Agnès Carcenac

Annie Cohade

Annie Cuer

Je Remercie Également

Laurent Gazull

Alejandro Guevara-Sanginés

José Alberto Lara-Pulido

Carlos Muñoz-Piña

Roberto Sanginés

Laura Brimont

Sébastien Desbureaux

Par

Céline, Emilie, Eric, Guo, Joël, Manu, Hervé, Laurène, Laurent, Pierre, Sékou, Sébastien, Sophie, Timothée Pierrick-Victor Je Remercie Également Ariane

Keywords: Analyse d'Impact, Conservation de la Forêt, Economie de l'Environnement, Environmental Economics, Forest conservation, Impact evaluation, Mexico, Payments for Environmental Services

En premier lieu, je souhaite remercier les membres du jury. Je remercie Pascale Combes-Motel d'avoir accepté de présider ce jury ainsi que Jean-Michel Salles et Serge Garcia d'avoir accepté de rapporter ma thèse. Je remercie également les deux suragants Alain Karsenty et Céline Dutilly. Leurs remarques me seront précieuses pour améliorer la qualité de mon travail.

Il est souvent dit qu'une thèse est un travail dicile et solitaire. Si je souscris volontiers à la première armation, je rejette formellement la deuxième puisque, dans mon cas, cette thèse aura été une traversée sans escale mais avec équipiers.

Je tiens ainsi à remercier ma directrice, Catherine Araujo-Bonjean, pour ses conseils avisés qui m'ont souvent empêché de dériver avec la marée au cours de ces cinq années, la rigueur dans la rédaction que m'ont apporté ses coups de crayon rouge et pour m'avoir soutenu dans cette aventure. Je souhaite également remercier Céline Dutilly pour son encadrement, notamment lors de la mise en place du terrain, et la passion de la recherche qu'elle a su me transmettre. Cette thèse n'aurait pas abouti, voire levé l'ancre, sans la présence attentive de mes deux encadrantes.

Je suis par ma formation et mes origines un Cerdien 100% pur beurre, j'adresse donc mes remerciements les plus chaleureux à l'institution qui m'a vu grandir. Je remercie tous mes professeurs et en particulier Jenny Aker, Claudio Araujo et Francesca Marchetta qui ont par la suite accepté de me coner des enseignements au CERDI. Tout au long de ma formation, j'ai pu bénécier d'excellentes conditions de travail en grande partie grâce à la gentillesse et la disponibilité de tout le personnel administratif avec une pensée particulère pour Bruno Barbosa, Martine Bouchut qui est aux doctorants en détresse informatique ce que la SNSM est aux naufragés en mer,

List of Acronyms

General Introduction L'environnement est l'envers imaginaire de la Nature, parée de tous les attraits de l'harmonie, de l'équilibre. Dans l'imaginaire collectif, la nature est propre, l'environnement est sale: ne parle-t-on pas, dans le même temps, des équilibres de la nature et des déséquilibres environnementaux? Tout se passe comme si la nature était d'essence divine, l'environnement étant création des hommes.

Le millénaire touche à sa n, engendrant des peurs.

Ce n'est plus, comme en l'An Mil, la comète qui nous tombera dessus, mais c'est encore la n du monde qui nous est promise : cette fois, les hommes eux-mêmes seraient, nous dit-on, les artisans de leur propre perte." Jacques Weber (1995) 11.1 Ecosystem services: A redenition of the interdependencies between human and natural capital Social costs and internalization of externalities, pollution and abatement or compensation, natural capital and sustainability, tragedy of the commons and socioecological systems, prisoner's dilemma and collective action... When it comes to environmental issues, one can not escape its own representation of how can human behavior impact nature and vice versa. In Weber's words: The way a management problem is posed is partially trapped in our representation of the context in which this problem arises or the way we make it emerge by formalizing it2 ". In this regard, the emergence of the ecosystem services concept in the late 1990's [START_REF] Daily | Nature's services: societal dependence on natural ecosystems[END_REF][START_REF] Costanza | The value of the world's ecosystem services and natural capital[END_REF] represents a crucial shift in the way States, non governmental organizations (NGOs), international institutions, academics or any actors directly or indirectly involved in environmental policies perceives environmental issues [START_REF] Kull | The political ecology of ecosystem services[END_REF].

According to [START_REF] Costanza | The value of the world's ecosystem services and natural capital[END_REF]'s denition, ecosystem services are ows of materials, energy, and information from natural capital stocks which combine with manufactured and human capital services to produce human welfare. The interdependencies between nature and human are at the core of this notion. Nature is here conceptualised as a stock: the natural capital. According to this denition, human behavior can degrade the natural capital, for instance by clearing forest, releasing CO2 into the atmosphere or polluting rivers. As this stock of natural capital decreases, the ow of ecosystem services also decreases.

What are the services provided by this natural capital? The usual typology classies them into four categories of ecological functions De [START_REF] De Groot | A typology for the classication, description and valuation of ecosystem functions, goods and services[END_REF]:

• Information to contribute to the maintenance of human health • Regulation related to the capacity of the nature to regulate ecological processes • Production of goods for human consumption • Habitat for the conservation of biological and genetic diversity Forest ecosystems are major providers of ecosystem services through various means such as carbon sequestration, ood or erosion mitigation bio-diversity conservation ans improvement in water quality among many other things [START_REF] Daily | Nature's services: societal dependence on natural ecosystems[END_REF][START_REF] Neary | Linkages between forest soils and water quality and quantity[END_REF][START_REF] Fiquepron | Land use impact on water quality: valuing forest services in terms of the water supply sector[END_REF]. We will here focus primarily on forests but other types of ecosystems are important providers of ecosystem services such as wetlands, rivers, agricultural lands etc... Following ecosystem services, environmental services (ES) subsequently appeared in the academic literature. Both terms are often used synonymously but slight dierences may exist depending on the author. For instance, [START_REF] Wunder | Payments for environmental services: some nuts and bolts[END_REF] and others tend to consider that the notion of ecosystem services is implicitly larger than the ES notion. [START_REF] Karsenty | Du mésusage des métaphores -les paiements pour services environnementaux sont-ils des instruments de marchandisation de la nature[END_REF] propose an alternative distinction arguing that ecosystems services are provided by nature to human beings and are not appropriable while ES are provided by human beings to other human beings in order to increase the natures's provision of ecosystem services. Let's use the example of a landowner that is planting trees on his land located in the upper watershed which enhance water quality. The ES is provided by the landowner by planting trees and the water quality is the ecosystem services provided by the trees.

The use of these terms has widely spread during the last 15 years but their relevancy for the conceptualization of environmental issues is highly debated. Many authors emphasize that conceptualizing nature as a stock that provides ows of ES may underestimate the diculty of governing the provision of ecosystem services [START_REF] Farley | Payments for ecosystem services: from local to global[END_REF][START_REF] Norgaard | Ecosystem services: From eye-opening metaphor to complexity blinder[END_REF][START_REF] Muradian | Between markets and hierarchies: the challenge of governing ecosystem services[END_REF]. Reaching sustainable solutions requires major institutional changes. Moreover, a static project-by-project approach that aims at enhancing the provision of ES in one area may fail to account for the intrinsic complexity of ecosystem functioning.

A large debate that derives from a lack of clarication around the notion of ES and its application concerns ecosystem services valuation. Many authors tries 1.2. Payments for Environmental Services: A direct approach to conservation 3

to estimate the value of services provided by nature in various places [START_REF] Costanza | The value of the world's ecosystem services and natural capital[END_REF][START_REF] Farber | Economic and ecological concepts for valuing ecosystem services[END_REF]Costanza et al., 2006, among others). The debate about economic valuation of nature is a traditional point of disagreement between environmental economics and ecological economics [START_REF] Gómez-Baggethun | The history of ecosystem services in economic theory and practice: from early notions to markets and payment schemes[END_REF].

As highlighted by Chevassus-au [START_REF] Louis | Approche économique de la biodiversité et des services liés aux écosystèmes: contribution à la décision publique[END_REF] and [START_REF] Salles | Valuing biodiversity and ecosystem services: Why put economic values on nature?[END_REF], ecosystem services valuation is important in highlighting the biophysical values of nature that may not be directly perceived by humans, in improving decision making and in bringing political attention to these topics, among other reasons. Nevertheless, it may not be relevant to oer payments based on these valuations. In this regard, Karsenty and Ezzine de Blas (2014)'s distinction is important. If payments are for ES and not for ecosystem services, then the remuneration is no longer related to the value of the ecosystem services. In the above example, the landowners in the upper watershed is paid to plant trees on his land not to enhance water quality.

Payments for Environmental Services: A direct approach to conservation

Based on the assumption that environmental problems come from an underprovision of ES, Payments for Environmental Services (PES) schemes have emerged around the globe. PES are dened by [START_REF] Wunder | Payments for environmental services: some nuts and bolts[END_REF] as voluntary transactions where a well-dened ES (or a land-use likely to secure that service) is being bought by a (minimum one) ES buyer from a (minimum one) ES provider if and only if the ES provider secures ES provision (conditionality)".

PES schemes have been implemented in various types of ecosystems but we will here again focus on forests. One can distinguish between use-restricting and asset-building PES. In the context of forest conservation, the former remunerates forest conservation while the latter proposes payments to restore the ow of ES through reforestation or agro-forestry, for instance. Four types of PES are usually acknowledged in the literature:

• Payments for carbon sequestration

• Payments for biodiversity protection

• Payments for watershed protection (or hydrological PES)

• Payments for landscape beauty Compared to Natural Protected Areas (NPAs), PES schemes are voluntary instead of being based on command-and-control. As stated by [START_REF] Wunder | Payments for environmental services: some nuts and bolts[END_REF], PES use economic incentives while NPA rely on the regulatory framework. PES schemes are also more direct than Integrated Conservation and Development Projects (ICDP) that were not directly conditional on forest conservation and wanted to achieve conservation by distraction" i.e. by providing alternative activities to land clearing. Hence, PES were supposed to be more eective than NPA or ICDP based 4

Chapter 1. General Introduction on the principle emphasized by [START_REF] Ferraro | Direct payments to conserve biodiversity[END_REF] that: the cheapest way to get something you want is to pay for what you want (e.g., protected rain forest), rather than pay for something indirectly related to it ". Due to this apparent effectiveness and simplicity of implementation, PES schemes have largely developed during the last 15 years [START_REF] Wunder | Taking stock: A comparative analysis of payments for environmental services programs in developed and developing countries[END_REF][START_REF] Pattanayak | Show me the money: Do payments supply environmental services in developing countries?[END_REF][START_REF] Wol | Payment for ecosystem services from forests[END_REF].

In line with [START_REF] Wunder | Payments for environmental services: some nuts and bolts[END_REF]'s denition, the concept of PES derive from the Coase theorem [START_REF] Coase | Problem of social cost, the[END_REF]. If transaction costs are low and property-rights well dened, the buyers and sellers can nd a monetary agreement over a land-use likely to secure an ES provision. As predicted by the Coase theorem, a PES scheme needs well dened property rights in order to function. Complete land titling is not necessary but PES schemes can not work without use and exclusion rights. It follows from the Coase theorem that PES schemes nanced directly by the ES users will only emerge if transaction costs are not too high. In the context of PES, transaction costs include costs for search and information, negotiation or monitoring among others. According to [START_REF] Engel | Designing payments for environmental services in theory and practice: An overview of the issues[END_REF], high transaction costs justify the intermediation by the State or NGOs on behalf of the users.

Many authors contest the Coasean vision of PES which they consider as institutionally blind and counterproductive in the long-run [START_REF] Muradian | Reconciling theory and practice: An alternative conceptual framework for understanding payments for environmental services[END_REF][START_REF] Vatn | An institutional analysis of payments for environmental services[END_REF][START_REF] Muradian | Payments for ecosystem services and the fatal attraction of win-win solutions[END_REF]. Although, as in the famous Vittel case [START_REF] Perrot-Maitre | The vittel payments for ecosystem services: a perfect pes case[END_REF], Coasean PES emerged on small scales, this Coasean conceptualisation of the instrument does not seem appropriate for the schemes nanced by States or NGOs. Many PES schemes are initiated, designed and nanced by this type of intermediary. These PES schemes are shaped by local institutions and barely looks like Coasean agreement. In such cases, the intermediary is undeniably the main actor governing the scheme [START_REF] Muradian | Reconciling theory and practice: An alternative conceptual framework for understanding payments for environmental services[END_REF][START_REF] Vatn | An institutional analysis of payments for environmental services[END_REF]. As argued by [START_REF] Engel | Designing payments for environmental services in theory and practice: An overview of the issues[END_REF], the intermediaries may act on behalf of ES beneciaries but it seems exaggerated to state that those beneciaries are voluntarily contributing to the scheme as they are required to pay taxes [START_REF] Vatn | Markets in environmental governancefrom theory to practice[END_REF]. For this reason, [START_REF] Muradian | Payments for ecosystem services as incentives for collective action[END_REF] refers to PES as incentives for better resource management more than Coasean agreement. The main innovation of PES is the conditionality [START_REF] Wunder | Revisiting the concept of payments for environmental services[END_REF], therefore, by many characteristics, PES can be analyzed as an environmental conditional-cash-transfer (CCT) [START_REF] Martin Persson | Conditional cash transfers and payments for environmental services -a conceptual framework for explaining and judging dierences in outcomes[END_REF]. Finally, when designing PES programs, many stakeholder interact in the policy making process and environmental schemes often tend to be hybridized into programs with multiple objectives including poverty reduction [START_REF] Jack | Designing payments for ecosystem services: Lessons from previous experience with incentive-based mechanisms[END_REF][START_REF] Le Coq | The governance of costa rica's programme of payments for environmental services: A stakeholders perspective[END_REF][START_REF] Shapiro-Garza | Contesting the market-based nature of mexico's national payments for ecosystem services programs: Four sites of articulation and hybridization[END_REF][START_REF] Kolinjivadi | Juggling multiple dimensions in a complex socio-ecosystem: The issue of targeting in payments for ecosystem services[END_REF]. This hybridization also tends to drive the program away from a Coasean environmental program.

A related debate concerns the supposed market-based nature of the instrument. The perception of PES as market based instruments ideologically attracted decision makers who associate market with eciency as much as it has repulsed others that accuse PES of selling nature as a commodity [START_REF] Lapeyre | Payments for environmental services and marketbased instruments: next of kin or false friends[END_REF]. The perception of nature as a stock providing services tends to give utilitarian values to nature and simplify complex ecosystems into commodities (Gómez-Baggethun et al., 1.3. PES: An eective instrument? 5 2010; [START_REF] Kosoy | Payments for ecosystem services as commodity fetishism[END_REF]. Nevertheless, as highlighted by [START_REF] Pirard | Market-based instruments for biodiversity and ecosystem services: A lexicon[END_REF], the term market-based" refers to a wide variety of arrangements and most of them are not directly related to a market. In the case of PES, the term market-based" may be used to distinguish the instrument from command-and-control initiatives but PES does not result in any transfer of land property rights [START_REF] Karsenty | Du mésusage des métaphores -les paiements pour services environnementaux sont-ils des instruments de marchandisation de la nature[END_REF][START_REF] Gómez-Baggethun | In markets we trust? setting the boundaries of market-based instruments in ecosystem services governance[END_REF]. In most cases, the existence of a PES scheme does not mean that a market for ES has been created [START_REF] Corbera | The equity and legitimacy of markets for ecosystem services[END_REF]. National PES schemes are often nanced through compulsory taxes, most buyers do not even know they are contributing to a PES scheme. In that case, PES programs do not rely on a market mechanism at least on the demand-side [START_REF] Vatn | Markets in environmental governancefrom theory to practice[END_REF].

PES: An eective instrument?

The eectiveness of PES for forest conservation relies on three criteria [START_REF] Wunder | Payments for environmental services: some nuts and bolts[END_REF][START_REF] Engel | Designing payments for environmental services in theory and practice: An overview of the issues[END_REF]:

• Additionality, dened as the avoided deforestation on protected parcels attributable to the PES

• Leakages, dened as the indirect impact of the PES on land-use in another area

• Permanence, dened as the ability of the PES to achieve a sustainable increase of ES provision over time

In the inuential Money for Nothing?", [START_REF] Ferraro | Money for nothing? a call for empirical evaluation of biodiversity conservation investments[END_REF] highlight that evaluating additionality goes beyond monitoring the number of hectares in a given PES program. Additionality is the PES'schemes capacity to make a difference compared to a counterfactual situation in the absence of the program. As highlighted by Martin [START_REF] Martin Persson | Conditional cash transfers and payments for environmental services -a conceptual framework for explaining and judging dierences in outcomes[END_REF], if payments are conditional to a type of land-use, the extent of additionality depends on the percentage of area enrolled that would not have met the conditionality in the absence of the scheme.

In the case of PES for conservation, eectiveness is measured by the avoided deforestation i.e. the decrease in deforestation attributable to the program. Much attention has been given to leakages of conservation projects [START_REF] Wu | Slippage eects of the conservation reserve program[END_REF][START_REF] Wu | Environmental and distributional impacts of conservation targeting strategies[END_REF][START_REF] Chomitz | Baseline, leakage and measurement issues: how do forestry and energy projects compare?[END_REF]. In the context of forest conservation, leakages emerge when conservation in one area leads to more deforestation in another area. Various mechanisms can lead to leakages. The most cited is activity shifting if the activity responsible for clearing is displaced to another area. Leakages can also occur because of market eects. If by reducing available land, the PES scheme impacts the price of timber or agricultural commodities, this can increase deforestation in another area. In the context of PES programs for forest conservation, direct leakages are very likely to occur if land is easily available [START_REF] Aukland | A conceptual framework and its application for addressing leakage: the case of avoided deforestation[END_REF].

In the literature, permanence has been given less attention than additionality and leakages. This may be due to the fact that most PES schemes are too recent Chapter 1. General Introduction to empirically explore their eects over the long run. We can distinguish two approaches to PES permanence. In his study of a Costa Rican national PES, [START_REF] Pagiola | Payments for environmental services in costa rica[END_REF] acknowledges that the positive impacts of PES are likely to be temporary if the payments stop. The PES must continue as long as necessary and permanence depends on the capacity to sustain and secure funding for the scheme over time.

Alternatively, one can think about PES schemes as temporary instruments likely to initiate a transition toward more sustainable use of the forest cover through agroforestry for example [START_REF] Wunder | Payments for environmental services: some nuts and bolts[END_REF]. As highlighted by [START_REF] Pirard | Questioning the theory of payments for ecosystem services in light of emerging experience and plausible developments[END_REF] and [START_REF] Karsenty | Combining conservation incentives with investment[END_REF], to have a sustainable impact on deforestation, PES schemes must be combined with investment in order to relax dependence on degrading activities.

Otherwise, PES programs may only have a short-term impact and are likely to generate leakages.

All authors agree that with scarce resources PES must make a dierence according to a business-as-usual situation and generate avoided deforestation. However, uncertainties remain about how to achieve eectiveness, especially in the case of schemes funded by States or NGOs. In line with a polluter-paid Coasean approach, to be additional, PES must target threatened forests that would be deforested without the payments and compensate the owners for the foregone income due to PES implementation: the opportunity cost (OC) (Alix-Garcia et al., 2008;[START_REF] Engel | Designing payments for environmental services in theory and practice: An overview of the issues[END_REF][START_REF] Wunder | How do we deal with leakage[END_REF][START_REF] Martin Persson | Conditional cash transfers and payments for environmental services -a conceptual framework for explaining and judging dierences in outcomes[END_REF]). Yet, as highlighted by [START_REF] Ferraro | Asymmetric information and contract design for payments for environmental services[END_REF] and [START_REF] Anthon | Incentive contracts for natura 2000 implementation in forest areas[END_REF], PES schemes are subject to adverse selection because of asymmetric information. The OC and the willingness to deforest are known only by forest owners so targeting credible threats of deforestation is not an easy task.

Moreover, targeting credible threats of deforestation may not be the most eective solution. As emphasized by many authors [START_REF] Muradian | Payments for ecosystem services and the fatal attraction of win-win solutions[END_REF], the polluterpaid logic of Coasean PES disregards many other issues that determine the long-run impact of the schemes. How the scheme considers the existing institutions that rule the use of the forest cover as well as the local perception of justice and fairness may be a crucial determinant of PES eectiveness [START_REF] Corbera | The equity and legitimacy of markets for ecosystem services[END_REF][START_REF] Swallow | Compensation and rewards for environmental services in the developing world: framing pan-tropical analysis and comparison[END_REF][START_REF] Pascual | Exploring the links between equity and eciency in payments for environmental services: A conceptual approach[END_REF]. Moreover, following a polluter-paid approach, PES must prioritize environmental benets over social goals and focus solely on OC compensation. By contrast, the approach of PES as incentives proposed by [START_REF] Muradian | Payments for ecosystem services as incentives for collective action[END_REF], acknowledges that PES may have multiple goals and may reward greener behavior even though they are not additional. Yet, it is often feared that directing payments to the agents that deforest in a polluter-paid approach may crowd-out any intrinsic motivation to conserve [START_REF] Rode | Motivation crowding by economic incentives in conservation policy: A review of the empirical evidence[END_REF]. 1.4 Studying the Mexican PSA-H Many authors recently have emphasized the need for more impact evaluation (IE) of PES and of conservation instruments in general [START_REF] Ferraro | Money for nothing? a call for empirical evaluation of biodiversity conservation investments[END_REF][START_REF] Pattanayak | Show me the money: Do payments supply environmental services in developing countries?[END_REF][START_REF] Miteva | Evaluation of biodiversity policy instruments: what works and what doesn't?[END_REF]. Until recently, IE has focused mainly on 1.4. Studying the Mexican PSA-H 7 additionality and leakages. A growing body of literature has emerged during the last ve years that looks at the impact of PES on deforestation in Costa Rica [START_REF] Arriagada | Do payments for environmental services aect forest cover? a farm-level evaluation from costa rica[END_REF][START_REF] Robalino | Ecopayments and deforestation in costa rica: A nationwide analysis of psa's initial years[END_REF], Mexico [START_REF] Honey-Roses | A spatially explicit estimate of avoided forest loss[END_REF][START_REF] Shapiro | Forest conservation and slippage: Evidence from mexico's national payments for ecosystem services program[END_REF], Bolivia [START_REF] Canavire-Bacarreza | Estimating the impacts of bolivias protected areas on poverty[END_REF] and Thailand [START_REF] Sims | Conservation and development: Evidence from thai protected areas[END_REF][START_REF] Ferraro | Conditions associated with protected area success in conservation and poverty reduction[END_REF] among others. This literature is mainly based on the application to environmental issues of IE methodologies developed in other elds of development economics such as micronance, education and health economics [START_REF] Ravallion | Evaluating anti-poverty programs[END_REF].

In line with this literature, this dissertation proposes an empirical analysis of the implementation and the impact of a Mexican PES scheme: The PSA-H. The PSA-H is a federal PES program that has oered payments for forest conservation since 2003. It is a hydrological PES partially nanced by a water use tax and managed by the federal forest commission: CONAFOR [START_REF] Muñoz-Piña | Paying for the hydrological services of mexico's forests: Analysis, negotiations and results[END_REF]. The particularity of Mexican forest relates to the Mexican land tenure system. As a result of one century of agrarian reforms in the rural sector, more than 80% of the forest cover is constituted of community forests (Bray et al., 2003b). While these forests are ultimately state-property, they are managed by communities called ejidos through traditional governance institutions including a community assembly of ejidatarios.

While, most of the PSA-H payments are made to ejidos, as highlighted by [START_REF] Corbera | Institutional dimensions of payments for ecosystem services: An analysis of mexico's carbon forestry programme[END_REF], the ultimate beneciaries are not clearly identiable. Through the assembly, ejidatarios can choose to redistribute the payments according to their own rules or invest it into common goods. Enrollment in the scheme is voluntary but all applying ejidos are not necessarily accepted into the scheme as CONAFOR ranks application according to predened objectives [START_REF] Rolon | The mexican pes programme: targeting for higher eciency in environmental protection and poverty alleviation[END_REF]. Since 2003, the program has gained political attention and nancial support but has been progressively hybridized into a multi-objectives program targeting both threatened forests and marginal areas [START_REF] Muñoz Piña | Payment for environmental services in mexico: Targeting challenges[END_REF][START_REF] Shapiro-Garza | Contesting the market-based nature of mexico's national payments for ecosystem services programs: Four sites of articulation and hybridization[END_REF]Sims et al., 2014). Once enrolled in the program, beneciaries commit themselves avoiding land-use change in a dened forested area and receive yearly payments proportional to the amount of forest enrolled.

Chapter 2 summarizes the evolution of the land tenure system in Mexico until the second agrarian reform in 1992 and presents Mexican environmental policies, with a strong emphasis on the PSA-H. In addition, this chapter presents the data used in the empirical analysis including the surveys realized through the ANR-funded project PESMIX. It also proposes useful descriptive statistics in order to introduce the empirical analysis.

Chapter 3 discusses the implementation of IE methodologies in the context of PES. As emphasized above, a growing body of literature has emerged on this topic in the last ten years. In Chapter 3, the main methodologies used to evaluate the impact of PES are introduced. This chapter emphasizes the main assumptions of the estimators and illustrates their uses based on examples from the literature. In a nal section, we discuss the relevance of these methodologies in the context of PES.

Many methodological choices and trade-os arise in the IES of PES schemes. We Chapter 1. General Introduction discuss the methodological trade-os, propose guidelines and identify new area of investigation for future research for IE.

Building on these conclusions, Chapter 4 proposes a new methodological approach to estimate PES additionality. This approach explicitly considers forest ownership and simultaneously estimates impact on protected land and leakages. Moreover, it takes into consideration heterogeneity of exposure to the treatment regarding time spent in the scheme. First, we use a unit of observation that combines land tenure, land cover, gridding and PSA-H protection. We build on spatial econometrics tools to conjointly estimate direct impacts of PSA-H and potential leakage eects. Rather than using a dummy variable, we introduce the time spent in the program in our estimation and take into account contract renewal. We pre-process our data using matching to account for the selection bias and propose a regression framework to estimate the impact of the program. Our analysis provides evidence that, in our study area, leakage eects undermined the additionality of the PSA-H.

Chapter 5 looks at the distribution of payments within the ejidos and how it is linked to ejidatarios' land use. As highlighted above, while PES instruments must be additional, a debate remains about which types of forest should be targeted. One viewpoint is that PES schemes should focus on threatened areas and compensate landowners for lost land opportunity costs. Another viewpoint is that PES schemes should constitute incentives for conservation and may reward greener behavior rather than compensate opportunity costs. During the last decade, CONAFOR has developed a complex targeting system in order to focus the PSA-H on threatened forests and overcome adverse selection. Through its targeting system, the program is clearly oriented toward compensation. However, since most forests are managed by ejidos, PSA-H payments are given, not to individual landowners, but to a board representing the assembly of ejidatarios, which decides how to share the payments in accordance with the assembly. We rst investigate, at the ejido-level, how land-use heterogeneity within the ejidos impacts inequality in the distribution of payments. Second, we analyze how the land-use of each ejidatario within the ejidos inuences the amount of payment received in the distribution. Our analysis shows that the way the PSA-H is being distributed by ejidatarios bypasses the initial compensation objective, and has transformed it into a reward for not using the forest.

Increasing attention has recently been given in the literature to the interactions between policy instruments at the landscape-level. In a search for a relevant policy-mix, Chapter 6 looks at the interaction between the PSA-H and Community Forestry. Community Forest Enterprises (CFEs) have developed since the early 1980's in Mexico and become a prominent actor in Mexico's timber value chain. These CFEs implement extraction activities in community forests and sometimes valorize the timber through a transformation process. They are regulated by a federal forestry management plan that ensures sustainability of extraction activities. Does paying for conservation discourage investment in CFEs? Or by contrast, can the PES scheme help ejidos create and stabilize CFEs? In this chapter, we argue that the PSA-H can have a positive impact on CFEs. First, because developing a CFE requires heavy investment and the PSA-H can relax credit constraints. 1.4. Studying the Mexican PSA-H 9 Second, because PES enhances ejidos' access to valuable information about CFEs. Finally, because following PSA-H enrollment, the ejidatarios must organize themselves to implement various tasks in the forest which creates the forest management institutions within the ejidos that are necessary for the development of CFEs. We explore the interactions between Mexican CFEs and the PSA-H using a sample of 223 ejidos composed of PSA-H beneciaries and non beneciaries surveyed both in 2003 and 2010. Our empirical results suggest that the PSA-H can contribute to the development and stabilization of CFEs.

In a nal section of this dissertation, we draw the conclusions from the empirical essays for the design of PES and for future research. 12 Chapter 2. Studying the impact of a PES in community forests:

Context and methodology

Land tenure in Mexico

In Mexico, around 80% of the forest cover is owned as commons by communities called ejidos (Bray et al., 2003b;[START_REF] Kaimowitz | Mexico: Market options and barriers for timber and sawnwood from michoacan, oaxaca, guerrero, campeche adn quintana roo. a summary[END_REF] as a result of a century of reforms in Mexico's rural communities. This section summarizes the evolution of land tenure system in Mexico based on De Janvry et al. (1997), Gordillo de [START_REF] Gordillo De Anda | Between political control and eciency gains: the evolution of agrarian property rights in mexico[END_REF], [START_REF] Dutilly | Action Collective et Coopération Partielle dans la Gestion des Ressources Communes: Le Cas des Ejidos Mexicains[END_REF] and [START_REF] Assies | Land tenure and tenure regimes in mexico: an overview[END_REF].

Colonial time and the Mexican revolution

The origin of the Mexican land tenure system can be traced back to the Spanish colonial era. Upon their arrival in 1519, the Spanish colonists were allocated land and assigned indigenous workers through the encomienda system. The term encomienda" literally suggests that the colonists were entrusted indigenous workers with the mission to evangelize them in return for their labor. During the period of the encomienda system, the Spanish colonists appropriated large areas of the best farming land. Progressively, the land passed from many small indigenous landowners into the hands of a few large landowners. The encomienda system led to many abuses by the colonists and the Spanish Crown eventually decided to promote the New Laws of the Indies in 1542 to regulate and eliminate the encomienda system and replace it with a new one. This new system, called repartimiento, obliged the indigenous workers to provide a certain amount of labor hours to the Spaniards but for a given wage. It also created pueblos de indios or villages where the indigenous were allowed to cultivate their own crops and breed livestock. Within these villages, the common land for livestock breeding was called an ejido.

Over the following centuries, the Spanish Crown continued allocating land to indigenous communities. However, most of the land was appropriated by the colonizers through various means, which led to the emergence of haciendas: large properties specialized in mining or crop cultivation. Their expansion continued after Mexican independence in the beginning of the 19th century. The main preoccupation for successive governments was to cultivate more land and increase agricultural productivity, and many parcels owned by the church or by ejidos were considered by ocials to be ineciently managed. In 1856, the Lerdo Law authorized the expropriation of land held by civil entities or the church at the prot of a private agent.

In 1857, ejido-land was included in the Lerdo Law consolidating the movement of land concentration into the hands of larger landowners.

This trend was reinforced during the rule of General Porrio Diaz. Mexican economic growth at the end of the 19th century was mostly driven by the exports of the haciendas. In order to identify land without owners, around 50 companies among family members and other prestanombres. Moreover, new industrial groups may have appropriated the most protable lands while poorer households received small parcels of poor quality land. This land redistribution was complemented with the creation of farm credit institutions. However, given land protability and the poor education of farmers, the credits remained inaccessible for small peasants.

President Cardenas also created corporatist structures such as the Confederacion Nacional Campesino that would eventually serve to ensure the dominance of the revolutionary party in rural communities.

In subsequent years, Mexican industry grew at a remarkable rate. 1934-1940: Lazaro Cardenas del Rio 18,786,131 728,847 1940-1946: Manuel Avila Camacho 7,287,697 157,816 1946[START_REF] Tinbergen | On the theory of economic policy[END_REF]: Miguel Aleman Valdes 4,633,321 80,161 1952-1958: Adolfo Ruiz Cortines 6,056,773 68,317 1958-1964: Adolfo Lopez Mateos 8,870,430 148,238 1964-1970: Gustavo Diaz Ordaz 24,738,199 278,214 1970-1976: Luis Echevaria Alvarez 12,773,888 205,999 1976-1982: Jose Lopez Portillo 6,397,595 243,35 1982[START_REF] Sargan | Testing for misspecication after estimating using instrumental variables[END_REF]: Miguel de la Madrid Hurtado 5,626,227 248,486 1988Hurtado 5,626,227 248,486 -1992: Carlos Salinas de Gortari 551,869 80,692 Source: [START_REF] Assies | Land tenure and tenure regimes in mexico: an overview[END_REF] based on [START_REF] Bizberg | Auge y decadencia del corporativismo[END_REF] states of Mexico including Michoacan, Guerrero and Oaxaca, which obliged the governments to get back to land redistribution. By the 1980's, over half of Mexican farmland was owned by ejidos or comunidades. There is a slight dierence between ejidos and comunidades in terms of property rights [START_REF] Brown | Ejido and comunidades in oaxaca, mexico: Impact of the 1992 reforms[END_REF]. Ejidos have three land classications: parcelized arable land, common land, and land for human settlement. Access to common land is regulated by the assembly. Comunidades have just two land classications: human settlement and common land. Within comunidades, each member is supposed to have equal access to the commons but there can be de facto repartition among the dierent members.

Para-state institutions were also created providing services paid by a federal institution such as agricultural credit, distribution of inputs or a guaranteed price for many commodities. However, following the Mexican crisis of 1981, state support to the rural world began to decline. Ejidos began illegally renting land to commercial farms and looking for o-farm opportunities through massive migration to the urban areas and to the United States.

PROCEDE: Second agrarian reform

During the seventy-ve years of the rst agrarian reform, more than half of rural Mexican land was reallocated to peasants under the form of ejidos and comunidades [START_REF] Dutilly | Action Collective et Coopération Partielle dans la Gestion des Ressources Communes: Le Cas des Ejidos Mexicains[END_REF]. In the late 1980's, Mexico joined the GATT (1986) and began negotiating NAFTA (North American Free Trade Agreement) and their entry into the OECD. It became crucial for the Mexican government led by Salinas de Gortari to improve productivity in the agricultural sector through reform of the land tenure system [START_REF] De Janvry | Mexico's second agrarian reform: household and community responses[END_REF][START_REF] Deininger | Land policies for growth and poverty reduction[END_REF].

As highlighted by [START_REF] Heath | Evaluating the impact of mexico's land reform on agricultural productivity[END_REF], following the implementation of the rst agrarian reforms, large landowners still faced the threat having lands expropriated above an uncertain size of holdings and it was de jure impossible to cultivate crops on lands labeled as pasture. Lots of land was abandoned as a consequence of rural migration but leasing was supposed to be prohibited which limited access to land to landowners willing to cultivate more. Access to credit was also limited since rural public banks mainly oered in-kind payments, such as inputs, and because the land was state-property, it could not be mortgaged. The government, considering the system insecure and leading to under-investment in agriculture, undertook a massive agrarian reform called PROCEDE (Programa de Certicacion de Derechos Ejidales).

The second agrarian reforms included many changes in government institutions or regarding access to credits among others [START_REF] De Janvry | Mexico's second agrarian reform: household and community responses[END_REF] but we here focus on the Article 27 reform regarding land property. This reform involved a recognition of the ejido's governance institutions and allowed it to choose its property regime.

The reform also eliminated land rental restrictions [START_REF] Deininger | Mexico's second agrarian reform: implementation and impact[END_REF]. PROCEDE was a voluntary land regularization run by federal institutions. For an ejido, implementing Procede involved two steps [START_REF] De Janvry | Liberal reforms and community responses in mexico[END_REF][START_REF] Brown | Ejido and comunidades in oaxaca, mexico: Impact of the 1992 reforms[END_REF] 1. An external assessment determined boundaries of ejidos and communidades and informed the assembly. If the assembly chose to go forward (simple majority vote) with this rst step of PROCEDE, these boundaries were ocially recognized by the Instituto Nacional de Estadistica Geograa y Informatica (INEGI) who created a map that demarcated the dierent classication of lands and individual parcels.

2. Considering the map produced, the assembly could choose to obtain full titling over the land plots. Implementing this step required a two-thirds majority at the assembly. The assembly could choose full or partial privatization. In the case, of partial privatization it allowed individual ejidatarios who wished to privatize to obtain complete property rights but these did not apply to the entire community.

The assembly could also choose to dissolve the ejido as a political and economic organization. PROCEDE granted individual land title to the ejidatarios. Note that, once the ejido completed PROCEDE, the title of ejidatario could only be transmitted to one heir and the ejido was no longer allowed to integrate new ejidatarios.

Three types of property rights were allocated:

• Certicado parcelios: Property right over an individual parcel • Certicado de derechos de uso comun: Rights to use of a percentage of the common land of the ejido • Titulos de propiedad de solares urbanos: Residential property titles Certicado parcelios gave ejidatarios the right to legally rent and sell their land.

Nevertheless, this did not amount to complete property rights as the landholder was Chapter 2. Studying the impact of a PES in community forests:

Context and methodology not allowed to sell his land to non members of the ejido without community agreement [START_REF] Key | Social and environmental consequences of the mexican reforms: Common pool resources in the ejido sector[END_REF]. Certicado parcelios could be converted to full property titles (Certicados de dominio pleno) upon the request of the ejidatarios [START_REF] De Janvry | Are land reforms granting complete property rights politically risky? electoral outcomes of mexico's certication program[END_REF].

According to the federal statistics of the Regitro Agrario Nacional reported by [START_REF] Assies | Land tenure and tenure regimes in mexico: an overview[END_REF], 76.2 million hectares were certied or titled during PROCEDE, which beneted approximately 3.8 million people or more than 95% of the ejidos. Among the 76.2 million hectares, 23 million were certied as parcels and 52 million as commons.

The consequences of the reform are highly debated. Before the reform, Heath (1992) noted that, apart from the rural public bank, other non institutional sources of credit existed before PROCEDE (family, money lenders, merchants...). Using the private sector and ejidos that did not complete PROCEDE as a control group, [START_REF] Deininger | Mexico's second agrarian reform: implementation and impact[END_REF] highlight that the reform had a small impact on land sales but a signicant positive impact on the rental market. Nevertheless, as emphasized by [START_REF] Bouquet | State-led land reform and local institutional change: Land titles, land markets and tenure security in mexican communities[END_REF], rental and land markets existed informally before PROCEDE. For instance, to make a land transaction appear legal, ejidatarios often disguised it as an inheritance or as a sanction against the landowners. Nevertheless, a commonly acknowledged positive impact of the reform is that the land titles are perceived today by the ejidatarios as more secure.

During the reform, most ejidos chose to keep the forests owned as commons. This is the reason why 80% of the Mexican forest cover is formed by community forests (Bray et al., 2003b;[START_REF] Kaimowitz | Mexico: Market options and barriers for timber and sawnwood from michoacan, oaxaca, guerrero, campeche adn quintana roo. a summary[END_REF].

The ejidos today

Nowadays, governance of the ejidos, including management of the common forests, is still based on the traditional institutions that endure as a result of the dierent agrarian reforms [START_REF] Haenn | The changing and enduring ejido: a state and regional examination of mexico's land tenure counter-reforms[END_REF]. Following PROCEDE, three types of households are acknowledged within the ejidos:

• Ejidatarios which have the right of access to the common land, an urban plot (solar ), an arable parcel of land, and voting rights at the assembly The title of ejidatario is transmitted by inheritance to the rst heir. Therefore, buying land from an ejidatario does not give vote right at the assembly. Posesionarios often are sons of ejidatarios that did not inherit from the title. Avecindados may only live in the ejido, have o-farm activities or, since PROCEDE, rent land within the ejidos.

Mexican forests

Four types of authorities (autoridades) are in charge of ejido's governance. They are usually non-remunerated, serving once for one of these authorities is considered as part of an ejidatarios' duty toward the ejido [START_REF] Antinori | Vertical Integration in Mexican Common Property Forests[END_REF][START_REF] Dutilly | Action Collective et Coopération Partielle dans la Gestion des Ressources Communes: Le Cas des Ejidos Mexicains[END_REF]:

• El consejo del comisariado de bienes comunales: The council is constituted of the president (el comisariado), a secretary (el secretario) and a treasurer (tresorero). They administer the communal property and represent it in all the activities related to the territory of the ejido. They are elected for three years. Being part of this council, and especially being elected president, is often perceived as a burden by the ejidatarios. It takes them away from their productive activities and they usually do not perceive any compensation.

• El consejo de vigilancia: This is also constituted of three elected members. Its role is to monitor the consejo del comisariado. It ensures that authorities full their missions and that funds are not diverted.

• La municipal : Two members of the ejido in charge of law enforcement

• La assemblea general : The assembly gathers all the ejidatarios. Important issues concerning the ejido are debated and voted by the assembly.

Decisions regarding commons management, land access, federal schemes and conict resolution, and other issues, are debated at the assembly. Enrolling commons in an environmental program, such as a PES, requires an agreement at the assembly. Posesionarios may have the right to attend and speak at the assembly but voting rights are normally reserved for the ejidatatarios. Even after the reforms, the rules of the ejido concerning land access and sales often coexist with federal law [START_REF] Haenn | The changing and enduring ejido: a state and regional examination of mexico's land tenure counter-reforms[END_REF][START_REF] Bouquet | State-led land reform and local institutional change: Land titles, land markets and tenure security in mexican communities[END_REF]. Therefore, selling or leasing land often requires agreement of the assembly.

Mexican forests

Deforestation in Mexico

Deforestation is a crucial matter of concern in Mexico. The FAO (2011) estimates that on average 195 thousands of hectares were deforested yearly between 2000 and 2010, which corresponded to an annual average rate of 0.3 percent. Nevertheless, deforestation has sharply decreased since the 1990's. Between 1990 and 2000, 354 thousands hectares were deforested annually (0.5% per year). The greatest deforestation pressure comes from cattle-ranching, cropping and logging in some specic areas.

Before PROCEDE implementation, [START_REF] Deininger | Poverty, policies, and deforestation: the case of mexico[END_REF] studied the deforestation rate in the 1980's using national land-use change maps at the scale of 1:250 000. Their results suggest that agricultural subsidies and poverty were important determinants of deforestation rates. The same authors later highlighted the Chapter 2. Studying the impact of a PES in community forests:

Context and methodology importance of transaction costs proxied by distances to the markets and agroecological characteristics such as slopes [START_REF] Deininger | Determinants of deforestation and the economics of protection: An application to mexico[END_REF]. Similar results were found by [START_REF] Muñoz-Piña | Pixel patterns of deforestation in mexico: 19932000[END_REF] between 1993 and 2000.

