Philippe Delacote
email: philippe.delacote@inra.fr

Gwenole Le Velly
email: gwenole.le-velly@supagro.fr

Gabriela Simonet

How do location and certification impact additionality of REDD+ projects? Theory and Evidence

Keywords: Deforestation, REDD+, Additionality, Spatial analysis. JEL codes: Q23, Q28, Q56

How is location of REDD+ projects chosen, and how do those location choices influence project additionality? This paper assesses these questions, presenting a simple theoretical model and using an original database of REDD+ projects in Brazil. We show that project location is strongly influenced by the type of project proponent, which appears to be a good proxy for its objectives, whether oriented toward environmental impacts, development impacts, or external funding. Our results suggest that the incentives behind REDD+ certification mechanisms can lead to low environmental effort or an investment in areas that are not additional.

Introduction

1

REDD+ is an international initiative aimed at compensating developing countries for their par-2 ticipation in the global effort of climate change mitigation through their reductions of deforestation 3 and forest degradation, as well as the conservation and enhancement of their forest carbon stocks.

4

At the local level, REDD+ has resulted in hundreds of REDD+ projects. Some of these projects 5 are financed through the sale of carbon credits, which are supposed to remunerate their addition-6 ality. The additionality of forest conservation projects can be defined as the avoided deforestation 7 attributable to the project (??). It is based on a comparison between the actual deforestation in the 8 area under conservation and an hypothetical counterfactual situation of no project implementation. 9

As explained by ?, additionality is determined by the share of area enrolled that would not meet 10 program requirement, for instance forest conservation, without program implementation.

11

In this article, we explore the interactions between the type of project proponents, the choice 12 of a location, the type of certification scheme and the additionality of REDD+ projects. Our 13 theoretical results shows that project proponents face a trade-offs between targeting additional areas 14 and efficient allocation of scarce resources. In some cases, given the uncertainty of carbon credit 15 certification, it can be optimal for the project proponent to implement a project in a location that 16 generates very low additionality in order to maximise funds from the carbon markets. Moreover, 17 the theoretical model also shows that pursuing conjointly social and environmental objectives might 18 lead to lower additionality.

19

We test these hypotheses by estimating empirically the additionality of 9 REDD+ projects lo-20 cated in the Brazilian Amazon. We consider two types of actors: Non-Governmental Organisations 21 (NGOs) and private-for-profit organisations; and two types of standards for certification: the Vol-22 untary Carbon Standard (VCS), which focuses on the carbon dimension of the projects, and the 23 Climate, Communities and Biodiversity (CCB) Alliance standard, which addresses the non-carbon 24 impacts of the projects. Our empirical analysis confirms the theoretical predictions of the model.

25

As a matter of fact, proponents that rely less on the carbon markets for funding, here NGOs com-26 pared to private-for-profit organizations, tends to implement more additional projects. Moreover, 27 projects using only the VCS certification appear to be more additional than those combining two 28 certification schemes.

29

In the second section, the REDD+ mechanism and the different certification standards are 30 presented. Section 3 develops a theoretical model that combines the type of projects proponents, 31 the choice of a location and the choice of a certification scheme, and derives conclusion for the Comment citer ce document : Delacote, P., LE VELLY, G., simonet, G. (2017). How do location and certification impact additionality of REDD+ projects? Theory and evidence. In: 4ème Conférence annuelle de la FAERE -12-13 Septembre 2017 -Nancy (p. 41 p.). Presented at 4. Conférence annuelle de la FAERE, Nancy, FRA (2017-09-12 -2017-09-13).

generated by REDD+ projects are well represented in the voluntary carbon markets. Indeed, in 2015, REDD+ projects (including tree-planting and improved forest management) generated 18 percent (or 15 megatons of CO2 equivalent) of the total volume of offsets transacted in this market (?).

Among REDD+ countries, Brazil is the country with the highest number of REDD+ projects, with 41 projects implemented as of 2014 (?). Brazil is a key player in the field of deforestation, because deforestation generates 44 percent of the total greenhouse gases emissions of the country Since its creation in 2005, the REDD+ mechanism has generated much academic debate. On the one hand, REDD+ has been presented as a promising tool, capable of channeling substantial funding to forest conservation, notably through carbon markets, and of delivering multiple benefits, by combining climate change mitigation, biodiversity conservation and poverty alleviation. On the other hand, REDD+ has raised considerable criticism, in particular as regards its environmental and social impacts. The environmental effectiveness of the mechanism has been questioned for several reasons. First, the risk of leakage has been highlighted, which refers to the fact that forest carbon emissions avoided by REDD+ programs or projects can create or increase CO2 emissions outside the territory covered by REDD+ activities (???). Second, forest carbon projects are subject to a risk of non-permanence, which corresponds to the risk of reversibility of the emissions reductions achieved by a project (?). Finally, the additionality of REDD+ projects, which corresponds to the environmental benefits that would not have happened without a project, has also been questioned, notably due to the difficulty in establishing accurate baseline scenarios of future deforestation (?).

In addition to these environmental issues, concern has been expressed by many academics and organizations defending human rights about the potential negative social impacts of REDD+ (?), which is feared to generate, among others, tenure conflicts, displacements of people for conservation reasons or 'green-grabbing', which is defined as the "the appropriation of land and resources for environmental ends" (?).

Comment citer ce document : Delacote, P., LE VELLY, G., simonet, G. (2017). How do location and certification impact additionality of REDD+ projects? Theory and evidence. In: 4ème Conférence annuelle de la FAERE -12-13 Septembre 2017 -Nancy (p. 41 p.). Presented at 4. Conférence annuelle de la FAERE, Nancy, FRA (2017-09-12 -2017-09-13).

Certification standards

To prevent the potential negative environmental and social impacts of REDD+, the UNFCCC Cancun Agreement established seven safeguards (Decision 1, CP.16). In the voluntary carbon markets, although there is no legal authority which controls and certifies carbon credits, several certification schemes emerged as an answer to the fear expressed by buyers that REDD+ carbon credits could be associated with non-permanence, lack of additionality or negative social impacts (?). In 2014, half of REDD+ projects were certified by one of the standards of the voluntary market (?). Data provided by ? indicates that 40 percent of REDD+ projects certified or in the process of certification are using the VCS, which is the most commonly used of the voluntary market standards (?). The VCS validates carbon monitoring methodologies proposed by project proponents and applies the same methodological principles as the CDM. Project proponents seeking VCS certification must submit a Project Design Document (PDD) that describes the methodology used to estimate the emissions reductions or carbon sequestration generated by the project, as well as the strategy used to deal with the risks of non-permanence, leakage and non-additionality.

The VCS dealt with the risk of non-permanence by the creation of a reserve of carbon credits, also called 'buffer', which represents between 10 to 40 percent of the total quantity of carbon credits, depending on the estimated risk of non-permanence of each project (?). The use of a buffer represents an innovation compared to the CDM, where the risk of non-permanence was addressed by the creation of 'temporary credits', which participated in the low attractiveness of CDM forestry credits, due to the complexity of their use. An assessment of each project against VCS rules by an independent third partiy, know as Validation and Verification Body (VVB), is necessary before the project proponent can sell carbon credits.

To answer buyers concern regarding the potential negative impacts of REDD+ projects on biodiversity or local people, projects proponents often combine the VCS certification with a certification by the Climate, Community and Biodiversity (CCB) Alliance standard, which focuses on the non-carbon benefits of the projects. ? reports than three-quarters of the VCS forestry credits transacted in 2014 were also certified by the CCB.

Under the umbrella of REDD+ projects, a vast heterogeneity of projects can be found, notably in terms of project type, location, proponents or funding sources (?). Given this heterogeneity among projects, it seems crucial when questioning the additionality of REDD+ projects to wonder, not only if REDD+ projects generate additionality, but which types of projects generate additionality.

Comment citer ce document : Delacote, P., LE VELLY, G., simonet, G. (2017). How do location and certification impact additionality of REDD+ projects? Theory and evidence. In: 4ème Conférence annuelle de la FAERE -12-13 Septembre 2017 -Nancy (p. 41 p.). Presented at 4. Conférence annuelle de la FAERE, Nancy, FRA (2017-09-12 -2017-09-13).

of REDD+ projects implemented in a country, and its position on the forest transition curve (??).

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Other showed that the location of REDD+ projects can be explained by the presence of protected 129 areas (?), as well as the baseline CO2 emissions, the forest carbon stock, the number of threatened 130 species, the quality of governance and the region, with a bias toward Latin America (?).

131

Other less explored sources of heterogeneity are the type of project proponent and the certifi-

u i = b i d i - v 2 d 2 i (1)
Comment citer ce document : Delacote, P., LE VELLY, G., simonet, G. (2017). How do location and certification impact additionality of REDD+ projects? Theory and evidence. In: 4ème Conférence annuelle de la FAERE -12-13 Septembre 2017 -Nancy (p. 41 p.). Presented at 4. Conférence annuelle de la FAERE, Nancy, FRA (2017-09-12 -2017-09-13).

Under no intervention, the optimal level of deforestation is thus: The representative agent's utility thus becomes:

d i = b i v . The
181 max d i u i = (2 -δ i e)(b i d i - v 2 ((1 + δ i e)d i ) 2 ) (2)
Comment citer ce document : Delacote, P., LE VELLY, G., simonet, G. (2017). How do location and certification impact additionality of REDD+ projects? Theory and evidence. In: 4ème Conférence annuelle de la FAERE -12-13 Septembre 2017 -Nancy (p. 41 p.). Presented at 4. Conférence annuelle de la FAERE, Nancy, FRA (2017-09-12 -2017-09-13).

The community's reaction to the REDD+ project is thus:

d * i (e) = b i v 1 (1 + δ i e) 2
(3)

u * i (e) = b 2 i 2v (2 -δ i e) (1 + δ i e) 2 (4)
The community reacts to the effort allocation in the following way:

183 d ie = ∂d * i (e) ∂e = b i v -2δ i (1 + δ i e) 3 < 0 (5) u ie = ∂u * i (e) ∂e = b 2 i 2v δ i (δ i e -5) (1 + δ i e) 3 < 0 ⇐⇒ δ i < 5/e (6)
Avoided deforestation from the project is:

184 AD i (e) = d i -d * i (e) = b i v δ i e(2 + δ i e) (1 + δ i e) 2 (7)
Avoided deforestation is increasing in e:

185

AD ie = ∂AD * i (e) ∂e = b i v 2δ i (1 -δ i e) (1 + δ i e) 2 > 0 ⇐⇒ δ i < 1/e (8)
Livelihoods improvement from the project is:

186 ∆ i (e) = u * i (e) -u i = b 2 i 2v (1 -δ 2 i e 2 -3δ i e) (1 + δ i e) 2 (9)
∆ ie = b 2 i 2v δ i (δ i e -5) (1 + δ i e) 3 < 0 ⇐⇒ δ i < 5/e (10)
For the remaining of the paper, we will focus on the case of low enough environmental effort The first-order condition implicitly gives the effort allocation e * of the Pp:

212 v um (e) = αU E (AD ie ) + βU L (∆ ie ) + (1 -α -β)U F (-p cm d ie + p um u ie ) = 0 (12)
The optimal allocation effort e * is chosen so that the marginal environmental benefit of in- obtaining funding since they all asked for certification. However, we study this extreme to analyse 220 the decisions made by the proponents less interested by funding. Second, we will focus on a project 221 2 Log functions will be used for the simulations.

