Please do not cite The reform of the grass ... - INRA Montpellier

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Please do not cite The reform of the grass ... - INRA Montpellier

DRAFT – Please do not citeAAE meeting – Reading April 2007The reform of the grass premium in FranceWhat design for an auction-based allocation mechanism?Sandra Saïd *Sophie Thoyer *** PhD student – Inra-Lameta 2 place Viala 34060 Montpellier cedex 1 (France) –e-mail:said@ensam.inra.fr** Assistant Professor, Department of agricultural and Resource Economics, Ensam-Lameta, 2place Viala 34060 Montpellier cedex 1 (France) e-mail: thoyer@ensam.inra.frThe authors wish to acknowledge the contribution of Annie Hofstetter, Inra-Lameta, who conductedthe numerical simulations and gave very useful advice on optimisation algorithms.Key words: Agri-environment, auctions, multi-criteria index, FranceJEL: Q58, H23, C0AbstractThis paper focuses on an auction for agri-environmental contracts providing bothenvironmental and income-support benefits. The idea is to combine spatially-differentiatedstewardship payments based on previous practices and environmental goods provided, and incentivepayments for additional efforts provided by farmers on vulnerable zones. The challenge with suchmulti-dimensional auctions is to design a index capable of aggregating these benefits in a single scoreused to rank bids. We first conduct an analytical study of the solutions. Then numerical simulationsare conducted, based on the Grass Premium data in France. We demonstrate that the relative maximumstewardship payments authorized in vulnerable and less vulnerable zones, as well as the relativeweights given to environmental objectives and income support objectives can lead farmers to adoptbidding strategies which are not desirable for the policy-maker.IntroductionAgri-environment schemes were introduced into the European Union (EU) agriculturalpolicy during the late 1980s as a financial instrument to support farming practicescontributing to protect the environment and to maintain the countryside. With the 1992Common Agricultural Policy (CAP) reform , it became compulsory for member States toinclude them as “accompanying measures” in their rural development policy. Farmers couldchoose, on a voluntary basis, to commit themselves for a minimum period of five years toadopt environmentally-friendly farming techniques on their private land. They signed up fora tailored contract which included a number of relevant measures chosen amongst a largemenu, which included extensification practices, management of low intensity pasturesystems, preservation of habitats and biodiversity, adoption of organic farming etc. Theyreceived in return payments that were calculated so as to compensate them for additional costs1


and loss of income arising from their altered farming practices. An additional 20% incentivepayment, above costs of compliance, was allowed by the European Commission.After implementation of Agenda 2000, agri-environmental schemes were included in the“second pillar” of the CAP and European budgets dedicated to agri-environmental payments(co-financed by national states) were increased significantly: in the 2000-2006 programmingperiod, they amounted to 13.5 billion € and represented more than one third of the EUcontribution to rural development. Across the EU, the share of agricultural land enrolled inagri-environmental measures increased from 15% in 1998 to 25% in 2005 (EC, 2005).Such payments often represent a non negligible –and secure- source of farm income andhave contributed to maintain farming in less favourable areas. It is a fact that agrienvironmentalpolicies have often been used by member States to supplement farm income, ina way which was compatible with the decoupling requirements of the World TradeOrganization. Although supervised by Brussels, allocation rules vary from one member Stateto another, often reflecting the relative weights that national decision-makers give to genuineenvironmental concerns and to income-support objectives. This ambiguity about the trueobjectives of agri-environmental scheme explains partly the disheartened evaluationconducted by the EC (EC, 2005; Primdahl et al, 2003) which pointed out the insufficientenvironmental outcomes of agri-environmental payments. The EC diagnostic was thatdisappointing outcomes resulted from ill-designed measures, dispersion of efforts as well asmultiple “windfall effects” (farmers being paid for what they were already doing or for whatthey would have done anyway).The French agri-environmental policy is a good illustration of this problem. The PHAE(Prime Herbagère Agro-Environnementale, formally Grass premium) is the main agrienvironmentalscheme in France, representing more than 50% of all agri-environmentalpayments to French farmers. It is a premium which was initially created in 1993 to encourageextensive livestock production on sown and natural pastures, with the declared objective ofmaintaining open landscapes and low soil and groundwater pollution levels. However, it wasbefore all a scheme designed to provide income support to stock breeders in mountainous andsemi mountainous areas. In 2005, PHAE payments amounted to 212 million € and covered3.2 million ha, for 56 600 farms. The average annual payment per farm was 3700 € and oftenamounted in less favoured areas for more than 60% of net farm revenue. The outcome of thispolicy is that the PHAE scheme has succeeded in maintaining extensive stockbreeding in ruralareas which had known before a drastic reduction in the number of farmers, but its trueenvironmental impact was fairly limited: the PHAE has mainly maintained existing practiceswithout truly creating a momentum for better environmental practices (CNASEA, 2004).The mid-term review of the CAP in July 2003 has put greater emphasis on environmentalsustainability by introducing cross compliance on farming practices: farmers have to observeminimum environmental standards as a condition for the full granting of direct farmpayments. The indirect result of the “greening of the first pillar” of the CAP is the increasinglevel of requirement in agri-environmental schemes. It is clearly stipulated in the Berlinagreement that agri-environmental measures should only purchase environmental servicesprovided beyond the baseline level - the Good Farming Practices defined within theframework of various environmental European Directives. Following an audit of theEuropean Court of Auditors, the European Commission also required that agri-environmentalschemes include quantifiable objectives, be more cost-effective, and encourage memberStates to adopt competitive bidding in the allocation process.At the same time, the ongoing2