After the implementation of PROCEDE, 80% of Mexican forests remained owned as commons by the ejidatatios (Bray et al., 2003b;[START_REF] Kaimowitz | Mexico: Market options and barriers for timber and sawnwood from michoacan, oaxaca, guerrero, campeche adn quintana roo. a summary[END_REF]. In Brazil, land tenure insecurity has been a strong determinant of deforestation according to [START_REF] Araujo | Property rights and deforestation in the brazilian amazon[END_REF].

The impact of the land tenure system on deforestation is widely debated in Mexico. [START_REF] Deininger | Poverty, policies, and deforestation: the case of mexico[END_REF], [START_REF] Deininger | Determinants of deforestation and the economics of protection: An application to mexico[END_REF], and Muñoz-Piña et al. ( 2004)'s results provide no evidence that ejidos' tenure arrangements have led to a higher level of deforestation. Using data from 350 ejidos, Alix-Garcia (2008) showed that a higher level of inequality within ejidos decreased deforestation rates. The underlying hypothesis that could explain this fact is that while inequality increases the use of the commons by wealthier households to a lesser extent than it decreases the use of the commons by poorer households.

One important issue raised by [START_REF]A spatial analysis of common property deforestation[END_REF] regarding deforestation in Mexico is related to the localization of deforestation. As a matter of fact, to understand deforestation in Mexico, one must not only take into account the absolute land quality but also the relative land quality within the ejido. Indeed, decisions to deforest depend rst, on the demand by some ejidatarios for pasture or agricultural land and second, on the relative value of the land within the ejido.

Since the entry into force of the General Law of Sustainable Forestry Development of 2006 (Ley General de Desarollo Forestal Sustenable), deforestation is de jure prohibited by law unless the landowners comply with a Management Plan insuring that extraction activities take place at a sustainable rate. Slash-and-burn (Roza Tumba Quema in Spanish) is explicitly prohibited by the law but tolerated de facto as the traditional form of agriculture. and [START_REF] Guevara-Sanginés | Evolución de los programas de conafor: origen, desarrollo y perspectivas a futuro[END_REF]. This program proposes among others:

Environmental policies in Mexico

• PES: Hydrological (the PSA-H) and Carbon and Biodiversity (the PSA-CABSA)

• Reforestation subsidies (PROCOREF)

• Subsidies for sustainable forestry and training program (PRODEFOR) and forest plantation (PRODEPLAN)

• Fire prevention

• Community forest development planning In the rst ten years of CONAFOR, PROCOREF is the program that received the largest share of CONAFOR's funding (25%). The PSA-H received 18% while the other PES, the PSA-CABSA, captured only 6% of CONAFOR's resources.

2.3

The PSA-H

Design and payments

The PSA-H was created in 2003 as a response to high deforestation pressures in Mexico and overexploitation of about 15% of the country's aquifers [START_REF] Rolon | The mexican pes programme: targeting for higher eciency in environmental protection and poverty alleviation[END_REF] Additional funding allowed CONAFOR to remunerate more service providers with higher payments. According to CONAFOr, the program covered only 127 millions of hectares in 2003 and 486 millions of hectares in 2010. Cumulating contracts over time between 2003 and 2010, the program had been granted to 4,893 beneciaries covering more than 2 billions of hectares of forests.

The PSA-H remunerates only conservation and should not be confounded with the PSA-CABSA which also remunerates sustainable forest management and reforestation [START_REF] Corbera | Institutional dimensions of payments for ecosystem services: An analysis of mexico's carbon forestry programme[END_REF]. Note that in order to apply collectively for the PSA-H, ejidos have to enroll at least 200 Ha of forests into the scheme. For individual owners of forests, it is also possible to apply individually with a minimum of 20 Ha.

Payments were originally set to 200 pesos per hectare and were later increased and dierentiated according to the type of forests and the deforestation risk. Mesophyll forests (cloud forests), for instance, are expected to provide more hydrological services. Since 2010, payments are set as shown in Table 2.3.

The deforestation risk index is computed by INECC in order to measure deforestation pressure. It is based on an econometric model developed by [START_REF] Muñoz-Piña | Paying for the hydrological services of mexico's forests: Analysis, negotiations and results[END_REF] that determines the probability of deforestation. Using satellite images, the authors computed the probability for a pixel to be deforested between 1993 and 2000 according to variables reecting agro-ecological characteristics, transaction costs, socioeconomic variables and regulation. They used their results to compute a map of deforestation risk covering all of the Mexican territory that is now used to target the PSA-H. The authors also found that most of the payments were allocated to low deforestation risk area. This nding justied the inclusion of deforestation risk in the criteria of the PSA-H in order to enhance additionality of the scheme. CONAFOR does not have a sta dedicated to the promotion, assistance and monitoring of the PSA-H in the ejido. If an ejido is accepted into the program, he receives extra money to contract with a technical advisor who will help him to comply with PSA-H rules. These CONAFOR-accredited advisors play the role of intermediary between CONAFOR and the ejidos. In order to get new contracts, advisors promote the PSA-H by inviting ejidos to apply for the program, helping them to ll-in their applications and gathering the necessary administrative documentation. The role of the technical advisor is crucial and the probability that an ejido will be accepted into the program is inuenced by the dynamism and capabilities of the advisors.

Applying for the program requires an agreement at the assembly. Nevertheless, this does not necessarily mean that all ejidatarios agree to enroll commons in the program. The ejidatarios can decide that only a specic part of the commons will be enrolled in the program and that some ejidatarios and posesionarios will not be concerned by the PSA-H. It is common, especially in large ejidos, that a substantial number of the ejidatarios decides not to enroll their share of commons and remain outside of the program. These ejidatarios will not receive payments from the PSA-H.

Note that the ejidos can ll-in dierent applications for PSA-H contracts over time on dierent parcels of forest. For instance, they can enroll 200 hectares in 2007 and 400 additional hectares three years later. In larger ejidos, the ejidatarios and posesionarios concerned by each application may even be dierent. An ejido can also apply to renew the PSA-H on enrolled land after the ve-year contract. On this occasion, ejidatarios often increase or reduce the size of the forest enrolled in the program.

Not all ejidos who apply receive the payments. CONAFOR have dened eligibility zones for each ProArbol programme, including the PSA-H, and only the ejidos located in these areas are eligible for the program. Moreover, because the PSA-H is underfunded in comparison to the number of applications received, CONAFOR has developed a scoring system in order to rank the applications.

Eligibility zones

In 2003, for the rst year of allocation, there were no scoring systems or eligibility zones planned. CONAFOR received too many applications for the funds available. Payments were allocated to the rst applicants until there were no more funds available. To avoid the recurrence of this problem, CONAFOR began to dene eligibility zones and developed a scoring system in order to rank the applications.

Since 2004, ejidos must belong to these eligibility zones in order to receive the PSA-H. Eligibility zones have been regularly modied. The eligible area has increased signicantly over the years as PSA-H's budget has simultaneously increased. In 2004, eligibility zones represented only 2% of Mexican territory while they covered about a quarter of the territory by 2012, according to our calculations2 . Each application inside the eligible zones receives a score. Since the PSA-H has been incorporated into ProArbol, we can distinguish the criteria that are commons to every ProArbol programme and those that are specic to the PSA-H.

All applications to ProArbol programs receive points if the ejido never received any support from CONAFOR, complies with a forestry management plan or is located in a marginal area or a zone with a majority of indigenous population among other things.

A second scoring system applies specically for the PSA-H. Points are added to those obtained for ProArbol. In 2010, there were 18 criteria in the scoring system: 11 of them are common with PSA-CABSA, the other national PES scheme. Other criteria deal with, for instance, location inside priority zones for the environment and water accessibility. Details about the scoring system can be found in the Appendix.

The scoring system has evolved considerably since 2003. As highlighted by [START_REF] Shapiro-Garza | Contesting the market-based nature of mexico's national payments for ecosystem services programs: Four sites of articulation and hybridization[END_REF], the targeting of the PSA-H is the result of the interaction measured 38 805 km 2 in 2004 and 486 640 km 2 in 2012.

The PSA-H

of many stakeholders such as CONAFOR, other federal ministries and commissions and civil society organizations. The program has taken a hybrid form combining environmental and social objectives. Looking at only four criteria (overexploited aquifers, INE risk of deforestation, water scarcity zones and marginalization), [START_REF] Rolon | The mexican pes programme: targeting for higher eciency in environmental protection and poverty alleviation[END_REF] show that the allocation of the PSA-H has been strongly inuenced by the constant evolution of the scoring system. Nevertheless, Sims et al. (2014) note that the targeting of the PSA-H evolved over time in order to combine environmental and social priorities and has succeeded in enrolling land on the basis of both. This PMPM denes the activities that will have to be undertaken for forest conservation. The ejidos must provide this program to CONAFOR in order to receive the rst payment 3 The advisor is also in charge of providing beneciaries a workshop twice a year to teach about environmental issues.

During the ve years of the reception of the payment, the ejido commits itself to complying with the following rules in the area enrolled in the PSA-H. The advisor must provide assistance to the ejido in order to comply with these commitments.

• Avoid land-use change

• Conserve the forest cover and avoid degradation • Set at least two notices (signs) announcing the implementation of the scheme in the ejido that will remain during the ve years.

• Avoid overgrazing

• Implement the activities planned by the PMPM

• Realize monitoring and prevention activities against res including the formation of at least one brigade.

This last commitment includes the necessity of patrolling in the forest (hacer las rondas in Spanish) or building rebreaks. As highlighted by Yanez-Pagans (2013), the ejidatarios must provide a certain amount of labour hours to receive payments. Therefore, the scheme tends to increase cooperation within the ejidos but not only in forest conservation activities. Since the ejidatarios are exposed to sanctions for Chapter 2. Studying the impact of a PES in community forests:

Context and methodology not participating to the ejidos' collective work, it also increases participation to collective activities that are not required by the PSA-H. If the ejido does not comply with these rules, sanctions can vary from simple notication to an exclusion from the program and a ban from any future CONAFOR scheme.

Payment repartition

The ejidos receive payments every year for ve years. ProArbol's rules state that a share of these payments must be used to remunerate the technical assistant [START_REF] Conafor | Réglas de operacion del programa proarbol 2010[END_REF]. In 2010, this amount varied from 22,000 to 60,000 Mexican pesos (1,700 to 4,600 USD 4 ). According to our surveys in Cono Sur (see Section 2.4.1), it represents on average 10% of the total amount received. The fact that the funds dedicated to technical assistants were transmitted to the ejidos led to some abuses either from technical assistants or from a collusion between ejidos' elite and the technical assistants. Therefore, to avoid misappropriation, CONAFOR has decided in recent years to remunerate the technical assistants directly.

Once the technical assistant has been remunerated, the remainder of the payments is left at the discretionary power of the ejidos' authorities. The use of payments is usually discussed during the assembly or between the ejidos that chose enter into the program. As emphasized by [START_REF] Corbera | Institutional dimensions of payments for ecosystem services: An analysis of mexico's carbon forestry programme[END_REF], a particularity of PES in commons is that the beneciary is not clearly identiable.

We distinguish three types of use for PSA-H payments. The rst is collective investment. A certain share of payments can be used for investment in public goods (roads, school...) but also in new income generation activities at ejido-level (community forest enterprises, palm oil plantation...). A second possibility is to use a certain share of payments to remunerate the collective work required to receive the PSA-H, including the days spent patrolling or building rebreaks. PSA-H payments can be used to remunerate these activities at a xed daily wage. A third use is to redistribute payments within ejidatarios and posesionarios as collective owners of the forests. The rule of distribution can vary from one ejido to another and ejidatarios do not systematically receive the same amount of payments. If no funds are specically allocated to the remuneration of rebreak building and patrolling, it is usually mandatory for PSA-H beneciaries to participate in these activities or, if unable to do so, to pay someone to do so in their place.

Note that the choice between these dierent types of payment use may be inuenced by the perceptions of PSA-H's objectives by regional oces and technical assistants. The use of payments as remuneration has never been found in our surveys in Yucatan while it has been frequently found in Chiapas.

Impact of the PSA-H

Alix-Garcia et al. ( 2012) studied the impact of the program on avoided deforestation based on a sample of early beneciaries in 2003 and rejected applicants as a control group. They use pre-matching to control for the selection bias. Their estimate suggests a low additionality of the scheme and the existence of leakage eects in neighboring parcels. The low additionality can be attributed to the weak capacity of CONAFOR to target threatened forests. This hypothesis is conrmed by earlier work by [START_REF] Muñoz-Piña | Paying for the hydrological services of mexico's forests: Analysis, negotiations and results[END_REF] that showed that the scheme failed to focus on the areas with the highest deforestation risk.

The lack of the PSA-H focus on endangered forest can be explained by its multiple objectives. If CONAFOR's position was to target threatened forests, interactions with civil society and other governmental organizations hybridized the program into a reward for conservation within the NPA and a poverty alleviation tool [START_REF] Muñoz Piña | Payment for environmental services in mexico: Targeting challenges[END_REF][START_REF] Shapiro-Garza | Contesting the market-based nature of mexico's national payments for ecosystem services programs: Four sites of articulation and hybridization[END_REF]. Combining multiple objectives drove the scheme away form its original environmental objective. On the same sample of early beneciaries and rejected applicants described above, Alix-Garcia et al. (2014) showed that the program had a limited impact on poverty. Combining both objectives might be dicult because higher impact on poverty have been achieved in areas of low deforestation risk and vice versa. Nevertheless, according to Sims et al. (2014), CONAFOR have succeeded over time in improving the focus of the program on threatened forests and marginal areas through adaptive management of the targeting criteria. The Yucatan Peninsula (formed by the states of Campeche, Quintana Roo and Yucatan) covers around 140 thousand Km2, of which 62,229 thousand of hectares are protected areas. It has a population of about 4 million inhabitants of whom more than 800 thousand rely on agricultural production.

Agriculture generally consists of 2 to 4 hectares of land reserved for (milpa) agriculture. Milpa is a traditional Mayan production process which includes maize, beans and squash. Milpa goes hand in hand with the roza-tumba-quema method: a form of slash-and-burn agriculture. The latter consists in working the land according to precise periods; trimming and weeding (roza), cutting and clearing (tumba) and, nally burning the wastes (quema). It is based on a rotating principal. Bovine production takes an extensive form in the Yucatan Peninsula implicating deforestation in order to create pastures. Finally, the area also relies on beekeeping and honey production. As Mexico was ratifying NAFTA and entering the GATT in the mid-90', the federal government tried to impulse an intensication process in an attempt to modernize Mexican agriculture [START_REF] Sadoulet | Cash transfer programs with income multipliers: Procampo in mexico[END_REF]. A process of agricultural intensication is undergoing in the peninsula today and concerns both cultivation of commercial crops, such as chili for instance, and traditional maize-bean intercropping. As highlighted by [START_REF] Pascual | Land use intensication potential in slash-and-burn farming through improvements in technical eciency[END_REF], the intensication process takes the form of either increased access to new technologies or improvement of eciency in the traditional sector. Using a stochastic frontier of production, [START_REF] Pascual | Land use intensication potential in slash-and-burn farming through improvements in technical eciency[END_REF] shows that, by operating at the eciency frontier, peasants could clear less land. This would involve a reduction in the fallow period.

In the southern part of the peninsula, [START_REF] Rueda | Understanding deforestation in the southern yucatán: insights from a sub-regional, multi-temporal analysis[END_REF] studied deforestation rates between 1984 and 1993 and between 1993 and 2000. Her ndings suggest that deforestation decreased in the latter period. The author attributes this decrease to the change in production behavior and intensication of agriculture. Nevertheless, as emphasized by [START_REF] Schmook | Shifting maize cultivation and secondary vegetation in the southern yucatan: Successional forest impacts of temporal intensication[END_REF], the total impact of agricultural intensication on the environment remains unknown. As a matter of fact, if the reduction of the fallow period is not a problem on the short-run, if yields are reaching a tipping point due to loss of soil phosphorous, this may lead farmers to expand their cultivation and start a new deforestation phase.

The impact of demographic variables and migration on deforestation have also been emphasized by multiple authors. Demographic pressures have been identied as strong determinants of deforestation [START_REF] Ellis | Is community-based forest management more eective than protected areas?: A comparison of land use/land cover change in two neighboring study areas of the central yucatan peninsula, mexico[END_REF][START_REF] Klepeis | Integrated land history and global change science:: the example of the southern yucatán peninsular region project[END_REF][START_REF] Turner | Sustainability and forest transitions in the southern yucatán: The land architecture approach[END_REF][START_REF] Abizaid | Land use and forest fallowing dynamics in seasonally dry tropical forests of the southern yucatan peninsula, mexico[END_REF]. Using t-tests, [START_REF] Schmook | International labor migration from a tropical development frontier: globalizing households and an incipient forest transition[END_REF] shows that, if migration may have led to a decrease in land pressure PESMIX surveys were implemented in Yucatan State. In Yucatan, PSA-H allocation is mainly concentrated in the southern part of the state in a zone called the Cono Sur (see Figure 2.3). For this reason, our surveys were implemented in these areas. Allocation has been concentrated in Cono Sur because the ejidos from this part of the State have been founded more recently than in other parts. Therefore, there are still large areas of forest owned as commons that have not been deforested.

Moreover, the Cono Sur has known several period of large deforestation in the past due to cattle ranching and the expansion of mechanised agriculture. In the 1970 and 1980's, the Mexican government proposed subsidies for land clearing and leveling in order to encourage intensive mechanized agriculture. While deforestation has slowed down since the 1990's and the end of agricultural subsidies, it remains an important matter of concern which justies the fact that most PSA-H funds are allocated in this areas.

To a lesser extent, PSA-H contracts can be found in other areas of Yucatan. The PSA-H is found in the periurban area around the capital Merida. Migration to Merida led to an increase in deforestation close to the city which justied allocation in these areas. Moreover, some PSA-H have also been found in the north-east of the State following the work of a private NGO.

The Cono Sur shares many of the features of the rest of the peninsula. Milpa remains the main agricultural activity. The degree of intensication is very heterogeneous across the Cono Sur. Taking advantage of the at topography of the region, some ejidos have managed to develop highly mechanized agriculture. Cattleranching is also very developed in the region. 2.4 present the ejidos surveyed during the eld work. Most ejidos of the area beneted from the program but at dierent periods in time. The main determinant of the reception year was eligibility.

The northern ejidos became eligible for the program later than the southern ejidos. This may be due to insucient funds at the beginning of the program. According to CONAFOR's regional oce in Yucatan, more information was available about the southern ejidos to justify their eligibility. Eligibility zones were later widened as emphasized in Figure 2.4. Nevertheless, the northern ejidos are on average less remote and closer to the main roads and cities than the southern ejidos.

Surveys at the ejido-level

Our sample is composed of all ejidos eligible for the program in 2012 with up to a maximum of 300 ejidatarios. We decided to limit the number of ejidatarios to 300 since ejidos with more than 300 ejidatarios include the main cities of Cono Sur such as Tekax, Tzucacab or Oxkutzcab that no longer rely on traditional ejido governance institutions such as the assembly or the comisariado. Note that none of The surveys were administered between November and December 2012 by two teams of two interviewers and lasted approximately 2 hours. For each ejido, the team rst gathered the authorities and all ejido members willing to participate. The number of participants for one survey varied from 3, which was the minimum required, to more than 50. The participatory mapping was crucial for the administration of the questionnaires. SPOT images together with ocial maps given to the ejido by procaduria agraria after PROCEDE implementation were used in order to help the ejidatarios locate the dierent types of land-use. Together with the ejidatarios, the team divided the ejido into multiple polygons corresponding to dierent land-use (see pictures in the Appendix). The rest of the questionnaire directly refers to these polygons in order to allow the ejidatarios to answer the multiple questions regarding land-use and economic activities.

Ejidos from Cono Sur are rather small both in size and number of inhabitants as showed in table 2.5. In a national representative surveys, the ENNAF survey presented in Section 2.4.2, ejidos are on average ve times bigger and the number of ejidatarios is multiplied by 3.

This can be explained by the fact that most of these are part of new centers of population founded in the second part of the 20th century. The founders of these Another important feature that characterizes the Cono Sur is its remoteness, as highlighted by the distance to the market shown in Table 2.5. Ejidos from the southern part of our study zone lack crucial infrastructure, including not only roads but also access to running water or electricity. These ejidos have been deserted by the population, which has led to the formation of labor-ejidos where ejidatarios cultivate crops and breed cattle but typically hold a primary residence in the nearest city: Tekax or Tzucacab for instance.

Figure 2.6 presents kernel density estimates for the percentage of land under each land tenure regime. Following PROCEDE, it is possible to distinguish commons from individualized parcels. Nevertheless, within commons, a certain share of land is divided de facto by agreement of the assembly. Ejido surveys have enabled us to distinguish commons, land parcelized by agreement of the assembly and land parcelized by PROCEDE. While the share of land parcelized by PROCEDE is small in most ejidos, we note that considering parcelization by the assembly gives more mixed results. One group of ejidos maintained a large share of commons while a smaller group has a larger share of land parcelized by assembly. Table 2.6 presents the percentage of ejidatarios involved in each activity per ejido. On average, between 20% and 35% of the ejidatarios are involved in each activity but this percentage varies highly from one ejido to another. Note that many of them combine dierent activities. A large variety of land use types can be found in Cono Sur, ranging from complete specialization of the ejido in one type of land use to a mosaic of various land-use types. We will now look at PSA-H allocation in the area. Table 2.7 shows the evolution of the beneciariy proles over time regarding total areas, number of ejidatarios, density of ejidatarios and distance to the market. One can note that the rst PSA-H beneciaries were ejidos with a smaller number of ejidatarios and a lower density of ejidatarios per hectare.

This suggests that the rst ejidos to enter the program were ejidos with a lower risk of deforestation and where it might be easier to reach an agreement at the 2.7 presents information regarding the number of hectares enrolled in the PSA-H and associated payments for beneciaries. On average, an ejido enrolled 1,486 hectares into the program which correspond to 63% of the total forest. The number of years of reception varies according to the date of the rst PSA-H contract and the decision to renew the contract. Some ejidos received payments for seven years between 2003 and 2012. On average, ejidos received payments for approximately 4 years. Remember that the PSA-H contracts last ve years.

The ejidos receive substantial payments through the PSA-H. On average, between 2003 and 2012, the ejidos of Cono Sur received more than 1.8 million Mexican pesos (or more than 140,000 USD). Per year of reception, ejidos received on average 423,000 Mexican pesos (or 32,000 USD). Divided by the number of ejidatarios, the payments received over ten years are on average 57,000 Mexican pesos (over 4,000 USD) per household. For a comparison, the daily minimum wage in Mexico in 2015 is around 80 pesos per day. Nevertheless, this amount can vary widely, as emphasized in Table 2.7.

Surveys at household-level

At the household level, the sample size could not exceed 200 households and for logistical reasons it was impossible to conduct household surveys in all ejidos. We randomly selected 27 ejidos, including 2 future beneciaries, and checked the rep-Figure 2.8: Ejidos that participate to the household surveys resentativity of this sample regarding demography, PSA-H payments and economic activities. Surveying 200 households corresponded to approximately 20% of the total number of ejidatarios in the 27 ejidos so in each ejido, we surveyed 20% of the total number of ejidatarios. Nevertheless, it was not relevant to systematically apply the same percentage for each ejido since the number of ejidatarios varies widely and the results regarding the distribution of the PSA-H within the ejidos would have been biased by the decisions made by larger ejidos. For this reason, we imposed a maximum limit of 15 households and a minimum limit of 5 households per ejido. We deliberately chose to focus the household survey on ejidatarios, as they are the main land owners and decision makers at the assembly.

The questionnaire was composed of six parts and mainly focused on economic activities and government support.

• I. Household composition and characteristics

• II. Land use 2.9 presents the number of hectares cultivated for each type of land use. As expected, for traditional agriculture, the ejidatarios cultivate on average 2 hectares. Areas cultivated with mechanized agriculture are much larger. On average, the Table 2.10 presents the amount of payments received by the ejidatarios in beneciary ejidos. On average, each ejidatario received around 55,000 Mexican pesos (4,200 USD) between 2003 and 2013. During the years of reception of payments, each ejidatario received on average 10,000 Mexican pesos (or 770 USD). Note that even in beneciary ejidos, some ejidatarios did not receive payments. They may have decided to remain outside of the program or may have been excluded by a decision of the assembly. The yearly amount received was as high as 60,000 Mexican pesos (more than 4,600 USD).

Table 2.10 show that the amount of payments are likely to represent signicant additional income for the ejidatarios. Table 2.11 shows how, on average, they claim to have used this additional income. If 60% of the payments were used for household expenses, about 30% of the payments were invested in either agriculture or cattle ranching. This suggests that the PSA-H may have impacted agricultural activities. The remote sensing analysis focused on the land owned by ejidos surveyed.

The remote sensing analysis combined information from the SPOT images with ground-truthing data and information from the participatory mapping in order to distinguish pasture, mechanized agriculture and traditional agriculture. The analysis distinguishes 5 main classes of land-use: 2002, 2004, 2006, 2007and 2010. The ejidos surveyed between 2002and 2007 were all dierent but, in 2010, some of the ejidos surveyed in 2002 have been surveyed again. Our sample is composed of the 223 ejidos that have surveyed both in 2002 and 2010.

Table 2.14 presents the main characteristics of the ejidos. There were on average 190 ejidatarios per ejido surveyed. The percentage of forest cover was computed using INEGI Serie IV data with a resolution of 1:250 000.

In this representative survey, 22% of the ejidos beneted from the PSA-H for at least one year between 2003 and 2010. Most of the beneciaries have completed, or are about to complete, the ve years of enrollment. We have already observed several ejidos that renewed their contracts. One of these ejidos received payments for 8 years.

Table 2.16 presents the number of hectares enrolled in the PSA-H and the amount Evaluating the impact of conservation instruments have been emphasized in the literature as a major area of research by many authors [START_REF] Ferraro | Money for nothing? a call for empirical evaluation of biodiversity conservation investments[END_REF][START_REF] Joppa | Reassessing the forest impacts of protection[END_REF][START_REF] Pattanayak | Show me the money: Do payments supply environmental services in developing countries?[END_REF][START_REF] Miteva | Evaluation of biodiversity policy instruments: what works and what doesn't?[END_REF]. Based on existing literature, this chapter proposes a review of the available techniques and the underlying assumptions for their implementation. We also discuss the main issues for IE of conservation instruments with a special emphasis on PES. We show that PES are no common treatment and that lots of caution should be taken in building and interpreting impact analysis. Following [START_REF] Rubin | Estimating causal eects of treatments in randomized and nonrandomized studies[END_REF], the problem of evaluation can be formalised using potential outcomes. Let us consider the implementation of a new policy, called the treatment, supposed to impact an outcome variable Y . In the context of PES, the treatment would be PES reception and the outcome variable a measure of deforestation. Let's note note Y i the outcome variable for each individual i. Y T i corresponds to the outcome when the individual receives the treatment and its counterfactual Y NT i when the individual do not receive the treatment. Hence, the impact of the program is: Two intuitive solutions could be considered to estimate Y NT i but both of them lead to biased estimates [START_REF] Joppa | Reassessing the forest impacts of protection[END_REF]):

E(Y T i -Y NT i ) ( 3
• Before/after: Comparing the group of beneciaries before and after the treatment implementation. One makes the assumption that Y NT i is equal to the value of Y i before the program ie that the value of the outcome variable would have not varied if the program had not been implemented. It is easy to understand why this assumption is unlikely to be veried. For example, let's consider a ctive country where the government decides to implement a PES scheme as a response to high deforestation pressures. Meanwhile, a variation in international timber prices makes deforestation less attractive: deforestation rates will decrease as a consequence of the decrease in timber prices. Therefore, comparing deforestation before and after PES for the beneciaries will lead us to falsely attribute the decrease of deforestation to PES implementation.

• With/without: Another appealing solution is to use a group of nonbeneciaries as a counterfactual for the beneciaries and assumes that Y NT i is equal to the outcome of non-beneciaries. This method is also likely to be biased. In the context of PES, let's consider two landowners, one that is willing to deforest in the future and another that is not. Without specic targeting system of the program, the latter is more likely to enter into the PES but would not have deforested anyway. Therefore, comparing non beneciaries with beneciaries will lead us to overestimate the impact of the program. Now let's consider that the scheme succeeds in targeting landowners with higher willingness to deforestation in the near-future. A control group of non beneciaries is likely to be constituted of landowners with smaller willingness to deforest. In that case, the impact might be underestimated. In both examples, the impact is biased upward or downward.

IE usually involves a group of beneciaries and a group of non beneciaries to reconstitute a counterfactual. We will now introduce the status of individuals regarding treatment D i and note D i = 1 if i is part of the group of beneciaries (treated group) and D i = 0 if he is part of non beneciaries (control group).

As explained by Chabe-Ferret ( 2008), the impact one is willing to identify is:

E(Y T i -Y NT i ) = E(Y T i -Y NT i |D i = 1)P r(D i = 1)+E(Y T i -Y NT i |D i = 0)P r(D i = 0) (3.
2) With/without estimations implicitly make the assumption that:

• E[Y NT i |D i = 0] -E[Y NT i |D i = 1] = 0 i.e.
the expected outcome of beneciaries if they had not received the program would have been identical to the outcome of non beneciaries

• E[Y T i |D i = 0] -E[Y T i |D i = 1] = 0 i.e.
the expected outcome of nonbeneciaries if they had received the program would have been identical to the outcome of beneciaries

Denitions

The main challenge for IE is to build a counterfactual accounting for the biases presented above. Each technique relies on dierent assumptions and have its own strengthes and weaknesses. This section presents a few necessary denitions to understand the dierent techniques.

Impact

We introduce four concepts: the average treatment eect (ATE), the average treatment eect on the treated (ATT), the intention to treat eect (ITT) and the local average treatment eect (LATE). The statistical denition of this concept is crucial to interpret the results of an IE and assess their external and internal validity.

Internal validity is the minimum one should question about the estimates. As explained by [START_REF] Ravallion | Evaluating anti-poverty programs[END_REF], it concerns the ability to obtain a reliable estimate of the counterfactual outcomes in the specic context ". External validity concerns the extrapolation of the results in other settings ie for another population or in other places for instance.

The average treatment eect is dened as follows:

AT E = E(Y T i -Y NT i ) (3.3)
We note that D i does not appear in equation 3.3. It means that, if the methodol- ogy allows to estimate an ATE, it can be interpreted as the impact of the treatment independently of the status of the individuals. This concept has the strongest internal and external validity: one can assume that the impact would have been the same for the whole population of study if they had received the program. ATE can be obtained if the treatment is randomised.

The average treatment eect on the treated is dened as follows:

AT T = E(Y T i |(D i = 1) -Y NT i |(D i = 1)) (3.4)
Comparing 3.3 and 3.4, it is straightforward that ATE has a larger validity than ATT. As a matter of fact, ATT's validity is limited to the treated group. One can not hypothesize that the impact would have been the same for the control group if they had received the program.

The intention to treat eect is estimated when treatments can be accepted or rejected by participants. Let's assume an invitation Z i to adopt the treatment D i . Z i = 1 if the individual is invited and Z i = 0 if he is not. The group can be divided between compliers, who accept the invitation and adopt the treatment and non-compliers that do not accept. The adoption of the treatment D i may remain unobserved. ITT is dened as:

IT T = E(Y i |(Z i = 1) -Y i |(Z i = 0)) (3.5)
In the context of PES for instance, consider that one knows which landowners have been proposed the program but can not clearly identify the parcel enrolled into the program. It is not possible to compute the impact of participation, the ATT, since one can not observe the landowners that accepted the invitation and on which parcel. But the ITT can be computed based on the invitation to participate.

Let us now consider that D i is observed but one suspects endogeneity of partic- ipation and uses Z i , for instance the invitation to participate, as an instrument to predict the treatment [START_REF] Angrist | Identication of causal effects using instrumental variables[END_REF] 1 . Our sample can be divided between compliers and deers as in table 3.1.

Z i = 0 Z i = 1 D i = 0 Complier / Never-taker Deer / Never-taker D i = 1
Deer / Always-taker Complier / Always-taker 3.1. Introduction: Evaluating public policies 47 [START_REF] Angrist | Lifetime earnings and the vietnam era draft lottery: evidence from social security administrative records[END_REF] evaluates the impact of Vietnam war on veteran life earning using the draft lottery as an instrument. In their study, deers are the veteran that would have enrolled voluntarily regardless of the result of the draft and the persons that did not enroll despite the draft selected them.

Using Z i as an instrument, it is possible to identify the LATE dened as:

LAT E = E((Y T i -Y NT i )|D i (Z i = z) = D i (Z i = w)) (3.6)
The LATE's validity is limited to the compliers i.e. observations for which an exogenous variation of the instruments induces a change of behaviour. The validity of the LATE is limited but it can be the most relevant identication strategy in order to insure that the estimates ar not biased.

Main hypotheses

A fundamental hypothesis of most estimators is the Stable Unit Treatment Value Assumption (SUTVA) [START_REF] Rubin | Randomization analysis of experimental data: The sher randomization test comment[END_REF]. The SUTVA hypothesis states that there are no diusion eects of the treatment ie that the outcome of one individual is only inuenced by its own status regarding the treatment. If SUTVA is veried, the fact that j = i receives or not the program does not impact the outcome Y i . In the context of PES, it means that the deforestation rate Y i of one parcel i does not depend on the fact that its neighbours decide to enroll or not into the PES. In the context of conservation instruments, validating SUTVA automatically requires hypothesis about leakage eects of the programs. We will further discuss this in Section 3.4.

The SUTVA hypothesis can also be violated because of John Henry Eects. This eect refers to changes of behavior by the control group to compensate for their disadvantage compared to the treated group. Indeed, in the case of PES, a growing literature emerged on the issue of crowding-in and crowding-out eects of PES schemes [START_REF] Rode | Motivation crowding by economic incentives in conservation policy: A review of the empirical evidence[END_REF]. If the treatment modies the behaviour of the control group, it automatically biases the estimation.

Another important hypothesis is the absence of Ashenfelter's dip in the estimation [START_REF] Ashenfelter | Estimating the eect of training programs on earnings[END_REF]. It states that individuals have not changed their behaviour prior to the program implementation. This change of behaviour could be due to:

• A shock aecting the population of study. In that case, the methods using pretreatment characteristics such as dierence-in-dierence (DID) for example (see Section 3.2.2) can not correctly identify the impact.

• Anticipation eects by individuals. If individuals change their behaviour before program implementation, for example in order to be eligible for the program, it might bias the results. We will also discuss this hypothesis in the context of PES in Section 3.4.

A last important hypothesis is called uncounfoundedness. It states that all relevant covariates inuencing both treatment reception and the outcome variable are included in the model. Econometrically, it means that the outcome variable is independent from the treatment conditionally on the observable characteristics included in the model. These covariates are called confounding factors and can be either observable or unobservable.

3.2 Evaluating PES as a treatment

Matching methods

Let's consider a vector of covariates X i and two groups of observations: the treatment group, D i = 1, and the control group D i = 0. Treatment and control groups are statistically dierent regarding covariates X i . Matching methods aims at bal- ancing covariates between the treated group and the control group to make them on average similar in all observable covariates but the treatment itself. The matching procedure weights observations from the control group in order to balance the covariates ad makes both groups statistically similar regarding X i . These methods have been emphasized by [START_REF] Joppa | Reassessing the forest impacts of protection[END_REF] among others as a good option to deal with selection bias in conservation instruments allocation. There are two main types of matching estimators: The main hypothesis of PSM is the selection on observable. To comply with unconfoundedness hypothesis, one must make the hypothesis that there are no unobservable covariates inuencing both the treatment and the outcome variable.

In order to get an unbiased estimation of the impact, the estimations must include all observable covariates jointly inuencing both treatment and outcome variables and, if possible, enough variables to capture the eect of unobservable characteristics [START_REF] Ravallion | Evaluating anti-poverty programs[END_REF]. Matching methods also requires the SUTVA hypothesis to hold (cf Section 3.1.2). Therefore, PSM relies on the same fundamental hypothesis as ordinary least square (OLS).

An exact matching would compare individuals with the same observable characteristics (X i ). If all relevant variables are included, the treatment becomes orthog- onal to the error term. But nding two observations, treated and non treated, with exactly the same characteristics might prove impossible. [START_REF] Rosenbaum | The central role of the propensity score in observational studies for causal eects[END_REF] theorem states that, if the outcome is orthogonal to participation conditionally on X i :

Y i ⊥ D i |X i (3.7)
Then it is also orthogonal to participation conditionally on the probability to be treated knowing X i :

Evaluating PES as a treatment

49

Y i ⊥ D i |P (X i ) (3.8)
Therefore, one can estimate the impact, not by comparing observations with similar X i , but with similar probability to receive the treatment knowing X i . This probability is called the propensity score.

Let's consider that our sample of N observations i is divided into two groups: a group of J observations j that received the treatment and a group of K observations k that did not. PSM procedure attributes, for each treated observation, one or more match from the control group. In order to nd similar matches for each treated individuals, it is recommended to have a control group much larger than the treated group.

We can distinguish 4 steps in PSM implementation. First, one must estimate the probability to receive the treatment (the propensity score) for each observation usually using a probit or a logit model:

P r(D i = 1) = f (X i ) (3.9)
Second, one restricts the sample to the common support by excluding from the control group the observations with a propensity score lower than the minimum of the treated group and vice versa with the treated group. The existence of a common support is a crucial assumption to the application of matching methods. Note that restriction to the common support is an advantage of matching methods compared to OLS estimations.

Third, weighting matrix ω jk is generated in order to balance covariates between the two groups. Numerous technique are available for the estimation of ω jk such as:

• Nearest-neighbour: Each treated observations is matched with its closer neighbour in terms of propensity score, each neighbour is assigned a weight of one.

• N-nearest neighbour: Each treated is matched with its three nearest neighbours, each neighbour is assigned a weight of 1/n.

• Radius-matching: Each treated individual is matched with all the observations within a certain tolerance limit regarding dierence in propensity score

• Kernel matching: Each treated is matched with the whole distribution of the control group weighted by their distance in terms of propensity score.

It is also possible to dene a tolerance limit between observations regarding dierences of probability. For example, choose the 3-nearest neighbour matching but impose that the dierence of propensity score with the treated observation should not exceed 5%. In that case, some treated observations may only have 1 or 2 matches in the control group.

Matching can also be implemented with or without replacement. As explained by [START_REF] Caliendo | Some practical guidance for the implementation of propensity score matching[END_REF], there is a trade-o between potential bias and variance. On one hand, matching with replacement reduces the possibility of bad matches" ie matches that would be highly dierent from the treated but, on the other hand, it reduces the size of the control group in the common support and consequently increases the variance [START_REF] Smith | Does matching overcome lalonde's critique of nonexperimental estimators[END_REF].

Matching can also be forced within strata. For instance, if there are structurally dierent groups according to one variable or to impose a matching between observations of the same geographic region, it is possible to restrict the matching within strata. To obtain unbiased estimation, the control group must be large enough within each strata in comparison of the treated group.

Eventually, the impact is computed as:

AT T = E(Y T j -Y NT j |(D i = 1)) = 1 J J j (Y j -E(Y NT j |D i = 1, P j )) (3.10) (3.11) With: E(Y NT j |D i = 1, P j ) = J j ω jk Y NT k (3.12)
The impact identied is computed conditionally on treatment status D i : it is an ATT. Its validity is limited to the treated group. One can not hypothesize that the results would have been the same if the control group had received the program.

As explained by [START_REF] Caliendo | Some practical guidance for the implementation of propensity score matching[END_REF], there are no clear rule of thumb about which techniques should be used and they should all converge asymptotically. The choice of the technique is case-specic and depends a lot on the context of the study. There are often trade-os between variance and bias and the choice depends on the availability of good matches in the control group.

Covariate matching and extensions

Covariate matching relies on the same assumptions as PSM (cf Section 3.2.1.1) but the matching between observation is based on vectorial distances instead of propensity scores. Covariate matching is usually based on Mahalanobis distance. Equation 3.13 presents the usual euclidian vectorial distance between i and j:

V D = (X i -X j )V -1 (X j -X i ) (3.13) V is the variance of X.
Euclidian distance takes into consideration the scale of the dierent variables included in X i by introducing the variance V . But it does not consider the correlation in the data. For this reason, covariate matching uses the Mahalanobis distance that introduce the covariance matrix S instead the variance V . This metrics puts less weight on the components of X that are more dispersed. The Mahalanobis distance is dened as:

MD = (X i -X j )S -1 (X j -X i ) (3.14)
Once computed Mahalanobis distance between each treated and control observation, the matching procedure weights the control group according to the technique chosen. The techniques available are the same as the ones presented in Section 3.2.1.1. The ATT is also computed using equation 3.10.

The trade-os between covariate matching and PSM has been discussed by [START_REF] Zhao | Using matching to estimate treatment eects: data requirements, matching metrics, and monte carlo evidence[END_REF]. Based on Monte-Carlo simulations, the author's ndings suggest that when the correlation between the covariates and the participation is high, PSM performs better but covariate matching can also be used. Nevertheless, with small sample, it is recommended to use covariate matching.

Studying large sample properties of covariate matching, [START_REF] Abadie | Large sample properties of matching estimators for average treatment eects[END_REF] show that when matching is not exact, the estimations include a bias that may not vanish with nite sample. They propose a correction for this bias making matching estimators consistent. They also provide a software in order to apply this correction.

