A CHOICE EXPERIMENT IN GARATE-SANTA BARBARA (GUIPUZCOA)

Transcription

1 FACULTAD DE CIENCIAS ECONÓMICAS Y EMPRESARIALES MASTER IN ECONOMICS: EMPIRICAL APPLICATIONS AND POLICIES A CHOICE EXPERIMENT IN GARATE-SANTA BARBARA (GUIPUZCOA) Welfare benefts of conservaton plans for a Natura 2000 Network ste Raquel Grandes Santolalla MASTER THESIS Supervsors: Petr Marel and Davd Hoyos Blbao September

2 ABSTRACT In economcs, we usually work wth goods and servces regulated by a market, but natural resources have no mechansms to determne prce. However, ths does not mply an absence of value. There exst several methods for the valuaton of envronmental goods and servces whose obectve s to measure the utlty and welfare provded by these goods. In ths study, we analyze dfferent land uses n a determned area usng a dscrete choce experment methodology. The ntroducton of socodemographcs allows us to consder heterogenety. Fnally, welfare measures for dfferent conservaton levels of natural resources are calculated. Keywords: dscrete choce experment, envronmental valuaton, wllngness to pay, Natura 2000 JEL Classfcaton: Q51, Z10 2

3 1. INTRODUCTION The envronment s an nfnte set of relatonshps between speces that works on ts own and assures the exstence of lfe on Earth. A black box wth complex mechansms nsde that provdes a budget of goods and servces that are ndspensable for our lves. From an economc pont of vew, the problem facng non-market valuaton s the absence of a market that regulates supply and demand. However, not havng a prce does not mply the absence of value, whch s why we should be conscous of the mportance of envronmental goods and be ready to face envronmental degradaton processes. Market falure that makes essental goods lack a market, or have a prce that wll never represent ther real value (e.g. the case of water), s well known n envronmental economcs. As Hoyos et al. (2009) ponted out, the estmaton of an economc value for envronmental goods and servces s ustfed, among other thngs, by the fact that they can be taken nto account n decson-makng tools such as cost beneft analyses. In envronmental economcs, the standard approach to evaluate polces assocated wth natonal protected areas and the ecosystem servces suppled by them s manly based on tradtonal cost beneft analyss. Dfferent economc valuaton technques have appeared wthn the theoretcal framework of envronmental economcs to estmate, n monetary terms, the value of non-market goods. Exstng approaches are broadly grouped nto revealed preference (RP) methods (e.g. hedonc prcng or the travel cost method) and stated preference (SP) methods (e.g. contngent valuaton and choce experments (CEs)) (Hoyos et al. 2008). However, RP data are sometmes scarce, and snce the early 1990s SP methods have receved growng attenton and acceptance, manly because of ther flexblty and ablty to measure not ust use values (as do RP methods) but also the non-use values of natural resources as well (Mtchell and Carson 1989). 1 Furthermore, SP methods are the only methodologes able to estmate hypothetcal changes n nature, as wll be the case n ths study. Wthn SP methods, the man dfference between the contngent valuaton method (CVM) and dscrete choce experment (DCE) methodology s that whereas n the former ndvduals 1 The concept use value refers to the value people derve from the drect use of a good. However, non-use value refers to the value that people derve from economc goods ndependent of any use, present or future, that people mght make of those goods. 3

4 face the valuaton of one good wth varyng prces, n the latter one ndvduals face the valuaton of several goods (or one good wth multple attrbutes) wth dfferent prces. The underlyng dea of DCEs s that f human-nduced changes n the state of an ecosystem can be coherently represented by a bunch of attrbutes, people s choces provde substantal nformaton about ther preferences regardng alternatve states of the envronment. The flexblty of the CVM has expanded to DCEs, thereby allowng the nvestgaton of many dfferent stuatons and polces. Thus, the use of the DCE methodology n ths context s ustfed for two reasons: frst, as a SP non-market valuaton technque, t allows for the estmaton of hypothetcal envronmental qualty changes; and second, t s able to separately estmate the preferences of ndvduals for dfferent envronmental attrbutes and provde them wth margnal values. The greater flexblty of DCEs allows for the assessment of a wde varety of potental damages to a dverse set of envronmental attrbutes. Recent applcatons of CEs to natural resource management can be found n Campbell et al. (2008), Czakowsk and Hanley (2009), Hoyos (2009) and Hoyos et al. (2009). The DCE methodology presents several advantages: DCEs facltate the valuaton of multple optons rather than evaluatng a sngle nterventon, represent an ntegraton of several theoretcal areas and are consstent wth Lancaster's (1966) characterstcs of the theory of demand: consumers have preferences for and derve utlty from underlyng attrbutes. DCEs are, therefore, consstent wth welfare economcs and consumer theory. The purpose of ths study s to analyze the welfare effects of the mplementaton of dfferent conservaton plans n the Natura 2000 ste of Garate-Santa Barbara (Basque Country, Span). The exstng nteractons among the nterests of multple actors and stakeholders are a source of land use conflct. Ths s why we have to proceed to estmate ndvduals utlty functons to try to obtan more nformaton and be able to generate a tool for polcymakers to fnd the land uses maxmzng the welfare of the populaton of the Basque Country. In ths work, a mx of ecologcal/bogeographcal varables, socodemographc aspects and monetary valuaton dmensons amng at helpng n the desgn and mplementaton of conservaton plans and polces n the Natura 2000 ste of Garate-Santa Barbara has been ncluded. Ths rest of the work s structured as follows. In the next secton, the DCE methodology and ts man features are explaned. Then, the case study s descrbed. Secton 4 presents and 4