disagreement among member States on their financial contributions to the European budgetand on budget shares allocated to new members has created tensions and uncertainties aboutthe future European agricultural budget. The Council Regulation on support for ruraldevelopment for the 2007-2013 programmation period has planned a reduction of theEuropean Agricultural Fund for Rural Development. In particular, France had to accept a 400million € cut for the 2007-2013 programmation period, compared to the previous 2000-2006period (from 6,4 billiion € to 6 billion €).The consequence is that the French Ministry of Agriculture has to revise its own contributionto agri-environmental schemes. In particular, it has been decided that the PHAE, which isalready under Brussel’s scrutiny because it did not comply with all allocation criteria of agrienvironmentalschemes – will be entirely paid for by the French budget as from 2008, withoutany contribution from the CAP budget. The French ministry of Agriculture is therefore tryingto decide on the new allocation rules for PHAE and is negotiating with farm lobbies and otherstakeholders to identify eligibility criteria for PHAE contracts. There is of course strongpressure from previous beneficiaries of PHAE to reconduct former rules and to maintain thestatus quo. At the same time, the government is aware that it has to give priority to incentivesinducing net environmental gains and avoiding windfall effects. Within this perspective, costeffectivenessrequires that the level of payments reflect better farmers’ true compliance costs,which are often imperfectly know by decision-makers. Moreover, there is a need for moreefficient targeting: contracts should be offered in priority to farmers who can produce thegreatest environmental gain in the most environmentally vulnerable area. The French ministryhas already decided that a mapping of high natural value agricultural systems (systèmesagricoles à haute valeur naturelle -HVN) at the French scale will help identifying priorityareas for PHAE contracts. The Commission of Economic Affairs of the French Ministry ofAgriculture has put forward a first proposal in January 2007 for the new PHAE contractallocation rules: for a fixed amount of 76 €/ha, eligible farmers will be required to adopt agiven set of farming practices on at least 20% of their eligible farming area. Only farmers whowere already enlisted into a PHAE contract will be allowed to enter the new scheme. Themain justification for such proposal is that it helps maintaining budget expenditures undercontrol and monitoring is made easier. However, it runs against the basic principles of whatwould be a cost-effective efficient mechanism since payments are fixed and contractualizedactions are uniformized at the national scale. Moreover, it already creates upheavals amongstfarmers who find unacceptable that previous PHAE payments be phased out withoutjustification.In this paper, we explore a different mechanism for the allocation of the new PHAE contracts.We suggest a scheme which leaves the choice to farmers: either to remain under the previousPHAE mechanism (maintaining the level of payments and the associated environmentalcommitments) or to enter into an auction system in which they would bid on three parameters:a reduction of their past PHAE payments, additional environmental efforts (beyond the effortalready supplied under the previous PHAE contract) and additional compensatory payments.Such competitive bidding combines stewardship payments to maintain farm income, andincentive payments for additional efforts provided by farmers. In order to rank bidders, weuse a scoring function designed as a linear combination of the various benefits (in terms ofpayments required and environmental gains) offered by bidders. Such auction encouragesfarmers who can provide environmental gains at a lower cost to accept a reduction of paststewardship payments in order to be better compensated on additional environmentalmeasures.3