One notable extension of covariate matching is the Genetic Matching developed by [START_REF] Diamond | Genetic matching for estimating causal eects: A general multivariate matching method for achieving balance in observational studies[END_REF]. The objectives of matching methods is to balance the covariates between the control and treated group. Genetic matching generates an additional weighting matrix G in order to improve the balance between covariates of treated and control group. The vectorial distance is dened by:

GM D = (X i -X j )GS -1 (X j -X i ) (3.15)
The matrix G is generated by an iterative algorithm. The IE of Costa-rican PES by [START_REF] Robalino | Ecopayments and deforestation in costa rica: A nationwide analysis of psa's initial years[END_REF] 2 between 1997 and 2000 and Robalino et al. (2008) between 2000 and 2005 show how matching methods (PSM and covariate matching) can reduce bias in the estimation compared to with/without or before/after estimations. As a matter of fact, the authors' ndings suggest that if deforestation rates are low in treated area, this is mainly due to a national decrease in deforestation rates. Once controlled for distances to the main markets, access to infrastructure and including dummy variable for the dierent areas, both studies nd very low impact of the PES that they attribute to mistargeting of the scheme. Similar methodologies have been applied to IE of protected areas in Costa Rica, Mexico or Thailand [START_REF] Andam | Measuring the eectiveness of protected area networks in reducing deforestation[END_REF][START_REF] Andam | Protected areas reduced poverty in costa rica and thailand[END_REF][START_REF] Pfa | Park location aects forest protection: Land characteristics cause dierences in park impacts across costa rica[END_REF][START_REF] Honey-Roses | A spatially explicit estimate of avoided forest loss[END_REF]. Genetic matching methods have been poorly used so far for IE of conservation instruments. The evaluation of protected areas in Bolivia by Canavire-Bacarreza and Hanauer (2013) constitutes a notable exception. The authors investigate the impact of protected area on a poverty index in Bolivian municipalities and nd that protected areas are associated with poverty reduction.

Spatial matching

The eciency of conservation instruments depends crucially on their localisation and spatial interactions with neighbouring observations matter a lot for IE. As a matter of fact, the characteristics of neighbouring observations are crucial confounding factors: they inuence both the probability to receive the program and the outcome variable. For instance, being located near a road inuences both deforestation and the probability to enter into a PES.

Honey-roses et al. ( 2011) recently developed a spatial-matching estimator in order to evaluate the impact of a natural protected area and PES in Mexico. The authors include in the estimation, not only the characteristics of the parcel i but also the characteristics of the neighboring parcel j.

The additional variables introduced by Honey-roses et al. ( 2011) are computed as:

X SL i = j γ ij X j (3.16)
γ ij is a contingency matrix that takes a value dierent from zero if i and j are considered as neighbours. γ ij can have dierent formulation depending on the denition of neighbouring and the standardisation chosen. More details about contingency matrix can be found in Section 3.3.3. The authors nd a positive impact of the conservation on the forest cover.

The rest of the procedure follows the steps presented in the previous sections. Remember that unconfoundedness (see Section 3.2.1.1) is the main assumption of matching estimators. Since spatially-lagged variable are crucial confounding factors for IE of conservation instruments, not including them automatically bias the estimations. Moreover, not introducing spatial interactions can lead to a violation of the SUTVA in the presence of diusion eects.

Testing the matching

In order to test that the matching succeeded in balancing covariates, it is recommended to implement a succession of t-test for each variable of X i to test if signi- cative dierence remain between the tow groups after the matching.

Without weighting the counterfactual, the null hypothesis of the t-test is:

1 J J j x j - 1 K K k x k = 0 (3.17)
And after weighting the control group and restricting to the common support, the null hypothesis is: :

1 J ( J j x j - J j ω jk x k ) (3.18)
Statistical dierence may appear in 3.17. However, they should not be signicant in equation 3.18 if the matchings performed well in balancing covariates. Moreover, for each covariate, one can compute the standardised bias between control and treatment groups before and after weighting such as:

( xj -xk ) 0.5(V (x j ) + V (x k )) (3.19)
With xj and V (x j ) the mean and the variance of variable x i in the treated group and xk and V (x k ) the mean and the variance in the control group. [START_REF] Caliendo | Some practical guidance for the implementation of propensity score matching[END_REF] also recommend to run the test suggested by [START_REF] Sianesi | An evaluation of the swedish system of active labor market programs in the 1990s[END_REF]. This test proposes to re-estimate the propensity score on the common support. There should not be any dierences in the distribution of covariates of both groups and the explanatory power of the model, the pseudo-R 2 , should be low. It is also possible to run an F-test on the joint-signicance of all regressors and compare the results of this test using the whole sample and the common support. We should reject the null hypothesis that all regressors are jointly null on the whole sample and accept it on the the common support. [START_REF] Rosenbaum | Observational studies[END_REF] proposes a test of sensitivity to hidden bias. This test checks how sensitive are the results to the existence of unobservable confounding factors or, in other words, how strongly these factors must aect selection into the treatment to undermine our conclusions" [START_REF] Andam | Measuring the eectiveness of protected area networks in reducing deforestation[END_REF]. Hidden bias could threaten the results if unobservable are inuencing selection into the treatment and the outcome variable. Following [START_REF] Andam | Measuring the eectiveness of protected area networks in reducing deforestation[END_REF], we note the odds ratio of the probability to be selected into as PES as Π i /1 -P i i . The log odds can be modeled as: [START_REF] Rosenbaum | Observational studies[END_REF] proposes to compute bounds on the signicance level of the estimates as τ = exp(γ) varies. The odds of being protected may vary as a result of a hidden bias but the impact can remain signicant. The test provides the maximum value of τ for which the results remains signicant. The more this limit value is important, the less hidden bias might impact the results. As noted by [START_REF] Andam | Measuring the eectiveness of protected area networks in reducing deforestation[END_REF], the test does not say anything about the existence of such a bias but about how strongly it should inuence the selection process to bias the results.

log( Π i 1 -P i i ) = κX i + γμ i (3.20) μ i are unobserved covariates scaled as 0 ≤ μ i ≤ 1.

Dierence-in dierence 3.2.2.1 Introducing control on unobservable

Dierence-in-dierence method is a combination between before/after and with/without situation. Let's note Y it the outcome of individual i at time t and D i the reception status.

The equation of the ATT presented in Section 3.1.2 is:

AT T = E(Y T i -Y NT i |(D i = 1)) (3.21)
Remember that the outcome for beneciaries if they had not beneted from the program, Y NT it+1 is unobservable by denition. DID estimator assumes that it is possible, based on pre-treatment data of treatment and control group, to approximate this missing outcome by:

E(Y NT it+1 |D i = 1) = E(Y NT it+1 |D i = 0) -[E(Y NT it |D i = 0) -E(Y NT it |D i = 1)] (3.22)
Equation 3.22 assumes that the mean outcome for beneciaries if they had not received the treatment is equal to the mean outcome of the control group after the treatment corrected by the mean pre-treatment outcome dierence between both groups. Combining 3.21 and 3.22, DID identies the ATT dened as:

AT T = E(Y T it+1 |D = 1) -E(Y NT it+1 |D = 0) -[E(Y NT it |D = 1) -E(Y NT it |D = 0)] (3.23)
The main assumption of DID is that the mean dierence between treated and control groups would have remained stable without implementation of the treatment ie that both groups were following parallel trends. If the parallel trends assumption is veried, we can identify an ATT (cf Section 3.1.2). While matching methods controlled for observable covariates, DID allows us to control for time-unvarying unobserved heterogeneity.

Let's consider a simple graphic example. Figure 3.1 plots the mean evolution of deforestation for PES beneciaries, lower line, and non-beneciaries, upper line, over time. PES is implemented at date t. In this gure, it is easy to understand why before/after methods and with/without methods presented in Section 3.1.1 lead to biased estimates. First, because deforestation is decreasing over time in both groups and second because non beneciaries (control group) have higher deforestation rates than beneciaries (treated group). Looking at gure 3.1, one can conclude that the parallel trend assumption seems to be validated by pre-treatment evolution of both groups (between t -1 and t). Let's assume that this parallel evolution would have continued without PES. The dotted line shows the hypothetical evolution of the beneciaries if they had not received the treatment: the ATT is represented by segment a. If a can not be computed directly, it is straightforward from gure 3.1 that it is equal to (bc) ie the dierence of deforestation rates between treated and control group minus the original dierence between the two groups.

DID can be estimated in a simple panel regression framework:

Y it = α + γ 1 T i + γ 2 t i + βT i t i (3.24)
Combining equations 3.23 and 3.24, it is easy to show that the ATT is equal to coecient β. DID can also be estimated using xed-eect estimator.

It is possible to test the parallel trend assumption using pretreatment data if they are available. It requires estimating equation 3.24 between t -1 and t. If the assumption is validated, β should not be signicantly dierent from zero.

DID controls for all time unvarying covariates: observable or unobservable. If DID method relies on less restrictive assumption than matching methods, one advantage of the latter is the restriction to the common support and the possibility to select a sample similar to the treated group. It is possible to combine the advantages of both methods using DID-Matching.

Combining DID and matching

Compared with simple matching, DID-matching introduces a control on timeinvariant unobservable covariates. The conditional independence assumption becomes:

(Y it+1 -Y it ) ⊥ D i |X i (3.25)
This assumption can also be called conditional parallel trend.

If we consider that the sample is divided between a group of J beneciaries j and K non beneciaries k, the ATT is estimated by:

AT T = E((Y T i -Y NT i )|D = 1) = 1 J J j [(Y T jt+1 -Y NT jt ) - J j ω jk (Y NT kt+1 -Y NT kt )]
(3.26)

The weighting matrix ω ij is computed using covariate matching with Abadie and Imbens (2006)'s correction or PSM. It is recommended to use pre-treatment data to construct the weighting matrix. As a matter of fact, ex-ante covariates are not inuenced by the treatment contrary to ex-post data. Matching using pretreatment data build a counterfactual that was similar to the treated group before the implementation of the treatment. Matching on post-treatment data might bias the estimations if the treatment has impacted the covariates X it+1 .

In the context of PES, this method has been used by [START_REF] Arriagada | Do payments for environmental services aect forest cover? a farm-level evaluation from costa rica[END_REF] and [START_REF] Chabé-Ferret | How much green for the buck? estimating additional and windfall eects of french agro-environmental schemes by did-matching[END_REF]. [START_REF] Arriagada | Do payments for environmental services aect forest cover? a farm-level evaluation from costa rica[END_REF] studied the impact of PES in a region of Costa Rica. They perform an analysis at farm-level and match on pre-treatment covariates such as initial forest cover or farm size. Their results suggest that DID matching performs better than covariate matching in balancing covariates. They also perform [START_REF] Rosenbaum | Observational studies[END_REF] test in order to check for the sensitivity of their results to potential unobservable confounder. As [START_REF] Pfa | Payments for environmental services: empirical analysis for costa rica[END_REF] before, they nd a low impact of the program that they attribute to mistargeting.

In their study of the impact of European Union agro-environmental schemes (AES), Chabé-Ferret and Subervie (2013) 3 develop a theoretical model in order to understand the decision of the farmers to adopt an AES. The authors identify the potential bias for their estimations by discussing SUTVA, conditional parallel trend assumption and common support in the context of their model. Once discussed the assumption of the estimator, they provide estimations of ATT for 5 AES at farmerlevel. Nevertheless, the multiplicity of AES and the possibility to cumulate them makes the denition of control and treated group more complicated. They dene the control group as the farmers that did not adopt any AES. The treated group for a given AES is composed of the farmers that adopted at least this AES of interest independently of the number of other AES adopted. Hence, the ATT identied for this given AES is a weighted average of the treatment eect of each combination of AES including the AES of interest.

Regression discontinuity design and pipeline regressions

Regression discontinuity design (RDD) and pipeline regression are two dierent IE methods both using eligibility rules of the program in order to estimate the impact [START_REF] Khandker | Handbook on impact evaluation: quantitative methods and practices[END_REF]. The rst one is based on the targeting criteria and the second on progressive entry into the program.

Regression discontinuity design

RDD uses eligibility cutos as an instrument to identify the impact of the program.

Let us consider that eligibility is based on a variable S, an index of deforestation risk for instance, with i eligible for program reception if s i > s * . The group of eligible observations is likely to be dierent from the one of non-eligible. Nevertheless, if we consider only the individuals which are located just below and just above the threshold: these two groups are likely to be similar. RDD uses individuals with s i slightly lower than s * as a control group for individuals with s i slightly higher than s * . We identify a LATE dened as: When the threshold is enforced, the discontinuity is said sharp. But there might be individuals located above the threshold that do not receive the treatment and vice-versa: in this case the discontinity is said fuzzy. In that case, the threshold is not used directly to identify the impact but as an instrument for the probability to receive the program (see Section 3.2.4 for more details about instrumental variable estimations). Let's consider a dummy variable E i = 1 if S i > s * , one can compute the probability to receive the program:

LAT E RDD = E(Y i |S i = s * +ε) -E(Y i |S i = s * -ε) (3.27)
P r(D i = 1|E i )
(3.28) If S i and E i are both included in the model, being above or below the threshold will not inuence the outcome variable: the threshold is said exogeneous. Then it is possible to estimate the impact using instrumental variables with the threshold as an instrument for participation to the program.

RDD allows us to estimate a LATE for the individuals located around the threshold. Its internal validity is very limited since results can not be extrapolated to other groups of beneciaries. Nevertheless, RDD can identify an unbiased impact of the program without additional assumption such as selection on observable or parallel trend. There is a trade-o between larger validity and unbiased identication of the impact. Note that RDD does not relax the SUTVA assumption.

An example of the implementation of fuzzy-RDD can be found in Alix-Garcia et al. ( 2013). The authors analyse the impact of a Mexican social program called Oportunidades on deforestation. Oportunidades oers cash payments to marginal households conditionally on children's school attendance and health checkups. The authors hypothesize that the program can relax a credit constraint and increase deforestation. Oportunidades is allocated to households in localities considered as marginal according to a marginality index based on measures of education, infrastructures or access to energy among others. The authors use the threshold as an instrument to identify the impact of the programm on deforestation. As a matter of fact, if marginality can impact deforestation, if we control for the value of the marginality index, we can credibly make the hypothesis that being above or below the threshold won't aect deforestation. Their results conrm that the program has increased deforestation.

Pipeline regressions

Pipeline regressions uses progressive entry into the program to evaluate its impact. It is based on the assumption that entry into the program is random given application. The control group is composed of observations that are about to receive the program in the forthcoming years. Pipeline regressions are similar to RDD but instead of using a threshold, they estimate the impact for each new cohorts entering into the program using the future beneciaries as a control group. [START_REF] Ravallion | Evaluating anti-poverty programs[END_REF].

Instrumental variable methods

The unconfoundedness assumption is highly restrictive. If one is unable to deal with observable and/or unobservable confounding factors, instrumental variable methods allows to identify a LATE.

Let us consider the estimation of a simple model using Ordinary Least-square (OLS):

Y i = α + βD i + γX i + ε i (3.29)
If one estimates this equation using OLS, the estimate of β might be biased because of endogeneity of D i . This endogeneity may arise for dierent reasons but we here focus on:

• Inverse causality: Y i impacts D i . Deforestation rates can inuence the reception of a conservation instruments such as PES

• Omitted variable: ε i is correlated with both D i and Y i . There are unobserved variable inuencing conjointly deforestation rates and reception of the scheme (unconfoundedness) If endogeneity arises, E(D i , ε i ) = 0 and the impact of the treatment can not be accurately identied. Let us now consider that the selection process can be modeled as:

D i = θ + φZ i + σX i + μ i (3.30)
With Z i a vector of exogeneous instruments. This vector of instruments must comply with the exclusion restriction stating that:

• E(Z i , ε i ) = 0 • E(Z i , D i ) = 0
OLS estimations are biased because of the correlation between ε i and μ i . There are two ways to account for endogeneity in the estimations. The rst one, control functions model, is based on predictions of μ i while the second, two-stage least square (TSLS), is based on predictions of D i .

In both cases, a LATE is identied an its validity is limited to the observations for which the instrumental variable impacts the reception of the treatment. Therefore, it is crucial to have instruments that are strong predictors of the treatment.

Control functions model

Control function model is part of Heckman-type" models. In Heckman sample selection model, also called Tobit type II [START_REF] Wooldridge | Econometric Analysis Cross Section Panel[END_REF], the explained variable is censored meaning that its true value is only observed if the observation satises with an underlying selection process. Heckman proposed to estimate the selection process in order to take into account this issue. In our case, we are not concerned about censoring of the explained variable but about a selection bias in the attribution of the program: the explanatory variable. Control functions have been developed as an extension of original Heckman model to deal with this issue [START_REF] Heckman | Using matching, instrumental variables, and control functions to estimate economic choice models[END_REF].

Control functions model is based on predictions of the error term of the selection process. In the previous section, we noted that the coecient γ obtained by an estimation of equation 3.29 using OLS was biased because of the correlation between ε i and μ i . Introducing predictions of μ i in 3.29 allows to account for this correlation in the estimation.

The rst step is to estimate the probability to receive the treatment using a probit model:

P r(D i = 1) = θ + φZ i + σX i + μ i (3.31)
Note that Z i must comply with the exclusion restriction as dened in the previous section.

From 3.31, it is possible to compute the Inverse-Mills Ratio (IMR) for participants and non-participants by estimating equation 3.32 and 3.33.

IM R p = E(μ i |Z i , X i , D i = 1) = φ(.) Φ(.) if D i = 1 0 if D i = 0 (3.32)
And for non participants:

IM R np = E(μ i |Z i , X i , D i = 0) = φ(.) 1-Φ(.) if D i = 0 0 if D i = 1 (3.33)
The IMR is the ratio of:

• φ(.): The density function of the probability to receive the program ie the predicted probability that D i = 1 conditionally on Z i and X i .

• Φ(.): The cumulative distribution function of the probability to receive the program.

We include both IMR in equation 3.29:

Y i = α + βD i + γX i + ρ p IM R p + ρ np IM R np + ε i (3.34)
By introducing the IMR in the estimation, we purge the error term ε i from the part correlated with the outcome variable and correct the selection bias. A test of joint-signicance of both Mills-ratio indicates if there is a selection bias in the OLS estimation.

Y i = α + β Di + γX i + ε i (3.36)
By using a predicted value Di , we isolate the exogeneous variation of D i ie non correlated with ε i . D i being a binomial variable and not a continuous variable, it would be necessary to estimate the rst step using a probit or logit model. To our knowledge, no estimators based on predicted value have yet been developed with a binomial model as a rst-stage. Therefore, the rst step is usually estimated using OLS and the procedure simplies into two-stage least square as presented in Section 3.3.2.

Nevertheless, estimating the rst step model using linear probability (OLS estimation with a dummy explained variable) does not take in consideration the binomial nature of D i . [START_REF] Imbens | Control function and related methods[END_REF] explain that this method is inconsistent and has no know properties. The authors propose to:

1. Estimate the probability to receive the program (equation 3.31) using a probit model 2. Extract the tted probabilities to receive the program from the probit estimations 3. Estimate the model by 2SLS and use the tted probabilities as instrument for reception [START_REF] Imbens | Control function and related methods[END_REF] state that this method is fully robust but might be less ecient than the control function method.

Selection and pre-processing of the data

An alternative way to estimate equation 3.29 in a regression framework is to deal with selection by preprocessing the data. We will here introduce pre-matching and propensity scores weighting.

Pre-matching methods

Matching methods can also be implemented as non-parametric pre-processing of the data [START_REF] Ho | Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference[END_REF]. Pre-matching uses a matching procedure to restrict the sample to the common support before estimating the impact using a parametric model. The sample can also be restricted to a matched control group by applying a matching techniques (nearest-neighbourg, caliper etc...). Through this process, one is trying to insure that treated and control groups are similar. Propensity scores can also be added to the vector of regressors X i in order to control for the heterogeneity of propensity scores.

The pre-processing relies on selection on observable assumption. Identifying a relevant control group remains the most crucial issue, pre-processing must only be considered as a complement in order to identify within the control group which observations are the most similar to the treated and exclude outliers.

It is the method chosen by Alix-Garcia et al. ( 2012) for IE of a Mexican's PES (the PSA-H). In their analysis, the authors used rejected applicants as a counterfactual for PSA-H beneciaries during the rst year of implementation. In 2003, too many ejidos applied for the scheme in comparison of its budget and applications got rejected only because the program lacked of funding. In the context of PES, one of the main unobservable confounding factor is the willingness to participate in the program. The pool of rejected applicants proved willingness to participate so they constitute a relevant counterfactual. Once restricted their sample to rejected applicants for lack of funds, Alix-Garcia et al. ( 2012) preprocess the data using [START_REF] Abadie | Large sample properties of matching estimators for average treatment eects[END_REF] bias-adjusted matching in order to select the most similar control group regarding observable characteristics. Once selected their control group, they use a Tobit model 4 to estimate the impact of PSA-H. They nd a small but signicant impact of the program on avoided deforestation. Pre-matching has also been used by [START_REF] Ferraro | Conditions associated with protected area success in conservation and poverty reduction[END_REF] to evaluate the eciency of protected area in Costa Rica and Thailand.

Propensity Score Weighting

Propensity Score Weighting uses propensity scores as sample weights in a regression framework. PSW allows us to balance covariates between treated and non-treated observations. It is implemented as follows [START_REF] Hirano | Estimation of causal eects using propensity score weighting: An application to data on right heart catheterization[END_REF][START_REF] Lunceford | Stratication and weighting via the propensity score in estimation of causal treatment eects: a comparative study[END_REF][START_REF] Austin | An introduction to propensity score methods for reducing the eects of confounding in observational studies[END_REF]:

1. Estimate propensity scores using a probit or a logit model 2. Generate sample weight using predicted propensity scores 3. Estimate the model using Weighted Least Squares (WLS) PSW has mainly been applied with a dummy variable in the second step of the estimation. In that case, [START_REF] Robins | Analysis of semiparametric regression models for repeated outcomes in the presence of missing data[END_REF] showed that the estimator was doubly robust. The estimates are unbiased if either one of the two models are correctly specied. Moreover, [START_REF] Hirano | Ecient estimation of average treatment eects using the estimated propensity score[END_REF] showed that using non parametric estimates of the propensity scores led to ecient estimation of the average treatment eect. Nevertheless, as emphasized by [START_REF] Wooldridge | Econometric Analysis Cross Section Panel[END_REF], it is dicult to estimate the asymptotic variance of the estimates using this method and little is probably lost using a logit or a probit model.

In a rst step, one must estimate the propensity score

pscore ij = P r(D i = 1) = θ + φW i + μ i (3.37)
In a second step, one can estimate the ATT using WLS. Sample weights are equals to:

w ij = pscore ij /(1 -pscore ij ) if psa ij = 0 1 if psa ij = 1 (3.38)
The covariates W i included in the estimation of the propensity scores do not have to be the same as X i included in equation 3.29.

Evaluating PES as a continuous variable

So far, most IE used a dummy variable for PES reception. As highlighted by [START_REF] Miteva | Evaluation of biodiversity policy instruments: what works and what doesn't?[END_REF], reception of the PES may also be analysed as a continuous variable. Exposure to the treatment varies according to the share of forest enrolled or the number of years of reception. In this section, we suggest methodologies that could be applied with a continuous treatment.

Generalised propensity scores

Generalised propensity scores (GPS) is a generalisation of propensity score methods to continuous treatment developed by [START_REF] Hirano | The propensity score with continuous treatments. Applied Bayesian modeling and causal inference from incomplete-data perspectives[END_REF]. It relies on the same assumptions as the one exposed in Section 3.2.1.1 regarding SUTVA and confounding factors. Let us consider the conditional density of a continuous treatment variable T i given covariates X i : r(t i , x i ).

Remember that PSM compares observations with similar probability to receive the treatment. By analogy, the GPS compares observations with similar value of r(t, x). As explained by [START_REF] Hirano | The propensity score with continuous treatments. Applied Bayesian modeling and causal inference from incomplete-data perspectives[END_REF], GPS has balancing property similar to that of the standard propensity score. Within the same strata of r(t i , x i ) the probability that T = t i does not depend on the value of X i .

The objectives of GPS is to estimate an average-dose-response function dened as:

E[Y (t)] (3.39)
GPS are implemented in three steps. First, one must estimate the conditional distribution of the treatment given the covariates r(t i , x i ). Using this estimated conditional distribution, it is possible to compute an estimated generalised propensity score Ri .

Second, one can estimate the expectation of outcome variable Y i conditionally on R i for each observation. [START_REF] Hirano | The propensity score with continuous treatments. Applied Bayesian modeling and causal inference from incomplete-data perspectives[END_REF] recommends using a quadratic form:

Evaluating PES as a continuous variable 63

E(Y i |T i , Ri ) = α 0 + α 1 T i + α 2 T 2 1 + α 3 Ri + α 4 Ri 2 + α 5 T i Ri (3.40)
Finally, the average expected outcome of treatment, the equation 3.41, is computed as the average value of 3.40:

Ê[Y (t)] = 1 N N i=1 ( α0 + α1 t i + α2 t 2 i + α3 r(t i , X i )+ α4 r(t i , X i ) 2 + α4 t i r(t i , X i )) (3.41)
The method has been applied to the Costa-rican PES by [START_REF] Arriagada | Private provision of public goods: applying matching methods to evaluate payments for ecosystem services in Costa Rica[END_REF]. The author considers the percentage of area under protection for each landowner. He estimates the average-dose response function following the procedure presented here but does not nd a signicant impact of the program on the whole distribution.

Nevertheless, by plotting the dose-response function, he identies a threshold in program impact. [START_REF] Arriagada | Private provision of public goods: applying matching methods to evaluate payments for ecosystem services in Costa Rica[END_REF]

Two stages least squares

Let us consider the estimation of the following model with Y i the deforestation rate in village i, T i the amount of payments received for PES scheme5 and X i a vector of control variable.

Y i = α + βT i + γX i + ε i (3.42)
For the reasons explained in Section 3.2.4, a selection bias generates endogeneity in the estimation. Nevertheless, with a continuous treatment, the reasons for which endogeneity may arise are slightly dierent. One is not only concerned about endogeneity of treatment reception, but also about the exposure to the treatment.

If treatment exposure is dened as the amount received or the time spent in the program, inverse causality means that deforestation rates are not only inuencing reception of scheme but also the amount of paiement or the order of entry into the program.

Two-stages least square (TSLS) proposes to correct for endogeneity using instrumental variables. One must rst predict T i by estimating equation 3.43:

T i = θ + φZ i + σX i + μ i (3.43)
and introduce it into the model as a second stage:

Y i = α + β Ti + γX i + ε i (3.44)
To be valid, instruments Z i must be strong predictors of the treatment T i and not have direct impact on the outcome variable Y i . In econometric terms, Z i must valid the exclusion restriction:

1. Be correlated with the treatment variable: cor(Z i , T i ) = 0 2. Be uncorrelated with the error term : cor(ε i , T i ) = 0 Three main tests are usually implemented following TSLS. First, one must perform an F-test of joint signicance of instruments Z i after estimating equation 3.43. Following the results obtained by [START_REF] Staiger | Instrumental variables regression with weak instruments[END_REF], a common rule of thumb is to consider that if the value of the F-test is inferior to 10, the instruments must be considered as weak and are not good predictors of T i [START_REF] Araujo | Econométrie. Bréal éd[END_REF][START_REF] Cameron | Microeconometrics: methods and applications[END_REF].

A second test is the Sargan (1988)'s overidentication test. This test checks the validity of the exclusion restriction ie that Z i is uncorrelated with ε i . It can only be implemented if the model is overidentied ie if the number of instrumental variables Z i is superior to the number of endogeneous variable T i . From equation 3.44, one can predict εi and estimate the following regression using OLS:

εi = φ 2 Z i + σ 2 X i + ς i (3.45)
In the null hypothesis, the instruments are valid and the explanatory power of this regression is null. Sargan's test is a χ 2 and test'statistics is S = N.R, N being the size of the sample, and R, the R 2 of the test equation.

The last test is the Dubin, Wu and Haussman test in the version proposed by [START_REF] Nakamura | On the relationships among several specication error tests presented by durbin, wu, and hausman[END_REF]. It checks for exogeneity of variables T i and is only valid if the instruments validates the exclusion restriction. If T i is exogenous, it is more appropriate to estimate the model using OLS. If T i is endogenous, there is a correlation between μ i and ε i . From equation 3.44, it is possible to predict μi and estimate the following equation:

Y i = α + βT i + γX i + ρμ i + ε i (3.46)
The test is a Student-ratio test on coecient ρ and in the null hypothesis ρ = 0. If the null hypothesis can not be rejected, T i can be considered as exogenous. In this case, OLS are convergent with minimal variance and it is recommended to use this estimator [START_REF] Araujo | Econométrie. Bréal éd[END_REF].

This methodology has been used by [START_REF] Sims | Conservation and development: Evidence from thai protected areas[END_REF] to evaluate the impact of protected areas on poverty reduction in Thailand. The author investigates the impact of protection on average consumption and poverty rates at locality level. The protection is instrumented using the positions of the localities regarding the majors tributary rivers. Historically, one of the objectives of protected area in Thailand was to protect upper watersheds of the rivers owing into the major cities in order to increase water quality. Therefore, position of the localities nearby these rivers inuences the share of land protected but is not supposed to directly impact poverty. The author nds a positive and signicant impact of protected area on poverty which they attribute mainly to the development of tourism in these regions.

Introducing spatial dimension

Geographical characteristics matter a lot for IE of conservation instruments and it is crucial to consider spatial interactions between observations. Non-spatial estimations rely on the assumption that observations are independent (Le [START_REF] Gallo | Econométrie spatiale: l'autocorrélation spatiale dans les modèles de régression linéaire[END_REF] but if they are not independent, the SUTVA does not hold.

Let us dene neighbors using a matrix of contingency ω ij that takes a value dierent of zero if i and j are considered as neighbours. Contingency can be dened in various ways such as:

• Neighbourhood: ω ij = 1 if i and j share a common border or are located within a certain distance

• K-nearest neighbour: ω ij = 1 if j is one of the k-nearest neighbour of i
• Distance: ω ij equals the distance between i and j. The distance can be computed from the centroid of the polygons, the border, etc... One can impose a maximal distance above which ω ij = 0 or choose a specic functional form such as the inverse of the distance

• Length of common borders

It can be relevant to standardize ω ij i.e. to divide the weights by j ω ij . Hence, the weights range from 0 and 1 for each observation and capture the relative distance from i to each neighbour j. 1. The deforestation rate of surrounding villages j inuences the deforestation rate in village i:

Y i = α + βT i + γX i + ρ j =i ω ij Y j + ε i (3.47)
The endogenous variable is said spatially lagged. If one does not consider the inclusion of a spatially lagged-variable, the coecients estimated are biased and estimations are inecient. This type of model can be estimated using instrumental variables or maximum-likelihood. More details can be found in LeSage and Pace (2009).

2. The reception of PES in surrounding villages j inuences the deforestation rate in village i:

Y i = α + βT i + ρ j =i ω ij T j + γX i + ε i (3.48)
The exogenous variable is spatially-lagged. In that case, not including the spatially lagged variable can be more or less problematic:

• If the spatially lagged variable inuences conjointly D i and Y i , omitting this variable generates endogeneity: the estimations are biased and inecient. This is the case when leakage occurs. Including the spatially-lagged exogeneous variable corrects for endogeneity.

ε i = φ j =i ω ij ε j + μ i (3.49)
Autocorrelation between error-term can arise because there are spatially autocorrelated variables, observable or unobservable, that are not included in the model or because shocks are spatially correlated. From this statement, it is straightforward that omitting an exogeneous or an endogeneous variable will automatically generate spatial correlation between error terms. An autoregressive process on the error term is usually specied to account for spatial autocorrelation. The purpose of this last section is to review possible methodological trade-os when designing an evaluation of the impact of a PES on forest cover. We focus on PES for forest conservation but some of the analysis can be extended to asset-building or practice-based PES. By analogy with the literature on CCT, the landowners who enroll land in a PES program are referred to as the beneciaries of the program and the term treatment refers the PES reception. The aim of the IE is to evaluate the additionality of the program, i.e. avoided deforestation that can be attributed to the PES program.

Methodological challenges and knowledge gaps

In this section, we track the design of an IE starting with the choice of the sample of beneciaries and non-beneciaries to the interpretation of the results, and discuss the dierent trade-os involved. Our aim is not to identify the best IE practices, but to propose guidelines, discuss methodological choices, and identify knowledge gaps. First, we discuss the boundaries of an IE and the choice of geographic scale, the unit of observations, and the timeframe of the evaluation as important aspects to frame PES evaluation. After which, we discuss impact estimation and more specically, the denition of treatment, the assumption of the estimators, including widely-used matching estimators, the choice of a control group, and impact heterogeneity. The rst step in designing an IE is choosing the geographic scale. Should all the beneciaries of a PES be included in the sample or should the focus be on a more homogeneous sub-area? Is it important that the area concerned is contiguous?

One group of studies uses an exhaustive or a representative sample of all PES contracts. Geographic boundaries are the same for the PES and the IE. Examples are [START_REF] Shapiro | Forest conservation and slippage: Evidence from mexico's national payments for ecosystem services program[END_REF] and [START_REF] Robalino | Ecopayments and deforestation in costa rica: A nationwide analysis of psa's initial years[END_REF], who studied the impact of a national PES using a sample of beneciaries and non beneciaries respectively in Mexico and Costa Rica. Other studies set boundaries by focusing on a specic zone at regional or sub-regional level. [START_REF] Honey-Roses | A spatially explicit estimate of avoided forest loss[END_REF] and [START_REF] Costedoat | How eective are biodiversity conservation payments in mexico?[END_REF] also studied the impact of national PES7 but at the regional level in dierent states in Mexico. [START_REF] Arriagada | Do payments for environmental services aect forest cover? a farm-level evaluation from costa rica[END_REF] evaluated a national PES in the Sarapiqui region of Costa Rica. All these authors used similar procedures to select a control group but the validity of the results and the assumption of the estimators were not the same.

Compared to many economic instruments, one characteristic of conservation instruments is that their expected impact is highly location-specic. Geographic characteristics matter because they are impact moderators (Ferraro et al., 2013) and the impact diers from one region to another. Second, geographic characteristics are crucial confounding factors that inuence not only deforestation but program reception. The main characteristics of the OC including soil fertility, land tenure, slope and other agro-ecological characteristics, are location-specic. Some of these 68 Chapter 3. Challenges for PES Impact Evaluation factors are observable, others are not which could bias the estimations.

Unobservables are more likely to be well balanced in regional studies, but obviously the validity of the results will remain limited to the sample used for the estimation and cannot be extrapolated to other regions. For these reasons, they can only be considered as case studies. But one crucial advantage of dealing with smaller area is that it might be possible to have a geographically contiguous sample. Dealing with a contiguous zone makes it possible to model spatial interactions, especially leakages, using tools provided by spatial econometrics (LeSage and Pace,

2009).

Conversely, national-scale analyses estimate the impact of the program on a representative sample. If they are well designed and if the assumption of the estimators hold, they have strong and large internal validity. The results can be extrapolated to the whole population therefore making them more relevant for decision-making.

However, the average treatment eects give us only limited information about the impact of a PES and it is crucial to investigate heterogeneity.

Unit of observation

The second choice concerns the unit of observation. PES evaluation relies to a great extent on remote sensing analysis but the question is, which pixel aggregation is a relevant unit of observation? Should the analysis be at landowner level? Should one pixel or a group of pixels be used? What is the optimal image resolution?

The choice of the relevant unit of observation for conservation instrument studies has already been discussed in the literature [START_REF]A spatial analysis of common property deforestation[END_REF], 2008;[START_REF] Andam | Protected areas reduced poverty in costa rica and thailand[END_REF]. At which level should forest cover change be computed? We will dierentiate three types of unit of analysis: pixel, gridding [START_REF] Robalino | Ecopayments and deforestation in costa rica: A nationwide analysis of psa's initial years[END_REF][START_REF] Costedoat | How eective are biodiversity conservation payments in mexico?[END_REF] or landowners [START_REF] Honey-Roses | A spatially explicit estimate of avoided forest loss[END_REF][START_REF] Arriagada | Do payments for environmental services aect forest cover? a farm-level evaluation from costa rica[END_REF].

Using a method based on pixels [START_REF] Pfa | Park location aects forest protection: Land characteristics cause dierences in park impacts across costa rica[END_REF][START_REF] Robalino | Ecopayments and deforestation in costa rica: A nationwide analysis of psa's initial years[END_REF] focuses on land use and makes it possible to accurately account for any geographic heterogeneity in the area under protection. It also allows multiplication of the size of the sample which is statistically very convenient. The optimal resolution depends on the ecological context, particularly on forest density, the size of the unit of observation, the magnitude of the expected land-use change and the underlying causes of deforestation. A particular resolution may be appropriate for identifying land-use changes attributed to planting soya in Brazil, but not for two hectares of maize planted by a peasant in Mexico. Moreover, some PES may reduce deforestation while others targets degradation which modify the type of satellite images needed [START_REF] Blackman | Evaluating forest conservation policies in developing countries using remote sensing data: An introduction and practical guide[END_REF]. In addition, although low resolution images may not enable accurate measurement of land-use changes at local level, higher resolution images can lead to misclassications in the remote sensing analysis if not combined with ground-truthing. Small-scale degradation could be classied as deforestation or ecological modications as reforestation. Using gridding allows these imperfections to be corrected by averaging deforestation in a given area. Note that the size of the grid should be carefully chosen as it can inuence the construction of the counterfactual and hence the results of the IE.

But, since the landowner is the decision maker, it seems more in line with theoretical work to aggregate pixels at the landowner's level to account for land tenure and governance of the resource8 . In this case, each unit of observation corresponds to one landowner. However, this approach could fail to account for the heterogeneity of the geographic characteristics of the area. In addition, as pointed out by [START_REF] Arriagada | Private provision of public goods: applying matching methods to evaluate payments for ecosystem services in Costa Rica[END_REF], at landowner level, one may have to deal with a mixture of forest that is protected and forest that is not. Building polygons overlapping land tenure, homogeneous land use, and PES reception, as suggested by Honey-roses et al. ( 2011) may be an appropriate intermediate solution if the size of the forest owned by each landowner is not too heterogeneous.

Timeframe

The next choice concerns the timeframe. During what period should the impact of the program be evaluated? Should the evaluation be limited to the period of the contract? What if something happened before or happens after the implementation of the program? What about contract renewal? How can a sample be selected when all the participants did not join the program at the same time?

PES are evaluated at one point in time and are usually xed-term contracts. A source of bias in IE that remains to be explored is related to possible ex-post and exante impacts of the PES. Some landowners may anticipate the arrival of the program and reduce their rate of deforestation in order to send a signal of good behavior to the authorities. A contrary hypothesis is that individuals will anticipate the future ban on converting land by cutting more trees before the ban becomes eective. Moreover, if the amount of payments is a function of a baseline, it may be tempting to increase deforestation to inuence the baseline [START_REF] Pattanayak | Show me the money: Do payments supply environmental services in developing countries?[END_REF]. Land use before the program is implemented is usually considered to be exogenous but if landowners anticipate the program, this assumption cannot be validated and we face an Ashenfelter's dip [START_REF] Ashenfelter | Estimating the eect of training programs on earnings[END_REF] and the direction of the anticipation eect is unknown.

PES may also have lagged impacts. One hypothesis is that, if deforestation slows down during the lifetime of the program, it is only delayed and the beneciaries will catch up after the program ends. PES could even increase deforestation if it temporarily relaxes a liquidity constraint but leads to more deforestation once the forest is no longer under protection [START_REF] Jayachandran | Liquidity constraints and deforestation: The limitations of payments for ecosystem services[END_REF]. On the contrary, PES can help nance a transition to non-degrading activities and permanently reduce deforestation [START_REF] Pirard | Questioning the theory of payments for ecosystem services in light of emerging experience and plausible developments[END_REF][START_REF] Karsenty | Combining conservation incentives with investment[END_REF]. PES may thus have both positive and negative ex-ante or ex-post eects on forest cover. The direction of the bias is theoretically unknown and can also vary from one beneciary to another.

Another issue concerns contract renewal. To gain in exibility and to be able to adjust PES schemes over time, contracts are usually short [START_REF] Rodríguez | Towards a unied scheme for environmental and social protection: Learning from pes and cct experiences in developing countries[END_REF]. Because PES are now well established, contracts are being renewed, a new selection process begins that has to be accounted for in the analysis. Landowners may choose to renew their contract on the same parcel, to enroll a new parcel or choose to leave the scheme. This decision will determine the long-term impact of the PES. Analyzing the sequence of renewal and abandon of a PES scheme can help understand a landowner's motivation for joining, along with potential crowding-in and crowding-out eects [START_REF] Rode | Motivation crowding by economic incentives in conservation policy: A review of the empirical evidence[END_REF].

The existence of these eects leads us to question the timeframe of the IE. PES should intuitively be evaluated over the period of the contract. But in this case, anticipated eects and lagged impacts are not accounted for and the estimates are consequently partial. On the other hand, if the impacts are evaluated over a longer period9 , it may be impossible to disentangle the dierent eects. Every year, new cohorts of beneciaries enter the program while the contracts of older cohorts come to an end. At a given point in time, a sample of beneciaries is inevitably composed of beneciaries who belonged to the program during the period of analysis, plus early beneciaries who are no longer in the program, and late beneciaries who were not in the program at the beginning of the analysis. If yearly panel data about the forest cover are not available, the impact identied is a mixture of treatment, and of anticipated and lagged eects. Both [START_REF] Shapiro | Forest conservation and slippage: Evidence from mexico's national payments for ecosystem services program[END_REF] in Mexico, and [START_REF] Robalino | Ecopayments and deforestation in costa rica: A nationwide analysis of psa's initial years[END_REF] in Costa Rica, used a sample of early beneciaries but, as the former pointed out this sample might not be representative of all beneciaries. Early beneciaries may have the closest relations with the authorities or have stronger intrinsic motivation for conservation. One possible way of dealing with this problem is to exclude contracts that have not been renewed [START_REF] Arriagada | Do payments for environmental services aect forest cover? a farm-level evaluation from costa rica[END_REF] but this implies focusing on a sub-sample. Running robustness tests on sub-samples that include and exclude beneciaries who leave the program would also be interesting. If one wants to include the lagged impacts of the program, the period of analysis needs to be extended and separate analysis would be required to analyze what happens after the PES contract. In any case, the actual duration of the program with respect to the period of time analyzed matters and is probably a variable that should be considered in the empirical analysis. Given the boundaries of the study, an appropriate treatment denition is required. Is PES reception systematically a dummy variable? If not, how should heterogeneous exposure be accounted for in the analysis? What are the possible denitions for continuous treatments?