5 dscusses the man results obtaned. Fnally, the last secton presents some conclusons that can be derved from the work. 2. A CE METHODOLOGY TO VALUE ENVIRONMENTAL DAMAGE The DCE methodology s an SP method of valuaton that converts subectve choce responses nto estmated parameters. CEs were frst used n marketng research durng the 1970s to analyze consumer choces. Later, ths technque was used n transport economcs and health economcs, and more recently t has ganed popularty n the felds of envronmental and ecologcal economcs as well as cultural economcs. To delve nto the evoluton of the methodology, the followng artcles can be examned: Louvere et al. (2000), Alpzar et al. (2001), Tran (2003), Snowball (2008) and Hoyos (2010). 2.1 Economc theory In mcroeconomcs, models of consumer behavor are based on utlty maxmzaton under a budget constrant. The theoretcal bases of the DCE methodology can be found n Lancaster s (1966) characterstcs of the theory of demand, welfare theory and consumer theory, n whch ndvduals derve utlty from the characterstcs of a good rather than formng the whole good. In addton, DCEs combne the Lancasteran theory of value wth the consumer demand models developed by Hanemann (1984a). The method nvolves the generaton and analyss of choce data through the constructon of a hypothetcal market usng a survey. Subectve choce responses are then converted nto estmated parameters. Surveys comprse several choce sets, each contanng hypothetcal optons between whch respondents choose. Each opton s descrbed by a set of attrbutes, and each attrbute takes one of several levels. The selecton of the preferred choce s decded mplctly by the tradeoff a consumer makes from the dfferent alternatves that are offered n the choce sets. Each ndvdual s assumed to solve the followng maxmzaton problem: Maxc, δ, xu[ δ1c1 ( A1 ), δ 2c2 ( A2),..., δ N cn ( AN ); Z] (2.1) N = = 1 s.t. () y p δ c ( A ) z + 5

6 () δ δ = 0, () z 0, δ 0 for at least one, where, U[ ] denotes a quasconcave utlty functon; δ s a dchotomous varable equal to one f the alternatve s chosen and zero otherwse; c (A ) s the alternatve combnaton as a functon of ts attrbutes, the vector A ; p s the cost/prce attrbute of each alternatve; y s the level of ncome; and z s a composte bundle of goods wth ts prce normalzed to one. Ths maxmzaton problem has the followng propertes (Alpzar et al. 2001): 1. All the relevant alternatves are defned and descrbed by all the relevant attrbutes. Therefore, the selecton of attrbutes and attrbute levels has a drect mpact on the utlty functon defned. 2. The prce varable n the budget constrant must be related to the full set of attrbutes confrmng each alternatve, thereby reflectng a contnuous dmenson. 3. Restrcton () mples that only one alternatve can be chosen n a gven choce set. 4. For a gven ncome level, the selecton of one alternatve (provded n an exogenously fxed quantty) mples that the amount of ordnary goods that can be purchased s also fxed. 5. Restrcton () mples that the ndvdual wll choose a non-negatve quantty of the composte good and that an opt-out or status quo opton s gven. Two further assumptons need to be made to solve the maxmzaton problem: frst, a purely dscrete choce s assumed; and second, weak complementarty s assumed (.e. the attrbute levels of the non-selected alternatves have no nfluence on the utlty functon of the chosen alternatve). Accordng to Hanemann (1984a): U If δ = 0, then = 0 A. (2.2) Followng Equatons (3.1) and (3.2), and gvenδ = 1, the condtonal utlty functon can be wrtten as: U = V c ( A ), p, y, z) = V ( A, y p c ). (2.3) ( 6

7 Gong back to the uncondtonal ndrect utlty functon (IUF) gven by the followng expresson: V [ A p, y] max[ V ( A, y p c ),..., V ( A, y p c )] =. (2.4), N N N It follows that, for a purely dscrete choce, the ndvdual chooses alternatve f and only f: V ( A, y p c ) > V ( A, y p c ),. (2.5) 2.2 Econometrcs of DCEs The analyss of the choces made n a DCE s based on random utlty theory. The random utlty approach developed by McFadden (1974) s used to lnk the determnstc model wth the statstcal model of human behavor. The randomness of utlty functon suggests that only an analyss of the probablty of choosng one alternatve over another s possble. Estmable choce models requre, thus, a dstrbutonal assumpton for the random component. As a consequence, a random dsturbance wth a specfc probablty dstrbuton, ε, s ntroduced nto the economc model. In ths context, an ndvdual wll choose alternatve f and only f: V ( A, y p c, ε ) > V ( A, y p c, ε ),. (2.6) Or n terms of probablty: { ( A, y p c, ε ) > V ( A, y p c, } P( δ = 1) = P V ε ). (2.7) The fnal specfcaton of the econometrc model wll ultmately depend on: () the specfcaton of the utlty functon (.e. how the random term enters the condtonal IUF); and () the dstrbutonal assumpton for the error component. The most common formulaton for the utlty functon s addtvely separable, so that the error component enters the utlty functon as an addtve term. The specfcaton of the utlty functon requres two addtonal decsons: the functonal form V and the relevant attrbutes 7