Since this auction scheme is multi-dimensional, the challenge for the decision-makeris to design an index capable of aggregating the various benefits embodied into the farmer’sbid into a single score used to rank bids. Based on the work by Cattaneo (2006), we calculatethe optimal bid strategies by farmers and analyse how the parameters of the scoring functionaffect the outcome of the auction. We apply this analysis to a French region, the Lozere,located in the Cevennes mountains, in the South east of France.The objectives of the paper are therefore to evaluate theoretically the advantages ofsuch auction compared to the fixed payment case which is currently under debate in France,and to measure the sensitivity of results to the choice of the scoring function parameters. Wethen discuss the feasibility of such scheme in the specific context of the Lozere region byconducting numerical simulations of optimal bidding strategies of Lozere farmers, and bymeasuring the performance of such auction.Section 1 of the paper describes in more details the auction mechanism and comparesit to other mechanisms used for the allocation of agri-environmental contracts. Section 2calculates optimal biding strategies by farmers. Section 3 presents numerical simulationsapplicable to the French PHAE case. Section 4 concludes.Section 1: Auctions and agri-environmental contractsThe vast majority of agri-environmental schemes involve the voluntary provision ofenvironmental services (above and beyond the regulatory duty of care level) by farmers ontheir private land in return for a compensatory payment by public authorities or environmentalgroups. Agri-environmental measures are usually the object of a contract between individualfarmers and the environmental service purchaser, specifying the actions that should beundertaken, the contract length, the control method and the payments made to farmers. Theunderlying rationale is that farmers, if they get the right incentive, can switch to moreenvironmentally-friendly production technologies, therefore supplying net environmentalgains to the society as a joint product of farming activities. The whole difficulty lies in thewords “right incentives”. In most cases, the net costs of technology switching is farmer’sprivate information. Decision-makers, when trying to calculate payment, either take the risk to“over-compensate” farmers, by providing them with a payment above their true costs ofcompliance, leading then to low budget returns, or to “under-compensate” farmers, leadingthen to low participation and insufficient provision of environmental services.Decision-makers in Europe have tried to overcome this difficulty by designing menucontracts tailored to the characteristics of each zone and by trying to measure as precisely aspossible the average costs of undertaking agri-environmental measures. Such method has notproved satisfactory for several reasons: it had extremely high transaction costs both in termsof initial design (cost of gathering information on farmers’ costs, describing a menu ofmeasures tailored to the zone) and monitoring; it has encouraged farmers to sign up formeasures they already complied with, creating windfall effects and reducing netenvironmental gains; it has often overvalued the compensation by neglecting the positiveeffects that the adoption of an environmentally-friendly technology could have on farms’profits (increased soil quality, higher quality of products) and on farmers’ utility when theycare for the environment (Dupraz et al, 2003).4


