The Effects of Experience on Preference Uncertainty: Theory and Empirics for Public and Quasi-Public Goods

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1 The Effects of Experence on Preference Uncertanty: Theory and Emprcs for Publc and Quas-Publc Goods Mkołaj Czajkowsk Nck Hanley Jacob LaRvere Strlng Economcs Dscusson Paper 0-7 August 0 Onlne at

2 The Effects of Experence on Preference Uncertanty: Theory and Emprcs for Publc and Quas Publc Goods Mkołaj Czajkowsk Faculty of Economc Scences, Unversty of Warsaw, Poland Nck Hanley Economcs Dvson, Unversty of Strlng, Scotland Jacob LaRvere 3 Department of Economcs, Unversty of Tennessee Keywords: Bayesan, demand estmaton, stated preference, generalzed multnomal logt, scale, scale varance. JEL Codes: C5, D83, Q5, H43 Abstract: Ths paper develops a model of demand estmaton n whch consumers learn about ther true preferences through consumpton experences. We develop a theoretcal model of Bayesan updatng, perform comparatve statcs over the model, and show how the theoretcal model can be consstently ncorporated nto a reduced form econometrc model. We then estmate the model usng data collected for two quas publc goods. We fnd that the predctons of the theoretcal exercse that addtonal experence wth a good wll make consumers more certan over ther preferences n both mean and varance are supported n each case. correspondng author, Długa 44/50, 00 4 Warsaw, Poland, (+48)554974, mq@wne.uw.edu.pl Strlng FK9 4LA, Scotland, UK, (+44) , n.d.hanley@str.ac.uk 3 55 Stokely Management Center, Knoxvlle, TN, , USA, (+) , jlarv@utk.edu

3 . Introducton Consumers often make decsons under uncertanty about ther preferences, such as when a frm ntroduces a new product. Experence goods are goods for whch consumers do not know ther preferences wth certanty, where nformaton about ther preference s learned wth each consumpton event (Nelson, 970; 974; Stgler et al., 977). Consumers are usually modeled as havng a true preference parameter, or type, whch they learn about through Bayesan updatng (Ackerberg, 003). Belefs regardng ther true preference for an experence good are then revealed n ther purchasng decsons. There s sgnfcant nterest n emprcally dentfyng how learnng about goods affects consumer demand (Erdem et al., 996; Ackerberg, 003; Osborne, 0). Identfyng how learnng affects preferences, and subsequently demand elastctes, s mportant for frms prcng decsons n order to effcently prce n an experence goods market (Crawford et al., 005; Goeree, 008). In ths lterature, a formal model of learnng and nformaton agglomeraton s often ntegrated nto the demand framework n a theoretcally consstent way (Ackerberg, 003; Israel, 005). It s somewhat surprsng, then, that learnng and experence have not been taken nto account n a theoretcally consstent way n demand estmaton for publc or quas publc goods such as outdoor recreaton demand or envronmental amentes, gven the nature of demand estmaton procedures for such goods and that experence has been shown to matter for preference uncertanty these contexts (Boyle et. al. 993; Whtehead et al., 995; Cameron et al., 997; Hanley et al., 009). Demand estmaton for publc or quas publc goods often nvolves non market valuaton because markets for such goods are ncomplete (Carson et al., forthcomng). For example, t s dffcult to value the establshment of a new natonal park or a bodversty conservaton program wth market data. As a result, demand estmaton for publc and quas publc goods makes use of a range of non market

