The Role of Deforestation Risk and Calibrated Compensation in Designing Payments for Environmental Services

Transcription

1 Th Rol of Dforstation Risk and Calibratd Compnsation in Dsigning Paymnts for Environmntal Srvics Jnnifr Alix-Garcia 1 Univrsity of Montana Dpartmnt of Economics 32 Campus Dr # 5472 Missoula, MT jnnifr.alix-garcia@mso.umt.du Alain d Janvry Elisabth Sadoult Dpartmnt of Agricultural and Rsourc Economics Univrsity of California at Brkly 207 Giannini Hall Brkly, CA Abstract: This papr discusss th gain in fficincy from including dforstation risk as a targting critrion in paymnts for nvironmntal srvics (PES) programs. W contrast two paymnt schms that w simulat using data from Mxican common proprty forsts: a flat paymnt schm with a cap on allowabl hctars pr nroll, similar to th program implmntd in many countris, and a paymnt that taks dforstation risk and htrognity in land productivity into account. W simulat th lattr stratgy both with and without a budgt constraint. Using obsrvd past dforstation, w find that whil risk-targtd paymnts ar far mor fficint, cappd flat paymnts ar mor galitarian. W also considr th charactristics of communitis rciving paymnts from both programs. W find that th risk-wightd schm rsults in mor paymnts to poor communitis, and that ths paymnts ar mor fficint than thos mad to non-poor jidos. Finally, w show that th risk of dforstation can b prdictd quit prcisly with indicators that ar asily obsrvabl and that cannot b manipulatd by th community. Kywords: Paymnts for nvironmntal srvics, rnwabl rsourcs and consrvation, markt-basd mchanisms, nvironmntal policy, simulation, rural dvlopmnt 1 Corrsponding author.

2 2 Th Rol of Dforstation Risk and Flxibl Compnsation in Dsigning Paymnts for Environmntal Srvics Summary Programs that pay for th nvironmntal amnitis providd by standing forsts ar bcoming incrasingly common. Most of th currnt programs, howvr, giv th sam paymnt for ach hctar of forst nrolld, or for ach hctar of diffrnt forst typs nrolld up to a cap on allowabl hctars pr nroll. This papr discusss th gain in fficincy from diffrntiatd paymnts and th inclusion of dforstation risk as a targting critrion in paymnts for nvironmntal srvics (PES) programs. W contrast two paymnt schms that w simulat using data from Mxican common proprty forsts: (i) a flat paymnt schm with a cap on allowabl hctars and a budgt constraint, similar to th program implmntd in many countris, and (ii) a paymnt schm without a cap that taks dforstation risk and variation in land valu into account (risk-wightd flxibl paymnts). W simulat th lattr stratgy both with and without a budgt constraint. Th program without a budgt constraint is a paymnt of th potntial incom for all hctars of forst which ar at risk of dforstation. W us th total amount of rsourcs spnt for this program as th constraint for th flat paymnts program, whr it is assumd that rcipints will not accpt paymnts lowr than th potntial incom gnratd by dforstd land. In ordr to illustrat how to dal optimally with a budgt constraint, w us 2/3 of th total budgt of th othr programs and allocat paymnts according to th forsts nvironmntal bnfits/cost ratio, paying th valu of th potntial incom gnratd by hctars at risk of dforstation (bnfit-maximizing paymnts). Our initial st of simulations assums prfct forsight by using obsrvd past dforstation, an assumption w latr rlax. W find that paymnts in our bnfit-maximizing schm provid mor than 4 tims th nvironmntal bnfits for ach dollar spnt than in th flat paymnts program. Th intuition bhind this rsult is that a larg part of th mony in th flat paymnts program is givn to forsts which would hav bn prsrvd vn in absnc of th incntiv. Th tradoff, howvr, is that cappd flat paymnts ar mor galitarian th Gini cofficint of paymnts is lss than half that of th bnfitmaximizing program. W also considr th charactristics of communitis rciving paymnts from both programs. W find that th risk-wightd flxibl paymnt schm rsults in mor, though smallr, paymnts to poor communitis, and ths paymnts ar mor fficint than thos to non-poor jidos. In th flat paymnt schm, paymnts to poor and non-poor ar qual, though th poor rciv lss of th budgt than in th bnfit-maximizing program. Finally, in ordr to addrss th problm of how to avoid stratgic bhavior, w simulat th bnfit-maximizing program using dforstation risk prdictd by asily obsrvabl variabls. Evn with rrors in prdiction, th bnfit-maximizing program is at last twic as fficint as a program of flat paymnts. Word count for txt (not including abstract, summary, rfrncs or tabls): 5,579 2

3 3 1. Introduction Programs of paymnts for nvironmntal srvics (PES) 2 ar bcoming an incrasingly popular way of crating, consrving, and rstoring natural rsourcs throughout th world. Mayrand and Paquin (2004) invntorid mor than 300 such schms. In rcnt yars, PES programs hav incrasingly bn introducd in dvloping countris, with on of th arlist fforts occurring in Costa Rica in 1997, and pilot programs mushrooming throughout Latin Amrica and Asia (World Bank, 2005). Paymnts for th consrvation of standing forsts ar th most frqunt of such programs in dvloping countris. Th targting stratgy has bn to pay a flat f pr hctar of standing forst, whr th forst ownrs whos forsts fall in spcific, usually nvironmntally snsitiv, gographical rgions voluntarily nroll hctars but whr thr is a limit on th numbr of allowabl hctars pr nroll. Exampls of this typ of targting can b found in Mxico, Costa Rica (Zbindn and L, 2005), and Ecuador (Echavarría, 2002), among othrs (for a usful rviw, s Pagiola t al (2002)). China s Grain for Grn program also offrs flat paymnts, although farmrs ar paid for consrvation activitis such as rforstation rathr than for prsrving standing forsts (Uchida t al, 2005). Th appal of this sort of non-diffrntiating stratgy clarly lis in its transparncy, as of implmntation, and imprssion of fairnss. Unfortunatly, sinc ths schms do not tak into account th risk of losing th forst nor th productiv capacity of th land nrolld, it is highly unlikly that on could find a situation in which such a program would maximiz th nvironmntal bnfits accrud pr dollar spnt. Givn limitd budgts for consrvation, th sarch for an fficint, radily implmntabl program dsign is imprativ. In this papr w us th cas of Mxico to illustrat th fficincy gain in including th risk of dforstation and calibratd paymnts in th dsign of paymnts for nvironmntal amnitis providd by standing forst. 2 Such programs ar also known as programs of paymnts for nvironmntal amnitis, consrvation paymnts and nvironmntal srvic paymnts. 3