income support. To respond to this issue, we investigate the outcome of a mechanism whichhelps revealing the trade-offs that farmers are willing to accept between farm-income supportand environmental incentive payments. The mechanism permits to select bidders non only ona cost-effectiveness criterion (ie additional environmental gain per € spent) but also on thepriority level of different area and on budget-saving criteria (the amount of previous paymentsforegone by farmers). It guarantees that the level of environmental gain obtained under theprevious PHAE scheme is maintained and that the new scheme is acceptable. The secondissue is the bidder’s ranking when bidding is multi-dimensional. The CRP adopted anenvironmental index which is a linear combination of several environmental parameters(characterizing improvements for soil, water, and air) and a cost factor, whereas the Bushtender adopted a cost effectiveness index calculating an environmental improvement score perunit of public fund spent. Johansson and Cattaneo (2006) demonstrate that the weights and thefunctional form of the index has an impact both on the selection of farmers and on theoutcome of the auction. Cattaneo (2006) analyses further the sensitivity of CRP and Equipauctions in the US to the choice of index weights and government’s reverse price. Ouranalysis draws on this interesting approach to adapt it to the specific case of PHAE auctionsin the French context.Section 2: the modelThe auctionSuppose that farmers have already signed a grassland conservation contract: for anenvironmental effort e 1 , they get a payment DP*e 1 . It is therefore a uniform stewardshippayment for maintaining previous good practice. It is also considered an income-supportpayment and as such, cannot be authoritatively eliminated. Since the PHAE contract specifiesa strict set of actions and authorized technologies (in terms of fertilizer use, stocking rate perhectare, and maintenance and clearing of invasive flora), it imposes that all signatory farmerssupply the same level of effort per unit of land. The total environmental effort e 1 in such caseis therefore measured by the total area under contract.In the auction, they submit a sealed bid b (e 2 , r, s) with three decision variables:- e 2 the level of additional environmental effort they would be willing to supply (either onland which is already under PHAE contract by accepting stricter conservation technology, orby extending conservation practices on land which was not under contract previously).- r the level of compensatory payment per unit of effort provided- s the share of the previous PHAE premium they are willing to forego.The auction is a multiple contract discriminatory procurement auction: winning farmers sign acontract in which they accept to see their stewardship payment reduced by DPe 1 s, theycommit themselves to maintain the level of effort e 1 and to supply environmental effort e 2 , forwhich they get paid re 2 .We assume that farmers are risk neutral and that they have private information about theirfarm profits as well as about their costs of providing environmental gains. A farmer will bidb = (e 2 ,r, s) in the auction if his expected utility of participation is greater than his reservationutility RU =Π+DPe1EU = [ Π−C(e2)+ re2+DPe1(1−s)]P+[ Π+ DPe1](1−P)6


Π is the total farm profit when the base conservation effort e 1 is provided (excludingstewardship payments).C(e 2 ) is the total cost of providing environmental effort e 2 . We assume a positive andincreasing marginal cost C’(e 2 ) >0 and C’’(e 2 ) >0P is the probability of winning the auctionThe bid’s scoreThe probability of winning depends on the rank of the farmer’s bid. Following Cattaneo(2006), we establish an additively separable scoring rule S(e 2 ,r,s)=I which combines linearlythe various dimensions of each bid in a single index value comprised between 0 and 1.( 2 )( )g e ⎛P P e s r1r ⎞I = w e + w + w s + w ⎜ − ⎟g emax− e1 ⎝ rmax⎠with:e p ⊂ [0,1] , the priority score given to the area in which the farmer is located. We assume herethat the decision-maker can decide to give greater priority to Natura 2000 areas or/and toenvironmentally vulnerable zones. For land which is less vulnerable to pollutions or which isalready degraded, e p is close to 0. For land which requires greater protection, for examplebecause it displays high value threatened biodiversity, or there is a vulnerable aquifer, e p iscloser to 1. It is therefore an exogenous measure of environmental gain, associated with thelocation of bidder’s land. It is common knowledge.g ( e2)is the function of environmental gain. It measures the environmental benefit ofadditional environmental effort e 2 . It can vary from one farmer to another due to hisproduction system or the characteristics of his land. We assume that g(.) is continuous, twicedifferentiable and thatg’(e 2 ) >0 and g’’(e 2 )