3 If the certification scheme only considers the avoided deforestation output, with no importance given to livelihoods as a co-benefit, we simply have pum = 0.

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proponent only interested in funding from carbon markets: the OnMo case. Finally, we will consider 222 the interaction between funding and the other two objectives: BoMo case.

223

NoMo Case: (α + β = 1) .

224

When the project proponent does not consider external funding in its objective function, the OnMo Case: (α + β = 0) .

236

If the project proponent only cares about funding from carbon markets, his effort will be entirely 237 focused on environmental purposes or development purposes. We have the following corner solution: BoMo Case: (0 < α + β < 1) .

238        e * = 0 ⇐⇒ p um u ie > p cm d ie e * =

Choosing project location 254

At first, the Pp has to select the right location for implementing the REDD+ project. Locations 255 are represented by the couples levels of marginal benefit from deforestation and potential project 256 efficiency: L(b i , δ i ). It is important to note here that the project impacts not only depend on the 257 effort repartition described in the previous period, but also on the initial conditions in the project 258 location.

259

Overall, as shown before, the optimal effort level depends on the two variables that define

260 location: e * (b i , δ i (b i ))
. Thus the choice of the project location is linked to the selection of the right

261 b i .
262

The project location is chosen so that:

263 max b i v i (e * (b i , δ i (b i ))) = αU E (AD i (e * (b i , δ i (b i )))) + βU L (∆ i (e * (b i , δ i (b i )))) (13) + (1 -α -β)U F (p c (γ c d i -d * i (e * (b i , δ i (b i )))) + p u (u * i (e * (b i , δ i (b i ))) -γ u u i )))
Location i is chosen if the following condition is satisfied:

264 v um (b i ) = (e * b i + e * δ i δ i (b i ))v um (e) + (1 -α -β)(p c γ c d i b i -p u γ u u i b i ) = 0 (14)
Therefore, when choosing the project location, the project proponent considers how location

Choosing the certification scheme 285

Finally, the Pp has to select the certification scheme m that best fits with his objectives, within 286 the M possible certification schemes. A certification scheme is a combination of credit prices, 287 requirement levels and certification cost k: C(p c , γ c , p u , γ u , k). The chosen certification scheme will 288 be the one maximizing:

289 max m∈[0,...,M ] v im (e * ) = (p c (γ c d i -d * i (e * )) + p u (u * i (e * ) -γ u u i )) -k ( 15
)
Location i is chosen if the following condition is satisfied:

290 v im (e * ) > v is (e * ), ∀ s = m (16)
Result 5: Project proponents will tend to choose the certification scheme associated to the 291 highest possible price, and the lowest possible additionality requirement. 2). This is especially the case when the Pp is only motivated by funding from carbon markets 300 (α = 0 and β = 0): this case converges towards a corner solution where his environmental effort e i 301 is null. Note that, if the level of environmental stringency of the carbon standard is low (γ cm >1) i.e.

302

if the baseline of deforestation is overestimated, the Pp can still get funding from carbon markets 303 even if his environmental effort is null.

304

Moreover, from Result 5, we understand that the choice of certification is related the the weight 305 given to environmental (α) and social preferences (β) in the objective function of the Pp. Therefore,

306
we can hypothesize that the Pp that chooses a double certification have higher social preferences 307 than that Pp that only obtained VCS certification. In this case, Result 4 suggests that Pp with 308 higher β will favor locations with higher b i even though the environmental efficiency will be low

327

Our sample is composed of 9 REDD projects that cover around 2 millions hectares of forests.

328

We georeference each project using the Project Design Documents (PDD) that the proponents of from the PDD with some of these geographic features to locate the 9 projects. Once the project are 349 located, we draw the polygons that correspond to each project. We use this methodology for the 9 350 REDD projects. We build polygons that measure on average 108% of the project areas declared by 351 project proponents. This ratio is heterogeneous but for 8 projects out of 9, the difference between 352 computed and declared areas is lower than 15% (33% for the last one).

353

In order to estimate the impact of the project on deforestation, we use deforestation data In the first stage of our analysis, we study the choice of a location by project proponents 372 according to the type of certification and the type of proponents. In Section ??, we defined three 373 groups of projects. In order to study the difference in the choice of location for each type of REDD+ 374 projects proponents, we restrict our sample to the cells included in one of the REDD projects.

375

We obtain a sample of 859 observations.

376

Given the small number of REDD projects, we can not claim to identify the impact of the 377 characteristics of a location on the probability of enrolment in one type of project or another.

378

For this reason, we rely on qualitative evidence based on difference-in-mean tests. We study the 379 characteristics of the locations using variables influencing the opportunity costs of deforestation.

380

We believe that these variables are strong determinants of b i in the theoretical model. Higher 381 b i are associated with higher opportunity costs and those areas are likely to be more additional.

382

Moreover, higher b i can decrease the environmental efficiency δ i (b i ) of the environmental effort e i .

383

We focus on six variables: the distance to the nearest road, the distance to the nearest location, Since there are three types of projects we compute three pairwise tests in order to compare the samples two by two. For each test, we analyse if the characteristics of the location chosen by one type of Pp differ from the one chosen by the two other types type of Pp separately.

H0 : Xi -Xj = 0 H1 : Xi -Xj = 0

Additionality

In the second stage of analysis, we estimate the additionality of each type of projects according to the categories specified in Table ??. Additionality can not be estimated only by comparing enrolled and non enrolled areas. As a matter of fact, there are factors, called confounding variables, influencing both deforestation and the enrolment in a REDD+ projects, making simple comparisons irrelevant. To estimate the additionality, we rely on impact evaluation methodologies and combine matching methods with panel estimations. In line with this literature, we define the areas enrolled in the REDD projects as treated areas and build a counterfactual using a control group of nonenrolled areas.

In order to build a relevant counterfactual, we use a pre-matching procedure for each REDD projects (?). The objectives of this procedure is to select a group of observations that are as similar as possible to the treated areas and only differs regarding the treatment. For each REDD project, we consider as treated the cells that are located within the polygon of the project. We define our control group as all the cells located within a distance of 20 to 150km (or 200 km for the largest project) around each project.

In order to obtain a valid estimation of the impact, the Stable Unit of Treatment Value Assumption (SUTVA) must hold. This hypothesis requires that the outcome of an observation, here deforestation, is only influenced by its own status regarding the treatment. In our case, it means that the project does not impact deforestation in the control group. For this reason, we consider that the direct buffer of 20km around the projects (approx. the four closest cells from the project) is likely to be influenced by the project through leakage for instance (?). If we do not exclude the neighboring cells, the SUTVA hypothesis would not hold and the estimation might be biased.

However, in order to identify the impact of the projects, the choice of the control and treated groups must take into account unobservable confounding factors that affect both deforestation and the location of the REDD projects. By restricting our control group to the cells that are located

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no further than 150km from the REDD project, we hope to balance unobservable covariates such 417 as agro-ecological conditions. We also exclude protected areas and/or other REDD+ projects from 418 the control group since they also are under conservation policies.

419

For each project, we use a propensity score matching procedure without replacement. For each 420 cell located within a REDD projects, we select the nearest neighbor in terms of propensity score.

421

For each project, we estimate the following model:

422 P r(REDD i = 1) = η + 9 l=1 ρ l X li + ν i ( 17
)
X li is a vector of L variables including geographic characteristics that are structural deter-

437

We define a panel dataset with two dimensions: i corresponds to forested cells and t corresponds 438 to the year of observation. In order to estimate the impact of REDD+ projets. First, we estimate 439 the following model on the whole sample:

440 Def Rate it = α + βREDD it + ΩDef it-1 + 1,718 i=1 θ i D i + 7 t=1 κT t + it (18)
Second, we estimate one model for each type of projects as displayed in for all time specific effects on deforestation. Given the progressive entry into the REDD projects 457 and our control on time-unvarying fixed effects and year-specific effects, we are confident that our 458 approach allows us to estimate the impact of the different types of REDD projects.

459

In order to take into account spatial and temporal autocorrelation in the standard errors of 460 our estimates, we rely on a correction developed by ? and ? using the routine proposed by ?.

461

This procedure allows us to estimate standard errors robust to spatial autocorrelation in a buffer of 462 20km (approximatively four cells) around each cells and temporal autocorrelation for three periods.

463

Note that in Section ??, we analyze the validity of our matching procedure using balancing 464 tests and placebo tests. markets, here NGOs compared to private-for-profit organisations, favor more threatened forests 470 (see Section ??). This hypothesis is consistent with the results of the tests.

471

NGOs tend to enroll areas in REDD+ projects where the pressure to deforest seems higher.

472

As can be seen in Table ??, there were more deforestation in 2006 around the areas enrolled in a 473 project managed by an NGO compared to the two other types of projects. However, these areas 474 are more remote from the cities, the roads or the nearest non forested area.

475

Regarding certification choices, Table ?? allows us to compare the choices made by private-

476

for-profit organisations that combine CCB and VCS certification and those that only choose VCS In order to assess the quality of the matching procedure, we provide balancing tests for the three

518

The pre-matching procedure succeeded in reducing the bias between treated and non treated 519 observations. There are still remaining imbalances regarding some variables but we are confident 520 that the introduction of fixed effects in the estimation allows us to control for these biases.

521

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Standard-errors clustered at individual level 522

In our estimations, we account for spatial autocorrelation in a buffer of 20km and temporal Given that our observations have heterogeneous sizes, we choose to use the yearly deforestation 529 rates as our endogenous variable. However, we propose to test the robustness of our results using 530 the number of hectares deforested as endogeneous variable. As a matter of fact, using deforestation 531 rates, our results could be driven by deforestation in small cells that would result in very high 532 deforestation rates.