4 4 Thr is a growing litratur on cost ffctiv dsigns for consrvation programs, much of it inspird by th US Consrvation Rsrv Program (Babcock t al, 1996, 1997; Parks and Schorr, 1997). Th main proccupation of much of th currnt rsarch is with th propr way of masuring nvironmntal amnitis. Svral conomists hav proposd biodivrsity mtrics, including Witzman s (1998) xpctd gntic divrsity ranking, Ando t al s (1998) total spcis masur, and Frraro s (2004) distanc function approach. Frraro (2003) compars th impacts of diffrnt indx masurmnts for watr quality bnfits, and finds that all of thm ar highly corrlatd. Our contribution to this discussion is to point out that, no mattr th choic of amnity masur, it is a wast of mony to pay for amnitis which ar not at risk of bing lost. Mxico provids an xcllnt opportunity to study th fficincy of paymnts for srvics providd by forsts, in part bcaus it is in th arly stags of implmnting a nationwid PES program for standing forsts. Prsntly, 80% of th country s forsts ar locatd in jidos. Ths communitis, which wr cratd by th post-rvolution land rform, hold thir forstry and grazing lands in common proprty. Thir larg shar of national forst holdings maks thm an ssntial plac whr to bgin addrssing th dforstation problm. Using data obtaind from a 2002 jido survy, w compar thr targting stratgis for paymnts. 1. Flat Paymnt pr Hctar, H: A flat paymnt ovr all forstd hctars with a cap on th numbr of hctars pr nroll. This schm is usd as a rfrnc as it is similar to that currntly obsrvd in many xisting PES programs. 2. Risk-wightd Flxibl Paymnts, R: A paymnt for all hctars at risk of dforstation at th lvl of th incom potntially gnratd by th dforstd land, without a budgt constraint or a cap. 4

5 5 3. Bnfit-Maximizing Paymnts, B: A paymnt that maximizs th xpctd nvironmntal bnfits pr dollar paid using an indx of nvironmntal valu and is qual to th potntial incom gnratd by hctar at risk, for a givn budgt and without a cap. Th main rsult of ths simulations is that whil th bnchmark schm, H, is vry galitarian, it is highly infficint in trms of nvironmntal bnfits pr dollar spnt. Th risk-wightd flxibl paymnts schm, R, gnrats mor than thr tims th nvironmntal bnfits at th sam cost as th flat paymnts program, H. Simulation of th xpctd bnfit maximizing program, B, incrass fficincy ovr th flat paymnts program, H, by ovr four tims. Th intuition bhind th rsult is simpl: paying vryon to prsrv thir forst may achiv th goal of incom rdistribution, but at a high cost much of th paymnts will go to forsts which would rmain standing vn in th absnc of incntiv schms. W also illustrat how to avoid stratgic bhavior on th part of rcipints by conducting a final simulation which uss th prdictd rathr than actual risk to implmnt th bnfitmaximizing program, B. W find that thr ar rrors in targting, but that fficincy is still much gratr than in a flat paymnts program. Sinc policymaking is both about fficincy and quity, w analyz th distributional implications of th first and last schms that w considr. Th rsults show that th budgt from th flat paymnts program, H, is mor quitably distributd amongst jidos of diffrnt siz and povrty classs, whil th bnfit-maximizing program, B, allocats mor funds to largr and poorr communitis. Program B, howvr, givs smallr paymnts to poor jidos on a pr capita basis, whil flat paymnts pr capita ar qual for th poor and non-poor. Th papr procds as follows: w first dscrib th thortical rational bhind th diffrnt targting stratgis considrd, which lads us to th mpirical stratgy discussd in sction 3. Sction 4 dscribs th data to b usd for th simulations and sction 5 givs th rsults. Sction 6 discusss 5

6 6 som practical considrations for th implmntation of our most fficint stratgy and th last sction concluds. 2. Altrnativ paymnt schms thortical considrations Thr ar many possibl ways of dsigning an nvironmntal paymnt schm. Any variation in dsign will chang th kind of nvironmntal srvics obtaind and th popl who rciv paymnts. Concptually, thr is a lowr and an uppr bound on th pric which on can pay pr hctar in a PES program: th lowr bound is th potntial incom of th land and th uppr bound th valu of th nvironmntal srvics providd by th land. In this sction w discuss gnral principls that apply rgardlss of th program dsign chosn. Basic principls Two ssntial componnts for th dsign of a PES program ar targting stratgy and paymnt lvl. In thory, ths should b jointly dfind to maximiz nvironmntal bnfits for a givn budgt. Bcaus bnfits can only b gaind from voluntary nrolls, th optimal schm dpnds upon th rspons function of th rcipints, in this cas, th jidos. To formaliz, lt U( F ΔF, c Δ F ; z ) b th utility an jido drivs from th rmaining standing forst, F Δ F, and from th incom gnratd by dforsting an ara Δ F, whr c is th incom from dforsting a givn hctar, and z ar jido charactristics. Th optimal dforstation lvl ΔF ~ is thus a function of th initial standing forst, th potntial incom pr hctar of forst, and th jido charactristics: (,, ) Δ F% =ΔF F c z. Th offr from th PES program is to not dforst at all against a paymnt P. Th jido will thus accpt th contract if: (, ; ) ( Δ%, Δ% ; ) U F P z U F F c F z. 6

7 7 Lt P,min b th minimum valu that satisfis this condition. Givn th nvironmntal bnfit b providd pr hctar in jido, th optimal transfr schm undr th budgt constraint P is th solution to: s.t. Δ ( ) max 1 P P,min F F, c, z b P 1 P P,min P P. (1) Idally, on would lik to know th montary valu quivalnt to th utility that jidos driv from standing forst. In th absnc of such valuation, on can us P,min c F = Δ % as a lowr bound for th accptability of th schm to th jido (this is quivalnt to ignoring th loss in utility associatd with th dcras in standing forst). Ejidos accpt th paymnt and agr to not dforst if th paymnt P is at last as high as th incom that would b gnratd by convrting th land into pastur/crops, and do not accpt th contract if th offrd paymnt is blow this magnitud: If P c ΔF( F, c, z ) Δ F% = 0 (2) If < Δ (,, ) Δ % =Δ (,, ) P c F F c z F F F c z. Th qustion that follows is: assuming full knowldg of all th variabls, should on pay th minimum valu ncssary to prsrv th nvironmntal bnfits, i.., th potntial incom c of th land, or th ntir valu of th good bing purchasd, i.., th nvironmntal bnfit b? In rality, this is a qustion of bargaining powr. In both cass, paymnts will only b incntiv compatibl if th bnfits offrd by th land ar gratr than or qual to th valu of th land in altrnativ activitis, b c. Th diffrnc is that in th first cas, th surplus gos to socity whil in th scond it accrus to th land ownr. If on looks at th formula abov, th optimal contract only pays for th hctars that would othrwis b dforstd, Δ F( F, c, z ), which varis with th dforstation rat. In actuality, on 7