4 valuaton methods. In stuatons where non use values are lkely to be mportant, stated preference methods wth dscrete choce experment elctaton format are often used. Choce experments nvolve elctng consumers wllngness to pay for a partcular envronmental amenty after sometmes lengthy descrptons of the potental uses, benefts and costs of the good beng evaluated. Dscrete choce experments, then, are a natural settng where prevous experence wth a publc good such as rver water qualty or wlderness land can nteract wth nformaton provded by the researcher n the course of a stated preference exercse to affect consumer s wllngness to pay. Ths paper nvestgates the theoretcal and emprcal mplcatons of explctly accountng for pror experence wth an envronmental good nsofar as t can affect consumers preferences. Assumng a Bayesan updatng rule, we show explctly how pror dstrbutons over utlty from a good wll be updated wth addtonal nformaton n the form of experence to nfluence posteror dstrbutons over utlty. We then apply a generalzed random coeffcents multnomal logt model (G MNL) whch captures the salent features of the theoretcal exercse. We estmate ths model usng two dfferent data sets. We fnd that experence, as shown n the theoretcal exercse, decreases the varance of utlty functon error term, but also the varaton of ths varance when t s allowed to dffer between respondents. Further, we fnd that the nformaton set (e.g., the sgnal) gven to respondents sgnfcantly changes both the varance of utlty functon error term, but also the varaton of ths varance. Both of these results suggest that respondents update ther preferences as a functon of consumpton experences and nformaton provded durng stated preference surveys n a way consstent wth Bayesan updatng. Our paper thus offers a theoretcally consstent and parsmonous emprcal technque for takng experencedrven and nformaton drven preference changes nto account for future researchers usng random utlty based valuaton approaches. 3

5 The remander of ths paper s organzed as follows: Secton develops a smple theoretcal model whch shows how nformaton can affect the dstrbuton of valuatons for experence goods. Secton 3 develops a generalzed random coeffcents logt model and dscusses ts propertes wth respect to the theoretcal exercse n Secton. Secton 4 ntroduces and explans the emprcal studes whch we evaluate. Secton 5 presents results and Secton 6 offers dscusson.. Theoretcal Motvaton Ths secton shows n a smple Bayesan framework the effects of experence on the observed choces of consumers when experence helps to refne consumpton choces. We demonstrate how ths framework can be carred over to the case of publc and quas publc goods wthn the standard random utlty model of demand (McFadden, 974; Hanemann, 984). Ignorng prevous experence wll make consumer choces appear more random than they actually are along two dmensons. Frst, prevous experence wll decrease the average magntude of the dosyncratc component of choces. Second, prevous experence wll decrease the varance of ths dosyncratc component. Put another way, Bayesan updatng predcts that prevous experence wll ncrease the scale and decrease the scale varance heterogenety for ndvdual consumers. In lne wth the standard random utlty model, assume the utlty derved from a good s: U β X () jt j j j jt where ndexes an ndvdual, j a good, and t tme. The trats of good j are gven by the vector margnal utlty over those trats of good j are X j and β j ; for exposton here, they are not assumed to be ndvdual specfc as wth a random coeffcents model but could be represented n that way wth no 4

6 loss. 4 The dosyncratc utlty component s assumed normal and d: jt N 0,. Assume further that there s an ndvdual/product fxed effect that can be thought off as an ndvdual s type, j. Each ndvdual s type s tself a realzaton of a random varable subject to a tme nvarant dstrbuton j j N 0,. We assume the varance of consumers true type utlty s constant across the populaton but that assumpton can be relaxed at no qualtatve cost. The consumers learnng problem can be thought of as learnng about the true propertes of ther type, j, and thereby leadng to changes n the dstrbuton of consumers utlty functon taste parameters. 5 Indvduals never receve a sgnal that perfectly reveals ther type n ths model. Instead t ndvduals observe the sum of ther tme nvarant type,. As a result, ndvduals must nfer j j jt what ther true type s by evaluatng the lkelhood they had the experence they dd gven ther prors over type and the dstrbuton of the dosyncratc error term. 6 Followng DeGroot (004) and Ackerberg (003), assumng prors over a consumer s type, 0 normal, j N 0, o can be represented as: j, are, after K consumpton experences wth the good, posteror belefs about type 4 We relax ths assumpton below and consder the mplcatons of more general utlty specfcatons n Appendx A. 5 Note that learnng could affect both means and varances of random taste parameters, and as a result, the mean and varance of wllngness to pay. Ths wll be dscussed n more detal below. 6 For example, when a new good s ntroduced, say a new restaurant, a consumer wll not lkely be able to dstngush between the possblty they lke that restaurant more than the average patron (e.g., ther j ) or f they happened to have a partcularly good experence on that occason (e.g., jt ). 5