533

The result of the estimation can be found in the Appendix ??. The results confirm our finding 534 regarding the additionality of the projects managed by NGOs and private-for-profit organisations 535 that only obtained VCS certification. We find an average impact of 1.97 hectares of yearly avoided 536 deforestation for the whole sample, 0.68 for the the projects that only obtained VCS certification 537 and 2.21 for the projects managed by a private-for profit organisation. Moreover, the magnitude 538 of the coefficient is in line with our previous findings. If we multiply the average size of a forested 539 cell (1,900 hectares) by the decrease in deforestation rates estimated in Table ??, the magnitude 540 is similar to the coefficient estimated 7 . As in Table ??, we do not find evidence of a significant 541 impact for the projects that obtained both certifications. for funding from voluntary markets. Moreover, the trade off between project opportunity cost and 550 the efficiency of the environmental effort is crucial. Because higher opportunity costs decreases 551 environmental efficiency, it might be optimal in order to maximise funding from carbon markets to 552 implement the project in non additional area.

553

Our empirical analysis supports these predictions. As a matter of fact, according to our estima-554 tions, the projects that only obtained VCS certification had an additional impact on the deforesta-555 tion We can not reject that the private projects that combined both CCB and VCS certification 556

were not additional even though they are located in areas with higher opportunity costs. Moreover,

557

NGOS tend to rely less on the carbon market for funding and the project supported by these actors 558 were located in more threatened areas and were more additional.

559

Given the conclusions of our theoretical model, we acknowledge that our approach suffers from 560 our lack of data regarding the social impact of REDD+ projects. As a matter of fact, the the-

Appendices

A Value of the parameters for the simulations 6 Area deforested

i ∈ [1; 5] v 5 δ i 1 b a i a 1.8 p c 1, 0.8 p u 0.1, 0.3, 1.5, 2 γ c 1 γ u 1

B Data sources

C Placebo test

D Balancing tests

E Standard errors clustered at individual level

(

  in 2012, according to data from the World Resource Institute) and because of the significant shift observed in the Brazilian deforestation since 2004. Indeed, the annual deforestation rate in Brazil fell by 70 percent between 2005 and 2013 due to the implementation of command-and-control measures, the expansion of protected areas, and interventions in the soy and beef supply chains, such as the Soy Moratorium established in 2006 (?). In 2009, Brazil received about one billion of USD to implement REDD+ projects, mainly by Norway, through the Amazon Fund. This fund makes Brazil the main recipient of REDD+ funding (?).

213

  creasing effort on forest preservation equals the marginal economic benefit of increasing effort on 214 analyze what drives effort allocation for the project proponent, we will consider 217 several cases. First, we consider what happens when the project proponent is not interested in 218 funding from certifying his project : the NoMo case. Project proponents are all interested in 219

225Figure 1 :

 1 Figure 1: Effort allocation for diverse values of b i , α, NoMo case

243

  Comment citer ce document : Delacote, P., LE VELLY, G., simonet, G. (2017). How do location and certification impact additionality of REDD+ projects? Theory and evidence. In: 4ème Conférence annuelle de la FAERE -12-13 Septembre 2017 -Nancy (p. 41 p.). Presented at 4. Conférence annuelle de la FAERE, Nancy, FRA (2017-09-12 -2017-09-13).

Figure 2 :

 2 Figure 2: Project value for various levels of b i , p c , p u , OnMo case

Figure 3 :

 3 Figure 3: Effort allocation for diverse values of b i , α, β, p c , p u , BoMo case

Figure 4 :

 4 Figure 4: Project value for diverse values of b i , α, NoMo case

Figure 5 :

 5 Figure 5: Project value for diverse values of b i , α, β, p c , p u , BoMo case

  results of our theoretical model, the objectives of the project proponents influ-294 ence his choice of a location, defined by a marginal benefit b i and an environmental effort efficiency 295 δ i (b i ), his environmental effort e and the certification scheme. We also show that interactions 296 between b i and δ i (b i ) influence the environmental effort.297We can hypothesize that b i strongly and negatively influences the environmental effort efficiency 298 δ i (b i ). In this case, we can show that the Pp will provide lower environmental effort (Result 1 and 299

  309 which, according to Result 1, lead to a low additionality. 310 By combining Results 1, 2, 4 and 5, we can focus on two testable hypotheses: 311 Comment citer ce document : Delacote, P., LE VELLY, G., simonet, G. (2017). How do location and certification impact additionality of REDD+ projects? Theory and evidence. In: 4ème Conférence annuelle de la FAERE -12-13 Septembre 2017 -Nancy (p. 41 p.). Presented at 4. Conférence annuelle de la FAERE, Nancy, FRA (2017-09-12 -2017-09-13).

329

  the projects must elaborate in order to obtain the certification. The projects that are promoted by 330 private-for-profit organizations tend to rely more financially on the voluntary market. In line with 331 our theoretical model, the income obtained from the projects is a strong objective for these actors 332 compared to NGOs. Qualitative evidence collected during the construction of the ID-RECCO 333 database showed us that NGOs rely only partially on the carbon market. As a matter of fact, most 334 NGOs already existed and had their source of funding before selling carbon credits while many 335 private-for-profit organisations merged for the purpose of selling carbon credits. This hypothesis is 336 supported by the fact that, according to the database, selling carbon credits appear as an objective 337 of the project in around 50% of the REDD projects proposed by private for profit organisations 338 against 36% for NGOs.Comment citer ce document : Delacote, P., LE VELLY, G., simonet, G. (2017). How do location and certification impact additionality of REDD+ projects? Theory and evidence. In: 4ème Conférence annuelle de la FAERE -12-13 Septembre 2017 -Nancy (p. 41 p.). Presented at 4. Conférence annuelle de la FAERE, Nancy, FRA (2017-09-12 -2017-09-13).

384

  the distance to the Amazon frontier that we approximate by the distance to the border of the 385 Eastern States of Mato Grosso and Para, distance to the first non-forest point, slope in percent 386 rise and the number of hectares of deforestation in the neighbouring cell in (the first year of 387 analysis). All distances are in hundreds of kilometers. 388 Comment citer ce document : Delacote, P., LE VELLY, G., simonet, G. (2017). How do location and certification impact additionality of REDD+ projects? Theory and evidence. In: 4ème Conférence annuelle de la FAERE -12-13 Septembre 2017 -Nancy (p. 41 p.). Presented at 4. Conférence annuelle de la FAERE, Nancy, FRA (2017-09-12 -2017-09-13).

423

  minants of deforestation (???) such as distances to the closest waters, roads and localities, the 424 distance to the Amazon frontier that we approximate by the distance to the border of the Eastern 425 States of Mato Grosso and Para, distance to the first non-forest point, slope in percent rise, ele-426 vation in meters and the number of hectares of deforestation in the neighbouring cell in 2006 (the 427 first year of analysis) and the size of the cell in hundreds of hectares. Theses variables capture the 428 expected average pressure to deforest in the cells. Details about the sources of the data can be 429 found in Appendix ??. This procedure allows us to build a relevant control group for each of the 430 9 REDD projects.431The pre-matching procedure only allows us to control for observable confounding factors. In 432 order to control for unobservable confounding factors, we estimate the impact of the REDD projects 433 on deforestation rates using fixed-effect estimator. The progressive involvement of the forest into 434 the REDD projects allows us to control for all time unvarying confounding factors and to identify 435 the impact of each project. As a matter of fact, the fixed effect estimator controls for all cell-specific 436 effects.

  presents the results of the difference-in-mean test presented in Section ??. Given the 468 results of the theoretical model, we expect that the projects that rely less on funding from carbon 469 Comment citer ce document : Delacote, P., LE VELLY, G., simonet, G. (2017). How do location and certification impact additionality of REDD+ projects? Theory and evidence. In: 4ème Conférence annuelle de la FAERE -12-13 Septembre 2017 -Nancy (p. 41 p.). Presented at 4. Conférence annuelle de la FAERE, Nancy, FRA (2017-09-12 -2017-09-13).

477certification.

  According to the difference-in-means test, we do no find evidence of statistical differ-478 ence between the two groups regarding the deforestation in neighbouring cells in 2006. However, 479 VCS certification are located further from the roads and the urban areas that are crucial determi-480 nants of deforestation. It also confirms the theoretical predictions that proponents with a double 481 objective favor locations with high opportunity costs since their projects are closer to the main 482 infrastructure in order to better target the populations and achieve social objectives. 483 Comment citer ce document : Delacote, P., LE VELLY, G., simonet, G. (2017). How do location and certification impact additionality of REDD+ projects? Theory and evidence. In: 4ème Conférence annuelle de la FAERE -12-13 Septembre 2017 -Nancy (p. 41 p.). Presented at 4. Conférence annuelle de la FAERE, Nancy, FRA (2017-09-12 -2017-09-13).

  ., LE VELLY, G., simonet, G.(2017). How do location and certification impact additionality of REDD+ projects? Theory and evidence. In: 4ème Conférence annuelle de la FAERE -12-13 Septembre 2017 -Nancy (p. 41 p.). Presented at 4. Conférence annuelle de la FAERE, Nancy, FRA (2017-09-12 -2017-09-13).Comment citer ce document : Delacote, P., LE VELLY, G., simonet, G. (2017). How do location and certification impact additionality of REDD+ projects? Theory and evidence. In: 4ème Conférence annuelle de la FAERE -12-13 Septembre 2017 -Nancy (p. 41 p.). Presented at 4. Conférence annuelle de la FAERE, Nancy, FRA (2017-09-12 -2017-09-13).

512

  types of projects and their matched samples. The results of these balancing tests can be found 513 in Appendix ??. For each type of project, the unmatched sample includes all the cells located in 514 the REDD projects (treated group) and the control group is composed of the cells located within 515 a distance of 20 to 150km around each project and excluding protected areas or other REDD+ 516 projects. The matched sample includes only the treated cells and the matched cells from the 517 control group.

523

  autocorrelation over three periods in the estimation of standard errors. In the results displayed in 524 Appendix ??, standard errors are clustered at individual level. This procedure seems less relevant 525 in our case given the spatial nature of our data. However, we provide the results as a robustness 526 tests. The results confirm our previous findings.

7

  , we study the interactions between the type of project proponent, the choice of 544 a location and the choice of a certification scheme in the context of REDD+ projects. To study 545 this issue, we develop a theoretical model and test the results on an original database of REDD+ 546 projects in Brazil. According to our results, location and additionality from the projects are closely 547 related to the project proponent preferences. Projects are likely to have stronger additionality when 548 the project proponents have larger preferences for environmental quality, and smaller preferences 549 For the whole sample:0.0014x1900=2.66; For the VCS projects: 0.0007x1900=1.33; For the NGOs projects: 0.0016*1900=3.04 Comment citer ce document : Delacote, P., LE VELLY, G., simonet, G. (2017). How do location and certification impact additionality of REDD+ projects? Theory and evidence. In: 4ème Conférence annuelle de la FAERE -12-13 Septembre 2017 -Nancy (p. 41 p.). Presented at 4. Conférence annuelle de la FAERE, Nancy, FRA (2017-09-12 -2017-09-13).