8 8 frquntly obsrvs a flat paymnt pr hctar of currntly standing forst F with a cap on hctars pr nroll st by th agncy managing th program. In many placs, this paymnt varis with quality of forst in trms of nvironmntal bnfits, but th point hr is that it dpnds nithr on th dforstation rat nor on th incom-gnrating potntial of th land (xcpt as a floor valu). Argumnts in favor of this flat paymnt ar th simplicity of implmntation, as it dos not tak into account dforstation bhavior, and th imprssion of fairnss that it givs. Rgardlss of th choic of paymnt schm, th contract must b mad ovr th ntir forstd ara of th jido. Nglcting this considration could lad to slippag (a trm coind by Wu (2000)), that is, if a contract is ovr a subst of th forst, thn dforstation may simply b transfrrd from a contractd to an uncontractd ara of forst. Hnc, typically, th contract should spcify a paymnt against no dforstation on all of th hctars that hav a potntial incom blow thir nvironmntal valu. This arrangmnt, howvr, dos not tak into account th fact that prvnting land from going into agricultural production may hav an ffct on prics of th corrsponding agricultural goods, which may rsult in slippag btwn jidos (Wu, Zilbrman, and Babcock, 2001). 3. Proposd simulations In this sction, w spcify th altrnativ paymnt schms that will b simulatd. Building from our thortical considrations, w hav slctd 3 paymnt schms: Th cappd flat paymnts program, H, provids th bnchmark cas for our simulations. W compar this dsign to two programs which incorporat dforstation risk and th potntial incom gnratd by dforstd land. Th first program, risk-wightd flxibl paymnts, R, offrs paymnts qual to th siz of th potntial incom gnratd pr dforstd hctar only for thos hctars that ar at risk of dforstation. It is calculatd without any limit on th budgt, and th total mony spnt on this program is usd to provid a point of comparison for th othr two programs. First, th magnitud of th flat paymnts is 8

9 9 stablishd in ordr to giv th sam total xpnditurs as in th flxibl paymnts program. Scond, two-thirds of this budgt (an arbitrary choic, but nutral on our argumnt) givs th constraint for th bnfit-maximizing program, B, in which paymnts ar distributd in ordr to maximiz th xpctd nvironmntal bnfits pr dollar spnt. Flat paymnts, H W assum a flat paymnt r pr hctar, up to a maximum of F hctars: PH, = r min Fj, F. j P H, is th total paymnt to jido, which is th rat pr hctar multiplid by ithr th sum of forst hctars of typ j or by th maximum allowabl hctars, which w st at 2000 hctars pr forst ownr, as is th cas of Mxico s program. Assum that thr is a constant annual dforstation rat τ j of forst of catgory j in th jido, and that th potntial incom of a hctar of land in th jido is c. All jidos ar offrd a contract, but an jido will only accpt th contract and thus participat in th schm if th potntial incom, P R,, of th ara it would othrwis dforst is lss than or qual to th offr: PH, cτjfj PR,. j In ordr to facilitat comparison of th schms, th rat r is stablishd at th lvl that qualizs th total budgt to th budgt of th schm R, which pays th potntial incom gnratd by ach hctar of land at risk of dforstation and will b dtaild in th nxt sction. It thus solvs for: 1 P H, P R, P H, = P R,. Risk-wightd Flxibl Paymnts, R W allow for htrognity of nvironmntal bnfits within jidos. Each hctar of forst of typ j is charactrizd by an nvironmntal bnfit b j. Idally, on would prfr an actual montary valu for th nvironmntal bnfits offrd by a givn pic of land. In rality, howvr, this is quit 9

10 10 difficult to stablish, as markts ar missing for ths srvics. So, for th purpos of our simulations, w stablish an indx valu b j that allows th ranking of ach hctar of forst by its rlativ nvironmntal valu. Not that this dos not allow us to xclud lands whos tru nvironmntal valu is lss than th potntial incom gnratd by dforstd land. Lt Fj b th numbr of hctars with nvironmntal bnfits b in jido, with F = F th total j j j forst ara in jido. Th first yar of th program, unchallngd dforstation would convrt τ F of forst of quality j into pastur. Th scond yar, an additional j τ of th rmaining forst ( τ ) j j 1 j Fj would b convrtd, and similarly th following yars. Th dforstd ara aftr t yars would thus b: t ( τ ) t Δ F = 1 1 F j j j. If th program is to prvnt dforstation ovr th yars, it should thus rnt an incrasing shar of th forst. Paymnts basd on th incom gnratd by dforstd land, assuming that th nvironmntal bnfits of all hctars xcd this lvl, would b: P t, t R = cδfj j Ths paymnts should b disbursd on a yarly basis for th srvics providd by th prsrvd forst. Bcaus w ar paying xactly th valu of th altrnativ us for th hctars of land thy wish to dforst, jidos will always accpt th contract. Th participating jidos ar thos that would othrwis dforst. Not that th contract is for no dforstation on th total initial jido ara with potntial incom blow nvironmntal bnfits. Hnc th ara nrolld in th contract is. Environmntal bnfits Fj j obtaind by contracts in th participating jidos ar: t t BR, = bjδfj. j In th rst of th papr, sinc w ar only concrnd with comparing programs, w will only considr th first yar of paymnt, and lav out th t suprscript. Assuming that all communitis hav a fixd dforstation rat, or that any changs in th rat occur qually for all communitis, calculating 10