According to recent literature on IE, PES programs are usually modeled using a dummy variable. The protection is binomial and using a dummy variable appears to be reasonably straightforward at least when pixels are used. With other units of observation, protection may be heterogeneous if the entire forest is not covered by the program. Moreover, as exposure to the treatment becomes heterogeneous with respect to the time spent in the program, the payments received and the area protected, protection is no longer binomial Continuous treatments can be dened in dierent ways. When dealing with commons or collective forests like in Mexico, dening payments is even more complicated since the amount received by one landowner depends not only on the number of hectares enrolled, but also on the number of people who share the commons, along with the allocation rules applied by the community concerned [START_REF] Corbera | The equity and legitimacy of markets for ecosystem services[END_REF][START_REF] García-Amado | Eciency of payments for environmental services: Equity and additionality in a case study from a biosphere reserve in chiapas, mexico[END_REF]. Recent literature [START_REF] Shapiro | Forest conservation and slippage: Evidence from mexico's national payments for ecosystem services program[END_REF][START_REF] Jayachandran | Liquidity constraints and deforestation: The limitations of payments for ecosystem services[END_REF] pointed out that credit constraints could inuence the impact of PES. Note that, despite the fact that the impact of the payments and the impact of conditionality cannot be disentangled in a voluntary scheme10 , the denition of the treatment implicitly focuses on either the payment or on the conditionality.

Choosing a continuous treatment also changes the way of dealing with endogeneity. The concern here is not only the date of entry into the program but also the percentage of land enrolled in the program or the order of entry as the characteristics of the landowners enrolled may change over time [START_REF] Rolon | The mexican pes programme: targeting for higher eciency in environmental protection and poverty alleviation[END_REF]Sims et al., 2014).

Estimator and assumption

The denition of the treatment aects the choice of an estimator and its underlying assumption. Which is the best estimator? What are the strengths and weaknesses of dierent estimators? What is a relevant control group?

Most estimators, including widely-used matching methods, rely on three main assumptions: Stable Unit Treatment Value (SUTVA), the absence of Ashenfelter's dip, and unconfoundedness. We already discussed the absence of an Ashenfelter's dip, stating that the program has no anticipation eects in the previous section, here we focus on SUTVA and confounding factors.

SUTVA states that the program has no diusion eects, i.e. that the outcome of an individual is only inuenced by his own status regarding PES. If SUTVA is validated, deforestation by one individual is not aected by the fact that other individuals belong or not to the program. Therefore, validating SUTVA automatically requires a hypothesis concerning the leakage eects of the program. Considerable attention has been paid to leakages in the academic literature on PES [START_REF] Wu | Slippage eects of the conservation reserve program[END_REF][START_REF] Wunder | How do we deal with leakage[END_REF]. What are the implications of leakage for impact identication? If leakages occur within observations of the sample, SUTVA does not hold and the impact of the program cannot be identied. A solution to this problem is to remove the buer zone from the control group [START_REF] Honey-Roses | A spatially explicit estimate of avoided forest loss[END_REF]. Under the assumption that no secondary leakages occurred due to market equilibrium eects, removing the buer zone makes it possible to correctly identify the impact. Nevertheless, it only captures the impact of the program on conserved land, while total estimates of additionality should also include leakages. Focusing only on protected land may overestimate the impact because leakages can reduce this additionality [START_REF] Pattanayak | Show me the money: Do payments supply environmental services in developing countries?[END_REF][START_REF] Miteva | Evaluation of biodiversity policy instruments: what works and what doesn't?[END_REF]. Moreover, as emphasized in Section 3.1.2, the IE can be biased as a result of behavioral changes through crowding-in or crowding-out eects [START_REF] Rode | Motivation crowding by economic incentives in conservation policy: A review of the empirical evidence[END_REF].

Turning now to confounding factors, it should be remembered that matching methods do not control for unobservables. It is important to control for spatiallylagged variables in the covariates [START_REF] Honey-Roses | A spatially explicit estimate of avoided forest loss[END_REF][START_REF] Costedoat | How eective are biodiversity conservation payments in mexico?[END_REF].

These variables are crucial confounding factors. Past deforestation trends should also be included in the model, either as control variables or, if panel data are available, more explicitly through panel estimation or synthetic control matching. [START_REF] Miteva | Evaluation of biodiversity policy instruments: what works and what doesn't?[END_REF] pointed out that, up to now, few authors have used instrumental variable methods, such as control functions [START_REF] Heckman | Using matching, instrumental variables, and control functions to estimate economic choice models[END_REF] or two-stage least squares, for IE of conservation instruments. Sims (2010) evaluation of the impact of natural protected areas on poverty in Thailand is a notable exception. The lack of use of these estimators is probably due to the diculty in nding relevant exogenous instruments that comply with the exclusion restriction. Unlike matching methods, these methods introduce a control on unobservable confounding factors. However, it is important to note that these estimators identify a LATE.

The validity of the LATE is limited to observations where the instruments inuence the reception of the program. The question of the relevancy of LATE for policy recommendations led to a lively debate among academics [START_REF] Deaton | Instruments of development: Randomization in the tropics, and the search for the elusive keys to economic development[END_REF][START_REF] Imbens | Better late than nothing: Some comments on deaton (2009) and heckman and urzua[END_REF][START_REF] Heckman | Comparing iv with structural models: What simple iv can and cannot identify[END_REF].

The choice between the dierent matching estimators have already been discussed [START_REF] Zhao | Using matching to estimate treatment eects: data requirements, matching metrics, and monte carlo evidence[END_REF][START_REF] Caliendo | Some practical guidance for the implementation of propensity score matching[END_REF].

With continuous treatment, Generalised Propensity Score estimator [START_REF] Hirano | The propensity score with continuous treatments. Applied Bayesian modeling and causal inference from incomplete-data perspectives[END_REF]) can be used as done by [START_REF] Arriagada | Private provision of public goods: applying matching methods to evaluate payments for ecosystem services in Costa Rica[END_REF]. Note that genetic matching methods [START_REF] Diamond | Genetic matching for estimating causal eects: A general multivariate matching method for achieving balance in observational studies[END_REF] port should be provided to assess the quality of the matching. It is also useful to perform the bounds test proposed by [START_REF] Rosenbaum | Observational studies[END_REF] to check for the sensitivity of the results to unobservable confounding factors [START_REF] Andam | Measuring the eectiveness of protected area networks in reducing deforestation[END_REF][START_REF] Arriagada | Do payments for environmental services aect forest cover? a farm-level evaluation from costa rica[END_REF].

It should not be forgotten that matching can be used to select a sample of observations but it cannot be used to identify pairs of observations. For instance, if a PES is assigned to three landowners, identifying three control landowners through matching will not balance covariates between the two groups and it will not be pos-

Control group

Many observable and unobservable variables can impact deforestation so the choice of the control group is crucial to balance confounding factors [START_REF] Ferraro | Counterfactual thinking and impact evaluation in environmental policy[END_REF]. Among If nding an appropriate control group is impossible, studying exposure heterogeneity may be the only relevant issue. Let us consider a watershed where all the landowners are expected to be accepted for the program. The only landowners who are not in the PES program are likely to be structurally dierent from the others and will thus not constitute a control group. Looking for a hypothetical control group inside or outside this watershed is bound to fail as important confounding unobservable factors will not be taken into consideration.

Impact heterogeneity

Finally, we discuss the interpretation of the impact. Is the average eect an appropriate estimate for decision making? How can studying impact heterogeneity help decision making? What are the possible impact moderators?

A moderate impact does not necessarily mean no impact at all. The impact might even be positive for some beneciaries and negative for others. For this reason, it is important to investigate impact heterogeneity and, go beyond average treatment eects" [START_REF] Ferraro | Heterogeneous treatment eects and mechanisms in information-based environmental policies: Evidence from a largescale eld experiment[END_REF]. As explained above, the impacts of PES programs are location-specic and it is indispensable to investigate heterogeneity and try to understand the transmission channel. Moreover, as highlighted by [START_REF] Vincent | Impact evaluation of forest conservation programs: Benetcost analysis, without the economics[END_REF], environmental benets can vary from one area to another but costs of implementation can also be heterogeneous so the higher impact will not automatically be found where intervention is the most eective in term of cost-benet analysis.

Many authors have already studied impact heterogeneity in protected areas [START_REF] Pfa | Park location aects forest protection: Land characteristics cause dierences in park impacts across costa rica[END_REF][START_REF] Sims | Conservation and development: Evidence from thai protected areas[END_REF][START_REF] Ferraro | Conditions associated with protected area success in conservation and poverty reduction[END_REF]Ferraro et al., , 2013) ) but few have investigated this issue in the context of PES. So far, impact heterogeneity has mainly been explored over time [START_REF] Pfa | Payments for environmental services: empirical analysis for costa rica[END_REF][START_REF] Robalino | Deforestation impacts of environmental services payments: Costa rica's psa program 20002005[END_REF], according to transaction costs or infrastructure [START_REF] Pfa | Park location aects forest protection: Land characteristics cause dierences in park impacts across costa rica[END_REF] or according to a poverty index and geographic area [START_REF] Shapiro | Forest conservation and slippage: Evidence from mexico's national payments for ecosystem services program[END_REF]. Other important moderators have received little attention so far, including those related to the economic activity of the landowner. PES do not operate in a vacuum" [START_REF] Corbera | Institutional dimensions of payments for ecosystem services: An analysis of mexico's carbon forestry programme[END_REF]: other institutional factors matter in determining its eciency. PES often interacts with other policy instruments, such as regulatory environment or other subsidies [START_REF] Ferraro | The future of payments for environmental services[END_REF], which modify the incentive structure. Agricultural subsidies or anti-poverty CCT may have perverse eects on the forest cover [START_REF] Schmook | Agricultural policy, market barriers, and deforestation: the case of mexico's southern yucatán[END_REF][START_REF] Mcintosh | The ecological footprint of poverty alleviation: evidence from mexico's oportunidades program[END_REF]. Another worthwhile investigation of the impact heterogeneity of PES would be to examine the policy-mix dened as the dierent instruments in place that interact in a given landscape [START_REF] Barton | Assessing the role of economic instruments in a policy mix for biodiversity conservation and ecosystem services provision: a review of some methodological challenges[END_REF].

Concluding remarks

In this last section, we explored the challenges that arise when trying to assess the impact of PES schemes on forest cover using experimental or quasi-experimental designs. Seven main recommendations can be derived from this section.

1. Both impact and confounding factors are location-specic, the design of the IE should take this into account while choosing geographic boundaries and the scale of the unit of observation.

2. Knowledge of land tenure and resources governance play a crucial role in the choice of a unit of observation.

3. There is no ideal timeframe for an IE, but ex-post and ex-ante eects of PES schemes should not be forgotten.

4. Modeling PES reception using dummy variables should not be systematic as exposure to the treatment may vary from one beneciary to another.

5.

Other estimators than matching methods can be implemented for PES IE and attention should be paid to the assumptions of the estimator, especially if leakages are likely to occur.

6. When choosing the control group, attention should be paid not only to the ocial allocation rules but also to the behavior of all the agents involved in the allocation process and the motivations of the forest owners in enrolling in the program.

7. The ATE has limited implications and impact heterogeneity should be investigated.

Although the main focus of this section was on the methodological challenges that arise when assessing additionality, other dimensions than additionality matter for PES evaluation. The impact of conservation instruments on poverty reduction is the subject of wide debate in the literature [START_REF] Grieg-Gran | How can market mechanisms for forest environmental services help the poor? preliminary lessons from latin america[END_REF][START_REF] Pagiola | Can payments for environmental services help reduce poverty? an exploration of the issues and the evidence to date from latin america[END_REF][START_REF] Zilberman | When could payments for environmental services benet the poor?[END_REF][START_REF] Zilberman | Putting payments for environmental services in the context of economic development[END_REF]. Important studies have examined the impact of protected areas or PES on poverty in Costa Rica and Thailand using matching methods and/or instrumental variables [START_REF] Andam | Protected areas reduced poverty in costa rica and thailand[END_REF][START_REF] Robalino | Conservation policies and labor markets: Unraveling the eects of national parks on local wages in costa rica[END_REF][START_REF] Sims | Conservation and development: Evidence from thai protected areas[END_REF][START_REF] Ferraro | Conditions associated with protected area success in conservation and poverty reduction[END_REF] but this question nevertheless deserves more attention. By focusing too much on short-term additionality, the other impacts of PES can easily be overlooked. Placing IE in a broader environmental and social evaluation framework is therefore crucial to identify alternative theories of change initiated by PES programs [START_REF] Mickwitz | Key insights for the design of environmental evaluations[END_REF]. More investigation outside the forest is needed to understand the long-term impact of PES schemes. Understanding the impact of PES on governance and social capital is also crucial in the long run. Behavioral aspects related to fairness or motivational eects also deserve more attention. And since most of the schemes oer only short-term contracts, examining the impact on agricultural production strategies, o-farm work and migration is essential to identify possible perverse lagged eects.

Chapter 4

Evaluating PES impact and leakages in Yucatan: A spatial approach Contents A version of this Chapter has been adapted as a working paper co-authored with Alexandre Sauquet and Sergio Cortina for future publication.

Introduction

A large debate surrounds PES eciency and the way payments should be allocated in order to achieve a sustainable decrease in deforestation [START_REF] Engel | Designing payments for environmental services in theory and practice: An overview of the issues[END_REF][START_REF] Muradian | Payments for ecosystem services and the fatal attraction of win-win solutions[END_REF]. In recent years, many authors have called for more IE of conservation instruments [START_REF] Ferraro | Money for nothing? a call for empirical evaluation of biodiversity conservation investments[END_REF][START_REF] Pattanayak | Show me the money: Do payments supply environmental services in developing countries?[END_REF][START_REF] Miteva | Evaluation of biodiversity policy instruments: what works and what doesn't?[END_REF]. However, as highlighted by [START_REF] Ferraro | Money for nothing? a call for empirical evaluation of biodiversity conservation investments[END_REF] evaluating eectiveness entails more than monitoring indicators such as the number of hectares protected.

To evaluate the impact of PES, one must take into account the selection bias in the estimations. Deforestation rates are likely to be dierent in beneciary and non beneciary parcels but these dierences may not be attributable to the program. Moreover, PES schemes for forest conservation are likely to generate leakages [START_REF] Aukland | A conceptual framework and its application for addressing leakage: the case of avoided deforestation[END_REF][START_REF] Wunder | How do we deal with leakage[END_REF]. Avoided deforestation in protected parcels may just have been displaced to other areas.

In this Chapter, we propose a new methodology to evaluate the impact of a PES scheme and apply it in the context of the PSA-H in a sub-region of Yucatan. First, we dene our units of observation as land cover overlapping with land tenure and protection by the PES but we combine these with gridding. Second, we consider leakage eects in our analysis and simultaneously estimate PES impact and leakages using tools provided by spatial econometrics. Third, we consider exposure heterogeneity regarding time enrolled in the program and contract renewal. Finally, we use pre-matching to account for selection bias in the regression analysis. Our empirical analysis is based on remote sensing analysis using 20m resolution SPOT images and ejido-surveys carried out in all of the ejidos in our sample.

The following section reviews previous IE of PES schemes and leakages. Section 4.3 presents our study area and the data. In Section 4.4, we present our methodology and apply it in the Section 4.5.

Evaluating PES additionality

A growing body of empirical literature looking at PES impact has recently emerged [START_REF] Shapiro | Forest conservation and slippage: Evidence from mexico's national payments for ecosystem services program[END_REF][START_REF] Arriagada | Do payments for environmental services aect forest cover? a farm-level evaluation from costa rica[END_REF]Robalino and Pfa, 2013, among others). This literature relies on existing IE methodologies developed in other elds of economics such as micronance, health economics and education [START_REF] Ravallion | Evaluating anti-poverty programs[END_REF]. Evaluating PES impact requires reconstituting a counterfactual in order to evaluate the additionality of the program ie the avoided deforestation on protected parcels. However it also requires taking into account leakages that may undermine the additionality. Leakages result in an increase of deforestation outside of the protected parcels. These eects, likely negative, may decrease or even oset the impact of the program.

Estimating avoided deforestation

Evaluating additionality requires reconstituting a counterfactual [START_REF] Ferraro | Counterfactual thinking and impact evaluation in environmental policy[END_REF]. A rst step toward IE was the acknowledgement that before and after or with and without comparisons were biased and could not be considered as relevant estimations of the impact of an environmental program (Joppa andPfa, 2009, 2010). Indeed, PES program beneciaries dier from non beneciaries according to many characteristics. Because these characteristics also aect the groups' respective deforestation rates, comparisons between the two groups are irrelevant. In econometric terms, there are confounding factors that aect both the PES reception and deforestation itself; hence, not accounting for these confounding factors in the estimation automatically biases the estimation of avoided deforestation attributable to the PES scheme. In the context of PES programs, many confounding factors are geographic variables, including the percentage of forest cover as well as major determinants of deforestation such as population density, slope and elevation and distances to roads, cities and agricultural elds [START_REF] Angelsen | Rethinking the causes of deforestation: lessons from economic models[END_REF][START_REF] Pfa | Park location aects forest protection: Land characteristics cause dierences in park impacts across costa rica[END_REF][START_REF] Honey-Roses | A spatially explicit estimate of avoided forest loss[END_REF]. Using instrumental variables, [START_REF] Robalino | Contagious development: Neighbor interactions in deforestation[END_REF] also nd that one landowner's decision to deforest is inuenced by those of his neighbors. This result suggests that spatial interactions must be taken into account in the estimation. Moreover, beyond geographic characteristics, unobservable confounding factors also include variables aecting the deforestation rates and participants' willingness to enter a PES scheme.

Reconstituting a counterfactual for a PES beneciary is likely to be very complex. Confounding factors are often unobservable and the control group must be very similar to the treated group in order to balance unobservable factors. IE usually chooses a control group in the same agro-ecological area as the beneciaries (Arriagada et al., 2012, among others). A sample of non beneciaries located in a buer zone around the beneciaries might appear to be an optimal control group to balance confounding factors but, as explained in Section 4.2.2 leakages should be controlled for. To control for the willingness to enroll in a PES scheme, [START_REF] Shapiro | Forest conservation and slippage: Evidence from mexico's national payments for ecosystem services program[END_REF], for instance, propose using as a control group applicants rejected for budget insuciency. We also drop non applicants from our sample in order to control for the willingness to enroll in the PES scheme. If no credible control group is available, the estimates using a dierent area would automatically be biased and lacking in relevance. For this reason, we focus here mainly on beneciaries and explore the heterogeneity of exposure to the program.

Most empirical analysis relies on matching methods such as PSM [START_REF] Robalino | Ecopayments and deforestation in costa rica: A nationwide analysis of psa's initial years[END_REF] or covariate matching [START_REF] Arriagada | Do payments for environmental services aect forest cover? a farm-level evaluation from costa rica[END_REF]. A notable exception is the study by [START_REF] Shapiro | Forest conservation and slippage: Evidence from mexico's national payments for ecosystem services program[END_REF] in Mexico that used matching as a preprocess in order to select a relevant control group and estimated the impact using a Tobit model1 . In the context of protected areas, other estimators have been employed including [START_REF] Sims | Conservation and development: Evidence from thai protected areas[END_REF] and [START_REF] Andam | Protected areas reduced poverty in costa rica and thailand[END_REF] who used a two stage least square Chapter 4. Evaluating PES impact and leakages in Yucatan: A spatial approach estimator in Thailand and Costa Rica and Canavire-Bacarreza and Hanauer (2013) who used genetic matching in Bolivia. PES impact studies also dier according to the choice of the unit of observation. Some authors look at deforestation in pixels while others compute deforestation by forest owners. [START_REF] Honey-Roses | A spatially explicit estimate of avoided forest loss[END_REF] proposes building polygons by overlapping pixels of land cover, land tenure and protection by PES or natural protected areas. Following [START_REF] Amin | A spatial econometric approach to spillover eects between protected areas and deforestation in the brazilian amazon[END_REF], we combine this approach with gridding in order to consider heterogeneity within the polygons.

Most studies have concluded that the impact of PES on deforestation is low and attribute this nding to weak capacity of schemes to focus on threatened forest and to overcome adverse selection. However, PES impact is likely to be very heterogeneous and the ATT might be a restrictive estimate for evaluating the impact of the program. Beyond ATE, a vast area of research investigates the impact of the PES in dierent areas and according to dierent impact moderators [START_REF] Miteva | Evaluation of biodiversity policy instruments: what works and what doesn't?[END_REF][START_REF] Ferraro | Heterogeneous treatment eects and mechanisms in information-based environmental policies: Evidence from a largescale eld experiment[END_REF]. We here investigate heterogeneity regarding the rst year of participation in the program and the decision to renew the PES contract.

Estimating leakage eects

For carbon sequestration projects, leakage is dened by the International Panel on Climate Change (IPCC) as the indirect impact that a targeted land use, land-use change and forestry activity in a certain place at a certain time has on carbon storage at another place or time " [START_REF] Watson | Land use, land-use change and forestry: a special report of the Intergovernmental Panel on Climate Change[END_REF]. This denition, while simple, can encompass a wide variety of mechanisms. An important distinction is made between activity-shifting and market eects2 [START_REF] Wu | Slippage eects of the conservation reserve program[END_REF]Schwarze et al., 2002, among others).

As explained by [START_REF] Schwarze | Understanding and managing leakage in forestbased greenhousegasmitigation projects[END_REF], activity-shifting is the displacement of an activity outside of the project's boundaries ". Let us consider the example of a landowner clearing forest to extract timber or to cultivate an agricultural commodity.

He may shift his activities to another area following the implementation of the PES scheme. In most cases, the beneciary of the project is the agent shifting activities but leakages can also result from the behavior of another agent. Indeed, if the beneciary of the PES begins purchasing agricultural commodities, this may increase deforestation by his neighbors [START_REF] Aukland | A conceptual framework and its application for addressing leakage: the case of avoided deforestation[END_REF]. In the case of PES for conservation, this type of leakage is likely to undermine the scheme's additionality. Leakages may even be larger than the positive eect of the PES if the scheme relaxes a credit constraint [START_REF] Jayachandran | Liquidity constraints and deforestation: The limitations of payments for ecosystem services[END_REF]. Market eects occur if an environmental project such as a PES scheme aects the prices of commodities. If conservation decreases the supply of commodities, their price may increase which generates an incentive for additional deforestation. Among indirect leakages, this eect through prices is the most cited in the literature [START_REF] Wu | Slippage eects of the conservation reserve program[END_REF][START_REF] Wu | Environmental and distributional impacts of conservation targeting strategies[END_REF] but conservation programs can have other indirect eects. For instance, let us consider a project proposing alternative livelihood options as in the ICDP, if these options are very attractive, the project may generate an inux of population and increase deforestation [START_REF] Aukland | A conceptual framework and its application for addressing leakage: the case of avoided deforestation[END_REF].

The type of project (conservation, reforestation, ICDP, etc...) and the context in which it will be implemented may inuence both the channel through which leakages are likely to emerge and their magnitude [START_REF] Aukland | A conceptual framework and its application for addressing leakage: the case of avoided deforestation[END_REF][START_REF] Wunder | How do we deal with leakage[END_REF].

On one hand, a PES scheme for conservation may increase labor supply and land demand in turn generating leakages through activity-shifting and a possible increase in timber prices. On the other hand, a reforestation PES scheme, will have an opposite eect on the labor market and may increase sustainable timber extraction instead of decreasing it. Leakages are also determined by the characteristics of the landscape such as the concentration of the forests or the agents [START_REF] Delacote | Deforestation, leakage and avoided deforestation policies: A spatial analysis[END_REF]. Scale matters as leakages can be found at the micro-level but also at the international-level, for example for carbon-markets [START_REF] Schwarze | Understanding and managing leakage in forestbased greenhousegasmitigation projects[END_REF][START_REF] Gan | Measuring transnational leakage of forest conservation[END_REF]. Output price eects should not be overlooked for large scale projects. [START_REF] Wu | Environmental and distributional impacts of conservation targeting strategies[END_REF] showed that the magnitude of the output price eect will depend on the program targeting and the correlation between environmental benets and land productivity. However, if capital and labor are not mobile, leakages due to market eects are very unlikely to emerge [START_REF] Schwarze | Understanding and managing leakage in forestbased greenhousegasmitigation projects[END_REF][START_REF] Wunder | How do we deal with leakage[END_REF].

As highlighted by [START_REF] Aukland | A conceptual framework and its application for addressing leakage: the case of avoided deforestation[END_REF], in a conservation project such as the Mexican PSA-H, leakages are very likely to occur and almost completely oset the impact of the program if no alternative livelihood options relaxing dependence on the forest cover are provided. Nevertheless, as [START_REF] Wunder | How do we deal with leakage[END_REF] reminds us, the existence of such an alternative does not guarantee that, over time, no leakages will occur.

If an alternative activity provides high returns, one can expect a negative rebound eect" through credit constraints which would generate more deforestation in the long-run.

Leakage eects are essential determinants of PES eectiveness. The impact of a program can be undermined or even annulled if it induces deforestation on neighboring parcels. To identify the net impact of PES, it is necessary to measure the direct and the indirect impact (leakages) of a given scheme. Moreover, if the buer zone outside the project area is used as a control group, not accounting for leakages will automatically bias the estimations [START_REF] Mas | Assessing protected area eectiveness using surrounding (buer) areas environmentally similar to the target area[END_REF][START_REF] Ewers | Estimates of reserve eectiveness are confounded by leakage[END_REF].

We distinguish two approaches in the literature that have attempted to evaluate the extent of leakages. The rst approach estimates a baseline of deforestation based on a theoretical model [START_REF] Geres | A qualitative method to consider leakage eects from cdm and ji projects[END_REF][START_REF] Chomitz | Baseline, leakage and measurement issues: how do forestry and energy projects compare?[END_REF][START_REF] Murray | Estimating leakage from forest carbon sequestration programs[END_REF][START_REF] Sohngen | Measuring leakage from carbon projects in open economies: a stop timber harvesting project in bolivia as a case study[END_REF]). The second approach uses econometrics and impact analysis tools. Our methodology derives from this second approach. In the context of Brazilian NPAs, [START_REF] Amin | A spatial econometric approach to spillover eects between protected areas and deforestation in the brazilian amazon[END_REF] use instrumental variables and a spatially interrelated cross sectional equation to take into account the fact that NPAs are not randomly created. The authors provide evidence that the decisions to deforest of neighboring municipalities are complements and that NPAs generate leakages. Using IE methodology, Alix-Garcia et al. ( 2012) dene as treated the unprotected parcels that are in the same ejido as a forest protected under the PSA-H 82 Chapter 4. Evaluating PES impact and leakages in Yucatan: A spatial approach and compare deforestation in these parcels with deforestation in rejected parcels. Honey-roses et al. ( 2011) compare the coecients' magnitude of the impact of the PES with and without bordering parcels included in the control group. They infer the leakage eects in the border area from this dierence. We here propose evaluating leakages using spatial econometrics tools and simultaneously estimate avoided deforestation and leakages.

Study area

As presented in Chapter 2, ejido-surveys were conducted in 76 ejidos in the Cono Sur which constitutes an exhaustive sample of all of the ejidos that were eligible for PSA-H reception in 2012. Among these ejidos, 40 participated in the program for at least one year in 2013. Most of the non beneciaries are located in the northern areas and had just been eligible for a few years. Half of the non beneciaries had already applied to the program previously. Following Alix-Garcia et al. ( 2012), we kept only the 22 non-beneciary ejidos that had previously applied to the program to control for the willingness to join the program. Among those 22 ejidos, 16 entered the program in the two next years. On average, rejected applicants were accepted into the program two years after their rst application. Our nal sample is composed of 62 ejidos. Figure 4.2 displays the initial PSA-H enrollment date.

Figure 4.1 presents the evolution of eligibility zones in the Cono Sur between 2004 and 2012. Eligibility of an area is decided at the federal level by CONAFOR but regional oces can suggest including new areas. According to CONAFOR's regional sta, the northern area became eligible later than the southern areas because information concerning the forest cover loss in the area was not available and funds were insucient at the federal level to cover all of the Cono Sur.

Remote sensing analysis was conducted using 20 meter resolution SPOT images for 1999, 2005 and 2012. This classication allows us to distinguish between forests, agricultural elds (including pasture) and roads and infrastructure. The remote sensing analysis combined various sources of information, including ground-truthing data and participatory mapping in each ejido.

Methodology

We propose a new methodology for evaluating PES impact. First, we propose building polygons by combining gridding, land tenure and PES reception. Second, we explicitly introduce leakages in our estimation using spatial econometrics. Third, we introduce the time spent in the program and heterogeneity in the enrollment date. Fourth, we propose a regression framework and use pre-matching to account for the selection bias. In order to be able to estimate additionality and potential leakages, our unit of observation must:

1. Explicitly consider land tenure. A unit of observation must not be located in two ejidos.

2. Be small enough to consider heterogeneity of deforestation risk across the ejido but large enough to correspond to a relevant unit of decision for the ejidatarios.

3. Be homogeneous regarding exposure to the treatment i.e. not only PSA-H reception but also the number of years spent in the program.

Include only forests in 2005.

In this section, we explain how, considering the prerequisites presented above, we build our unit of observation. Our nal units of observations are polygons combined with gridding.

First, we combine ejido borders with a gridding of 500 m in order to take into account the heterogeneity of exposure within the ejidos. Some polygons are more likely to be deforested than others, for example those located near the agricultural elds. We use a grid of 500 m (each cell is 25 ha). According to our data, the median size of one grassland eld is around 50 ha while the median size of an agricultural elds is around 40 ha. Given these measurements, a 25 ha grid seems appropriate to capture land-use change. Note that on average the polygons are 25 ha but may be smaller at the borders of the ejidos. We drop polygons of less than 0.5 hectares since they result mainly from misoverlap of the original PSA-H polygons. These observations represent less than 1% of our study area. Our nal sample is composed of 10,352 polygons. There are, on average, 170 polygons per ejido. In our study area, forest covered more than 174,000 hectares in 2005. Approximately 77,000 hectares have been enrolled in the PSA-H at various points in time between 2005 and 2013. During this period, 7,900 ha of forest have been lost, which corresponds to more than 4.7% of the total forest cover in 2005.

To conclude, this approach allows us to have homogeneous polygons in terms of exposure to the PES scheme. The approach also explicitly considers land tenure when building the unit of observation. Decisions by the landowners to clear are made in plots and not in pixels. Thus, aggregating pixels using a 25 ha grid seems more in line with theoretical work than using pixel as units of observation. Moreover, desegregating all of the forest through gridding allows us to consider forest heterogeneity within the ejido.

Introducing leakages 4.4.2.1 Leakages within the ejido

We start by presenting the traditional estimation of a (direct) scheme impact. The estimated equation takes the following form:

def ij = α + βpsa ij + τ X ij + πX j + ε ij (4.1)
The unit of analysis is the polygon i from ejido j. def ij is dened as:

def ij = (forest05 ij -forest12 ij ) forest05 ij (4.2)
Our measure of deforestation is the percentage of forest cover loss between 2005 and 2012. As explained above, our polygons are generated by overlapping gridding, PSA-H protection, land tenure and forest cover in 2005.

Our unit of analysis is dened based on the area classied as forest in 2005.

Therefore, the variable def ij does not capture forest regeneration but only defor- estation within each forested polygon. This denition is consistent with the PSA-H's conservation objectives. Comparing forested areas under conservation with areas in regeneration may bias our results since PSA-H areas are, by denition, already entirely covered by forest.

We dene psa ij as a dummy variable for PSA-H reception if polygon i is protected by the program. We will introduce heterogeneity of exposure to PES treatment in the next section. The impact of the scheme is captured through the parameter β. X ij and X j are vectors of control variables at polygon and ejido level.

Recall that the activity shifting eect leads to leakages inside the ejido. Inside leakages are captured by estimating the following equation:

def ij = α + βpsa ij + δwpsa 1 j + τ X ij + πX j + ε ij (4.3) with wpsa 1 j = K k Ω ikj forest05 kj .psa kj K k Ω ikj forest05 kj (4.4)
Ω ik is equal to one if the polygon k is in the same ejido as i. Hence, wpsa 1 j is the percentage of forest protected by the PSA-H program in ejido j. Once controlled for psa ij , δ captures the leakage eects i.e. the impact of the program on unprotected land. We implicitly assume that the greater the constraint induced by the PES on the forest cover, the larger leakages eects on unprotected land will be. Note that the denominator is the amount of forest in the ejido:

forest05 j = K k Ω ikj forest05 kj (4.5)

Leakages in neighbouring ejidos

The PSA-H may also lead to leakages outside of the ejido either because the ejidatarios choose to deforest outside of their ejido (activity shifting) or because the ejidatarios want to buy more agricultural commodities from their neighbor, increasing deforestation pressure in surrounding ejidos (outsourcing). Outside leakages are captured by introducing a second spatially lagged exogenous variable to the estimated equation: The new weighting factor ω jm is equal to one if the ejido m is located in a buer zone around ejido j. forest05 m is the amount of forest in 2005 in ejido m. Hence, wpsa 2 j captures the presence of the PSA-H in surrounding ejidos and the coecient γ captures leakages in neighbors' ejidos. For this study we set the buer at 10km around the centroid of the ejido.

def ij = α + βpsa ij + δwpsa 1 j + γwpsa 2 j + τ X ij + πX j + ε ij
Using this methodology, we are not able to capture market's eects (or secondary leakages) since all ejidos are part of the same market zone. If there is a market eect, through commodity prices, it aects all of the area making its identication impossible. Nevertheless, as highlighted by [START_REF] Wunder | How do we deal with leakage[END_REF], market eects are likely to emerge if markets are well-functioning, which is probably not the case in the Cono Sur. We now introduce heterogeneity of exposure to the treatment regarding time spent in the program. We study the impact of the PES scheme over 8 years between 2005 and 2012. PSA-H contracts last 5 years and the time spent in the program by each observation is heterogeneous as shown in gure 4.6. For this reason in equation 4.6, we replace psa ij by tpsa ij : the time spent in the program.

def ij = α + βtpsa ij + δwtpsa 1 j + γwtpsa 2 j + τ X ij + πX j + ε ij (4.8) with wtpsa 1 j = K k Ω ikj forest05 kj .tpsa kj K k Ω ikj forest05 kj (4.9)
and

wtpsa 2 j = M m =j ω jm forest05 m .wtpsa 1 m J j =m ω jm forest05 m (4.10)
In equation 4.8, coecient β gives the impact on deforestation of one year of enrollment in the program by polygon i. Coecients δ and γ capture the leakages eect but the interpretation is more subtle than in equation 4.6. Coecients δ and γ capture the impact on deforestation in polygon i of the average time spent in the PSA-H by one hectare of forest located in ejido j (wtpsa 1 j ) and in surrounding ejidos located in the buer zone (wtpsa 2 j ).

Enrollment date

Our sample is not homogeneous in terms of the time of program entry. On the contrary it is composed of early beneciaries that left the program after ve years, Evaluating PES impact and leakages in Yucatan: A spatial approach late beneciaries that only participated in the program at the end of our period of analysis and beneciaries that were enrolled in the program at the beginning and subsequently renewed. The latter were enrolled in the PSA-H for almost 8 years.

In order to disentangle the impact for these dierent cohorts, we split the variable tpsa ij into three variables: tpsaearl ij corresponds to the time spent by polygons that were enrolled before 2008 but whose ejidos did not renew the PSA-H contract. In 2013, these polygons had all been enrolled for only ve years and had not been protected for two to ve years. The tpsaalw ij variable corresponds to the time spent in the program by polygons enrolled before 2008 and whose contract had been renewed. In 2013, these polygons had been enrolled in the program for at least six years. The tpsalate ij variable corresponds to the time spent in the program by polygons enrolled after 2008. In 2012, these polygons had not yet completed their rst contract. We estimate the following equation:

def ij =α + β 1 tpsaearl ij + β 2 tpsaalw ij + β 3 tpsalate ij + +δwtpsa1 j + γwtpsa2 j + τ X ij + πX j + ε ij (4.11)
Our period of analysis is between 2005 and 2012. Nevertheless, we were able to observe contract renewal in 2013. Therefore, we choose 2008 as a threshold as the contracts made before 2008 had come to an end at least two years before 2013. This left enough time for the ejido to enroll the land again. We use a regression framework to analyze the impact of the PSA-H for two reasons. First, it allows us to directly estimate leakage eects through spatially lagged variables. Second, contrary to usual IE techniques, we use a continuous treatment to take into consideration the time spent in the program.

Only 43% of the polygons in our sample receive the PSA-H. There are confounding factors impacting both deforestation and reception of the program so we must take into account the selection process in order to correct the bias. In order to be able to estimate our model using OLS, we use a matching procedure based on propensity scores to select our sample.

We estimate the following model for propensity scores:

pscore ij = P r(psa ij = 1) = θ + φZ ij + σZ j + μ (4.12)
The probability of a polygon i receiving the PSA-H depends on the decision of the ejido to join the PSA-H and the choice of the ejido to enroll polygon i rather than another. For this reason we include variables at the ejido and polygon-level.

Based on the distribution of the propensity scores for treated and control groups, we restrict our sample to the common support. Moreover, for each treated observation, we keep in our sample the three nearest neighbors in terms of propensity score with replacement. Through this process, we ensure that our control group is similar to our treated group. In order to take into account heterogeneity of propensity scores, we introduce the propensity score as a control variables in our estimation. The variable pscore ij is the probability that the polygons received the PSA-H conditionally on Z ij and Z j .

Given the large number of observations in our sample, one may fear that the signicance of our results might be partially driven by the sample size. To avoid this caveat, we cluster our error terms by ejidos.

An alternative approach would be to use PSW as developed by [START_REF] Robins | Analysis of semiparametric regression models for repeated outcomes in the presence of missing data[END_REF] in order to estimate the impact of the PSA-H on the forest cover. However, to our knowledge, a PSW estimator allowing clustering of the error terms has not yet been developed. Nevertheless, our results are robust to this alternative estimation.

Unobserved heterogeneity

Our results rely on two important assumptions regarding unobserved heterogeneity. First, we assume that, in our sample, there are no unobservable confounding factors aecting both the selection and the deforestation rate 4 . Regarding this unobserved heterogeneity, recall that we only kept in our sample non-beneciaries that had already applied to the program but had not yet been approved 5 . Nevertheless, they are likely to be accepted in the forthcoming years. Non applicants represent 12,000 hectares and our sample includes 162,000 hectares of forest in 2005. The deforestation rate with the non-applicants is 4.7% and it decreases to 4% after dropping the non-applicants. This conrms our hypothesis that non-applicants have higher intrinsic motivation to deforest so they do not constitute a relevant counterfactual.

Second, we assume that once considered the selection process, there are no omitted confounding factors aecting the time spent in the program and deforestation rates. Order of entry into the program is mainly inuenced by eligibility to the program. We introduce the variable telig i j in our control variables at both steps of the estimation (X i j and Z i j). This variable captures how many years polygon i has been eligible. Hence, we control for potential dierences due to the evolution of PSA-H targeting.

Moreover, we include temporally lagged values of our explained variable at the ejido-level and in a buer zone in order to control for unobserved heterogeneity.

The variable lagdef ejid j is the deforestation rate, as computed in equation 4.2, but at ejido level and between 1999 and 2005. And the variable lagbuffer ij is the average of past deforestation in ejido j in a buer zone of 1km around the frontiers of polygon i. Note that it only captures the average past deforestation in the same ejido as i: it is the average pressure in this area of the ejido. If there is adverse selection in the allocation of the PSA-H, we expect this variable to be negatively correlated with the reception of the program.

Estimation

Many control variables are included at both steps of the estimation. As explained in Section 4.4.4.2, we control for past deforestation at the ejido-level (lagdefejid ij ) and in a buer zone around the polygon (lagbuffer ij ) as explained above. The polygons are heterogeneous in size so we control for the size of the forest in 2005 (forest05 ij ). To control for adverse selection, we introduce various variables inuencing the probability to be deforested in the future. We control for the average slope (slope ij ) and distances to the roads and agricultural elds within the ejidos in 2005 and to the nearest city of more than 2,500 inhabitants in km (droad05 ij , dagri05 ij and dcity ij ). We control for the number of years of eligibility (telig ij ) and the conditional probability that the polygon received the PSA-H (pscore ij ) as explained in Section 4.4.4 At the ejido-level, we control for the size of the ejido in thousands of hectares, percentage of forest cover and the number of ejidatarios per hectare (areaejid j and ejiddens j ). We also control for two major PSA-H selection criteria likely to inuence deforestation: the marginality index computed by CONAPO in 2005 and the deforestation risk index (defrisk j and margi05 j ) [START_REF] Muñoz-Piña | Paying for the hydrological services of mexico's forests: Analysis, negotiations and results[END_REF]. 4.1 presents the results of the estimation of propensity scores. Column (1) includes only polygon-level variables and column (2) includes variables computed at the ejido-level. In order for our study to be relevant, it is crucial to be able to discriminate between polygons from the same ejido which have a higher probability to be accepted into the program. We note that the model presented in column (1) explains around 15% of the total variance and many covariates computed at the polygon-level remain signicant in column (2).

The deforesting pressure in the area is captured by the lagbuffer ij variable. PSA-H polygons tend to be located in areas with a lower ex ante deforestation rate. This result illustrates the adverse selection issues in PES allocation highlighted by [START_REF] Ferraro | Asymmetric information and contract design for payments for environmental services[END_REF] and studied by Muñoz [START_REF] Muñoz Piña | Payment for environmental services in mexico: Targeting challenges[END_REF] in the context of the PSA-H. Moreover, PSA-H polygons also tend to be located farther from agricultural land and in less densely populated ejidos. Both of these variables are crucial determinants of deforestation, [START_REF] Angelsen | Rethinking the causes of deforestation: lessons from economic models[END_REF] which conrms the adverse selection in the allocation of the PSA-H. The eect of the margi05 j and def risk j variables Estimation (2) in Table 4.1 allows us to compute propensity scores. Figure 4.7 maps the propensity scores in the Cono Sur. We restrict our sample to the common support and to the three nearest neighbors as proposed in Section 4.4.4 and drop 1,620 observations. Our nal sample is composed of 8,732 observations. The distribution of propensity scores for PSA-H beneciaries and non-beneciaries once restricted to this subsample are presented in gure 4.8. We note that both distributions are highly skewed but overlap well. A ve year contract generates an average additionality of 2% which, for a polygon of 25 ha corresponds to 0.5 ha. Note that higher propensity scores are also associated with lower deforestation which conrms adverse selection and the necessity to account for the selection bias in our results. Regarding control variables, one should be careful in interpreting the estimates. For many of them including dagri05 ij or def risk j , their impact is captured by the propensity scores. Introducing them allows us only to correct for remaining imbalances between the control and the treated group after controlling for pscore ij . Column ( 3) and ( 4) introduce the two variables that capture leakages. The coecient for variable wtpsa2 j that captures leakages in surrounding ejidos is not signicant. However, the variable wtpsa1 j that captures leakages within the ejidos has a signicant eect and is of the expected sign.