8 A that wll determne the utlty level for each alternatve. The determnstc component of utlty s usually assumed to be a lnear and addtve functon of the attrbutes of the good and the characterstcs of the respondent (x ): ' V = β x, (2.8) where β s a vector to be estmated. Utlty s a latent varable, whereas choces are the only observable ndcator of utlty. The second decson regardng the fnal specfcaton of the econometrc model relates to the specfcaton of the probablty dstrbuton of the error term. Assumng that the DCE has M choce sets (S m ), each formed by K m alternatves wth A attrbutes such that S m ={A m,,a km }, the probablty of choosng alternatve from a choce set S m can be wrtten as: { ( A, y p c ) + ε > V ( A, y p c ) + ; S ; } P( δ = 1 S ) = P V ε. (2.9) m m m One crucal assumpton relates to the way that ncome enters the utlty functon. A constant margnal utlty of ncome s usually assumed because t facltates the estmaton of welfare measures, although t mght not be always reasonable. Regardng the nfluence of selected attrbutes and nteractons t s mportant to note that the data collected n a DCE are based on a specfc expermental desgn that wll condton the estmaton of nteracton effects between the relevant attrbutes. A second mportant pont relatve to the specfcaton of the model s the assumpton that error terms of the utlty functons are ndependently and dentcally dstrbuted followng a type I extreme value (Gumbel) dstrbuton, and that the choce can be estmated usng a multnomal logt (MNL) specfcaton (McFadden 1974; Louvere et al. 2000). Ths means that the unobserved part of utlty for one alternatve s unrelated to the unobserved part of utlty for the other alternatves. Accordng to Carlsson and Martnsson (2008) 2, a lnear random utlty functon s assumed, where the ndrect utlty for the household n for alternatve consstng of a determnstc component v and a random part ε s: 2 The notaton has been modfed from the orgnal to mantan the notaton followed durng the study gven the other bblography consulted. 8

9 U ' = V + ε = β a + γ ( I c ) + ε, (2.10) where a s a vector of attrbutes n alternatve, β s the correspondng parameter vector, I n s ncome, c n s the cost assocated wth the alternatve, γ s the margnal utlty of ncome and ε n s an error term. The MNL statstcal model represents the probablty of choosng an alternatve gven that the utlty of that alternatve s greater than the utlty of all other alternatves. The probablty of ndvdual choosng an alternatve s: P = e h c v e v σ σ. (2.11) Accordng to Tran (2003), the advantages and dsadvantages of MNL can be determned as follows: 1) MNL can represent systematc taste varatons (those related to observed characterstcs of the respondents) but not random taste varatons. 2) MNL exhbts restrctve substtuton patterns because t mples proportonal substtuton across alternatves gven the specfcaton of the utlty functon. 3) MNL can handle stuatons where unobserved factors are correlated over tme for each respondent. Observed heterogenety can be ncorporated nto the systematc part of the model by ncludng nteractons between attrbutes or constant terms and the socoeconomc characterstcs of the respondents because ths wll show the case of study. However, the MNL cannot ncorporate unobserved heterogenety. The substtuton patterns of MNL models satsfy the property of the ndependence of rrelevant alternatves (IIA), that s the rato of the probabltes of choosng one alternatve over another (gven that both alternatves have a non-zero probablty of choce) s unaffected by the presence or absence of any addtonal alternatves n the choce set (Louvere et al. 2000): 9

10 v P e =. (2.12) P k e v k IIA depends on the choce and the varables ncluded n the specfcaton of the utlty functon that are assumed to be IID. In case of a volaton of IIA, the parameters estmaton would be based. The classcal econometrc specfcaton for estmatng CEs, the MNL model, s generally overcome by random parameter logt (RPL) specfcaton (Tran 2003). In the RPL model, a random term whose dstrbuton over ndvduals and alternatves depends on underlyng parameters s added to a classcal utlty functon assocated wth each alternatve. The RPL model accounts for unobserved heterogenety by allowng the parameters of the utlty functon to be random. It consders that each respondent makes choces n more than one choce stuaton. Ths methodologcal framework has recently been used n envronmental and ecologcal economcs applcatons (e.g. Hoyos et al. 2009; Abdullah and Marel 2010). Ths specfcaton of the RPL model dffers from the tradtonal logt model on whch β s fxed. In fact, f parameter β t were observable, the choce probablty of alternatve condtonal on parameter β t would be gven by ths expresson: P ( / t e X tβt β t ) = J, (2.13) X β = 1 e t t whch s the standard logt model. However, snce t s not possble to be observed, the noncondtonal probablty s the ntegral of P t (/β t ) over all the possble values of β t : P ( ) = P ( / β ) f ( β ) d( β ). (2.14) t β t t Fnally, as regards to the second aspect, the flexblty of the RPL model allows one to represent dfferent correlaton patterns among non-ndependent alternatves. Ths flexblty allows us to avod the assumpton of IIA. In fact, t does not exhbt the restrctve substtuton patterns of the logt model because the rato of probabltes P t /P t depends on all the data, ncludng the attrbutes of alternatves other than and. 10

11 2.3 Aspects to consder n a DCE The man ponts to take nto account after mplementng or applyng a DCE are presented n Table 1. Table 1: Checklst of factors to consder n undertakng and assessng the qualty of a DCE 1. Conceptualzng the choce process Was a choce rather than rankng, ratng task used? What type of choce was used: bnary response, pars, multple optons? Was a generc or labeled choce used? Was an opt-out, status quo opton or nether ncluded? If a forced choce was used, was a ustfcaton provded? Was the task ncentve compatble? 2. Attrbute selecton How were they derved and valdated? Was the number of attrbutes approprate? Was the coverage approprate? What form was used: generc or an alternatve? Was prce ncluded? If so, was an approprate payment vehcle used? Was rsk ncluded? If so, was t approprately communcated? 3. Level selecton How were they derved and valdated? Was the number of levels per attrbute approprate? Was an approprate range used? Were the levels evenly spaced? 4. Expermental desgn What type of desgn was used? Full factoral? Fractonal factoral? If fractonal, whch effects were dentfed: man effects + hgher order nteractons? How were the profles generated and allocated to choce sets? What are the propertes of the desgn? What s the effcency of the desgn? Was dentfcaton checked (e.g. s the varance covarance matrx block dagonal)? Was the desgn blocked nto versons? If so, how were choce sets allocated to versons? Were the resultng propertes of the verson checked? Were respondents randomly allocated to versons? How many choce sets were consdered per respondent? If some profles were mplausble how was mplausblty defned and how was t addressed? 5. Questonnare desgn Was an approprate level of background and contextual nformaton provded? Were the task nstructons approprate? Was the medum used to communcate attrbute/level nformaton (e.g. words, pctures, multmeda) approprate? 6. Plotng Was coverage of attrbutes and levels checked? Was understandng and complexty checked? Was the length and tmng checked? 7. Populaton/study perspectve 8. Sample and sample sze Was ths approprate for the research queston? Were ncluson/excluson crtera explct? Was sample sze approprate for model estmaton? 9. Data collecton What recrutment method was used? How were the data collected (e.g. mal, personal ntervew, web survey)? What was the response rate? Were ncentves used to enhance response rates? 10. Codng of data Was codng explctly dscussed? Was the codng approprate for effects to be estmated? 11. Econometrc analyss Were the estmaton methods approprate gven the expermental desgn and type of choce response? Was the functonal form of the IUFs approprate gven the expermental desgn? Were the alternatve specfc constants ncluded? Were socodemographcs and other covarates ncluded? Was goodness of ft consdered? 12. Valdty Was nternal or external valdty nvestgated? Were answers for any respondents deleted and f so on what bass? 13. Interpretaton Was the nterpretaton approprate gven codng of data? Were results n lne wth a pror expectatons? Were relatve attrbute effects compared usng a common and comparable metrc? 14. Welfare and polcy analyss Was wllngness to pay (WTP) estmated usng welfare theoretc compensatng varaton? Was probably analyss undertaken? Were margnal rates of substtuton calculated? Source: Lancsar and Louvere (2008) 11