11 11 program outcoms for th first yar will giv us th sam rlativ rsults for paymnt schms that dpnd upon dforstation risk. Not that by rstricting ourslvs to th first yar of paymnts w ar showing th flat paymnt schm in th bst possibl light. This is bcaus th flat paymnts do not chang ovr tim, but jidos with a constant rat of dforstation will hav to b paid for incrasingly larg land aras as tim gos on. Thrfor, th first yar givs th maximum numbr of communitis that will accpt th flat paymnts at any givn tim. Bnfit-maximizing paymnts, B If th total paymnt PR, xcds th availabl budgt, th optimal schm consists of ranking th jidos by dcrasing ratio of bnfits ovr cost, bc: bc bjτj Fj j = and paying th potntial incom c τ F j j j gnratd by th hctars at risk of dforstation PB, = PR, = cτjfj to thos with th highst bc ratio until th budgt is xhaustd. j 4. Data and Mthodology In th summr of 2002, Mxico s National Ecology Institut (INE), togthr with th Ibroamricana Univrsity, th Cntr for Economic Education and Rsarch (CIDE), th Univrsity of California at Brkly, and th World Bank, conductd a nationwid survy of Mxican jidos. Th purpos of th survy was to undrstand th dforstation procss in ths communitis in ordr to inform th dsign of a PES program 3 that th Mxican govrnmnt was intrstd in introducing. Th survy randomly sampld 407 jidos largr than 100 hctars locatd in th forstd rgions of th country. Th total univrs of jidos with forst ovr 100 hctars is 7,679. Th total amount of 3 This program was ffctivly introducd in

12 12 forst covrd by our sampl is 2,106,592 hctars of primary and scondary forst. Tabl 1 shows th distribution of communitis across rgions. Th sampl includd jidos in all stats with th xcption of Baja California, Coahuila, Guanajuato, Zacatcas, Morlos, and Aguascalints. In ordr to masur forst covr and its chang ovr tim, w us th Forst Invntoris for 1994 and 2000, which wr constructd by visual intrprtation from satllit imags with pixls of 30 mtrs at a scal of 1:250,000 (Vlázquz t al., 2002). In addition, w obtaind slops from digital lvation modls with 100 mtr pixls availabl from th Mxican govrnmnt. Ovrall, 86% of th jidos in our sampl currntly hav primary forst. Th ara of primary forsts is largly rlatd to jido siz, which varis considrably across jidos. Total jido ara rangs from 180 to 170,143 hctars in our sampl. Th avrag prcntag of a givn jido in primary forst is 34.7%. On a pr capita basis, th distribution of th forst is quit skwd. Though th avrag numbr of hctars pr capita is 37, th mdian is 6.5 and th Gini cofficint.83. This suggsts that any paymnt program disbursd on a pr hctar basis will b highly unqual in its distribution across communitis and individual mmbrs. Th dforstation rat ovr th total forst in our sampl is 1.2% pr yar from 1994 to 2000, which is comparabl to what Torrs and Flors (2001) trm th consrvativ stimat of 1.3% pr yar calculatd using all th forstd ara in Mxico. In our sampl, th avrag jido dforstd about 1.3% pr yar ovr th priod % of th jidos in th sampl dforstd ovr th study priod, whr dforstation is dfind as th chang from primary or scondary forst to agricultur or pastur. Amongst thos who dforst, th avrag rat of forst loss is 2.1% pr yar, a vry fast pac by intrnational standards. 12

13 13 Calculation of th cost In ordr to masur th potntial incom of forstd land, w us th rainfd land rntal rats rportd in th 2002 jido survy. Bcaus jido land cannot actually b rntd, th numbrs rportd ar th farmrs assssmnts of th land rntal rat for a pic of land similar to th on that had bn dforstd. Ths rats wr obsrvd for thos jidos that xprincd dforstation and rfr to aras from which thy had rmovd forst btwn 1994 and W considr this rat to b a fair stimat of th potntial incom of th land that is most likly to b dforstd, although it is possibly not as accurat for th mor rmot aras of th jido. Bcaus this rntal rat was not rportd for som jidos, w us as a masur of th potntial incom of forstd land th valu of th rntal rat prdictd by th rgrssion quation in Tabl 2. Th variabls in th quation includ all th availabl physical variabls which might affct soil quality, and hnc land productivity (altitud and slop), and th yild of maiz pr hctar (which is only availabl at th stat lvl). In addition, th distanc from th villag to th forst (calculatd using GIS data) is includd to account for th tim cost associatd with travling to th land in ordr to cultivat it, th distanc to town (givn in th survy) to account for transactions costs in bringing products to markt, and th total siz of th jido to giv som proxy for land scarcity. Th rgrssion was fittd using OLS and valus wr prdictd for thos jidos for which no rntal rat was availabl in th survy. Th R-squard valu for th rgrssion is.23. As on might hav anticipatd, both distanc masurs ar ngativly corrlatd with th rntal rat, whil land at highr altitud is associatd with a lowr rat. Land in stats with highr avrag maiz yild has highr rntal rats. Th on countrintuitiv rsult is that largr jidos ar corrlatd with highr rntal rats, though this might simply rflct unobsrvd variabls, such as a highr propnsity towards cattl-raising in largr 13

14 14 jidos, which would thn rais th valu of th land. Th prdictd avrag rntal rat pr hctar is $103 4 (sd $70) and th Gini cofficint of th pr hctar rat is.37. Calculation of th nvironmntal bnfits indx Idally, b should b xprssd in montary trms. This would rquir us of a valuation tchniqu j for srvics whos markts fail, or on of th tchniqus advocatd by th authors mntiond in th introduction. For srvics whr markts xist, such as hydrological bnfits, valus rmain highly dbatd, with stimats ranging from $20/ha (Chomitz t al, 1998) to $188 (Hrnándz, t al., 2003). Still othr studis suggst that a mixtur of pastur and forst covr gnrats vn highr hydrological bnfits that contiguous forst (Aylward and Togntti, 2002). Much of this variation is surly du to th fact that hydrological bnfits ar vry sit spcific. Hsitant to ntr into this valuation dbat, w hav instad stablishd an nvironmntal indx basd upon both th scal of paymnts for th xisting PES schm in Mxico and th country s nvironmntal prioritis. Mxico s currnt paymnt program, which givs highr paymnts for cloud forsts as opposd to forsts of othr typs, is intndd to rflct th highr hydrological valu of cloud forsts. Whil changing th masur of bnfits will altr th distribution of paymnts, th rsult that it is mor fficint to pay for bnfits which ar at risk of bing lost will rmain unaffctd. Mxico s PES program is intndd to prsrv th hydrological bnfits providd by standing forst. W thrfor dsign a simpl indx which rflcts this objctiv as follows. Forsts that ar closr to major rivrs ar givn a highr valu than forsts that do not hav this attribut. Bcaus a dtaild rivr map of th country is not availabl, w usd digital lvation modls to stablish whr th highst flow of watr across th landscap would b. Around aras of high flow, w calculatd a buffr distanc of on kilomtr as th ara whos rosion would most affct watr quality and 4 All montary valus ar givn in US dollars. 14

