poaching control strategies and prevent human-elephant conflicts". Then, each respondent was asked whether he would be willing to contribute to the program by paying some monthly amount if the Regional Management Unit were to demand financial support from all the inhabitants of the village. The payment vehicle presented was direct cash payment of secure funds that would be held by the Tridom program. This means of payment is the most familiar, credible and feasible based on the economic situation in the area.

We used both the closed-ended and open-ended elicitation format. We first apply the closed-ended format, particularly, the Double-bounded dichotomous choice model (DBDC) proposed by Hanemann (1985) and Carson (1985). This technique involves asking the respondent whether he would be willing to pay an initial amount for elephant conservation. A lower bid was presented to those with a negative answer and a higher bid was presented to those with a positive answer. In the OE format, respondents were asked to specify their WTP. To minimize "Yea-saying" and "Naysaying" bias2 as well as the starting point bias, (1) we asked each respondent to consider their monthly income, their sources of income, and their usual monthly expenditures, and ( 2) we asked each respondent to be realistic, making sure that he/she could actually pay the stated monthly amount for the next 10 years before answering. Furthermore, the respondents were randomly assigned to one of six starting bids developed and validated during the pre-test steps. Finally, we asked follow-up questions to examine the reasons for zero observations to be able to identify protest bidders in the database before model estimation (Arrow et al., 1993).

A full description of this technique as well as the data are imbedded in the third chapter. The following sub-section present our contributions and the organisation of this thesis.

Contribution and thesis organisation

Our contribution is multiple. First, following the reasoning presented in section 1, it provides a complete analysis of households behavior as regard to their livelihood and environment. Indeed, we first consider the households' livelihoods strategies choice. Then we investigate how those livelihoods strategies influence their land-use decisions and deforestation, and thus their environmental impact. Finally, we adopt the reversal analysis, i.e. how do they value wildlife conservation.

Our approach is essentially micro-econometric, with a strong emphasis on spatial issues. Indeed, we showed earlier that spatial interactions are important to take into account in our context of interest. Yet, the lack of reliable data prevent from implementing such type of analysis. We present in this piece of work much evidence that, indeed, it is essential to investigate spatial spillovers in order to fully understand the environment-development nexus which rural households of developing countries are confronted to.

This thesis is organized around three chapters.

(1) What drives livelihoods strategies in rural areas? Evidence from the

Tridom-TCL using Spatial Probit Analysis

The first chapter investigates the factors which drive the household's livelihoods strategies in rural regions, using a Spatial Probit Model. Very few scientific studies have focused on the determinants of such choice in the Congo Basin. More precisely, the role of human, financial, natural and location assets in the portfolio choice is investigated. A unique dataset is used from our recent survey with random and stratified 1035 households in 108 villages of the Tridom-TCL to investigate household preferences between (1) specialization and diversification strategies, (2) land-conversion and non-land-conversion activities, and (3) between strategies relying on forest vs other strategies. The Spatial Autoregressive Probit Model shows a significant similarities on the likelihood of closer households to prefer a given livelihoods strategy. Beside socioeconomic characteristics, the existence of human-wildlife conflict as well as the indigenousness directly lead households heads to make the choice of diversified strategies including activities related to land-conversion with some significant spillover effects on the likelihood of neighboring households heads to adopt the same strategies.

(2) Households livelihoods and deforestation in the Tridom-TCL : A

spatial analysis

This chapter aims to examine the determinants of household' deforestation in the Tridom-TCL. It investigates the role of household livelihoods strategies. It also tests the effects of crop damage caused by elephant (human-elephant conflict) in the households' decision of deforestation, using a unique dataset gathered with 1035 households in the Tridom-TCL. The results of the spatial autoregressive model state that: (1) households tend to imitate deforestation decisions of their neighbors. (2) When account is taken of cash crops in a diversification livelihoods strategy, households' deforestation increases significantly with a significant indirect impact on neighboring households' deforestation. In fact, a one-unit increase in the income of such households leads to six to sevent times increases in households' deforestation compared to other strategies. Also, (3) the indirect effects of these choices on neighboring households' deforestation have almost the same magnitude as the direct impact sesulting from other strategies. (4) In addition, there is no substitutability between leisure and work in land use choices. Indeed, the Pygmies, who constitute the pincipale labor with low cost, are employed in activities related to larger deforestation, while household labor duration do not significantly drive households' deforestation.

The share of household self-consumption is associated with a low deforestation, therefore, a better integration of the market can lead to increased deforestation, if practices remained unchanged.

(3) The value of endangered forest elephants to local communities in a transboundary conservation landscape

The last chapter seeks to determine and characterize social and cultural preferences for the conservation of endangered forest elephants in the the landscape, given the households activities. We combine double-bounded dichotomous choice with open-ended elicitation formats to assess the willingness-to-pay (WTP) for EFE conservation. We find that local households are willing to pay CFA 1,139.4 (e1.74) per month to prevent EFE extinction. This totals CFA 753.9 million (e1.15 million) per year for all inhabitants. Indigenousness positively influences the WTP for EFE conservation. Spatial data suggest that local communities prefer that elephants remain far from their crops. The existence of human-elephant conflicts has a neutral effect on preferences for EFE conservation. Therefore, our study suggests that local communities would engage in biodiversity preservation when the public benefits of conservation are accompanied by private benefits, such as human-elephant conflict avoidance.

Introduction

Most rural households in the Congo Basin rely primarily on small-scale agricultural activities, harvesting and direct use of forest ecosystem goods and services for their livelihoods1 including food security, fuelwood, water supply and primary healthcare (Sonwa et al., 2009;Megevand, 2013;Verchot, 2014). In the Tri-National Dja-Odzala Minkébé trinational conservation landscape (Tridom-TCL), approximately 78% of indigenous and local communities declare slash and burn small-scale farming (40.9%), cocoa and rubber or cash crop (19.6%), game hunting for bushmeat and NTFP gathering of or (14.8%) as well as traditional gold mining (3%) as predominant subsistence activities with a full time employment. A more detailed look of household heads' behavior in this landscape shows that an important share of household heads develop survival strategies based on activities portfolios, mixing two or three of above activities. Factors governing the decision to adopt a particular land-use or livelihood's strategy and the extent of their effects are complex and vary considerably from one place to another (Angelsen et Kaimowitz, 1999;Babigumira et al., 2014;VanWey et al., 2005). These individual decisions will have significant impact on the household's wellbeing, on the environment, and on the economic value of the landscape (Brown, 2004) and should therefore be taken into account by policy makers (Carrión-Flores et al., 2009).

In these conditions, there is a crucial need to investigate the factors that drive households' livelihoods portfolio in rural areas. We test following hypotheses.

• Household specific assets, including social capital (Community group membership, autochthonous, household's size and marital status), human capital (education, age and seniority3 ), access to natural capital (land-use conflict, human-wildlife conflict) and financial assets (loan and money transfers) drive household' heads preferences among livelihoods strategies.

• Geographical assets including environmental state (distance to the nearest protected area) and infrastructure (distance to market) influence household's heads preferences among livelihoods strategies. Indeed, beside household-specific assets, location is supposed to significantly influence households' livelihoods choices (Soltani et al., 2012). Further, distance to market can be considered as cost of market access (Losch et al., 1954;Von Thünen et Hall, 1966) • Finally, we hypothesize that preferences among livelihoods strategies of proximal households are correlated and changes in the level of household assets and characteristics will impact both own preferences and neighbouring household preferences over strategies. Indeed, according to the strategic-interaction models, a farmer's decision is supposed to indirectly affect other farmers' decisions within the same neighbourhood (Brueckner, 2003;Anselin, 2002).

This paper is structured as follows. Section (1.2) presents the literature review. Section ( 1.3) describes the case study. Section (1.4) presents the theoretical framework, and section (1.5) specifies the Spatial Autoregressive Probit (SAR-Probit) econometric model. Results are presented in section (1.6). Finally, section (1.7) present discussion and conclusions.

Literature Review

Households' livelihoods strategies have been a subject of numerous papers, encompassing the framework for investigating sustainable rural livelihoods, global studies and case-specific analysis. Literature on the SLF asserts that the ability to pursue different livelihoods strategies is dependent on the basic material and social, tangible and intangible assets that people have at their possession (Scoones, 1998). In different contexts, sustainable livelihoods can be achieved through access to natural, economic, human, physical and social capitals or resources. Jansen et al. (2006) have contributed to framing the livelihoods analysis by introducing geographical determinants as a sixth fundamental factor. Soltani et al. (2012) argued that, beside specific assets, location has a important effect on households' livelihoods strategies. They propose dividing this location asset into infrastructures and environmental state. These assets are often combined to define different livelihoods strategies including agricultural intensification, specialization or diversification (Ellis, 2000a;Scoones, 1998;Knutsson, 2006;Coomes et al., 2004;Angelsen, 2011;DfID, 1999).

Among the numerous case-specific studies on livelihoods strategies, Soltani et al. (2012) examined poverty and forest degradation in rural areas of Zagros (Iran) accounting for location assets beside the traditional SLF approach in other to identify the most sustainable households' livelihoods strategies. They studied three continuous indices of poverty, over-grazing and over-harvesting on a stratified random sample of 79 households. They found that 64% of the households surveyed adopt mixed strategies, while, the livelihoods strategies of poorest households (27%) are highly dependent on forest extraction and livestock grazing and that the remaining 9% combine cash crop with non farm work and earn higher income. They also found that locational capitals namely, development and marginality are important drivers of choice among livelihoods strategies in addition to household traditional assets. They finally found some evidence of an Environmental Kuznets Curve. Indeed, over-harvesting and overgrazing indices are the highest for households in the mixed cluster and lowest for households in the non-farm/commercial one.

These diverse strategies have some key characteristics of interest. They can be decomposed in a set of diverse choices. In this paper, we focus on three types of strategic choices: choice of diversification, choice of land conversion and choice of relying on forest resources.

Diversification: depending on available assets, households may prefer to diversify their activities as a survival strategy or safety net to cope with shocks in order to maintain or enhance their capabilities or for income motive, while, contextual effects can lead to specialization in a particular activity (Ellis, 2000b;Scoones, 1998). A range of global level studies have been focussing on the reliance on environmental income for gap-filling and as a safety net to cope with risk. Indeed, agriculture is subject to myriad risks including weather, price, pests, diseases and fire (Pattanayak et Sills, 2001), and diversification activities can be seen as a way to cope with risk, in a context of lacking insurance and credit markets. Angelsen et al. (2014); Delacote (2009Delacote ( , 2007)); Vedeld et al. (2007) and Wunder et al. (2014) are among the main researchers who pointed out the importance of environmental amenities and income for seasonal gap-filling and as rural safety net to shocks. They found that households with poor human, natural and social capital were more likely to go to the forest after a shock; while forest extraction has limited importance for seasonal gap-filling (Wunder et al., 2014). They found that forest income contributes between 22.2% to 27% of total households' income with large and systematic variations among regions, and that, the poor rely more heavily on subsistence products such as wood fuels and wild foods, and on products harvested from natural areas other than forests (Angelsen et al., 2014;Vedeld et al., 2007;Nguyen et al., 2015). A similar result was found by Belcher et al. (2015) in a case study in Jharkhand (India). However, this category of households (poor households) are more likely to be trapped in common-property resources extraction activities, that provide only minimum requirement. This is especially true for households with a large need of insurance (Delacote, 2009).

Land conversion: the choice of land-conversion activities has been investigated, mainly through forest clearing and deforestation choices for example, using the PEN dataset. Considering the SLF presented above, Babigumira et al. (2014) introduced mediating and vulnerability indicators to examine the factors that influence rural household decisions to clear forestland in 24 developing countries and to identify robust global correlation between forest clearing and rural livelihoods. They found that shock patterns do not drive forest clearing, and that male-headed households with abundance of male labour living in recently settled places with high forest cover, as well as households with medium to high asset holdings and higher market orientation were more likely to clear forest than the poorest and vulnerable households.

Forest reliance: Coomes et al. (2016) used a large scale census with 919 communities in four major river sub-basins in eastern Peru under the PARLAP initiative to analyse the drivers of the economic orientation of rain forest communities. The authors found that initial environmental endowments and market access of communities are important in shaping their economic orientation, however, they interact in different ways depending on the key natural resource upon which they rely. Further, they found a strong correlation between rich endowment of terrestrial activities components and participation in land-based extractive activities including hunting, non-timber-forest product (NTFP) and timber extraction. An important proportion of case-specific analysis of livelihoods strategies has been carried out in Southern and Eastern Africa. Some results are similar to those from global studies stated above in terms of the importance of forest income for the poorest households (Mamo et al., 2007;Kamanga et al., 2009), the differential influence of livelihoods assets on the choice of households strategies (Babulo et al., 2008) in Ethiopia and Malawi.

One of the very few scientific publications in the Congo Basin on this issue was carried out by Nielsen et al. (2012). They used data collected in a typical CIFOR-PEN study in Democratic republic of Congo to point out the importance of including asset-based measures of wealth in studies of poverty-environment relations with poverty groups of households. Considering poverty as a transitory phenomenon resulting from an array of push and pull factors, and considering the importance of assets' wealth for seasonal gap-filling, four poverty groups were identified by dividing the sample into five annual income quintiles and into five equally sized productive asset quintiles.

The authors found a strong reliance of the chronic poor on forest income and a strong reliance of richest households on business. They also found that the transient poor consume a higher share of harvested forest products, while the transient rich have higher agricultural productivity and absolute forest income.

Case study: The Tridom-TCL in the Congo

Basin

The Tridom-TCL is a cross-border conservation landscape created in 2005 by an agreement between Cameroon, Gabon and Congo governments, as one of the twelve "priority landscapes" of the Congo Basin Forest Partnership (CBFP)4 . The management of the Tridom-TCL is entrusted to a Regional Management Unit (RMU) with representative members in each of the three countries. Each representative member manages the corresponding segment of the landscape according to the national law.

The RMU is prepared to act beyond national borders with the following missions:

(1) sustainable management of transboundary biodiversity in the inter-zone, (2) maintaining the ecological, the economic and the social connectivity of the complex, (3) ensuring long term conservation of its protected area system and (4) fostering local development with reduced impact on environment. The Tridom-TCL consists of a network of 10 protected areas, namely, the Dja biosphere reserve, the Boumba-Bek NP, and the Nki-Mengamé gorilla sanctuary (Republic of Cameroon), the Odzala-Kokoua NP and the Lossi NP (Republic of Congo), the Minkébé NP, the Ivindo NP and the Mwagne NP (Gabonese Republic). These protected áreas, spread over 35, 968 square kilometers, are connected by a wide inter-zone of more than 111, 000km 2 of which 36% (40,000 km 2 ) is inhabited. It represents 7.5% of the total area of the Congo Basin Tropical Forests in Central Africa. The inhabited inter-zone hosts indigenous and local people. These people depend on slash and burn agriculture, and forest-based activities such as hunting, NTFP gathering, fishing, traditional forest management and traditional mining for their survival. The human population density is between 1-7.9 inhabitants/km 2 and is currently growing due to resource exploitation (Ngoufo et al., 2012). The field work was carried out in the Cameroon and Gabon segments as shown in the figure (A.1.1) in appendix. Both segments are inhabited by more than 43 tribes, dominated by Bantu, with a minority group of the indigenous baka tribe.

Data

The paper uses data from a representative face-to-face survey with a random and stratified sample of 10355 households. The total number of households is approximately 651406 . The survey lasted 8 months between December 2013 and July 2014 in 108 villages representing all the 26 administrative units of the Cameroonian and the Gabonese part of the landscape. The villages visited are spread over nearly 27,000 km 2 , which is two third of the landscape livable inter-zone. The random sampling of households in the villages was based on the village inhabitants' registry held by the chief of the village.

The interviews lasted between 1 to 3 hours. In addition, there were evening visits in various households surveyed to quantify and measure daily, weekly and per annum livelihoods production. The survey was supervised by the first author. Ten Masters students selected after five training seminars participated as surveyors. Every village provided at least two local translators in the case the household's heads could not communicate in French. Every household was geo-localized with a GPS. The per annum value of livelihoods were derived in two ways: Some well organized household's heads usualy record their daily, weekly and per annum livelihoods statistics. We trusted and collected these statistics. For the remaining households, we used contextual material to measure daily and weekly production. We extrapolated to have the per annum livelihoods production according to the recall data they stated and the seasonality of each species or product. For instance, among the forest-based livelihoods, (1) Ricinodendron Heudelotti is harvested every year for two-months period, it is measured with a 5 liters pail that weights 7.5kg, and a bag of 90kg is made of 12 pails. The total value for two months were considered as the per annum value. ( 2) Likewise, Irvingia gabonensis is harvested for three-months period a year. Some products are harvested during 12 months (Gnetum africanum, Piper nigrum, Afromomum dalzeillii...) and others are harvested during three to four months (Fungi, Corylus avellana, Garcinia cola, Monodora myristica etc.). The table A.2.1 in appendix shows the measurement indicators for the 20 NTFP, including fuel wood, that account for forest-based strategies. We also used a mechanical dynamometer to measure and to weight bushmeat, crop (banana, casava, potato, vegetables etc.) and cash crop (including rubber and cocoa) daily and weekly production. We used the average local sales price to estimate the value of such livelihoods resulting from each strategy. Indeed, price variation within rural areas is normally quite small (Angelsen et Kaimowitz, 1999).

Livelihoods portfolios

Based on an initial analysis we defined the households as member of one of six groups. Indeed, as illustrated in the figure A.1.2 in appendix, 35% of the Tridom-TCL households base their livelihoods strategies on small-scale agriculture and forest products extraction (AF ). Besides these two activities, cash crop is included as a third activity in a mixed strategy with three activities (ACF ) by 17% of the households. 14 % mix small-scale agriculture and cash crop (AC) and the remaining households specialize in forest-based (F , 27%), cash crop (C, 4%) and small-scale agriculture (A, 3%). The figure (1.1) below displays the distribution of the livelihoods strategies on the Tridom-TCL. Finally, one can distinguish portfolios relying on forests and the others: , AF, ACF ]. This distinction allows us to investigate which factors leads household heads to engage in forest-related activities. This is a matter of interests, as when household heads consider forests as assets, they may be more likely to consider them as valuable and thus be actors of forest protection.

[A, C] vs [F, CF

A simple microeconomic model of livelihoods portfolios

Consider a household's head i that chooses among a set of livelihoods portfolios

L i = [A, C, F, CF, AF
, ACF ] to maximize his/her utility U i . X i is a vector of the household's head i socio-economic variables, Z ip stands for observed attributes of the activity portfolio p for the household's head i (Lancaster, 1966) 7 . Household i's utility function may encompass income derived from the livelihoods portfolios, but also other non-observable outcomes such as household's vulnerability. Thus, the household characteristics X i may influence not only the economic return from the portfolios, but also other households matters of interest. Furthermore, we consider that household i's utility may be influenced by his/her neighborhood. L j is the livelihoods portfolios selected by household i's neighbors, that are likely to influence his/her decision. The household maximisation problem is given by: max

L i U i (L i , X i , Z ip , L j ) (1.1)
The first-oder condition implicitly gives the optimal portfolio L * i (X i , Z ip , L * j ) for household i:

∂U i (L * i , X i , Z ip , L * j ) ∂L * i = 0 (1.2)
As suggested by the trade-offs described in the above sub-section, households portfolio choice can be substituted by sets of binary strategies (S = 1, 2). Henceforth, S will be used for livelihoods strategies instead of L. The model described in equation (1.1) leads to binary or discrete outcomes. Models of discrete choices have been known since McFadden (1974); Bhat (1997); Horowitz (1991); Schnier et Felthoven (2011); Albert et Chib (1993) and Thurstone (1927) and formulated as Random Utility Model (RUM). Basically, the theory of random utility assumes that the utility function is comprised by two components. The first is a systematic component representing the observed attributes of different strategies, the socioeconomic characteristics of an individual as well as the neighboring characteristics. The second is a random component that captures the effects of unobserved attributes and characteristics that may influence individual choices. Therefore, the utility function U i presented in equation (1.1) driven by household i's livelihoods strategy becomes:

     U is = V is + is V is = aZ is + b s X i + c s f (S * j ) (1.3) Where i = 1, 2, 3...n and s = s = 1, 2.
In equation (1.3), V i denotes the systematic component of the RUM, Z is is the vector of characteristics of the binary strategy S adopted by household 's i, X i is the vector of household i socioeconomic characteristics. a, b and c denote parameters associated to the covariates and is is the random variable that captures the random component of the utility function and / or the unobserved attributes of the strategy S.

Spatial Probit Model

The model

The household i's head will adopt the optimal livelihoods strategy S * i that provides him/her with the greatest utility. Since binary dependent variable observations can be treated as indicators that relate to underlying latent or unobservable level of utility (Albert et Chib, 1993;LeSage et Pace, 2009), the decision to choose or not to choose, or to choose among two strategies can therefore be guided by the difference in utilities brought about by both realizations. Therefore, assuming the couple (0, 1) as the observed choice indicator of the strategy S by household i, the following holds:

S i = U 1i -U 0i = a (Z i1 -Z i0 ) + (b 1 -b 0 ) X i + (c 1 -c 0 ) f (S j ) + ( i1 -i0 ) S i = a Z s + βX i + ρf (S j ) + µ i (1.4)
In equation (1.4), S i is not observed. It is considered as an unobserved latent dependent variable, representing the relative utility derived by the household i's head from his/her observed strategy S * i . The microeconomic model of livelihoods strategies developed above defines the household i's strategies and the resulting utility as function of neighboring households' matters of interest. Hence, the observed choice of a household may be similar or dissimilar to strategies of nearby households. Modeling such discrete variables generated by spatially interdependent processes requires defining a spatial weight matrix and an operator that help accounts for the intensity of spatial interaction among households as well as the resulting spatial spillover effects. In this paper, we are interested in the strength of spatial interdependence among households as well as its impacts on households' likelihood to choose a particular livelihoods strategy. Therefore, the spatial lag discrete dependent model is the most convenient among the spatially explicit econometrics models (Anselin, 2007). In the following, we removed the component a Z s from the model. Indeed, the main characteristics of households strategies are themselves the matter of the study. Thus, equation (1.4) becomes:

S = Xβ + ρW S + µ µ ∼ 0, σ 2 µ I n (1.5)
In equation (1.5), β is a kx1 vector of parameters to be estimated, ρ denotes the intensity of spatial interdependence, W is the spatial weight matrix and W S(nxn) is the spatial operator that denotes the mean dependant variable of neighboring households. The term µ is a multivariate normal distribution corresponding to a Probit model. Under the hypothesis ρ = 0 and (1 -ρW ) = I n , the matrix notation of the data generating process is given by equation (1.6) below:

S = (1 -ρW ) -1 Xβ + (1 -ρW ) -1 µ (1.6)
The observed value of the limited dependent variable S * i is defined as follows:

S * i =      1 if S > 0 0 if S ≤ 0 (1.7)
Following LeSage et Pace (2009), the probability of choosing a particular strategy is given by:

P r (S * = 1 | X, W ) = P r (S > 0) = P r (1 -ρW ) -1 Xβ + (1 -ρW ) -1 µ > 0 = P r η < (1 -ρW ) -1 Xβ (1.8) In equation (1.8), η = (1 -ρW ) -1 µ.
In the case where ρ = 0 and (1 -ρW ) = I n , the expression (1.8) follows a standard Probit model, and Maximum likelihood estimation (ML) techniques can be used. If ρ = 0 and (1 -ρW ) = I n , S and µ follow a Truncated Multivariate Normal Distribution, and the standard ML techniques are not suitable. Indeed, the inclusion of neighboring strategies W S as a determinant of the latent variable S involves n-dimensional integral in the likelihood function and generates some tractability and computational problems. Further, the reduced form of the latent process is nonlinear. This relates to the consistency of the model (Baltagi et al., 2014). In this case, estimation can be achieved using the Bayesian Markov Chain Monte Carlo (MCMC) simulation methods by Gibbs and Metropolis-Hastings sampling (Albert et Chib, 1993;Geweke, 1991;LeSage, 2004LeSage, , 2000;;LeSage et Pace, 2009), using the Recursive Important Sampling (RIS) or the GHK (Geweke-Hajivassiliou-Keane) simulation methods (Geweke et al., 1994;Hajivassiliou, 2000) to name a few. In this paper, we use the Bayesian approach to estimate the SAR-Probit model with MCMC simulations and Gibbs sample with 1000 drawn, 20% of the draws used as burn-in, assuming a non-informative prior distribution for β. For a detailed presentation of the Bayesian approach to modelling limited dependent variable, see LeSage (2000); Wilhelm et de Matos (2013);McMillen (1992).