12 1.1. Conceptualzng the choce process DCE choce questons must be ncentve compatble to encourage respondents to reveal ther true preferences. The methodology nvolves askng respondents to make dscrete choces. It should be consdered to allow respondents to opt out, choose nether opton or choose status quo optons, especally f the purpose of the study s to derve welfare measures. A status quo mght be a reference pont for gans and losses consstent wth prospect theory (Kahneman and Tversky 1979). Ths poston s a do nothng scenaro (Hanley et al. 2001), also known as the busness-as-usual poston, because t does not vary across the choce sets (Mogas et al. 2006) Attrbute selecton and level selecton Envronmental attrbutes and the level of provson become crtcal aspects of any CE gven that the only nformaton about preferences provded by respondents takes the form of choces between these optons (Hensher 2007). Accordng to Lancaster (1991), an envronmental attrbute can be consdered relevant f gnorng t would change our conclusons about consumer s preferences. The constructon of the choce sets ncluded n an experment requres a correct defnton of the change to be valued and the attrbutes levels that would be used. Attrbutes can be quanttatve or qualtatve and are generally dentfed from the lterature, qualtatve research such as sem-structured ntervews and/or focus groups wth samples of relevant respondents and experts. Prce s typcally one of the attrbutes n the choces. A suffcently wde range of levels should be used to avod respondents gnorng attrbutes because of small dfferences between the levels Expermental desgn An expermental desgn s a sample from all possble combnatons of attrbute levels used to construct choce alternatves and assgn them to choce sets. There s a growng concern about the mportance of expermental desgn as an mportant factor n dscrete choce analyss. The expermental desgn nfluences the types of IUFs that can be estmated from choces, so IUFs' functonal forms should be consdered a pror. If IUFs are not strctly addtve the man effects are lkely to be based (Lancsar and Louvere 2008). 12

13 1.4. Questonnare desgn A proper survey desgn s requred. The analyst should pursue an ncentve compatble choce queston to avod respondents not gvng ther true preferences, and the choce format should mmc the actual choce context as much as possble Plotng It s mportant to guarantee the coverage of attrbutes and levels. To assure understandng and gude development as well as the testng of DCE surveys, teratve face-to-face plot testng s needed Populaton/study perspectve At ths stage, t s mportant to check f the groups selected could be sgnfcant for the study Sample and sample sze Samplng requres the consderaton of the populaton to whom the results wll be generalzed, opportunty costs regardng how programs are funded and the relevant perspectve. Sample sze should be chosen to allow the estmaton of relable models, subect to research budget and other constrants. The calculaton of optmal sample szes for estmatng non-lnear dscrete choce models from DCE data s complcated as because t depends on the true values of the unknown parameters estmated n choce models Data collecton The recrutment method wll be very mportant and the mode of data collecton depends on study obectves, beng convenent to prepare ncentves to ncrease response rates Codng of data The effects of codng or dummy varable codng are typcally used, partcularly for qualtatve attrbutes. It should be checked f codng s approprate for the effects that wll be estmated Econometrc analyss Lancsar and Louvere (2008) recommended estmatng the most dsaggregated possble model by ncludng parameters estmated for every attrbute level but one 13

14 and mappng obtaned parameters aganst the attrbute level to vsualze ther functonal forms. Although the ncluson of the status quo opton mght reduce effcency, t s ustfed on the grounds of better congruency wth consumer theory and real choces Valdty In some cases, t s recommended to delete the answers of some respondents that could affect the results negatvely, for example, n the presence of protest respondents. It s convenent to check both external and nternal valdty. The frst concept refers to the results obtaned from smlar studes and the second to the aspects of the model Interpretaton Results should always be nterpreted by takng realty and avalable nformaton nto account Welfare and polcy analyss DCEs provde rch data sources for economc valuaton and decson makng and make t able to estmate not only the WTP for a dscrete change n envronmental qualty but also estmate the WTP for the dfferent attrbutes that form the goods. DCEs are ncreasngly used to elct WTP for ndvdual characterstcs of goods/servces and monetary measures of benefts (Lancsar and Louvere 2008). Owng to the lnearty of ncome n the utlty functon, the margnal WTP for an attrbute s the rato between the attrbute s coeffcent and the cost or payment coeffcent. DCEs allow the estmaton of trade-offs that respondents make between attrbutes or ther MRS 3. Followng standard consumer theory, MRS s calculated by partally dfferentatng the IUF wth respect to the frst and second attrbutes, and calculatng ther rato: MRS x 1, x 2 V X V X 1 =, (2.15) 2 3 MRS: the margnal rate of substtuton measures the rate at whch an ndvdual must gve up one asset to obtan a sngle addtonal unt of a second asset, whle keepng overall utlty constant. 14