15 15 infiltration, and gav highr valu to forsts in ths aras. In addition, w giv highr valus to thos communitis locatd in watrshds which hav bn classifid as ovr-xploitd. Ovr-xploitd watrshds hav bn idntifid and mappd by INE, and ar thos which hav had long trm withdrawal rats gratr than thir rats of aquifr rcharg. Finally, givn that cloud forst is of particular concrn bcaus of its status as an ndangrd cosystm in Mxico and is thought to produc a highr valu of watr srvics, w giv thm xtra points as wll. This scoring systm rflcts th pric diffrntial in th PES program currntly implmntd in Mxico paymnts pr hctar for cloud forsts ar 400 psos, whil for othr typs of forst thy ar 300. Th currnt program was dsignd for paymnts to rflct th highr nvironmntal valu of cloud forst for watr consrvation. All forst typs wr dfind according to th classification in th 2000 Forst Invntory. Clarly, many of ths dcisions could b calld arbitrary. Th indx has bn dsignd to dmonstrat only on out of many possibl configurations of nvironmntal bnfits indics, though w bliv it to b th most likly. In addition, whil th currnt program is focusd on th domstic bnfits of watrshd prsrvation, on could crtainly imagin a indx that includd global xtrnalitis. Tabl 3 dscribs th valus that w us for ranking th nvironmntal bnfits providd by diffrnt typs of forst in diffrnt locations. Th avrag bnfits pr hctar ar 30.6 points (sd 5.3) and th Gini cofficint is.11. Th simpl corrlation cofficint btwn avrag bnfit pr hctar and prdictd rntal rat is small and ngativ, qual to This implis that th bnfit to cost ratio falls with th rntal valu of th land, and hnc that th program should nlist a maximum amount of inxpnsiv land. 5. Empirical rsults Paymnts and participation 15

16 16 To compar th paymnt schms, w bgin by assuming prfct forsight in prdicting dforstation by using th obsrvd dforstation rat btwn 1993 and W will rlax this assumption in sction 6 by using a prdictiv quation for dforstation in discussing implmntation of th optimal schm. W simulat th thr schms as if thy wr put into plac in 1994 and w ar obsrving th rsults on yar latr. Th rsults ar rportd in Tabls 4 and 5. Th flat paymnt ndd up bing $5 pr hctar with th budgt from th risk-wightd flxibl paymnts program, R, as a constraint. In th program R, all of th dforsting jidos (61% of th sampl) ar paid in 1994, though th paymnts ar quit unqually distributd, with a Gini cofficint of.81. In th flat paymnt program, H, th participation rat is much highr, at 87 prcnt, du to th fact that many jidos without dforstation participat, though w do los som of th dforstrs with potntial incom highr than th flat paymnt offr. Th distribution of ths paymnts is much mor quitabl than in th othr two programs, with a Gini of.32. In th bnfit-maximizing program, B, whr w us 2/3 th budgt of th first program, w hav lss than 2/3 participation (57%) and a Gini cofficint of 0.77, indicating th high inquality of paymnt distribution. Tabl 5 shows what is gaind by programs R and B in fficincy in xchang for th highr lvls of inquality dtaild in Tabl 4. Th total numbr of hctars dforstd in th sampl btwn 1994 and 2000 is 22,667, which is th amount nrolld in program R. Dspit its highr participation, program H nrolls lss than a third of th total hctars at risk of dforstation, 6,732 hctars, and th amount of nvironmntal bnfits pr dollar spnt, at.08, is also lss than a third that of th program R. This is du to th fact that it nrolls many jidos that do not hav positiv dforstation. Th bnfit-maximizing program, shown in column thr, rsults in th nrollmnt of 19,225 hctars, which is narly all of thos at risk, and an fficincy lvl of.35, four tims highr than that of th flat paymnts schm. 16

17 17 Tabl 6 illustrats anothr masur of fficincy th dollar amount paid for ach hctar at risk of dforstation. Not that this is xtrmly larg for schm H (dspit th fact that thos with zro dforstation cannot b includd sinc th masur is undfind whn thr ar zro hctars at risk) at $7,610 pr hctar and smallst for stratgy B, which pays $86 pr hctar on avrag. This vry high numbr from th flat paymnts rsults from th fact that a high pric is paid for hctars that hav a vry low risk of dforstation. This is ffctivly a form of lakag of program funds to noncritical forsts. Who gts th paymnts? In this sction w xamin th distribution of both th flat paymnts program, H, and th most fficint paymnt program, B, ovr diffrnt structural and social charactristics of participants. W do not includ program R hr du to th fact that its rsults ar vry clos to thos of B, givn that thy ar ssntially th sam program with and without a constraint. Tabl 7 shows th distribution of programs H and B ovr siz and povrty classs. For paymnts by povrty class, w considr pr capita rcipts rathr than total paymnts. Povrty class was dtrmind by th prcntag of jido mmbr housholds prdictd to rciv Progrsa, an ducational and halth subsidy program targtd at th poor. This prdiction was don using houshold lvl data. This is calculatd undr th assumption that paymnts at th community lvl will b shard qually btwn mmbrs. Participation in H is highr than in B in all ara classs and rlativly constant across classs. For B, participation incrass across ara classs. This is du to th fact that dforstation in largr communitis is highr. Efficincy is incrasing with ara in th flat paymnts program bcaus as th proprtis gt largr, th flat paymnts program is mor likly to nroll hctars at risk of dforstation. W s an intrsting rsult with rgards to quity. Although participation in both programs is highr 17

18 18 for th poor than it is for th non-poor, it is rlativly gratr in program B 63 vrsus 50% - than it is in th flat paymnts program 89 vrsus 85%. In addition, th bnfit-maximizing program allocats a gratr shar of th budgt to th poor 61% as opposd to 54 %. Th largr shar of th budgt allocatd to poor communitis is likly du to th fact that th poor, in gnral, hav largr forst aras (th simpl corrlation btwn povrty and forst ara is.10). At an individual lvl, howvr, w find that paymnts pr capita to th poor, $51, ar much lowr than to th non-poor, who rciv $135 pr capita. This is likly a combination of two factors. First, whil forsts hld by poor communitis hav slightly highr avrag nvironmntal valu (th simpl corrlation btwn th two is.05), thy hav much lowr prdictd rntal rats (th corrlation btwn povrty and prdictd rntal rat is -.15). In addition, poorr jidos ar also mor likly to hav mor mmbrs among which to divid th paymnts. 6. Implmntation with prdictd dforstation rat, I In ordr to implmnt paymnt schms which tak dforstation risk into account, it is ncssary to us th prdictd rathr than th actual dforstation rat. W labl this schm I. Using th actual dforstation to pay for hctars at risk would induc stratgic bhavior on th part of jidos. This sction focuss on th application of two prdictions of th dforstation rat to th most fficint program. Whatvr prdiction is chosn, it must b basd xclusivly on dtrminants x that ar truly xognous to th bhavior of th jido (so that th schm dos not rward bad bhavior), i.., physical ndowmnts of th jido (ara of diffrnt typs of land, mayb on pr capita basis), and structural charactristics such as distanc, population, thnicity, tc. W assum a uniform dforstation rat pr jido (i.., all catgoris j of forst hav th sam dforstation rat τ ), and prform th stimation on th obsrvd sampl of jidos. Although this stimation givs prdiction of th xpctd conditional dforstation rat ˆ τ ( x ) in th population of jidos of charactristic x, th actual optimal rat of 18