Figure 6 :

 6 Figure 6: Balancing tests V CS P ri

Figure 7 :

 7 Figure 7: Balancing tests CCB P ri

Figure 8 :

 8 Figure 8: Balancing tests CCB N gos

1 Buisiness-as-usual cases 151

	132
	133	cation scheme adopted. Regarding project proponents, the large majority of REDD+ projects is
	134	implemented by the private sector, either by non-for-profit organizations such as NGOs that see
	135	REDD+ projects as a new source of financing for forest conservation projects, or by for-profit car-
	136	bon companies that seek to start capital-generating projects focused on carbon. Public sector and
		research institutes represent less than 20 percent of the proponents (?). The certification process is
	152	We consider a set of N ∈ [1, ..., n] potential REDD+ projects locations. Each location i is
	153	represented by a benefit b i for each unit of deforestation d i . v is the cost of deforestation, in-
	154	cluding non-market benefits from forest conservation. We assume convex costs, with a quadratic
	155	specification. The representative agent in location i thus maximizes livelihoods:
		max d i

137 also very heterogeneous as some certification addresses only carbon issues and others consider the 138 social and biodiversity impacts of the projects.

139

In the rest of the paper, we focus on projects of avoided deforestation, which represent around 140 half of the REDD+ projects worldwide (?). 141 3 Modelling additionality and location selection 142 A project proponent (Pp) aims to set an avoided deforestation project, with a set of various 143 objectives: avoided deforestation, livelihood improvement, and income from the project. For that 144 purpose, three choices have to be made: (1) first, a certification scheme m; (2) a project location 145 i; (3) an effort allocation e. 146 We proceed backward: We first consider the business-as-usual scenario of deforestation and 147 livelihoods levels, as well as the community response to the REDD+ project. Second, we consider 148 how the Pp allocates his effort between the avoided deforestation and livelihood objectives. Third, 149 the choices of location and certification schemes are considered.

150 3.

3 Community's reaction to the REDD+ project 168

	156		level of livelihoods
	157	is: u i =	b 2 i 2v . Those levels are considered as the business-as-usual scenarios.
	158	3.2 REDD+ project
	159	The project proponent has three kinds of objectives: (1) a weight α is given to the outcome in
	160	terms of avoided deforestation (which is our indicator of additionality); (2) a weight β is given to
	161	the livelihood quality of the community where the project is implemented; (3) a weight (1 -α -β)
	162	is given to financial aspects, approximated by the amount of money received from selling REDD+
	163	credit on voluntary carbon markets.
	164	The project proponent first selects one location among the N possible ones, as well as a certifi-
	165	cation scheme. He then chooses his effort allocation between environment (avoided deforestation)
	166	and development (improving livelihoods). Indeed, the outcome of the project is twofold: a level of
		deforestation and a level of livelihoods. We proceed backward.
	169	The Pp allocates his effort between reducing deforestation (e) and improving livelihood in
	170	the community (1 -e). We consider that effort allocated to environmental objectives increases
	171	the benefit from forest conservation for the community, and that effort allocated to livelihoods
	172	improvement increases the net benefit from the community's activities.
	173	Moreover, depending on the selected project, effort may be more effective for environmental
	174	purpose than for development purpose, or vice versa. Thus effort efficiency is δ i e. For δ i > 1 (resp.
	175	< 1), effort is more (less) productive for environmental purpose than development ones. Further
	176	more, effort efficiency is likely to depend on the marginal benefit from deforestation: δ i (b i ). Indeed,
	177	opportunity costs from avoided deforestation are larger when the marginal benefit of deforestation
	178	is larger, which decreases the effort efficiency for avoided deforestation. On the contrary, larger
	179	marginal benefits may represent larger development potential, and thus larger effort efficiency in
	180	terms of livelihood improvement. Thus we can consider that δ i (b i ) < 0. 1

167

3.

187 efficiency: δ i < 1. 188 3.4 A simple model of effort allocation in a REDD+ project 189 3.4.1 Optimal effort allocation 190

  um is the price premium that may be paid to the project proponent if such co-204 benefit is taken into account by the certification scheme 3 . γ um is the stringency level of livelihood

	196	ment (U L ) and utility from project funding (U F ) are all increasing and concave. 2
	197	AD i (e) is the actual level of avoided deforestation (supposedly known by the Pp). ∆ i (e) is the
	198	project impacts in terms of livelihood improvement. p cm (γ cm d i -d * i (e)) is the amount of money
		received from selling REDD+ credits on voluntary markets under certification m. p cm is the price
	191	As noticed before, the project proponent has three kinds of objectives: avoiding deforestation,
	192	improving livelihoods and increasing income from the REDD+ project. The objective of the Pp is
	193	thus to allocate his effort in order to maximize its utility from the project, taking location i and
	194	certification m as given:
		max e	v im (e) = αU E (AD i (e))+βU L (∆ i (e))+(1-α-β)U F ((p cm (γ cm d i -d * i (e))+p um (u * i (e)-γ um u i )))
			(11)
			Comment citer ce document :
		Delacote, P., LE VELLY, G., simonet, G. (2017). How do location and certification impact
	additionality of REDD+ projects? Theory and evidence. In: 4ème Conférence annuelle de la FAERE -
	12-13 Septembre 2017 -Nancy (p. 41 p.). Presented at 4. Conférence annuelle de la FAERE, Nancy,
			FRA (2017-09-12 -2017-09-13).

199

of carbon credits, while γ cm is the level of stringency relating avoided deforestation to credits. 200 A low (< 1) γ cm represents strong requirements and/or low uncertainty in baseline estimation, 201 while a large (> 1) γ cm represents low levels of stringency and/or high uncertainty regarding the 202 baseline. p um (u * i (e) -γ um u i ) represents the payment related to livelihoods improvement under 203 certification m. p 205 improvement measurements. If γ um < 1, the initial level of livelihoods is underestimated, which 206 tends to overestimate livelihoods improvements. Thus the label is considered loose in terms of 207 livelihoods measurements. In contrast, it is considered stringent if γ um > 1. 208 Overall, the certification scheme m is composed of 4 elements: a carbon price p cm , a level 209 of environmental stringency γ cm , a price premium to livelihood improvement p um and a level of 210 livelihoods stringency γ um . 211

244

  When the project proponent cares both about projects impacts and certification funding, this 245 tends to put an extra-weight on avoided deforestation or livelihoods. This extra-weight depends on When the project proponent considers both impacts from the project and certifica-

	246
	247	the condition:
	       ∂e 248
	249	tion funding, increasing the importance given to avoided deforestation (resp. livelihoods) increases
		(decreases) environmental effort if the price given to avoided deforestation p c is high enough, if

* ∂α < 0 and ∂e * ∂β > 0 ⇐⇒ p um u ie > p cm d ie ∂e * ∂α < 0 and ∂e * ∂β < 0 ⇐⇒ p um u ie < p cm d ie Result 3: 250 the price given to livelihood improvement p u is low enough, if the environmental effort efficiency δ i 251 is high enough, if the marginal benefit b i is high enough. It will be decreasing (increasing) in the 252 contrary. 253 Comment citer ce document : Delacote, P., LE VELLY, G., simonet, G. (2017). How do location and certification impact additionality of REDD+ projects? Theory and evidence. In: 4ème Conférence annuelle de la FAERE -12-13 Septembre 2017 -Nancy (p. 41 p.). Presented at 4. Conférence annuelle de la FAERE, Nancy, FRA (2017-09-12 -2017-09-13).

  • A Pp only motivated by funding from carbon markets favors areas with lower (or null) op-Pp that chooses a double certification favor areas with high opportunity costs but generates built an international database of REDD+ projects around the world. This database is 318 available online 4 and contains 454 projects located in 56 countries. As of May 2017, the database 319 included information about 57 projects in Brazil, of which 31 are ongoing projects of avoided defor-

	314
	315	less additionality.
	316	4 Empirical analysis
		4.1 Data

312

portunity costs and generates less additionality.

313

• A 317 ? 320 estation (REDD). However, a vast majority of these projects were not certified. Given the scope of 321 this article, we focus on projects that relied, or will soon rely, on funds coming from the voluntary 322 carbon markets. Therefore, we choose to focus on projects that already obtained the VCS and/or 323 the CCB certifications. Moreover, we choose to focus on conservation projects (REDD) instead of 324 reforestation ones for two reasons. First, it is easier to monitor deforestation than reforestation us-325 ing satellite images. Second, reforestation projects are smaller, making georeferencing complicated 326 if not impossible.

Table 1 :

 1 List of projects

		Certification	Actors	Variable	Number of projects
		VCS only	Private for-profit V CS P ri = 1 / T ype i = 1
		CCB and VCS Private for-profit CCB P ri = 1 / T ype i = 2
		CCB and VCS	NGO's	CCB N gos = 1 / T ype i = 3
	340	Our sample of projects is composed of three groups, further detailed in Table ??. All the
	341	projects obtained VCS certification but only six of them obtained CCB certification. The three
		projects that did not obtain CCB certification are implemented by private for-profit proponents.

342

Three projects are implemented by NGOs and they all obtained both CCB and VCS certifications.

343

The PDDs include a map of the projects in PDF format that can be projected using a GIS 344 software but lacks of geographic coordinates. In order to locate each project, we use shape files 345 mapping waters, urban areas and roads, provided online by the Instituto Brasileiro de Geografia e 346 Estatística (IBGE) and Digital Chart of the World, and the shape file of protected areas provided 347 by the International Union for Conservation of Nature (IUCN). We overlap each image extracted 348

Table 2 :

 2 Starting date of the projects

		Starting year	2002 2006 2007 2008 2009 2011 2012
		Number of projects	1	1	1	1	3	1
	364	analysis using PRODES data and create a new shape file of forested cells. All non forested areas in
	365	2005 are excluded from our sample. Eventually, we intersect this shape file with the REDD projects
	366	and protected areas boundaries. Therefore, each forested cell is either entirely within or outside
	367	protected areas and/or REDD projects. This procedure allows us to compute yearly deforestation
		between 2006 and 2014 within each cell. We drop cells of less than 1000 hectares as they mainly

368

result from mis-overlap and may bias our results. 369 4.2 Methods 370 4.2.1 Choice of a location 371

Table ?

 ?