As with the standard Probit model, estimates of the coefficients of the SAR-Probit do not have direct economic interpretation as the normal probability distribution is non linear. The sign of parameters is the only usable information, indicating the direction of the impact. The sensitivity of the probability to choose or not to choose a given strategy relative to one-unit change in the independent variables is measured by a single value standing for marginal effect in the standard Probit model. This does not hold for the SAR-Probit model. Indeed, the SAR-Probit model involves spatial lag of the dependent variable. This implies some spillover or indirect effect brough about by a one-unit change of the i th household characteristics on the neighboring households' likelihood to adopt a strategy, in addition to the usual direct effect. Following LeSage et Pace (2009), the SAR-Probit yields three values of interest namely, the direct, the neighborhood and the total effect derived from expression (1.9).

∂E (S

* | X k ) ∂X k = φ [1 -ρW ] -1 I n - X k β k [1 -ρW ] -1 I n β k (1.9)
Where X k denotes the k th independent variable with -X k as mean value, φ is a standard normal distribution, and stands for element-wise multiplication.

The main diagonal elements of expression (1.9) represent the direct effect. That is the effect of a one-unit change in an independent variable of household i on the own likelihood to choose or not to choose a particular livelihoods strategy. The average value of each row results in the total impact brought about by one-unit change in the corresponding covariates. The neighborhood (indirect) effect is measured by the difference between the total effect and the direct effect. In the SAR-Probit, this number captures the effect of a one-unit change in an independent variable of the household i on the likelihood of the neighboring household to choose or not to choose a particular livelihoods strategy.

Spatial operator

The consideration of social interactions and / or spatial interdependence is made possible through the spatial operator W S that represents the neighboring livelihoods strategies. The spatial weight matrix W that summarizes the spatial relations between observations depends on the neighboring structures. A large body of research has used the distance weight matrix, contiguity weight matrix or K-nearest-neighbours (KNN) matrix corresponding respectively to distance-based households structure, common boundary structure and nearest neighbors structure (Bivand et al., 2013). As pointed out by LeSage et Pace (2014); Fingleton et Arbia (2008), there is near universal agreement that estimates and inferences from spatial regression models are sensitive to particular specifications of the spatial weight structure.

To account for this "biggest myth in spatial econometrics" (LeSage et Pace, 2014), we consider three different structures of W , namely, the Gabriel graph weight matrix, the KNN weight matrix and the row-standardize distance-based weight matrix.

The Gabriel relative neighborhood graph is a geographic connectivity network between the sampling households. According to Matula et Sokal (1980) and Gabriel et Sokal (1969), two households i and j in the Tridom-TCL are considered to be contiguous if and only if all other households are outside the ij circle. In this weight matrix, two households i and j are considered contiguous unless there exists another household l such that in the triangle ijl, the distance between i and j is less than the sum of the squares of the distances to any other locality l.

The KNN weight matrix bases its estimation on a fix number of k households closest to household i. The selection of the KNN households is made possible via the euclidean distance dij

(x i , x j ) = x i -x j = (x i -x j ) (x i -x j ) 0.5 . With j = 1...k.
In this study, we consider 3NN, 5NN and 10NN spatial weight matrices. We choose 10 as maximum number because it is close to the minimum number of households in many villages surveyed.

In the row-standardize distance-based weight matrix, the i th row contains the spatial weight of neighboring households influencing household i, such that each row sums up to unity. We consider the sparse matrices8 in the three different structure of W we used.

What drives livelihoods strategies in the

Tridom-TCL?

This section first presents descriptive statistics (2.6.1). Then, it presents the specification used for the results, choosing among the standard Probit model and the best spatial model given various types of weight matrix to control the so called "biggest myth in spatial econometrics" (2.6.2). It ends with estimates and marginal effects for each of the three trade-offs (1.6.3).

Descriptive statistics

Among the 1035 households surveyed, this paper considers 987 households who were successfully geo-localised with GPS, representing 95.4% of of surveyed households.

Regarding the first dependent variable that opposes specialization strategies and diversification strategies as presented in the first trade-off presented in table (A.2.2) in appendix, about 66% of household's heads diversify their strategy, mixing small-scale farming, cash crop and forest. As regards the second dependent variable related to land-use, 73% of the households adopt land-conversion activities and the remaining prefer forest-based activities. The final trade-off opposes 67% of the sample choosing strategies relying on single or mixed strategies with at least 30% income provided by forest-based activities to households who do not or who rarely value forest.

the figure (A.1.3) in appendix shows that, among these trade-offs, households who diversify, those who choose land-conversion and those who rarely value forest would cause higher average deforestation (5.26ha, 5.57ha and 7.14ha respectively), with lower per annum return or yields (CFAF0.40 * 10 6 , CFAF0.40 * 10 6 and CFAF0.37 * 10 6 respectively, that is $84, $84 and $77.7 )9 while those who choose to specialize, to adopt forest-based activities or to rely on forest with a mixed of single strategies deforest twice less, with a two-time higher return.

Following these rough statistics, a tentative conclusion is that forest resource would be socially beneficial, and could therefore constitute a safety net for rural households. Specialization, as well as strategies that value forests would be viewed as sustainable strategies. However, this result does not provide detailed information about those who specialize, those who adopt forest-based activities, or those who value forest regardless of diversification or not. Subsection (1.6.3) below will provide further insight about the factors that lead households to make the choice of a particular strategy.

Table 1.1 displays a description of variables included in the analysis to test the hypotheses. We basically distinguish social assets, human capital, natural capital, financial assets, the environmental context, and infrastructure assets. Regarding social assets, in 2014, 70% of the households in the overall sample were married. The average household size was 6.5 with a standard deviation of 4. Twenty-six percent of the households belong to a community group of interest, households employ on average 1.87 member of pygmy society. Regarding human capital, 56% of the households have reached at least secondary-school educational level. Regarding natural capital, we choose not to introduce land-holding as a determinant of livelihoods strategies, as there is neither a market of lands nor a binding regime on access to land. Yet, we introduce human-elephant conflict which caused CFA620 mean damage cost per month. We also introduce the land conflict among households. Indeed, 18% of the households have faced conflicts with their neighbors. Both variables should also provide some information on the behavior of vulnerable households especially when facing a shock. The standard Probit model is contrasted to the SAR-probit model as suggested by LeSage et Pace (2009Pace ( , 2014)). Further, we check the sensitivity of the results to various specifications used for the spatial weight structure in the SAR-probit. It finally concludes on the nature of the spatial dependence in various trade-offs, considering the best model.

The table in appendix (A.2.3) displays the results of the standard Probit model that assumes non-spatial relationship among observations alongside with those of the spatial autoregressive model for the trade-off between specialization and diversification. This table shows some stark contrasts between the estimates and marginal effects of both standard Probit model and SAR-probit. It also suggests a significant effect of distance to market as well as distance to the nearest protected area. On the other hand, these effects are not significant for the SAR-Probit. In the latter model, the spatial autocorrelation coefficient, ρ, differs statistically from zero. It is thus clear that estimates and marginal effects of the standard Probit model are biased and inconsistent, allowing non pertinent causations as regards to both distances to the market and the nearest protected area. As result, the decision to choose among specialization and diversification is generated by spatially interdependent processes.

The table in appendix (A.2.4) contrasts the results of various SAR-Probits given the structure of neighbors to illustrate the impact of changing the type of the weight matrix. It shows that varying the type of the matrix does not lead to similar results. Indeed, the Gabriel-relative neighborhood graph (Gabgrahp) reports the absence of spatial patterns. The value of ρ Gabgraph is close to zero and non significant (ρ Gabgraph = 0.05), while the KNN and the distance-based matrix report the influence of spatial effects on the households' likelihood to adopt a livelihoods strategy. Further, differing the number of neighbors yields different results. As the number of neighbors increases, the strength of spatial interaction increases. The spatial weight matrix based on 10 nearest neighbors (10NN) presents higher spatial dependence with ρ = 0.36 compared to 3NN (ρ 3N N = 0.23) and 5NN (ρ 5N N = 0.29).

The distance-based matrix yields spatial effects that are closer to the 10NN.

This analysis validates the sensitivity of results to the weight matrix specification postulated by LeSage et Pace (2009Pace ( , 2014)). Therefore, a good analysis of households livelihoods strategies should involve testing and accounting for spatial effects using spatially explicit econometric models, as well as checking the accuracy of the results with the form of the spatial weight matrix. In the following, we consider the distancebased weight matrix. Indeed, as the households were geo-localized during the field work, the distance-based weight matrix (that yields almost similar results with the 10NN matrix in our study) is better. This matrix allows for the magnitude of interaction among two individuals to be proportional to the inverse euclidian distance among them, while the KNN matrix tends to attribute the same weight to all the k individuals. The quantitative explanatory variables were checked for multi-collinearity, tables (A.2.5) and (A.2.6) in appendix suggest the independence among them.

Spatial dependence

Table 1.2 shows an evidence of spatial autocorrelation among the likelihood of proximal households to choose among livelihood strategies. Indeed, the value of ρ is positive and significant at 1%. This suggests that households tend to mimic the livelihood strategies of their neighbors.

The range of the ρ parameter suggests a difference among the strength of mimicry among the three trade-offs. Indeed, the dependence among closer households' likelihood appears to be stronger regarding strategies that value forest rather than choosing or not to choose between forest-based strategies and land-conversion based strategies, or between specialization and diversification.

Coefficient Estimates and Marginal Effects

Table 1.2 presents the sign and the possible variables that drive household heads' decision in various trade-offs. Tables 1.3, 1.4 and 1.5 present the magnitude or the incremental change resulting from a one-unite change in the independent variables on both own and neighboring likelihood of choosing or not to choosing a strategy in various trade-offs. As a reminder, the independent variables include households' human, social, natural and financial assets; geographical assets of location and infrastructure; remaining households characteristics and spatial spillover effects.

Specialization vs diversification

Table 1.3 displays the results for the first trade-off where households choose between specialization (either on forest activities, or on cash-crop or small-scale farming) and diversification (mixing cash crop, forest and small-scale farming). One can argue that households specializing in one activity tend to be more income-maximizing oriented, while those tending toward diversification are more risk-coping oriented. Yet, other kinds of characteristics have to be taken into account: some households may have to specialize because there are some barriers to diversification, related to low levels of some assets. • Social asset Among households' social assets, being part of a community group of interest, marital status and pygmies employment do not have any direct or indirect impact on the household's head likelihood to choose between diversification and specialization. The direct and indirect effect resulting from the household size is negative and significative. This suggests that larger households will be less likely to specialize than others. Moreover, they tend to have negative spillover on their neighborhood. Further, regarding the magnitude, a one-unit increase in household size will reduce both own probability and neighboring households' probability to specialize by 1.93% and 1.16% respectively. This leads to a total impact of 3.09%, (i.e. 1.93%+1.16%). Finally, autochthonous (wether indigenous pygmy or not) exerts a positive and significant direct and indirect influence on specialization strategy. Indeed, indigenous Baka pygmies are more likely to specialize in forest-based activities. Being a pygmy is associated with 15.08% more chances to specialize and tends to increase his/her neighboring households' likelihood to specialize by 9.16%, leading to a total impact of 24,23%.

• Human asset

With respect to human capital, the direct and the indirect effect associated with secondary school education level as well as age is not significant. Yet, households who settled long ago in villages (seniority) are less likely to specialize in their livelihoods strategies. An additional seniority year increases the likelihood to diversify by 0.18% with a positive spillover effect of 0.11% on the likelihood of neighboring households to diversify.

• Natural asset

Human-wildlife conflict as well as land conflict with the neighboring households were considered as an indicator of access to natural capital. Table 1.3 also shows a non significant impact of land conflict within the household's neighborhood.

It also shows that households who face human-wildlife conflict are more likely to specialize in a particular livelihood strategy. More precisely, a CFAf1000 ($2.1) additional elephants damage cost per month will lead to increasing own probability to specialize by 1.65% with a positive spillover effet of 0.98% on the probability of those living within their neighborhood to specialize, that is a total effect of 2.62%.

• Financial asset

Household heads' financial capital endowment and more precisely, loan and money transfers increase both own and neighboring households' likelihood to specialize. A CFAf1000 ($2.1) increase of the household financial capital will increase own and neighboring likelihood to specialize by 0,12% and 0.07% respectively, that is a total effect of 0.19%. This result gives the insight that higher financial assets alleviate some barriers to specialization. Moreover, financial assets constitute a risk-management tool, which reduces the need for other risk-coping strategies such as diversification.

• Geographical asset or location

Both location assets, including environmental state (distance to the nearest protected area) and infrastructure (distance to market), do not significantly influence households' choices between specialization and diversification.

• Gender and Country effect Gender seems to have no impact on the choice of specializing versus diversifying. Also, Cameroonian households are less likely to specialize in their livelihood strategies.

Non-land-conversion activities vs land-conversion activities

Table 1.4 displays the marginal effects for the trade-off between non-land-conversion activities (which consists of forest-based specialization) vs land-conversion activities. This brings interesting insights, as it can help to understand which factors lead households to engage in activities related to land-use change and deforestation. As a matter of fact, households taking the non-land-conversion strategy were associated with average levels of deforestation of 1.78 ha, while those engaged in land-conversion activities had average deforestation levels of 5.57 ha.

• Social asset

The household size and the indigenous status of the households are both the social assets that influence households' likelihood to adopt either forest-based or land-conversion activities. Increasing household i i 's size by one unit will significantly decrease both own and neighboring households' likelihood to adopt forest-based activities as livelihoods strategy by 1.69% and 1.58% respectively. This result suggests that land-conversion activities are more labour intensive than others, and increased household size facilitates conversion. Belonging to the pygmy ethnicity is associated with a greather likelihood (18.8%) to adopt forest-based strategy, with a significant indirect spatial effect of 17.9% on the neighboring preferences for such strategy.

• Human asset

This table suggests that education level does not influence households' decision between forest-based strategy and land-conversion strategy. However, the direct and the indirect, and thus the total impact of the household age and seniority on this trade-off, is significant, the first being positive and the second negative. In detail, when a household becomes one year older, their probability to practice non-conversion strategy increases by 0.15% with a positive spillover effect of 0.14% on their neighborhood. The resulting total effect is an increase in the likelihood to specialize by 0.29%. With regard to seniority, households who settled long ago in villages will be more likely to base their livelihoods strategies on land-conversion activities. They are associated with an additional 0.21% probability to keep such strategies and with 0.2% positive spillover effect on their neighborhood.

• Natural asset

Among the factors that influence access to natural assets, land conflict in a neighborhood does not have any effect. Moreover, human-wildlife conflict remains non neutral as in the preceding subsection. An increase of CFA1000 in crop damage by elephants increases the likelihood of the concerned household's head to prefer non-conversion strategies by 1.74% with a 1.62% positive knockon effect on the neighboring household likelihood to adopt the same strategies.

• Geographical asset or location

Distance to the nearest protected area remains non significant as previously.

In contrast, households living far from local markets are more likely to rely on non-conversion strategies for their livelihoods, with a positive spillover effect within their neighborhood. This may reflect transport costs that are larger, which leads to lower profitability of agriculture and especially cash crops.

• Other assets

Financial assets, gender and country do not exert any effect on the household decision in this trade-off.

• Social asset

Social capital, such as being part of a community group of interest, pygmies employment and the household size do not exert any influence on the households trade-off between forest-based activities and land-conversion strategies; while marital status and the indigenous status of the households do. Being married decreases the household head's likelihood to expect at least 30% of his/her income from forest-based activities by 6.4% without indirect spatial spillover effect within their neighborhood. In contrast, being a pygmy household head is associated with 25.6% more chances to exert livelihood strategies that value forest. This result is consistent with the main activity of pygmy communities being related to forests, specialization and non-land-converting-activities.

• Human asset

Age is the only households' human asset that impacts the likelihood to choose or not to choose among both strategies in this trade-off.

• Natural asset

Land-use conflict among households does not influence household heads' choice in this trade-off. But the marginal effect resulting from increasing the cost of crop damage caused by wildlife increases the likelihood of the household to choose strategies associated with at least 30% of income from forest.

• Other assets

Financial asset, geographical asset, including distance to the nearest protected área, and distance to the nearest market, gender and country do not influence the preferences within the third trade-off.

Discussion and conclusion

In rural areas of the developing world, livelihood strategies have key choices determining population wellbeing, sustainability and natural resource management. Yet, these choices are constrained by many types of variables, including assets at households' disposal. It is then of crucial importance to understand to what extent these assets influence the choice of livelihoods strategies. In this paper, we argue that the choice of activities portfolios can be decomposed in diverse strategic choices. We focus on the choices of diversification versus specialization, land-conversion activities versus non-land-conversion activities, and forest-based activities versus non-forest-based activities. Moreover, we give a special emphasis to spatial spillovers, relying on the assumption that individual choices have external impacts within his/her neighborhood. For that purpose, we rely on a unique dataset of households survey, conducted in the Tridom-TCL.

Our paper brings interesting insights. First, belonging to the pygmy community has some key influence on livelihood choices: Pygmies tend to specialize in forest-based activities and to avoid land-converting activities. This result is not a surprise per se, but it underlines that autochthonous status has a strong influence on livelihood choices. Pygmies have historically been living in the forest and depending on forest products. Their traditional livelihood strategies are hunting bushmeat and gathering NTFP; this is why they are usally labelled as hunter-gatherers.

Second, we find evidence of a few drivers of specialization. Financial assets increase the likelihood to specialize. This can be explained by the fact that such assets can be seen as a safety net in case of shocks. Thus, higher financial assets decrease households' vulnerability, and increase the need to diversify to cope with risks. Seniority and household size increase the likelihood to diversify. These factors may have two kinds of explanation: (1) a larger household may increase the need to cope efficiently for all the risks the family faces; (2) these factors can also represent a higher capacity of larger households with deep roots in the community to diversify.

Third, the choice of land-converting activities is driven by household size, newer arrivants in the community, and smaller distance to markets. Indeed we can expect larger households to be able to engage in activities that are more land extensive, as it can represent a larger labour force. Smaller distance to markets represents smaller transportation costs, and thus higher profitability of land-converting activities.

Fourth, the role of wildlife conflict has to be pointed out. Larger crops losses implicitly increase forest valuation and the need to cope with the crops losses but also increase land conversion. Following this argument, it is interesting to analyze how humanwildlife conflicts influence the household preferences for wildlife conversation (this is done in Ngouhouo Poufoun et al. (2016)). Environmental state is also important when dealing with livelihood choices. Proximity to protected areas does not seem to impact these choices. This gives the insight that conservation policies do not influence much households in the neighborhood. Thus it appears that environmental protection is not carried out at the expense of setting more constraints on households' livelihood choices. Likewise, it appears that land conflicts do not influence the choice of livelihoods strategies. This can indicate that access to land is not scarce in the study area. In this case, land access does not seem to impose a strong constraint on households in their livelihood choices.

Finally, it is crucial to note the existence of spatial spillovers. In all cases, the direct effects of the assets on livelihood choices are combined with indirect effects on the neighborhood. This result can be related to agglomeration effects: households of the same types tend to live in the same neighborhood. It can be also related to mimicking or other types of spatial dissemination.

The three strategic choices that we analyse in this paper have an impact in terms of well being, but also in terms of land use. A quick look at the figure (A.1.2) in appendix shows that households that specialize have better economic outcomes, both in terms of yields and income, than those diversifying their activities. This is consistent with the fact that households choosing specialization strategies tend to be income maximizers, while those choosing diversification tend to have others objectives, such as risk coping or they make a constrained choice. In terms of land use, specialized households tend to have smaller deforestation levels than others. Thus if economic development reduces households' vulnerability to shocks (e.g., through better access to markets), this can bring larger deforestation rates if those households decide to specialize10 .

When comparing households focusing on land-conversion activities, they unsurprisingly tend to have larger deforestation levels than others. In terms of economic outcomes, they have slightly lower levels of income, which may seem surprising. In such case, it is likely that higher income from households relying on cash crops are balanced by lower income for small scale farming households. In the same manner, one can see that households relying on forest resources have lower deforestation levels, while their income is comparable to others. Thus it appears that giving incentives to households so that they rely on forest resources would help to protect forests, while not hurting their livelihoods.

This work opens the field for further research using our database. First, the impact of the livelihood choices on deforestation requires a deeper analysis. Second, as mentioned, risk is an important matter in rural areas. Analyzing factors influencing households risk preferences, and the way those risk preferences influence livelihoods, deforestation and ecosystem services preferences is of crucial matter.

abstract

This chapter aims to examine the determinants of household' deforestation in the Tridom tansboundary conservation landscape (Tridom-TCL). It investigates the role of households' livelihoods strategies. It also tests the effects of crop damage caused by elephants (human-elephant conflict) on the households' decision of deforestation, using a unique dataset gathered with 1035 households in the Tridom-TCL. The results of the spatial autoregressive model show that: (1) households tend to imitate deforestation decisions of their neighbors. ( 2) When account cash crops is part of a diversification livelihoods strategy, households' deforestation increases significantly with a significant indirect impact on neighboring households' deforestation. In fact, a one-unit increase in the income of households that include cash crops in their portfolio, leads to six to seven times increases in households' deforestation compared to the effect of income increase in households choosing other livelihoods strategies. Also, (3) the indirect effects of these choices on neighboring households' deforestation have almost the same magnitude as the direct impact resulting from other strategies. ( 4) We find no substitutability between leisure and work in land use choices. Indeed, the Pygmies, who constitute the pincipale labour with low cost, are employed in activities related to larger deforestation, while household labour duration does not significantly drive households' deforestation. The share of households' self-consumption is associated with a low deforestation, therefore, a bigger integration of the market can lead to increased deforestation, if practices remained unchanged.

keywords : Small-scale farming, Forest-based livelihood , Landscape approach, Deforestation, Spatial Spillover Effects, Neighborhood Peer Effects.