19 19 dforstation of a spcific jido rmains unknown to outsidrs: ˆ = ˆ( x ) + u, whr rprsnts th τ τ u idiosyncratic shock or bhavior of th jido, drawn from th stimatd distribution ( ˆ 2 0, ) N σ. Armd with this analysis, w simulat th most fficint schm basd on th prdictd dforstation rat ˆ τ as follows: W first rank jidos by dcrasing ratio of nvironmntal bnfits ovr potntial incom (which is indpndnt of th dforstation rat), as w did abov. W thn pay th prdictd conditional xpctd dforstation ˆ τ F j proportionatly to th xpctd potntial incom ( 1 ) PI, = c + μτˆ j j F starting with thos with th highst ratio until th budgt P is xhaustd. A spcific jido will accpt th schm if th paymnt compars favorably to th opportunity cost of its optimal dforstation rat, i.., if: P c ( τ + u ) F which can also b writtn: ˆ τ u ( 1 ) I, ˆ j j This shows that if th paymnt is st at th xpctd potntial incom, i.., μ = μτˆ., all th jidos with highr dforstation rats than th prdictd avrag will not accpt th contract. Convrsly, all jidos with prdictd rats lowr than th prdictd avrag ar compnsatd for thir good bhavior. By proposing a highr paymnt, μ > 0, th program facs a trad-off in paying mor than ncssary for many jidos but attracting mor of thm into th schm. It follows that th optimal valu for th paymnt lvl μ is dtrmind by th ovrall optimization program: ( ( )) max Pr u μτ ˆ b j ˆ τ + E u u μτ ˆ j P j s.t. Pr u μτˆ P, I P. F (3) Two prdiction quations Much of th litratur on dforstation uss th pixl, th smallst unit of a satllit imag, in ordr to masur changs in forst covr. Thr hav bn a varity of analyss conductd using this approach, including Puri and Griffths (2001), Monro, Southworth, and Tuckr (2002), Godoy and 19

20 20 Contrras (2001), and Vanc and Goghgan (2002). Chomitz and Thomas (2003), Diningr and Mintn (1999), and Pfaff (1999) ar municipal lvl studis. Bcaus th unit of dcision-making in our cas is th jido, w us as a dpndnt variabl th ara dforstd in a givn jido ovr th study priod W prsnt in Tabl 8 two diffrnt prdiction quations for dforstation. Th first is a parsimonious spcification, containing only asily obsrvabl, mostly physical variabls most of which hav bcom standard in dforstation studis, whil th scond includs a rang of variabls associatd with dforstation bhavior in common proprty communitis (s Alix-Garcia, d Janvry, and Sadoult, 2005). Th formr rprsnts a tchniqu suitabl for application in program sttings. Th intntion of prsnting both options is to s how much targting prcision is lost in omitting variabls rprsnting community bhavior. Th first stimation includs total jido ara, forst ara, forst squard and forst cubd, th avrag distanc to, slop and altitud of th forstd ara of th jido, intractions of ths trms, a dummy for if th jido practics forstry or not, and th numbr of jido mmbrs in Non of ths variabls ar asily altrd by community mmbrs. Among ths variabls, th largst impacts on dforstation ar through th siz of th forst in 1994, th avrag slop of th forstd ara, and if th jido practics forstry or not. Som of th variabls which on might xpct to b significant hr, lik th gographical ons, ar likly not so du to thir corrlation with th prcntag of total ara in forst. Th scond spcification includs, in addition to th physical variabls, som charactristics that might influnc group bhavior, such as th numbr of popl pr houshold with scondary ducation, avrag siz of individual parcls, th Gini cofficint of th distribution of privat parcls, th ratio of mmbr to total population in th jido, and th prdictd proportion of th population rciving Progrsa an ducational subsidy program distributd to th poor. Th largst impacts com from th Gini cofficint and th prdictd proportion of th population 20

21 21 rciving Progrsa. It is important not to giv causal valu to ths variabls, as thr ar possibl ndognity problms with th proxy for povrty. Howvr, sinc this rgrssion is bing usd mrly for prdictiv purposs, issus of ndognity ar of no importanc. Th scond spcification shows a small gain in th rgrssion adjustd R-squard ovr th parsimonious spcification from.38 to.42. This suggsts that th scond spcification has suprior prdictiv powr ovr th first. In gnral, howvr, an R-squard of.38 is quit rspctabl for such a cross-sctional stimation. Sinc thr is not much gain from xpansion of th variabl st in quation (2), Tabl 9 shows th paymnts in a B program calculatd using only th parsimonious quation (1) spcification. As in th program calculatd using actual dforstation rats, th Gini cofficint of paymnts for th prdictd program is considrably highr than that of th flat paymnts program, which has a valu of.32. Th fficincy lvl, 0.15, is considrably lowr than that of th most fficint program using th actual dforstation rats, at 0.35, but is still twic as fficint as th flat paymnts.08. This lads us to th qustion of whr th misallocation of paymnts causing th lowr fficincy lvl occurs. Tabl 10 shows th charactristics of communitis with paymnts in diffrnt rror catgoris. Th typ II rror coms ntirly from dforstation rats that ar stimatd to b positiv for jidos that in rality had no obsrvd dforstation during th priod. Ths communitis also hav vry high bnfits and low potntial incom from dforstd land, which mans that thy rankd quit high on our bnfits to cost scal. Communitis with typ I rror hav vry high dforstation rats (and wr undr-prdictd). In addition, thir potntial incoms ar larg rlativ to th bnfits that thir land provids. In sum, th mor worrying typ I rror coms from jidos with vry high potntial incoms and vry high rats of dforstation. Ths ar also communitis with somwhat low nvironmntal bnfits pr hctar. In avoiding th stratgic bhavior associatd with using obsrvd dforstation rats, w 21