			257		7
		Def Rate it = α2 + β2CCB P ri it +	θ2 i D i +	κ2T t + it	(20)
			i=1	t=1
			1,273	7
		Def Rate it = α3 + β2CCB N gos it	+	θ3 i D i +	κ3T t + it	(21)
			i=1	t=1
	443	Equations ?? to ?? are estimated on different samples. Equation ?? is estimated on the cells
	444	located in the three REDD projects that only obtained VCS certification and are managed by
	445	private-for-profit organizations (cf Table ??), and a control group composed of all the observations
	446	obtained using pre-matching for these projects. Similarly, the samples used for equations ?? and ??
	447	are composed of treated and control cells for the REDD projects that obtained both VCS and CCB
		certifications and are promoted by private-for-profit organizations (Equation ??) and the REDD
	441					?. We estimate
		the three following models:
			185		7
				θ1 i D i +	κ1T t + it	(19)
			i=1	t=1

442

Def Rate it = α1 + β1V CS

P

ri it + Comment citer ce document : Delacote, P., LE VELLY, G., simonet, G. (2017). How do location and certification impact additionality of REDD+ projects? Theory and evidence. In: 4ème Conférence annuelle de la FAERE -12-13 Septembre 2017 -Nancy (p. 41 p.). Presented at 4. Conférence annuelle de la FAERE, Nancy, FRA (2017-09-12 -2017-09-13). 448 projects that obtained both VCS and CCB certifications and are promoted by NGOs (Equation 449 ??). 450 In Equations ?? to ??, V CS P ri it , CCB P ri it , CCB N gos it are respectively equals to one if cell i is 451 located within a REDD project at time t. Def Rate it is the deforestation rate in cell i between year 452 t and t -1. We favour this measure since our cells may have slightly different sizes and propose in 453 Section ?? a robustness test using the area deforested. D i and T t correspond to the individual fixed 454 effects and yearly dummies. Introducing fixed effects allows us to control for all time unvarying 455 confounding factors influencing both project allocation and deforestation. Yearly dummies control 456

Table 4 :

 4 Additionality of REDD+ projects: Deforestation rates

		(1)	(2)	(3)	(4)
	VARIABLES Deforestation rate Deforestation rate Deforestation rate Deforestation rate
	REDD	-0.0014***
		(0.0005)
	V CS P ri it		-0.0007***
			(0.0003)
	CCB P ri it			0.0002
				(0.0005)
	CCB N gos it				-0.0016**
					(0.0007)
	Observations	15,462	1,674	2,322	11,466
	R-squared	0.0109	0.0098	0.0065	0.0146
	Number of Fixed Effects	1,718	186	258	1,274
	Number of yearly dummies	8	8	8	8
	Standard errors in parentheses are robust to spatial autocorrelation in
	a buffer of 20 km and temporal autocorrelation over three periods
		*** p<0.01, ** p<0.05, * p<0.1
	4.4

.2 Balancing tests 511

Table 5

 5

		: default
	Variable	Value
	b

Table 6 :

 6 Data sources

	Variable

Comment citer ce document : Delacote, P., LE VELLY, G., simonet, G. (2017). How do location and certification impact additionality of REDD+ projects? Theory and evidence. In: 4ème Conférence annuelle de la FAERE -12-13 Septembre 2017 -Nancy (p. 41 p.). Presented at 4. Conférence annuelle de la FAERE, Nancy, FRA (2017-09-12 -2017-09-13).

Table 7 :

 7 Parallel trend: Difference in deforestation in 2006 between treated and matched controls

	Type	Statistics Observations Treated Untreated Difference
	V CS P ri it	Mean difference	41	0,0004	0,0002	0,0002
		Standard-error	41	0,0002	0,0002	0,0003
	CCB P ri it	Mean difference	129	0,002	0,0003	0,0018
		Standard-error	129	0,0014	0,0001	0,0014
	CCB N gos it	Mean difference	435	0,0098	0,0076	0,0022
		Standard-error	435	0,0015	0,0009	0,0018

Comment citer ce document : Delacote, P., LE VELLY, G., simonet, G. (2017). How do location and certification impact additionality of REDD+ projects? Theory and evidence. In: 4ème Conférence annuelle de la FAERE -12-13 Septembre 2017 -Nancy (p. 41 p.). Presented at 4. Conférence annuelle de la FAERE, Nancy, FRA (2017-09-12 -2017-09-13).

Comment citer ce document : Delacote, P., LE VELLY, G., simonet, G. (

2017

). How do location and certification impact additionality of REDD+ projects? Theory and evidence. In: 4ème Conférence annuelle de la FAERE -12-13 Septembre 2017 -Nancy (p. 41 p.). Presented at 4. Conférence annuelle de la FAERE, Nancy, FRA (2017-09-12 -2017-09-13).

Table 8 :

 8 Balancing tests V CS P ri

	Variable

Comment citer ce document : Delacote, P., LE VELLY, G., simonet, G. (2017). How do location and certification impact additionality of REDD+ projects? Theory and evidence. In: 4ème Conférence annuelle de la FAERE -12-13 Septembre 2017 -Nancy (p. 41 p.). Presented at 4. Conférence annuelle de la FAERE, Nancy, FRA (2017-09-12 -2017-09-13).

Table 9 :

 9 Balancing tests CCB P riComment citer ce document : Delacote, P., LE VELLY, G., simonet, G. (2017). How do location and certification impact additionality of REDD+ projects? Theory and evidence. In: 4ème Conférence annuelle de la FAERE -12-13 Septembre 2017 -Nancy (p. 41 p.). Presented at 4. Conférence annuelle de la FAERE, Nancy,

	Variable

FRA (2017-09-12 -2017-09-13

).

Table 10 :

 10 Balancing tests CCB N gosComment citer ce document : Delacote, P., LE VELLY, G., simonet, G. (2017). How do location and certification impact additionality of REDD+ projects? Theory and evidence. In: 4ème Conférence annuelle de la FAERE -12-13 Septembre 2017 -Nancy (p. 41 p.). Presented at 4. Conférence annuelle de la FAERE, Nancy,

	Variable

FRA (2017-09-12 -2017-09-13

).

Table 11 :

 11 Robustness test: Standard errors clustered at individual level Comment citer ce document : Delacote, P., LE VELLY, G., simonet, G. (2017). How do location and certification impact additionality of REDD+ projects? Theory and evidence. In: 4ème Conférence annuelle de la FAERE -12-13 Septembre 2017 -Nancy (p. 41 p.). Presented at 4. Conférence annuelle de la FAERE, Nancy,

		(1)	(2)	(3)	(4)
	VARIABLES Deforestation rate Deforestation rate Deforestation rate Deforestation rate
	REDD	-0.0014**
		(0.0006)
	V CS P ri it		-0.0007**
			(0.0003)
	CCB P ri it			0.0007
				(0.0006)
	CCB N gos it				-0.0016*
					(0.0009)
	Constant	0.0048***	0.0003**	0.0010*	0.0062***
		(0.0004)	(0.0001)	(0.0005)	(0.0005)
	Observations	15,462	1,674	2,322	11,466
	R-squared	0.0109	0.0098	0.0065	0.0146
	Number of Fixed Effects	1,718	186	258	1,274
	Number of yearly dummies	8	8	8	8
		Robust standard errors in parentheses
		*** p<0.01, ** p<0.05, * p<0.1

FRA (2017-09-12 -2017-09-13

).

Table 12 :

 12 Additionality of REDD+ projects: Area deforested

		(1)	(2)	(3)	(4)
	VARIABLES	Area deforested Area deforested Area deforested Area deforested
	REDD	-1.9687***
		(0.7803)
	V CS P ri it		-0.6776***
			(0.2442)
	CCB P ri it			0.5356
				(0.4649)
	CCB N gos it				-2.2056**
					(1.0760)
	Observations	15,462	1,674	2,322	11,466
	R-squared	0.0142	0.0074	0.0116	0.0193
	Number of Fixed Effects	1,718	186	258	1,274
	Number of yearly dummies	8	8	8	8
	Standard errors in parentheses are robust to spatial autocorrelation in
	a buffer of 20 km and temporal autocorrelation over three periods
		*** p<0.01, ** p<0.05, * p<0.1

For the simulations, we consider that δi(bi) = 1/b a i .

http://www.reddprojectsdatabase.org/

http://www.dpi.inpe.br/prodesdigital/prodes.php

Remember that the size of the cells is 2,500ha

Acknowledgement

The authors would like to thank Annie Hofstetter for her help in building the database. The authors also would like to thank Martino Pelli and all participants to the Rencontre Montpellier -Nîmes -Sherbrooke in Montpellier (June 2017). The Laboratory of Forest Economics contributes to the Labex ARBRE ANR-11-LABX-0002-01.

The theoretical model presented above also suggests that the type of proponent and the choice of 485 certification impacts additionality through the choice of a location.

491

Once we focus on sub-samples per type of projects, we find an additional impact of 0.16% of 492 avoided deforestation per year for the cells included in the projects managed by NGOs (Column

493

(3)) 6 . We also find evidence of additionality for the projects that only obtained VCS certification.

494

We find an additional impact of 0.07% of avoided deforestation per year for the cells included in 495 the projects managed by a private-for profit organisation. Note that this impact is lower than for 496 the projects managed by NGOs. Those results suggest that the projects managed by NGOs are 497 more additional that the ones managed by private-for-profit organisations.

498

Regarding the choice of certification, we can only compare private-for-profit organisations. We 499 have no evidence about the additionality of the projects that obtained both certifications but our 500 results clearly suggest that the projects that only obtained VCS certification have been additional.

501

These results confirm that the choice of a location studied in the previous Section (Table ??) affects 502 additionality. All these findings are consistent with our theoretical model.


Delphine Lallias

Mekki Boussaha

Maria Bernard

Céline Ciobotaru

Nicolas Dechamp

Mathilde Dupont-Nivet

Edwige Quillet

In-depth genomic characterization of a unique collection of rainbow trout isogenic lines

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

IN-DEPTH GENOMIC CHARACTERIZATION OF A UNIQUE COLLECTION OF RAINBOW TROUT ISOGENIC LINES

Delphine Lallias*, Mekki Boussaha, Maria Bernard, Céline Ciobotaru, Nicolas Dechamp, Mathilde Dupont-Nivet and Edwige Quillet GABI, INRA, AgroParisTech, Université Paris-Saclay 78350 Jouy-en-Josas France delphine.lallias@inra.fr A unique collection of 17 isogenic homozygous rainbow trout lines has been established and maintained in INRA fish experimental facilities. These lines exhibit a wide range of phenotypes for most of the traits screened for. Such isogenic lines have many advantages in experimental approaches, including homozygosity, within-line genetic homogeneity, the availability of the same 'individual' genetic background at different places and times or the possibility to carry out investigations at different levels (cell, tissue, whole individual) for a given genetic background. In particular, these lines are being used to investigate the molecular bases of complex traits: lines with contrasted phenotypes are used for functional and genetic analyses (such as transcriptomics or QTL mapping).