JEL Classification: C31,Q23 & 24, R14

Introduction

In most developing world, rural households rely on both small scale farming and forest-based activities to develop their survival strategies. Small scale agriculture is known as a source of livelihoods for about 86 percent of the three billion people living in rural areas (Burch et al., 2007), while forests-based activities are widely recognized to provide safety nets to mitigate agricultural risk (Pattanayak et Sills, 2001) and contributes between 22.2% to 27% of total households income (Vedeld et al., 2007;Angelsen et al., 2014). As pointed-out by the literature on sustainable livelihoods strategies by Ellis (2000); Scoones (1998), rural households switch between specialisation and diversification to optimize their livelihoods provisioning. In the Dja-Odzala-Minkébé trinational tansboundary conservation landscape (Tridom-TCL), Ngouhouo Poufoun et Delacote (2016) investigates the variables determining the household choice to specialize or diversify its activities. The authors have also shown that choosing a livelihoods strategy in the Tridom-TCL can be seen as strategic choice between forest-based and non-forest-based portfolios including small-scale farming and/or cash cropping (cocoa and rubber). Yet, depending on the orientation between land-converting activities and forest resource extraction, effort allocation by households might either increase deforestation, increase forest degradation, or enhance both (Delacote et Angelsen, 2015). Indeed, agriculture is estimated to be the proximate driver for around 80% of deforestation worldwide, contributing for 10 to 12% of the total global annual anthropogenic emissions of GHGs (Verchot, 2014), while poaching and non-sustainable harvesting of non-timber forest products (NTFPs) can have significant adverse impacts on forest ecosystems and lead to forest degradation (Belcher et Schreckenberg, 2007;Hosonuma et al., 2012;Angelsen, 2009;KissinGer et Herold, 2012), reducing the capacity of forest to regenerate and to produce ecosystem services.

Our recent household survey at the Tridom landscape-scale provides some evidence that about 85,45% of households are responsible for changing forest cover to other uses regardless of their livelihoods strategies. There is a low population density (less than 7 inh./km2 ) and local households are less likely to practice optimal crop rotation. As there is no binding regime 1 of land acquisition in the non-permanent forest estate 2 that is seen as common access resources, they can clear relatively large area of land at low cost.

In almost all cases, land conversion is done without revival of forest neither artificially nor naturally. Indeed, rural households do not apply any agroforestry system and they are not involved in the reforestation activities, while primary or secondary forest is progressively replaced by corkwood, whose carbon storage potential is very low. In many case, extensive and unsustainable household farming based on slash-andburn cultivation accentuates small-scale deforestation and forest degradation. After removal or selling the softwood lumber in surrounding forests, all the remaining plants and material in the forest are burnt and land is used for extensive agricultural and cash-crop production. Soil fertility and crop yields are continuously declining (Boahene et al., 1998) and could cause a food crop production loss of at least $2.4billion to $5 billion to across the Congo Basin (Ernst et al., 2010). Without an optimal fallow system, local people are experiencing poor yields per hectare regarding cash-cropping and crop production. Indeed, at least 75% cocoa and plantain yield observed are less than 0,338t/ha and 3.59t/ha respectively with an average of 0.236t/ha and 3,09t/ha. This average yield is below the average performance given limited farm means of production that is 0.5t/ha and 16.5 t/ha, respectively. The potential yield of Cocoa is 0.73t/ha and 1.22t/ha when cocoa plantations are associated with timber shade and leguminous tree species and when there is a good use of the litter fall resoectively.

The maximum yield can reach 2.4t/ha (Somarriba et Beer, 1998, 2006). Regarding plantains, the potential yield can reach 30t/ha/year (MINCOMMERCE, 2010). The diminishing returns due to the unsustainable practices contribute to the perpetration of poverty (Alemagi et al., 2014). Hence, the high level of forest dependence may not necessarily correspond to a high and sustainable potential to reduce poverty (Angelsen et Wunder, 2003). Rather, this may lead to over-exploitation of common access resources and constitute poverty trap when rural households face a large need of insurance (Delacote, 2007).

In light of the above considerations, analyzing the full set of potential drivers of households' deforestation, prioritizing or distinguishing among them in order to inform policy makers and facilitate appropriate political decision process to curb deforestation in the medium and long term perspectives is of crucial interest (Pfaff, 1999;Tegegne et al., 2016). Our paper contributes to the literature, and seeks to answer the following questions : How and how much do the different livelihoods strategies developed by households, given wildlife constrains such as human-wildlife conflict impact small-scale deforestation ?

The following sections develop the literature review and our contribution (section 2.2), Objectives and hypothesis (section 2.3), a simple microeconomic model (section 2.4). The spatial economic procedure in presented in section 2.5, the results in section (2.6) and discussion and conclusion in section (2.7).

Literature Review and contribution

Literature review

The substantial academic research on the causes of tropical deforestation includes (1) conceptual framework for analyzing deforestation, (2) macro-level empirical studies including regional and national levels, (3) micro-level empirical studies and (4) spatially explicit analyses.

Conceptual Framework related studies

The first analysis that combined the results of multiple studies to frame the causes of tropical deforestation was realized by Angelsen et Kaimowitz (1999). The authors have synthesized the results of more than 140 economic models using five types of variables3 to build a helpful framework for both understanding deforestation processes and classifying modeling approaches. According to the authors, the agent or the source of deforestation (plantation companies, small farmers, etc.) has to be identified. Further, agents' decisions have to be considered, accounting for (1) their characteristics, including their preferences, their background (seniority of a households head), labour allocation as well as their initial resource and for ( 2) their decision parameters such as property regime, agricultural price, timber prices and income. These variables represent immediate or proximate causes. Finally, underlying variables, i.e., broader forces like macroeconomic variables or policy instruments that influence the source or agents and indirectly drive deforestation have to be taken into account. Proximate drivers stand for human-induced factors that influence directly households' deforestation, while underlying driving forces are fundamental social processes, that underpin the proximate causes and either operate at the local level or have an indirect impact from the national level (Geist et Lambin, 2002;KissinGer et Herold, 2012). From their meta-analysis, Angelsen et Kaimowitz (1999) derived two categories of models. Microeconomic models should focus on immediate causes, while macroeconomic models tend to deal with underlying causes. The authors also suggest distinguishing between models based on perfect markets and models assuming imperfect markets.

Geist et Lambin ( 2002) have contributed to the building of this conceptual framework via a meta-analysis of 152 case studies taken from 95 articles published in 40 scientific journals. The authors' main contribution was the breakdown of numerous factors found in the existing literature into (1) three aggregate proximate causes that are agricultural expansion, wood extraction and expansion of infrastructure; into (2) five broad categories of underlying driving forces which are demographic, economic, technological, policy/institutional, cultural or socio-political factors; and a group of "other variables" associated with deforestation, comprising land characteristics, biophysical drivers and social trigger events (economic crises, war, etc.).

More recently, Combes et al. (2015) have contributed to the conceptual framework with a theoretical model that emphasize a substitution effect between seigniorage and deforestation income. This contribution complies with the framework presented above. Indeed, Combes et al. (2015) considered the triple Environment-Economic-Social crises, which Geist et Lambin (2002) refer to as social trigger event, and proposed a link or a trade-off between macroeconomic and environmental outcomes, using an explicit model. This contribution is valuable to the traditional framework developed by Angelsen et Kaimowitz (1999). It presents a very feasible transmission channel between broad underlying drivers and deforestation. For instance, international transfers, public debt and saving can be used by the government to optimize intertemporal allocation of natural resources and spending (Combes et al., 2015).

Macro-level empirical studies

A broad existing literature addresses causes of tropical deforestation at national, regional, and global scales using macro-level data in developing countries, considering many type of forests, macroeconomic variables, institutional and policy factors (Kaimowitz et Angelsen, 1998;Pfaff, 1999;Palo, 1999;Bhattarai et Hammig, 2001;Barbier et Burgess, 2001;Geist et Lambin, 2002;Nguyen Van et Azomahou, 2007;Culas, 2007;Damette et Delacote, 2012;Wolfersberger et al., 2015;Tegegne et al., 2016;Combes et al., 2015). Major conclusions from a meta-analysis using results of 150 deforestation models by Kaimowitz et Angelsen (1998) in Brazil, Cameroon, Costa Rica, Indonesia, Mexico, Thailand, Ecuador, the Philippines and Tanzania are that deforestation tends to be greater when economic liberalization and adjustment policy reforms increases; when forested lands are more accessible; when agricultural and timber prices are higher; when rural wages are lower and there are more opportunities for long distance trade. Nguyen Van et Azomahou (2007) use a panel dataset of 59 developing countries over the 1972-1994 period to study the deforestation process. They found no evidence of an Environmental Kuznets Curve and they pointed out political institution failures as factor that can worsen the deforestation process in developing countries. More generally, the evidence supporting the existence of an EKC for deforestation is contrasted (Choumert et al., 2013). Hosonuma et al. (2012) derive deforestation and degradation drivers using empirical data synthesized from existing reports on national REDD+ readiness activities. They assessed the relative importance as well as the drivers of variability by continent reflecting approximately the period 2000-2010. They used the forest transition model, considering deforestation rate and remaining forest cover in 100 subtropical non-Annex I countries4 . They found that, similarly to Asia, the importance of deforestation drivers in Africa varies with different forest transition phases and with different areas. The impact of commercial agriculture on deforestation rises until the late-transition phase and the relative importance of subsistence agriculture remains fairly stable throughout the different phases.

Micro-level empirical studies relating to agent livelihoods decision

There is strong evidence that forests have an important role in insuring livelihood over time and in some cases, contribute to poverty alleviation (Sunderlin et al., 2005). Yet, few studies have investigated the relation between agent livelihoods decision and tropical deforestation at household's level. Babigumira et al. (2014) use the CIFOR-PEN dataset, comprising of 7172 households from 24 developing countries to analyze which household and contextual characteristics affect land use decision in the developing world. The authors consider the sustainable livelihoods framework and assess the role of various asset types on households' deforestation. The authors found out that 27% of rural household have cleared forest for agricultural based livelihoods. They also found out that asset poverty does not drive deforestation. Indeed, households with medium to high asset holdings and higher market orientation were more likely to clear forest than the poorest and market-isolated households. They found out that households that cleared forests were closer to the forest and came from villages with higher forest cover.

Relying on a rich panel dataset collected from the Tsimane' communities in Bolivia, Perge et McKay (2016) analyze the relationship between forest households' livelihoods strategies, and forest clearing, and the relationship of both to welfare. The authors identify four livelihoods strategies based on households' reported sources of cash earnings, namely, sale, wage, diversified and subsistence strategy. They find that forest clearing is positively linked to welfare especially for households whose income results from combining agricultural sales and wage activities compared to households adopting other strategies. Households with subsistence strategy are not able to accumulate assets in the long run. As one of the main conclusions, the authors state that households clear only small areas of forest with a positive effect on welfare, enabling accumulation of assets.

Pacheco ( 2009) define a typology of smallholders that accounts for both livelihoods, farming systems and wealth to analyze smallholders' deforestation in Uruará and Redenção in the Brazilian Amazon. The author use households survey data from 136 interviews in Uruará and 82 interviews in Redenção area, and find that cattle ranching is associated to greater impact than cash cropping or subsistence agriculture. Contrary to Perge et McKay (2016), a strong correlation between deforestation and the wealth of the farmers is found.

Spatially patterns studies

Spatially explicit econometric studies of drivers of deforestation have taken more importance in the last few years (Ferretti-Gallon and Busch, 2014). According to these studies, most deforestation tends to be located outside the reserve and mountainous area and deforestation occurred primarily within the more accessible Eastern counties and at areas near deforested areas. This illustrates the spread effect of deforestation in the Brazilian Amazon (Mertens et al., 2002). In the same vein, Pfaff et al. (2007) find evidence of spatial spillovers from roads in the Brazilian Amazon's deforestation.

Considering local administrative entity, Amin et al. (2014) found that deforestation activities of neighboring municipalities are correlated with some leakage. As a point of fact, protected areas may shift deforestation to neighboring municipalities.

Using a general spatial two stage least squares model to analyze the determinants of deforestation in 24 Sub-Saharan African countries during the period spanning 1990 to 2004, Boubacar (2012) found that deforestation in one country is positively correlated to deforestation in neighboring countries and that determinants of forest clearing are region specific.

Contribution

More often, data on tropical deforestation including household scale-level have been debatable and notoriously unreliable or non-available (Palo, 1999;Geist et Lambin, 2002). Furthermore, there is a very poor level of econometric studies on the drivers of deforestation in tropical Africa including Congo Basin countries (Gbetnkom, 2009). While, unlike Southeast Asia and the Amazon regions, where large-scale agricultural operations play an important role, most deforestation in the Congo basin can be attributed to small-scale farmers using extensive slash-and-burn techniques (Weise et Ngobo, 2004;Kotto-Same et al., 2002). In the same vein, a meta-analysis by Ferretti-Gallon et Busch (2014) reveals a geographical lag of spatially explicit studies of tropical deforestation in peer-reviewed academic journals as regard to Africa from 1996-2013, underlined the availability of data as the main constraint.

In this context, our contribution is multiple:

• We assess the impact of livelihood choices on deforestation. This research is among the pioneering studies that investigate the factors that govern households' deforestation in the Congo basin using a household-level survey. Indeed, west and central Africa account among regions that lag in econometric analysis of deforestation (Ferretti-Gallon et Busch, 2014;Geist et Lambin, 2002). At the same time, the factors affecting deforestation are more complex and vary significantly from one location or continent to another (Rudel et Roper, 1997;Murali et Hedge, 1997;VanWey et al., 2005;Babigumira et al., 2014).

• We refer to the standard protocol of analyzing deforestation. This is crucial as it allows for improved comparisons in future research (Geist et Lambin, 2002).

Our research considers and tests the influence of (1) agents' decision parameters such as family income and factor constraints (2) agents' characteristics and (3) other contextual variables such as choice and biophysical variables on the agent' deforestation. Further, our research used a microeconomic model, therefore, we focus on immediate causes as suggested by (Angelsen et Kaimowitz, 1999). The figure 2.1 shows the adaptation of our research to the conceptual framework for analyzing households' deforestation.

• We consider the landscape approach and test the impact of human wildlife conflicts on households' deforestation. In the same vein, we consider testing the impact of land conflict among households on households' deforestation.

• The influence of spatial spillovers is investigated. Beside direct effects on households' characteristics, we consider endogenous and exogenous interactions among households and test the possible resulting spillover effect on households' deforestation within their neighborhood.

Objective and Hypothesis

This paper investigates the factors that drive households' deforestation in the Tridom-TCL, with a particular interest in the impact of households' choice of livelihoods strategies. More precisely, we test the following hypothesis:

Decision variables influence deforestation.

(1) The impact of family income on small-scale deforestation is closely related to the households' livelihoods strategies. This hypothesis will allow comparing the incremental change on households' deforestation resulting from a one-unit increase in income given their livelihoods portfolio and strategies. ( 2

SOURCES OF DEFORESTATION

Agents of deforestation: choice variables

DEFORESTATION

Source: Authors, Adapted from Angelsen and Kaimowitz (1999). Elements in blue color represent our contribution to the framework. (3) Financial asset (Walker et al., 2000(Walker et al., , 2002) ) and factor constraints such as both Human-Human and Human-Wildlife conflicts drive households' deforestation. (4) Environmental state and policy, captured by the distance to the nearest protected area, rank among households' deforestation drivers. Due to their legal status, protected areas are supposed to be associated to lower deforestation (Pfaff et al., 2015;Ferretti-Gallon et Busch, 2014). Yet they may also have an impact on surrounding deforestation, for instance through leakage (Amin et al., 2014).

Households' characteristics influence deforestation. Gender, households' head age and education, marital status, the household's size, ethnicity as well as duration of residence (seniority) account for the drivers of small-scale deforestation.

Households' Choice variables influence deforestation. Following Pichón (1997); Walker et al. (2000Walker et al. ( , 2002)), labour allocation between work and leisure and hired labour (especially Pygmies employment with very low cost in our case) increases forest clearing. ( 2) Social capital, like belonging to a group of interest, has an impact on households' deforestation. (3) Finally, following Chowdhury (2006a,b) we test that biophysical variables namely, rainfall have a strong impact on small-scale deforestation.

Spatial patterns influence deforestation. This paper tests the presence of endogenous interaction of households' deforestation. Indeed, proximity among households in the Tridom-TCL implies the existence of cultural and social interaction that could yield spatial spillover effect leading to similarities in deforestation decision. Further, it was shown that, households' deforestation as a social and cultural phenomenon is likely to be characterized by spatial autocorrelation (Mertens et al., 2002;Pfaff et al., 2007;Boubacar, 2012;Amin et al., 2014;Brueckner, 2003;Anselin, 2002). The observations we did during the 8 months fieldwork in Cameroon and Gabon reveal some competition about land holding among household heads. This observation calls for testing the existence of spatial effects within a household's neighborhood.

A simple microeconomic model of deforestation choices

Consider household i choosing his/her level of deforestation D i to maximize its utility:

max D i U i (D i , L i , X i , D j ) (2.1)
L i is the livelihoods strategy selected by the household, as defined in citeN-gouhouoPoufoun2016lvlhd. 6 different household strategies are considered: agriculture (A), cash crop production (C), forest-based activities (F ), and combinations of cash crops/forest-based (CF ), agriculture/forest-based (AF ) and agriculture/cash crops/forest-based (ACF ); such that

L i = [A, C, F, CF, AF, ACF ].
X i is a vector of household i socio-economic variables susceptible to influence deforestation. Household i's utility function may encompass income, but also other non-observable outcomes such as household's vulnerability. Thus, the household characteristics X i may influence not only the household economic return, but also other household matters of interest. Furthermore, we also consider that household i's utility may be influenced by its neighbors. D j is the level of deforestation chosen by household i's neighbors, that is likely to influence its decision. This type of strategic interactions is close to the resource-flow model presented by Brueckner (2003) and Anselin (2002).

The first-order condition implicitly gives the optimal level of deforestation

D * i (L i , X i , D * j )
for household i:

U D i = ∂U i (D * i , L i , X i , D * j ) ∂D * i = 0 (2.2)
Optimal deforestation strongly depends on the livelihoods strategies chosen by the households:

D * i (L i , X i , D * j ) = D * i (L i , X i , D * j ), ∀L i = L i . (2.
3) Moreover, one can then infer the impact of livelihoods strategies, other variables and neighbors deforestation on household i deforestation level:

∂D * i (L i , X i , D * j ) ∂X i = - ∂U D i ∂X i ∂U D i ∂D i
(2.4)

∂D * i (L i , X i , D * j ) ∂D j = - ∂U D i ∂D j ∂U D i ∂D i (2.5)
In the next section, we will investigate the impact of livelihood choices on deforestation levels (sign of equation ( 2.3)), the impact of other control variables (sign of equation (2.4)) and the nature of spatial spillovers (sign of equation (2.5)).

Spatial Econometric Procedure

The common observation that individuals belonging to the same group tend to behave similarly can de explained by three hypotheses of the standard linear model (SLM) that are the endogenous effects, the exogenous effects, and the correlated effects (Manski, 1993). The endogenous and the exogenous effects express distinct ways that persons might be influenced by their social environments. The first assumes that, all else equal, individual behavior (deforestation (D i ) tends to vary with the average behavior (deforestation of the group or neighbor (D -i )). The second effect assumes that individual behavior is in some way influenced by the characteristics of the group or neighbors (Z -i ). The correlated effects express nonsocial phenomena. According to these similarities in individuals' behavior may results from spatially dependent omitted variables, interaction among error terms ( ) or environmental similarities (Brueckner, 2003;Manski, 1993;Elhorst, 2014;Anselin, 1988b). In the following, we present a short description of various cross-sectional spatial econometric models (2.5.1). Then, we present the selection procedure we used (2.5.2).

Cross-sectional spatial econometric models

The matrix form of the generalized nested spatial model that accounts for all the three effects was defined by Manski (1993) in equations (2.6-2.7). This model is also called the Manski model.

D = α * I N + ρW D + Zβ + W Zθ + , ( 2.6)
= λW + µ (2.7)

.

In this expression, I N is a n by n identity matrix. W D denotes the endogenous effects, representing the average deforestation of neighboring individuals (D -i ). The ρ parameter measures the strength of spatial dependence. W is a row-standardized weights matrix such that the elements (w ij ) in each row (i) sum to one and the diagonal elements set to zero, each element (w ij ) measures the intensity of interaction among household's (i) and its relevant neighbors (Anselin, 1995). W Z stands for the exogenous effects representing the average value of neighboring households' characteristics, scaled by the parameter θ. The parameter β captures the direct impact of independent variables. W denotes the interaction among the disturbance terms. the parameter λ measures the spatial autocorrelation intensity among error terms. After testing the equations (2.6-2.7), Manski (1993) found that data on equilibrium outcomes cannot distinguish both endogenous and exogenous interactions from contextual effects based on testing the model ( .

The Spatial Autoregressive Model (SAR) was proposed by Anselin (1988b) to test only the endogenous interaction, using the lag value of the dependent variable. The SAR model allows only spatial autocorrelation of households' deforestation, without spillover effects neither from the neighborhood, nor from own behavior on neighboring households.

D = α * I N + ρW D + Zβ + (2.11)
Among other models, (1) the Spatial Error Model (SEM) was developed by Anselin (1988b) to account only for the correlated effects. This assumes that ρ = 0 in the SDM model. ( 2) The Spatial Durbin Error Model (SDEM) includes spatial lags of independents varables (θ = 0) and the spatially lagged error term (λ = 0) in equation (2.6-2.7).

(3) The Spatial Lag of the explanatory variable (SLX), (ρ = 0), (θ = 0) and (λ = 0) in equation ( 2.6-2.7)

Selection procedure

The consideration of spatial effects in econometric models require some specific processes to avoid model misspecification (Le Gallo, 2002).

The usual standard approach is to start with a specific SLM. Further, test if the error terms and/or the dependent variable are spatially correlated, to specify the spatial model that is consistent with the data generation process. This is called the specific-to-general approach. The second approach is to start with the Mansky model and test progressively the existence of various spatial effects (Elhorst, 2014).

In this study, we started with the standard approach that is most common in spatial analyses, following Anselin (1988a). After estimating a SLM, we first tested for the existence of spatial autocorrelation using the Moran i statistic on the residual of the linear model. Further, we proceeded to the Lagrange Multiplier test that helps to find the type of spatial effects that fit with our data generation process. Tables 2.2 and ?? in the subsection 2.6.2 display our procedure of model specification.

An issue that arises in applied econometrics is the need to compare models (LeSage, 2008). Indeed, a universal criticism of spatial regression models is the sensitivity of the estimates and inferences to the form of spatial weight matrix (LeSage et Pace, 2014). After specifying our econometric model, we use four types of weight matrix namely, the Gabriel graph weight matrix, the five nearest neighbors (5NN), the ten nearest neighbors (10NN) and the distance based weight matrix to account for this criticism. These weight matrices are presented in detail in Ngouhouo Poufoun et Delacote ( 2016).