22 22 nd up with a lowr lvl of nvironmntal bnfits pr dollar spnt, though this is still narly twic as high as th fficincy lvl gnratd by using a flat paymnts program. 7. Conclusion Th most important contribution of this papr is to mpirically dmonstrat that including risk into th targting of nvironmntal srvics programs can gratly incras thir fficincy and to show that this is quit fasibl to implmnt. W illustrat this point by comparing a flat paymnt schm to a schm which taks into account th risk of dforstation. W simulat thr programs: a cappd flat paymnt schm, a paymnt of potntial incom for ach hctar of forst at risk of dforstation, and, in ordr to illustrat th optimal mannr of daling with a budgt constraint, a program which distributs paymnts according to th highst bnfit/cost ratio and pays th potntial incom gnratd by ach hctar of forst at risk of dforstation. Comparing ths approachs, w find that th most galitarian approach is to pay a flat rat pr hctar pr yar with a cap on th numbr of allowabl hctars pr rcipint. This is, howvr, th last fficint stratgy in trms of nvironmntal bnfits pr dollar spnt. Th highst fficincy coms from maximizing nvironmntal bnfits pr dollar spnt. Th driving forc bhind ths rsults is th lakag inhrnt in ignoring dforstation risk in th targting procss in a program which givs th sam paymnt for any hctar of forst, a high cost is incurrd to consrv hctars of forst that wr at no risk of bing lost in th first plac. Whn w considr th distribution of paymnts of th flat paymnts and bnfit-maximizing programs according to charactristics of rcipint communitis, w find that in th cas of th bnfitmaximizing program, largr jidos rciv th lion s shar of th budgt, although thy ar not always th most fficint in providing nvironmntal srvics. W also find that poor jidos hav highr participation rats, gt a largr proportion of th budgt, and provid highr bnfits pr dollar spnt than non-poor jidos. In th optimal program, paymnts pr mmbr to th poor and indignous ar 22

23 23 much lowr than to thir countrparts, rflcting th fact that nvironmntally valuabl rsourcs pr capita ar highr among th non-poor. In th flat schm with a cap, th budgt is distributd rlativly qually across siz and povrty classs, and paymnts to th poor and non-poor ar qual. Finally, w also addrss on of th important factors in implmnting a schm which accounts for dforstation risk. In ordr to avoid stratgic bhavior, on must us prdictd dforstation using non-manipulabl variabls. W show that thr is littl advantag in vnturing byond asily obsrvabl variabls in ordr to mak this prdiction. Thr is of cours an fficincy loss in using th prdiction as opposd to th actual rat of forst loss, but a program using th prdictd dforstation rat is still twic as fficint as a flat paymnt program. Works Citd Alix-Garcia, J., d Janvry, A., and Sadoult, E. (2005), A Tal of Two Communitis: Explaining Dforstation in Mxico, World Dvlopmnt 33: Ando, A., Camm, J., Polasky, S. and Solow, A. (1998), Spcis Distributions, Land Valus, and Efficint Consrvation, Scinc 279: Aylward, B. and Togntti, S. (2002), Valuation of hydrological xtrnalitis of land us chang: Lak Arnal cas study, Costa Rica Land-watr linkags in rural watrshds, Cas study sris. FAO, Rom. Babcock B.A., Lakshminarayan, P.G., Wu, J. and Zilbrman, D. (1996), Th Economics of a Public Fund for Environmntal Amnitis: A Study of CRP Contracts, Amrican Journal of Agricultural Economics 78: Babcock B.A., Lakshminarayan, P.G., Wu, J. and Zilbrman, D. (1997), Targting Tools for th Purchas of Environmntal Amnitis, Land Economics 73 : Chomitz, K., Brns, E., and Constantino, L. (1998), Financing Environmntal Srvics: Th Costa Rican Exprinc and its Implications, Papr prpard for th Environmntally and Socially Sustainabl Dvlopmnt, Latin Amrica and Caribban Rgion, World Bank. Chomitz, K. and Thomas, T. (2003), Dtrminants of Land Us in Amazonia: A Fin Scal Spatial Analysis, Amrican Journal of Agricultural Economics 85: Diningr, K. and Mintn, B. (1999), Povrty, Policis, and Dforstation: Th Cas of Mxico', Economic Dvlopmnt and Cultural Chang 47:

24 24 Echavarría, M. (2002), Financing Watrshd Consrvation: Th FONAG Watr Fund in Quito, Ecuador, in Pagiola, S., Bishop, J., and Landll-Mills, N. (ds.) Slling Forst Environmntal Srvics: Markt-basd Mchanisms for Consrvation and Dvlopmnt, London: Earthscan Publications: pp Frraro, P.J. (2003), Consrvation Contracting in Htrognous Landscaps: An Application to Watrshd Protction with Thrshold Constraints, Agricultural and Rsourc Economics Rviw 32: Frraro, P.J. (2004), Targting Consrvation Paymnts in Htrognous Landscaps: A Distanc Function Approach and Application to Watrshd Managmnt, Amrican Journal of Agricultural Economics 86: Godoy, R. & Contrras, M. (2001), A Comparativ Study of Education and Tropical Dforstation among Lowland Bolivian Amrindians: Forst Valus, Environmntal Extrnality, and School Subsidis, Economic Dvlopmnt and Cultural Chang 49: Hrnándz, O., Cobos, C., Ortiz, A. and Méndz, J.C. (2003), Valoración Económica dl Srvicio Ambintal d Rgulación Hídrica dl Lado Sur d la Rsrva d la Biosfra Sirra d las Minas, Guatmala, Papr prpard for Foro Rgional sobr Sistmas d Pago por Srvicios Ambintals. Aréquipa, Pru. Mayrand, Karl and Paquin, Marc (2004), Paymnts for Environmntal Srvics: A Survy and Assssmnt of Currnt Schms, Unisfra Intrnational Cntr (for th Commission for Environmntal Coopration of North Amrica) Unisfra_n.pdf Monro, D., Southworth, J., and Tuckr, C. (2002), Th Dynamics of Land-covr Chang in Wstrn Honduras: Exploring Spatial and Tmporal Complxity, Agricultural Economics 27: Pagiola, S., Bishop, and J. Mills, N., ds. (2002), Slling Forst Environmntal Srvics: Markt-basd Mchanisms for Consrvation and Dvlopmnt, London: Earthscan Publications. Parks, P. and Schorr, J. (1997), Sustaining Opn Spac Bnfits in th Northast: An Evaluation of th Consrvation Rsrv Program, Journal of Environmntal Economics and Managmnt 32: Pfaff, A. (1999), What Drivs Dforstation in th Brazilian Amazon?, Journal of Environmntal Economics and Managmnt 37: Puri, M. C. J. and Griffths, C. (2001), Prdicting th Location of Dforstation: Th Rol of Roads and Protctd Aras in North Thailand, Land Economics 77: Torrs-Rojo, J.M. and Flors-Xolocotzi, R. (2001), Dforstation and Land Us Chang in Mxico, in Dor, MHI (ds.), Climat Chang and Forst Managmnt in th Wstrn Hmisphr, Binghamton: Haworth Prss, Inc.: pp