Genomic characterization of isogenic lines is pivotal to realize the whole benefit of this material in integrative approaches aimed at dissecting complex traits. Having access to the genomic variability among lines is essential in expression or QTL studies in order to identify polymorphism(s) responsible for the phenotypes of interest. So far, the main information has come from RAD-tag genotyping, resulting in no more than a few thousand SNPs per line, which limits the accuracy of investigations at whole genome level or in targeted areas.

The objective of this study was to carry out in-depth genomic characterization of the trout isogenic lines, by investigating both small genomic variations (SNPs and InDels) and structural variants (SVs). SVs are defined as genomic alterations that affect large DNA segments ≥ 50 nucleotides, thereby causing modifications in either DNA quantity (insertions, deletions and duplications) or DNA structure (inversions). Although SVs have received increasing interest in many species and were shown to be associated with several diseases and phenotypes, they are poorly documented in fish.

All isogenic lines (one or two individuals per line) have been resequenced at a depth of coverage ranging from 10X to 32X, on an Illumina HiSeq X-Ten platform, in paired-end 2x150 bp configuration. Analysis of small genomic variants was performed according to the GATK Best Practices. The identification and characterization of SVs was done by using 4 different tools corresponding to three distinct but complementary approaches: i) Pindel and Delly (split-read approach); ii) BreakDancer (paired-end approach); iii) CNVnator (depth of coverage approach). After SVs annotation, a subset of SVs with potential relevant biological effects will be validated experimentally.

Funding: CRB-Anim (Biological Resource Centers for Domestic Animals)


Trinh Thi Huong

Simioni Michel

Thomas-Agnan Christine

A new perspective on the relationship between calorie intake and income in China and Vietnam using semiparametric modeling

Keywords: Nutrition, Income, Semiparametric Econometrics, China, Vietnam JEL Classication: I31, J21, J22

. This methodology is implemented using successive waves of the Chinese Health and Nutrition Survey and of the Vietnam Household Living Standard Survey. The application delivers some new and interesting insights on nutritional transition in the two countries between 2004 and today.

Introduction

Policies aimed at reducing starvation and redressing nutritional deciencies remain among the most widely accepted policies in the world as emphasized by [START_REF] Banerjee | Policies for a better-fed world[END_REF]. These policies can take many dierent forms, from subsidized prices of basic foodstus to cash transfers, and their eectiveness depends on the existence of a sensitivity of food demand to income variation and its magnitude. Several papers in development economics were thus interested in the estimation of the relationship between food demand measured in calories and household income. [START_REF] Ogundari | Examining the heterogeneity in calorieincome elasticities: A meta-analysis[END_REF] recently summarized this literature by providing a meta analysis of results given in forty papers published on this issue for several countries in the world, i.e. covering 99 estimated calorie intake income-elasticities. Unlike other literatures in applied econometrics , the choice of the functional form to characterize the relationship between calorie intake and income does not seem much discussed. 86 estimated income-elasticities were thus obtained using the parametric double-log specication. Nonlinearity is sometimes introduced through the addition of the square of the logarithm of income, after having checked the concavity of the relationship between logarithm of calorie intake and logarithm of income with nonparametric regression tools [START_REF] Abdulai | Nonparametric and parametric analysis of calorie consumption in Tanzania[END_REF]. Only few papers use semiparametric specications to deal with the issue of nonlinearity (see, for instance, [START_REF] Gibson | How elastic is calorie demand? Parametric, nonparametric, and semiparametric results for urban Papua New Guinea[END_REF], Vu 2009a[START_REF] Tian | Using semiparametric models to study nutrition improvement and dietary change with dierent indices: The case of China[END_REF][START_REF] Nie | A fresh look at calorie-income elasticities in China[END_REF]. The last two papers investigate the relationship for China and use the same data sets but produce conicting results. On one hand, [START_REF] Nie | A fresh look at calorie-income elasticities in China[END_REF] suggests that no clear nonlinearity, regardless of whether parametric, nonparametric, or semiparametric approaches are used, while, on the other hand, [START_REF] Tian | Using semiparametric models to study nutrition improvement and dietary change with dierent indices: The case of China[END_REF] claim that nutrition improvement and dietary change will continue in China but will slow down in the future with further income growth.

Our paper aims to contribute to the aforementioned literature in several ways. First, we want to compare changes over time in the relationship between calorie intake and income for China and Vietnam. We can think that China began its nutritional transition much earlier than Vietnam, as China began economic reforms ten years before Vietnam. The relationship between calorie intake and income may have converged to a similar shape in both countries, as both countries have undergone profound changes over the last 30 years. To our knowledge, this is the rst time this issue is addressed in the literature while many papers have studied the dierences in growth between the two countries since they have experienced economic reforms (see, among others, [START_REF] Vu | Economic Reform and Growth Performance: China and Vietnam in Comparison[END_REF]).

To answer the previous question, it is necessary to seek the functional form that best describes the relationship between calorie intake and income. Here is our second contribution. A natural choice would be to adopt a fully nonparametric specication of the relationship. The estimate of the relationship involves many control variables (age, education, region . . . ) and then we would be faced with the problem of the curse of dimensionality. We then choose to estimate various semi-parametric additive specications in which the control variables included in the parametric part of the model. Income is then supposed to impact calories intake through a smooth function of unknown form. A similar choice has also been done by [START_REF] Gibson | How elastic is calorie demand? Parametric, nonparametric, and semiparametric results for urban Papua New Guinea[END_REF], [START_REF] Tian | Using semiparametric models to study nutrition improvement and dietary change with dierent indices: The case of China[END_REF], and [START_REF] Nie | A fresh look at calorie-income elasticities in China[END_REF]. We consider more general specications belonging to the family of generalized additive models, or GAM [START_REF] Wood | Generalized additive models: an introduction with R[END_REF]. The conditional distribution of calorie intake given income and various control variables is thus chosen in a list of conventional statistical distributions. The conditional expectation of calorie intake given income and control variables is expressed as a linear function of the control variables and a smooth function of income, up to a monotone transformation or link function. The papers cited just above actually use GAM specications where the conditional distribution is the classical normal distribution and the link function the identity function.

Several potential options are possible for the GAM specications to describe the relationship between calorie intake and income, following the approach suggested above, and we must choose among them.

Here is the third contribution of our paper. We use a cross-validation procedure proposed recently by [START_REF] Racine | Data-Driven Model Evaluation: A Test for Revealed Performance[END_REF], namely revealed performance test. This procedure is a data-driven method for testing whether or not two competing approximate models are equivalent in terms of their expected true errors, i.e., their expected performance on unseen data from the same data generating process. The proposed test is quite exible with regard to the types of models that can be compared (nested versus non-nested, parametric versus nonparametric) and is applicable in cross-sectional and time-series settings.

The proposed test can be also applied to model selection [START_REF] Kiefer | The smooth colonel and the reverend nd common ground[END_REF].

The comparison between China and Vietnam, based on the approach described above, is illustrated on the China Health and Nutrition Survey, or CHNS, and the Vietnam Household Living Standards Survey, or VHLSS, in recent years. The methodology outlined above is carried out for dierent years from the early 2000s until today.

The analysis of the evolution of the calorie intake -income relationship over the studied period is not easy because this relationship is estimated from samples whose structure has evolved over time to remain representative of the population of Chinese or Vietnamese households. Nevertheless, estimates of the relationship between calorie intake and income for each survey wave can be used to decompose the dierence between average calorie intakes between two waves in two eects: the eect of change in the surveyed populations and that due to changes in eating habits as reected by the dierences between the estimates of the calorie intake -income relationship. This is the objective of decomposition methods in economics initiated by [START_REF] Oaxaca | OECD Framework for Statistics on the Distribution of Household Income, Consumption and Wealth[END_REF] and [START_REF] Blinder | Journal of Human resources[END_REF]. We modify the approach proposed by [START_REF] Machado | Counterfactual decomposition of changes in wage distributions using quantile regression[END_REF] by applying it to the case of a dierence between mean values and by incorporating in it the semi-parametric estimates of the relationship under investigation.

The paper is organized as follows. Section 2 presents the methodology used in this paper. Section 3 is devoted to the presentation of the CHNS and VHLSS data. Results are presented and discussed in Section 4. Section 5 concludes.

Methodology

Generalized Additive Models

Following [START_REF] Abdulai | Nonparametric and parametric analysis of calorie consumption in Tanzania[END_REF], most empirical works about estimating the relationship between calorie intake and income, use the classical double-log specication, i.e.

log(PCCI

) = α 0 + α 1 log(INCOME) + α 2 (log(INCOME)) 2 + j β j x j + ε (1)
where PCCI denotes per capita calorie intake, INCOME is total household income (sometimes replaced by total food expenditure), and x j are other covariates (usually discrete covariates describing the structure of the household) . The squared term, (log(INCOME))

2

, is introduced to capture the nonlinearity of the income elasticity of calorie intake as a function of income. The unknown coecients, or α 0 , α 1 , α 2 , and the β j , can be easily estimated by using the classical estimation techniques for linear models.

Although apparently exible, the double-log specication constrains the form of the response of calorie intake to a change in income. Of course, it is easy to give a direct interpretation to the estimated values of coecients associated with log(INCOME) and its squared value in terms of income-elasticity, which explains the frequent choice of this specication in empirical studies. However, taking the conditional expectation of the logarithm of the calorie intake as the object to be estimated rather than directly the conditional expectation of calorie intake can lead to misleading conclusions about the relationship studied as shown by [START_REF] Silva | The log of gravity[END_REF]. More general, or less restrictive, specications belonging to the family of generalized additive models, or GAM [START_REF] Wood | Generalized additive models: an introduction with R[END_REF], can be chosen to provide clearer statistical foundations to the estimation of the relationship between calorie intake and income and to capture nonlinearities in this relationship.

GAMs can be viewed as extensions of Generalized Linear Models, or GLM, which have the following structure

g(µ) = η = Xβ (2)
where g(.) is a smooth monotonic link function, µ ≡ E (y|X), y is a vector of independent response variables (Y 1 , . . . , Y n ) , n is the sample size, η is called the linear predictor, X is an n × k matrix of k covariates, and β represents the k × 1 vector of unknown regression coecients. The response variable Y follows an exponential family distribution whose probability density functions are of the form

exp yθ -b(θ) a(φ) + c(y, φ) (3)
where b(.), a(.) and c(.) are arbitrary functions, θ is known as the canonical parameter of the distribution, and φ is the dispersion parameter. For practical modelling, a(φ) is usually set to φ. The expected value and variance of such a distribution are E (Y ) = ∂b(θ)/∂θ = µ and V ar (Y ) = φ∂µ/∂θ = φV (µ), where V (.) denotes the variance function. Among the most popular distributions of the exponential family are the Gaussian, Poisson and Gamma distributions.