15 where V s the IUF and X 1 and X 2 are attrbutes of the good and servce, respectvely. The numerator (denomnator) s nterpreted as the margnal utlty of attrbute 1(2). If the IUF s lnearly addtve, the expresson above equals the rato of the estmated attrbute parameters. Compensatng surplus (CS) welfare estmates for DCEs can be obtaned from Hanemann (1984b) and Tran (1998): CS 1 = α 0 1 [ ln( exp( βx ) ln( exp( βx )], (2.16) where α s the margnal utlty of ncome (usually represented by the coeffcent of the payment attrbute) and 0 X, and 1 X, represent the vector of envronmental attrbutes as change by the ntal level (status quo) and after the change levels, respectvely. Therefore, Hcksan compensatng varaton measures a change n expected utlty because of a change n the level of provson n the attrbute or attrbutes by weghtng ths change by the margnal utlty ncome. Smplfyng, we obtan the margnal value of a change n one attrbute, whch wth respect to another s measured through the ratos of the two coeffcents: WTP β attrbute =. (2.17) β payment Therefore, the WTP for a margnal change n the level of the provson of each envronmental attrbute s obtaned by dvdng the coeffcent of the attrbute by the coeffcent of the payment attrbute. In ths case, t wll be the cost. Once the IUF s estmated, t can be used n polcy analyss n varous ways, such as comparng the relatve mportance of product/program attrbutes. 3. CASE STUDY: GARATE-SANTA BARBARA (GUIPUZCOA) In May 1992, European Unon governments adopted legslaton desgned to protect the most serously threatened habtats and speces across Europe. Ths legslaton s called the Habtats Drectve and complements the Brds Drectve adopted n At the heart of both these drectves s the creaton of a network of stes called Natura Specal protecton areas are classfed under the Brds Drectve to help protect and manage areas that are mportant for rare and vulnerable brds because they use them for breedng, feedng, wnterng or 15

16 mgraton. Specal areas of conservaton are classfed under the Habtats Drectve and provde rare and vulnerable anmals, plants and habtats wth ncreased protecton and management. Across the EU a dverse range of habtats are protected from flower-rch meadows to vast expanses of estuares and even cave systems, and a huge varety of anmals throughout the EU beneft from ths. Member states are responsble for ensurng that all Natura 2000 stes are approprately managed by the conservaton authortes n each country. These organzatons often work n partnershp wth other authortes, voluntary bodes, local or natonal chartes and prvate landowners. Member states are expected to pay for the management of the stes n ther country, but to help countres pay for urgent or nnovatve conservaton work the EU has set asde money under a fund called LIFE-Nature, whch s managed by the Envronment Drectorate of the European Commsson. There are also a number of other communty funds that can be used for Natura 2000 stes, such as structural funds and agr-envronment measures. Sometmes certan actvtes have to be restrcted or stopped when they are a sgnfcant threat to the speces or habtat types for whch the ste was desgnated as a Natura 2000 ste. These are always addressed on a case-by-case bass. Keepng speces and habtats n a good condton s not necessarly ncompatble wth human actvtes; n fact, many areas are dependent on certan human actvtes for ther management and survval, such as agrculture. Ths s an mportant pont to take nto account and an ncentve to make an effort n the studes to assure decson/polcymakers have enough nformaton to desgn and mplement polces. In the Basque Country, there are 52 stes of communty nterest and sx specal protecton zones for brds. The case study regon, Garate-Santa Barbara, s located n the provnce of Gupuzcoa, between the vllages of Zarautz and Getara. It has 142 hectares of whch 81 belong to Getara and 61 to Zarautz. All of them are under a prvate property regme. Ths area s part of the Basque Country s lst of hghly valuable envronmental areas, especally gven the exstence of endemc bodversty n the area, whch also ncludes the exstence of a partcularly scarce forest of cork oak trees, whch are abundant n the Medterranean bogeographcal regon. The regon was ncluded n the European lst of stes of communty nterest n 2004 because of the exstence of fve dfferent types of envronmentally valuable habtats: cork oak forest, 16

17 holm oak forest, European dry heaths, endemc or Medterranean heaths wth gorse and lowland hay meadows (Garmenda et al. 2009). 4. RESULTS AND DISCUSSION The study started wth exstng data from a prevous study conducted n The survey was admnstered through n-person computer-aded ndvdual home ntervews. The relevant populaton consdered was that of the Basque Autonomous Communty, accountng for 1.8 mllon people aged 18 and over. In each locaton, the questonnare was dstrbuted usng a random survey. Alternatves were constructed n an nteractve manner between the group of researchers and the numerous socal actors n the evaluaton process. These alternatves were dentfed based on plausble future scenaros consderng the actual legal framework and the envronmental potental assessed by means of the bo-geographc assessment. The land use scenaros were used to generate alternatve management optons consderng the presence or absence of payment schemes to compensate landowners for changes n ther land use for promotng conservaton measures that would enhance the bodversty of the area. The database conssted of the results obtaned from 402 completed questonnares. Each ndvdual responded wth sx choce cards, so there were ntally 2412 observatons. Followng standard practce n SP surveys, those answers n whch the ndvduals protested aganst answerng or showed that they dd not really understand the obectves were deleted. Furthermore, rratonal answers were also reected from the database. The fnal database accounted for 1326 observatons. The man obectve of the study was to assess the dfferent values assocated wth the potental land uses that could occur n the area because of dfferent management plans. Sx attrbutes were dentfed as beng assocated wth dfferent land uses: percentage of land area covered by cork oak tree forest (varable AUT, rangng from 2 to 30%), percentage of land area covered by forest plantatons, based on productve pnes (varable FOR, rangng from 15 to 40%), percentage of land area covered by vneyards (varable VIN, rangng from 10 to 40%), bodversty, based on the number of endangered speces of flora and fauna n the area (varable BIO, rangng from 5 to 25), the level of conservaton of recreatonal and cultural 17