These results tend to show that if the program eectively decreases pressure in an enrolled parcel, this pressure is displaced to other areas of the ejido. Nevertheless, it is not possible to directly interpret the dierence of magnitude between the two coecients. The variable wtpsa1 j is the average protection of one hectare in the ejido and does not only depend on the number of hectares protected but also on the total stock of forest. Moreover, looking at the issue of heterogeneity leads us to reconsider the results presented in Table 4.2.

Heterogeneity over time

Table 4.3 presents the results of the estimation of equation 4.11. In this model, we dierentiate between polygons that were only in the program in the early years and did not renew their contracts (tpsaearl ij ), those that only entered at the end of the program (tpsalate ij ) and those that were in the program continuously (tpsaalw ij ) Looking at Table 4.3, we note that the coecient of tpsaearl ij is not signicant 6 . Two hypotheses can be advanced to explain this result. One explanation would be that these forests were not threatened. Sims et al. (2014) show that over the years, the PSA-H has improved its focus on threatened forest. This could explain why these areas have not been renewed. Another plausible explanation would be that the ejidatarios decided to withdraw those lands from the program in order to clear them. Hence, if the PSA-H protected the forest for ve years, the ejidatarios caught up on their original deforestation rate in the subsequent years. Moreover, we note that once this heterogeneity is taken into account, the magnitude of the impact for the variables tpsalate ij and tpsaalw ij is very similar to the gures shown in Table 4.2.

With a 95% condence interval, the avoided deforestation between 2005 and 2012 in protected parcels is between 1,000 and 2,800 ha. Proportionally to the numbers of hectares enrolled, this corresponds to an additionality between 1.3 and 3.6%. However, we estimate that leakage eects within the ejidos would vary between 200 ha and more than 3,000 ha. This estimation tends to show that most of the avoided deforestation in the ejido has been displaced to other areas of the ejido. In the more optimistic scenario involving the maximum of avoided deforestation and the minimum leakages, the additionality of the PSA-H corresponds to approximately 3% of the areas enrolled. In the worst case scenario, leakages completely oset the impact of the PSA-H.

These results concerning leakage eects should be interpreted with caution. As highlighted in Chapter 2, deforestation pressure decreased in the Cono Sur prior to the program's implementation. Therefore, additionality of the PSA-H is quite low in the area and small leakage eects would be sucient to entirely oset the impact.

Robustness tests

We run fours robustness tests using dierent estimators and withdrawing the larger ejido. Results of the robustness tests are displayed in the Appendix B.

Propensity Score Weighting

We use PSW as developed by [START_REF] Robins | Analysis of semiparametric regression models for repeated outcomes in the presence of missing data[END_REF] in order to estimate the impact of the PSA-H on the forest cover. It might be more appropriate to deal with the selection bias with PSW than a pre-matching. However, to our knowledge, this methodology does not allow clustering of the standard errors. For this reason, we preferred to perform a pre-matching. PSW allows us to balance covariates between 100 Chapter 4. Evaluating PES impact and leakages in Yucatan: A spatial approach [START_REF] Hirano | Estimation of causal eects using propensity score weighting: An application to data on right heart catheterization[END_REF][START_REF] Lunceford | Stratication and weighting via the propensity score in estimation of causal treatment eects: a comparative study[END_REF][START_REF] Austin | An introduction to propensity score methods for reducing the eects of confounding in observational studies[END_REF]:

1. Estimating propensity scores using a probit or a logit model 2. Generating sample weight using predicted propensity scores 3. Estimating the model using Weighted Least Squares (WLS)

Based on the estimation of the propensity score presented in Table 4.1, we estimate the ATT using WLS. We estimate equation 4.6 with sample weights equals:

w ij = pscore ij /(1 -pscore ij ) if psa ij = 0 1 if psa ij = 1 (4.13)
Results are presented in the Appendix in Table B.1.

Pre-matching using Covariate matching

We used PSM in our analysis in order to select a control group and account for the selection bias. An alternative to this approach is to use Covariate matching which is based, not on propensity scores, but on Mahalanobis vectorial distance.

This method might more appropriate if we are not able to predict correctly the propensity scores [START_REF] Zhao | Using matching to estimate treatment eects: data requirements, matching metrics, and monte carlo evidence[END_REF].

It is crucial to control for the propensity scores in the estimations so, for the purpose of consistency, we have chosen PSM to select our sample. Nevertheless, we provide the results using Covariate matching with three nearest neighbors as a robustness test.

Results are presented in Table B.2 in the Appendix. The estimates are not signicantly dierent from those obtained with PSM and remain signicantly dierent from zero.

Withdrawal of the larger ejido

As can be seen in gure 4.2, there is one ejido located in the north-east of the Cono Sur that is much larger than the others. This ejido represents approximately 10%

of our area of study. In order to ensure that this ejido alone is not driving our results, we run a robustness test without this ejido. The results of this estimation are presented in Table B.3 in the Appendix and conrm our results.

Selection and leakages estimation

The pre-matching allows us to take into account the selection process in the estimation. Section 4.5.1 showed that the polygons under protection were less threatened.

Therefore, the pre-matching conducts us to keep less threatened forest in our sample, which could bias our estimations of leakages. In fact, leakages may be lower in these parcels. approach

To check for the existence of such bias, we run the same model on the sample of beneciary and applicant ejidos without sample selection, propensity scores and clustering of the error terms. The result of this naive estimation are presented in columns ( 1) and ( 2) in Table B.4 in the Appendix. The coecient for wtpsa1 ij is not statistically signicantly dierent from zero.

Note that the coecient for tpsa ij is also similar to the one found in Table 4.2. This is not surprising considering the fact that about a third of the polygons received the PSA-H. The control variables, including lagged measures of deforestation, introduce a control on observable covariates. The pre-matching would more useful if the proportion of treated polygons was lower.

Column (3) proposes an estimation without any control variables. The result of this estimation clearly shows that, without any control on observable covariates, the impact of tpsa ij and leakages would be highly overestimated.

Concluding remarks

We propose a new methodological approach to evaluate PES impact and leakages. Following [START_REF] Honey-Roses | A spatially explicit estimate of avoided forest loss[END_REF], our approach explicitly considers land tenure in the unit of observation but also allows us to take into account heterogeneity within forests owned by the same landowner. Moreover, we simultaneously estimate direct impact and leakages using tools provided by spatial econometrics. We also introduce heterogeneity of exposure to the treatment as we consider the time spent in the program and contract renewal. Our econometric model is estimated on a sample of beneciaries and applicants as [START_REF] Shapiro | Forest conservation and slippage: Evidence from mexico's national payments for ecosystem services program[END_REF]. We estimate an ATT using matching to pre-process the data and introduce an ex-ante spatially lagged measure of deforestation to control for unobservable heterogeneity.

We apply this methodology in the context of the Mexican PSA-H in the Cono Sur of Yucatan State. Our results suggest a strong adverse selection issue of PSA-H allocation within the ejidos. Indeed, most PSA-H contracts are found in areas with low deforestation pressure. Moreover, we show that leakages should not be overlooked. As emphasized by [START_REF] Aukland | A conceptual framework and its application for addressing leakage: the case of avoided deforestation[END_REF], leakages are very likely to occur following the implementation of conservation projects if land is easily available and if the project does not provide alternative livelihood options. In our study area, most of the avoided deforestation has been displaced to other areas within the ejidos. Moreover, looking at impact heterogeneity, we note that no additionality has been found in the areas where the ejidatarios decide not to renew their contracts after ve years. One possible explanation is that the ejidatarios withdraw lands from the program in order to clear them.

The implications of our study are twofold. First, for future IE, we demonstrate that care should be taken in monitoring the indirect eects of a PES program over space and time. Leakage eects may undermine or even oset the additionality of the program. Moreover, one may fear a negative rebound-eect after the end of the program on protected parcels. Therefore, evaluating the program's impact on protected land and only during the time the area is under conservation may lead to strongly overestimating the program's impact.

Second, from a policy perspective, monitoring leakages and rebound eects will not prevent these eects from occuring. PES schemes are based on voluntary enrollment and conservation cannot be enforced on all forest parcels over time without the beneciary's agreement. Providing alternative livelihood options and sustainable use of the forest cover through agroforestry or sustainable forestry seem a relevant alternative to ensure permanence and minimum leakages. Nevertheless, PES designers should learn from ICDP's failures in order not to generate negative rebound eects. If PES schemes do not provide a new path for sustainable development, they may be doomed to reproduce the same failures as other conservation instruments with uncertain indirect impact over both time and space.

Introduction

PES are innovative forest conservation instruments that have developed extensively during the last decade [START_REF] Engel | Designing payments for environmental services in theory and practice: An overview of the issues[END_REF]. But a erce debate remains about the most eective solution to achieve additionality. A rst paradigm is that PES must remunerate landowners at the OC of the land. It also follows that payments should not be directed toward landowners that already conserve the forest since their OC on the forest cover is null [START_REF] Ferraro | Asymmetric information and contract design for payments for environmental services[END_REF][START_REF] Martin Persson | Conditional cash transfers and payments for environmental services -a conceptual framework for explaining and judging dierences in outcomes[END_REF]. To achieve maximum additionality, PES must be designed as compensation instruments for the loss of future income due to forest conservation or restoration [START_REF] Pascual | Exploring the links between equity and eciency in payments for environmental services: A conceptual approach[END_REF]. Analyzed as such, the emergence of PES represented an original shift from the polluter-pays principle to a Coasean polluter-paid agreement [START_REF] Wunder | Payments for environmental services: some nuts and bolts[END_REF][START_REF] Wunder | Revisiting the concept of payments for environmental services[END_REF].

This vision of PES has been criticised by many authors as it ignores the social and institutional process behind PES. This second paradigm considers PES as incentives for greener behavior [START_REF] Muradian | Between markets and hierarchies: the challenge of governing ecosystem services[END_REF][START_REF] Muradian | Payments for ecosystem services as incentives for collective action[END_REF]. Making the scheme acceptable and ecient on the long-run often requires better considering equity and legitimacy in the attribution of payments [START_REF] Adger | Governance for sustainability: towards athick'analysis of environmental decisionmaking[END_REF][START_REF] Corbera | The equity and legitimacy of markets for ecosystem services[END_REF]. It is accepted that PES may reward current conservation more than compensate OC. This second paradigm is based on the assumption that compensation of OC is impossible to implement for many reasons. Moreover, on the long-run, these incentives may impulse behavioral changes from the agents that deforest CONAFOR has developed a complex targeting system in order to focus its intervention [START_REF] Muñoz Piña | Payment for environmental services in mexico: Targeting challenges[END_REF][START_REF] Rolon | The mexican pes programme: targeting for higher eciency in environmental protection and poverty alleviation[END_REF]. Interactions between CONAFOR and dierent stakeholders has led to an hybridization of the program into a multiple objectives program [START_REF] Shapiro-Garza | Contesting the market-based nature of mexico's national payments for ecosystem services programs: Four sites of articulation and hybridization[END_REF]Sims et al., 2014). Reecting the objectives of dierent stakeholders, the scheme targets threatened forests but also NPA or marginal areas. But CONAFOR's main objective is to focus on threatened forest in order to maximize additionality which shows that they conceive PES as a compensation for foregone revenues and not as reward for current and past conservation. As we will demonstrate, the scheme's design still has a strong compensation component reecting CONAFOR's conception of the instrument.

However, as emphasised by [START_REF] Corbera | The equity and legitimacy of markets for ecosystem services[END_REF], since forests are owned in commons, the nal beneciary is not clearly identiable. As a matter of fact, once payments are received by the ejido, an assembly of ejidatarios decides how to use the payments. The ejidatarios can choose either to invest the payments in public goods or to share them among themselves. The nal allocation of the PSA-H depends on how the members of the assembly decide to redistribute the payments. Whilst the scheme has been designed as a compensation instrument, other conceptions of fairness at the ejido-level may exist, which inuence the nal allocation of the program [START_REF] Pascual | Exploring the links between equity and eciency in payments for environmental services: A conceptual approach[END_REF].

In this article, we study how the land-use heterogeneity impacts PSA-H redistribution. Within ejidos, the ejidatarios have dierent economic activities and rely more or less on the forest cover. Does the land-use aects inequality in payments 5.2. PES: A conservation instrument designed as a compensation? 107 redistribution? Are the payments allocated higher or lower for the ejidatarios with higher OC? Studying the interaction between land-use type and redistribution of the PSA-H within the ejidos allows us to understand how the program's objectives may have been appropriated by the ejidos and what conception of fairness their distribution choices reect. Within the ejido dierent types of land users more or less dependent on the forest cover coexist. We show that inequality in payment allocation is directly linked to the land-use type involved and that ejidatarios are likely to receive relatively higher payments if they are less dependent on the forest cover. The PSA-H is not understood as a compensation but as a reward for greener practices.

The next section of this article presents an overview of the literature regarding fairness and PES allocation. Section 5.3 presents the targeting system of the PSA-H and discusses payment repartition within the ejidos. Section 5.4 presents the Cono Sur of Yucatan. Finally Section 5.6 presents the results of our empirical analysis regarding payment redistribution within an ejido in Cono Sur.

PES:

A conservation instrument designed as a compensation?

PES as a compensation

According to Wunder's denitions [START_REF] Wunder | Payments for environmental services: some nuts and bolts[END_REF][START_REF] Wunder | Revisiting the concept of payments for environmental services[END_REF], PES are a direct application of Coase theorem [START_REF] Coase | Problem of social cost, the[END_REF]. Through their production activities, landowners generate a negative externality which decreases the quantity of ES provided. This externality is not valorised by any market but if transaction costs are low and property rights well-dened, the buyer and the provider of the ES can nd a monetary agreement to ensure that the provider will change his behaviour in order to provide the ES. To be environmentally eective, PES must demonstrate additionality and focus on credible threats of deforestation [START_REF] Wunder | The eciency of payments for environmental services in tropical conservation[END_REF]. Following the Coaean approach, payments should be set to, at a minimum, compensate landowners for land OC [START_REF] Wunder | Payments for environmental services: some nuts and bolts[END_REF][START_REF] Engel | Designing payments for environmental services in theory and practice: An overview of the issues[END_REF]. Indeed, providers won't accept a price below the revenues that could be obtained from the land if it was used for production. Payments may also include an additional premium to ease and secure the provision of ES especially for "asset-building" PES1 [START_REF] Pagiola | Paying for biodiversity conservation services in agricultural landscapes[END_REF].

The paradox of PES is that to be environmentally eective they must target landowners with non-null OC, but in order to minimize the costs, they have to focus on areas where protection is cheaper, i.e. where OC are lower [START_REF] Wunder | Payments for environmental services: some nuts and bolts[END_REF][START_REF] Wunder | The eciency of payments for environmental services in tropical conservation[END_REF][START_REF] Wünscher | Spatial targeting of payments for environmental services: a tool for boosting conservation benets[END_REF]. In areas with high OC, protection is too costly to be achieved by a PES and other types of instruments are needed.

Empirical studies of Costarican and Mexican PES highlight that the eectiveness Chapter 5. Reward or compensation? PES distribution in Mexican common forests of the mechanisms was undermined by their weak ability to target nal beneciaries [START_REF] Muñoz Piña | Payment for environmental services in mexico: Targeting challenges[END_REF][START_REF] Shapiro | Forest conservation and slippage: Evidence from mexico's national payments for ecosystem services program[END_REF][START_REF] Arriagada | Do payments for environmental services aect forest cover? a farm-level evaluation from costa rica[END_REF][START_REF] Robalino | Ecopayments and deforestation in costa rica: A nationwide analysis of psa's initial years[END_REF]. This adverse selection in PES programs due to asymmetric information has also been highlighted by [START_REF] Ferraro | Asymmetric information and contract design for payments for environmental services[END_REF]. The phenomenon generates informational rents for the landowners with lower OC. [START_REF] Ferraro | Asymmetric information and contract design for payments for environmental services[END_REF] proposes using reverse auctions, as practiced in Indonesia, Malawi [START_REF] Ajayi | Auction design for the private provision of public goods in developing countries: lessons from payments for environmental services in malawi and indonesia[END_REF] or Australia [START_REF] Comerford | Understanding why landholders choose to participate or withdraw from conservation programs: A case study from a queensland conservation auction[END_REF] or to base targeting on costly-to-fake signals. Using a theoretical model, Alix-Garcia et al. ( 2008) also provide evidence that indexing payments on the deforestation risk increases the eciency of PES schemes.

PES as a reward

This Coasean vision of PES has been criticised by many authors because it ignores the institutional process that leads to the emergence of the PES. In reality, PES are shaped by local institutions [START_REF] Muradian | Reconciling theory and practice: An alternative conceptual framework for understanding payments for environmental services[END_REF][START_REF] Muradian | Payments for ecosystem services and the fatal attraction of win-win solutions[END_REF] and/or existing regulations [START_REF] Brimont | Achieving conservation and equity amidst extreme poverty and climate risk: The makira redd+ project in madagascar[END_REF] and often do not look like a Coaesean agreement. This is especially true when dealing with PES funded by an intermediary such as a State or an NGO. The intermediary, in our case the state, often becomes the key decision-maker in PES implementation [START_REF] Corbera | Institutional dimensions of payments for ecosystem services: An analysis of mexico's carbon forestry programme[END_REF][START_REF] Kosoy | Payments for ecosystem services as commodity fetishism[END_REF][START_REF] Vatn | Markets in environmental governancefrom theory to practice[END_REF] and the instrument becomes, in many respects, similar to CCT [START_REF] Rodríguez | Towards a unied scheme for environmental and social protection: Learning from pes and cct experiences in developing countries[END_REF][START_REF] Martin Persson | Conditional cash transfers and payments for environmental services -a conceptual framework for explaining and judging dierences in outcomes[END_REF]. In that case, designing PES as a compensation may not be possible or eective for many reasons.

First, because estimating OC remains challenging and highly sensitive to the calculation methods. Moreover, OC vary widely from one area to another and from one land user to another. In Mexico, [START_REF] Borrego | Estimating the opportunity costs of activities that cause degradation in tropical dry forest: Implications for redd+[END_REF] showed that OC can be highly heterogeneous according to the type of land use implemented. In their study, cattle ranching provide the largest return so compensation should be higher for these types of land users.

Second, PES are shaped by local institutions and perceptions of fairness. [START_REF] Pascual | Exploring the links between equity and eciency in payments for environmental services: A conceptual approach[END_REF] emphasises that the focus on compensation is not systematic. Compensating income loss attributed to conservation might be seen as a fair allocation for PES but it automatically excludes the landowners that do not wish to deforest since their OC is null. Another fair allocation of PES is to reward forest owners that do not deforest. It should not be forgotten here that PES are exceptions to the polluter-pays principle. A scheme may not be seen as legitimate and fair if it only remunerates landowners that are willing to deforest [START_REF] Adger | Governance for sustainability: towards athick'analysis of environmental decisionmaking[END_REF]. From an economic perspective, designing PES as a compensation for foregone revenues and focusing on threatened forest is optimal in order to maximise additionality at minimal cost on the short-run. Nevertheless, such a polluter-paid solution may be perceived as unfair by landowners and prove counterproductive on the long-run.

Third, paying only for threatened forest may change the behavior of the forest owners that already conserve the forest. These landowners may try to blackmail the buyers and start clearing in order to receive payments. One may need to remunerate these landowners to avoid this change of behavior [START_REF] Wunder | The eciency of payments for environmental services in tropical conservation[END_REF]. Moreover, this 5.3. Design of the PSA-H 109 change of behavior may not be the results of a voluntary blackmail. As highlighted by [START_REF] Rode | Motivation crowding by economic incentives in conservation policy: A review of the empirical evidence[END_REF] through a number of case studies, paying for good practices can crowd-out intrinsic motivations to conserve without payments. The authors cite numerous mechanisms through which PES can crowd-out intrinsic motivations to conserve and associated case studies.

According to many authors such as [START_REF] Corbera | The equity and legitimacy of markets for ecosystem services[END_REF], [START_REF] Muradian | Payments for ecosystem services and the fatal attraction of win-win solutions[END_REF] or [START_REF] Pascual | Social equity matters in payments for ecosystem services[END_REF], to be eective, PES must pay attentions to local conceptions of a fair allocation2 . It may be more eective to encourage good practices in accordance with local institutions and procedural justice than to try to compensate the forest owners that deforest. Payments are here conceived more as an incentive for good practices and collective action than as a compensation mechanism [START_REF] Muradian | Between markets and hierarchies: the challenge of governing ecosystem services[END_REF][START_REF] Muradian | Payments for ecosystem services as incentives for collective action[END_REF]. It may involve rewarding forest owners that already conserve in order to send a signal acknowledging the good practices. On the long-run, one can hope it will impulse a shift in motivation to conserve, a crowding in eect, for the forest owners that deforest.

The distinction between reward and compensation is very subtle. As a matter of fact, reward can be conceived as compensation received ex post. We here consider two main distinctions. First, if PES are rewards, payments must rst be directed toward the landowners that deforested less in the past. Second, if PES are compensation, the payments must increase with the OC.

Design of the PSA-H

Was the PSA-H conceived as compensation or a reward mechanism? As highlighted by [START_REF] Mcafee | Payments for ecosystem services in mexico: Nature, neoliberalism, social movements, and the state[END_REF] and Shapiro-Garza (2013), interactions with civil society and other stakeholders, such as NPA commissions, forced CONAFOR to consider multiple objectives. These interactions tended to hybridize" the program into a multiple objectives scheme targeting not only toward threatened forests but also marginal areas. Sims et al. (2014) showed that, through adaptive management of the selection criteria, the PSA-H succeeded in combining both objectives. Studying allocation rules of the program, one can distinguish three types of criteria: administrative, marginality and environmental priority. We here focus on environmental priority. From CONAFOR's point of view, was the environmental objective of the program to protect threatened forests by compensating the OC or to reward good forest managements with payments? We will show in the forthcoming section that CONAFOR conceived the PSA-H as a compensation instrument.

According to CONAFOR's rules, the primary objective of the PSA-H and the PSA-CABSA is to "oer payments for environmental services generated by forest ecosystems" [START_REF] Conafor | Réglas de operacion del programa proarbol 2010[END_REF]. Rural development only appears as one of many common forests Every year, CONAFOR denes eligibility zones based on land-use change maps, advice and propositions from regional oces and a deforestation risk index. The eligibility zones are dened as "areas or forest ecosystems where degradation process endangers biodiversity and environmental services4 "(CONAFOR, 2009). The deforestation risk index is computed by the Instituto Nacional de Ecologia y Cambio Climatico [START_REF] Muñoz-Piña | Paying for the hydrological services of mexico's forests: Analysis, negotiations and results[END_REF]. It predicts, at the pixel-level, the risk of deforestation based on past deforestation trends, agro-ecological characteristics and socio-economic variables5 . Figure 5.1 shows the evolution of eligibility zones between 2004 and 2012. Eligible areas have been widely enlarged since 2003 and now cover about a quarter of the Mexican territory 6 .

Every year, CONAFOR receives new applications for the scheme and ranks these applications according to predened ranking criteria. Because of budget constraints, only the highest ranked ejidos are accepted into the program. The deforestation risk index is also part of the ranking criteria together with other environmental indicators such as soil degradation, over-exploitation of aquifers or water availability, among others [START_REF] Conafor | Réglas de operacion del programa proarbol 2010[END_REF].

Eventually, the amount of payments allocated per hectare of forest enrolled varies according to the deforestation risk index. Originally, the payments were set at the average income that could be obtained from one hectare of maize in order to reect the OC of one hectare of forest [START_REF] Muñoz-Piña | Paying for the hydrological services of mexico's forests: Analysis, negotiations and results[END_REF]. The payments were later diversied per type of forest and deforestation risk to capture OC heterogeneity. The amount of payments are presented in Table 5.1.

Note that NPA, and other priority zones such as areas under the RAMSAR conventions, are de facto eligible. Moreover, in the allocation criteria, localization inside an NPA increases the chance to receive the program. It results from interaction with other stakeholder such as the NPA commission (CONANP). CONANP's argument was that through the NPA system, they generated additionality without compensation for many years so they should be eligible to receive compensation ex-post. Environmentally, the emphasis put on NPA shows that the scheme also rewards good practices and past avoided deforestation. If CONAFOR conceived the PSA-H as compensation instrument, this reward elements is a good illustration of how the program has been hybridized to take into consideration multiple claims. Nevertheless, apart from NPA, the above elements shows that CONAFOR conceived the PSA-H as a compensation mechanism as it targets threatened forest and as the amount of payments are intended to reect income loss attributable to conservation. As highlighted above, interactions with civil societies and other groups of interest forced CONAFOR to consider other conceptions of PES and integrate new objectives. This hybridization may be responsible for the low additionality observed in the early cohorts of beneciaries [START_REF] Muñoz-Piña | Paying for the hydrological services of mexico's forests: Analysis, negotiations and results[END_REF][START_REF] Muñoz Piña | Payment for environmental services in mexico: Targeting challenges[END_REF][START_REF] Shapiro | Forest conservation and slippage: Evidence from mexico's national payments for ecosystem services program[END_REF]. Nevertheless, Sims et al. (2014) highlight that, through adaptive management of the targeting criteria, the designers succeeded over time at combining both objectives. One can hope that additionality increased if the scheme focused more on threatened forest.

In this article, we analyse the distribution of the payments within the ejidos in order to understand how the objectives of the PSA-H are understood and whether the internal distribution complies or conicts with compensation logic. We explore this issue in a sub-region of Yucatan State: the Cono Sur.

The Cono Sur in Yucatan

Economic activities and associated opportunity costs

In line with Coasean vision of the PES, we dene the OC of enrollment as the income that would be obtained from the land if the ejidos had not enrolled in the PSA-H. This OC depends on the protability of the type of land-use that could be implemented but also on the probability that the owner will deforest in the nearfuture. As a matter of fact, landowners may not be willing to deforest because of a trade-o between on-farm and o-farm activities or because they are creditconstrained among many things.

There are four main economic activities in the area. The ejidatarios often combine these four sources of income:

• Traditional slash-and-burn agriculture: Shifting cultivation of maize inter-cropped with beans is the traditional form of agriculture called milpa.

In our sample, traditional producers cultivate 2 hectares on average. Producers shift parcels every two or three years and fallow periods last for 10 to 15 years. Yields that can be obtained from this activity remain low and vary according to the climatic conditions (around 600 kg/ha/year). Being poorly protable, traditional agriculture tends to be abandoned or is maintained in combination with other activities. In our sample, 50 ejidatarios implemented slash-and-burn agriculture in 2005 and 47 in 2013.

• Mechanised agriculture: This form of intensive non-rotative agriculture developed in Cono Sur in the late 1970's with the implementation of PRON-ADE. The area under cultivation in our sample varies from 1 to 22 hectares.

The yields that can be obtained are three to four times superior to those obtained by slash-and-burn if combined with an irrigation system. The OC may seem greater for those producers but they are not directly dependant on the forest cover. They are sedentary, at least in the short-run. Indeed, although 5.5. Distribution of the PSA-H in the ejidos 113 this form of agriculture is protable, cultivating new land requires high investment in land-leveling that most producers cannot aord. In our sample, 81 ejidatarios engaged in this type of activity in 2005 and 85 in 2013.

• Cattle-ranching: The Cono Sur is also a traditional cattle-ranching area. In our sample, 30% of the ejidatarios have at least one head of cattle but some of them only breed a few heads at home for savings or short-term prot. More than 20% of our sample have individual pastures or use common pasture and forest for grazing. In our sample, 29 ejidatarios engaged in cattle-ranching activities in 2005 and 41 in 2013.

• O-farm: O-farm wages and remittances are also important sources of income for agricultural households. A quarter of the surveyed ejidatarios have temporary or permanent o-farm activities.

These activities yield heterogeneous returns and have varying opportunity costs on the forest cover. Mechanised agriculture is sedentary in the short-run so the OC of these land-users is close to zero. Traditional producers cultivate very small areas with moderate returns so the amount necessary to compensate these producers is also low. Moreover, as highlighted by [START_REF] Borrego | Estimating the opportunity costs of activities that cause degradation in tropical dry forest: Implications for redd+[END_REF], if fallow period are long enough, this type of agriculture is likely to be sustainable. In Jalisco, the authors showed that the highest return on clearing activities were obtained by cattle ranchers Moreover, land cleared for pasture is larger than for traditional agriculture.

In this context, cattle-ranchers are the main agents to compensate in order to achieve additionality.

If PES is seen as a compensation, cattle-ranchers are the main agents to target and should receive more payments than the other land users since their OC is higher.

However, if the PES is seen as a reward, cattle ranchers, the main agents that deforest, should receive less payments than mechanised and traditional producers.

As a matter of fact, mechanized producers do not rely on the forest cover and traditional producers clear very small areas.

5.5 Distribution of the PSA-H in the ejidos 5.5.1 Appropriation of the PSA-H by the ejidos Section 5.3 demonstrated that CONAFOR designed the PSA-H as a compensation instrument. However, as explained above, once payments are allocated to the ejido, the assembly decides how to distribute the payments to the ejidatarios. Payments can be redistributed among ejidatarios, invested in public goods (roads, school, new income generating activities etc...), or used to remunerate activities related to the program such as patrolling into the forest or building rebreak. This last use of PSA-H payments has been found in other Mexican States but, in our sample, excepting minor collective investment, all the payments were redistributed among the 114 Chapter 5. Reward or compensation? PES distribution in Mexican common forests ejidatarios8 . However, dierent ejidatarios from the same ejido won't necessarily receive the same amount of payments. Qualitative surveys with the authorities allowed us to explore the distribution rules and their justication in the 47 ejidos.

In order to understand how the ejidatarios share the payments, we provide three meaningful examples of the distribution rules. The rules vary over time and between ejidos but are often a combination of these three examples. A rst distribution rule consists in dividing all the payments equally among the ejidatarios. In this example, remuneration is not directly linked to the forest as some ejidatarios use the commons more than others. Nevertheless, since all of them have land-use rights and voting rights at the assembly, they receive equal payments. Another rule is to divide the payments, not equally among each member, but rather inversely proportional to the share of common forests they have cleared. Each ejidatario owns an equal share of commons and therefore an equal share of the common forest. This forest cover can be converted to agricultural elds or pasture for personal use with the permission of the assembly. The more the ejidatario has converted of his share of forest, the less remuneration he receives. A last example of a distribution rule involves sharing payments but only among a certain group of ejidatarios willing to participate. The ejidatarios that do not wish to join the program are either those who were absent during the assemblies or those who want to keep their share of forest out of the program. The latter, often cattle-ranchers, do not join either because they consider that payments are not high enough or because they feel that environmental programs threaten their activities. None of these three examples of allocation rules follows the compensation logic of the PSA-H. On the contrary, the latter two tend to show that the PSA-H is directly linked to land use but is perceived as a reward for not using the forest more than as a compensation for foregone revenues.

Land use and allocation of the payments

As highlighted by [START_REF] Corbera | The equity and legitimacy of markets for ecosystem services[END_REF] when resources are owned as commons, the provider of the ES is not clearly identiable. In Chiapas, multiple authors showed that participation and distribution of the payments within the community was not egalitarian and tended to reect existing power relationships within the community [START_REF] Corbera | The equity and legitimacy of markets for ecosystem services[END_REF][START_REF] García-Amado | Eciency of payments for environmental services: Equity and additionality in a case study from a biosphere reserve in chiapas, mexico[END_REF][START_REF] Hendrickson | Participation dynamics and institutional change in the scolel té carbon forestry project, chiapas, mexico[END_REF].

Our studies complete these results with an emphasis on the type of land-use and the logic of the distribution rules.

We hypothesise that the scheme has been reinterpreted at the ejido-level as a reward for good behaviour. Community forests are perceived as areas managed by a homogeneous group. Focusing on more threatened areas addresses between-ejido heterogeneity but the design of the PSA-H implicitly assumes the absence of the heterogeneity that exists within ejidos. This heterogeneity leads to the emergence of a second adverse selection problem. Within the ejido, some ejidatarios benet cattle ranching more from the scheme than others, and these dierences are directly related to the land use. [START_REF] Borrego | Estimating the opportunity costs of activities that cause degradation in tropical dry forest: Implications for redd+[END_REF] showed that OC varied across land users in Mexico, studying allocation rules allows us to understand if the distribution, at ejido-level follows logic of compensation or reward.

Traditional producers are dependent on the forest cover but only cultivate a small area (about two hectares). Moreover, their production behaviour is rotative which allows for forest regeneration. Mechanised producers cultivate larger areas but are sedentary in the short-run. Moreover, they cultivate mainly on parcelised land and not on commons. In compensation logic, these types of land-uses would not be targeted. However, if the scheme is a reward for not using the forest, these land users would be the main beneciaries. Cattle ranchers are the main forest-users. They use the forest for grazing, which is forbidden by the PSA-H, and convert it into pasture. Thus, they are the primary agents to compensate in order to avoid deforestation.

Figure 5.2 presents the distribution of the Gini coecient of inequality in the distribution of payments in the 47 ejidos according to the presence of the three main economic activities in the ejido. Calculations of the Gini are based on the total amount of payments received by each ejidatario in the 47 ejidos that received payments between the beginning of the PSA-H and 2012. We note that the presence of cattle-ranching in the ejido tends to be associated with more unequal distribution. This pattern seems less marked with mechanised and traditional agriculture.

At the household-level, Figure 5.3 presents the yearly amount of payments received by the 163 ejidatario surveyed according to the three types of land use: mechanised agriculture, cattle-ranching and traditional slash-and-burn. The land use can be impacted by the PSA-H so we use activities in 2005, before PSA-H reception, to avoid endogeneity. Moreover, we use the average reception per year because the total amount is highly inuenced by the year of entry of the ejido, making irrelevant the comparison between households of dierent ejidos. According to Figure 5.3, the ejidatarios who practiced mechanised agriculture tended to receive more payments, whilst ejidatarios who practiced cattle-ranching received less. Note that the amount of payments received by the ejidatarios depends on the amount received by their ejido. In order to analyse the distribution of the payments, it is crucial to compare the amount of payments received by one ejidatario, not with the amount received by the entire sample, but by the other ejidatarios living in the same ejido. In the econometric analysis presented in the following section, we use a pseudo-panel to capture between-ejido heterogeneity and focus on within-ejido heterogeneity.

Econometric analysis

This section proposes an empirical analysis of the repartition of the payments within the ejidos. The rst section of the econometric analysis investigates the determinants of inequality in payment distribution at the ejido-level. The second section explores the determinants of the yearly amount of payments received by one ejidatario compared to other ejidatarios from the same ejido. We explore the determinants of inequality of payment distribution within the ejidos by estimating an OLS model with the Gini index of payment distribution between 2003 and 2012 as our explained variable. Our hypothesis is that inequality in distribution is directly linked to land-use heterogeneity within the ejido. We estimate the following model:

psa_gini j = α + βL j + γX j + ε j (5.1)
In equation 5.1, L j corresponds to the dierent land use in ejido j as presented in Section 5.4. We use the 2005 level of each variable in order to avoid endogeneity. In a rst step, we consider dummy variables that are equal to one if mechanised agriculture (mec05_ejid j ), traditional agriculture (trad05_ejid j ) or cattle-ranching (ranch05_ejid j ) are present in the ejido. In a second step, we introduce the percentage of ejidatarios implementing each activity9 (per_mec05 j , per_trad05 j and per_ranch05 j ). X j include control variables likely to inuence inequality such as number of ejidatarios (ejidat j ), the average and standard deviation of ejidatarios' age (age j and age_sd j ), average participation to the assembly (partasemb j ), area of common forests in hectare (forest05 j ) and percentage of total supercie of the ejido (per_forcom j ). We also include a dummy variable for labor-ejido dened as the ejido with less that 20 inhabitants10 (labejid j ). Eventually we control for the average annual amount received by the ejido (psaejid_yr j ). We estimate equation 5.1 on a sample of 47 beneciary ejidos.

Results

Table 5.2 presents the results for the estimation of equation 5.1. Column (1) presents the results of the estimation with only the control variables. Column (2) adds dummy variables for each activity and column (3) adds the percentage of ejidatarios implementing each activity. Comparing the R-squared of column (1) with columns Chapter 5. Reward or compensation? PES distribution in Mexican common forests

(2) and ( 3), we note that considering the type of land-use in the estimation adds to the explanatory power of the model.

As suggested by the box-plots presented in Figure 5.2, the presence of cattle ranching seems to increase distribution inequality. According to the results of column ( 3), the more cattle ranchers there are in the ejido, the more unequal is the distribution. Our results also suggest the opposite results for mechanised agriculture.

The more ejidatarios with mechanised plots, the less unequal is the distribution.

Our results also suggest that the distribution has been more equal in the laborejido and in the ejidos with higher participation rate at the assembly. Moreover, the larger is the share of forest, the more equal is the distribution. Eventually, the distribution may be more equal when yearly payments received are higher but the coecient of psaejid_yr j is only signicant in column ( 3).

This result suggests that inequality in payment distribution is linked to the type of land-use involved. As a matter of fact, whilst ejidos relying on mechanised agriculture tend to have more equal rules of allocation, the ejidos involved in cattle ranching have more heterogeneity in individual payments, conrming our hypothesis.

Nevertheless, these results do not tell us who benets more from the program. In the next section, we study the amount received by each ejidatario according to their land use.

Distribution of the payments

Identication strategy

Our hypothesis is that the objectives of the PSA-H have been reinterpreted by the ejidatarios as a reward for conserving the forest. We test this hypothesis using a pseudo-panel. The pseudo-panel estimation allows us to capture between-ejido heterogeneity and focus on within-ejido heterogeneity, i.e. the dierence between the payments received by one ejidatario compared with other ejidatarios from the same ejido. We test the impact of the type of land use implemented by the ejidatario before PSA-H implementation on the amount of PSA-H received.

We estimate the following models respectively with xed eects (5.2) and random eects (5.3):

psahyrij = α + βA ij + γX ij + ηD j + ε ij (5.2)
psahyr ij = α + βA ij + γX ij + ε ij ε ij = ρ j + μ ij (5.3)
In equation 5.2 and 5.3, psahyr ij corresponds to the average yearly amount of payments received by ejidatario i in ejido j during one year of reception. Some ejidatarios joined the program several years after the other ejidatarios either due to absence from the ejido or a reluctance to participate at the program's onset. For this reason, we chose to use average yearly payments during the years of reception rather than total payments. In order to check whether or not the random eect estimator is convergent, we run an overidentifcation test as suggested by [START_REF] Cameron | Microeconometrics: methods and applications[END_REF]. Contrary to Hausman's test, this test is robust to heteroskedasticity. We cannot rule out that both estimations are convergent so the random-eects estimator is the most ecient.

Nevertheless, we present both models in Table 5.3.

A ij are dummy variables that correspond to the type of economic activities of household i. As in Section 5.6.1, we use the level of the variables in 2005 in order to avoid endogeneity. Three of these variables are linked to land-use: mec05_hh ij , trad05_hh ij and ranch05_hh ij respectively for mechanised agriculture, traditional slash-and-burn and cattle ranching. We consider as cattle ranchers only households using either pasture or common land for grazing. We also include one dummy variable to control for permanent o-farm activity (of f 05 ij ). X ij include control variables such as the age of household's head (age), a dummy variable equals to one if the household knows how to write and read (write ij ), the size of the household (size05 ij ) and a measure of the assets computed using principal component analysis (assets05 ij ). Eventually, we include a dummy variable equals to one if the ejidatario is a member of the ejido authorities (orgejid ij ) and the average percentage of as- semblies in which he participated (partasemb_hh ij ). Our sample is composed of 163 ejidatarios.

Results

Table 5.3 presents the results of the econometric analysis regarding distribution of the payments. Column ( 1) and ( 2) presents the results of the estimation of the models presented in equations 5.3 and 5.2. Our three main variables of interest, mec05_hh ij , trad05_hh ij and ranch05_hh ij , are all signicant. The main beneciaries were the ejidatarios who engaged in mechanised activities and traditional agriculture. Producers with cattle-ranching activities received less payment than others.

Our results are robust in many settings. Columns ( 3) and ( 4) exclude from the sample the ejidatarios that decided to remain outside of the program and did not receive any payments. Columns ( 5) and ( 6) display the results using the total amount of payments received psahtot ij rather than average yearly payments respectively with the whole sample and excluding the ones that decided to remain outside of the program. Our variables of interest remain signicant in all specications, which conrm the robustness of our results.

As explained in Section 5.4, cattle ranchers clear large areas of forest with high returns. On the contrary, traditional producers clear moderate areas with lower return. Moreover, this farming system could be sustainable if fallow periods are long enough [START_REF] Borrego | Estimating the opportunity costs of activities that cause degradation in tropical dry forest: Implications for redd+[END_REF]. Mechanised producers are sedentary, at least on the short-run, and do not clear the forest anymore. In compensation logic, cattle ranchers should be the main beneciaries of the PSA-H. These results conrm our hypothesis that the distribution of payments within the ejido does not follow the PES logic of compensation. On the contrary, the PSA-H appears to be perceived 5.7. Concluding remarks 121 by the ejidatarios as a reward for greener behaviour.

Concluding remarks

In this Chapter, we explore the distribution of the PSA-H payments at the ejido and household levels. We here oppose two conceptions of PES' eciency. The rst one regards PES as a compensation mechanisms for income loss attributable to conservation: it targets threatened forests in order to achieve maximum additionality at minimum costs. The second conception of PES pays more attention to local institutions and considers that, to be ecient on the long-run, PES allocation may reward existing conservation eorts. The PSA-H was designed as a compensation mechanism for income loss attributable to conservation: it targets threatened forests in order to achieve maximum additionality at minimum costs. Since most of the Mexican forests are commons, PES payments are allocated to the ejidos, and the assembly of each ejido divides payments among the ejidatarios according to their own rules and perception of fairness.