7 k t K t t j N, () K K o o Note that f an ndvdual consumes product j n each perod, then K t. By nspecton, addtonal experence has an ambguous effect on the mean of belefs over type; the relatve strength of an ndvdual s experence must be compared to the reducton n mean from addtonal experences. The varance of belefs over type s fallng n experence (e.g., the second term falls as K ncreases). Now consder how ths model of learnng would manfest tself n the dynamcs of consumpton decsons. For exposton, assume an ndvdual s true type s half of one standard devaton below the mean type:.5, and that the varance of both the pror and true type s one. In ths example, we j plot the posteror gven an expected draws (e.g., the posteror condtonal on draws of the ndvdual s true type: t.5 ). Put another way, we parameterze ths example so that there s no nose j j jt ntroduced by the dosyncratc term. Fgure shows the updatng of the posteror dstrbuton of belefs over type, K j, for one and two draws respectvely. There are two mportant features of Fgure. Frst s that the consumer s posteror mean type, E j K, falls gven new consumpton experences because each sgnal s below the mean of ther pror. Second, the varance around the posteror mean s decreasng as successve sgnals mechancally decrease the varance of posteror belefs. In ths smple example, we assume that both consumpton sgnals are the mean true sgnal for expostonal purposes, but ths assumpton can be relaxed wthout qualtatve loss. 7 Fgure and equaton above both show that a model of Bayesan learnng dctates that addtonal 7 One could perform Monte Carlo smulatons over the entre dstrbuton not condtonng on realzed sgnals. There would be no qualtatve dfferences, though, as t would have the effect of ncreasng the sze of the tals n each posteror dstrbuton. 6

8 consumpton experences wth a good wll decrease the varance of a consumer s utlty for that good. Alternatvely, one can model uncertanty over an ndvdual s type and the dosyncratc error term as formng a composte error term. Wth ths approach, the varance of the composte error term should be allowed to decrease as experence levels wth a good ncrease. Effectvely, the magntude of the composte dosyncratc component of utlty decreases relatve to the determnstc component as experence wth the good ncreases. Now consder the mplcatons of updatng behavor for a multple good or multple attrbute dscrete choce model n a random utlty framework (McFadden, 974). Once agan, the utlty assocated wth any choce alternatve can be dvded nto a sum of contrbutons that can be observed by a researcher, and a component that cannot, and hence s assumed random. Specfcally consder the followng emprcal specfcaton of a random parameters multnomal choce model: U Alternatve j U βx, (3) t jt jt jt where: U jt represents respondent s utlty assocated wth selectng alternatve j out of a set of J avalable alternatves at tme occason t ; the stochastc component of the utlty functon may be nterpreted as resultng from researcher s nablty to observe all attrbutes of choce and all sgnfcant characterstcs of respondents (McFadden, 976), or as decson maker s choce from a set of hs decson rules (Mansk, 977). Pragmatcally, ntroducng the error term s equvalent of assumng that utlty levels are random varables, as t s otherwse mpossble to explan why apparently equal ndvduals (equal n all attrbutes whch can be observed) may choose dfferent optons; 7

9 x jt s a vector of respondent and alternatve specfc choce attrbutes,.e. goods or ther characterstcs; β represents a vector of ndvdual specfc taste parameters assocated wth margnal utltes of the choce attrbutes. Denotng the multvarate dstrbuton of these parameters n the sample as f, β fb, Σ, where b s a vector of sample means and Σ s a varance covarance matrx, wth a vector of square roots of dagonal elements s whch represent standard devatons of random taste parameters; s the scale parameter whch allows one to ntroduce the desred level of randomness to respondents choces. 8 The scale parameter can ndeed be allowed to be ndvdual specfc, as t s reasonable to allow dfferent agents n an economy to have relatvely larger or smaller dosyncratc components as opposed to determnstc components of the utlty functon. 9 The scale heterogenety of the agents can be descrbed wth the parameter, such that gven the scale dstrbuton g, g,. 0 The above specfcaton of the random utlty model accounts for unobserved preference heterogenety n terms of both taste parameters and scale. In addton, one can ntroduce observed preference heterogenety n the model by ncludng ndvdual specfc covarates of means of random taste parameters b, ther varances, the scale parameter or ts varance. A convenent reduced form 8 Note, that the scale cannot be econometrcally separated from the other parameters of the utlty functon and the estmates of taste parameters are n fact multplcatons of the underlyng taste parameters and scale (Febg et al., 00). Wth no loss of generalty one can normalze scale to and, due of the ordnal nature of varous utlty functons, treat the estmates of utlty functon taste parameters as true taste parameters, whch can only be nterpreted n relaton to each other. 9 For example, when one agent has very well defned preferences, one would expect ther determnstc component of utlty to be large n magntude relatve to the dosyncratc component. 0 In ths case the mean of the scale parameter s normalzed to. 8