Results

Variables and Descriptive statistics

Dependent variable : our measure of deforestation followed two steps. We first asked the households to fill the information about their land-holding in a table. After that, we randomly chose one plot among the total plots declared by the household to visit. The visited plot was tracked using a Global Positioning System (GPS) to have the real area. A total of 3338 plots of land were declared by overall sample that is on average 3.2 plots held by each household. A total of 526 plots were tracked with a GPS. The data declared were adjusted using the tracked data to obtain the value used in this study. On average, household heads declared clearing 4.75 ha of forest. We found after statistical adjustment that the average land clearing of each household stands at 4.41 ha.

Figure 2.2: Livelihoods and mean deforestation in the landscape

Independent variables : we distinguish our independent variables in three categories.

First, we consider the household income, depending on the livelihoods strategy chosen. In our case study, like many contexts in rural areas of developing countries, access to land can be considered open. Figure (2.2) above displays the distribution of mean deforestation and livelihoods in the Tridom-TCL.

Second, diverse households' characteristics can have a part in explaining deforestation.

We consider here several potential constraints to deforestation. Credit constraints are approached through credit and money transfer received by the household. Finally, land-use conflict is a proxy for constraints on land access, while the damage costs from wildlife conflicts represent environmental damages.

Third we consider a set of contextual variables, such as distance to markets, as a proxy for transaction costs. Proximity to protected areas represents constraints brought by environmental policies. Table 2.1 displays the variables' definition and descriptive statistics. The Lagrange multiplier test presented in table 2.3 is used to diagnose the type of spatial dependence that governs our data generation process among the endogenous effects, i.e. spatial lag of the dependent variable (ρ = 0) and the correlated effects or the spatial autocorrelation of the disturbance term (λ = 0). This test suggests rejecting all the specifications that allow spatial autocorrelation in the disturbance term. Therefore, we avoid estimating the SARAR, the SEM and the generalized nested Manski spatial model. In the following, we estimate the SAR as it fits with our data generation process. We avoid displaying the results of the SDM as it yielded counter-intuitive findings.

Overall, following the findings in tables 2.2 and 2.3, we cannot reject our hypothesis of a spatial effects, giving rise to a presumption of the existence of a positive relation between households' deforestation and the average deforestation of neighboring households. In section 2.6.3 below will confirm or reject this presumption via the ρ parameter and present the drivers of households' deforestation. The SAR models show a significant spatial dependence between deforestation of each household and the average deforestation of neighboring households. This suggests some similarities in the deforestation decision of households located nearby. The expected deforestation of each household in the Tridom-TCL is determined by both own characteristics and a linear combination of neighboring households' deforestation scaled by ρ. The SLM estimates have larger size compare to the SAR models considering all types of weight matrice. It attributes the variabilities in households' deforestation only to the independent variables. Also, the SAR model suggests that the variability of deforestation across households is partially explained by neighbors' deforestation behaviour. Further, the spatial lag of households' deforestation is treated as an endogenous variable and the error term is influence by the same process. As a result, although the Q-Q plot in the figure (B.1.1) in appendix reveals normal distribution of households' deforestation, the SLM is biased and yields inconsistent estimates due to simultaneity bias. In these conditions, the SAR is a proper specification to account for this endogeneity (Anselin, 2001). Following Anselin (1988bAnselin ( , 2001) ) our spatial lag model of deforestation was estimated using the maximum likelihood technique.

As shown in table 2.4, the strength of spatial dependence (ρ) varies along with the type of the weight matrix. The scale of the ρ parameter varies increasingly from 0.027 for the the Gabriel graph weight matrix to 0.235 for the distance-based weight matrix. It equals 0.089 for the 5NN and 0.179 for the 10NN weight matrices. Further, the estimates vary decreasingly from the from the Gabriel graph matrix to the distance-based matrix. The warning in LeSage et Pace (2014) regarding the sensitivity of the estimates and inferences to the type of matrix is confirmed. Among these four candidate models, the 10NN base model, displayed in the third column with bold characters, performs better as it minimizes the information loss. This model has the maximum log likelihood with the minimum Akaike Information Criterion (AIC) compared to others. The goodness-of-fit test confirms that the SAR model based on the 10NN weight matrix is the best to fit the households' deforestation. Indeed, combining the Wald test (W ), the the Log-likelihood Ratio test (LR) and the Lagrange Multiplier test (LM ) as suggested by Anselin (1988b), we found that the inequality W ≥ LR ≥ LM is verified only for the 10NN based model that is (W = 10.399) ≥ (LR = 10.16) ≥ (LM = 1.014). In the following, estimates from the SAR model based on 10NN weight matrix are used to derive the drivers of households' deforestation.

The β coefficient of the SAR model cannot be interpreted as partial derivatives of households' deforestation in the Tridom-TCL with respect to a one-unit change of various independent variables as in conventional linear regression model (Pace et LeSage, 2006). The subsection 2.6.3.2 below presents the impact of the various independent variables on the households' deforestation.

Direct, Indirect and Total effects

Livelihoods Strategies:

The direct effects for household income are all positive and significant regardless of the livelihoods strategy, except for the income of households who practice forest-based activities.

Diversification strategies and specialization strategies have comparable impacts on deforestation. Strategies that encompass cash crops production have the highest impact. On the other hand, family income of households specializing in forest-based activities unsurprisingly do not tend to impact deforestation. Further, the indirect effects of these incomes are positive and significant except for households who adopt a diversified portfolio comprised of agriculture and forest-based activities. More precisely:

• ACF: A one-unit increase in the monthly income of a household's head, who diversifies choosing agriculture, cash-crop and forest-based activities (ACF), leads to increasing own deforestation by 0.0102 ha; with a positive spillover effect of 0.0023 ha within his/her neighborhood. The resulting total effect is 0.0125 ha. Translating into dollars, using the 2014 exchange rate 5 , this approximately equates to an incremental increase of own deforestation by 4.9 ha with a spillover effect of 1.1 ha within the neighborhood, resulting from per annum $1000 increase in the household income. That is a total effect of 6ha.

• CF: Likewise, a one-unit increase in the monthly income, of a household's head who chooses cash crop and forest (CF), leads to increasing own deforestation by 0.0113 ha; with a positive spillover effect of 0.0025 ha within his/her neighborhood. This approximately equates to an incremental increase of own deforestation by 5.4 ha with a spillover effect of 1.2 ha within the neighborhood, resulting from per annum $1000 increase in the household income. The total effect is of 6.6 ha.

• AF: A one-unit increase in the monthly income, of a household's head choosing agriculture and forest, leads to increasing own deforestation by 0.0025 ha; This approximately equates to an incremental increase of own deforestation by 1.2 ha resulting from per annum $1000 increase in the household income. Households' heads choosing (AF) portfolio do not exert any significant spillover effect within their neighborhood.

• A: The direct effect of increasing of households specializing in agriculture (A) is a significant increase by 0.0030 ha of own deforestation. The resulting significant and positive spillover effect within the neighborhood is 0.0007 ha.

The resulting total effect is 0.0037 ha. This approximately equates to an incremental increase of own deforestation by 1.43 ha with a spillover effect of 0.33 ha within the neighborhood, resulting from per annum $1000 increase in the household income. That is a total effect of 1.76 ha.

• C: The incremental change of deforestation resulting from a one-unit increase in the monthly income of households choosing cash crop (C) is an increase of 0.0180 ha. This leads to increasing deforestation of households sharing the same neighborhood by 0.0040 ha. The resulting total effect is 0.022 ha. This approximately equates to an incremental increase of own deforestation by 8.6 ha with a spillover effect of 1.9 ha within the neighborhood, resulting from per annum $1000 increase in the household income. The total effect is of 10.5 ha.

As regard the capital and factor constraint decision's variables, financial asset as well as "the damage cost from human-elephants conflict have both significant direct and indirect effects (table 2.5). Yet,the existence of land conflicts among households (Human-Human conflict) were insignificant (see table (B.2.1) in appendix).

An additional unit of loan contracted (or transfer received) by a household's head leads to a marginal increase of own deforestation of 0.0102 ha with a positive spillover effect within his/her neighborhood of 0.0043 ha. That is a total effect of 0.0145 ha. This equates to an increase of own deforestation by 6.9 ha with a spillover effect of 2.04 ha within the neighborhood, resulting from per annum $1000 increase in the financial asset. That is a total effect of 8.94 ha. This indicates that increasing money transfer in favor of households living in the landscape may foster engagement in forest clearing by households.

Unlike the financial asset, monthly cost of crops damage by elephants exert a negative and significant direct and indirect effect on the households' deforestation. Indeed, additional unit of damage cost reduces both own and neighboring deforestation by 0.217 ha and 0.048 ha. The resulting total effect is 0.265 ha. This approximately equates to an incremental decrease of 103 ha and 22.8 ha respectively, resulting from per annum $1000 increase in damage cost. This result translates into two complementary effects. Firstly, a business discouragement effect that could lead to the abandonment of spaces nearby the promenade area of elephants; on the other hand, it may cause a switch from activities relating to land use to forest extraction activities that seem least risky with a possibility of increasing forest degradation.

The existence of land conflict among households (Human-Human conflict) were insignificant (see table (B.2.1) in appendix). The third result gives the insight that there is little constraint on land access in our case study: deforestation and agricultural expansion is not impacted by neighbor conflicts.

Households characteristics: Among the socioeconomic variables, education, the marital status and the ethnicity do not influence households' deforestation as shown in table (B.2.1) in appendix. Those with significant effects include gender, age, households' size and the residence duration or seniority.

Table 2.5 shows that men are associated with 0.58 ha more deforestation than women without spillover effects within the neighborhood.

Deforestation increases slowly and significantly with the household's head age with some threshold effect. For every year they get older, deforestation increases by 0.03 ha, with a negligible spillover effect. Larger household's size induces more deforestation. Indeed, an additional member in a family increases own deforestation by 0.16 ha with a spillover effect of 0.034 ha. As pointed out by Kaimowitz et Angelsen (1998), the residence duration is positively associated to forest clearing with the same level as age.

When it comes to labour, it is interesting to note that Pygmy employment is related to larger deforestation, while household labour duration has no influence (see table (B.2.1 in appendix). Thus both types of labour do not seem to be substitutes for households' labour. Pygmy labour appears to be more land-intensive practices, while household labour seems not to be related to land-use choices, i.e. more related to labour-intensive practices.

Contextual variables: Among contextual variables presented in table 2.1, "distance to market", "distance to the nearest protected area" and the first result suggest that distance to markets has no direct effect on deforestation. However, as shown by citeNgouhouoPoufoun2016lvlhd, distance to markets influences the livelihoods strategies: thus the effect of distance apparently passes through the livelihoods strategies transmission channel. The second result indicates that public policies such as protected areas do not bring constraints on land use decision. Moreover, we do not find evidence of leakage between protected areas and neighboring households.

Finally, both the direct and the indirect effects of country are positive and significant. Indeed, households living in Cameroon are associated with 0.85 ha more deforestation compare to those living in Gabon, with a spillover effect of 0.178 ha on proximate households in Gabon. This result suggests paying additional attention in the Cameroonian segment of the landscape.

Discussion and conclusion

The aim of this study is to better understand how livelihoods strategies have an impact on deforestation. To that matter, this paper is a natural extension of citeN-gouhouoPoufoun2016lvlhd, which determines the variables influencing livelihoods strategies. We also develop a spatial approach in order to take into account spatial interactions between agents. Our analysis relies on an original household survey collected in the Tridom-TCL.

Our results are multiple. First, when it comes to livelihoods, diversification and specialization strategies roughly seem to have the same impacts. Yet, we show that strategies incorporating agricultural activities tend to have an impact on deforestation. The corollary to this result is that only agents specializing in forest-based activities do not influence deforestation. More precisely, households integrating cash-crops in their activity portfolio are those getting the largest impact on deforestation. Further, spillover effects from strategies with cash-crop (C, CF, ACF) have almost the same scale than the direct effect of the remaining strategies (AF, A). Cash-crop as a specialization strategy has the highest influence on forest cover compared to the other strategies, with a spillover effect that is almost twice as large as the direct effect caused by households mixing agriculture and forest. These results bring the insight that, if development leads to households switching from small-scale agriculture to cash-cropping as a main activity, this would result to a major increase in deforestation. As an example, one extra dollar earned in cash-crops appears to have about a 7-times larger effect on deforestation than an extra dollar in agriculture. In the same vein, we show that the share of auto-consumption is negatively related to deforestation. Here again, if economic development brings a better market access and lower auto-consumption shares, this may positively influence deforestation.

Second, we show that land conflicts and distance to protected areas do not seem to influence deforestation. This result brings the insight that competing land-uses is not really a matter of constraints for households, nor does it represent a source of leakage. In contrast, land-wildlife conflicts do seem to have a negative impact on deforestation. Therefore, if policies are set with the aim to protect wildlife in rural areas and decrease human-wildlife interactions at the same time (Ngouhouo Poufoun et al., 2016), it is a crucial matter to monitor and to involve local populations in order to avoid a bump up in deforestation.

Third, our paper underlines the importance of assessing deforestation factors in a spatial context, using a landscape approach. Indeed, spatial spillovers tend to be of large magnitude: indirect effects may reach up to 20% of the direct effects. This result is important, as it shows that micro-economic analysis of deforestation factors should take into account those spatial interactions, in order to have an accurate understanding of the mechanisms in place. This statement also holds for the choice of livelihoods strategies (Ngouhouo Poufoun et Delacote, 2016).

Fourth, labour allocation is important. While the household labour duration does not seem to impact deforestation, employing Pygmy labour tend to increase it. Therefore, both types of labour cannot be considered as substitutes, especially when it comes to land use. Household labour appears to be allocated to labour-intensive activities, while Pygmy labour seems to be allocated to activities requiring more land and thus more deforestation. If we consider a Chayanovian approach (Kaimowitz et Angelsen, 1998), it seems that the trade-off between household labour and leisure does not influence deforestation.

Overall, our paper brings some reflexion on how development may influence deforestation in such a landscape. Three transmission channels are to be distinguished: an income channel, an activity portfolio channel and a market integration channel. First, economic development comes with larger income. We show that, except for households specializing in forest-based activities, an increase in income is related to more deforestation. Second, the portfolio of activities is likely to change with economic development, with an increased importance of cash-cropping. This would also result in larger deforestation rates. Finally, when households have a better access to markets, they tend to decrease their share of auto-consumption, which can also have an tendency to increase deforestation. It is important for development projects and policies to take those three channels into account, when dealing with possible environmental adverse effects.

Introduction

Forest elephant (Loxodonta africana cyclotis)1 poaching in tropical Africa is a major threat to the dynamics of this iconic species. In 2011, the Congo Basin's forest elephant population was less than 10% of its potential size and occupied less than 25% of its potential range (Maisels et al., 2013;Blake et al., 2007;Martin et Stiles, 2000). The Tri-national Dja-Odzala-Minkebe (Tridom) cross-border landscape, spanning Cameroon, Congo (R), and Gabon, is considered to have ecological and biodiversity uniqueness, and hosts the most important population of forest elephants in the world, with the highest density in the Minkébé National Park (MNP). The MNP lost more than 11,000 individuals between 2004 and 2012, representing more than 50% of the 2004 elephant population (Maisels et al., 2013). Despite the ivory trade ban by CITES2 to protect the African elephant (Van Kooten, 2005), the current growing demand for ivory for jewellery, leisure and Asian medicine as well as the increasing deforestation and land pressure are the main drivers of the elephant population's devastating decline. It is evident that the elephant is much appreciated for these material and provisioning services. However, elephants also contribute to the maintenance of ecological equilibrium and to the provision of social and cultural services.

The forest elephant can be considered a flagship species, as its protection implies the protection of other species in the same ecosystem. Indeed, the elephant disseminates the seeds of important tropical fleshy fruit trees over long distances and contributes to the regeneration of these tree species throughout the Congo Basin (Blake et al., 2009;Wang, 2008;Beaune et al., 2013). For instance, Baillonella toxisperma (moabi), a traditional multi-use species among Bantu and Baka villagers in the Tridom region is now an endangered species because of its high commercial value in the wood market. Moabi contributes significantly to balanced diets among forest people. Indeed, its fruits and nuts are eaten raw and its oil is used in cooking, disease treatment and cosmetics. By disseminating its seeds, the elephant contributes to the restoration of the forest; therefore, it indirectly contributes to carbon storage.

Hence, elephant conservation is consistent with the REDD+ mechanism3 , which has emerged as an important framework for forest conservation. Forest elephants help improve the forest habitat and thus restore the ecological services provided by the forest (i.e., they regulate the ecosystem).

Moreover, forest elephant populations are crucial to the cultural identity of the indigenous Baka ethnic group. Their main rituals occur after an elephant hunt, and 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 elephant hunt is also the most important spiritual and religious event for this population. The hunt brings together dispersed groups, all of which have specific responsibilities (e.g., the vital contribution of women to the mystical preparations for a safe hunt). Only the oldest Baka are permitted to kill elephants, and they undergo rigorous preparation, in which they learn from experienced hunters over many years before obtaining permission 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 has been consumed (Lewis, 2002). Therefore, elephants participate in maintaining the spiritual enrichment, cultural identity and knowledge of the Baka community. This cultural service implies that elephant extinction entails an opportunity cost in terms of the loss of cultural values (Garrod et Willis, 1999). Figure 3.1 provides a synoptic description of the ecosystem services and the total economic value of the elephant in the Tridom landscape. Considering the importance of forest elephants to ecological, cultural and socio-economic equilibria, possible crop damage (human-elephant conflicts) notwithstanding, endangered forest elephant (EFE) extinction would severely and directly affect local and indigenous welfare. Considering the interdependence of the elephant, Baillonella toxisperma and rural communities as described above, "nature too would seem to lose" (Tisdell, 1990, pp. 83). This irreplaceable loss to Tridom society makes the EFE a priority in biodiversity conservation decisions. In addition, the social value of biodiversity is unknown, and thus, the potential impact of this loss in biodiversity on social wellbeing is uncertain (Turpie, 2003). An assessment of the economic value of the ecosystem services that the elephants provide for local communities will yield important information for policy makers and conservation managers. The findings may also increase awareness of the importance of biodiversity conservation. Therefore, the key question addressed in this paper is the following: What are local households willing to pay to prevent the elephant's extinction?

A large body of research has contributed to enriching the literature on the economics of endangered species conservation (Bishop, 1978;Barbier et al., 2013;Kremer et Morcom, 2000;Tisdell et al., 2002;Bulte et Kooten, 2002). However, only a few studies have assessed the indirect use value, bequest value and existence value of the elephant (Bandara et Tisdell, 2005, 2003a,b), even though this iconic species plays important roles in socio-cultural and ecological integrity (Lewis, 2002;Blake et al., 2009). Bandara et Tisdell (2001) used data from a face-to-face contingent valuation (CV) study of a sample of 300 urban residents in Colombo (Sri Lanka) to assess their willingness to pay (WTP) for elephant conservation. Their assessment differentiated between the values reported by users and non-users for the Asian elephant. Respondents who had used elephant facilities at least once were willing to pay Rs. 137.38 (e2.05), while non-users were willing to pay Rs. 82.96 (e1.24) for elephant conservation, yielding an average of Rs. 110.17 (e1.65) per month. The results reveal that urban residents are willing to pay for elephant conservation because they want to secure the existence of the elephant (non-use value) and because the presence of elephants has a use value, i.e., importance for recreation and tourism. The results also indicated that the probability of a positive WTP is significantly and positively influenced by pro-conservation attitudes and higher incomes (Bandara et Tisdell, 2004). The study found that the total WTP is sufficient to compensate for the value of annual crop damage. While a small number of studies have investigated farmers' valuations of the use value (Smith et Sullivan, 2014), as well as the option value and non-use value, of Asian savannah elephants (Vredin, 1997;Bandara et Tisdell, 2003a,b, 2004), no research has addressed the value of EFEs to local and indigenous communities. Our first contribution is thus to measure the value of EFEs to local and indigenous communities in Central Africa to contribute to the growing literature on CV implementation in developing countries. Additionally, this paper considers the relations between landscape factors, such as the distance between the household and the nearest concentration of elephants (i.e., protected areas), the elephant population density in the protected area and the households' land ownership, and the WTP. Furthermore, our methodological contribution involves combining closed-ended and open-ended (OE) elicitation formats to target robust WTP value. This analysis is facilitated by the collection of a novel, unexploited dataset obtained through face-to-face interviews with 1,035 households geo-localized by GPS.

The purpose of this study is to calculate local and indigenous households' WTP for EFE conservation and to analyse the factors that influence its value. We test four hypotheses. First, the extinction of the forest elephant may lead to a significant net loss in household welfare. Second, WTP for elephant conservation varies with the distance between the household and the nearest protected area. The effect of this distance may be positive or negative. Indeed, following the distance decay hypothesis, WTP declines as the distance between the respondent location and the site providing environmental services increases (Bateman et al., 2006;Loomis et al., 2000;Schaafsma et al., 2013). However, the distance to the protected area may also be an indicator of elephant scarcity. Therefore, if we assume decreasing marginal utility of forest elephant presence, household heads living close to a protected area with higher elephant density would be likely to express lower marginal WTP for elephant conservation. Third, WTP is significantly influenced by the indigenous status of households, as cultural services such as traditions and religious practices depend on the elephant's existence. Knowledge of spatial and ethnic differences in WTP may be used to design spatially explicit and culturally adapted conservation policies. Fourth, the presence of human-elephant conflicts is expected to reduce WTP for elephant conservation.

The following sections present the case study of the Congo Basin Tridom landscape's EFEs (section 3.2), the methodology (section 3.3), the results (section 3.4) and the discussion and conclusion (section 3.5).

Case Study: The Tridom Landscape's EFEs in the Congo Basin

The Tridom is a cross-border conservation landscape covering a geographical area of 191,541 km 2 , representing 7.5% of the total area of tropical forests in the Congo Basin of Central Africa. The Tridom was created in 2005 by an agreement among the Cameroon, Gabon and Congo governments, as one of the twelve Congo Basin Forest Partnership (CBFP) priority landscapes. The agreement targets the promotion of long-term biodiversity and protected areas' system conservation, the rational use of natural resources and sustainable development, including poverty reduction. It encompasses 10 protected areas representing 37,498 km 2 . Five of these areas are located in the Cameroonian 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 protected areas is a liveable inter-zone totaling 40,000 km 2 , with the remaining area being non-livable. The Tridom landscape includes rich and diversified flora and fauna and is abundant in highly commercially valuable timber species. In addition to elephants, this landscape has a relatively high density of large mammals, such as buffaloes, bongos, giant pangolins and gorillas. The range of human population density is 1-7.9 inhabitants/km 2 and is increasing as a result of resource exploitation (Ngoufo et al., 2012). The Tridom inter-zone is home to numerous economic activities, including forest management. Aproximately 78% of the indigenous and local communities rely on small-scale farming (40.9%), cash-cropping (19.6%), hunting-gathering bushmeat and non-timber forest products (14.8%), and Traditional gold-mining (3%) as their predominant livelihood with full-time employment. Among these activities, cash-cropping and small-scale farming constitute the main sources of income in the region.