GAMs extend GLMs by allowing the determination of possible non-linear eects of predictors on the response variable. The linear predictor of a GAM is typically given by

η = Xβ + j s j (z j ) (4)
where β represents the vector of unknown regression coecients for the covariates acting linearly in the matrix X (usually discrete covariates), and the s j are unknown smooth functions of the covariates z j .

The smooth functions can be function of a single covariates as well as of interactions between several covariates.

In recent papers, [START_REF] Tian | Using semiparametric models to study nutrition improvement and dietary change with dierent indices: The case of China[END_REF] and [START_REF] Nie | A fresh look at calorie-income elasticities in China[END_REF] generalize the usual double-log model by introducing an unknown smooth function to capture the impact of income on per capita calorie intake. They estimate models whose expressions can be summarized as

PCCI = α 0 + s(INCOME) + j β j x j + ε (5)
This equation can be viewed as a special case of the general GAM specication presented above. We also estimate more general semiparametric specications whose expression is

g (E(PCCI|INCOME, x j )) = α 0 + s(INCOME) + j β j x j . (6)
The logarithmic transformation is chosen as the link function, i.e., g(.) = log(.), ensuring that the conditional expectation is always positive. Dierent assumptions are then made as to the conditional distribution of calorie intake given income and various control variables.

Revealed Performance test

Various models will be estimated later to describe the relationship between calorie intake and income, as explained above. So we will be facing the problem of choice among these models. We approach the issue of selecting among these models from the perspective that tted statistical models are approximations.

The revealed performance test proposed by [START_REF] Racine | Data-Driven Model Evaluation: A Test for Revealed Performance[END_REF] uses random sample splits of the available data to construct evaluation and training data sets, estimating the competing models with the training data sets and then engaging out-of-sample prediction with the evaluation data. This process is repeated a large number of times and then the average out-of-sample squared prediction error, or ASP E, is used to compare models. The model with the smallest ASP E is deemed the model with the lowest average prediction error.

Assuming that the data represent independent draws, as they would in a standard cross-sectional setup, the steps involved in the procedure proposed by [START_REF] Racine | Data-Driven Model Evaluation: A Test for Revealed Performance[END_REF] 2

(y * i , x * i ) n i=n1+1 , i.e. y i,j = m j (x * i ), i = n 1 + 1, . . . ,
These draws are used to discriminate between models. Paired t-test of dierence in means for the two distributions can be used to choose between these models.

Decomposition methods

The procedure presented above allows us to select a specication for the relationship between calorie intake and income for each wave of the surveys we use (see below). It is then interesting to see in the

∆P CCI t0→t1 = E t1 (P CCI) -E t0 (P CCI) (7)
where the two waves are denoted by t 0 and t 1 , and E t (P CCI) denotes the expectation of calorie intake using the distribution of the explanatory variables for wave t. Using the law of iterated expectations, the dierence ∆P CCI t0→t1 can be written as

∆P CCI t0→t1 = E t1 (E (P CCI|IN COM E, Z)) -E t0 (E (P CCI|IN COM E, Z)) (8)
Note that E (P CCI|IN COM E, Z) = m t (IN COM E, Z) where m t (.) denotes the model chosen for wave t by the revealed performance test. Equation (8) becomes

∆P CCI t0→t1 = E t1 (m t1 (IN COM E, Z)) -E t0 (m t0 (IN COM E, Z)) (9)
Finally we can write the dierence as

∆P CCI t0→t1 = E t1 (m t1 (IN COM E, Z)) -E t1 (m t0 (IN COM E, Z)) + E t1 (m t0 (IN COM E, Z)) -E t0 (m t0 (IN COM E, Z)) (10)
where E t1 (m t0 (IN COM E, Z)) is the counterfactual expectation of calorie intake using the model chosen for wave t 0 and the distribution of explanatory variables of wave t 1 .

Decomposition (10) can be viewed as a generalization of the well-known Oaxaca-Blinder decomposition [START_REF] Oaxaca | OECD Framework for Statistics on the Distribution of Household Income, Consumption and Wealth[END_REF][START_REF] Blinder | Journal of Human resources[END_REF]) to semiparametric models. The rst term in the right han side of equa-

tion (10), or E t1 (m t1 (IN COM E, Z)) -E t1 (m t0 (IN COM E, Z))
, measures what is usually called the structure eect. This eect can capture the chang of impact of household behavior in their choice of consumption due to changes in their environment. For instance, such changes may make these choices more or less income sensitive. The second term, or

E t1 (m t0 (IN COM E, Z)) -E t0 (m t0 (IN COM E, Z)),
measures the composition eect and refers to the eect of the change in the distribution of the characteristics of households.

The dierent terms of the decomposition (10) can be estimated by taking empirical counterparts of the expectations, i.e. average values of the predicted values of P CCI from the dierent models using either the contemporaneous or the counterfactual observations. Condence intervals can then be calculated by adapting the bootstrap procedure proposed by [START_REF] Machado | Counterfactual decomposition of changes in wage distributions using quantile regression[END_REF].

2 Here too, there is no theoretical guidance as to the number B in [START_REF] Racine | Data-Driven Model Evaluation: A Test for Revealed Performance[END_REF]. They just advise to take a large number such as B = 10, 000.

3 Data and Variables

Calculating per capita calorie intake

Once estimated the number of calories consumed per household, it is common practice to convert household-level calorie intake into individual-level calorie intake using equivalence scales. Household total calorie intake, or T HCI, can be expressed as

T HCI = CI h + i neqh CI i g,a
where CI h is calorie intake of the head of the household, taken as the reference, and CI i g,a is calorie intake of the non-head household member i of gender g and age a. Calorie intake of the adult reference member can then be computed as

CI h = T HCI 1 + i =h 1 i∈{g,a} θ g,a
where θ g,a = CI i g,a /CI h denes the equivalence scale for a non-head member of the household of gender g and age a.

It is no frequent to observe calorie intake for each member of a household, making it impossible to calculate directly the equivalence scales. Most papers in the literature do not use any equivalence scales, and calculate the adult equivalent of household calorie intake by dividing household total calorie intake by the total number of members in the household. Hence, θ g,a = 1, whatever the age or gender of the household members. However, this issue can be addressed using OECD equivalence scales, i.e. setting θ g,a = 0.7 for each adult other than the head of the household, whatever the gender, and θ g,a = 0.5 for the children, whatever their age or gender (OECD 2013). Here, to calculate our equivalence scales, we proceed as [START_REF] Aguiar | Deconstructing life cycle expenditure[END_REF] and we estimate the following regression model

log(T HCI) = γ 0 + γ 1 Gender + γ 2 N a + γ 3 F amily + ε. (11)
where T HCI is total household calorie intake, Gender is the gender of the head of the household (male is taken as the reference), N a is the number of adults in the household other than the head, and F amily counts the numbers of children by gender and age categories (0 -2, 3 -5, 6 -13, and 14 -17). This regression is estimated separately by area of residence, i.e. rural or urban, and by year as in Santaeulàlia-Llopis and Zheng ( 2016). Then we use the exponentiated predicted value of T HCI, normalized by the value for singleton households, i.e. exp( γ 0 ) if the individual is a male, or exp( γ 0 + γ 1 ), otherwise, as the equivalence scale. An equivalence scale is thus dened for each household. Per capita calorie intake, or adult equivalent calorie intake, is then computed as the ratio of household total calorie intake and household equivalence scale.

[Table 1 about here.] Table 1 reports the average value of adult equivalent calorie intake for each year in the survey and each country and compares it with other studies. The average values we obtained for the two countries are consistent with those obtained in the other papers using the same survey data. They are just a little higher, which we could foresee as the other studies do not use any equivalence scale.

Income and control variables

The following variables are used as explanatory variables when estimating the relationship between calorie intake and income: IN COM E: total household income per year; IN COM E was converted to 2006 dollars to make comparisons between the two countries easier; U RBAN : dummy variable = 1 if the household is located in an urban area, = 0 if not; HSIZE: household size (this variable is discretized in several classes: ve for China and six for Vietnam, the last class for households with 5 (resp. 6) or more members in China (resp. Vietnam)); HAN for China or KIN H for Vietnam: ethnicity of the head of household, = 1 if the head of the household belongs to the major ethnic group of the country (Han for China or Kinh for Vietnam), = 0 if not; EDU CH: the highest education level of the head of the household (this ordered variable takes three levels: = 1 for primary school, = 2 for secondary school, and = 3 for university); GEN DER: gender of the head of the household, = 1 if male, = 0 if not; W A: this variable indicates if the household is located in a house having access to clean water or not; AREA: the province where the household is located. In China, we use the nine provinces which are surveyed. We divide Vietnam in six biggest regions.

4 Main results

Chosen models

We estimate four dierent models. The rst one is the classical double-log model, or DLM,

log(P CCI) = α 0 + α 1 log(IN COM E) + α 2 log(IN COM E) 2 + β F actors + , (12)
where F actors include U RBAN , HSIZE, ET HN IC, W A, EDU CH, GEN DER, AREA. The second model is the semiparametric model where the distribution of PCCI belongs to the Gaussian family and

E(P CCI|INCOME, F actors) = α 0 + s(IN COM E) + β F actors. ( 13
)
This model is denoted by GAMGauSId. This model corresponds to models used by [START_REF] Gibson | How elastic is calorie demand? Parametric, nonparametric, and semiparametric results for urban Papua New Guinea[END_REF], [START_REF] Tian | Using semiparametric models to study nutrition improvement and dietary change with dierent indices: The case of China[END_REF], and [START_REF] Nie | A fresh look at calorie-income elasticities in China[END_REF]. The third and fourth models are semiparametric models such that log(E(P CCI|INCOME, F actors)

) = α 0 + s(IN COM E) + β F actors, (14)
where the distribution of PCCI belongs to the Gaussian family, model denoted by GAMGauSLog, or to the Gamma family, model denoted by GAMGamLog.

Table 2 reports the results of the t-paired tests used to compare the out-of-sample predictive performances of the four models for each year. This table should be read like that. Consider, for example, the value obtained of the test statistics shown for China in 2004 at the intersection of the line for DLM and the column for GAMGauId, namely 78.80. This gure indicates that on average the dierence between the ASPE criteria obtained for the two models for each of the 10, 000 splits of the Chinese data using the procedure presented in subsection 2.2 is positive. On average, the value of ASPE for the DLM is therefore higher than that obtained for GAMGauId, and this dierence is signicatively dierent from zero, indicating that DLM underperforms when compared to GAMGauId. A negative and signicantly dierent from zero value of the test statistics would have indicated the opposite.