Based on ejido and household surveys in a sub-region of Yucatan State, we explore the interaction between the land-use type and internal payment distribution. In our study area, the cattle ranchers are the more likely to clear land in the near future and will obtain higher return from clearing than the other type of land-users. They are the main agents to compensate but if PES is seen as a reward, they should receive less payment. We estimate the impact of land use type on both the Gini coecient of distribution at the ejido-level and the yearly payment amount received by each eijdatario at the household-level. Our econometric results suggest that the more prevalent cattle ranching is in the ejido, the more unequal the distribution tends to be. Moreover, within the same ejido, the ejidatarios that already conserve the forest receive higher payments whilst the cattle-ranchers, receive less remuneration than their peer-ejidatarios. The ejidatarios share the payments according to past behavior, the agents that deforested the most receives less payments either because it is considered that they already cleared their share of commons or because it is not considered as fair to remunerate more the land users that clears the forest. In no cases, the distribution follows the compensation logic that more payments should be allocated to the agents with higher OC. The ejidatarios appear to have reinterpreted this instrument conceived by federal authorities as a compensation as a reward for good practices.

As all compensation mechanisms, PES are complex exceptions to the polluterpays principle [START_REF] Pirard | Questioning the theory of payments for ecosystem services in light of emerging experience and plausible developments[END_REF]. The idea that payments must rst be directed to the agents that deforest is far from being appropriated by all forest owners and the only agents willing to join might be the one with higher intrinsic motivation to conserve [START_REF] Lapeyre | Payments for environmental services in indonesia: What if economic signals were lost in translation?[END_REF]. The institutional process of allocating compensation from the federal-level to the ejidos involves many levels of decision making, leaving room for reinterpretation of the program's objectives. PES might have been progressively transformed into a subsidy for the agents that do not clear the forest Moreover, at the federal level, it is possible to discriminate between eligible areas in order to focus the program on areas with higher deforestation rates. However, within the ejido, remunerating only producers that are likely to clear the forest cover and not those who are already conserving it might prove impossible. Making the scheme acceptable requires taking into consideration local procedural justice and institutions [START_REF] Adger | Governance for sustainability: towards athick'analysis of environmental decisionmaking[END_REF][START_REF] Corbera | The equity and legitimacy of markets for ecosystem services[END_REF][START_REF] Pascual | Social equity matters in payments for ecosystem services[END_REF] and the ejido may have reinterpreted the program's objectives according to their own conception of fairness.

Once confronted with reality, the implementation of the PES concept can result in unexpected outcomes. Compensation as an exception to the polluter-pays principle may not be well appropriated by the economic agents especially when dealing with commons. It also highlights the diculty to involve the land users willing to deforest in the process. When dealing with commons, it seems ethically impossible to compensate income loss within a community without rewarding those already conserving the forest. Nevertheless, more eort is needed to include the deforesting agents in a process that they currently regard as a threat to their activity. A version of this Chapter has been adapted as a working paper co-authored with Carlos Muñoz-Piña for future publication.

Introduction

During the last years, a wide variety of heterogeneous instruments including PES of various forms or incentives for sustainable management emerged. As these dierent instruments widespread, they eventually come to interact within the same areas. In recent researches, [START_REF] Barton | Assessing the role of economic instruments in a policy mix for biodiversity conservation and ecosystem services provision: a review of some methodological challenges[END_REF] and [START_REF] Ring | Instrument mixes for biodiversity policies[END_REF] drew attention on the concept of policy mix at landscape-level. As instruments interact, they may oset each other's impact or be complementary and create synergy. Therefore, they should not be considered or evaluated separately. In a search for relevant policy-mix, we here provide an example of positive interactions in Mexico between PES and Community Forestry.

The specicity of Mexican forests is that, as a result of one century of agrarian land reforms, 80% of these forests are owned today as commons by communities called ejidos (Bray et al., 2003b;[START_REF] Kaimowitz | Mexico: Market options and barriers for timber and sawnwood from michoacan, oaxaca, guerrero, campeche adn quintana roo. a summary[END_REF]. To counteract high deforestation rates, the Mexican government relies on a wide set of conservation instruments including the PSA-H [START_REF] Muñoz-Piña | Paying for the hydrological services of mexico's forests: Analysis, negotiations and results[END_REF]. But since the 1980's, the timber value chain in Mexico has been characterized by the prominent role played by community forest enterprises (CFEs) [START_REF] Antinori | Vertical Integration in Mexican Common Property Forests[END_REF][START_REF] Scheer | Making markets work for forests communities, forests trends[END_REF]. These CFEs are managed directly by the ejidatarios and constitute a substantial source of income and an early stage of industrial development at the ejido-level. Moreover, many authors emphasized CFE development as sustainable options for forest conservation in Mexico [START_REF] Ellis | Is community-based forest management more eective than protected areas?: A comparison of land use/land cover change in two neighboring study areas of the central yucatan peninsula, mexico[END_REF][START_REF] Bray | Toward post-redd+ landscapes. mexico community forest enterprises provide a proven pathway to reduce emissions from deforestation and forest degradation[END_REF][START_REF] Porter-Bolland | Community managed forests and forest protected areas: An assessment of their conservation eectiveness across the tropics[END_REF]. As a matter of fact, in order to sell timber on the legal market, CFEs must comply with a management plan that ensures sustainability of logging activities.

How do the PSA-H and CFEs coexist and interact within the same landscape? Does paying for conservation discourage investment in CFEs? Or by contrast, can PES help communities to create and stabilise CFEs? We hypothesize that the PSA-H can contribute to CFE development for three reasons. First, because the PES can relax credit constraints. Extracting timber through CFEs requires investment that needs to be renewed regularly and ejidos may be unable to nance it without additional funding. Second, because PES reception improves ejido's access to information. Ejidos are often unaware of the possibility of exploiting their timber resources through CFEs. Enrolling in the PSA-H enhances their interactions with CONAFOR's sta and other ejidos through meetings and workshops and increases their knowledge about CFEs. Third, because the PES can help ejidos create forest management institutions. To comply with PES' commitments, the ejidatarios must organize various tasks in the forest such as patrolling or building rebreaks. It creates institutions of forest management, thus likely fostering the emergence of additional CFEs

We propose an empirical analysis of the interactions between CFEs and the PSA-H. We use a national database of 223 ejidos. The results of our empirical analysis support our hypothesis. The concept of policy mix derives from macroeconomic policy and can be dened as the art to combine and harmonize economic policies to achieve economic objectives. Following seminal works by [START_REF] Tinbergen | On the theory of economic policy[END_REF] or [START_REF] Mundell | The appropriate use of monetary and scal policy for internal and external stability[END_REF], it often refers to the interdependencies between scal and monetary policies. In environmental policy, the concept of policy mix has been applied to the interactions between heterogeneous environmental policies at dierent scales. For instance, [START_REF] Sorrell | Carbon trading in the policy mix[END_REF] study interactions between cap-and-trade and quotas in the context of carbon trading. But the concept of policy mix has also been applied to interactions at micro-level [START_REF] Barton | Assessing the role of economic instruments in a policy mix for biodiversity conservation and ecosystem services provision: a review of some methodological challenges[END_REF][START_REF] Ring | Instrument mixes for biodiversity policies[END_REF]. [START_REF] Ring | Instrument mixes for biodiversity policies[END_REF] note that policy instruments in the context of biodiversity protection, are considered and evaluated independently even though they often interact in the same landscape. The authors dene this policy mix as a combination of policy instruments which has evolved to inuence the quantity and quality of biodiversity conservation and ecosystem service provision in public and private sectors". As emphasized by this denition, policy mix here emerge de facto and do not result from a coordinated process. Environmental policies also interact with other types of economic instruments including agricultural subsidies or social programmes. The outcome of the interactions between environmental policies and these programs is uncertain and potentially harmful to economic eciency [START_REF] Rodríguez | Towards a unied scheme for environmental and social protection: Learning from pes and cct experiences in developing countries[END_REF]. As highlighted by [START_REF] Barton | Policyscapes. nature-based policy mixes for biodiversity conservation and ecosystem services provision[END_REF], the challenge is to think the policy mix, and resulting interactions, at landscape level. It includes a careful mapping of the instruments, and associated land uses, discussed with stakeholder and acknowledging the areas of higher environmental benets and economic costs for the land users.

Many policy instruments interact in the rural Mexico. We add to the existing empirical evidence on this matter. Computing the multiplier eect of agricultural subsidies called PROCAMPO on household income, [START_REF] Sadoulet | Cash transfer programs with income multipliers: Procampo in mexico[END_REF] note that the programme has the potential to relax a credit constraint. It can explain why this program together with cattle ranching subsidies has been found to increase deforestation in Mexico [START_REF] Barbier | Economic analysis of deforestation in mexico[END_REF][START_REF] Schmook | Agricultural policy, market barriers, and deforestation: the case of mexico's southern yucatán[END_REF]. Using regression discontinuity design, [START_REF] Mcintosh | The ecological footprint of poverty alleviation: evidence from mexico's oportunidades program[END_REF] nd that exposure to a social conditional cash transfer called Oportunidades1 increases deforestation.

The results show that, because of the credit constraint, additional income signicantly increases consumption which increases deforestation. Recently, Sims et al. (2014) compared the additionality of protected areas and the PSA-H and interactions between both instruments. As expected, their results show that PSA-H's additionality is low within protected areas which suggest that the protected areas were already well-enforced. We here empirically explore the interactions between two other environmental policies in Mexico: CFEs development and PES. CFEs emerged in Mexico in the early 1980's and expanded widely in the subsequent years. They are dened by [START_REF] Antinori | Community forest enterprises as entrepreneurial rms: Economic and institutional perspectives from mexico[END_REF] as businesses based on collective ownership or secured access to a forest resource by a community, with governance derived from or inuenced by local community traditions". CFEs have become prominent actors of the Mexican timber value chain. It is dicult to know the number of ejidos with activities of timber extraction and transformation. Bray et al. (2003a) show that estimates can vary from one study to another. The most recent federal survey states that more than 1,700 communities had extraction activities in 2007 [START_REF] Bray | Nueva evidencia: los bosques comunitarios de méxico protegen el ambiente, disminuyen la pobreza y promuevan la paz social[END_REF]. More than forest or environmental policy, the main factor explaining the emergence of CFEs in Mexico lies in the land tenure system [START_REF] Bray | The mexican model of community forest management: The role of agrarian policy, forest policy and entrepreneurial organization[END_REF]. In fact, most of the timber resources are owned by ejidos as commons. Moreover, the governance of CFEs relies on ejidos' already existing traditional governance institutions such as the assembly of ejidatarios.

CFEs are regulated by the General Law of Sustainable Forestry Development of 2006 (Ley General de Desarollo Forestal Sustenable). This law strictly forbids the extraction of timber if the ejido is not complying with a forestry management plan (Plan de Manejo Forestal ) ensuring that logging activities are implemented at a sustainable rate. The management plan denes the volume of timber that can be extracted annually for the subsequent ten or twenty years: this volume is called the annuity. The management plan also establishes the length of the rotation cycle, ie the time necessary for regeneration. An ejido cannot enroll the same area in a management plan before the end of the rotation cycle. The regeneration time depends on multiple characteristics such as, climatic conditions, tree and soil type and land-use history. At the end of the plan, the ejido can decide to pay for another plan and enroll a new area. To comply with their management plan, CFEs contract with private CONAFOR-qualied forest engineers. These engineers monitor and supervise extraction activities and ensure a good level of regeneration of the forest cover according to silviculture standards. The engineer's wages and the management plan are paid by the ejido but in some cases the timber buyer, a private sawmill for instance, may take over these costs.

Within the CFEs, day-to-day decisions are taken by a general manager (gerente general or coordinador ) elected by the ejido [START_REF] Antinori | Vertical Integration in Mexican Common Property Forests[END_REF]. The forest engineer is consulted to supervise the logging activities. More developed CFEs may also have a chief of sales (jefe de ventas) and/or a chief of nance (jefe de nanzas). Apart from the engineer, all the decision-makers are members of the ejidos. [START_REF] Antinori | Collective choice and community forestry management in mexico: An empirical analysis[END_REF] studied decision-making in CFEs. Their ndings suggest that more technical decisions are left to an external expert such as the forest engineer. Decisions concerning commercialization can be taken either by the assembly or delegated to the authorities of the ejido (such as the comisariado or elected managers) but key policy decisions such as prot distribution or the purchase of a sawmill are discussed at the assembly.

Various authors have studied the impact of CFEs on the forest cover or carbon mitigation. Their conclusions suggest that CFEs development, and communitybased management are more generally associated with reduced deforestation. With the implementation of REDD+ mechanisms, some authors advocate for the inclusion of community-based management as carbon-reducing activities [START_REF] Klooster | Community forest management in mexico: carbon mitigation and biodiversity conservation through rural development[END_REF][START_REF] Bray | The institutional drivers of sustainable landscapes: a case study of the mayan zonein quintana roo, mexico[END_REF][START_REF] Ellis | Is community-based forest management more eective than protected areas?: A comparison of land use/land cover change in two neighboring study areas of the central yucatan peninsula, mexico[END_REF][START_REF] Bray | Toward post-redd+ landscapes. mexico community forest enterprises provide a proven pathway to reduce emissions from deforestation and forest degradation[END_REF][START_REF] Porter-Bolland | Community managed forests and forest protected areas: An assessment of their conservation eectiveness across the tropics[END_REF]. Nevertheless, despite the fact that CFEs comply with most international standards of sustainable forestry, few of them apply for Forest Stewardship Council (FSC) certication. According to Gerez Fernandez and Alatorre-Guzman ( 2005), the premium for certication perceived by ejidos is too low in comparison to its cost and can be captured at a higher level of the timber value chain. Moreover, CFEs mainly sell on local markets that do not value the certication.

CFE and the timber value chain

In 1997, PROCYMAF (see Section 6.2.2.3) developed an ordered classication of CFEs presented in Table 6.1. We will use this classication in our econometric analysis to capture the development of CFEs Table 6.1: PROCYMAF Classication of CFEs Type I Potential producers: Owners or possessors of forestlands with capacity for sustainable commercial production that currently do not carry out logging because they lack an authorized forest management plan or sucient means to pay for its elaboration. Type II Producers who sell timber on the stump: Owners or possessors of parcels subject to timber exploitation where the activity is carried out by third parties through commercial contracts without the owner or possessor participating in any phase of the extraction process. Type III Producers of forest raw material: Owners and/or possessors of forest parcels that have authorized participate directly in some phase of the production chain. Type IV Producers with capacity for transformation and marketing: Producers of raw forest materials that have infrastructure for its primary transformation and directly carry out the marketing of their products. Source: (Bray et al., 2003a) This classication reects early levels of industrial development. Type I are potential producers but do not carry extraction activities under a management plan. Type II producers only sell their annuities to private sawmills and are usually con-130 Chapter 6. Can PES enhance Community Forestry? sidered as rentiers (rentistas ) [START_REF] Conafor | Logros y perspectivas del desarollo forestal en mexico 2007-2012[END_REF][START_REF] Bray | Community forestry in mexico: twenty lessons learned and four future pathways. The community forests of Mexico: managing for sustainable landscapes[END_REF]. The ejidos are not in charge of timber extraction, private sawmills use their own equipment and labor forces 2 to cut the trees and transform them into sawnwood. The ejido only receives a payment for the annuity allowed by the management plan Type III and IV producers provide labor and capital to cut and transform the timber before selling it.

Type III producers sell sawnwood and Type IV producers further transform it into nal products such as boards.

All the ejidos with extracting activities, Types II to IV, must invest in a management plan. However, only Type III and IV producers are directly involved in timber transformation and management. According to the denition of [START_REF] Antinori | Community forest enterprises as entrepreneurial rms: Economic and institutional perspectives from mexico[END_REF] presented in Section 6.2.2.1, CFEs correspond to these producers.

Eventually, one should note that higher stages of development are associated with higher investment requirements and better organizational capacity.

Dependance on external supports

The main threat for CFE sustainability lies in their dependance on private and public support for nancial and technical assistance.

At the federal-level, CFEs have beneted from two main programs: PROCY-MAF (Proyecto de Conservacion y Manejo Sostenable de Recursos Forestales), a World Bank-funded program, and PRODEFOR (Programa de Desarollo Forestal ).

PROCYMAF was started in 1999 and ended in 2008. This program oered technical assistance in land-use planning and managerial capacities for ejidos willing to invest in a CFE or for ejidos that had already developed extraction activities [START_REF] Scheer | Making markets work for forests communities, forests trends[END_REF][START_REF] Merino-Perez | Forest and conservation policies and their impact on forest communities in mexico[END_REF] CFEs also rely on external support from timber buyers. Given the land tenure of Mexican forests, timber buyers, such as private sawmills, cannot integrate backwards without working with ejidos [START_REF] Antinori | Vertical integration in the community forestry enterprises of oaxaca. The community forestry of Mexico: managing for sustainable landscapes[END_REF]. They provide assistance to the ejidos and nance small investments in order to secure their access to timber resources.

However, the buyers do not assume large investments because, without long-term 2 Some ejidos informally force the sawmill to employ some persons from the ejido 6.3. Database 131 contracts with the ejido, they are unable to secure an investment for longer than the length of the management plan. Despite federal and private support, CFEs remain fragile and their development is constrained by a lack of liquidity, the diculty of organizing tasks within ejidos and the low absorption capacities of local markets. For these reasons, CFEs tend to extract less annually than the volume of timber authorized by their management plan [START_REF] Antinori | Vertical integration in the community forestry enterprises of oaxaca. The community forestry of Mexico: managing for sustainable landscapes[END_REF][START_REF] Torres-Rojo | The managerial economics of sustainable community forestry in mexico: A case study of el balcon, tecpan, guerrero[END_REF] and struggle to sustain their activities.

Can PSA-H contribute to CFE development?

PSA-H coverage of Mexican forests has considerably increased during the last decade and interactions between the PSA-H and CFEs has become crucial for forest management. As feared by [START_REF] Bray | Community forestry in mexico: twenty lessons learned and four future pathways. The community forests of Mexico: managing for sustainable landscapes[END_REF], paying for conservation may reduce the incentive to invest in a CFE and slow down their development. Ejidos may favor conservation over timber exploitation which is more costly and risky. We argue that the PSA-H can be combined with CFEs and contribute to their development for three reasons:

• PES can relax a credit constraint: Developing and sustaining CFEs requires investment that needs to be regularly renewed. Ejidos are often creditconstrained, which prevents them from nancing investments. The PSA-H can relax these constraints.

• PES can increase access to information: Ejidos are often unaware of the possibility of valorizing their resources through CFEs. By enrolling in the PSA-H, ejidos become closer to CONAOR sta and participate in environmental workshops and meetings with other ejidos. This can improve their knowledge about federal programs and increase their willingness to invest in a CFE.

• PES can help to create and reinforce institutions for forest management: Collective action is a crucial determinant of CFE development and [START_REF] Yanez-Pagans | Cash for cooperation? payments for ecosystem services and common property management in mexico[END_REF] shows that the PSA-H tends to reinforce cooperation in collective activities within the ejidos. When enrolling land in the PSA-H, the ejidatarios commit themselves to implement dierent tasks such as patrolling in the forest or building rebreaks. The PSA-H can increase the involvement of ejidatarios in forest-related activities and can help to create and reinforce CFEs.

Our hypothesis is that PSA-H can help the CFEs to develop and stabilise over time. We test this hypothesis using a database of 223 ejidos.

Database

We test our hypothesis on the representative sample of ejidos surveyed in 2002 an 2010 for the ENNAF survey presented in Chapter 2. Our sample is composed of 223 Chapter 6. Can PES enhance Community Forestry? ejidos. There are on average 190 ejidatarios in an ejido but this number varies from 10 to 3,500. The area of ejidos also varies from less than 200 ha to more than 127,000 ha. The mean area of an ejido in our database is 10,000 ha. Of our 223 ejidos, 49 received the PSA-H between 2004 and 2010. The distribution of beneciary ejidos according to the year of reception is presented in Table 6.2 Table 6 Note that 10 out of 49 ejidos subscribed to more than one PSA-H contract between 2003 and 2010. These ejidos either enrolled dierent parcels of forest at dierent times or renewed their contract after the initial ve year period.

We do not look only at PES reception but also at the heterogeneity regarding the time spent in the program (tpsah j ) and the total amount of payments spent in the program (psahval j ). As a matter of fact, we suspect that the impact of PSA-H on CFE development increases with increasing exposure to the treatment. Figure 6.1 shows the distribution of our interest variables for the beneciaries of the scheme. The absolute payment varies from 170,000 pesos (approximately 13,000 USD in 2010) to more than 6,300,000 pesos (approximately 484,600 USD in 2010).

We will now look conjointly at PES reception and CFEs. Table 6.3 shows the distribution of PSA-H beneciaries and non-beneciaries according to CFE type as presented in Table 6.1. The higher percentage of PSA-H beneciaries is among the more developed CFEs. While only 21,97% of our sample beneted from the PSA-H, more than half of the ejidos that owned a sawmill received the PSA-H. tion of the CFEs. We distinguish three groups: the rst group (cf e j < cfe02 j ) corresponds to CFEs that regressed between 2003 and 2010 according to the typology presented in Table 6.1. The second group corresponds to CFEs that stabilized (cf e j = cf e02 j ) and the third group gathers CFEs that expanded (cf e j > cfe02 j )

i.e. according to the typology, they are more developed in 2010 than in 2002. We note that higher percentage of beneciaries are found in the last two groups. that the ejidos that increased their activities between 2002 and 2010 received higher amounts and had been enrolled longer than the others. This also supports our hypothesis.

6.4 Econometric estimation 6.4.1 Econometric model We are interested in estimating the impact of PSA-H reception on the level of CFE development. As other authors before [START_REF] Antinori | Vertical Integration in Mexican Common Property Forests[END_REF]Bray et al., 2003a;[START_REF] Antinori | Collective choice and community forestry management in mexico: An empirical analysis[END_REF], we use the PROCYMAF classication (see Table 6.1) in order to capture the level of development of the CFEs. This classication reects an increasing involvement by the ejidos in the extraction and transformation process.

Our variable cf e j for ejido j is constructed as follows:

1. Potential producers 2. Producers who sell timber on the stump 3. Producers of raw materials (roundwood) 4. Producers with capacity for transformation and marketing (sawmill)

The impact of the PSA-H on CFE development is expected to be larger with increasing exposure to the treatment. We will consider here that exposure varies according to the time spent in the program and the amount of payments received.

Therefore, as recommended by [START_REF] Miteva | Evaluation of biodiversity policy instruments: what works and what doesn't?[END_REF], we use continuous measures of PSA-H reception. Our variables of interest are dened as: • psahval j : The sum of payments received by the ejido j between 2003 and 2010.

These variable have been computed thanks to CONAFOR's information.

Our explained variable cf e j is multinomial and discrete. Therefore, we use an ordered probit to estimate the impact of PSA-H reception. We estimate the following models for two latent variables y * j and z * j :

y * j = ρ 1 tpsah j + λ 1 X j + η 1 (6.1) z * j = ρ 2 psahval j + λ 2 X j + η 2 (6.2)
Given the threshold for each level of CFE, we will derive marginal eects of tpsah j and psahval j on each level of development of the CFE at average value of X j .

In equation 6.1 and 6.2, X j includes control variables such as the initial level of development of the CFE before PSA-H implementation (cf e02 j ). We control for basic characteristics of the ejido including the number of ejidatarios (ej idat10 j ), the size of the ejido in tens of thousands of hectares (size j ), the percentage of forest cover in 2002 3 (perf or02 j ), the mean slope (meanslope j ) and population density in 2010 (density10 j ). We introduce proxies for remoteness and transactions costs using the distance to the closest road (distroad j ) and to the closest city of more than 2,500 habitants (distcity j ). We also control for the reception of technical assistance before 2002 (at02 j ) and the illiteracy rates (analph10 j ). Eventually, we control with

Checking for endogeneity

The main challenge to identifying the impact of PSA-H on CFE development is to deal with endogeneity of PSA-H reception. Endogeneity may arise for three reasons:

• Ejidos with a CFE are closer to CONAFOR's sta, including technical advisors, than others. They have better access to information about the PSA-H, which increases their probability of enrolling in the program.

• Ejidos that have already developed a CFE are more inclined to favor environmental initiatives. Therefore, they are more likely to apply for the PSA-H.

• CONAFOR ranks applications for the PSA-H according to many selection criteria. Complying with a Management Plan is one of these criteria. At the margin, complying with a Management Plan can increase the probability that the PSA-H application will be accepted. We introduce the lagged value of cf e j in our estimations to control for most of the confounding factors generating endogeneity. Nevertheless, we test the presence of endogeneity using two-stage least squares (TSLS). The explained variable cf e j being ordered and discrete, it would be better to run our estimations using an ordered probit but, to our knowledge, no instrumental variable estimators with an ordered probit as a second stage have been developed. The second stage of TSLS does not take into account the discrete nature of cf e j but allows us to test the exogeneity of our interest variables.

The rst step of the TSLS estimation must include instruments excluded from the second step. These instruments must be strong predictors of tpsah j and psahval j without directly impacting cf e j . Based on PSA-H's operational rules, we identied two variables. The rst is the area of mesophyll forests (meso02 j ). This type of forest (often called cloud forest) is supposed to provide more environmental services and is remunerated with higher payments. The second variable is the number of years of eligibility of the ejidos (telig j ). As funds allocated to the PSA-H increased, more ejidos became eligible. We believe that the longer ejidos have been eligible, the more likely they are to apply and be accepted into the program. Nevertheless, it is unlikely that eligible zones have been dened based on a CFE's location.

We estimate the following models for the number of years spent in the program: (6.3) cf e j = α 1 + β 1 X j + σ 1 tpsah j + ε j (6.4) And the amount of payments received: (6.5) cf e j = α 2 + β 2 X j + σ 2 psahval j + ε j (6.6) tpsah j and psahval j respectively are predicted values of tpsah j and psahval j , X j is the vector of control variables and Z j corresponds to the vector of instrumental variables (meso02 j and telig j ).

tpsah j = θ 1 + ρ 1 X j + γ 1 Z j + μ j
psahval j = θ 2 + ρ 2 X j + γ 2 Z j + μ j
The results of the estimations are presented in Table 6.5 and 6.6. Columns 1, 2 and 3, respectively, display the results of the model estimated using OLS and the rst and second steps of TSLS both estimated using a linear functional form.

In both cases, the instruments are signicant and of expected signs. The F-test of joint signicance is superior to 10 which allows us to reject the hypothesis that our instruments are weak. The over-identication test cannot reject the hypothesis that our instruments validate the exclusion restriction. Eventually, the exogeneity test conrms that the variables psahval j and tpsah j can be considered as exogeneous.

Note that the coecient of the variables psahval j and tpsah j remain signicant and of similar magnitude as in the OLS estimation.

Since we cannot reject the hypothesis that our variables of interest tpsah j and psahval j are exogeneous, we now run estimations of the impact of the PSA-H on CFEs development using an ordered probit in order to correctly account for the discrete nature of cf e and be able to compute marginal eects. 6.4.3 Estimating the impact of PSA-H on CFE development 6.4.3.1 Results We now turn to the estimation of equations 6.1 and 6.2. The results of the ordered probit estimations and associated marginal eects are presented in Table 6.7 and 6.8 which respectively estimate the impact of tpsah j and psahval j on CFEs.

According to the results presented in Table 6.7, enrolling in the PSA-H for ve years decreases the probability that the ejido has no CFEs by 14.8% and increases the probability of being at stage 2, 3 and 4 by 3.05%, 5.25% and 6.5%, respectively.

Note that the magnitude increases with the level of development of the CFEs. If, following [START_REF] Antinori | Community forest enterprises as entrepreneurial rms: Economic and institutional perspectives from mexico[END_REF], we consider that CFEs are stage III and IV, a ve-year contract increases by 11.75% the probability of having a CFEs. Table 6.8 looks at the impact of the amount of payments received. An increase of one standard deviation of psahval j among beneciaries (167 thousand pesos or about 128 thousand USD) decreases the probability of having no CFEs by about 8.5% and increases the probability at stage 2, 3 and 4 by 1.7%, 3% and 3.8%, respectively. Here again, the magnitude of the coecient increases with the type of CFEs.

Moreover, technical assistance appears to be a strong determinant of CFEs development and the number of ejidatarios negatively impacts CFEs, which might be attributable to the diculty of reaching an agreement in larger ejidos.

The variable psahval j captures the total amount of payments received by the ejidos. It implicitly captures both the impact of the time spent in the program (ie tpsah j ) and the impact of the amount of payments received every year. In order to disentangle the impact of the amount of payments from the impact of the time spent in the program, we introduce both variables in our estimations.

The results are displayed in Table 6.9. These results should be taken with caution since multicolinearity is likely to arise when introducing both variables in the same estimation.

In table 6.9, the impact of tpsah j on CFEs remains positive, signicant and of similar magnitude to that in Table 6.8 but the impact of psahval j is no longer signicant. This result suggests that the time spent in the program matters more than the amount of payments received. In Section 6.2.3, we identied three transmission channels through which the PSA-H can positively impact the development of CFEs. Results from Table 6.9 suggest that the credit constraint may not be an obstacle to CFEs development or that the amount received by all beneciaries of the PSA-H is sucient to overcome it. Therefore, positive impact of the PSA-H on CFEs development can be attributed to one of the two other channels: the access to information and the development of institutions of forest-management.

Robustness tests

In Section 6.4.2, we tested the endogeneity of the variable of interest in our estimations. Since this issue is crucial to validate our identication strategy, we run three robustness tests. Results of the robustness tests are displayed in the appendix D. First, we test our results on a sub-sample of eligible ejidos. Eligible ejidos share some common characteristics and non-eligible ejidos may not constitute a relevant control group. We restrict our sample to the ejidos that have been eligible in at least one year between 2004 and 2010. Our sample is now constituted of 159 ejidos: 110 non beneciaries and 49 beneciaries6 . The results of the ordered probit presented in Table D.1 are very similar to those obtained using the whole sample.

The results may also be biased because of proximity between CONAFOR and the ejidos that already had a CFE before PSA-H implementation. We check the robustness of our results on a sub-sample composed of ejidos that already had a Management Plan in 2002 (Types II to IV). These ejidos are likely to share the same proximity with CONAFOR and have access to the same information. The results of the ordered probit estimations are reported in Table D.2. The impact of the PSA-H remains positive and signicant which conrms our previous results7 .

Eventually, we run a placebo regression by estimating the impact of PSA-H reception on the level of CFE before PSA-H implementation in 2002. If the PSA-H remains a signicant predictor of the lagged value of CFEs, our results might be driven by unobservable confounding factors. The results are presented in Table D.3. The variables tpsah and psahval are not signicant in this model which conrms the validity of our empirical strategy.

Conclusion

We explore the interactions between the PSA-H, a Mexican federal PES, and community forestry in Mexico. With the expansion of PSA-H coverage, the scheme has come to interact with existing forms of sustainable forest management, such as CFEs, and one may wonder if paying for conservation can discourage investment into a CFE. Our results suggest that the PSA-H does not negatively impact CFEs and can contribute to their stabilization and development. After rejecting the endogeneity of PSA-H reception using TSLS, we run an ordered probit in order to estimate the PSA-H's impact on CFEs development. Our empirical analysis conrms that the PSA-H positively impacts the development of CFEs. Controlling for the initial development of the CFE and other covariates, we nd evidence that the amount of payments received and the number of years of reception have a signicant and positive impact on the level of development of the CFE.

According to our estimations, the number of years of reception matters more for a CFEs development than the amount of payments. This result suggests that the positive impact may not be due to credit constraints or at least that, given reception, the amount of payments received are sucient to overcome this constraint. Other alternative transmission mechanisms include information access and organizational capacities. First, the PSA-H brings ejidos closer to CONAFOR and increases their knowledge about CFEs. Second, the PSA-H can help to create and reinforce forest management institutions at the ejido-level. In both cases, CONAFOR's technical advisors, who are the main agents in contact with the ejidos, play a crucial role in providing the information, helping the ejidatarios to organize and suggesting how PSA-H money can be invested into a CFE.

Permanence of PES impact after the end the contracts is questionable so one should wonder how to impulse sustainable changes in forest management [START_REF] Pirard | Questioning the theory of payments for ecosystem services in light of emerging experience and plausible developments[END_REF][START_REF] Karsenty | Combining conservation incentives with investment[END_REF]. Interactions between economic policies may have the potential to impulse that shift. In the inuential PES: some nuts and bolds", [START_REF] Wunder | Payments for environmental services: some nuts and bolts[END_REF] suggested that the PES could act as a temporary instrument allowing a desired shift toward sustainable forestry" in protable forests. We here provide an example of positive interactions between two instruments. If Community Forestry has the potential to generate long-term incentives for forest conservation [START_REF] Bray | Toward post-redd+ landscapes. mexico community forest enterprises provide a proven pathway to reduce emissions from deforestation and forest degradation[END_REF]Porter-Bolland et al., 2012, among others), they struggle to emerge and stabilize over time for many reasons enounced in Section 6.2.2.1. PES alone cannot overcome all the constraints but has the potential to be a rst step toward sustainable management. Nevertheless, these positive interactions of the PSA-H with Community Forestry emerged as a side-eect of the environmental policies and not from a coordinated policy process. Our study shows that, in order to promote this type of synergy, interactions must be monitored both at federal and landscape level through a policy mix able to enhance permanence of conservation projects.

Chapter 7

General Conclusion

This dissertation examines the eectiveness of a Mexican federal PES program: the PSA-H. Answering the call by [START_REF] Ferraro | Money for nothing? a call for empirical evaluation of biodiversity conservation investments[END_REF] and [START_REF] Miteva | Evaluation of biodiversity policy instruments: what works and what doesn't?[END_REF] for more IE of conservation instruments, we propose three empirical essays based on primary survey data collected in a sub region of Yucatan State, the Cono Sur, and a representative database of forested ejidos.

Once the academic debates around PES design and eectiveness and the context of our study are presented, the dissertation discusses the various IE techniques based on the existing academic literature [START_REF] Arriagada | Do payments for environmental services aect forest cover? a farm-level evaluation from costa rica[END_REF][START_REF] Shapiro | Forest conservation and slippage: Evidence from mexico's national payments for ecosystem services program[END_REF]Robalino and Pfa, 2013, among others). We also show that applying a classic IE estimator to the case of PES scheme requires numerous adjustments.

The rst empirical essay, presented in Chapter 4, proposes a new methodological approach to PES impact estimation and its application to the PSA-H in Mexico' Cono Sur. Our approach allows us to simultaneously estimate the program's impact on protected parcels and leakages using tools provided by spatial econometrics. We deal with selection bias using pre-matching. Our results conrm that leakages can undermine or even oset the additonality of PES on protected parcels.

Chapter 5 looks at payment redistribution within Mexican ejidos. Based on household and ejido surveys, we study how the heterogeneity of types of land use within the ejidos in the Cono Sur has inuenced inequality in payment redistribution. Our study shows that the type of land use is a strong determinant of inequality in redistribution and that lower payments are allocated to cattle ranchers who are the primary deforesting agents. Following the Coaesean denition of PES, the Mexican authorities designed the PSA-H as compensation for the agents that would deforest in the absence of the program. However, the scheme is understood as a reward by the ejidatarios demonstrating that the polluter-paid principle is both dicult to appropriate and likely contrary to ejidatarios' conception of fairness.

Based on a nationally representative database of ejidos, Chapter 6 looks at the interactions between the PSA-H and Community Forest Enterprises. One might fear that paying for conservation could discourage investment in CFE. However, our empirical results suggest the contrary: the PSA-H has helped the CFE to develop and stabilize. This result may be attributable to various mechanisms. First, the PSA-H can relax credit constraints allowing investment in a CFE. Second, the scheme may increase interactions with CONAFOR's sta and improve access to information concerning the possibility to valorize timber resources through a CFE. Third, to receive PSA-H payments, the ejidos must implement a number of tasks related to forests management. The PSA-H has the potential to create forest management institutions that are necessary for the emergence and stabilization of the CFEs.

Policy perspective

From a policy perspective, the dissertation highlights two main obstacles to PES eectiveness.

First, today, the PSA-H is highly exible for the ejidos. As highlighted in Chapter 4, the PSA-H oers payments for a period of ve years on areas of forests which are unlikely to generate additionality because of adverse selection within the ejidos.

Moreover, the scheme does not include further restriction on unprotected forests.

In essence, PES are voluntary schemes so one cannot oblige forest owners to put all their forest endowment under conservation. In such a situation, leakages are very likely to occur. Moreover, the scheme includes no guarantee that the areas will not be cleared at the end of the ve years.

Nevertheless, as suggested by Chapter 6, PES schemes can be complementary with additional intervention and can be the rst step toward forest cover sustainable management within ejidos. The need to combine PES with investment has also been emphasized by [START_REF] Pirard | Questioning the theory of payments for ecosystem services in light of emerging experience and plausible developments[END_REF] and [START_REF] Karsenty | Combining conservation incentives with investment[END_REF]. Most ejidos that were eligible showed a willingness to join the program. For conservation policy, PES schemes can be the foundation of a sustainable transition. However, achieving such a transition requires investment in new income generating activities that would relax landowners' dependence on forest clearing.

Second, Chapter 5 demonstrates that more eorts are needed to involve deforesting agents in the process of forest conservation. In line with Wunder's denitions [START_REF] Wunder | Payments for environmental services: some nuts and bolts[END_REF][START_REF] Wunder | Revisiting the concept of payments for environmental services[END_REF], PES schemes are complex instruments based on polluter-paid principles. The forest owners and intermediaries between ES sellers and buyers may not accept and appropriate such an approach. This is especially problematic when dealing with commons because the appropriation of the program by the forest owners determine the nal allocation. In the case of the PSA-H, the scheme is perceived as a reward and deforesting agents do not feel concerned by the program or are partially excluded from its benets.

Modifying the form of the payments could be a way to involve more deforesting agents in the conservation process. Individual payments could be oered to all ejidatarios proportionally to the total share of commons owned, combined with a joint liability to conserve the forest cover. In this scheme, payments would be individualized but the conditionality would be collective.

In line with sustainable transition, CONAFOR could encourage the investment of the payments in alternative activities. The investment could be combined with other CONAFOR's sustainable forest management programs. However, the alternative activities would need to be adapted to the types of land-use found in the ejido. Land users, in our case cattle ranchers, may be less reluctant to enroll in the PSA-H and abandon a production mode they have been relying on for many years if they are oered alternatives for ecological intensication or sylvo-pastoralism.

Our conclusions makes it clear that PES schemes must be included in a larger policy-mix [START_REF] Ring | Instrument mixes for biodiversity policies[END_REF][START_REF] Barton | Policyscapes. nature-based policy mixes for biodiversity conservation and ecosystem services provision[END_REF]. State and NGO interventions must be coordinated at the ejido level through relevant land use planning that combines areas of forest conservation, sustainable forestry and the development of sustainable agricultural systems. As highlighted by Chapter 4, PES alone are likely to have a limited impact over the long term but may be the st step toward a larger sustainable transition.

Prospects for future research

Given the demand for more IE, more research is needed on the identication of additonality and leakages. It is undeniable that using IE methodologies reduces the selection bias but most existing IE of PES impacts rely on contestable assumptions such as the selection on observable or the absence of diusion eects (SUTVA). More importantly, as these IE techniques for the evaluation of environmental projects have become widely employed, a great deal of caution should be taken in their application. As emphasized in Chapter 3, each technique relies on important assumptions. Hence, to make the most of scarce resources, it is crucial to consider the assumptions of the estimator in order to build the best identication strategy at the very beginning of the evaluation process and especially before the sampling process.

Moreover, as emphasized by [START_REF] Ferraro | Counterfactual thinking and impact evaluation in environmental policy[END_REF], IE requires an adequate theory of changes and rival explanations to explain a result. The heterogeneity of the impact over space and time may be more helpful than the ATT. Looking at other outcomes and particularly at the impact on the economic activities of beneciary households might be relevant in building a theory of change and in assessing permanence and leakages. Moreover, in no case, should IE substitute for institutional approaches to scheme analysis [START_REF] Corbera | Institutional dimensions of payments for ecosystem services: An analysis of mexico's carbon forestry programme[END_REF][START_REF] Shapiro-Garza | Contesting the market-based nature of mexico's national payments for ecosystem services programs: Four sites of articulation and hybridization[END_REF].

Another unexplored area of research concerns the cost side of conservation policies. According to [START_REF] Ferraro | Money for nothing? a call for empirical evaluation of biodiversity conservation investments[END_REF] and [START_REF] Vincent | Impact evaluation of forest conservation programs: Benetcost analysis, without the economics[END_REF], since IE methodologies have been applied to conservation issues, more attention has been given to the outputs through the measures of additionality and heterogeneity over space. However, the costs of public policies are also likely to be very heterogeneous. A valuation of PES' benets would also be helpful to compare with its costs [START_REF] Pagiola | Assessing the economic value of ecosystem conservation[END_REF][START_REF] Louis | Approche économique de la biodiversité et des services liés aux écosystèmes: contribution à la décision publique[END_REF]. The higher cost-benet ratio may not be found where the ATT is higher, which may dramatically change the priority of the program.

Finally, the term PES is used in many contexts and often refers to a wide variety of instruments. More research is needed to compare the varying designs of PES schemes according to their dierent allocation mechanisms (auctions vs selection with targeting criteria), the form of the payments (in-kind vs in-cash) and the timing of payments (proportional over time vs higher front-payments) [START_REF] Jack | Upstreamdownstream transactions and watershed externalities: Experimental evidence from kenya[END_REF][START_REF] Ajayi | Auction design for the private provision of public goods in developing countries: lessons from payments for environmental services in malawi and indonesia[END_REF][START_REF] Clot | Present bias predicts participation in payments for environmental services: Evidence from a behavioral experiment in uganda[END_REF]. Lab-in-eld experiments or Randomized Controlled Trials (RCT) would be extremely helpful in such comparisons. Shaping PES schemes dierently may help to involve deforesting agents in the process and increase additionality.