Each farmer can calculate his score. His bid is accepted provided his score is greater than thecut-off value I c . The cut-off score is computed by decision-makers after all bids have beensubmitted. If it is a target auction in which public authorities want to allocate n contracts, thenI c is the score of the n th farmer accepted in the scheme (that is the value of the n th score whenranked from the highest to the lowest). It is more likely that the auction is in fact limited by amaximum budget. In such case, I c is the score of the last winning farmer once the wholeplanned budget has been allocated to winning contracts. It is important to note here that thej jj jtotal expenditure is measured by e DPe s for the j winning farmers.The probability to win∑jr2−1Following Latacz-Lohmann and Van der Hamsvoort (1997), we assume that each bidder,although he cannot know I c or the score I j of other bidders, forms expectations about theirdistribution. Let’s call f(I) the density function of this distribution and I the score valueunder which the bidder’s expectation to win is zero. The probability of being accepted in theauction is the probability that his score I is superior to the cut-off value score I cI=∫ f(I)dI=P ( I>Ic)F(I)IIf we assume that I is uniformly distributed between I and I (the score above which thefarmer’s expectation to win is 1) , then the cumulative distribution function F(I) is written⎧0 si I p I⎪⎪ I − IF ( I ) = ⎨ si I ∈ ⎡ I , II I ⎣⎪ −⎪⎩1 si I f I⎫⎪⎤⎪⎦ ⎬⎪⎪⎭In the following section, we assume that I = 0 and I = 1. Therefore, F(I) = IThis assumption just indicates that bidders are very uncertain about the outcome of theauction. We can expect however that if the auction was repeated several times over the years,farmers would get an opportunity to learn more about the score value of other bidders andwould therefore form a narrower range of expectations for I and I .The optimal bidding strategyThe farmer maximises his expected utility EU subject to a participation constraint and sixlimits (3 inferior limits and 3 maximum limits) on variables.The bidder is individually-rational if he is better-off when winning the auction than when notwinning: The individual rationality constraint can be written as:Π+ re2 − C( e2) + DPe1(1 −s)≥ Π+ DPe1The optimisation programme of the bidder is therefore the following:8


( e2, s, r)S / C0 ≤ s ≤1( 2 ( 2)1 ) ( )Max re −C e − DPe s F I + DPe0 ≤ e ≤ e − e0 ≤ r ≤ r2 max 1max( )re −C e − DPe s ≥ 02 2 1By taking the first-order derivatives of (2) with respect to e 2 , r et s, we can look for there −C e −DPe s fnecessary conditions for an optimum solution, when ( )1(1)2 2 10We then need to check whether the solutions found are a maximum (the Hessian must besemi-definite negative at this point) and whether they fit the constraints. The only obvious* * *analytical result ( e , r , s ) is not a maximum and had to be discarded:e=w** r12wsrmax2DPewermax( )w g ( e − e )* ' '2 2rmax 1( )r = C e − × × e g e2s*1g( e2)( − )⎛⎛ r ⎞⎞⎜we p p+ we + wr⎜1−⎟⎟( re2 − C ( e2))g emax e1 ⎝ rmax⎠= −⎝⎠2DPe2wsCorner solutionsWe therefore had to analyse corner solutions in table 1. Only the most interesting results arecommented. The full analysis is available on request.Table 1 : Corner solutionse2= 0 0 pe2 p emax− e 1e2 = emax− e1r = 0N/R N/R0 pr p rs = 0s = 1 s = 0maxN/RN/RN/AR case 1case 2 case 3r = rs = 0 0psp1s = 1 s = 0 0psp 1maxcase 5 case 6 case 7 case 8 case 9s = 1case 4s = 1case 10We have 27 potential corner solutions cases. But a number of them are not relevant (N/R)because the participation constraint (PC) is not verified. Either PC


Out of the 10 possible corner solutions, we focus on two specific cases: the best outcome forthe decision-maker (when s=1, e2 = emax −e1, 0 pr prmaxcase 4), and the worst outcome forthe decision-maker (when s=0, 0 pe2 pemax − e1, r = rmaxcase 5).• Case 4 : s = 1, e2 = emax −e1,0 pr prmaxThe optimal compensatory payment provided is* rC( e − emax) + DP*er = ( wpep + we + wr + ws)+2w2* e − ersmax 1 1( )max 1To obtain the maximum additional environment effort together with a total renouncement toprevious PHAE payments, r max and index weights w i must be set by the policy-maker underthe following necessary conditions:w f w e + w + w(2)r p p eandrmaxµ( − ) +( e − e )C e e DP*emax 1 1f (3)max 1wrwith µ =f 1w −w e −w −wr p p e sr max has to be increased in the following cases: when e1is small relative to emax(in otherwords when the previous PHAE contract rate per farm is low); when previous PHAE paymentper unit of effort (DP) is high; when costs of providing environmental efforts are high; whenwr is low.• Case 5 : s = 0 , 0 pe2 pemax − e1, r = rmaxC( e2 )'A slack necessary condition is prmax p C ( e2).e2When the maximum level of compensatory payment lies between the average cost and themarginal cost of providing e 2, there are cases when farmers’ bids will maintain the wholeprevious PHAE payment and require the maximum level of compensatory payment r, for anenvironmental additional effort which is not maximized.e 2• The most common case, case 1: s = 0 , 0 pe2 pemax − e1,0 pr prmaxThe optimal compensatory payment is:* r ⎛g( emax2)⎞ C e2r = wpep we*wr2w ⎜+ + +rg( e e ) ⎟⎝ − ⎠ 2*e( )max 1 2Three conditions are necessary in order to find a solution falling under case1:10