Data were collected from a representative face-to-face survey of a random, stratified sample of 1,035 households (from a total of approximately 65,140 households in the study area). Because of administrative constraints and limited field work funding, our survey was conducted in the Cameroon and Gabon segments but not in Congo (figure 3.2). These segments are inhabited by more than 43 tribes, which are dominated by the Bantu, although the minority indigenous Baka tribe is also present (table C.2.1 in appendix). Random sampling of the households in the villages was based on the inhabitants' registers held by the village chiefs. The survey was administrated for 8 months between December 2013 and July 2014 in 108 villages representing all 26 administrative units of the Cameroonian and Gabonese part of the landscape (table C.2.2 in appendix). The visited villages are spread over nearly 27,000 km 2 , which represents two-thirds of the landscape's liveable inter-zone. Each household was geolocalized using a GPS. Interviews lasted between 1 and 3 hours. In addition, evening visits to various surveyed households were conducted to measure daily production. The field work was supervised by the first author. Ten master's degree students were selected after five training seminars to participate as surveyors. Each village provided us with at least two local translators in case the household heads could not communicate in French.

Methodology: Combining Open-Ended and

Closed-Ended CV Methods

Overview of CV methods

Major differences between CV approaches include open-ended (OE) and closed-ended formats, or discrete-choice contingent valuation (DCCV) (Cameron et James, 1987). In the OE format, respondents are asked to specify their WTP, while in the close-ended format, respondents are asked to indicate whether they would pay a specified amount (Kealy et Turner, 1993). Regardless of the level of information that respondents receive, the predicted WTP may be unstable with respect to the elicitation format (Brown et al., 1996). Indeed, the DCCV and OE mechanisms may yield significantly different preferences for public goods because of the varying incentives for strategic behaviour (Kealy et Turner, 1993). We use both mechanisms to approximate the true WTP. The DCCV approaches include single-, double-, or multiple-bounded dichotomous-choice approaches (Hanemann, 1985;Carson, 1985;Hanemann et Kanninen, 1999). In the double-bounded dichotomous choice (DBDC) approach, the respondents, having been asked a DCCV question, are asked a second DCCV question that depends on the answer to the first.

We first apply the DBDC model proposed by Hanemann (1985) and Carson (1985). The advantage of DBDC over the single-bounded format is that we obtain more information from the respondents. The former is therefore asymptotically more efficient than the single-bounded method developed by Bishop et Heberlein (1979); Hanemann et al. (1991); Alberini (1995); Haab et McConnell (2002). However, DBDC is more complicated to implement and requires more advanced econometric approaches to analyse the data. Furthermore, the initial bid may provide an anchor for the uncertain respondent, implying a bias in the response to the second question. For example the respondent may believe that the first bid may represent the "true" value of amenity valuated and may consider this information when responding to the second question (Hanemann et al., 1991;Herriges et Shogren, 1996). However, a good starting point has the benefit of preparing and encouraging respondents to reveal their maximum WTP (Brouwer et Martin-Ortega, 2012;Bateman et al., 2008;Frew, 2010a;Fischhoff et Furby, 1988).

Our methodological approach is to combine the DBDC format and the OE format. Once the starting points are well defined, the use of DBDC in first stage is expected to provide the best basis for an OE question. In fact, stand-alone OE questions often suffer from a high share of protesters as the respondents have difficulties when they are not accustomed to valuing a service or good [START_REF] Zeiler | The willingness to pay/willingness to accept gap, the endowment effect, subject misconceptions and experimental procedures for eliciting valuations[END_REF]Bateman et al., 2011;Brouwer et Martin-Ortega, 2012). According to the discovered preference hypothesis, executing DBDC yields repetition and experience and therefore facilitates convergence towards stable and theoretically consistent preferences [START_REF] Zeiler | The willingness to pay/willingness to accept gap, the endowment effect, subject misconceptions and experimental procedures for eliciting valuations[END_REF]Bateman et al., 2008). Hence, following Bateman et al. (2008), the DBDC format may be considered a learning design that may help to reduce the preference anomalies that occur under the OE format as well as the non-response rate and the number of protesters. Asking an OE question after the DBDC provides more information when the respondent answered either 'yes' to both amounts or 'no' to both amounts (Mahieu et al., 2012b). Indeed,the WTP stated by these respondents is unbounded when only the DBDC format is used.

Survey design

CV questions were included in a questionnaire addressing the characteristics, behaviours and motivations of the households. The CV first described the socio-economic and ecological attributes of the elephant, as well as the potential change in socio-cultural services associated with possible extinction given that forest elephants are currently present only in the Congo Basin. The respondents were asked to state their preferences for the entire bundle of services provided by forest elephants because addressing services separately leads to double counting. For instance, existence value will ensure bequest value, as well as option value (Loomis et Larson, 1994;Bandara et Tisdell, 2003a). Next, we proposed the following hypothetical scenario, given that we are valuing a non-market good without an implicit market. "Considering the trend towards the extinction of forest elephants, if action is not taken quickly, this multi-use, iconic species will disappear in the next few years. To stop this tendency towards extinction and make the species more abundant, the Tridom Regional Project Management Unit may develop a 10-year elephant conservation programme that aims to seize weapons currently used by poachers and to effectively fight cross-border poaching by (1) creating joint checkpoints on the landscape scale and ( 2) recruiting more young people from villages and involving them in a communication network to improve anti-poaching control strategies and prevent human-elephant conflicts". Then, each respondent was asked whether he would be willing to contribute to the programme by paying some monthly amount if the Regional Management Unit were to demand financial support from all the inhabitants of the village.

The payment vehicle presented was the direct cash payment of secure funds that would be held by the Tridom programme. This means of payment is the most familiar, credible and feasible based on the economic situation in the landscape. To minimize "yea-saying" and "nay-saying" bias4 as well as the starting point bias, (1) we asked each respondent to consider his monthly income, his sources of income, and his usual monthly expenditures, and ( 2) we asked each respondent to be realistic, in ensuring that he could actually pay the stated monthly amount for the next 10 years before answering. Furthermore, the respondents were randomly assigned to one of six starting bids developed and validated during two pretest steps with 40 households in four villages (Meyomessi, Oveng, Kongo and Mbieleme) of two subdivisions of the study area using an OE elicitation format (Boyle et Bishop, 1988). A lower bid was presented to those who gave a negative answer to the starting bid, and a higher bid was presented to those who gave a positive answer. The bid card structure is presented in the figure (C.1.1 in appendix). After the double-bounded elicitation format was used, the respondents were assigned an OE question asking the maximum amount they would be willing to pay for forest elephant conservation. Finally, we ask follow-up questions to examine the reasons for zero observations to be able to identify protest bidders in the database before model estimation (Arrow et al., 1993).

Theoretical model specification

Household preferences for forest elephant conservation in the Tridom can be described by a random utility model as developed by McFadden (1973) and formalized by Manski (1977) and Hanemann et al. (1991). The indirect utility function is given by the following:

U ij = V ij (Y i -a, E(a), X i ) + ε ij (3.1)
In equation (3.1), j = 1 ∧ a > 0 if household i is willing to pay an amount a, or j = 0 ∧ a = 0 otherwise. Y i is the household head's income, V ij is the deterministic component of the utility function measuring the indirect utility to respondent i in the state j, and X i is vector of socio-economic and geographical characteristics influencing households preferences, ε ij is the unobserved random component of the utility function. This function is supposed to be increasing and concave in available income Y i as well as in the level of elephant protection E(a). The household head i

Econometric model specification

Following Cameron (1988), we model the random WTP generated by closed-ended and OE question formats using the following formulation:

W T P i (X i , µ i ) = X i β + µ i (3.5)
In equation (3.5), W T P i represents the WTP vector of the i th respondent, X i is a vector of explanatory variables, β is a parameter vector, and µ i a normally distributed error term.

In the following, we specify four econometric models, using both the DBDC and the OE elicitation procedures described above. These models represent different assumptions about the reasons for respondents of not giving a positive bid. Our sample included a significant number of zero-bidders. Furthermore, as explained below, the different model assumptions concerns also how we combine information from the closed-ended and OE elicitation formats. The first two models are variants of the interval regression model (IRM) initially proposed by Hanemann et al. (1991) to analyse DBDC data. The other two models are corner solution models, i.e., a Tobit model and Cragg's truncated normal double-hurdle model (DHM).

Interval regression models

First, we estimate a standard interval regression model (IRM) based on the data obtained from the DBDC elicitation format. We estimate the model with and without zero-bid protesters. In our questionnaire, zero-bid protesters are identified as zero bidders who may have a true positive WTP but stated zero WTP in the OE question. Strazzera et al. (2003); Jorgensen et Syme (2000) and Halstead et al. (1992) recommend excluding them from the analysis only if they are similar to other respondents. Indeed, socio-demographic characteristics do not differ significantly between zero-bid protesters and non-protesters (see Table 3.2). By excluding zero-bid protesters, we assume that they have similar preferences, on average, as non-protesters.

In the standard application of IRMs (DBDC), a respondent who refuses to pay the lowest bid ("no/no" respondents) is left-censored at the lowest bid. If the respondent accepts both bids, the true WTP is right-censored at the upper bid.

The IRM is adapted to account for point data. Applying OE responses in cases in which the respondents reply "no/no" or "yes/yes" under a DBDC format helps significantly reduce the share of unbounded intervals. Our hypothesis is that combining these two datasets increases statistical efficiency (Mahieu et al., 2012a;Haab et McConnell, 2002).

Furthermore, we use follow-up questions to identify respondents who may have a negative WTP in the proposed conservation scenario. It is assumed that respondents may have a negative WTP if they answer zero WTP for the OE question and reply to a follow-up question that elephants are considered a cost5 . We estimate the modified interval regression model (IRM1) assuming that respondents who consider elephants a cost after facing a human-elephant conflict and who answer zero WTP in the OE question may effectively have a negative WTP. This approach involves left-censoring at zero. The remaining respondents answering zero in the OE format are considered true zero bidders and are integrated as point data. These respondents' main motives for zero WTP include "I cannot afford to pay for elephant conservation", "I do not receive any benefit from elephants", and "I do not see any problem if they disappear". The second estimation of the modified IRM left-censors household heads who expressed a possible loss in utility from the presence of elephants and who do not necessarily face human-elephant conflicts. This IRM has expected negative preferences (IRM2).

The standard IRM or DBDC model described above yields four possible interval outcomes with respect to the "yes/yes", "yes/no", "no/yes" and "no/no" answers.

The added value of the modified IRM is that, in addition to the output formats of the DBDC, it accounts for point data. Hence, Table 3.1 presents various possible specifications of the dependent variable.

a OE i ≥ 0 "no/no" and WTP < 0 1 [-∞, 0[ a s ij a l ij 0 "yes/yes" a OE i a s ij a u ij a OE i ≥ a u ij Interval "no/yes" a l ij , a s ij a s ij a l ij a l i ≤ a OE ij ≤ a s ij Interval "yes/no" a s ij , a u ij a s ij a u ij a s i ≤ a OE ij ≤ a u ij 1
These are "no/no" respondents who respond 0 to the OE and who faced or expect to face crop damage caused by elephants.

Here, a s ij , a l ij and a u ij represent the starting point, the lower bid and the upper bid, respectively, of the j th random bid card assigned to the i th household. According to Table 3.1, the probability that the i th household is willing to pay an unobserved amount within one of these intervals is given by the following:

P lc = P (µ i < 0 -X i β) = Ψ 0 -X i β σ = Ψ -X i β σ
(3.6)

P id = P (F B i ≤ X i β + µ i < SB i ) = Ψ SB i -X i β σ -Ψ F B i -X i β σ (3.7)
where Ψ( * ) is the cumulative normal distribution function. Equation (3.6) represents the left-censored (lc) data, that is, zero bidders who face or expect to face crop damage caused by elephants. Equation (3.7) represents the interval data (id), that is, the no/yes and yes/no respondents in the DBDC format. In this equation, F B i is the first bid or starting point a s ij , and SB i is the secondary bid, with a l ij or a u ij as the value for the "no/yes" or "yes/no" respondents, respectively Stewart (1983) suggests using the maximum likelihood technique when estimating the IRM. Hence, following Wooldridge (2012), the log-likelihood function can be expressed as follows:

ln(L(β)) = N i=1 b lc i ln Ψ -X i β σ + N i=1 b id i ln Ψ SB i -X i β σ -Ψ F B i -X i β σ + N i=1 1 2 b OE i a OE i -X i β σ + ln 2πσ 2 (3.8)
The first element of equation (3.8) accounts for left-censoring at zero observations. The second element accounts for interval data ("no/yes" and "yes/no"). The last element accounts for "no/no" or "yes/yes" respondents who are willing to pay an amount a OE

Corner solution models

A total of 358 households replied "no/no" in the DBDC and gave zero as an answer to the OE question; thus, there remains a non-trivial proportion of the population that did not state positive preferences for forest elephant conservation. These zeros may represent true zeros when the respondent does not obtain any utility from the proposed conservation scenario or cannot afford to pay for the conservation programme. A stated zero WTP can also be given if respondents have a negative WTP but are asked to only state a positive WTP. Alternatively, respondents may obtain positive (or negative) utility from elephant conservation but refuse to reveal their preferences, e.g., because they may find it difficult to put a value on such an asset (Mitchell et Carson, 1993). If some household heads stating a zero WTP in fact have a negative WTP for conservation, then the full possible range of respondents' preferences for elephant conservation is not observed. In this case, the dependent variable is a corner solution outcome [START_REF] Wooldridge | Econometric analysis of cross section and panel data[END_REF]Cameron et Trivedi, 2005). Econometric models that can cope with corner solution outcomes include Type I Tobit models (Tobin, 1958), Heckman selection or incidental truncation models (Heckman, 1979) and Cragg DHMs (Cragg, 1971).

Given the high number of zero bidders we apply a Tobit model and a DHM model that allows us to investigate the reasons for not participating (giving a positive bid). In the Tobit model, the likelihood of participation and the intensity of participation (in elephant conservation) are determined in the same way using the same covariates. This assumption is not generally reasonable (Katchova et Miranda, 2004). For example, in our case, households living very far from a protected area with high elephant density may be less likely to participate in elephant conservation and those that do participate may pay larger amounts because they are often close to urban areas. Furthermore, Tobit models do not remain consistent under heteroscedasticity (Brooks, 2014;Amemiya, 1984). DHMs provide the option of separating the parameterization of both decisions under a conditional independence assumption for drivers of participation and intensity (Burke, 2009;Solomon et Bekele, 2014;[START_REF] Wooldridge | Econometric analysis of cross section and panel data[END_REF]). Cragg's DHM allows heteroscedasticity in the second hurdle without conceding model misspecification in the first hurdle (Burke, 2009). The paper considers and compares traditional Tobit models and Cragg's DHMs. As the Tobit model is nested within Cragg's alternative, the following specifies only Cragg's model. The first hurdle, or participation decision d i , and the second hurdle, explaining the intensity of participation (Y 2i ), are defined as follows:

Participation equation:

Y * 1i = X 1i α + ε i (3.9)
Threshold participation equation:

d i =    1 if Y * 1i >0 0 otherwise (3.10)
Observed contribution intensity:

Y * 2i = X 2i γ + µ i (3.11)
Threshold contribution intensity:

Y 2i =    Y * 2i if Y * 2i >0 0 otherwise (3.12)
In equations (3.9), (3.10), (3.11) and (3.12), Y * 1i and Y * 2i are latent variables. Here, d i equals 1 if the household participates in EFE conservation and 0 otherwise, X 1i is a vector of socio-demographic and geographical covariates, α a vector of coefficients, and ε i is the iid error term. Y * 2i is the household's latent contribution to EFE conservation, X 2i is the vector of socio-demographic and geographical covariates that drives the intensity of payment, γ is the vector of coefficients, and µ i is an iid error term. Following Moffatt (2005), the DHM log-likelihood function is specified as follows:

ln(L) = Y 2i =0 ln 1 -ψ (X 1i α) Ψ X 2i γ σ i + Y 2i >0 ln 1 σ i ψ Y 2i -X 2i γ σ i Ψ X 1i α σ i (3.13)
In equation (3.13), ψ( * ) and Ψ( * ) are the probability density functions and cumulative normal distribution functions, respectively. The first element represents the non-participants, while the second represents the summation over observed positive contributions to EFE conservation. The expression σ i is the standard error or the value of σ for each observation. If α σ i = γ and X 1i = X 2i , then the Tobit model and the truncated DHM are mathematically identical. For detailed presentations of both models, see Cragg (1971) and [START_REF] Wooldridge | Econometric analysis of cross section and panel data[END_REF]. Following Burke (2009), this paper considers three values of interest to characterize the households' likelihood and intensity of participation in EFE conservation. The first is the partial effects of the covariates on the probability of participation, and the second and third are the partial effects of the covariates on the conditional and unconditional expected preferences derived using the maximum likelihood outcome obtained from the truncated DHM.

Partial effects of the covariates j on the probability of participation:

∂P (Y * 1 > 0 | X 1 ) ∂X 1j = α j ψ (X 1 α) (3.14)
Partial effects of the covariates on the conditional expected preferences:

∂E (Y 2 | Y 2i > 0, X 2i ) ∂X 1j = γ j 1 -λ X 2 γ σ i X 2 γ σ i + λ X 2 γ σ i (3.15)
Partial effects of the covariates on the unconditional expected preferences:

∂E (Y 2 | X 1 , X 2 ) ∂X 1j = α j ψ (X 1 α) X 2 γ + σλ X 2 γ σ i + Ψ (X 1 α) * γ j 1 -λ X 2 γ σ i X 2 γ σ i + λ X 2 γ σ i (3.16)
In equations (3.14) and (3.16), α j is an element of α that appears as the coefficient on X 1 . In equations (3.15) and (3.16), γ j is an element of γ that appears as the coefficient on X

2 ; λ = ψ (X 1 α) /Ψ (X 1 α) is the inverse Mills ratio.
We also considered a Heckman selection model allowing for dependence in the error terms of the participation and contribution equations (Martínez-Espiñeira, 2006). However, our tests indicated no significant dependence, and the results of the Heckman selection model are not presented. Variables such as land holdings, distance between the respondent and the nearest protected area and proximity to a protected area with relatively high elephant density may also influence the preferences of the respondents for elephant conservation. The first two of these variables were determined using a GPS and the ArcMap software. Indeed, land holdings refer to the area of land holdings of a household head. This variable was generated using GPS tracking to capture the exact area. Approximately 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, and 3% own between 15 and 25 ha. A few household heads own between 25 and 57 ha of land. With regard to proximity to a protected area (with relatively high elephant density), the elephant density of the various protected areas is considered a continuous variable (this variable was determined using the UICN elephant database).

Results

Variable Descriptions and Descriptive Statistics

Table 3.3 provides the distribution of the 936 answers to the DBDC questions, excluding identified protest bidders. As expected, the frequency of "yes" and "yes/yes" respondents decreases with the starting bid. On average, 56% responded "no/no". The WTP reported by 18.4% of the remaining respondents was in the interval between the lower bid and the starting bid (6.5%) or between the starting bid and the upper bid (11.9%). Approximately 25.4% were willing to pay more than the higher bid. Table 3.4 shows the outcome summary with respect to the various econometric models estimated. The DBDC model without protesters left-censors all "no/no" observations, that is, 56.2% of the sample; it considers 43.8% of positive DBDC question, that is, 56.2% of the sample surveyed, 18% stated a positive WTP in response to the OE question, and 25.4% were willing to pay more than the higher bid. The follow-up questions revealed that 104 households reported a zero WTP because they considered elephants to be a cost for them. Of these, 27 households had experienced crop damage from elephants.

Econometric results

Predicted WTP for EFE conservation and extrapolation

Table 3.5 presents the factors influencing households' WTP, as well as the estimated WTP. We display the results from the diverse models described in the previous section in order to underline how estimated WTP are influenced by the chosen models.

Overall, we see that the estimated WTP is sensitive to the estimation techniques and assumptions about protesters and zero bidders. Indeed, the first two columns display the results based on the standard DBDC model with and without the zero-bid protesters. As expected, the predicted WTP is higher when zero-bid protesters are removed. Indeed, the remaining respondents are assumed to be representative of the population, as they have nearly the same characteristics as the zero-bid protesters.

The removal of protesters leads to an increase in the monthly predicted WTP from CFA 368.84 (e0.56) to CFA 742.92 (e1.13) per household.

The DHM model resulted in an unconditional expected WTP for EFE conservation of CFA 1,326.87 (e2.02). As expected the conditional expected WTP, based on the respondent having a positive WTP, was significant higher CFA 2,081.84 (e3.17) because of the non-trivial share of respondents who did not state positive preferences for forest elephant conservation. IRM1 produced a monthly predicted WTP of CFA 1,245.66 (e1.89). This model left-censors only the 27 households that experienced both human-elephant conflicts and crop damage. IRM2 left-censors the 104 households that experienced elephant-related costs as well as those citing potential elephants-related cost as the reason for a zero bid. This model generated a predicted WTP of CFA 1,138.17 (e1.74) or CFA 13,658 (e20.82) annually per household.

To estimate the expected aggregate WTP for elephant conservation by the population living in both the Cameroonian and Gabonese segments of the Tridom landscape, the simple transferring point estimate is used as the sampling of respondents was random and representative. This method produces a robust aggregate with less bias than a benefits function transfer approach (Bandara et Tisdell, 2004;Loomis et al., 2000;Brouwer et Spaninks, 1999). The aggregate population size in both segments of the Tridom is 418,855 inhabitants (Bucrep, 2010;Gabon, 2010

Drivers of participation, intensity of participation and the impact of covariate change

The drivers of households' participation decisions and their intensity of participation are analysed using the Tobit model and DHM. Applying a log-likelihood test, we test if the more general DHM can be reduced to a Tobit model. See, for example, Katchova et Miranda (2004). The calculated L statistic given by -2 * (DHM LL-T obitLL), which equals 178, is greater than χ 2 (13) at the 5% critical value (27.69). This outcome shows that the data do not support the more restricted Tobit model. Indeed, participation in and the size of the contribution to elephant conservation cannot be examined using one-step parameterization. In addition, these decisions are not explained by the same covariates. The household heads' activities influence the participation decision, but not the intensity of participation. These variables are removed from the second hurdle, as the restricted model provides a smaller Akaike Information Criterion value compared with the value obtained from the model including all covariates.

Drivers of participation and intensity

The coefficients of the first hurdle in the fourth column of Table 3.5 indicate that the likelihood of participating in the elephant conservation programme is negatively influenced by age and positively influenced by education level; indigenousness; specific activities, such as traditional gold mining or having a job in a forest management unit, forest administration or other administration unit; and the land holdings of the household heads. Some of the covariates have conflicting effects on both the decision to participate (first hurdle) and the intensity of participation (second hurdle). This also motivates the choice of the double parameterization technique under the DHM. Indeed, as a household head grows older, he is less likely to participate in the elephant conservation programme, yet the age variable does not influence the intensity of participation.