18 facltes (varable REC, rangng from low to very hgh) and the cost of conservaton programs (varable COST, rangng from 0 to 100 Euros). These attrbutes were selected based on () a prevous nsttutonal analyss, () bogeographc analyss () focus groups and (v) expert advce. The fnal verson of the questonnare had sx choce sets; each formed by the status quo plus two alternatve protecton programs (program A and program B). Fgure 1 presents an example of a choce set used n the valuaton exercse. Fgure 1: Example of a choce set used n the valuaton exercse. In the frst step, a MNL specfcaton was mplemented to test the sgnfcance of attrbutes and the dfferent socoeconomc varables ncluded. Then, a random parameter model, wth random parameters assumed to follow a normal dstrbuton, was appled to obtan a better ft of the model and capture the heterogenety to explan the absence of sgnfcance for the recreatonal and cultural facltes (REC) and forest plantatons (FOR) attrbutes. 18

19 As mentoned, the target populaton of ths study was the Basque populaton of the Basque Autonomous Communty. Table 2 presents descrptve statstcs of the most mportant socodemographc varables ncluded n the survey. These statstcs were close to the aforementoned populaton statstcs, showng that the analyzed sample was representatve. Table 2: Descrptve statstcs Varable Mean Std. Dev. Mnmum Maxmum Cases Mssng GENDER PERSONAL INCOME HIGH INCOME (HI) AGE BORN IN CAPV YEARS IN CAPV ADULTS CHILDREN STUDIES NGO FAMILY INCOME LABOR Data from the experment were examned and an MNL model specfcaton was mplemented assumng the utlty functon was lnear n the parameters and addtvely separable. After the estmaton of many models wth dfferent combnatons of socodemographc varables (Table 2) and attrbutes, the determnstc part of the model defned n (2.8) wth the best ft obtaned, n whch the explcatve varables ncluded were the attrbutes dentfed, the varable gender multpled by cost and the varable hgh ncome multpled by cost was the followng: V = β + β COST + β AUT + β VIN + β FOR + β BIO + β REC β GENDER * COST + β HI * COST 7 8 The varable hgh ncome (HI) s a dummy varable takng a value of one when the ndvdual ncome per month s hgher than 2500 Euros and zero otherwse. The other socodemographc, the varable gender (GENDER), s a dummy varable that takes a value of one f the ndvdual s a man or zero f the ndvdual s a woman. The nteracton of socoeconomc varables wth attrbutes accounts for group heterogenety among ndvduals. 19

20 In the model, β₀ represents the constant term, β 1 represents the effect of a unt ncrement n the cost of the program on the ndvduals utlty and β 2, β 3 and β 4 represent the effect of a unt ncrement on percentage of land area covered by cork oaks, vneyards and pnes, respectvely. The parameter β 5 shows how the utlty changes f the number of speces n danger ncreases by one unt and β 6 represents the effect of a change n the level of conservatonal and cultural facltes. Lookng at the socodemographc varables we can nterpret the coeffcents n the followng way: β 7 explans the effect of gender f the cost ncreases by one unt and β 8 wll measure the effect of an ncrease on the level of ncome n the WTP among those who have a hgh ncome. Table 3 presents the varance nflaton factor (VIF), whch s commonly used n regresson analyss for detectng problems of multcollnearty, where values greater than 10 ndcate hghly collnear data. In ths case, all values are very low, so no problem of multcollnearty s expected n the model estmated. Table 3: VIF VIF COST AUT VIN FOR BIO REC COST*GEN COST*HI The model appled frst was the MNL and the RPL was then appled durng the second stage. In both cases, the econometrc software used was NLOGIT The estmaton results wll be shown n the followng tables. The results of the MNL estmaton are presented n Table 4. As can be observed, the attrbutes COST, AUT and BIO are sgnfcant at the 1% level and the attrbute VIN s almost sgnfcant at 10%. All of them have the expected sgns, suggestng that utlty ncreases f the level of protecton of the envronmental attrbutes does, and decreases as the 20

21 cost of the program ncreases. The nteractons between cost and gender and cost and hgh ncome are sgnfcant at the 10% level and 5% level, respectvely. Table 4: MNL model estmaton results Varable Coeffcent Standard error T p-value FOR REC CONSTANT COST *** AUT *** VIN BIO *** COST*GEN * COST*HI ** No. of observatons: 1326 Log lkelhood functon: Sgnfcance: *(10%); **(5%); ***(1%) All coeffcents ncluded n the MNL model n Table 4 were tested for ther possble randomness usng t-statstcs related to the standard devatons of the random parameter. Two were found to be random, namely FOR and REC. That s why an RPL was estmated. An RPL, unlke the prevous model, allows for the specfcaton of unobserved heterogenety among ndvduals (Table 5). Table 5: RPL Varable Coeffcent Standard error t p-value Random parameters n utlty functons FOR REC Non-random parameters n utlty functons CONSTANT COST *** AUT *** VIN BIO *** COST*GEN * COST*HI ** Derved standard devatons of parameter dstrbuton FOR ** REC *** Number of observatons: 1326 Log lkelhood functon : Log lkelhood functon (constants only) : Sgnfcance: *(10%); **(5%); ***(1%) 21