g( e2)( − )w w e r p p+ w eg e e max 1f (4)⎛⎞⎜⎟wrLet µ = ⎜⎟f 1, then⎜ g( e2) ⎟⎜wr −wpep −weg ( emax− e1 ) ⎟⎝⎠From the participation constraint, we have:g( e )( − )g( e2)g( e − e )r ⎛⎞C emaxwe w w C( e)2w ⎜+ + p −r ⎝ g e e ⎠2( )( )r C emaxµ f e( )2 '2p p e r⎟2max 1e2we + w + wp p e rmax 1Let = < 1η2wC(e )C eµ < < ( ) − ηe2'2rmax ( C e2)22e2r22A simplified setting : the case when r is fixedWe assume that the payment per unit of effort r is fixed by the decision-maker. In such case,the index is simplified as:⎛g ( e2 )⎞I = ⎜wPeP+ we+ wssg ( emaxe1)⎟⎝−⎠with w + w + w = 1p e sThe bidder’s maximization program is:( e2, s)S / C0 ≤ s ≤1( 2 ( 2)1 )Max re −C e − DPe s I + DPe0 ≤ e ≤ e − e2 max 1( )re −C e − DPe s ≥ 02 2 1The first order conditions of (1) give two necessary conditions for an optimum solution:DPe I = ( re −C( e ) −DPe s) w(5)1 2 2 1g ( e )( r− C ( e )) I =−( re −C( e ) −DPes) w (6)ge ( e )'' 22 2 2 1 emax−1sAssuming that the participation constraint is strictly respected, and by dividing (6) by (5), wederive a condition on e 2 for candidate solutions111


We apply the above model to an hypothetical auction taking place in Lozere in theSouth of France. Lozere is a semi-mountainous area, north of Montpellier. The agriculturalsector is mainly extensive cattle and sheep ranging, on natural and semi-natural pasture. Thelow profitability of this farming system, associated with harsh climatic conditions, has led to asharp decline in agricultural activity between the 60s and the late 90’s. As a consequence ofless grazing, the landscape has changed with the growth of patchy pine forests and large areascovered by bush and scrubs, less favourable to traditional biodiversity. Public authorities havesought to restore the traditional –more open - landscape by encouraging livestock extensivegrazing through CAP agri-environmental measures as well as income-support paymentstargeted at less favourable area. The grassland premium (PHAE) represents a significant shareof farm revenue in Lozere and the prospect of the PHAE reform has triggered concernsamongst farmers.We conduct a numerical simulation of the auction mechanism described above tocompare its outcome with the fixed price system which is currently envisaged by the Frenchministry of Agriculture. We build an hypothetical sample of 120 farmers. The distribution ofe max and e 1 in our sample is the same as the true distribution of pasture area per farm andaverage pasture area currently under PHAE contracts in the population of Lozere farmers.We have identified high priority areas (corresponding to the 25% of Lozere classified asNatura 2000) and low priority areas, not in the Natura 2000 zoning. Environmental costfunctions were not assessed. We used hypothetical cost functions but we checked that averagecosts obtained with such functions are equivalent to measured average costs in the zone for anaverage area under contract of 50 ha. We assumed that there are only two cost functions C Hand C L : a high cost function C H for farmers whose farm is located on slopes and bushy areasand a low cost function C L for farmers who have mainly flat pastures. We also assumed twoenvironmental gain functions g H and g L : a high environmental gain function g H is associatedto areas identified as high natural value (HNV) agricultural systems in which landscape andgrassland are vulnerable components and can be maintained by livestock extensive grazing(75% of Lozere, as identified by Solagro study, 2006). The low environmental gain isassociated to the remaining 25% of Lozere which are not in the HVN zone.Sources of heterogeneity in our sample are therefore: a distribution of e max between 30and 120; a distribution of e 1 between 20 and e max , two quadratic cost functions and twoenvironmental gain functions. The functional forms are:32C e ) = α * e + β * e + γ * e , i = H,L , α > α , β = β , γ = γgi(2 i 2 i 2 i 2= δ , i = H,L , δ > δi( e2)i* e2HLHLHLHLWe conducted the simulations for different relative values of the index weight, as well as fortwo different values of r max. The simulation scenarios are presented in table 2 and the mainoutcomes of the simulations are presented in table 3.13