Ecosystem services and PES schemes have been at the core of environmental policies for more than 15 years and continue to fuel academic debates. These debates reappear in the public sphere as highlighted by the controversy over the use of so-called market-based instruments and their eectiveness in reducing deforestation. This dissertation discusses the eectiveness of the Mexican PSA-H but its conclusions could be applied in many settings. Despite some pessimistic ndings about leakages and permanence of the scheme, the dissertation also highlights the potential benets of this PES instrument and its attractiveness to forest owners. The scheme alone is unlikely to have a long-term impact but synergies can be found with other instruments to begin a transition toward sustainable activities. We do not take a position against the use of PES instruments for conservation but advocate for their adaptation and integration into a coordinated policy mix. Mots clés: Analyse d'Impact; Conservation de la Forêt; Economie de l'Environnement; Mexique; Paiements pour Services Environnementaux Abstract This dissertation contributes to the literature regarding Payments for Environmental Services (PES) eectiveness with a specic focus on the impact of a federal Mexican PES scheme: the PSA-H. After a general introduction retracing the emergence of PES and the current academic debates, Chapter 2 introduces the particularity of the land tenure system in Mexico, the country's environmental policies and the PSA-H scheme and the data used in the empirical analysis. Chapter 3 looks at impact evaluation methodologies and how they have been used in the context of forest conservation instruments. Once discussed these challenges, we propose three empirical essays based on primary and secondary data. Chapter 4 presents a new methodology allowing us to estimate the additionality and leakages of the PSA-H in our study area. Chapter 5 studies how land use can inuence the allocation of PES payments within the beneciary community. Chapter 6 studies the interactions between the PSA-H and Community Forestry. Finally, in the conclusion, we discuss the implications of the empirical essays for the design of PES schemes and future research.

Keywords: Environmental Economics; Forest conservation; Impact evaluation; Mexico; Payments for Environmental Services

(

  mostly based in the United States) were given concessions in exchange for running a demarcation process. These companies received one third of the area demarcated (around 20 million hectares) and the remaining 40 million hectares became public land before being widely sold to private owners such as the haciendas. At the beginning of the 20th century, 87% of Mexico's rural land was owned by 0.2% of the landowners In 1910, General Diaz's reelection gave rise to the rst protests announcing a decade of civil war. As emphasized by the southern revolutionary slogan tierra y libertad ", claims for land redistribution were at the heart of the Mexican revolution.In 1913, Francisco Villa promoted an expropriation decree in the northern states while in the south, Emiliano Zapata called for an immediate return of lost lands to the communities and a redistribution of hacienda land among landless peasants.These propositions were gathered in the Plan de Ayala and constitute the basis of Article 27 of the new Mexican Constitution of 1917, which concerned land tenure and the rst agrarian reform.2.1.2 The rst agrarian reformArticle 27 stated that all land and waters ultimately belong to the nation but may be transmitted to private parties under certain conditions. With the new constitution began the rst agrarian reform allowing groups of former hacienda workers or indigenous communities to ask for restitution, dotation or amplication of their common land endowment. This land remains owned in commons: it is ultimately the property of the state and can not be sold, rent or mortgaged. The land commonly owned by a group of peasants is called an ejido. In the aftermath of the revolution, redistribution remained modest under the rules of Alvaro Obregon and Plutarco Elias Calles as priority was given to reconstruction and the stabilization of the country. The main redistribution process happened during the rule of Lazaro Cardenas between 1934 and 1940. The Wall Street Crash of 1929 directly impacted Mexican growth through a decrease in agricultural exports and the expulsion of about one million Mexican immigrants from the United States. Facing more claims for land redistribution, President Cardenas began a vast land redistribution process. More than 20 million hectares were redistributed during this period to nearly 800,000 peasants. Nevertheless, although haciendas were dismantled by the reform, the eect on land concentration remains unclear. The land may just have been redistributed

  . The program was originally designed by researchers from INE 1 (Instituto Na- cional de Ecologia) and part of SEMARNAT (Environment and Natural Resources Department), Universidad Iberoamericana, Centro de Investigacion y Docencia Economica and the University of California at Berkeley between 2001 and 2003 with the support of the World Bank (Muñoz-Piña et al., 2008). The PSA-H aims at enhancing the provision of hydrological services in overexploited aquifers. Chapter 2. Studying the impact of a PES in community forests: Context and methodology

Chapter 2 .Figure 2 . 1 :

 221 Figure 2.1: Evolution of eligible areas

1

 1 Study area: the Cono Sur of Yucatan

Figure 2 . 2 :

 22 Figure 2.2: Peninsula de Yucatan and Cono Sur

Figure 2 . 3 :

 23 Figure 2.3: PSA-H allocation in Yucatan

Chapter 2 .Figure 2 . 4 :

 224 Figure 2.4: Evolution of eligibility between 2004 and 2012 in Cono Sur

Figure 2 . 5 :

 25 Figure 2.5: The Cono Sur and the rst year of PSA-H enrollment by ejido

Figure 2 . 6 :Figure 2 . 7 :

 2627 Figure 2.6: Kernel density estimates of the percentage of land in each land tenure regime per ejido (a) Percentage of common land (b) Percentage of land parcelized by PROCEDE

. 1 )

 1 In equation 3.3, Y NT i corresponds to an hypothetical outcome: the outcome of the treatment beneciaries if they had not been part of the program. Contrary to Y T i , this outcome is by denition unobservable. The main challenge of evaluation is to reconstitute this missing outcome.

•

  Propensity Score Matching (PSM) based on the probability to receive the program • Covariate Matching based on the vectorial distance 3.2.1.1 Propensity score matching

Figure 3 . 1 :

 31 Figure 3.1: Dierence-in-dierence and parallel trend

Chapter 3 .

 3 Challenges for PES Impact Evaluation 3.2.4.2 Two-stage least squaresContrary to control function model, TSLS method is based on the introduction of predicted value of D i in equation 3.29. Lets's compute the predicted value of D i such as: Di = θ + φZ i + σX i(3.35) and introduce it into the model as a second stage:

  Let us consider the estimation of the simple model presented in equation 3.42 in the context of PES. Spatial correlation could arise for three reasons (Le Gallo, 2002):

  used by Canavire-Bacarreza and Hanauer (2013) with Bolivian protected areas has not been yet applied in the context of PES. Matching can also be combined with DID as done by Chabé-Ferret and Subervie (2013) to control for time unvarying unobservable. Balancing tests and details about the common sup-

  many other examples,[START_REF] Shapiro | Forest conservation and slippage: Evidence from mexico's national payments for ecosystem services program[END_REF] used applicants who were rejected from a PES program due to insucient funding to control for willingness to join the program, while[START_REF] Arriagada | Do payments for environmental services aect forest cover? a farm-level evaluation from costa rica[END_REF] used landowners located in a buer zone around areas under PES contracts. There can be no pre-dened rule for the choice of a control group, as it relies on a good knowledge of the allocation mechanism over time and space. In the case of national PES, understanding allocation often goes beyond studying the ocial allocation rules. During the decentralisation process, many actors have a stake in the allocation process and the same program may be interpreted and allocated dierently in dierent regions or states. The choice of the control group should be based on a deep understanding of the entire process and should exclude landowners who are structurally too dierent from the beneciaries and unlikely to be accepted into the program. Pre-matching methods may be useful to select the sample, but it should not be forgotten that pre-matching does not control for the unobservable[START_REF] Shapiro | Forest conservation and slippage: Evidence from mexico's national payments for ecosystem services program[END_REF].

Figure 4 . 1 :

 41 Figure 4.1: Eligibility between 2004 and 2012 in Cono Sur

1

 1 Figure 4.2: PSA-H rst reception

  Second, following[START_REF] Honey-Roses | A spatially explicit estimate of avoided forest loss[END_REF], we overlap the gridding of the ejidos with PSA-H reception. Since we want to consider heterogeneity in the number of years of reception, we need to merge the PSA-H contract using data provided by CONAFOR. Within an ejido, a rst polygon can have been enrolled in the program in 2005 followed by a second polygon in 2010. Similarly, within an area enrolled in 2005, half of this area may have been enrolled again in a new contract ve years later and another half left outside of the PSA-H. Within the ejidos, we do not only distinguish between PSA-H and non PSA-H land but also according to the number of years a parcel has been enrolled in the program. To take into consideration this heterogeneity, we merge PSA-H beneciary polygons as presented in gure 4.3.

Figure 4 .Figure 4

 44 Figure 4.3 presents a zoom in an area of Cono Sur with an example of PSA-H polygons for years 2007, 2008 and 2012 and merged polygons. We note that the polygons do not overlap between 2007 and 2008 3 . In 2012, contracts made in 2007 come to an end and can be renewed. Looking at merged polygons, we can observe that the southern PSA-H areas enrolled in 2007 have not been completely renewed in 2012. In the merged PSA-H polygons, each polygon corresponds to a homogeneous area regarding the number of years of reception. Thus, a PSA-H polygon can be split into two or more parts if some of these areas have been renewed after ve years and some have not. In gure 4.3, for illustration, the polygon that represents 2007 contracts located in the south east has been split into two parts: the area that has been renewed in 2012 and the area that has not. Now that we have built polygons that are homogeneous in terms of years spent in the PSA-H program, we merge these polygons with the gridding of the ejidos as presented in gure 4.4. Therefore, a cell of the grid can be split in two if one area has been enrolled in the PSA-H and another has not. Similarly, the cell of the grid can be split in two if one part of it has been enrolled for eight years and another for only ve years. Finally, we overlap these polygons with the land cover classes from the remote sensing analysis and restrict our sample to the areas classied in 2005 as forest as shown in gure 4.5. The nal polygons used as units of observations are in green in gure 4.5 and were composed only of forest in 2005. These polygons are slightly heterogeneous in size but homogeneous in terms of program status (i.e., protected or not), number of years of reception and land tenure. By overlapping forest polygons of 2005 and 2012, we can compute the percentage of forest loss within each polygon during the rst years of PSA-H implementation. For each forest polygon, we also compute geographic variables such as distances to the roads and agricultural elds, elevation, slope and spatially lagged variables presented in Section 4.4.2.We complete these data with information at the ejido-level from the ejido-surveys conducted in December 2012.

Figure 4 . 5 :

 45 Figure 4.5: Final polygons

Figure 4 . 6 :

 46 Figure 4.6: Number of years spent in the PSA-H by polygons

Figure 4 .

 4 Figure 4.7: Propensity scores

Figure 4 .

 4 Figure 4.8: Common support

Figure 5 . 1 :

 51 Figure 5.1: Evolution of eligible areas

Figure 5 . 2 :

 52 Figure 5.2: Gini of PSA-H distribution inequality by type of economic activities

Chapter 5 .

 5 Reward or compensation? PES distribution in Mexican common forests

Figure 5 . 3 :

 53 Figure 5.3: PSA-H yearly payments received by the households by type of activities

Figure 6 . 1 :

 61 Figure 6.1: Number of year of PSA-H reception and amount received (a) Time spent in the program

Figure 6 . 2 :

 62 Figure 6.2: Time under PSA-H and amount received according to the evolution of the CFE (a) Time spent in the program

  dummy variable for three other economic policies that can possibly inuence CFE development: PRODEFOR, PSA-CABSA 4 and NPAs. CFE development, reception of federal programs and demographic variables comes directly from the ENAFF survey. Data regarding PSA-H reception has been provided by CONAFOR. Illiteracy rates have been computed by CONAPO (National Population Council) at AGEB's level 5 (Area Geostatistica Basica). The other variables have been computed using GIS thanks to information from the Mexican Federal Statistical Institute (INEGI). More details about the denition and sources of each variable can be found in Table D.4 in the Appendix.

2 .

 2 Parcelado por acuerdo; 3.Uso común; 4. Parcelado PROCEDE pero común por acuerdo

FODULILFDU/

  Figure A.1: SPOT Images 2012 (20m) classied

Table 2 .

 2 1: Land redistribution by presidential term

	Period	Hectares redistributed Number of beneciaries

Table 2 .

 2 2: Natural Protected Areas in Mexico in 2014

	Categorie Number Size (km2) Reservas de la Biosfera 41 12,751,149 Parques Nacionales 66 1,411,319 Monumentos Naturales 5 16,269 Areas de Proteccion de Recursos Naturales 8 4,503,345 Areas de Proteccion de Fauna y Flora 38 6,786,837 Santuarios 18 148,332 Total 176 25,617,251 Source:CONANP (2015)

Table 2 .

 2 3: Payments areas by vegetation types and deforestation risk Budget allocated to the program increased since 2003 from 200 millions pesos to 965 millions pesos in 2009. The scheme is supposed to enhance water quality. Therefore, the program is partly nanced by a fee on water users. In accordance with the General Law of Sustainable Forestry Development, 200 millions of water fees are earmarked for the PSA-H. It represented about 20% of the program's budget in 2009, the rest is nanced by federal funds allocated to CONAFOR.

	Type of forests Area 1 Bosque Mesolo Area 2 Bosque Mesolo Area 3 Bosque de coniferas, selva subca-ducifolia and bosque de encino Area 4 Selva alta perennifolias	Deforestation risk Very high Low to high Very low to very high 550 Pesos per Ha Payment 1.100 Pesos per Ha 700 Pesos per Ha Very low to very high 382 Pesos per Ha Source:(CONAFOR, 2009)

  2.3.3 The PSA-H in the ejidos 2.3.3.1 Commitment In order to receive payments, ejidos must comply with certain rules (CONAFOR, 2009). The technical advisor accredited by CONAFOR is in charge of establishing a Program of Better Management Practices (PMPM in Spanish: Programa de Mejores Practicas de Manejo).

Table 2 .

 2

	4: Number of ejidos that enrolled for the rst time per year between 2005 and 2012
	2005 3 2010 7	2006 6 2011 4	2008 19 2012 6	2009 5 No-PSA-H 27
	these urban centers receive the PSA-H for common forests. The nal sample was composed of 77 ejidos. The questionnaire is composed of 12 parts that investigate dierent characteris-tics of the ejido, including economic activities, governance and repartition of PSA-H payments. The questionnaire was organized as follow:
	• I. History and demography	• VII. Rules for land-use
	• II. Participatory mapping	• VIII. Collective action
	• III. PSA-H		• IX. Organization of the ejido
	• IV. Land tenure		• X. Infrastructure
	• V. Land-use		• XI. Other federal program
	• VI. Economic activities	• XII. Prospects for the future
	This questionnaire and pictures of the eld work can be found in the Appendix.

Table 2 .

 2 are part of the last ejidatarios that beneted from land allocation under the rule of the rst agrarian reform. This fact also explains why there are very few posesionarios. Posesionarios are often sons of ejidatarios that did not inherit the title of ejidatario. The number of posesionarios can increase over time but this has not happened yet in Cono Sur and the vast majority of heads of household are

	5: Main characteristics of the ejidos
	Number of ejidatarios Number of posesionarios Number of avecindados Total area (Ha) Distance to the nearest market (km) Mean age of the ejidatarios	Mean Stand. dev. Skewness Min Max 55.44 63.42 2.95 11 368 1.455 4.930 4.412 0 30 16.948 53.202 5.596 0 401 2,749.43 4034.20 6.32 594 34,000 36.28 26.98 0.57 0 100 51.33 4.72 -0.01 40.29 65

ejidos ejidatarios.

Table 2 . 6

 26

	: Percentage of ejidatarios or posesionarios involved in each activity by
	ejido
	Mean Stand. dev. Skewness
	% With mechanised agriculture 0.335 % With cattle ranching 0.206 % With traditional agriculture 0.306 % With only traditional agriculture 0.208 % With only o-farm activities 0.318	0.334 0.215 0.343 0.286 0.303	0.559 1.228 0.855 1.362 0.688

Table 2

 2

	.7: Total number of hectares enrolled and PSA-H payments (in thousands of pesos) received by beneciary ejido
	Mean Stand. dev. Skewness 1,486.629 1,217.556 1.647 160.314 5,664.786 Min Max Percentage of forest enrolled 63.1% Number of hectare of forest enrolled 24.7% -0.493 9.2% 99.4% Amount received 1,867.837 1,570.252 1.300 84.000 6,915.082 Number of years of recep-4.143 1.791 -0.283 1 7 tion Amount per year of recep-423.478 285.433 1.527 35.041 1,562.668 tion Amount per ejidat. or pose-sionarios 57.032 49.309 1.037 1.515 198.464
	assembly. As explained in Section 2.3.2, technical assistants from CONAFOR are in charge of promoting the program and helping the ejidos to ll in an application for the program. Their remuneration depends on the number of hectares they enroll into the PSA-H. It is quite likely technical assistants favored ejidos with large amounts of forest and low deforestation pressure at the beginning of the program. Nevertheless, verifying this hypothesis would require more qualitative surveys with the technical assistants than we were unable to implement. Table

Table 2

 2

	.8: Principal households characteristics
	Size of the household Standard deviation Mean Minimum Maximum	4.69 2.28 1 16
	Gender of household head Male household head Female household head	95.12% 4.88%
	Age of household head Standard deviation Mean Minimum Maximum	54.47 13.84 24 89
	Education of the households' head Do not write and read 23.41% Write and read 44.88% Finished primary school 22.44% Finished secondary school and more 9.27%
	Main residence Outside the ejido In the ejido	56.59% 43.41%
	Economic activities Traditional agriculture Mechanized agriculture Cattle ranching O-farm activities	33.66% 48.78% 24.39% 59.02%
	years old. Twenty two percent of them are illiterate. Only 55% of the household lives in the ejido, which illustrates the phenomena of labour-ejido emphasized in Section 2.4.1.1. About a third of our sample does traditional agriculture (milpa) and almost half of the sample practices mechanized agriculture. About a quarter of the sample have cattle-ranching activities. Many households keep a few head of cattle at home but are not really involved in cattle ranching activities. We consider cattle ranchers here as households that use commons of individual pasture for graz-ing. Around a quarter of our sample has cattle ranching activities. More than half of the ejidatarios has permanent or temporary o farm activities.
	Table

Table 2 .

 2 9: Numbers of hectare cultivated pet type of land use

	Mean Stand. dev. Skewness Min Max Traditional agriculture 2.377 1.790 1.816 0.320 9 Mechanised agriculture 7.609 11.047 4.463 0.500 87 Pasture 18.766 27.707 4.764 0.080 200
	Table 2.10: PSA-H payments reception in beneciary ejidos
	Mean Stand. dev. Skewness Min Max Number of years of reception 4.43 2.21 -0.344 0 8 Total amount received 55.09 62.92 2.991 0 504 Average yearly amount re-ceived per yr of reception 10.99 9.69 1.750 0 63
	ejidatarios cultivate 7.6 hectares but this area can vary widely. A pasture is, on average, 18 hectares but can be shared between ejidatarios.

Table 2 .

 2 11: Use of the payments according to the ejidatarios' statements

	Household expenses 62.5% Agriculture 23.9% Cattle ranching 5.6% Debt reimbursement 2.2% Other 5.8%

Table 2

 2

	.12: Land-use in 2012
	Land use Hectares Percentage Infrastructure 1,526 0.709% Forest 187,004 86.942% Mechanized agriculture 15,758 7.326% Traditional agriculture 1,499 0.697% Pasture 9,302 4,325% Total 215,089 100%
	2.4.1.4 Remote sensing analysis
	In order to assess land-use change over time, remote sensing analysis was performed using SPOT images of 1999, 2005 and 2012. The remote sensing analysis was conducted by CIRAD in Montpelier (France) and ECOSUR in San Cristobal de Las Casas (Chiapas, Mexico). The classied images are presented in the Appendix.

of ejidos were randomly selected. The number of ejidos per State is displayed in table 2.13. Surveys took place in

	Table 2.13: Ejidos from the ENAFF surveys by state
	• Infrastructure (Roads, villages...) State Number of ejidos Percentage Campeche 7 3.13% Chiapas 26 11.61% Chihuahua 14 6.25% Colima 6 2.68% Durango 7 3.13% Guerrero 14 6.25% Hidalgo 11 4.91% Jalisco 27 12.05% Mexico 4 1.79% Nayarit 11 4.91% Oaxaca 31 13.84% Puebla 8 3.57% Queretaro de Arteaga 1 0.45% Quintana Roo 13 5.80% San Luis Potosi 11 4.91% Sinaloa 6 2.68% Tabasco 3 1.34% Tlaxcala 1 0.45% Veracruz-Lllave 20 8.93% Yucatan 3 1.34%
	• Forest
	• Mechanised agriculture • Traditional agriculture (milpa) the Centro de Investigacion y Docencias Economicas and focuses on forest manage-ment.
	• Pasture deforestation rates around 4.5% 5 between 2005 and 2015 is 4.9%. Between 1999 and 2005, the trend is similar with tion due to land abandonment and forest regeneration. The gross deforestation rate and 2013, more than 9,200 hectares were deforested, but we also observed reforesta-The area for each land-use type in 2012 is presented in Table 2.12. Between 2005 The sampling was based on a stratication by size of ejidos. Within each strata, a representative number
	2.4.2 ENAFF surveys
	The ENNAF (Encuesta Nacional a los Nucleos Agrarios Forestales) survey is a representative survey of forested ejidos in Mexico. These surveys have been imple-mented by the Instituto Nacional de Ecologia, the Universidad Iberoamericana and

Table 2

 2

	.14: Main characteristics of ejidos in the ENAFF surveys
	Mean Stand. dev. Skewness 189.21 343.74 6.89 Area (Ha) 10,439.78 Number of ejidatarios 20,140.25 3.83 190.21 127733.76 Min Max 10 3500 Percentage of forest in 2002 0.46 0.28 0.44 0 1.00
	Table 2.15: Number of ejidos by years of PSA-H reception in the ENNAF survey
	Number of years Number of ejidos Percentage 0 175 78.13 1 3 1.34 2 4 1.79 3 7 3.13 4 15 6.70 5 12 5.36 6 5 2.23 7 2 0.89 8 1 0.45 Total 224 100

Table 3 .

 3 1: Compliers and deers for LATE estimation

•

  If the spatially lagged variable inuences only Y i and do not have indirect impact through D i , omitting this variable do not bias the coecient but the estimations remains inecient. Moreover, if the estimation of β is unbiased, it only captures the impact of the program reception on Y i but does not cap- ture the fact that reception of the program in neighbouring areas may also impact Y i . The total impact of the program is composed of both eects and not including the neighbouring observations leads to partial estimates of the impact.

3. Error terms are spatially correlated such as:

  3.4. Methodological challenges and knowledge gaps 73sible to identify the impact of the program. Matching balances covariates between a group of beneciaries and a group of non-beneciaries. If both groups are large enough, matching methods can weight the observations in the control group to build a counterfactual that is statistically similar to the treatment group. The dierence between individual pairs of observations cannot be interpreted independently of the rest of the distribution. Moreover, since the intervention is at village level, most of the confounding factors are measured at village level and investigating many households in only a few villages will not solve this issue.

Table 4 .

 4 2 presents the results regarding additionality estimated using OLS. Column (1) presents the results using a dummy variable. Column (2) introduces the time

	4.5.2 Additionality

Table 4 . 2 :

 42 Impact of the PSA-H and leakages

		(1)	(2)	(3)	(4)
	VARIABLES	def	def	def	def
	psa	-0.0445***
		(0.0072)
	tpsa		-0.0042***	-0.0058***	-0.0058***
			(0.0012)	(0.0017)	(0.0016)
	wtpsa1			0.0057*	0.0059**
				(0.0030)	(0.0030)
	wtpsa2				0.0021
					(0.0060)
	pscore	-0.6609***	-0.6692***	-0.6746***	-0.6790***
		(0.1980)	(0.2150)	(0.2166)	(0.2155)
	forest05	-0.0001	-0.0001	0.0000	0.0001
		(0.0006)	(0.0006)	(0.0006)	(0.0006)
	slope	-0.0018	-0.0015	-0.0015	-0.0014
		(0.0016)	(0.0017)	(0.0017)	(0.0017)
	droad05	-0.0000	0.0001	0.0000	0.0000
		(0.0010)	(0.0010)	(0.0010)	(0.0010)
	dagri05	0.0271**	0.0262**	0.0272**	0.0278**
		(0.0108)	(0.0117)	(0.0118)	(0.0117)
	dcity	-0.0016***	-0.0016***	-0.0015***	-0.0016***
		(0.0005)	(0.0005)	(0.0005)	(0.0005)
	telig	0.0269***	0.0267***	0.0263***	0.0261***
		(0.0086)	(0.0091)	(0.0091)	(0.0094)
	lagbuer	-0.9230*	-0.8872	-0.9076	-0.9175
		(0.5241)	(0.5641)	(0.5689)	(0.5686)
	areaejid	-0.0096***	-0.0095***	-0.0093***	-0.0094***
		(0.0028)	(0.0030)	(0.0030)	(0.0030)
	lagdefejid	-2.2936***	-2.2747***	-2.2573***	-2.2691***
		(0.7229)	(0.7527)	(0.7447)	(0.7414)
	ejiddens	-4.7676***	-4.8240***	-4.5446***	-4.5287***
		(1.5102)	(1.5700)	(1.5919)	(1.6013)
	margi05	-0.0304***	-0.0299***	-0.0300***	-0.0298***
		(0.0092)	(0.0096)	(0.0094)	(0.0096)
	defrisk	-3.0060**	-2.9666**	-2.8395**	-2.8406**
		(1.2613)	(1.3050)	(1.2994)	(1.2865)
	Constant	0.4493***	0.4384***	0.4215***	0.4207***
		(0.1175)	(0.1254)	(0.1265)	(0.1268)
	Observations	8,732	8,732	8,732	8,732
	R-squared	0.1948	0.1765	0.1782	0.1784
	Standard errors in parentheses adjusted for 62 clusters
		*** p<0.01, ** p<0.05, * p<0.1

Table 4 .

 4

		3: Impact heterogeneity
		(1)
	VARIABLES	def
	tpsalate	-0.0114***
		(0.0029)
	tpsaalw	-0.0090***
		(0.0019)
	tpsaearl	-0.0001
		(0.0026)
	wtpsa1	0.0069**
		(0.0031)
	wtpsa2	0.0026
		(0.0060)
	pscore	-0.7001***
		(0.2097)
	forest05	0.0003
		(0.0006)
	slope	-0.0014
		(0.0017)
	droad05	0.0003
		(0.0009)
	dagri05	0.0294**
		(0.0116)
	dcity	-0.0018***
		(0.0005)
	telig	0.0273***
		(0.0092)
	lagbuer	-0.9932*
		(0.5551)
	areaejid	-0.0097***
		(0.0029)
	lagdefejid	-2.3386***
		(0.7242)
	ejiddens	-4.5886***
		(1.5250)
	margi05	-0.0292***
		(0.0094)
	defrisk	-2.8699**
		(1.2665)
	Constant	0.4259***
		(0.1214)
	Observations	8,732
	R-squared	0.1878
	Standard errors in parentheses adjusted for 62 clusters
	*** p<0.01, ** p<0.05, * p<0.1

Table 5 .

 5 1: Payments areas by vegetation types and deforestation risk

	5.4. The Cono Sur in Yucatan

  5.4.1 Sample at ejido and household levelAs presented in Chapter 2, our sample is composed of 47 ejidos receiving the PSA-H. They constitute a quasi exhaustive sample of all of the Cono Sur beneciaries once excluding outliers7 . At the ejido level, we conducted participatory surveys with Chapter 5. Reward or compensation? PES distribution in Mexican common forests the authorities and with ejidatarios willing to participate. The surveys explored land-use, household economic activities, governance, and infrastructure and PSA-H

	payment distribution, among other criteria.
	At the household level, the sample is composed of 163 households living in an
	ejido enrolled in the PSA-H. Among those 163 households, 151 declared that they
	had received payments from the PSA-H. The average households' size is 4.8 mem-
	bers. The household's head is on average 54 years old and 22% of them are illeterate.
	Total payments received between 2005 and 2013 range from 3,000 to 180,000 Mexi-
	can pesos (230 to 13,850 USD in 2010). Most of this heterogeneity is explained by
	the fact that some ejidos have only received the program for one year whilst others
	participated during the entire period of analysis. Nevertheless, if on average the
	ejidatarios received around 10,000 Mexican pesos (770 USD in 2010) per year of
	reception, this amount varies between 600 and 34,000 per ejidatarios (46 to 2615
	USD in 2010).

Table 5 .

 5

	2: Determinants of inequality in PSA-H payments' distribution
	(1) VARIABLES psa_gini psa_gini psa_gini (2) (3)
	mec05_ejid trad05_ejid ranch05_ejid per_mec05 per_trad05 per_ranch05 ejidat age age_sd labejid partasemb forestuc per_forcom -0.5013*** -0.6280*** -0.6121*** -0.2436** (0.1093) -0.0061 (0.0902) 0.1828* (0.1078) -0.3945*** (0.1397) -0.1391 (0.1366) 0.4682*** (0.1131) -0.0025 -0.0036 -0.0029 (0.0016) (0.0022) (0.0019) -0.0048 -0.0033 -0.0038 (0.0082) (0.0087) (0.0075) -0.0203 -0.0285** -0.0334** (0.0141) (0.0139) (0.0138) -0.1430* -0.1953** -0.2391*** (0.0788) (0.0910) (0.0742) -0.4321** -0.3770** -0.3950* (0.1885) (0.1836) (0.2001) 0.0002** 0.0002*** 0.0002*** (0.0001) (0.0001) (0.0001) (0.1627) (0.1696) (0.1516) psaejid_yr -0.0030 -0.0018 -0.0050** (0.0023) (0.0021) (0.0024) Constant 1.4807*** 1.5319*** 1.7335*** (0.5105) (0.4656) (0.4495)
	Observations R-squared Standard errors in parentheses 46 46 0.3615 0.4234 *** p<0.01, ** p<0.05, * p<0.1	46 0.4884

Table 5 .

 5

	3: Determinant of the amount of payment received by the household (Pseudo-panel estimation)
	(1) psahyr FE 3.7685** 4.0199** 3.2428** 3.4172** 15.6547** 16.4676** (2) (3) (4) (5) (6) psahyr psahyr psahyr psahtot psahtot RE FE RE FE RE (1.5625) (1.5897) (1.3301) (1.4209) (7.4804) (7.7884) 2.9831** 2.7545** 3.1669** 2.8861** 15.2408** 14.8110** (1.0749) (1.0801) (1.1714) (1.1617) (6.3771) (6.4336) -1.3585* -1.5841** -1.5768* -1.7717** -6.2461 -6.6456* (0.7622) (0.6742) (0.8756) (0.8204) (4.3225) (3.9195) 0.9919 1.0519 0.3901 0.4677 7.3725 7.8755* (0.9932) (0.9693) (0.9270) (0.9064) (4.7309) (4.6554) -0.0291 -0.0304 -0.0113 -0.0119 -0.0103 -0.0148 (0.0196) (0.0187) (0.0205) (0.0195) (0.1137) (0.1115) -0.0444 -0.0452 0.1124 0.0988 -0.1982 -0.1503 (0.1893) (0.1906) (0.1514) (0.1559) (1.0701) (1.0636) 0.5179 0.3545 0.9791 0.8417 3.4609 2.9056 (0.9711) (0.9860) (0.9218) (0.9461) (5.1679) (5.2300) 0.7290 1.2451 1.2907 1.6733 8.2075 9.1767 (3.1291) (3.0621) (2.0297) (2.0634) (15.8287) (15.8626) partasemb_hh 1.4608 mec05_hh trad05_hh ranch05_hh o05 age size05 write assets05 1.4626 -1.1758 -1.1143 7.0490 7.1015 (1.7820) (1.7165) (1.1317) (1.1294) (6.4985) (6.4011) orgejid 1.5611 1.7396 0.6013 0.7934 6.1104 6.5513 (1.0862) (1.0725) (0.7597) (0.7650) (5.6804) (5.5953) Constant 6.9080*** 8.7716*** 7.5950*** 9.5237*** 24.3260* 32.8560** (2.1561) (2.3138) (2.2118) (2.2593) (14.1033) (14.5378)
	Observations R-squared Robust standard errors in parentheses. FE=Fixed eects, RE=Random eects 163 163 151 151 163 163 0.2006 0.1991 0.1856 0.1838 0.1403 0.1399 Standard errors clustered by ejido *** p<0.01, ** p<0.05, * p<0.1

  Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 6.2 PSA-H and CFE as a policy mix . . . . . . . . . . . . . . . . 127 6.2.1 Policy Mix in Mexico . . . . . . . . . . . . . . . . . . . . . . 127 6.2.2 Community Forests Enterprises . . . . . . . . . . . . . . . . . 128 6.2.3 Can PSA-H contribute to CFE development? . . . . . . . . . 131

	Chapter 6 Can PES enhance Community Forestry?
	Contents
	6.1

6.3 Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 6.4 Econometric estimation . . . . . . . . . . . . . . . . . . . . . . 134 6.4.1 Econometric model . . . . . . . . . . . . . . . . . . . . . . . . 134 6.4.2 Checking for endogeneity . . . . . . . . . . . . . . . . . . . . 136 6.4.3 Estimating the impact of PSA-H on CFE development . . . . 140 6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

  .2: First year of enrollment in the PSA-H by ENNAF ejidos

	First year of reception 2003 2004 2005 2006 2007 2008 2009 2010 Total Number of ejidos 2 11 6 2 14 7 4 3 49

Table 6 .

 6 3: Type of CFE and PSA-H reception

	CFEs type 2010	Non PSA-H	PSA-H ben-	Total % of PSA-H
			beneciaries	eciaries		beneciaries
	1: Potential producers	144	32	176	18,18%
	2:	Producers who sell	10	1	11	9,09%
	timber on the stump
	3:	Producers of forest	15	4	19	21,05%
	raw material
	4:	Producers with ca-	5	12	17	70,59%
	pacity for transforma-
	tion and marketing
	Total	174	49	223	21,97%

Table 6 .

 6 4 presents the percentage of PES beneciaries according to the evolu-

Table 6 .

 6

	4: Evolution of the CFEs' level of development and PSA-H reception
	Non PSA-H beneciaries		PSA-H beneciaries
	cfe02>cfe 19	90,48%	2	9,52%
	cfe02=cfe 14	56,00%	11	44,00%
	cfe02<cfe 15	71,43%	6	28,57%
	Figure 6.2 presents the distribution of our two interest variables for PSA-H
	beneciaries according to the evolution of the CFE. Looking at Figure 6.2, we note

Table 6 .

 6

	5: CFE and number of years of PSA-H reception between 2003 and 2010: Two-stage least square
		(1)	(2)	(3)
	VARIABLES	cfe	tpsah	cfe
	tpsah	0.0911***		0.0985*
		(0.0330)		(0.0566)
	cfe02	0.4155***	0.2067	0.4139***
		(0.1380)	(0.2553)	(0.1327)
	size	0.0578*	0.0580	0.0570*
		(0.0314)	(0.0645)	(0.0299)
	perfor02	-0.3463	0.4204	-0.3528*
		(0.2229)	(0.5109)	(0.2108)
	at02	0.4473	-0.3575	0.4480*
		(0.2783)	(0.5306)	(0.2662)
	meanslope	0.0048	0.0089	0.0046
		(0.0076)	(0.0167)	(0.0079)
	analph10	0.0034	-0.0076	0.0034
		(0.0061)	(0.0141)	(0.0060)
	density10	-0.0030	0.0689	-0.0036
		(0.0589)	(0.1481)	(0.0573)
	distcity	-0.0139	0.1411	-0.0143
		(0.0520)	(0.1485)	(0.0507)
	distroad	-0.1135	-0.1180	-0.1123
		(0.1347)	(0.2889)	(0.1294)
	ejidat10	-0.0002**	0.0001	-0.0002**
		(0.0001)	(0.0004)	(0.0001)
	prodefor	0.1663	0.0751	0.1642
		(0.1770)	(0.2924)	(0.1686)
	cabsa	-0.1373	-0.2740	-0.1361
		(0.2007)	(0.5282)	(0.1918)
	npa	-0.0645	0.9235**	-0.0754
		(0.1504)	(0.4362)	(0.1771)
	telig		0.2938***
			(0.0535)
	meso02		0.0004***
			(0.0001)
	Constant	0.7628***	-0.8445**	0.7674***
		(0.1901)	(0.3489)	(0.1888)
	Observations	223	223	223
	R-squared Notes: *=signicant at the 1% level, **=signicant at the 5% level, 0.4533 0.3449 0.4531 *=signicant at the 10% level. Standard errors clustered at state-level. F-stat of excluded instrument=31.11(0.000). Sargan-Hansen statstic= 0.244(0.6215). Wu-Haussman's statistics=0.010(0.9209)

Table 6 .

 6

	6: CFE and amount of PSA-H received between 2003 and 2010: Two-stage least square
		(4)	(5)	(6)
	VARIABLES	cfe	psahval	cfe
	psahval	0.0133**		0.0106*
		(0.0063)		(0.0063)
	cfe02	0.4568***	-2.1255	0.4525***
		(0.1369)	(1.6467)	(0.1355)
	size	0.0484	1.2121*	0.0522*
		(0.0317)	(0.6181)	(0.0314)
	perfor02	-0.3392	1.9244	-0.3243
		(0.2188)	(2.5869)	(0.2143)
	at02	0.3987	2.7030	0.4070
		(0.2736)	(3.5806)	(0.2759)
	meanslope	0.0046	0.0662	0.0052
		(0.0077)	(0.0976)	(0.0080)
	analph10	0.0029	-0.0461	0.0029
		(0.0060)	(0.0788)	(0.0057)
	density10	0.0052	0.3434	0.0052
		(0.0611)	(0.5650)	(0.0591)
	distcity	-0.0208	1.6651	-0.0184
		(0.0537)	(1.0333)	(0.0513)
	distroad	-0.1131	-1.0779	-0.1161
		(0.1335)	(2.0439)	(0.1286)
	ejidat10	-0.0002**	0.0041	-0.0002**
		(0.0001)	(0.0037)	(0.0001)
	prodefor	0.1794	0.3979	0.1820
		(0.1749)	(1.2478)	(0.1679)
	cabsa	-0.1574	-0.1522	-0.1563
		(0.2031)	(2.7646)	(0.1989)
	npa	0.0186	2.3256	0.0291
		(0.1428)	(2.1296)	(0.1436)
	telig		0.7873***
			(0.2226)
	meso02		0.0053***
			(0.0011)
	Constant	0.7631***	-2.7458	0.7518***
		(0.1983)	(2.0750)	(0.1951)
	Observations	223	223	223
	R-squared Notes: *=signicant at the 1% level, **=signicant at the 5% level, 0.4427 0.4374 0.4420 *=signicant at the 10% level. Standard errors clustered at state-level. F-stat of excluded instrument=21.37(0.000). Sargan-Hansen statstic= 1.485(0.2230).

Table 6 .

 6

	7: CFE and number of years of PSA-H reception between 2003 and 2010: Ordered probit and marginal eects
		(1)	(2)	(3)	(4)	(5)
	VARIABLES	cfe	Pr(cfe=1) .	Pr(cfe=2)	Pr(cfe=3)	Pr(cfe=4)
	tpsah	0.1599***	-0.0296***	0.0061**	0.0105***	0.0130***
		(0.0527)	(0.0096)	(0.0024)	(0.0038)	(0.0048)
	cfe02	0.4438**	-0.0822**	0.0170*	0.0291**	0.0360**
		(0.1963)	(0.0350)	(0.0090)	(0.0144)	(0.0145)
	size	0.1587**	-0.0294**	0.0061**	0.0104**	0.0129**
		(0.0649)	(0.0124)	(0.0030)	(0.0047)	(0.0059)
	perfor02	-0.7647	0.1416	-0.0294	-0.0502	-0.0620
		(0.5286)	(0.0974)	(0.0224)	(0.0372)	(0.0411)
	at02	0.7568**	-0.1401**	0.0291**	0.0497**	0.0613**
		(0.3526)	(0.0633)	(0.0147)	(0.0225)	(0.0312)
	meanslope	0.0108	-0.0020	0.0004	0.0007	0.0009
		(0.0179)	(0.0033)	(0.0007)	(0.0012)	(0.0014)
	analph10	-0.0019	0.0004	-0.0001	-0.0001	-0.0002
		(0.0144)	(0.0027)	(0.0006)	(0.0009)	(0.0012)
	density10	0.0565	-0.0105	0.0022	0.0037	0.0046
		(0.1291)	(0.0241)	(0.0050)	(0.0085)	(0.0107)
	distcity	-0.0378	0.0070	-0.0015	-0.0025	-0.0031
		(0.1069)	(0.0198)	(0.0041)	(0.0070)	(0.0087)
	distroad	-0.2814	0.0521	-0.0108	-0.0185	-0.0228
		(0.2255)	(0.0425)	(0.0093)	(0.0152)	(0.0191)
	ejidat10	-0.0019**	0.0003**	-0.0001*	-0.0001**	-0.0002**
		(0.0008)	(0.0002)	(0.0000)	(0.0001)	(0.0001)
	prodefor	0.3111	-0.0576	0.0119	0.0204	0.0252
		(0.2777)	(0.0524)	(0.0115)	(0.0186)	(0.0233)
	cabsa	-0.3846	0.0712	-0.0148	-0.0253	-0.0312
		(0.4560)	(0.0859)	(0.0184)	(0.0310)	(0.0375)
	npa	-0.2958	0.0547	-0.0114	-0.0194	-0.0240
		(0.2737)	(0.0517)	(0.0112)	(0.0182)	(0.0233)
	Cut1	1.4475***
		(0.4017)
	Cut2	1.7606***
		(0.4143)
	Cut3	2.4779***
		(0.4900)
	Observations Notes: Pseudo-R 2 =0.2804. *=signicant at the 1% level, **=signicant at the 5% 223 223 223 223 223 level, *=signicant at the 10% level. Standard errors clustered at state-level. Marginal eects are computed with dummy variables as balanced and at means of other variables.