The higher the household head's education level is, the more likely he is to participate (and participate more intensively) in EFE conservation. The positive influence of education suggests positive feedback between better education and environmental awareness. This result suggests positive environmental benefits that arise from addressing education improvement and facilitating access to higher education in developing regions. The effect of working in a forest management unit or a forest conservation NGO is positive, which implies a virtuous circle of environmental preservation: increasing the importance of forest protection also increases household awareness of biodiversity protection.

The impact of changes in covariate drivers of participation and intensity

The impact of a change in a covariate on the decision to participate is measured using the marginal effects based on the first hurdle equation. See the first column of Indeed, a one-unit increase in the age of the household head will lead to an approximate decrease of 0.3 percentage points in the probability of programme participation. Given the unconditional average partial effect in the third column simultaneously with the first two columns of Table 3.6, it appears that the overall effect of being one year older is a decrease of CFA 3.04, as the negative effect on participation dominates the positive conditional average partial effect. When a household head reaches secondary school, the probability of participation increases by 10.7 percentage points. This would lead to an average partial increase in the expected payment for EFE conservation of CFA 635 (e0.968) given the participation of the household head. The unconditional expected contribution to EFE conservation increases by CFA 615.6 (e0.939) compared with a contribution of a household head who has not enrolled in secondary school.

The likelihood of participating in such a programme would increase by 15.1, 23.9, 34.8 or 15.2 percentage points if the household head is a Baka pygmy, traditional gold miner, forest administration or other administration worker, respectively. Conditional on agreeing to participate, an indigenous household head would pay an additional CFA 807.4 (e1.23). Not conditional on the agreement to participate, when the household head is an indigenous Baka, WTP increases by CFA 813.4 (e1.24) compared with the WTP of the rest of the population. Finally, household heads would pay an additional CFA 2.13 (e0.003) for an additional elephant per hectare in protected areas that are far from their crops.

Discussion and Conclusion

The aim of our paper is to determine local households WTP for forest elephant conservation. Our methodological contribution consists of combining OE and closedended CV methods. The above estimates provide four major outcomes with respect to the hypotheses. The extinction of Loxodonta cyclotis may lead to a significant net loss in household welfare. In fact, the predicted monthly WTP by household head is CFA 1,138.17 (e1.74). This value is close to the results found by Bandara et Tisdell (2005). Indeed, they found that respondents in general were willing to pay Rs. 110.17 (e1.65) per month for elephant conservation. The corresponding annual social value that we found is CFA 889.7 million (e1.36 million), and the net present value over 10 years is CFA 8.67billion (e13.2 million). By comparison, despite the expected annual budget of e1.5 million for the Tridom landscape biodiversity conservation project, the completed budget was e0.905 million on average per year between 2007and 2011(Baastel, 2012;;Undp, 2013) 6 . This conservation project concerns slightly more than 9,000 species, including flagship species, such as buffaloes, gorillas, chimpanzees, bongos, otters, Nile crocodiles, giant hornbills, and giant pangolins in all Tridom segments, including Congo. As a matter of comparison, the estimated social value of improving conservation management for local people only, in the Cameroon and Gabon part of the Tridom of one species, the forest elephant, represents 150% of the total biodiversity conservation effective cost. This suggest that the programme is under-funded relative to the social benefits of biodiversity conservation.

The variable resulting from crossing distance and the protected area's elephant density does not influence the households' participation decision (i.e the first hurdle in the Table 3.5 parameterization, the likelihood of participation in EFE conservation). However, household heads who live far from a protected area with high elephant density are willing to pay larger amounts to avoid EFE extinction. The positive sign of this variable tells us that local communities prefer elephants, albeit they prefer them far from their crops. Referring to the second hypothesis, distance as an indicator of scarcity is supported rather than the distance decay assumption. The Tridom Regional Management Unit should promote elephant mobility corridors, and these corridors should be located far from local and indigenous peoples' areas of interest in forests.

As expected in the third hypothesis, indigenousness (i.e., being a Baka pygmy) has a positive and significant influence on household preferences, while the distribution of the mean WTP does not seem to be conditional on ethnicity among the remaining Bantou ethnic groups (figure C.1.3 in appendix). Baka pygmies are likely to pay larger amounts. This information is important, as elephant extinction would severely threaten the spiritual enrichment, cultural identity and way of life of the Baka ethnic minority group.

Furthermore, contrary to the fourth hypothesis, the estimates show that the existence of human-elephant conflicts influences neither the household decision to participate nor the intensity of its preferences. Additionally, households with more access to land are more likely to contribute to the conservation programme. Theoretically, households with more access to land are more likely to face human-elephant conflicts, but the impact of such conflicts is likely to represent a smaller share of their income. Overall, the second effect seems to dominate the first. The relevance of these results is that local and indigenous people feel that their concerns about offsetting human-elephant conflicts are adequately addressed by the hypothetical scenario. This result makes the implementation of the proposed hypothetical scenario very important, as it simultaneously (1) insures the preservation of a public good (EFE) and its ecosystem services and (2) improves private benefits by preventing human-elephant conflicts. Therefore, our study suggests that local communities may be willing to engage in biodiversity preservation when the public benefits of conservation are accompanied by private benefits and when it prevents human-elephant conflicts.

EFE conservation decisions by policy makers should reflect a range of considerations, such as socio-economic and cultural issues, zero-conflict-oriented elephant habitats and optimal harvest rules with less poaching. However, our results cannot disentangle the WTP for EFE conservation from the WTP associated with avoiding human-elephant conflicts. Nevertheless, even if the policy does not succeed in preventing human-elephant conflicts, local demand for elephant conservation is likely to remain non-negligible. Indeed, considering the CFA 28,140 (e43) annual mean damage incurred by 27.7% of the sampled households, the net benefit for the overall population in both the Tridom segments is CFA 3.73 billion (e5.68 millions). This finding suggests that the present benefits of the 10-year hypothetical scenario would be greater than the present value of crop damage caused by elephants. This result suggests that, conditional on the implementation of the hypothetical scenario, EFE conservation is socially beneficial. It may also indicate tolerance by farmers for the presence of EFE in their agricultural fields. Indeed, the underlying assumption of this result is that the 10-year hypothetical elephant conservation programme is effective in terms of both reducing poaching and preventing human-elephant conflicts, thus offsetting the damage faced by local people. A critical issue here is thus the ability of policy makers to cope with objectives that may be partially conflicting.

Overlapping maps of social value with elephant density and human-elephant conflicts represent one way to proceed.

Biodiversity conservation solutions generally involve optimizing trade-off between land use activities and the natural habitat of fauna. Therefore, the issues of where habitat is needed and how it should be managed are at the core of the problem (O'Laughlin, 1998). The Tridom regional manager should proceed with the integration of elephant and large mammals in the national and international mobility corridor (see figure 3.4) in landscape land use planning. To enhance zero-conflict-oriented elephant habitats, corridors should be consistently placed relative to high elephant-concentration zones without crossing into zones with denser human populations. In (Figure 3.5), we propose to set some glades in protected area on the side of highly prevalent human-elephant conflict zones. We also propose an additional segment of the international corridor starting from the Ivondo National Park to Odzala Kokoua National Park via Mwagna National Park with some glades favoring elephant mobility within the natural habitat and far from communities.

The Tridom manager should devote additional effort to reducing poaching. The international standard for management of protected areas is "one eco-guard for 5,000 ha" (IUCN et UNESCO, 2006), while the ratio in the Tridom protected areas is one agent per 6,000 ha to 9,000 ha7 . There is a need for recruiting and training 18 additional guards on average per protected area for a total of 160 guards, and providing them with convenient equipment and setting up surveillance infrastructure.

Considering the average monthly wages (including bonus) of e100, this additional 2) create vehicle and foot transboundary patrols to strengthen cross-border cooperation for anti-poaching surveillance. As introduced in the hypothetical scenario, recruiting young people from the villages and involving them in anti-poaching communication networks remain feasible actions for improving EFE management, as they are low-cost actions relative to the social value of EFE (4%). Our results can also serve as the basis for determining optimal harvest rules for forest elephants while accounting for the social value of EFE in the social planner maximization programme. This important issue requires future research.

General Conclusion

What drives livelihoods strategies of rural households of developing regions? How do those strategies influence land-use choices and the willingness of local population to protect endangered species? The ambition of this thesis is to bring some elements of response on those important research questions.

Our work uses a unique households survey that we conducted for several months, interviewing more than 1000 households in the Dja-Odzala Minkebe Tri-national

Transboundary Conservation Landscape (Tridom-TCL).. The database encompasses information on livelihoods strategies, income, and other socio-economic characteristics.

It also bring information on the households past deforestation behavior. Finally, it has the unique advantage to present data from a choice experiment of protected forest elephant. In other word, the survey we collected presents the rare advantage to provide information on the links between livelihoods, deforestation and preference for biodiversity conservation.

Our contribution is multiple. First, we make a downstream assessment of the choice of livelihoods strategies and their impact on deforestation. Further, we analyze how households are influenced in their perception of wildlife conservation. Our study makes a strong emphasis on the need to analyze those choices taking into account spatial interactions. Indeed, we show that spatial spillovers may represent a strong part of the total effect of the investigated variables.

First, when it comes to livelihoods, we show that the autochthonous status has a strong influence on the choice of forest-based specialized strategies. We also find evidence that financial assets bring incentive for households to specialize in their main activity. It means that when households are less credit constraint, they tend to specialize, which can be good in terms of income and well-being, but this may be at the expense of larger deforestation. We finally show that people using forest-products harvesting tend to deforest less, which let think that increasing the value of NTFP can be a way to protect tropical forests.

Second, we find indeed that households specializing in forest-based activities are the only ones not impacting deforestation. For all the other livelihoods strategies, an increase in family income leads to higher deforestation. This is especially true for strategies that include cash-cropping. Also, households with larger share of auto-consumption tend tu deforest less. Thus, when considering development in its whole process, increase of income, the rise of cash-cropping as an important activity, and market integration, appears to be three potentiel factors of deforestation.

Third, rural households appear to have significant willingness to pay for the protection and conservation of forest elephant in their neighborhood. Yet, this WTP is conditional on the fact that human-wildlife conflicts are avoided. However, in chapter 2, we find evidence that smaller damage costs are related to higher deforestation.

Therefore, when implementing some conservation policy with the view to reduce conflicts between wildlife and rural households, it is very important to monitor land-use change and to let those households participate to the project in order to avoid a bump up in deforestation.

Overall, we find that livelihoods strategies are determined by autochtonous status, financial assets (money transfer and access to loan), distance to market and larger crop looses resulting from human-wildlife conflicts. Further, we show that livelihoods strategies are important for deforestation. Therefore, the commitments to reducing small-scale deforestation may be favored by a good consideration of factors that drive households' livelihoods strategies. We find out that spatial issues seems to be important. Proximity among households yields spatial shift effects and spatial spillover effects that are likely to amplify small-scale deforestation.

:

  Strategies de subsistance des ménages, Déforestation, Conflits homme-faune, Conservation des éléphants de forêt, Paysage, Econometrie spatiale JEL Codes: C11 & 21 & 23 & 24 & 25 & 31; D12; Q12 & 24 & 23 & 29 & 57for analyzing households' livelihoods strategies, deforestation and conservation in the Tridom-TCL . . . . . 2 Study area and location of households surveyed . . . . . . . . . . . . 3 One of group of villages surveyed in Ngoyla-Mintom forest . . . . . . 1.1 Livelihoods strategies on the landscape . . . . . . . . . . . . . . . . . 1.2 Trade-offs in livelihoods strategies . . . . . . . . . . . . . . . . . . . 2.1 Adaptation of deforestation framework to Tridom-TCL case study . . 2.2 Livelihoods and mean deforestation in the landscape . . . . . . . . . 3.1 Ecosystem Services and Total Economic Value (TEV) of Forest Elephants (Adapted from Brahic et Terreaux (2009); MEA (2005)). . . . 3.2 The Study Area. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Aggregate WTP ( * 10 6 CF A ) by division and elephant densities in protected areas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 WTP, Human-elephant conflicts and corridor projects. . . . . . . . . 3.5 Glades proposition and elephants' mobility corridor modification proposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv xvi

Figure 1 :•

 1 Figure 1: Tentative landscape approach for analyzing households' livelihoods strategies, deforestation and conservation in the Tridom-TCL

Figure 2 :

 2 Figure 2: Study area and location of households surveyed

Figure 3 :

 3 Figure 3: One of group of villages surveyed in Ngoyla-Mintom forest

Figure

  Figure 1.1: Livelihoods strategies on the landscape

Figure 1 . 2 :

 12 Figure 1.2: Trade-offs in livelihoods strategies

Figure 2 . 1 :

 21 Figure 2.1: Adaptation of deforestation framework to Tridom-TCL case study

  of CV methods . . . . . . . . . . . . . . . . . . 3.3.2 Survey design . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.3 Theoretical model specification . . . . . . . . . . . . . . . 3.3.4 Econometric model specification . . . . . . . . . . . . . . 3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 Variable Descriptions and Descriptive Statistics . . . . . . 3.4.2 Econometric results . . . . . . . . . . . . . . . . . . . . . 3.5 Discussion and Conclusion . . . . . . . . . . . . . . . . .

Figure 3

 3 Figure 3.1: Ecosystem Services and Total Economic Value (TEV) of Forest Elephants (Adapted from Brahic et Terreaux (2009); MEA (2005)).

Figure 3 . 2 :

 32 Figure 3.2: The Study Area.

i≥ 0 .

 0 In equation (3.8), b lc i , b id i and b OE i are dummy variables indicating the type of interval to which a houshold relates.

Figure 3

 3 Figure 3.3: Aggregate WTP ( * 10 6 CF A ) by division and elephant densities in protected areas.

Figure 3

 3 Figure 3.4: WTP, Human-elephant conflicts and corridor projects.

Figure 3 . 5 :

 35 Figure 3.5: Glades proposition and elephants' mobility corridor modification proposition

Table 1 :

 1 Table 1 displays additional descriptive statistics comparing both the declared and the adjusted values of households deforestation. Descriptive statistics: Declared deforestation VS Ajusted value

		Mean	St. Dev.	Min Max	Percentiles 1% 5% 10% 25% 50% 75% 90% 95% 99%
	Deforestation declaration	4.76 5.39	0	64	0	0	0.5 1.55 3.34	6	10	14	23
	Deforestation ajustement	4.42 5.24	0 56.24	0	0	0.4 1.36 2.88 5.59 9.67 12.99 23.71

Chapter 1 What drives livelihoods strategies in rural areas? Evidence from the Tridom Conservation Landscape using Spatial Probit Analysis Co-author : Philippe Delacote Working paper "Les Cahiers du Lef " Sommaire 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . 1.3 Case study: The Tridom-TCL in the Congo Basin . . .

	1.3.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
	1.3.2 Livelihoods portfolios . . . . . . . . . . . . . . . . . . . .
	1.4

Theoretical Model specification . . . . . . . . . . . . . . .

	1.4.1 Strategic trade-offs in livelihood portfolio selection . . . .
	1.4.2 A simple microeconomic model of livelihoods portfolios .
	1.

5 Spatial Probit Model . . . . . . . . . . . . . . . . . . . . .

	1.5.1 The model . . . . . . . . . . . . . . . . . . . . . . . . . .
	1.5.2 Spatial operator . . . . . . . . . . . . . . . . . . . . . . .
	1.6

What drives livelihoods strategies in the Tridom-TCL?

	1.6.1 Descriptive statistics . . . . . . . . . . . . . . . . . . . . .
	1.6.2 Spatial dependance and sensitivity . . . . . . . . . . . . .
	1.6.3 Coefficient Estimates and Marginal Effects . . . . . . . .
	1.7

Discussion and conclusion . . . . . . . . . . . . . . . . . . Abstract

	Very few scientific studies have focused on the determinants of households' livelihoods
	strategies in the Congo Basin. The aim of this paper is to understand which
	factors drive the choice of portfolio activities in rural regions. More precisely,
	the role of human, financial, natural and location assets in the portfolio choice is
	investigated. A unique dataset is used from our recent survey with 1035 random
	and stratified households in 108 villages of the Tridom landscape to investigate
	household preferences between (1) specialization and diversification strategies, (2)
	land-conversion and non-land-conversion activities, and (3) between strategies relying
	on forest vs other strategies. Our results show significant similarities on the likelihood
	of households living in the same neighborhood to prefer a given livelihoods strategy.
	Beside socioeconomic characteristics, the existence of human-wildlife conflict, as
	well as the indigenousness, directly lead household's heads to make the choice of
	diversified strategies, or to choose activities related to land-conversion. These choices
	lead to some significant spillover effects on the likelihood of neighboring household's
	heads to adopt the same strategies.
	keywords : Forest-based livelihoods, Agriculture and cash crop, Diversification
	strategies, Deforestation, Spatial Spillover Effects, Spatial Autoregressive Probit.
	JEL Codes: C11 & 21 & 25, D12; Q12 & 23

2 Spatial dependance and sensitivity 1.6.2.1 Data generation process and results' accuracy with the weight matrix type

  Financial assets are approximated by loans and money transfers with a mean value of CF A8.66 * 10 3 per month. Regarding the environmental context, households are located on average at 29.3km to the nearest protected area with a standard deviation of 22.58. As an indicator of transport cost and infrastructure assets, the average distance to the subdivision's market is 51.65km with a deviation of 35.7. Female-headed households account for 23% of the sample, and the average age is 48.44 years. The table in appendix (A.2.2) provides a more detail description of these variables with respect to various strategies and trade-offs.

	Table 1.1: Variables and descriptive statistics of overall sample
	Variables	Description of variables	Mean StDev
	Social asset
	Marit_single	Matrimonial status. Dummy (1=Maried)	0.70	0.46
	Hsize	Household size (continuous)	6.50	4.01
	Pygmee_employmt	Pygmies employment (continuous)	1.86	2.95
	CommunityGroup	Community Interest Companies . Dummy (1=yes)	0.28	0.45
	Autochbaka	Indegenouesness . Dummy (1=pygmy. 0=Bantou)	0.05	0.22
	Human Asset
	Age	Household's head age (continuous. in years)	48.44 14.61
	Seniority	Seniority in the village (continuous. in years)	27.01 20.71
	Schoolcycl	Education level . Dummy (1=secondary school)	0.56	0.50
	Naturel asset (access to)
	Land holding	Deforestation per household (in ha)	4.55	5.31
	Landconflict	Land use conflict . Dummy (1=yes)	0.18	0.38
	Human_Wildlife	Damage cost of wildlife conflict (CFA/month)	0.62	1.45
	Financial asset
	Finance_asset	Credit and money fransfert (CFAF/month)	8.66 33.54
	Location asset
	Distance to PA	Distance to the nearest Protected Area (in Km)	29.30 22.58
	Distmarket	Distance to market (in Km)	65.05 58.69
	Other drivers
	Gender	Gender. Dummy (1=Male)	0.77	0.42
	Country	Country. Dummy (1=Cameroon. 0=Gabon)	0.73	0.44
	1.6.

Table 1

 1

		.2: MCMC SAR-Probit estimates
		Specialization	Forest-based	Valuing Forest
		VS	VS	VS
		Diversification	Land-Conversion	Non Valuing Forest
		Estimate Std. Dev Estimate Std. Dev Estimate Std. Dev
	Social assets
	Marit_single(1=Maried)	0.0640 0.107	-0.0310 0.106	-0.191 0.106 *
	Hsize(continious)	-0.0584 0.012 ***	-0.058 0.012 ***	-0.014 0.011
	Pygmee_employmt(cont)	0.0095 0.015	-0.018 0.017	-0.018 0.015
	CommunityGroup(1=yes)	-0.1531 0.098	-0.12 0.109	-0.086 0.102
	Autochbaka(1=pygmy)	0.4552 0.194 **	0.651 0.201 ***	0.762 0.252 ***
	Human assets
	Age(continious)	0.0038 0.003	0.005 0.003 **	0.007 0.003 ***
	Seniority(years in the vlge)	-0.0056 0.002 **	-0.007 0.003 ***	-0.004 0.003
	Schoolcycl(1=2ndschool)	-0.0857 0.087	-0.08 0.095	-0.057 0.095
	Natural assets (access to)
	Landconflict.(1=yes)	0.0631 0.112	0.129 0.119	0.024 0.123
	Human_Wildlife(Damage cost)	0.0497 0.03 *	0.06 0.03 **	0.091 0.033 ***
	Financian asset
	Finance_asset(CFAF)	0.0036 0.002 **	0.001 0.001	-0.001 0.001
	Location assets
	Distance(in Km)	0.0023 0.002	0.001 0.002	-0.001 0.002
	Distmarket (in Km)	0.001 0.001	0.001 0.001 *	0.001 0.001
	Other drivers
	Gender(1=Male)	0.0258 0.095	-0.011 0.102	0.15 0.104
	Country(1=Cameroon)	-0.2464 0.109 **	-0.165 0.114	0.01 0.098
	ρ	0.3616 0.122 ***	0.484 0.094 ***	0.732 0.049 ***
	L_{i}(0.1)	(654. 333)	(721. 266)	(321. 666)
	AIC	1199.268	1064.261	1224.836
	logLik.sarprobi	-583.63	-516.13	-596.41
	N draws	1000	1000	1000
	burn-in	200	200	200

Table 1

 1

	.3: Marginal effect Specialization VS Diversification
		Direct effects	Indirect effects	Total effects
		Mean z(1.96, 5%) Mean z(1.96, 5%) Mean z(1.96, 5%)
	Social asset
	Marit_single(1=Maried)	0.0212 0.7211	0.0133	0.764	0.0345	0.748
	Hsize(continious)	-0.0193 -6.2294 ** -0.0116 -2.0044 ** -0.0309 -3.9114 **
	Pygmee_employement	0.0032 0.7392	0.002 0.7896	0.0051 0.7678
	CommunityGroup(1=yes)	-0.0507 -1.7847	-0.0306 -1.219	-0.0813 -1.7702
	Autochbaka(1=pygmy)	0.1508 2.7952 ** 0.0915 2.2696 ** 0.2423 2.6349 **
	Human Asset
	Age(continious)	0.0013 1.7187	0.0008 1.7912	0.002 1.7292
	Seniority(years in the vlge)	-0.0018 -2.7086 ** -0.0011 -1.5184	-0.0029 -2.4753 **
	Schoolcycl(1=2ndschool)	-0.0283 -1.1339	-0.0164 -0.8347	-0.0447 -1.1135
	Naturel asset (access to)
	Landconflict.(1=yes)	0.021 0.6682	0.0128 0.6911	0.0338 0.6751
	Human_Wildlife(Damage cost) 0.0165 1.9409 *	0.0098 1.9419 *	0.0263 1.9458 *
	Financial asset
	Finance_asset(CFAF)	0.0012 2.6644 ** 0.0007 2.3046 ** 0.0019 2.6551 **
	Location asset
	Distance(in Km)	0.0008 1.6009	0.0004 1.6234	0.0012 1.6066
	Distmarket	0.0003 1.8139	0.0002 1.8315	0.0005 1.8077
	Other drivers
	Gender(1=Male)	0.0085 0.3258	0.0046 0.2816	0.0132 0.3022
	Country(1=Cameroon)	-0.0813 -2.7045 ** -0.0465 -1.5516	-0.1278 -2.8956 **

Table 1

 1

	.4: Marginal effect Non-land-conversion VS Land-conversion
		Direct Effect	Indirect Effect	Total Effect
		Mean z(1.96, 5%) Mean z(1.96, 5%) Mean z(1.96, 5%)
	Social asset
	Marit_single(1=Maried)	-0.009 -0.3605	-0.0084 -0.3319	-0.0174 -0.3451
	Hsize(continious)	-0.0169 -5.427 ** -0.0158 -2.813 ** -0.0327 -4.6673 **
	Pygmee_employement	-0.0052 -1.2117	-0.0049 -1.0165	-0.01 -1.1997
	CommunityGroup(1=yes)	-0.0347 -1.319	-0.0328 -1.0939	-0.0675 -1.2449
	Autochbaka(1=pygmy)	0.1886 3.9571 **	0.179 3.0834 ** 0.3676 3.6718 **
	Human Asset
	Age(continious)	0.0015 2.2978 ** 0.0014	2.222 ** 0.0029 2.2329 **
	Seniority(years in the vlge)	-0.0021 -3.3589 ** -0.002 -2.1831 ** -0.0041 -2.8667 **
	Schoolcycl(1=2ndschool)	-0.0232 -1.0763	-0.0207 -0.9471	-0.0439 -1.0341
	Naturel asset (access to)
	Landconflict.(1=yes)	0.0374 1.2849	0.0352 1.3231	0.0726	1.303
	Human_Wildlife(Damage cost) 0.0174	2.341 ** 0.0162 2.2367 ** 0.0336 2.3308 **
	Financial asset
	Finance_asset(CFAF10 3 )	0.0004 1.1673	0.0003 1.2182	0.0007 1.1754
	Location asset
	Distance(in Km)	0.0002 0.5494	0.0002 0.5213	0.0005 0.5464
	Distmarket	0.0004 2.2866 ** 0.0004 2.2933 ** 0.0008 2.3702 **
	Other drivers
	Gender(1=Male)	-0.0032 -0.1267	-0.0029 -0.1214	-0.0061 -0.1268
	Country(1=Cameroon)	-0.0472 -1.641	-0.0421 -1.5131	-0.0894 -1.7657
	1.6.3.