Table 2 : Simulation scenariosSimulations 1 2 3 4 5 6 7 8W e 0.25 0.25 0.05 0.05 0.1 0.1 0.4 0.4W p 0.25 0.25 0.05 0.05 0.1 0.1 0.1 0.1W r 0.25 0.25 0.4 0.4 0.1 0.1 0.4 0.4W s 0.25 0.25 0.5 0.5 0.7 0.7 0.1 0.1R max 70 95 70 95 70 95 70 95Table 3 : Simulation outcomes :Simulations 1 2 3 4 5 6 7 8Number of participants CP>0 53 72 69 98 52 78 83 95Number of enrolled farmers 32 24 69 61 52 51 57 52Average index 0,41 0,46 0,32 0,34 0,31 0,33 0,54 0,57Average uniform stewardshippayment: DP*e12962,5 3393 3080 3449 2792 3161 3337 3777Budget spending or budgetcutoff (*)49847 51517 32723 49363 45188 49970 49776 49833Average budget spending(per bidder)1558 2147 474 809 869 980 976 958Cost of additionalenvironmental effort : 50121 51901 38746 71794 67515 90925 49776 49833sum r*e 2Total retrieved budget:sum DP*e 1 *s247 384 6023 22431 31327 40955,5 0 0Rate of net environmentalgain :sum g(e 2 ) enrolled / sum34% 26% 51% 44% 45% 44% 73% 42%g(emax-e 1 ) of 120 farmersUnit cost of additionalenvironmental effortpurchased :64 92 36 67 62 95 63 60sum r*e 2 /sum e 2Bidders net profits :sum (r*e 2 -C(e 2 )-DP*e 1 *s)29377 35464 19273 26787 22034 26702 33259 37046Budget return efficiencysumC(e 2 )/sum(r*e 2 -DP*e 1 *s)41% 31% 46% 46% 51% 46% 33% 26%All figures concerning auction outcomes are calculated for successful bidders only (unless indicated otherwise)(*) the budget is 50 000 but spending might be a bit over or under this amount according tothe bid of the last winnerWe conducted four simulations for various relative weights in the index. Each simulation ofthis simulation was conducted for two different values of r max (table 2)• Simulations 1 and 2 are the benchmark simulations. All the weights have the samevalues. It is an interesting case since weights effects are neutralized.• In simulations 3 and 4, we gave more weight to the budget criteria than theenvironmental criteria. This occurs when the decision maker has a tight budget andgives priority to low cost farmers.14