Table 6

 6

	.8: CFE and amount of PSA-H reception between 2003 and 2010: Ordered probit and marginal eects
		(1)	(2)	(3)	(4)	(5)
	VARIABLES	cfe	Pr(cfe=1) .	Pr(cfe=2)	Pr(cfe=3)	Pr(cfe=4)
	psahval	0.0274**	-0.0051***	0.0010**	0.0018**	0.0023**
		(0.0108)	(0.0020)	(0.0005)	(0.0008)	(0.0010)
	cfe02	0.5575***	-0.1042***	0.0212**	0.0371**	0.0460***
		(0.1921)	(0.0338)	(0.0091)	(0.0146)	(0.0150)
	size	0.1431**	-0.0268**	0.0054**	0.0095**	0.0118**
		(0.0607)	(0.0118)	(0.0027)	(0.0044)	(0.0056)
	perfor02	-0.7942	0.1485	-0.0302	-0.0528	-0.0655
		(0.5212)	(0.0974)	(0.0220)	(0.0373)	(0.0417)
	at02	0.5963*	-0.1115*	0.0227*	0.0397*	0.0492*
		(0.3347)	(0.0611)	(0.0137)	(0.0219)	(0.0289)
	meanslope	0.0101	-0.0019	0.0004	0.0007	0.0008
		(0.0183)	(0.0034)	(0.0007)	(0.0012)	(0.0015)
	analph10	-0.0013	0.0002	-0.0000	-0.0001	-0.0001
		(0.0140)	(0.0026)	(0.0005)	(0.0009)	(0.0012)
	density10	0.0806	-0.0151	0.0031	0.0054	0.0066
		(0.1284)	(0.0242)	(0.0049)	(0.0086)	(0.0108)
	distcity	-0.0611	0.0114	-0.0023	-0.0041	-0.0050
		(0.1104)	(0.0206)	(0.0042)	(0.0073)	(0.0092)
	distroad	-0.2785	0.0521	-0.0106	-0.0185	-0.0230
		(0.2203)	(0.0419)	(0.0090)	(0.0149)	(0.0191)
	ejidat10	-0.0022***	0.0004***	-0.0001**	-0.0001**	-0.0002**
		(0.0009)	(0.0002)	(0.0000)	(0.0001)	(0.0001)
	prodefor	0.3072	-0.0574	0.0117	0.0204	0.0253
		(0.2708)	(0.0515)	(0.0112)	(0.0183)	(0.0231)
	cabsa	-0.4148	0.0776	-0.0158	-0.0276	-0.0342
		(0.4664)	(0.0885)	(0.0187)	(0.0321)	(0.0389)
	npa	-0.1550	0.0290	-0.0059	-0.0103	-0.0128
		(0.2497)	(0.0474)	(0.0098)	(0.0167)	(0.0212)
	Cut 1	1.4387***
		(0.3971)
	Cut 2	1.7471***
		(0.4104)
	Cut 3	2.4576***
		(0.4886)
	Observations Notes: Pseudo-R 2 =0.2749. *=signicant at the 1% level, **=signicant at the 5% 223 223 223 223 223 level, *=signicant at the 10% level. Standard errors clustered at state-level. Marginal eects are computed with dummy variables as balanced and at means of other variables.

Table 6

 6

	.9: CFE, amount and number of years of PSA-H reception between 2003 and 2010: Ordered probit and marginal eects
		(1)	(2)	(3)	(4)	(5)
	VARIABLES	cfe	Pr(cfe=1) .	Pr(cfe=2)	Pr(cfe=3)	Pr(cfe=4)
	tpsah	0.1220*	-0.0226*	0.0046	0.0081*	0.0099*
		(0.0691)	(0.0128)	(0.0028)	(0.0048)	(0.0058)
	psahval	0.0110	-0.0020	0.0004	0.0007	0.0009
		(0.0137)	(0.0025)	(0.0005)	(0.0009)	(0.0011)
	cfe02	0.4865**	-0.0900***	0.0185**	0.0321**	0.0393***
		(0.1917)	(0.0337)	(0.0089)	(0.0142)	(0.0143)
	size	0.1529**	-0.0283**	0.0058**	0.0101**	0.0124**
		(0.0630)	(0.0121)	(0.0029)	(0.0046)	(0.0057)
	perfor02	-0.8052	0.1489	-0.0306	-0.0532	-0.0651
		(0.5302)	(0.0975)	(0.0224)	(0.0376)	(0.0412)
	at02	0.6946**	-0.1285**	0.0264*	0.0459**	0.0562*
		(0.3326)	(0.0597)	(0.0138)	(0.0216)	(0.0289)
	meanslope	0.0093	-0.0017	0.0004	0.0006	0.0008
		(0.0181)	(0.0033)	(0.0007)	(0.0012)	(0.0014)
	analph10	-0.0019	0.0004	-0.0001	-0.0001	-0.0002
		(0.0142)	(0.0026)	(0.0005)	(0.0009)	(0.0012)
	density10	0.0668	-0.0124	0.0025	0.0044	0.0054
		(0.1301)	(0.0243)	(0.0050)	(0.0087)	(0.0107)
	distcity	-0.0477	0.0088	-0.0018	-0.0031	-0.0039
		(0.1100)	(0.0203)	(0.0042)	(0.0072)	(0.0089)
	distroad	-0.2824	0.0522	-0.0107	-0.0187	-0.0228
		(0.2229)	(0.0420)	(0.0091)	(0.0150)	(0.0189)
	ejidat10	-0.0021**	0.0004**	-0.0001**	-0.0001**	-0.0002**
		(0.0009)	(0.0002)	(0.0000)	(0.0001)	(0.0001)
	prodefor	0.3081	-0.0570	0.0117	0.0203	0.0249
		(0.2776)	(0.0524)	(0.0114)	(0.0187)	(0.0233)
	cabsa	-0.4167	0.0771	-0.0159	-0.0275	-0.0337
		(0.4565)	(0.0860)	(0.0183)	(0.0313)	(0.0376)
	npa	-0.2814	0.0520	-0.0107	-0.0186	-0.0227
		(0.2715)	(0.0513)	(0.0110)	(0.0182)	(0.0231)
	Cut 1	1.4220***
		(0.3991)
	Cut 2	1.7349***
		(0.4118)
	Cut 3	2.4581***
		(0.4890)
	Observations Notes: Pseudo-R 2 =0.2820. *=signicant at the 1% level, **=signicant at the 5% 223 223 223 223 223 level, *=signicant at the 10% level. Standard errors clustered at state-level. Marginal eects are computed with dummy variables as balanced and at means of other variables.

Actividad productiva; 2. Infraestructuras públicas; 3. Infraestructuras forestales; 4. Micro-crédito; 5. Otro (especificar ) La inversión no debe incluir los montos de remuneración

			Distribución	Polígono $ # pers.	Milpa # Años de Rendi. (Kg/Ha)	cult.(Ha) cultivo del primer año
	C. PSA-H	15. Repartición del PSA-H:	8.a. ¿Reciben el PSA-H? (1.Sí; 0.No) 10. ¿Cuál es la regla con respecto a la repartición de los pagos? Remuneración Inversión colectivo Técnico. # Ha Valor total (+ técnico) $ Año # pers. $ Tipo* $ Año	Contrato 1 # Años de Rendi. (Kg/Ha) # Ha Prom Monte/pers cultivo. 11. Con respecto de la remuneración: Contrato 2 del primer año # Ha Max Monte/ pers Milpa cult.(Ha) # Años de cultivo Rendi. (Kg/Ha) del primer año # Ha Prom Monte/ pers Milpa cult.(Ha) # Ha Min Monte/ pers # Pers # Ha Prom pers Monte/ 2003 2004 ¿Quien puede ser remunerado?_______________________ Políg Rotacion*	Contrato 3 1: Ejidatarios; 2005 2: Ejidatarios y posesionarios;	Solicitud en trámite 1 3: Todos	Solicitud en trámite 2 ¿Qué tipo de trabajo? _______________________________________________________________________________ 2006	2007	8.b. ¿Si no recibieron PSA-H, ya lo solicitaron? (1.Sí; 0.No) 12. Con respecto de la distribución: 2008 ¿Quien puede recibir?_________ Si sí, año:	Si sí, por qué no lo recibieron: 2: Ejidatarios y posesionarios; 2009 1: Ejidatarios;	3: Todos	8.c. ¿Si no recibieron PSA-H, porqué no lo solicitaron? ¿Cuál es la regla de distribución? 2010	1: Igualmente; especificar 2011 2: Proporcionalmente especificar	3: Un solo grupo especificar	4: Otros especificar 2012	Rotación: 1a.	13. Con respecto al técnico: Solicitud (%)	9. ¿Cuando escucharon por primera vez hablar del PSA-H? Nombre del técnico :	¿Cómo? ____________ Empresa :	T . Técnico;	Ej. Otros ejidos; ¿Cuántas veces venía el técnico al ejido en 2012?	Of. Oficiales;	M . M e d i a ; 14. ¿Cuántas veces participaron a pláticas sobre los programas de la CONAFOR en los últimos cinco años?	O .Otro especificar

*Inversión: 1. !!!!! E. Uso del suelo 25. Agricultura de milpa tradicional (Monte de uso ( 1 ))

Parcelada (milpa y más en el polig) ¡Quitar todo lo que no es para hacer la milpa en rotación (agricultura mecanizada, potreros…)!!!! 1b. Parcelada (sola milpa); 2. Rotación por polígono; Podemos poner varios polígonos por línea (si son usados por el mismo grupo de personas) -> A = (∑Superficie de los polígonos) / (# Pers. en el grupo) 3. Rotación libra; A= superficie del polígono / # Productores 4 . O t r a :

		Si Milpa:	rendimiento
				1999
			Max en el pasado
	ser más de un polígono si la tecnología cambia 27.a. Agricultura mecanizada y/o con agroquímicos (7, 8 y 9)-Puede	Cult* # ha cultivados Riego (1. Si; 0. No) 1999 2012 2005 # Pers Fecha de desmonte Políg. PROCEDE/ Por acuerdo	2005 2012 Tractores	# En sociedad	# Individual	27.c. Tecnología	26. ¿Si hay cambios entre los años, por qué?

* 1. Milpa; 2. Arboles; 3. Horticultura 27.b.

Evolución de la tecnología de cultivo de maíz mecanizado Rendimiento promedio

  Tipología de actividades y repartición del PSA-H por contrato (repartición entre los diferente grupos como estaban el año de recepción del PSA-H)

	O. prog. (UMA) G. Regla de utilización de la tierra Prom 36. Max Reglas Polígono después PSA-H Polígono antes de PSA-H PSA-H N°2 Año: % Pers Min Vender Madera Se puede No se puede Se Vender Leña puede		Descripción		# bovino L/Ej/PA/LPSA* 2005 1999	Supervisión (por año en 2012) Quien** Frecuencia Control de plagas*	# Infracciones los últimos 3 años	Sanción***
	No se
	puede Prom Se 2005 1999 puede Min Max No se puede Se puede No se puede PSA-H N°1 Año: 1999 % Pers construcción Extraer de # ha leña para auto-consumo Vender de Carbón Extraer material para Se puede No se puede Cazar para venta Se puede No se puede Políg. Ten. (PROCEDE; Por acuerdo; UC) #Pers si son parceladas 2012 F. Actividades económicas 30. Hogares de ejidatario y posesionarios 2012 2005 Se Cazar para puede auto-consumo No se puede Acceso del ganado al monte de uso común Se puede No se puede Acceso de bovino después Se puede cosecha No se puede Se puede No se puede Se puede No se puede	Hogares de ejidatario y posesionarios cuyas tierras se trabajan	Trad	Ganado	Meca	Trad+Ganado	Trad + Meca	Meca + Ganado	Trad + Meca + Ganado	29. Uso de los pastos (9) Hogares de ejidatario y posesionarios cuyas tierras no se trabajan	# ha PROCEDE/ Por Off-farm solo (incl. migrante) No trabajan (enfermedad, edad…) 2005 2012 1999 Políg. acuerdo /UC # Pers 2012	Avecindados	TOTAL	Meca= Milpa mecanizado +cultivo comercial	Preparación de tierra* Control de malezas* Siembra* Fertilización* Riego (Si/No) Cosecha*	2012	2005 31.a. ¿Cuántos productores venden maíz?	1999 31.b. ¿A qué precio lo venden?

* 0. Manual; 1.Mecanizado; 2.No hay 10 28. Uso del Monte (2, 3, 4 o 5) * L: Federal y Estado; Ej : Ejidal (escrita); PA: Informal (por acuerdo); LPSA: Regalamiento del PSA-H ** S: Agente Publico; CV: Consejo de vigilancia; R: Persona remunerada por el ejido; PM: Peer Monitoring *** 3. M: Multa; A: Advertencia; N:

No se aplica H. Acción colectiva 37

  .a. Mantenimiento de los bienes comunes

	Faenas/ Mantiene infraestructuras Frecuencia en los últimos 5 anos	Cuantas personas se necesitan para hacer el trabajo	% de defección los últimos cinco anos	Obligatorio (1:Si/0:No)	R/NR/NRV**
	Cercos
	Rutas
	Guarda rayas
	Ronda del PSA-H
	Vigilancia
	Limpieza espacios comunes

** R.

Remuneración; NR. No remunerado; NRV: No remunerado pero de facto personas son remuneradas (faenas colectivas cuando un ejidatario paga a alguien para hacerla).

	I. Organización del ejido
	38. ¿Cuánto veces se junto la asamblea en 2012?
	39. ¿Quién puede participar?
		1 : Todos
		2 : Ejidatarios únicamente
		3 : Ejidatarios + Posesionarios
	40. ¿En promedio cuántas personas participan en 2012?
	41. Lista los 3 últimos comisariado
	Nombre	Fecha	Edad a la fecha	Número de	Resultado de la
				candidatos	elección (%)
	37.b. ¿Si hubo remuneración, cual es el salario por día de trabajo?	Pesos
	42. Lista las organizaciones que existen en el ejido:
	Organización		Pers. del ejido	Tipo*

* 1

. Organización de productores (asociación o cooperativa); 2. Grupo de actividad social (mujeres, jóvenes, deportiva, cultural)

	43. ¿Los últimos 10 años, cuantos desacuerdos hubo entre los habitantes con respecto? :
	Desacuerdos	I/E*	Describir
	Límites de parcelas
	Uso de bosques
	Uso de pastos
	Límites del ejido

* I. Interno ; E. Externo

, &RPSRVLFLyQ \ FDUDFWHUtVWLFDV GHO KRJDU

Table B

 B Descriptives statistics before and after matching Table B.6: Descriptives statistics before and after matching : *=signicant at the 1% level, **=signicant at the 5% level, *=signicant at the 10% level. Standard errors clustered at state-level. Marginal eects are computed with dummy variables as balanced and at means of other variables.

		207
	.2: Robustness tests: Selection using covariate matching Table B.3: Robustness tests: Estimation without the larger ejido Table B.4: Robustness tests: Sample selection and leakages D.1 Robustness test
	C.2 Variable description at household level	(1) def -0.0456*** (0.0073) -0.5884*** -0.5846*** -0.5924*** -0.6045*** (2) (3) (4) def def def -0.0036*** -0.0054*** -0.0056*** (0.0012) (0.0017) (0.0016) 0.0075** 0.0079*** (0.0032) (0.0029) 0.0077 (0.0051) (0.1925) (0.2100) (0.2135) (0.2157) -0.0006 -0.0008 -0.0006 -0.0005 (0.0005) (0.0006) (0.0006) (0.0006) -0.0019 -0.0016 -0.0017 -0.0013 (0.0018) (0.0017) (0.0017) (0.0018) 0.0004 0.0002 0.0000 0.0004 (0.0016) (0.0016) (0.0016) (0.0015) 0.0226** 0.0209* 0.0225* 0.0246** (0.0109) (0.0118) (0.0121) (0.0122) -0.0012** -0.0011** -0.0010* -0.0011** (0.0005) (0.0005) (0.0005) (0.0005) 0.0242*** 0.0238** 0.0231** 0.0225** (0.0085) (0.0091) (0.0091) (0.0094) -0.7764 -0.7141 -0.7441 -0.7707 (0.4856) (0.5285) (0.5378) (0.5446) -0.0086*** -0.0084*** -0.0080** -0.0085*** (0.0028) (0.0030) (0.0030) (0.0031) -2.0758*** -2.0266*** -2.0168*** -2.0439*** (0.6844) (0.7274) (0.7187) (0.7139) -4.3677*** -4.3907*** -4.0691** -3.9832** (1.4613) (1.5459) (1.5522) (1.5721) -0.0269*** -0.0261*** -0.0264*** -0.0257*** (0.0088) (0.0092) (0.0089) (0.0093) -3.2272*** -3.0911** -2.8402** -2.8576** (1.1637) (1.2431) (1.2446) (1.1946) 0.4262*** 0.4033*** 0.3779*** 0.3721*** (0.1162) (0.1237) (0.1251) (0.1242) 7,490 7,490 7,490 7,490 0.1929 0.1740 0.1771 0.1788 Standard errors in parentheses adjusted for 62 clusters VARIABLES psa tpsa wtpsa1 wtpsa2 pscore forest05 slope droad05 dagri05 dcity telig lagbuer areaejid lagdefejid ejiddens margi05 defrisk Constant Observations R-squared *** p<0.01, ** p<0.05, * p<0.1 (1) (2) (3) (4) VARIABLES def def def def psa -0.0482*** (0.0080) tpsa -0.0042*** -0.0058*** -0.0059*** (0.0012) (0.0018) (0.0017) wtpsa1 0.0053* 0.0056* (0.0029) (0.0029) wtpsa2 0.0033 (0.0054) pscore -0.6828*** -0.7080*** -0.7108*** -0.7188*** (0.2084) (0.2246) (0.2260) (0.2251) forest05 -0.0001 -0.0001 -0.0000 0.0000 (0.0006) (0.0007) (0.0007) (0.0007) slope -0.0026 -0.0024 -0.0023 -0.0021 (0.0018) (0.0018) (0.0018) (0.0019) droad05 0.0010 0.0011 0.0009 0.0010 (0.0016) (0.0016) (0.0016) (0.0016) dagri05 0.0299** 0.0293** 0.0302** 0.0313** (0.0123) (0.0131) (0.0131) (0.0130) dcity -0.0022*** -0.0023*** -0.0022*** -0.0022*** (0.0007) (0.0007) (0.0007) (0.0006) telig 0.0285*** 0.0291*** 0.0286*** 0.0283*** (0.0090) (0.0095) (0.0095) (0.0097) lagbuer -0.9805* -0.9851 -0.9983* -1.0160* (0.5542) (0.5916) (0.5957) (0.5954) areaejid -0.0065** -0.0071** -0.0069** -0.0070* (0.0031) (0.0033) (0.0034) (0.0035) lagdefejid -2.3007*** -2.3395*** -2.3196*** -2.3391*** (0.7295) (0.7612) (0.7580) (0.7526) ejiddens -4.9800*** -5.1534*** -4.8807*** -4.8695*** (1.6462) (1.6937) (1.7279) (1.7408) margi05 -0.0316*** -0.0315*** -0.0315*** -0.0312*** (0.0091) (0.0095) (0.0094) (0.0096) defrisk -2.8966** -2.9924** -2.8695** -2.8634** (1.2792) (1.3327) (1.3460) (1.3269) Constant 0.4592*** 0.4585*** 0.4416*** 0.4407*** (0.1256) (0.1329) (0.1354) (0.1359) Observations 7,879 7,879 7,879 7,879 R-squared 0.1932 0.1731 0.1746 *** p<0.01, ** p<0.05, * p<0.1 Standard errors in parentheses adjusted for 61 clusters 0.1749 (1) (2) (3) VARIABLES def def def tpsa -0.0062*** -0.0061*** -0.0132*** (0.0008) (0.0008) (0.0009) wtpsa1 0.0045*** 0.0045*** 0.0112*** (0.0015) (0.0015) (0.0013) wtpsa2 -0.0005 -0.0147*** (0.0023) (0.0016) forest05 -0.0021*** -0.0021*** (0.0002) (0.0002) slope -0.0007 -0.0007 (0.0012) (0.0012) droad05 -0.0012*** -0.0012*** (0.0004) (0.0004) dagri05 -0.0158*** -0.0158*** (0.0014) (0.0014) dcity -0.0003 -0.0003 (0.0002) (0.0002) telig -0.0002 -0.0001 (0.0012) (0.0013) lagbuer 0.7067*** 0.7067*** (0.0525) (0.0525) areaejid 0.0007*** 0.0007*** (0.0001) (0.0002) lagdefejid 0.0289 0.0284 (0.0752) (0.0753) ejiddens 0.8155*** 0.8046*** (0.1945) (0.2029) margi05 -0.0056*** -0.0057*** (0.0020) (0.0020) defrisk 1.4226*** 1.4173*** (0.2771) (0.2777) Constant 0.0183 0.0189 0.0909*** (0.0146) (0.0148) (0.0045) Observations 10,352 10,352 10,352 R-squared 0.1893 0.1893 0.0369 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 B.3 Before pre-matching After pre-matching Variable Mean Stand. dev. Skewness Min Max Mean Stand. dev. Skewness Min Max def 0.0573 0.1487 3.4690 -6.78e-06 0.9999 0.0499 0.1405 3.7675 -6.78e-06 0.9999 psa 0.4794 0.4996 0.0824 0 1 0.5684 0.4953 -0.2761 0 1 tpsa 1.6748 2.2597 0.8665 0 7 1.9856 2.3317 0.6016 0 7 wtpsa1 1.8588 1.5415 0.6683 0 6.4504 2.0445 1.5408 0.5408 0 6.4504 wtpsa2 2.1993 1.0207 -0.6431 0 4.6446 2.2146 0.9552 -0.5372 0 4.6446 pscore 0.4798 0.2229 -0.2982 0.0003 0.9573 0.5308 0.1964 -0.4813 0.0151 0.9573 forest05 15.6355 8.2613 -0.2239 0.5022 25 15.4657 8.2195 -0.1765 0.5022 25 slope 2.0644 1.2433 1.3645 0.1055 11.3654 2.0496 1.1919 1.2443 0.10552 10.074 droad05 2.1879 3.2019 2.5430 0 22.9556 2.0484 2.8841 2.3843 0 22.9556 dagri05 0.4495 0.6754 2.1683 0 5.4260 0.4881 0.6972 2.0690 0 5.4260 dcity 26.9323 9.1147 -.2347 1.5931 48.4144 27.8193 8.7454 -0.1801 1.5931 48.4144 telig 8.0640 2.0066 -2.4557 0 9 8.3076 1.6957 -3.0013 0 9 lagbuer 0.0398 0.0602 2.6030 -2.11e-07 0.5042 0.0337 0.0498 2.4925 -2.11e-07 0.4941 areaejid 8.3539 11.3183 1.7560 .5263 34.4718 6.6991 9.5091 2.3666 0.5263 34.4718 lagdefejid -0.0375 0.0399 -2.2087 -0.2083 -0.0005 -0.0374 0.0393 -2.3366 -0.2083 -0.0005 ejiddens 0.0188 0.0132 1.6419 0.0060 0.0641 0.0179 0.0121 1.7059 0.0060 0.0641 margi05 0.4396 0.7435 0.4811 -0.9292 2.2204 0.4113 0.7438 0.6454 -0.9292 2.2204 defrisk 0.03419 0.0057 1.4125 0.024 0.0553 0.0344 0.0059 1.2857 0.024 0.0553 Table C.1: Variable description at ejido level Variable Description Source Mean Stand. dev. Skewness Min Max psa_gini Gini of payments distribution at ejido level PESMIX Sur-veys 0.3663 0.2530 0.1927 0 0.9730 mec05_ejid Presence of mechanised agriculture in the ejido in 2005 (Dummy variable) PESMIX Sur-veys 0.7447 0.4408 -1.1223 0 1 trad05_ejid Presence of traditional agriculture in the ejido in 2005 (Dummy variable) PESMIX Sur-veys 0.5745 0.4998 -0.3012 0 1 ranch05_ejid Presence of cattle-ranching in the ejido in 2005 (Dummy variable) PESMIX Sur-veys 0.7872 0.4137 -1.4037 0 1 per_mec05 Percentage of ejidatarios with mechanised agriculture in the ejido in 2005 PESMIX Sur-veys 0.4183 0.3484 0.1703 0 1 per_trad05 Percentage of ejidatarios with traditional agriculture in the ejido in 2005 PESMIX Sur-veys 0.2266 0.3108 1.3568 0 1.0000 per_ranch05 Percentage of ejidatarios with cattle ranching in the ejido in 2005 PESMIX Sur-veys 0.2180 0.2215 1.2540 0 1 ejidat Number of ejidatarios PESMIX Sur-veys 43.7447 46.1324 3.1312 11 244 age Average age of the ejidatarios PESMIX Sur-veys 50.5718 4.4163 -0.5558 40.2941 58.2143 age_sd Standard deviation of the ejidatarios' age PESMIX Sur-veys 10.1551 2.6069 0.5739 5.5572 17.2711 labejid Labour-ejido (dummy variable) PESMIX Sur-veys 0.3191 0.4712 0.7759 0 1 partasemb Average participation rate at the assembly PESMIX Sur-veys 0.7829 0.1949 -0.3865 0.4000 1 forest05 Total supercies of common forest in the ejido in 2005 SPOT images (20m) 1672.9407 1636.4204 1.7820 0 8254.8799 per_forcom Percentage of common forest in 2005 PESMIX Sur-veys 0.6421 0.3812 -0.4841 0 1.3935 psaejid_yr Average PSA-H payments received by the ejido during reception years CONAFOR and PESMIX Surveys 39.4345 21.7216 0.5776 3.5041 93.8586 Source Min Max 7.4880 1.1183 0 34 42.4930 1.4485 0.0000 180 0.4994 0.1750 0 1.0000 0.4744 0.6828 0 1 0.3666 1.8653 0 1 0.4669 0.7819 0 1 0.4191 -1.3127 0 1 14.0688 0.3923 24 89 2.1919 0.2231 0 11 0.1470 1.7968 0.0053 1 0.4157 0 1 Table D.1: Robustness test: Eligible ejidos (1) (2) (3) VARIABLES cfe cfe cfe tpsah 0.1588*** 0.1165* (0.0556) (0.0702) 1.3483 0.0000 1.0000 0.3318 -0.0272 psahval 0.0285** 0.0126 (0.0121) (0.0147) cfe02 0.4995** 0.6639*** 0.5705** (0.2434) (0.2349) (0.2395) size 0.2309** 0.1920* 0.2141** (0.1078) (0.0980) (0.1036) perfor02 -0.6065 -0.6958 -0.6749 Mean Stand. dev. Skewness PESMIX Sur-veys 8.3376 PESMIX Sur-veys 38.4248 PESMIX Sur-veys 0.4564 PESMIX Sur-veys 0.3385 PESMIX Sur-veys 0.1590 PESMIX Sur-veys 0.3179 PESMIX Sur-veys 0.7744 PESMIX Sur-veys 54.5128 PESMIX Sur-veys 4.8256 PESMIX Sur-veys 0.1734 PESMIX Sur-veys 0.2205 PESMIX Sur-veys 0.5893 (0.6293) (0.6290) (0.6328) at02 0.5994 0.3546 0.4936 (0.4479) (0.4172) (0.4204) meanslope -0.0031 -0.0050 -0.0053 (0.0229) (0.0234) (0.0232) analph10 0.0026 0.0021 0.0023 (0.0159) (0.0156) (0.0159) density10 -0.0210 -0.0155 -0.0199 (0.1410) (0.1414) (0.1408) distcity -0.1581 -0.1820 -0.1693 (0.1463) (0.1505) (0.1508) Table C.2: Variable description at household-level Variable Description psahval_an_hh Average yearly PSA-H payments received by the households during years of reception by the ejido (Thousands of pesos) psahval_hh Total PSA-H payments received by the households be-tween 2005 and 2012 (Thousands of pesos) mec05_hh Ejidatarios with mechanised agriculture in 2005 (Dummy variable) trad05_hh Ejidatarios with traditional agriculture in 2005 (Dummy variable) ranch05_hh Ejidatarios with cattle ranching in 2005 (Dummy vari-able) o05 Ejidatarios with permanent o-farm activities in 2005 (Dummy variable) write Writing and reading abilities of the household's head (Dummy variable) age Age of the household's head size05 Size of the household in 2005 assets05 Assets of the household in 2005 orgejid Involvement in the ejidos' distroad -0.2571 -0.2748 -0.2679 (0.2675) (0.2612) (0.2639) ejidat10 -0.0026** -0.0028** -0.0027** (0.0012) (0.0012) (0.0012) authorities (Dummy vari-able) prodefor 0.3962 0.3721 0.3871 (0.3206) (0.3140) (0.3213) cabsa -0.4162 -0.4657 -0.4571 (0.4991) (0.5142) (0.4996) npa -0.2428 -0.1210 -0.2266 (0.2705) (0.2538) (0.2687) Cut 1 1.2773** 1.2244** 1.2427** (0.5272) (0.5178) (0.5227) Cut 2 1.6261*** 1.5664*** 1.5912*** (0.5401) (0.5326) (0.5354) Cut 3 2.2567*** 2.1938*** 2.2304*** (0.6027) (0.5982) (0.6008) R-squared 0.3007 0.2952 0.3030 partasemb_hh Average participation rate at the assembly Observations 159 159 159

Notes

Table D .

 D 2: Robustness test: Ejidos with a CFE in 2002 : *=signicant at the 1% level, **=signicant at the 5% level, *=signicant at the 10% level. Standard errors clustered at state-level. Marginal eects are computed with dummy variables as balanced and at means of other variables.

		(1)	(2)	(3)
	VARIABLES	cfe	cfe	cfe
	tpsah	0.4170***		0.2259**
		(0.0900)		(0.1128)
	psahval		0.1032**	0.0597
			(0.0427)	(0.0425)
	cfe02	1.0110***	1.2868***	1.1898***
		(0.3219)	(0.3346)	(0.3505)
	size	0.1273	0.0828	0.1050
		(0.1048)	(0.1103)	(0.1089)
	perfor02	-0.7997	-1.0065	-0.9988
		(0.8137)	(0.8478)	(0.8395)
	at02	1.5446**	1.2045*	1.3320**
		(0.6618)	(0.6479)	(0.6606)
	meanslope	0.0894**	0.0763*	0.0788*
		(0.0441)	(0.0441)	(0.0452)
	analph10	-0.0241	0.0264	0.0028
		(0.0398)	(0.0394)	(0.0424)
	density10	0.0504	0.2193	0.1412
		(0.1845)	(0.1844)	(0.1922)
	distcity	0.0999	0.0800	0.1023
		(0.1638)	(0.1674)	(0.1679)
	distroad	-0.1492	-0.0883	-0.1423
		(0.3811)	(0.4146)	(0.4028)
	ejidat10	-0.0023	-0.0040*	-0.0033
		(0.0023)	(0.0023)	(0.0024)
	prodefor	-0.0797	-0.1261	-0.1410
		(0.3648)	(0.3539)	(0.3523)
	cabsa	0.4773	-0.3872	-0.0249
		(0.7986)	(0.8271)	(0.8414)
	npa	-0.3298	0.2018	-0.0838
		(0.4655)	(0.4265)	(0.4210)
	Cut 1	4.2600***	4.7628***	4.5003***
		(1.3457)	(1.3401)	(1.3764)
	Cut 2	4.7237***	5.2331***	4.9748***
		(1.2944)	(1.2808)	(1.3190)
	Cut 3	6.3632***	6.9046***	6.6988***
		(1.4618)	(1.4527)	(1.4908)
	R-squared	0.3788	0.3821	0.3946
	Observations	51	51	51

Notes

Table D .

 D 3: Robustness test: Placebo regressionLa thèse contribue à la littérature concernant l'ecacité des paiements pour services environnementaux (PSE) en se concentrant plus spéciquement sur l'impact d'un programme fédéral mexicain de PSE: le PSA-H. Après une introduction générale retraçant l'émergence des PSE et les débats académiques autour de l'instrument, le second chapitre présente les spécicités du système foncier mexicain, les politiques environnementales et plus spéciquement le PSA-H ainsi que les données utilisées dans l'analyse empirique. La thèse s'intéresse ensuite dans un troisième chapitre aux méthodes d'analyse d'impact et à leur application dans le contexte des instruments de conservation de la forêt. Par la suite, nous proposons trois analyses empiriques s'appuyant sur des données primaires et secondaires. Le quatrième chapitre présente une nouvelle méthode permettant d'estimer l'additonnalité et les eets de fuite du PSA-H dans notre zone d'étude. Dans le cinquième chapitre, la thèse étudie comment l'usage de la terre peut inuencer l'allocation des paiements au sein des communautés bénéciaires. Le sixième chapitre étudie les interactions entre le PSA-H et la foresterie communautaire. Enn, dans une conclusion générale, la thèse discute les implications des analyses empiriques pour la conception des PSE et les perspectives de recherche qui en découlent.

	THE EFFECTIVENESS OF PAYMENTS FOR ENVIRONMENTAL SERVICES IN MEXICAN COMMUNITY FORESTS
		(1)	(2)	(3)
	D.2 Variable description and sources	VARIABLES tpsah psahval cfe90 size perfor02 at02 meanslope analph10 density10 distcity distroad ejidat10 prodefor cabsa npa Cut 1 Cut 2 Cut 3 R-squared Observations Notes: *=signicant at the 1% level, **=signicant at the 5% level, cfe02 cfe02 cfe02 0.0458 0.1172 (0.0505) (0.0769) -0.0021 -0.0181 (0.0094) (0.0160) 0.9790*** 0.9753*** 0.9557*** (0.2044) (0.2074) (0.2062) 0.1093 0.1132 0.1173 (0.0962) (0.0982) (0.0988) 0.9980** 1.0255** 1.0192** (0.4928) (0.4984) (0.5005) 2.4817*** 2.5019*** 2.5443*** (0.3473) (0.3629) (0.3676) 0.0298 0.0332 0.0343 (0.0237) (0.0239) (0.0244) 0.0052 0.0067 0.0034 (0.0190) (0.0187) (0.0201) 0.2993 0.3027 0.2720 (0.2001) (0.1968) (0.2044) 0.0272 0.0205 0.0292 (0.1075) (0.1099) (0.1090) 0.0934 0.1066 0.1193 (0.1965) (0.2007) (0.2013) -0.0024 -0.0023 -0.0022 (0.0017) (0.0017) (0.0018) 0.5683* 0.6022** 0.5663* (0.2927) (0.2992) (0.2899) 0.9166** 0.9238** 0.9670** (0.4180) (0.4214) (0.4153) -0.6398* -0.5684 -0.6822** (0.3518) (0.3635) (0.3406) 4.1694*** 4.2244*** 4.2333*** (0.7095) (0.7257) (0.7312) 5.3490*** 5.4055*** 5.4170*** (0.8185) (0.8357) (0.8449) 7.4338*** 7.4713*** 7.5215*** (1.0088) (1.0196) (1.0306) 0.6154 0.6142 0.6180 223 223 223 *=signicant at the 10% level. Standard errors clustered at state-level. Marginal eects are computed with dummy variables as balanced and at means of other variables. Variable Description cfe Type of products produced by the CFE's in 2010 following PROCYMAF classication (discrete variable) cfe02 Lagged value of CFE's in 2002 psah Reception of PSA-H between 2003 and 2010 (dummy) psahval Amount of payments for PSA-H received be-tween 2003 and 2010 (thousand of Mexican pe-sos) size Total size of the ejido (tens thousands of hectares perpor02 Share of forest cover in 2002 in percentage of total size of the ejido (Hectares) at02 Reception of technical assistance to develop timber extraction before 2002 (Dummy) meanslope Mean slope in the ejido in percentage analph10 Illiteracy rate 2010 (AGEB level) density10 Population density in 2010 distcity Linear distance to the closest city of more than 2,500 habitants ((Tens of Km) distroad Linear distance to the closest road (Tens of Km) ejidat10 Number of ejidatarios in 2010 prodefor Reception of PRODEFOR between 2003 and 2010 (dummy) cabsa Reception of PSA-CABSA between 2003 and 2010 (dummy) npa Location of the ejido inside a Natural Pro-tected Areas (dummy) telig Number of years spent by the ejido inside the eligibility zones since 2003 meso02 Area of Mesophyll forests (Ha) Source ENNAF ENNAF CONAFOR CONAFOR INEGI Serie III / IN-EGI ENNAF Nasa's Shuttle Radar Topog-raphy Mission CONAPO ENNAF INEGI INEGI ENNAF ENNAF CONAFOR INEGI CONAFOR Serie III / IN-EGI Table D.4: Variable description 1.4484 0.9376 1.4260 .8556 0.9148 1.8712 374.8398 1049.736 1.0482 2.0176 0.4603 0.2788 0.2108 0.4088 11.3511 7.3014 13.3314 8.1830 0.5676 0.8207 1.4045 1.2101 0.4170 0.4750 189.8386 344.3888 0.2242 0.4180 0.0628 0.2431 0.1390 0.3467 2.9462 2.6126 205.0195 924.0211 Mean Stand. dev. Skewness Min Max 1.8588 1 4 1.8309 1 4 1.8312 0 8 3.6676 0 6382.083 3.8206 0.0190 12.7734 0.4460 0.0001 1 1.4183 0 1 0.2741 0 29.6752 1.4220 2.1658 52.0571 2.6985 0 5.2395 1.7044 0.01315 7.1168 6.8802 1.3225 3.605 2.0869 0.2214 6.1954 2.2828 0.0012 2.877 10 3500 0 1 0 1 0 1 0 7 0 8730.484 Résumé

Source: Weber (2013). Gestion des ressources renouvelables: fondements théoriques d'un programme de recherche. Collection Indisciplines, Editions Quae

 2 Translation by the author: La façon de poser un problème de gestion est en partie prisonnière de notre représentation du contexte dans lequel ce problème émerge, ou dont nous le faisons émerger en le formalisant.

Now called INECC (Instituto Nacional de Ecologia y Cambio Climatico)

Calculations have been made with GIS software using CONAFOR's data. Eligibility zones

The PMPM should not be confounded with the Forestry Management Plan which allows the ejidos to extract and sell timber.

During all the dissertation, we use exchange rate on the 1st January 2010: 1USD is approximatively equal to 13MP

Slight dierences between these statistics and others found in the rest of the dissertation are due to the areas of analysis. The remote sensing analysis included areas around ejido borders. There are dierences in absolute terms but deforestation rates are similar.

3.4.3 Concluding remarks . . . . . . . . . . . . . . . . . . . . . . .

cf Section 3.2.4 and 3.3.2 for instrumental variable estimations

Also see[START_REF] Pfa | Payments for environmental services: empirical analysis for costa rica[END_REF] for a more detailed version.

The measure of deforestation used by the author is censored: it is the reason why they use a Tobit model. Note that more details about the methodology can be found in the working paper version:[START_REF] Shapiro | Forest conservation and slippage: Evidence from mexico's national payments for ecosystem services program[END_REF]

Ti can also be dened as the time spent in the program or the number of hectare under protection. See Section 3.4 for more discussions about the denition of the treatment

Among other programs for[START_REF] Honey-Roses | A spatially explicit estimate of avoided forest loss[END_REF]

Note that the decision-maker can be a household or a community when dealing with forest in commons. In the latter case, one can suspect more unobserved heterogeneity, related to social capital for instance. It might be more dicult to nd a relevant control group and data collection on motivational aspects can be heavier than for households

Note that in the context of carbon PES, the forest needs to be maintained for many decades in order to sequester carbon. So, even though it is not practically speaking feasible, the appropriate timeframe for an IE would be over a century.

Landowners choose to enroll land in exchange for payments. Without payment, they would not enroll their land in the scheme, so it is dicult to dissociate the impact of the conditionality from the impact of the payment.

The data available was censored as it only included information on deforestation and none on reforestation thus justifying the use of a Tobit model.

This distinction is often made in the academic literature, sometimes under dierent terminology (eg: primary, direct or substitution eects vs secondary, indirect or output price eects).

The only overlapping areas are due to small geo-referencing errors.

An approach such as control function[START_REF] Heckman | Using matching, instrumental variables, and control functions to estimate economic choice models[END_REF]) may seem more appropriate to control for unobserved heterogeneity but it would be dicult to implement such an approach for at least three reasons. First, it would be dicult to nd relevant instruments validating the exclusion restriction at the polygon-level. Second, the spatially lagged variables capturing leakages, including internal leakages should be included in the probability and it would not make sense to explain the enrollment in the PSA-H at the polygon-level by the reception at the ejido-level. Third, control functions have not been used thus far with a continuous explanatory variable in the second step of estimation and the properties of such an estimator are unknown

Unlike Alix-Garcia et al. (2012), we were unable to precisely determine the reason why their application was rejected. Nevertheless, most of the applications in the area get rejected at least once for a lack of adequate administrative papers and because of budget limitations. Therefore, the ejidos are likely to receive the PSA-H in the forthcoming years.

Note that even once the selection process is taken into account, the decision of contract renewal may not be exogenous. We acknowledge that there may be unobservable factors impacting both deforestation and the decision to enter the program later or not to renew. Therefore, one should be cautious when comparing the magnitude of the coecient for these three variables.

[START_REF] Wunder | Payments for environmental services: some nuts and bolts[END_REF] dierentiates land-use restricting PES involving conservation of existing forest from asset building PES involving reforestation for instance

Beyond eectiveness and in a social justice perspective, compensation is problematic when dealing with poor landowners. The OC of poor landowners is close to zero so one can achieve maximum additionality by targeting poorer landowners. But it involves paying landowners at a price that is not sucient to increase their capabilities and transforming them in conservation rentiers" at the expense of economic development[START_REF] Karsenty | Des rentes contre le développement? les nouveaux instruments d'acquisition mondiale de la biodiversité et l'utilisation des terres dans les pays tropicaux[END_REF].

Translation by the authors

Translation by the authors

See Muñoz-Piña et al. (2008) for more details regarding the econometric analysis

Authors' calculations based on data provided by CONAFOR

Two ejidos were much larger than the rest of the distribution and received larger payments. Given the size of our sample, they were very likely to bias the results

We hypothesize that the use of the payments can be inuenced by regional authorities' vision of the program. This is probably the reason why all payments have been redistributed in our study area.

Note that we do not include o-farm activities here. First because they are not directly related to a land-use and second because we only have poor information on this matter at ejido-level.

Remember that, as explained in Chapter 2, economic activities are implemented in the ejido despite the fact that the ejidatarios are currently living outside the ejido

Formerly known as PROGRESA)

The variable capturing the size of forests is computed using data from INEGI's Serie III with a resolution of 1:250000. It does not allow us to precisely estimate the forest cover at the ejido-level, but these data remain credible proxies for forest size.

There are only 14 beneciaries of PSA-CABSA in our sample

In our sample, AGEB more or less overlap with municipalities.

Note that there are no deers (always taker) in our sample: all the beneciaries were eligible.

It would also be relevant to test the robustness of our results on the other sub-sample which is composed of the ejidos that did not have a CFE in 2002. Nevertheless, since most of these ejidos still do not have a CFE in 2010, the variable CFE would be highly censored.

Figure A.3: SPOT Images 1999 (20m) classied

Remerciements

A. Historia y población

Oportunidades

N° de hogares

PROGAN (en # de hogares)

Bovino

Abeja Borrego

Eco-certificación

Año Obs :

Otros programas:

55. ¿Existen superficie susceptibles de ser mecanizadas que no lo son ahora? (1.Sí;