[Table 2 about here.]

The last column of Table 2 indicates which model is chosen after the revealed performance test for each The relationship appears to be concave for both countries. For Vietnam, the relationship is strongly increasing for low income levels up to a point at which it continues to grow but at a much slower rate (or even zero rate). Also this general shape of the relationship is fairly constant over time. The results are more mixed for China, but the general shape of the curve still is concave.

[Figure 1 results from the change in the relationship between calorie intake and explanatory variables. Figure 2 3 The chosen household for China is living in a rural area in Heilongjiang region, with three members from HAN ethnicity, with a male head having secondary school degree, and having access to clean water. As for the household chosen for Vietnam, it comes from a rural area in Mekong province. Its head is a man with primary education level. It comprises four members from Kinh ethnicity and has access to clean water.

reports the results of the decomposition described in equation ( 10). More precisely, we report a boxplot of the bootstrapped distribution of the dierence of average P CCI between a given survey wave and 2004, for each country, and the boxplots of the corresponding bootstrapped distributions coming from its decomposition into a structure and a composition eects.

[Figure 2 about here.]

Figure 2 shows a contrasting trend over the period considered for China. The dierence between the average calorie intake between 2006 and 2004 is not signicantly dierent from zero and the elements of its decomposition also. The dierence between the average calorie intake between 2008 and 2004 is positive and larger than that between 2006 and 2004, but it is still not signicantly dierent from zero.

This is now due to the compensation between the two eects, the structure eect being predominantly negative, while the composition eect is always positive. The latter eect increases when comparing 2011 to 2004. This positive composition eect is completely counterbalanced by the signicant negative structure eect. This results in a signicant decrease in the average calorie intake between the two waves.

The introduction of the three autonomous cities in 2011 has certainly changed the composition of the CHNS sample by increasing the proportion of well-educated, small (less than two members), and urban households in it. Nevertheless, this also had the consequence of modifying signicantly the estimated relation between calorie intake and income as seen in Figure 1.

Results are much clearer for Vietnam. They are more stable, with the noticeable exception of the 2008 wave, an atypical year already mentioned above. The dierence in average calorie intake between 2006 and 2004 is not signicantly dierent from zero and this is due to the two eects that compensate over the period. Wave comparisons of 2010, 2012 and 2014 to 2004 show a stagnation of the dierence between average calorie intakes which is positive and signicantly dierent from zero. Now the two eects are also positive and signicantly dierent from zero, the structure eect being always larger than the composition eect. It should be noted that the samples for the 2010, 2012 and 2014 waves are composed of more urban and small (less than two members) households than the 2004 wave. The dierence between the average calorie intakes is certainly due to an eect coming from the dierence in the composition of the samples but it is also the result of a signicant change in the relationship between calorie intake and income, as reected in the structural eect.

Conclusion

This paper revisits the issue of estimating the relationship between calorie intake and income, and presents and compare estimates of this relationship for China and Vietnam. For this, we use various recent tools in semiparametric econometrics, in model choice, and in decomposition in economics. The application uses dierent waves of CHNS surveys for China and VHLSS for Vietnam over a similar period, from 2004

to the present day.

The dierent models chosen at the end of the model selection procedure include both the classical doublelog model and more general semiparametric specications. Most of them highlight a relationship between caloric intake and income that is strongly increasing for low income levels and that becomes increasing with a much lower slope or even constant from a certain income threshold. The analysis of the evolution of these curves is not easy because they are estimated from samples whose structure has evolved over time to remain representative of the population of Chinese or Vietnamese households. Nevertheless, estimates of the relationship between calorie intake and income for each survey wave can be used to decompose the dierence between average calorie intakes between two waves in two eects: the eect of change in the surveyed populations and that due to changes in eating habits as reected by the dierences between the estimates of the calorie intake -income relationship. The analysis reveals a signicant negative dierence between the average calorie intakes in China between 2011 and 2004, a nding already made by [START_REF] You | Declining nutrient intake in a growing China: Does household heterogeneity matter?[END_REF]. This dierence is mainly due to the negative and signicant eect of the change in consumption patterns which is barely compensated by the positive and also signicant eect due to the change in the structure of the studied population between the two years. Unlike China, the two eects play in the same direction over the period 2004 -2014 for Vietnam. They are positive and signicantly dierent from zero.

Their addition explains the increasing evolution of the average calorie intake observed over this period.

  evolution of the distribution of calorie intake between two waves what comes from the change in the distribution of explanatory variables and what results from the change in the chosen models. For this we will focus on the decomposition of average calorie intake between two waves and break it down into two eects: one specic to the change in the distribution of the explanatory variables and the other related to the model change. Or, put dierently, we focus on

  country and each survey year. The results clearly indicate that DLM is never chosen when compared to semiparametric models for China. GAMGauId is chosen three times over the four years studied when compared to other semiparametric models. GAMGAULog is only chosen in 2006. The results obtained for Vietnam show the best predictive capability for DLM for the rst three years. This model is rejected for the three following years where GAMGauId is chosen two times, in 2012 and 2014, and GAMGauLog only one time in 2010.

Figure 1

 1 Figure 1 reports per capita calorie intake as a function of income, the control variables being xed to their model values in 2004, 3 for the dierent waves for China and Vietnam. The nonlinearity of the relationship clearly appears in the view of the dierent curves traced in Figure 1. This conrms the tests performed above. The relationship appears to be concave for both countries. For Vietnam, the

  about here.] The comparison of the evolution of the curves over time has little meaning since these curves are estimated from samples whose structure varies over time and for years that may have been subject to external shocks on households' consumption in a given country. The sample structure of the CHNS survey changed between 2009 and 2011 due to the addition of the three autonomous cities, Beijing, Shanghai, and Chongqing, in 2011. In addition, the year 2008 was a year of strong decrease in food consumption in Vietnam due to dicult climatic year and a very signicant increase in food prices due to double-digit ination. Nevertheless, it is interesting to disentangle, in the evolution of the distribution of calorie intake between two waves, what comes from the change in the distribution of explanatory variables and what

Figure 1 :Figure 2 :

 12 Figure 1: Estimated calorie intake and income relationships for China and Vietnam

  . Compute average out-of-sample squared prediction error, or ASP E, for each model j as Repeat steps 1 -4 a large number B of times, yielding B draws for each model j, or (ASP E jb ) B b=1 .

	ASP E j =	1 n 2	n i=n1+1 (y i -y i,j ) 2
	5.

n.

1

[START_REF] Racine | Data-Driven Model Evaluation: A Test for Revealed Performance[END_REF]

do not give any theoretical guidance in selecting n 2 , or equivalently n 1 , as a function of the sample size. They just advise the user to investigate the stability of their results with respect to the choice of n 2 . 4

  This study is based on two surveys conducted in both countries over several years. The rst survey is China Health and Nutrition Survey, or CNHS. This survey is an on-going project between the Carolina Population Center at the University of Chapel Hill and the National Institute of Nutrition and Food Safety at the Chinese Center for disease Control and Prevention. Diet, health, demographic and socioeconomic information from about 4000 households is collected in nine geographically diverse Chinese provinces since 1989. In this study, we use the most recent four waves of the CNHS conducted in2004, 2006, 2009, and 2011. This study relies also on Vietnam Household Living Standard Surveys, or VHLSS.VHLSS is conducted by the General Statistics Oce of Vietnam, or GSO, with technical assistance of the World Bank, every two years since 2002. Each VHLSS survey contains modules related to household demographics, education, health, employment, income generating activities, including household businesses, and expenditures. The survey is conducted in all the 64 Vietnamese provinces and data are collected from about 9000 households for each wave. The survey is nationally representative and covers rural and urban area. In this study, we use the most recent six waves of the VHLSS conducted in2004, 2006, 2008, 2010, 2012, and 2014.

	3.1	Data sets

The surveys of the two countries we used in this study are very dierent in terms of how household consumption data are collected. The Nutrition Survey, an integral part of the CHNS, documents a variety of food items that each and all household members consume (in grams), both at home and away from home, over a three-day window. The result is a highly detailed account of hundreds of types of food consumed during the day, whose precision is suitable for nutrition studies as emphasized by

[START_REF] Batis | Longitudinal analysis of dietary patterns in Chinese adults from 1991 to 2009[END_REF]

. These very precise data are converted to energy intake and amounts of macronutrients using food composition tables for China (see

[START_REF] Popkin | Understanding the nutrition transition: measuring rapid dietary changes in transitional countries[END_REF]

, for more details). Calorie intake is thus available at the individual level for each member of a surveyed household in CNHS data set.

The main objective of VHLSS is to collect data on Vietnamese household living standards, as measured by households income and expenditure, as well as household members occupation, health and education status. This survey is not, by denition, constructed to assess the nutritional status of Vietnamese households. Only data on food expenditures and consumption are collected in this survey. Information on food expenditures and consumption are obtained for both regular and holiday expenses. These data are collected for both purchased goods and self-supplied food (home production) for 56 food items.

All food consumption is transformed into calories based on the calorie conversion table constructed by

Vietnam National Institute of Nutrition in 2007. The Vietnamese food composition table used in this study diers from that used in

[START_REF] Mishra | Dietary pattern, calorie intake and undernourishment: the vietnamese experience[END_REF]

who use the food composition table builded by FAO international (the rst FAO food composition table was published in 1949) to obtain calorie consumption. The calorie conversion table used in this study should reect better calorie consumption in Vietnam because it is based on Vietnamese diets while the FAO table is constructed based on the most common food items consumed around the world.


François Allal

Sebastien Ferrari

Khaled Horri

Marie-Odile Vidal

François Ruelle

Marc Vandeputte

Marie-Laure Bégout

Béatrice Chatain

Heritability of coping styles in farmed European sea bass

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers,

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  Allal F., Ferrari S., Horri K., Vidal M.-O., Ruelle F., Vandeputte M., Chatain B. , Bégout M.-L. ISGA XII, 2015 Laboratoire Adaptation & Adaptabilité des Animaux et des Systèmes Low hypoxia avoidance heritability (h² = 0.23 ±0.10) • High boldness heritability (h² = 0.42 ±0.12) • Hypoxia avoidance and Risk taking tests do not address the same personality trait in sea bass •Link between growth and personality  proactive < reactive Looking for a boldness related QTL in sea bass! Conclusions Laboratoire Adaptation & Adaptabilité des Animaux et des Systèmes Thanks for your attention!