3 Forest-based vs non forest-based

Table 1 .

 1 5: Marginal effect strategies valuing forest VS Non valuing forest

		Direct effect	Indirect effect	Total effect
		Mean z(1.96, 5%) Mean z(1.96, 5%) Mean z(1.96, 5%)
	Social asset
	Marit_single(1=Maried)	-0.0642 -2.0832 ** -0.1713 -1.8273	-0.2355 -1.9699 **
	Hsize(continious)	-0.0046 -1.4943	-0.0121 -1.3673	-0.0167 -1.451
	Pygmee_employement	-0.0059 -1.3073	-0.0158 -1.2937	-0.0217 -1.3438
	CommunityGroup(1=yes)	-0.0287 -1.0346	-0.0776 -0.9803	-0.1063 -1.0561
	Autochbaka(1=pygmy)	0.2561	3.899 ** 0.6889 3.4181 **	0.945 3.6702 **
	Human Asset
	Age(continious)	0.0023 3.2291 ** 0.0061 3.2878 ** 0.0084 3.2939 **
	Seniority(years in the vlge)	-0.0014 -1.9094	-0.0036 -1.757	-0.005 -1.822
	Schoolcycl(1=2ndschool)	-0.0194 -0.7046	-0.0521 -0.6465	-0.0715 -0.6632
	Naturel asset (access to)
	Landconflict.(1=yes)	0.008 0.2375	0.0225 0.2645	0.0305 0.2547
	Human_Wildlife(Damage cost) 0.0304 3.2818 ** 0.0812 3.2239 ** 0.1116 3.3479 **
	Financial asset
	Finance_asset(CFAF10 3 )	-0.0004 -1.1009	-0.001 -1.0613	-0.0013 -1.0746
	Location asset
	Distance(in Km)	-0.0004 -0.9262	-0.0012 -0.8947	-0.0016 -0.9092
	Distmarket	0.0002 1.0903	0.0006 1.0749	0.0008 1.0843
	Other drivers
	Gender(1=Male)	0.0502 1.7329	0.1351 1.7548	0.1854 1.7539
	Country(1=Cameroon)	0.0037 0.1363	0.0108 0.1428	0.0145 0.1399

Table 2

 2

	Variable	Definition of variables	Mean Std. Dev.
	AGENT'S

.1: Variables and descriptive statistics (N=986 households)

DECISION PARAMETERS Income of households adopting diversification livelihoods strategies (FCFA10 3 )

	2.6.2 Spatial dependence diagnostic
	FamilyIncome_ACF _households	Income of households mixing Agriculture &Cashcrop & Forest	38.96	117.06
	FamilyIncome_AF _households	Income of households mixing Agriculture & Forest	54.97	126.6
	FamilyIncome_CF _CashcropF orest Income of households mixing Cashcrop & Forest	21.28	73.34
	Income of households adopting specialisation livelihoods strategies (FCFA10 3 )
	FamilyIncome_F _F orestbased	Income of households specializing in Forest-based activities	70.18	372.79
	FamilyIncome_A_Agriculture	Income of households specializing in Agricultural	12.39	121.41
	FamilyIncome_C_Cashcrop	Income of households specializing in Cashcrop	8.85	67.93
	Other decision variables
	Autocons_Share (% of total value)	Autoconsumption share in the total income	0.26	0.19
	Capital & factor constraints
	Finance_asset	Credit and money transfer (CFAF/month)	8.67	33.55
	Human_Wildlife (FCFA10 3 )	Damage cost of wildlife conflict (CFA/month)	0.62	1.45
	Landconflict Dummy (1=yes)	Land use conflict, Dummy (1=yes)	0.18	0.38
	HOUSEHOLDS CHARACTERISTICS
	Gender	Gender, Dummy (1=Male)	0.77	0.42
	Age	Household head age (continuous, in years)	48.44	14.61
	Ages_thr	Age centered and squared	213.34	246.72
	Marit_single	Matrimonial status, Dummy (1=Maried)	0.7	0.46
	Hsize	Household size (continuous)	6.5	4.01
	Schoolcycl_2	Education level, Dummy (1=secondary school)	0.56	0.5
	Autochbaka	Indegenouesness, Dummy (1=Pygmy. 0=Bantou)	0.05	0.22
	Seniority	Seniority in the village (continuous, in years)	27.01	20.71
	CommunityGroup	Community Interest Companies, Dummy (1=yes)	0.28	0.45
	Pygmy_employmt	Pygmies employment (continuous)	1.87	2.96
	Labourduration	Working hour per day	5.49	4.39
	CONTEXTUAL VARIABLES
	Country	Country, Dummy (1=Cameroon, 0=Gabon)	0.73	0.44
	Distmarket	Distance to market (in Km)	65.06	58.69
	Distance to P. Area	Distance to the nearest Protected Area (in Km)	29.3	22.58
	Biophysical factor
	Rainfall	The per annum amount of rain that falls (mm)	1638.30	113.66

Table 2 .

 2 2 below displays the Moran coefficient index computed after running the SLM. This statistic tests the existence of the spatial autocorrelation. Except for the Gabriel Graph weight structure, the index value is positive and statistically greater than 0. It appears a positive spatial clustering of deforestation among nearby households in the Tridom-TCL.

Table 2 .

 2

		2: Spatial autocorrelation test
			Global Moran I		Moran I test under randomisation
		Moran I	E(I)	z(I)	P-value Moran I	E(I)	z(I)	P-value
	gabhsld.w	0.0171 -0.0032 0.7060	0.2401	0.1191 -0.0010 4.2133	0.0000
	3NN weight matrix	0.0377 -0.0032 1.7138	0.0433	0.1461 -0.0010 6.2109	0.0000
	4NN weight matrix	0.0339 -0.0032 1.7903	0.0367	0.1396 -0.0010 6.8335	0.0000
	5NN weight matrix	0.0336 -0.0032 1.9876	0.0234	0.1301 -0.0010 7.1017	0.0000
	10NN weight matrix	0.0363 -0.0030 2.9635	0.0015	0.1352 -0.0010 10.2690	0.0000
	17NN weight matrix	0.0217 -0.0028 2.4270	0.0076	0.1218 -0.0010 12.0470	0.0000
	Distance based weight matrix	0.0171 -0.0025 3.1368	0.0009	0.0993 -0.0010 15.4710	0.0000

What are the immediate causes of households' defor- estation in the Tridom-TCL? 2.6.3.1 Robustness check and spatial dependenceTable 2 .

 2 4 displays the estimates' results based on the SLM and four variants of SAR model considering four different types of weight matrix. This result considers only the factors that significantly drive households' deforestation. The table (B.2.1) in appendix display the results from the model with overall variables presented in the subsection 2.6.1 above. Insignificant variables were removed progressively until we got the reduced set of significant variables. The post estimation test (Akaike Information Criterion (AIC), Wald and LR tests) confirm the reduced model as best-suited compared to the full model.

Table 2

 2

	.5 displays the factors that proximately drive households' deforestation in
	the Tridom-TCL. These factors are regrouped into (1) Livelihoods Strategies; (2)
	households characteristics and (3) contextual variables. Variables with insignificant
	coefficients are displayed in the full model in table (B.2.1) in appendix.

DECISION PARAMETERS Income of households adopting diversification livelihoods strategies (FCFA10 3 )

	FamilyIncome_ACF _households	0.0109 0.0013 *** 0.0108 0.0013 *** 0.0106 0.0013 *** 0.0102 0.0013 *** 0.0102 0.0013 ***
	FamilyIncome_AF _households	0.0026 0.0012 **	0.0026 0.0012 **	0.0025 0.0011 **	0.0025 0.0011 **	0.0025 0.0011 **
	FamilyIncome_CF _CashcropF orest 0.0120 0.0021 *** 0.0119 0.0020 *** 0.0116 0.0020 *** 0.0112 0.0020 *** 0.0113 0.0020 ***
	Income of households adopting specialisation livelihoods strategies (FCFA10 3 )
	FamilyIncome_A_Agriculture	0.0029 0.0012 **	0.0029 0.0012 **	0.0029 0.0012 **	0.0029 0.0012 **	0.0028 0.0012 **
	FamilyIncome_C_Cashcrop	0.0186 0.0021 *** 0.0185 0.0021 *** 0.0183 0.0021 *** 0.0180 0.0021 *** 0.0179 0.0021 ***
	Autocons_Share (% of total value)	-2.1269 0.7737 *** -2.1210 0.7667 *** -2.1518 0.7649 *** -2.0922 0.7620 *** -2.1346 0.7633 ***
	Capital & factor constraints
	Finance_asset (FCFA10 3 )	0.0106 0.0043 **	0.0106 0.0042 **	0.0104 0.0042 **	0.0102 0.0042 **	0.0099 0.0042 **
	Human_Wildlife (FCFA10 3 )	-0.2135 0.0979 ** -0.2125 0.0970 ** -0.2143 0.0968 ** -0.2179 0.0965 **	-0.2183 0.0966 **
	HOUSEHOLDS CHARACTERISTICS
	Gender (1=Male)	0.6200 0.3450 *	0.6127 0.3419 *	0.5928 0.3411 *	0.5816 0.3398 *	0.6210 0.3404 *
	Age (continuous. in years)	0.0293 0.0115 **	0.0293 0.0114 **	0.0291 0.0114 **	0.0301 0.0113 *** 0.0303 0.0114 ***
	Ages_thr	-0.0013 0.0006 ** -0.0013 0.0006 ** -0.0013 0.0006 ** -0.0013 0.0006 **	-0.0013 0.0006 **
	Hsize (continuous)	0.1692 0.0379 *** 0.1677 0.0376 *** 0.1669 0.0375 *** 0.1650 0.0374 *** 0.1675 0.0374 ***
	Seniority (continuous. in years)	0.0401 0.0080 *** 0.0402 0.0079 *** 0.0404 0.0079 *** 0.0399 0.0078 *** 0.0390 0.0079 ***
	CommunityGroup Dummy (1=yes)	0.5566 0.3263 *	0.5605 0.3233 *	0.5777 0.3226 *	0.5718 0.3214 *	0.5922 0.3220 *
	Pygmy_employmt (coutinuous)	0.1428 0.0507 *** 0.1462 0.0503 **	0.1522 0.0502 *** 0.1584 0.0500 *** 0.1539 0.0501 ***
	CONTEXTUAL VARIABLE
	Country (1=Cameroun. 0=Gabon)	1.3424 0.3556 *** 1.2738 0.3617 *** 1.0840 0.3722 *** 0.8565 0.3826 **	0.8475 0.4080 **
	R-squared:	0.32
	F-statistic: (16; 969)	28.38	*
	Rho (ρ)			0.0274	0.0892	**	0.1799	***	0.2354	***
	Log Likelihood			-2856	-2855		-2852		-2853
	ML residual σ			4.39	4.38		4.36		4.3667
	AIC Criterion	5750		5751	5748		5742		5744
	Wald Statistic			0.737	3.996	**	10.399	***	7.848	***
	LR test value			0.694	4.207		10.165		8.010
	LM for Residual autocorrelation			0.122	0.262		1.014		0.128
	Observations	986		986	986		986		986
	*, ** and *** = significance level at 1% 5% 10% respectively

Table 2 .

 2 5: Directs, Indirects and Total Effects

	Coeff.	Direct Effects Mean SD	Indirect effects Mean SD	Total effects Mean SD
	AGENT'S

DECISION PARAMETERS Income of households adopting diversification livelihoods strategies (FCFA10 3 )

	FamilyIncome_ACF _households	0.0102 0.0102 0.0012 *** 0.0023 0.0009 *** 0.0125 0.0017 ***
	FamilyIncome_AF _households	0.0025 0.0025 0.0012 **	0.0006 0.0003	0.0030 0.0014 **
	FamilyIncome_CF _CashcropF orest 0.0112 0.0113 0.0020 *** 0.0025 0.0010 **	0.0138 0.0025 ***
	Income of households adopting specialisation livelihoods strategies (FCFA10 3 )
	FamilyIncome_A_Agriculture	0.0029 0.0030 0.0012 **	0.0007 0.0004 *	0.0036 0.0015 **
	FamilyIncome_C_Cashcrop	0.0180 0.0180 0.0021 *** 0.0040 0.0016 *** 0.0220 0.0029 ***
	Autocons_Share (% of total value)	-2.0922 -2.1099 0.7635 *** -0.4726 0.2496 *	-2.5825 0.9487 ***
	Capital & factor constraints
	Finance_asset (FCFA10 3 )	0.0102 0.0102 0.0043 **	0.0023 0.0013 *	0.0125 0.0053 **
	Human_Wildlife (FCFA10 3 )	-0.2179 -0.2172 0.0975 ** -0.0488 0.0297 *	-0.2660 0.1213 **
	HOUSEHOLDS CHARACTERISTICS
	Gender (1=Male)	0.5816 0.5802 0.3326 *	0.1300 0.0944	0.7102 0.4122 *
	Age (continuous, in years)	0.0301 0.0301 0.0113 *** 0.0067 0.0037 *	0.0368 0.0140 ***
	Ages_thr	-0.0013 -0.0013 0.0006 ** -0.0003 0.0002	-0.0017 0.0008 **
	Hsize (continuous)	0.1650 0.1649 0.0369 *** 0.0369 0.0164 **	0.2018 0.0475 ***
	Seniority (continuous, in years)	0.0399 0.0403 0.0080 *** 0.0090 0.0039 **	0.0494 0.0105 ***
	CommunityGroup Dummy (1=yes)	0.5718 0.5729 0.3161 *	0.1281 0.0896	0.7010 0.3914 *
	Pygmy_employement (coutinuous)	0.1584 0.1592 0.0495 *** 0.0356 0.0177 **	0.1948 0.0622 ***
	CONTEXTUAL VARIABLE
	Country (1=Cameroun. 0=Gabon)	0.8565 0.8515 0.3778 **	0.1786 0.0921 *	1.0300 0.4406 **
	*, ** and *** = significance level at 1% 5% 10% respectively

Table 3

 3

	.1: Various specifications of the dependent variable
		Data	First Bid (FB) Secondary Bid (SB)	OE
	"no/no" and WTP ≥ 0	a OE i	a s ij	a l ij

Table 3 .

 3 2 describes the independent variables used in the various econometric models, as well as the descriptive statistics. Of the 1,035 household heads surveyed, 99 (9.6%) were excluded as protesters, leaving a sample of 936 of non-protesters. The Baka indigenous group, an ethnic minority group, represented 5% of the households. Their traditional way of life is closely linked to the elephant's existence. The main activity or type of land use may also influence a household's preferences for forest elephant conservation. Of the 936 respondents, 19% produce cash crops (e.g., cocoa) as their main use of land, while 41% are small-scale farmers producing crops for subsistence and small-scale trading. Only 3% of households use forest land for traditional gold mining. Using the forest for hunting and gathering was reported by 15% of the sample, while 3% work in biodiversity conservation organizations, forest administration, or forest concession management. Among the remaining respondents, 9% work for other administrations, and 10% engage in animal husbandry, work in fisheries, or engage in trade. Experiencing conflicts with elephants may have a negative impact on households welfare and thus on households preferences for EFE conservation. Indeed, conflicts have been reported by 259 households (28%), with approximately CFA 28,140 or 123 e43 of damages reported per household. This cost is rigorously calculated, as it is typically registered by households that faced conflict in obtening compensation from decentralized administrations. To date, no household has received compensation.

		Table 3.2: Descriptive statistics
	Variable	Description of household head characteristics	Without protesters (n=936)	Protest bidders (n=99)	Overall obs. (n=1,035)	Comparison test
			Mean (Std)	Mean (Std)	Mean(Std)	Chi 2 (1) <3.84 [ t-test (5%, 1,033)] <1.96
	GENDER	1 if male, 0 otherwise	0.76 (0.42)	0.77 (0.42)	0.76 (0.42)	0.007
	AGE	age in years	48.29 (14.68)	50.79 (13.52)	48.53 (14.59)	[-14.07]
	HSIZE	household size	6.43 (4.05)	7.02 (3.90)	6.49 (4.04)	[-0.0194]
	EDUCATION LEVEL	1 if at least secondary school, 0 otherwise	0.55 (0.50)	0.68 (0.47)	0.56 (0.50)	6.21
	MONTHLY EXP	monthly expense	46,604 (59,463)	59,792 (68,242) 47,865 (60,446)	[-2.40E+19]
	INDIGENOUSNESS	1 if indigenous BAKA (Pygmies), 0 otherwise	0.05 (0.22)	0.03 (0.17)	0.05 (0.21)	0.77
	SMALL FARMER	1 if small scale farmer, 0 otherwise	0.41 (0.49)	0.42 (0.50)	0.41 (0.49)	0.09
	TRAD GOLD MINER	1 if traditional gold miner, 0 otherwise	0.03 (0.16)	0.00 (0.00)	0.03 (0.16)	2.82
	HUNTER GATHERER	1 if hunter-gatherer, 0 otherwise	0.15 (0.36)	0.09 (0.29)	0.15 (0.36)	2.81
	FMU OR FOREST AD	1 if works in the forest administration or a FMU	0.03 (0.18)	0.02 (0.14)	0.03 (0.18)	0.48
	OTHER ADMIN	1 if works in other administration	0.09 (0.28)	0.10 (0.30)	0.09 (0.29)	0.16
	HUM/ELEPH CONFLICT 1 if human-elephant conflict, 0 otherwise	0.28 (0.45)	0.23 (0.42)	0.27 (0.45)	0.88
	LAND HOLDINGS	land area (ha) ownership	4.32 (5.32)	5.36 (4.32)	4.42 (5.24)	[-0.09.73]
	DIST_NAREA	distance to the nearest protected area (km)	28.98 (22.26)	27.60 (22.14)	49.06 (493.20)	[40.17]
	ELEPHANT DENSITY	elephant density of the nearest protected area	0.94 (0.84)	0.83 (0.72)	0.93 (0.83)	[0.01.51]

Table 3

 3 = Starting bid; a l = Lower bid; a u =Upper bid preferences. The OE format assigned to the respondent after the DBDC yielded 578 positive stated WTP values, that is, for 61.75% of the respondents. As a corollary, the level of information obtained about forest community preferences improved considerably in two ways. Among the household heads responding "no/no" to the

				.3: Answers to bids
	Bid cards a s /a l /a u1	Bid card Statistics Freq. Perc.	Yes to a s	No to a s	Answers to bids YY NY YY NY Perc. (%) YN NN YN NN
	1,000/500/1,500	191	20% 46% 54% 56	15 5.98 1.60
							31	89 3.31 9.51
	1,500/1,000/2,000 161	17% 47% 53% 53	5	5.66 0.53
							22	81 2.35 8.65
	2,000/1,500/2,500 148	16% 34% 66% 33	14 3.53 1.50
							18	83 1.92 8.87
	2,500/1,000/3,000 163	17% 33% 67% 40	5	4.27 0.53
							13	105 1.39 11.22
	3,000/1,500/3,500 115	12% 38% 70% 31	10 3.31 1.07
							3	71 0.32 7.59
	3,500/2,000/4,000 158	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
		1 a s Table 3.4: Dataset description (percentages)
		DBDC standard IRM	Tobit	Double-hurdle model	IRM + point data
		Total (1,035) Without protesters (936)	First hurdle Second hurdle IRM 1	IRM 2
	Left-censored data	60.4		56.2	38.20	-		0.00	2.90	11.10
	Right-censored data	23		25.4	0.00	-		0.00	0.00	0.00
	Interval data	16.6		18.4	0.00	-		0.00	18.20	18.20
	Point data	0		0.00	61.80	100	61.80	79.00	70.70
	Truncated at zero	-		-	0.00	-		38.20	-	-
	Total	100		100	100	100	100	100	100

Table 3

 3

				.5: Coefficient estimates
		CLOSE-ENDED MODELS	OPEN-ENDED -CORNER SOLUTION MODELS	COMBINING DBDC and OE
	Predictors	DBDC Overall sample	DBDC without protesters	Tobit model	Heteroscedastic double-hurdle estimates 1 st hurdle 2 nd hurdle Het.	IRM 1 + effective negative utility	IRM 2 + potential negative utility
		Coef. (Std)	Coef. (Std)	Coef. (Std) Coef. (Std) Coef. (Std)	Coef. (Std)	Coef. (Std)	Coef. (Std)
	AGE	-28.476*** (8.938)	-24.444*** (8.537)	-22.153*** (7.482)	-0.008** (0.003)	16.136 (15.671)	-20.297** (8.669)	-13.373*** (4.786)	-15.855*** (5.325)
	EDUCATION LEVEL	814.450***	966.276***	639.181***	0.296***	2,204.333*** -678.365***	337.981***	405.907***
		(261.024)	(251.769)	(197.365)	(0.092)	(643.792)	(247.065)	(117.385)	(130.888)
	MONTHLY EXP	0.007***	0.009***	0.00784***	6.00E-07	-0.005	0.022***	0.007***	0.006***
		(0.002)	(0.002)	(0.002)	(8.00E-07)	(0.011)	(0.005)	(0.001)	(0.001)
	INDIGENOUSNESS	1,678.716***	1,684.426***	881.164**	0.417**	2,802.750*** -1,002.568***	580.169***	629.073***
		(536.186)	(512.805)	(347.409)	(0.210)	(666.533)	(283.159)	(209.415)	(225.923)
	SMALL FARMER	66.121	53.891	181.726	0.126	-	-	53.789	84.957
		(289.733)	(281.481)	(218.536)	(0.103)			(142.599)	(152.844)
	TRAD GOLD MINER	2,516.476***	1,993.225***	1,776.781**	0.660**	-	-	1,310.554**	1,385.737**
		(774.178)	(722.910)	(686.945)	(0.305)			(595.34)	(611.959)
	HUNTER GATHERER	497.433	419.813	406.523	0.195	-	-	209.180	224.920
		(388.632)	(373.119)	(280.632)	(0.141)			(177.173)	(191.492)
	FMU OR FOREST AD	1,880.433	1,665.085***	1,408.703***	0.962***	-	-	767.973**	849.319**
		(665.502)**	(639.716)	(456.845)	(0.304)			(378.348)	(381.100)
	OTHER ADMIN	1,102.013	1,012.012**	868.687**	0.418**	-	-	472.233*	514.322*
		(450.792)	(440.923)	(355.409)	(0.177)			(274.521)	(291.193)
	LAND HOLDINGS	469.118*	569.682**	589.087***	0.177*	-8,663.312	2,960.953*	350.103***	360.581**
		(264.529)	(257.990)	(211.915)	(0.096)	(7,696.3)	(1,642.55)	(133.111)	(143.537)
	HUM-ELEPH CONFLICT	-43.790	-61.491	48.305	-0.013	-1,394.463	905.122**	-131.466	0.664
		(267.087)	(257.591)	(202.818)	(0.098)	(1,231.3)	(452.839)	(138.767)	(139.280)
	DISTANCE*DENSITY	2.152	0.942	-0.763	-0.001	7.398**	0.315	-0.342	-0.588
		(2.509)	(2.421)	(1.911)	(0.001)	(2.890)	(2.409)	(1.153)	(1.279)
	INTERCEPT	334.306	391.343	336.557	0.298	-1,956.741	2,553.671***	1,132.088***	1,054.191***
		(561.368)	(535.129)	(393.581)	(0.196)	(1,203.5)	(582.020)	(286.764)	(301.033)
	/lnsigma	-	-	-	-	-	-	7.518***	7.580***
								(0.133)	(0.131)
	SIGMA
	_CONS	3,017.586	2,814.215	2,582.161	-	-	-	1,841.317	1,959.043
		(198.43)	(183.290)	(314.105)				(244.970)	(257.955)
	Number of obs	1,035	936	936	936	578		936	936
	Left-censured	625	526	358	-	-

Table 3 .