• In simulations 5 and 6, we gave more weight to the effort made by farmers to foregotheir PHAE premium share. This occurs when the government hopes to reduce itssunk payments without looking for any additional environmental gain.• In simulations 7 and 8, we gave more weight to the additional environmental effortand the compensatory payment. It reflects the case when the government’s objective isto obtain additional environmental efforts, without wanting to reduce past premiums.Simulation results are summarized in table 3.Auction performance can be measured by three criteria: (1) the rate of net environmental gain(the performance increases when the rate is closer to 1), (2) the budget return efficiency whichmeasures the informational rent distributed to farmers (if efficiency = 100%, it means thatpayments exactly compensate true compliance costs), (3) a third criterion would be theallocative efficiency of the auction: compare the true costs of environmental efforts providedwith the lowest possible cost to provide this effort. This last indicator is not available yet. Itcan be immediately noted that the budget return efficiency is quite disappointing since it isless than 50% on average. This is because we have conducted the analysis for high r max . Nosimulation out-performs the others on the two criteria simultaneously. As usual, there is atrade-off between budget efficiency and environmental efficiency. We see that auction 7 (lowr max , high weights for additional environmental efforts) is the best auction in terms ofenvironmental efficiency and that auction 5 (low r max and high weight to retrieved budget) isthe best in terms of budget efficiency.The choice of r max is of course crucial since it will guide the bids on financial compensationsan environmental efforts. Whatever the relative values of the index weights, the number ofparticipating farmers increases when r max increases but the number of enrolled farmersdecreases and, more importantly, the rate of net environmental gain ( total environmentalgain purchased over total potential environmental gain) decreases as well as budget returnefficiency (total true cost of environmental effort over total budget to purchase it). Too high ar max will therefore reduce the auction performance. On the other hand, a low r max reducesparticipation rate and forces the auction organizer to select non efficient bidders.Index weights also have an impact on the relative efficiency and budget returns of the auction.We note that average budget spending is higher in the benchmark case than in any othersimulation. The higher the weight w s , the greater the budget return efficiency but also thegreater the net payment per farmer. It has to be noted that in simulations 3 and 5, all eligiblefarmers are enrolled, for a budget inferior to the maximum budget. Therefore the ex-postresult is the absence of selection in the final outcome although farmers could not anticipate itbefore and therefore behaved as if they were under competitive pressure. It shows that forhigh w s and low r max (


(reserve price and index weights) interact with the producers’ characteristics and affect theoutcome of the auction.We derive optimal bids from the farmer’s perspective, and analyse corner solutions.Assigning a higher weight for retrieved premium share leads to a lower income farmersassociated to a lower average budget spending for more enrolled farmers with lower averageindex. Also it leads to higher budget efficiency for moderate net environmental gain rate.However, assigning greater weights for additional environmental gain and the compensatorypayment leads to the highest net environmental gain rate with the lowest budget returnefficiency.This initial work will allow us to make a number of recommendations on the structure of suchauction to improve its performance. In particular, after this initial analysis, we need to analysehow the design of separate auctions (with different weights and different reserve price) fordifferent types of farmer’s populations (on the basis of easily observable criteria such as sizeor location) could help improving the performance of the auctions.ReferencesBabcock B., Lakshminarayan P., Wu J. and Zilberman (1996). The economics of a public fundfor environmental amenities: a study of CRP contracts. American Journal of AgriculturalEconomics, 78, pp 597-604Cason T N., Gangadharan L. (2004). Auction design for voluntary conservation programs,American Journal Agricultural Economics 86, n° 5 pp 1211 - 1217Cattaneo A. (2006). Auctioning conservation payments using environmental indices,Contributed paper to the International Association of Agricultural Economists Conference,Gold Coast, Australia, 12-18 August 2006, 17 pClayton H. (2005). Market incentives for biodiversity conservation in a saline-affectedlandscape : farmer response and feedback, 49 th annual conference of the Australianagricultural and resource economics society, Coff Harbour, 9-11 February, 35 pCNASEA Centre national pour l’aménagement des structures des exploitation agricole(2004). évaluation à mi-parcours portant sur l’application en France du règlement CE n°1257/1999 du Conseil, concernant le soutient au développement rural. Synthèse du rapportd’évaluation. Janvier 2004, 24 pDobbs T L., Pretty J N. (2001). The United Kingdom's Experience with Agri-environmentalStewardship Schemes : Lessons and Issues for the United States and Europe, Joint Papern° 2001-1, Department of Economics, South Dakota State University, Brookings, andUniversity of Essex Center for Environment and Society, Colchester, England. 30 pDupraz, P ; Vermersch, D ; Henry De Frahan, B ; Delvaux, L (2003). The environmentalsupply of farm households: a flexible willingness to accept model. Environmental andResource Economics 25, pp 171 - 189.EC European Commision (2005). Agri-environment measures, overview on generalprinciples, types of measures and application, Directorate general for agriculture and ruraldevelopment, Unit G-4 Evaluation of mesures applied to agriculture, studies. Mars 2005, 24 p16


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