10 way of accountng for prevous experence s by estmatng the term explctly as a functon of pror experence, z, so that g φz,. Note that ths s equvalent to experence nfluencng all the taste parameters n the same way. Provded that all utlty functon taste parameters are random and they are allowed to be correlated, ths effect may already be to some extent accounted for by offdagonal elements of Σ (Hess et al., forthcomng). Collectng the common effect for all taste parameters has, however, a very nterestng behavoral nterpretaton allowng scale to be a functon of prevous experence permts the magntude of the error term, j, to be systematcally related to experence. As a result, the relatve mportance of observable characterstcs n determnng utlty s exactly what s mpled by Fgure above: as experence ncreases, agents learn ther type wth more certanty so that the relatve mportance of observable characterstcs of the good and the consumer become relatvely more mportant (.e. choces become less random). Fnally, just as experence related covarates n the mean scale collect common effects for all taste parameters, ntroducng experence related covarates n the varance of the scale parameter collects common effects for varances of all random parameters. In ths case, the scale varance can be modeled as a functon of experence, g, expξz. Behavorally, ths effect allows experence to cause respondents to become more smlar/dfferent wth respect to how determnstc ther choces are. Ths paper proposes a way n whch experence can be accounted for n econometrc modelng of consumers preferences n a random utlty framework as ntroduced above. In partcular, by focusng on effects of experence on scale and scale varance, whch collect common effects for all means and varances of random taste parameters as explaned n the precedng paragraph, we allow for Bayesan updatng to nform the econometrc specfcaton. We demonstrate how ths can nfluence preferences Another way to nterpret ths s that the errors n the random utlty model are heteroskedastc n prevous experence. 9

11 and, as a result, wllngness to pay estmates. The method developed n ths paper s wdely applcable to both stated and revealed preference data. 3. Econometrc treatment In ths secton we set out a method for accountng for the effects of experence on consumers preferences n dscrete choce models, by allowng for experence related observable and unobservable preference and scale heterogenety n a manner consstent wth the theoretcal treatment of the precedng secton. We later apply these methods usng two case study data sets to nvestgate how experence and famlarty wth the good nfluences respondents preferences and scale. The random utlty framework presented n the prevous secton convenently lends tself to econometrc modelng random utlty theory s transformed nto dfferent econometrc models by makng assumptons about the dstrbuton of the random error term and the random parameters. Typcally, s assumed to be ndependently and dentcally (d) Extreme Value Type dstrbuted across ndvduals and alternatves; n addton, assumng that all the random taste parameters are multvarate normally dstrbuted 3 and that the ndvdual scale parameter s log normally dstrbuted 4 leads to the j There are other ways n whch experence has been ntroduced n demand estmaton studes and we brefly revew them n Appendx A. Ths model, though, s can be ntegrated wth those alternatve technques as well, but the technque we develop here s smlar to those dscussed n Appendx A f consumers, on average, have unbased prors. 3 β MVN b, Σ, so β bγως, where ΓΩ s a lower trangular matrx resultng from Cholesky decomposton of the varance covarance matrx Σ of random taste parameters ( Σ ΓΩΓΩ wth the vector of square roots of dagonal elements s ), such that Γ has ones on the dagonal and possbly non zero below dagonal elements accountng for correlatons of random taste parameters, Ω s a dagonal matrx of standard devatons s 0