22 The mprovement n the log lkelhood functon suggests that the RPL clearly outperforms the MNL. The LR test of the null hypothess that the two standard devatons of random parameters are zero s and corresponds to a p-value of 5%. We found that the attrbutes cost, autochthonous forest, vneyard and bodversty were non-random. However, Table 5 shows that for the attrbutes percentage of land area covered by forest and the level of conservatonal and cultural facltes we can reect the null hypothess of zero standard devatons of parameter dstrbuton at 5%. As expected, the negatve sgn of the attrbute cost ndcates that the utlty of ndvduals decreases as the cost of the program ncreases. The utlty of ndvduals also ncreases f the percentage of land area covered by cork oaks or vneyards ncreases, whereas the utlty decreases f there are more speces n danger. By contrast, regardng the random parameters, the sgn of the effects for the attrbutes forest and recreatonal and cultural facltes can change for dfferent ndvduals. Ths s the evdence of the exstng heterogenety that could not be modeled by socodemographc varables. The two nteractons of the socoeconomc varables wth the cost attrbute, namely gender and hgh ncome, are sgnfcant at 10% and 5%, respectvely. We can conclude that men are less wllng to pay than women. Ths could be explaned by the senstvty of women to conserve the envronment and resources more than men because of ther chldren. The postve sgn of HIGHINCOME*COST ndcates that the hgher the ncome, the hgher the WTP. Ths s an expected result because those wth more earnngs should have a hgher WTP WTP The man research obectve s to analyze the cost of mantanng dfferent land uses. Because some SED 4 varables were ncluded n the RPL, the smulated WTP values depend on these SED values. We have to defne a base scenaro that wll be used as a benchmark for the WTP comparsons computed for dfferent values of the SED varables. In the base scenaro, the dummy varables, related to gender and hgh ncome, were set to zero. In ths way, by settng the dummy varables to one, the effect of gender and hgh ncome on WTP can be analyzed. Table 6 reports the WTP estmates for the defned envronment attrbutes n dfferent scenaros dependng on the values of the SED varables. 4 SED: socodemographc varables 22

23 Table 6: WTP GROUPS/ATTRIBUTES AUT VIN BIO FOR REC WEIGHTED WTP WOMEN WITH LOW MEDIUM INCOME WOMEN WITH HIGH INCOME MEN WITH LOW MEDIUM INCOME MEN WITH HIGH INCOME (3.835) (14.555) (3.014) (7.156) (13.616) (51.674) (10.701) (25.408) The attrbutes "AUT," "VIN" and "BIO" are non-random; however, "FOR" and "REC" are random parameters, and ther means and standard devatons (n brackets) were calculated after smulaton usng 10,000 random numbers. The frst row shows the amount of money that an average ndvdual would pay for an ncrease of 1% of the land area covered by oak trees (2.87 Euros per year), an ncrease of 1% of the land area covered by vneyards (0.45 Euros per year), a decrease of one unt of the number of speces n danger (2.60 Euros per year), an ncrease of 1% of the land area covered by forests (0.08 Euros per year) and an mprovement n the conservaton of recreaton and cultural facltes (0.41 Euros per year). It s nterestng for our analyss to compare the dfferences n the WTP consderng dfferent values of socoeconomc varables. In general terms, we could conclude that women wth hgh ncomes would be most lkely to pay and men wth low or medum ncomes would be least lkely to pay. For the cork oak forest, we can observe that women wth hgh ncomes would pay 8.40 Euros more than women wth low medum ncomes and 9 Euros more than men wth low medum ncomes. The same occurs for the bodversty attrbute followng the same profle but wth slghtly lower numbers. Lookng at the results for vneyards, at frst sght there seem to be lower dfferences among groups but the proporton s exactly the same as n the prevous cases. Summarzng for the fx attrbutes ( AUT, VIN and BIO ), we could conclude that men wth hgh ncomes would pay 3.80 tmes more than the other men, whereas women wth hgh ncomes would pay 2.37 tmes more than the other women. Ths can be nterpreted as the fact that level of ncome s less mportant for women than for men when the obectve s to protect an attrbute that has value for them. Makng comparsons between gender, and consderng the same level of ncome, t can be concluded that women wth low medum 23

24 ncomes would pay 1.27 tmes more than men. For the group of hgh ncome the coeffcent s 2.03 n favor of women too. From the strct pont of vew of ncome, there s a smaller dfference between genders when they each have less ncome. When both have hgh earnngs, women are wllng to pay double. Fnally, t s nterestng to compute the same coeffcent between the most lkely to pay group, women wth hgh ncomes, and the least lkely to pay group, men wth low medum ncomes. We obtan a coeffcent of 4.83, that s, women wth hgh ncomes wll pay 4.81 tmes more than men wth low medum ncomes (Table 7). Table 7: Approxmated summarzed comparson between the dfferent WTP values among groups (for the attrbutes: AUT, VIN and BIO ) Women hgh Men hgh Women hgh Women low Women hgh ncome/women ncome/men ncome/men medum ncome/men low medum low medum nc. hgh nc. ncome/men low low medum nc. medum nc. nc CONCLUSION The results satsfy n part the man obectve of the study, whch was to obtan nformaton about ndvduals preferences relatve to dfferent land uses. The dea was to evaluate future possble management plans and provde useful nformaton to polcymakers about an ndvdual's WTP, whch s a reflecton of the utlty of ndvduals gven the dfferent land uses and natural resources. Followng a DCE methodology, the attrbutes prevously dentfed were tested and a set of socodemographc varables were ncluded n the model. It s mportant to dentfy the heterogenety among ndvduals ncludng socodemographc varables because ths shows that dfferent preferences nfluence choce. Thus, the valuaton of envronmental goods and servces wll also be affected by these heterogeneous preferences. Such factors, when they nteract wth cost, reveal that, for example, n our case, gender and ncome of households mpact WTP. 24