25 25 Vlázquz, A., Mas, J. y Palacio, J. Análisis dl cambio d uso dl sulo Convnio INE-IGg (UNAM) (Oficio d autorización d invrsión 312.A , Instituto d Gografía, UNAM, Enro Witzman, M.L. (1998), Th Noah s Ark Problm, Economtrica 66: World Bank (2005), Currnt World Bank Work on Paymnts for Ecological Srvics, rntprojcts Wu, J. (2000), Slippag Effcts of th Consrvation Rsrv Program, Amrican Journal of Agricultural Economics 82: Wu, J., Zilbrman, D., and Babcock, B. (2001) Environmntal and Distributional Impacts of Consrvation Targting Stratgis, Journal of Environmntal Economics and Managmnt 41: Uchida, E., Xu, J., and Rozll, S. (2005), Grain for Grn: Cost-ffctivnss and Sustainability of China s Consrvation St-Asid Program, Land Economics 81: Zbindn, S. and L, D. (2005), Paying for Environmntal Srvics: An Analysis of Participation in Costa Rica s PSA Program, World Dvlopmnt 33:

26 26 10 Tabls Tabl 1. Distribution of forst jido univrs by rgion, Mxico Rgion Ejidos* % of population Sampl % of sampl Pninsula Gulf South Cntral North Total ,152 2,488 1,499 7, *Data providd by th Instituto Nacional d Ecología Tabl 2. Prdiction of Rainfd Land Rntal Rat Dpndnt Variabl: Dollars pr hctar of land pr yar Variabl Cofficint t-statistic Avrag distanc from villag to forst in km Avrag distanc squard Avrag altitud of forst in mtrs Avrag slop of forst in dgrs Distanc*slop Distanc* altitud Total siz in 1000 ha Stat lvl maiz yild pr ha Yild*slop Yild*altitud Distanc to narst town in kilomtrs Constant Obsrvations R-Squard * ** 2.2**

27 27 * dnots significanc at th 10% lvl and ** at th 5% lvl. Tabl 3. Constructing an nvironmntal indx Charactristic Points pr hctar Cloud forst Primary Scondary Othr typs of forst Primary Scondary Addd to ach hctar of abov: Ovrxploitd watrshd Within ½ mil of a rivr Primary Scondary Tabl 4. Participation and paymnts in diffrnt programs Paymnt rul Flat paymnts Riskwightd flxibl paymnts Bnfit-maximizing paymnts H R B Prcnt of jidos nrolld Avrag paymnt pr participating jido $7,341 $10,202 $7,418 Mdian paymnt pr participating jido $7,234 $1,744 $1,586 Gini cofficint of paymnts ovr participants

28 28 Tabl 5. Costs and bnfits of diffrnt paymnt programs Paymnt rul Flat paymnts Riskwightd flxibl paymnts Bnfit-maximizing paymnts H R B Total hctars nrolld 1,022,133 1,836,535 1,534,405 Hctars at risk nrolld 6,732 22,667 19,225 Environmntal bnfits 216, , ,729 Total budgt $2,598,870 $2,550,596 $1,713,509 Efficincy (nvironmntal bnfits/potntial incom) Tabl 6: Paymnts pr hctar at risk of dforstation Flat paymnts Risk-wightd flxibl paymnts Bnfit-maximizing paymnts H R B Man paymnt pr hctar at risk $7,610 $96 $86 Minimum paymnt pr hctar at risk $34 $5 $5 Maximum paymnt pr hctar at risk $654,222 $331 $275 28

29 29 Tabl 7. Distribution of paymnts from schms H and B ovr jido siz and povrty classs Ara and Distanc classs Participation rat (%) Avrag paymnt pr capita pr community Efficincy (b /paymnt) j Prcntag of ovrall budgt H B H B H B H B Ara: 1 st quartil $3,216 $1, nd quartil $6,748 $3, rd quartil $9,698 $4, th quartil $11,300 $17, Povrty: Non-poor $120 $ Poor $120 $ Th thrsholds for th ara quartils ar 1,240, 2,270, and 5,160 hctars. Ejidos ar classifid as poor if at last 53% of th population is prdictd to rciv Progrsa. 29

30 30 Tabl 8. Prdiction quations for dforstation Dpndnt variabl: Hctars of forst lost btwn Variabl (1)Parsimonious (2) spcification Full spcification Total ara of th jido in hctars (0.91) (1.22) Hctars of forst in (3.58)** (3.58)** Forst squard -3 x x 10-6 (2.95)* (2.98)** Forst cubd 3 x x (2.41)* (2.95)* Prcntag of total ara in forst, (2.50)* (2.25)* Avrag distanc to forstd ara in km (0.29) (0.83) Avrag slop of forstd ara in dgrs (0.75) (0.93) Avrag altitud of forstd ara in mtrs (1.45) (1.89) Avrag distanc*avrag slop (1.36) (1.20) Avrag distanc*avrag altitud (1.91) (1.91) Ejido practics forstry (dummy variabl) (1.16) (1.17) Numbr of jidatarios in (1.06) (1.73) Numbr of jidatarios squard 9 x 10-5 (0.81) Distanc to narst city in kilomtrs (0.77) (0.72) Avrag numbr of popl pr hh with scondary ducation (2.38)* Avrag parcl siz of jidatarios in hctars (1.83)* Numbr of jidatarios*gini cofficint of privat parcls (2.82)* Mmbrship ratio*forstry jido (0.44) Ratio of mmbrs to total population in jido (3.04)** Gini cofficint of privat parcls (1.53) Prdictd shar of population rciving Progrsa (0.40) Constant (1.21) (2.70)** Obsrvations Adjustd R-squard Robust t-statistics in parnthss. * significant at 5% lvl; ** significant at 1% lvl 30