 3 6. The conditional and unconditional average partial effects on the intensity of participation are derived from the second hurdle; see the second and third columns, respectively, of Table3.6.

Table 3 .

 3 6: Conditional and Unconditional Partial Effects (CFA)

	Predictors	Partial effect on prob(participation)	Conditional average partial effect on E(WTP)	Unconditional average partial effect on E(WTP)
	AGE	-0.003	4.648	-3.036
	EDUCATION LEVEL	0.107	635.031	615.652
	MONTHLY EXP	2.29E-07	-0.001	0.0004
	INDIGENOUSNESS	0.151	807.42	813.368
	SMALL FARMER	0.046		-1,431.913
	TRAD GOLD MINER	0.239		-260.821
	HUNTER GATHERER	0.071		0.491
	FMU OR FOREST AD	0.348		93.277
	OTHER ADMIN	0.152		487.291
	LAND HOLDINGS	0.064	-2,495.752	143.959
	HUM-ELEPH CONFLICT	-0.005	-401.721	709.561
	DISTANCE*DENSITY	-4.14E-04	2.131	308.764
	INVERSE MILLS RATIO		1.344

A.1.3 Trade-offs in livelihoods, Deforestation and Per Annum Yiels/ha

	A.2.3 Standard Probit VS SAR-Probit A.2.
	8,00		Standard Probit Model		SAR-Probit Model
	7,00		Estimates	Marginal	Estimates	Direct	Indirect	7,14 Total
	6,00 5,00 Country		5,26 -0.4210 ***		5,57 -0.1418 -0.2464 ** effect	effects -0.0813 -0.0465 -0.1278 effects effects
	3,16 Marit_single Age 4,00 3,00 Gender 2,00	3,25	0.0025 0.0180 0.0604	2,09		1,78 0.0008 0.0038 3,12 0.0061 0.0258 1,76 0.0203 0.0640	2,24	3,30 0.0013 0.0085 0.0212	0.0008 0.0046 2,40 0.0133	0.002 2,64 0.0132 0.0345
	1,00 Hsize		1,03 -0.0596 ***	0,40	0,40 -0.0201 -0.0584 *** -0.0193 -0.0116 -0.0309 0,73 0,37
	0,00 Pygmee_employt	0.0098			0.0033 0.0095		0.0032	0.002	0.0051
	Specialization CommunityGroup -0.1440 Diversification	Forestbased -0.0485 -0.1531 Land conversion activities -0.0507 -0.0306 -0.0813 Strategies with at least 30% income Other
	Seniority		TRADE-OFF I -0.0058 **		TRADE-OFF II -0.0019 -0.0056 **	from forest TRADE-OFF III -0.0018 -0.0011 -0.0029
	Autochbaka		Deforestation (ha) 0.4056 **	Value (CFA10^6) 0.1366 0.4552 ** Per Annum Yield(CFA10^6/ha) 0.1508 0.0915	0.2423
	Schoolcycl		-0.1045			-0.0352 -0.0857		-0.0283 -0.0164 -0.0447
	Landconflict.		0.0733			0.0247 0.0631		0.021	0.0128	0.0338
	Human_Wildlife	0.0476			0.0160 0.0497 *		0.0165	0.0098	0.0263
	Distance		0.0032 *		0.0011 0.0023		0.0008	0.0004	0.0012
	Distmarket		0.0016 **		0.0005 0.0010		0.0003	0.0002	0.0005
	Finance_asset	0.0030 *		0.0010 0.0036 **	0.0012	0.0007	0.0019
	ρ		-				0.3620 **

4 results' accuracy with the weight matrix type

	B.1.
			Specialisation vs Diversification
		Gabgrah	3NN	5NN	10NN	Distance
	ρ	0.0501	0.2322*** 0.2974*** 0.3682*** 0.3616**
	AIC Criterion	1200.15 1194.85	1194.12	1190.58	1199.26
	logLik.sarprobit -584.07	-581.42	-581.06	-579.29	-583.63
	A.2.5 Variance Inflation Factor
	Variable			VIF 1/VIF
	Age			1.56	0.64
	Country			1.51	0.66
	Seniority			1.40	0.71
	Marit_single			1.30	0.77
	Distmarket			1.26	0.79
	Schoolcycl			1.22	0.82
	Pygmee_employment		1.13	0.88
	Autochbaka			1.13	0.89
	Distance to nearest Protected Area	1.12	0.89
	Hsize			1.09	0.92
	Gender			1.09	0.92
	CommunityGroup			1.08	0.93
	Landconflict			1.05	0.95
	Financial Asset			1.05	0.96
	Human_Wildlife ConflictDamage cost 1.04	0.96
	Mean VIF			1.20

2 Livelohoods Strategies and Per Annum Yiels/ha

	B.2 Tables	C.
		1,855 B.2.1 Full Spatial Autoregressive Model 0,771 2,763 2,911 0,528 0,835 0,113 0,361 0,241 Agriculture Cashcrop + Forest Agriculture + Cashcrop +Forest Cashcrop (cofee + rubber) SPATIAL AUTOREGRESSIVE FULL MODEL 3,932 Coef. SD z value Pr(>|z|) (Intercept) -4,1857 2,2804 -1,8355 0,0664 * AGENT'S DECISION PARAMETERS Income of households adopting diversification livelihoods' strategies (FCFA10 3 ) FamilyIncome_ACF _households 0,0101 0,0013 7,905 0 *** FamilyIncome_AF _households 0,0024 0,0012 2,0399 0,0414 ** Land convertion Portfolio FamilyIncome_CF _CashcropF orest 0,0109 0,0021 5,326 0 *** Agricultural + Forest based Income of households adopting specialisation livelihoods' strategies (FCFA10 3 )
	Forest	Forest based livelihoods FamilyIncome_F _F orestbased FamilyIncome_A_Agriculture based FamilyIncome_C_Cashcrop	3,125 -0,0002 0,0004 -0,4287 0,6682 1,756 0,0026 0,0012 2,2638 0,0236 ** 0,0178 0,0021 8,3798 0 ***
		Other decision variables	0	0,5	1	1,5	2	2,5	3	3,5	4	4,5
		Per annum Yield ((CFA10^6)/ha) Autocons_Share (% of total value) -2,2003 0,7772 -2,8308 0,0046 *** Mean income (CFA10^6) Capital & factor constraints
		Finance_asset (FCFA10 3 )		0,0089 0,0043 2,0988 0,0358 **
		Landconflict Dummy (1=yes)		0,33 0,3763 0,8771 0,3804
		Human_Wildlife (FCFA10 3 )		-0,2033 0,0978 -2,0785 0,0377 **
		HOUSEHOLDS CHARACTERISTICS
		Gender (1=Male)		0,6824 0,3508 1,9451 0,0518 *
		Age (continuous. in years)		0,0208 0,0121 1,7115 0,087 *
		Ages_thr		-0,0014 0,0006 -2,2333 0,0255 **
		Marit_single (1=Maried)		0,42 0,3485 1,2051 0,2282
		Hsize (continuous)		0,1582 0,0376 4,2037	0 ***
		Schoolcycl_2 (1=secondary school)	-0,1703 0,314 -0,5423 0,5876
		Autochbaka (1=Pygmy. 0=Bantou)	-0,5574 0,6761 -0,8244 0,4097
		Seniority (continuous. in years)		0,0391 0,008	4,859	0 ***
		CommunityGroup Dummy (1=yes)	0,524 0,3235		1,62 0,1052
		Pygmy_employement (coutinuous)	0,1518 0,0504 3,0118 0,0026 ***
		Labourduration		-0,0483 0,0319 -1,5107 0,1309
		CONTEXTUAL VARIABLES
		Country (1=Cameroun. 0=Gabon)	0,8112 0,4593 1,7661 0,0774 *
		Distmarket Km		-0,0037 0,0029 -1,2952 0,1952
		Distance to P. Area (Km)		0,0006 0,0066 0,0903 0,928
		Biophysical factor
		Rainfall		0,0014 0,0014 0,9974 0,3186
		Rho (ρ)		0.1947	**
		Log Likelihood		-2849
		ML residual σ		4.350
		AIC Criterion		5754
		Wald Statistic		4.8115	**
		Observations		986
		residual autocorrelation		0.0728
		*, ** and *** = significance level at 1% 5% 10% respectively
				167

1 Figures C.1.1 Bid structure C.2 Tables C.2.1 Ethnic Representativeness (without protesters)

		C.2.2 Spatial Representativeness
		Division	Subdivision	Freq. Perc. Cum.
	CAMEROONIAN SEGMENT
			Sangmelima		5.02	5.02
			Freq. Perc. Meyomessala	Cum. 5.66 10.68
	Baka Fang-Beti-Bulu DJA ET South LOBO	Bengbis Meyomessi Djoum	47 369	5.02 39.42 44.44 2.35 13.03 5.02 2.88 15.91 5.13 21.04
	Bangando	Oveng	34	3.63	48.08 3.53 24.57
	Kota et Bakota MVILLA	Mintom Mvangan	64	6.84	1.82 26.39 54.91 2.14 28.53
	Mahongwe	Ngoyla	40	4.27	59.19 6.2	34.73
	Kounabembe	Lomie	20	2.14	61.32 5.56 40.29
	Mvong Mvong et Mpumpong HAUT Messamena 22 NYONG Somalomo	2.35	3.42 43.71 63.68 4.59 48.3
	Djem Est		Dja	65	6.94	70.62 3.42 51.72
	Badjoue	Messock	66	7.05	77.67 4.59 56.31
	Migration, Yambassa, Bamoun... 59 BOUMBA Mouloundou ET Yokadouma	6.30	4.91 61.22 83.97 5.34 66.56
	Nzime	NGOKO	61 Salapoumbe	6.52	90.49 2.67 69.23
	Other ethnic groups (20) GABONESE SEGMENT	89	9.51	100.0
	Total Ogooue Ivondo	IVINDO LA ZADIE	936 Makebe Bakouaka Makokou Batouala Mvadhi Mekambo	100.00	3.1 2.14 74.47 72.33 1.5 75.97 2.14 78.11 4.49 82.6
		LA LOPE	Booue			3.21 85.81
		LA MVOUNG Ovan			2.03 87.84
	Woleu Ntem	HAUT-NTEM Minvoul WOLEU Oyem OKANO Mitzic			3.74 91.58 5.98 97.56 2.46 100
		Total			936	100

The Congo Basin Forest Partnership (CBFP) was launched as a multi-stakeholder partnership (with 70 partners, including African countries, donor agencies and governments, international organizations, NGOs, scientific institutions and the private sector) at the

World Summit on Sustainable Development in Johannesburg, South Africa. It aims to enhance natural resource management and improve the standard of living in the Congo Basin ( http://www.pfbc-cbfp.org/)

Yea-sayers and Nay-sayers are respondents who try to please or to counter, respectively, the interviewer without considering the specific amount they are asked about(Carson et Hamenann, 2005;[START_REF] Frew | Benefit assessment for cba studies in healthcare using cv methods[END_REF].

A livelihood comprises the capabilities and activities required for a means of living. A livelihood is sustainable when it can cope with and recover from stress and shocks, maintain or enhance households' capabilities and assets, and provide sustainable livelihood opportunities for the next generation(Chambers et Conway, 1992). However, in this paper, the term livelihood is restricted to subsistence goods and services from small-scale farming and from forest-based activities.

The PEN is an initiative with more than 50 research partners that built a dataset containing detailed socio-economic data collected quarterly. It contains more than 8000 households at the village level in 40 study sites from 25 developing countries. (http://www1.cifor.org/pen)

Seniority is the quality of households that has settled long ago in the village.

The Congo Basin Forest Partnership (CBFP) was launched as a multi-stakeholder partnership (with 70 partners, including African countries, donor agencies and governments, international organizations, NGOs, scientific institutions and the Tridom-TCL private sector) at the 2002 World Summit on Sustainable Development in Johannesburg, South Africa. It aims to enhance natural resource management and improve the standard of living in the Congo Basin (http://www.pfbccbfp.org/)

The sample size required at a confidence level of 95% (typical value of 1.96) is 384.

The aggregate population size in both segments of the Tridom-TCL is 418,855 inhabitants(Bucrep, 2010; Gabon, 2010). Considering the mean household size of the sample (6.43), the number of households is around 65,140.

According to Lancaster's theory, consumers derive their utility from the attributes that describe the product.

Compared to the usual dense matrix, a sparce matrix stores only non-zero elements. It generates economy in terms of memory, it reduces the complexity of systems' resolutions and greatly speed up the calculations(Erhel, 2014).

In 2014, year of the field work, CFAF1 = $0, 0021)

However, this result may be driven by the presence of cash crops in the diversified portfolios. This would require further investigations in terms of land-use analysis.

A binding regime or more secure property rights motivates efficient resources management by landowners(Angelsen et Kaimowitz,

2001).

The forest sector in Congo Basin Countries is divided into (i) 'Permanent Forest Estate', that includes logging concessions, ought to remain forest and mandated to maintain the biological diversity, and (ii) 'Non-Permanent Forest Estate', that can be turned to alternative use including sustainable agriculture.

The five types of variables used in the 140 models of deforestation are : (1) The magnitude and location of deforestation; (2) the agents of deforestation, namely, individuals, households, or companies involved in land use change and their characteristics; (3) the choice variables (decisions about land allocation that determine the overall level of deforestation for the particular agent or group of agents); (4) Agents' decision parameters and (5) macroeconomic variables and policy instruments affecting forest clearing indirectly through their influence on the decision parametersAngelsen et Kaimowitz (1999).

Annex I countries parties to the Convention includes the OECD countries and economies in transition that accepted to returning individually or jointly to their 1990 levels of greenhouse gas emissions by the year 2000. The other countries are referred to as Non-Annex I countries.

In 2014, year of the field work, $ CF AF 1 = $0, 0021)

There are two elephant subspecies, the forest elephant and the savannah elephant. This paper focuses on the forest elephant.

CITES stands for Convention on International Trade in Endangered Species of Wild Fauna and Flora Signed in Washington, D.C., on

March 1973

This emerging policy aims to Reduce Emissions from Deforestation and Forest Degradation (REDD+). It aims to create financial value for the carbon stored in forests, offering incentives for developing countries to reduce emissions from forested lands and invest in low-carbon paths to sustainable development. REDD+ goes beyond deforestation and forest degradation and includes the role of conservation, the sustainable management of forests and improvement of forest carbon stocks.

Yea-sayers and nay-sayers are respondents who try to please or to counter, respectively, the interviewer without considering the specific amount they are asked about(Carson et Hamenann, 2005; Frew, 2010b).

The motives offered by these respondents include "elephants are a cost for me because they destroy my crops".

The total project disbursements amounted to $6.03 million between 2008 and 2012, including $4.08 million between 2008 and 2011 and $1.95 million in 2012, yielding an annual disbursement of $1.2 million. Given the exchange rate prevailing in 2014(reference date for the social value), this amount is equivalent to e0.905 million.

approximation fromRapac (2014)

[START_REF] Zeiler | The willingness to pay/willingness to accept gap, the endowment effect, subject misconceptions and experimental procedures for eliciting valuations[END_REF]. The willingness to pay/willingness to accept gap, the endowment effect, subject misconceptions and experimental procedures for eliciting valuations. American Economic Review.

Acknowledgements (1/2) Acknowledgements (2/2) Acknowledgements This paper has been presented at the AERC Biannual Research Workshop on May 29 -June 2, 2016, in Nairobi, Kenya. This paper will be presented at the next AERC biannual research Workshop on December 4-8, 2016 in Dakar, Senegal. I am grateful to the AERC for its financial support including complete fundings for 2 AERC's biannual workshop in Nairoby and Senegal. The Laboratory of Forest Economics contributes to the Labex ARBREANR-11-LABX-0002-01. This research is part of the CIFOR-GCS [Center for International Forestry Research-Global Comparative Study] project with funding provided by the Norwegian Agency for Development Cooperation (NORAD), grant no. QZA-12/0882. This research is part of the CGIAR research program on Forests, Trees, and Agroforestry (FTA). I am very grateful to the French Government (SCAC services) for its financial support to my thesis.

Acknowledgements

This paper was presented at the first Annual FLARE Network Conference 27 -30 Nov 2015, Musée de l'Homme, Paris, France. This paper was presented at the ISEE-2016 Annual Conference, Washington DC, June 30 -July 04, 2016. I thank the discussants of both conferences for usefull comments. This research is part of the CIFOR-GCS [Center for International Forestry Research-Global Comparative Study] project with funding provided by the Norwegian Agency for Development Cooperation (NORAD), grant no. QZA-12/0882. This research is part of the CGIAR research program on Forests, Trees, and Agroforestry (FTA). I am very grateful to the French Government (SCAC services) for its financial support to my thesis. The Laboratory of Forest Economics contributes to the Labex ARBREANR-11-LABX-0002-01. Acknowledgements This paper has been presented at the 27 th International Congress for Conservation Biology (ICCB) and the 4 th European Congress for Conservation Biology (ECCB) in Montpellier, France 2-6 August 2015 in the Conservation and Ecological Economics Discipline. This paper has been presented at the 2 nd Annual Conference of the French Association of Environmental and Resource Economists (FAERE), Toulouse Business School 10-11 September 2015.

We thank Sylvain Caurla and the Editing Team of the American Journal Expert, precisely Asia K. and Ana G. for editing service. We would like to thank the four anonymous reviewers for their helpful suggestions and comments that helped improve our paper.

We are grateful to Richard Suffo Kankeu for GIS assistance.

We thank Iason, Sophia, Geraldine, Lea and Claudio for their good comments and suggestions. We thank Bruno Jeandidier and Sabine Chaupain-Guillot for their good Comments on the survey questionnaire. This research is part of the CIFOR-GCS [Center for International Forestry Research-Global Comparative Study] project with funding provided by the Norwegian Agency for Development Cooperation (NORAD). I am very grateful to the French Government and its Department for Cultural Action and Co-operation (SCAC)-French Embassy in Cameroon for its financial support for my thesis. The Laboratory of Forest Economics contributes to the Labex ARBRE ANR-11-LABX-0002-01.

Abstract

This paper seeks to determine and characterize social and cultural preferences for the conservation of endangered forest elephants (EFEs) in the Congo Basin's Tridom Landscape. Using unique data from a stratified, random, face-to-face survey with 1,035 households in 108 villages in 2014, we combine double-bounded dichotomous choice with open-ended elicitation formats to assess the willingness-to-pay (WTP) for EFE conservation. We find that local households are willing to pay CFA 1,139.4 (e1.74) per month to prevent EFE extinction. This totals CFA 753.9 million (e1.15 million) per year for all inhabitants. Indigenousness positively influences the WTP for EFE conservation. Spatial data suggest that local communities prefer that elephants remain far from their crops. The existence of human-elephant conflicts has a neutral effect on preferences for EFE conservation. Therefore, our study suggests that local communities would engage in biodiversity preservation when the public benefits of conservation are accompanied by private benefits, such as human-elephant conflict avoidance.

Keyword: Forest Elephant Extinction, Indigenous People, Contingent Valuation, WTP, Interval Regression Model, Double-Hurdle Model.

JEL Classification : Q 57, 29, C24 will then be willing to pay if the following holds:

A household would be willing to contribute to EFE conservation if doing so provides greater utility than not paying. Therefore, the maximum W T P i of household head i can be expressed in equation (3.4): We also argue that, cross-cutting solutions towards a sustainable landscape considering these three side issues involve optimizing trade-off between households' livelihoods strategies, forest and the natural habitats of fauna. Therefore, the issues of community land security, of where natural habitat is needed and of how it should be managed are at the core of the problem. For example, decision-makers should proceed with the integration of large mammals mobility corridors far away from the community settlements in order to enhance zero-conflict-oriented elephant habitats;

corridors should be consistently placed relatively to high elephant-concentration zones without crossing into zones with denser human populations.

Many more work can be done using our household survey. For instance, risk

Ricinodendron Heudelotti

Ndjassang 5-liter pail 1 pail=7,5kg 1 bag=90kg Human-Wildlife ConflictDamage cost v11 -0,03 -0,07 -0,03 1,00 Seniority v12 0,47 0,10 -0,01 -0,02 1,00

Gnetum africanum

Distance to nearest Protected Area v13 0,10 -0,02 -0,03 0,00 -0,03 1,00 Distmarket v14 -0,13 -0,04 0,09 0,01 -0,02 -0,29 1,00

Financial Asset v15 0,03 0,03 0,00 -0,02 -0,10 0,06 -0,09 1,00 160