25 After mplementng an MNL model, an RPL model was appled, ncludng the unobserved heterogenety, whch was not possble to model on SED varables by locatng the preferences assocated wth socoeconomc factors. We concluded that four attrbutes were fxed and two were random, but all were sgnfcant at the standard sgnfcance level. In addton, the ncluson of the SED varables allowed us to present nterestng conclusons. Our analyss confrms that women and ndvduals wth hgh ncomes have a hgher WTP to conserve the envronmental attrbutes selected. For the cost attrbute, the estmated coeffcent s negatve, as expected, because the utlty of ncreasng the conservaton of the dfferent envronmental optons decreases wth hgher payments. The bodversty attrbute s negatve too, and ths ndcates that people s utlty decreases f the number of endangered speces n the area ncreases. The postve coeffcents of autochthonous forest and vneyard ndcate that people s utlty ncreases when the percentage of land area covered by these speces ncreases. However, the coeffcents for forest plantaton and recreatonal and cultural facltes are not sgnfcant at any level of confdence and are establshed as random parameters. DCEs could contrbute to polcy and resource allocaton, and we can confrm that the negatve coeffcent of cost attrbute ndcates that the probablty of acceptng an annual contrbuton for conservng ths area decreases as the prce ncreases. Although t s nterestng to note the dfferences n WTP between genders, t s dffcult to mplement dfferent polces between them because t could be dscrmnatory. However, ths nformaton could be useful because f polcymakers want to mprove one of the attrbutes they could target the dfferent populaton groups dentfed wth specfc advertsng campagns dependng on ther WTP. A good use of the provded nformaton to polcymakers would probably help mprove the welfare of socety. The polcy mplcatons emergng from ths study are that researchers should acknowledge test lmtatons when selectng and specfyng one econometrc model over another. Indeed, the selecton of approprate polcy programs depends on computng the rght WTP values usng the estmaton of the model chosen. WTP values generated from models that do not consder heterogenety or an erroneous type of heterogenety because of the napproprate selecton of random parameters affect the evaluaton of cost and benefts for specfc proects. As a consequence such proects or polcy programs can be erroneously establshed. To 25

26 summarze, acknowledgng ths heterogenety among the populaton results n effcent WTP values that can assst polcymakers target approprate programs to specfc groups. 26

27 6. REFERENCES Abdullah, S. and Marel, P., Choce experment study on the wllngness to pay to mprove electrcty servces. Energy Polcy, 38: Alpzar F., Carlsson F. and Martnsson P., Usng choce experments for non market valuaton. Economc Issues, 8: Campbell D., Hutchnson W.G. and Scarpa R., Incorporaton dscontnuous preferences nto the analyss of dscrete choce experments. Envronmental and Resource Economcs, 41: Carlsson F. and Martnsson P., Does t matter when a power outage occurs? A choce experment study on the wllngness to pay to avod power outages. Energy Economcs, 30: Czakowsk M. and Hanley N., Usng labels to nvestgate scope effects n stated preference methods. Envronmental and Resource Economcs, 41: Garmenda E., Etxano I., Hoyos D., Pascual U., Cadñanos J.A., Díez M.A. and Lozano P., A decson support tool to ntegrate multple dmensons and dversty of actors n protected areas plannng and management: an applcaton to Natura 2000 network n Europe. Workng paper. Unversty of the Basque Country. Hanley N., Mourato S. and Wrght R.E., Choce modellng approaches: a superor alternatve for envronmental valuaton? Journal of Economc Surveys, 15: Hanemann W.M., 1984a. Dscrete/contnuous models of consumer demand. Econometrca, 52: Hanemann W. M., 1984b. Welfare evaluatons n contngent valuaton nformaton wth dscrete responses. Amercan Journal of Agrcultural Economcs, 66: Hensher D.A., Attrbute processng n choce experments and mplcatons on wllngness to pay. In: BJ Kannnen (Edtor), Valung envronmental amentes usng stated choce studes. A common sense approach to theory and practce. Sprnger, The Netherlands, pp Hoyos, D., Rera, P. Fernández-Macho, J, Gallastegu, C. and García, D., Valung envronmental mpacts of coastal development proects: a choce modellng applcaton n Span. Bltok workng papers. Hoyos, D., 2009, Recent Advances n Envronmental Valuaton wth Stated Preference Methods. PhD Thess UPV/EHU. 27

28 Hoyos, D., Marel, P. and Fernández-Macho, J., The nfluence of cultural dentty on the WTP to protect natural resources: some emprcal evdence. Ecologcal Economcs, 68: Hoyos, D., The state of the art of envronmental valuaton wth dscrete choce experments. Ecologcal Economcs, 69: Kahneman D. and Tversky A., Prospect theory: an analyss of decson under rsk. Econometrca, 47: Lancaster K., A new approach to consumer theory. Journal of Poltcal Economy, 74: Lancaster K., Modern consumer theory, Edward Elgar, Brokfeld. Lancsar, E. and Louvere, J., Conductng dscrete choce experments to nform healthcare decson makng. Pharmaeconomcs, 26(8): Louvere J., Hensher A. and Swat J., Stated choce methods: analyss and applcatons, Cambrdge Unversty Press, Cambrdge. McFadden D., Condtonal logt analyss of qualtatve choce behavour. In: P Zarembka (Edtor), Fronters n Econometrcs. Academc Press, New York. Mtchell, R.C. and Carson, R.T., Usng Surveys to Value Publc Goods: The Contngent Valuaton Method, RFF Press, Washngton D.C. Mogas J., Rera P. and Bennett J., A comparson of contngent valuaton and choce modellng wth second order nteractons. Journal of Forest Economcs, 12: Snowball J.D., Measurng the Value of Culture. Methods and Examples n Cultural Economcs, Sprnger, Berln. Tran K., Recreaton demand models wth taste dfferences over people. Land Economcs, 74: Tran K., Dscrete Choce Methods wth Smulaton, Cambrdge Unversty Press, New York. 28