12 Generalzed Multnomal Random Parameters Logt model type II (G MNL; Febg et al., 00). Followng the notaton ntroduced n secton II, respondent s utlty assocated wth choosng alternatve j s: U b υ x, (4) jt jt jt where the ndvdual specfc random taste parameters are now represented by a vector of ther populaton means b and ndvdual specfc devatons from these means υ. The new subscrpt t represents dfferent choce tasks the same respondent may face n dscrete choce experments an ndvdual s usually confronted wth numerous choce tasks whch allows the researcher to extract more nformaton from each respondent of the study, and facltates dentfcaton of preference and scale heterogenety (Ruud, 996; Revelt et al., 998; Fosgerau, 006; Hess et al., 0). The key focus of our theoretcal treatment s on the representaton of the effects of famlarty n a random utlty model. Therefore, n order to emprcally nvestgate these possble relatonshps between respondents famlarty wth the goods and the taste parameters and scale n ther utlty functons, we adapt the G MNL model to account for the effects of experence, as explaned n secton II. Ths can be done by ntroducng ndcators of experence or famlarty wth the good z as covarates or means and varances of random taste parameters β MVNb φz, Σexpψz scale and ts varance LN φz, ξz. 5 and/or as covarates of random, and ς s a vector of random, normally dstrbuted unobserved taste varatons assocated wth taste parameters (wth mean vector 0 and covarance (dentty) matrx I). 4 LN,, so exp where N 0 0 0, and. 5 Snce experence related covarates enter dagonal elements of Σ only (.e. only the varances of random taste parameters), dag exp Ω s ψ z.

13 The resultng model s flexble enough to capture observed and unobserved preference heterogenety, as well as observed and unobserved scale heterogenety. Importantly, allowng for scale heterogenety provdes a convenent way n whch the behavor of the error term can be a functon of prevous experence whch Secton II shows must be allowed for n order for the emprcal model to be consstent when allowng for Bayesan learnng. The above model specfcaton results n the followng probablty of observng respondent choosng alternatve j out of the J avalable alternatves at choce occason t : exp exp b φz υ xjt Pryt j Jt exp b φz υ xkt k where: φz exp ξz 0 υ ΓΩς exp Ω dag s ψ z. (5) Snce the probablty s condtonal on the random terms, the uncondtonal probablty s obtaned by multple ntegraton, the expresson for whch does not exst n closed form. Instead, t can be smulated by averagng over D draws from the assumed dstrbutons (Revelt et al., 998). As a result, the smulated log lkelhood functon becomes: logl exp Jt exp d d b φz υ xkt T d b φ z υd x I D jt log D d t k. (6) In the Results secton of ths paper we estmate the above emprcal model for two dfferent stated preference choce experments. Because of the mportance of nformaton processng n motvatng our approach, we are partcularly nterested n the coeffcents on z, whch wll be proxes for pror

14 experence wth the good beng studed, and whch enter the utlty functon as covarates of the scale parameter and ts varance, thus allowng for Bayesan updatng. 4. Descrpton of Data In ths paper, we make use of two dfferent choce experment data sets to explore the effects of respondent famlarty wth two dfferent envronmental goods, usng the theoretcal and econometrc framework set out above. Ths secton descrbes those two data sets. In the frst dataset, two dfferent nformaton treatments were gven to randomly selected respondents. Ths allows us to later test whether dfferng nformaton can sgnfcantly affect scale and scale varance heterogenety. The frst data set also uses prevous levels of experence wth a good to explan scale and scale varance heterogenety. The second dataset uses only prevous experence levels wth a good n order to explan scale and scale varance heterogenety as nformaton treatments do not vary across the survey sample. 4.. Raptor conservaton on heather moorland Management of heather moorlands n the uplands of the UK for Red Grouse shootng has led to declnes n several speces of brds of prey (Newton, 998), snce the am of grouse management s to maxmze numbers of brds avalable for shootng n the autumn, and brds of prey are seen as threats to grouse numbers. Grouse moor management nvolves a mxture of vegetaton management (e.g. heather burnng) and predator control (Hudson et al., 995). One partcular conflct whch has arsen n ths context concerns the management of Hen Harrers (Crcus cyaneus) on sportng estates. Hen Harrers are lsted as endangered raptors (brds of prey) due to populaton declnes n the last 00 years (Balle et al., 009). Economc costs to grouse moor owners arse because harrers prey on grouse, and arguments 3

15 between the conservaton lobby and the sportng estate communty have become polarzed over tme (Redpath et al., 004; Thrgood et al., 008). Evdence shows that () Hen Harrer denstes can ncrease to the extent that they make management for grouse shootng economcally unvable; () llegal kllng has resulted n a suppresson of harrer populatons n both England and Scotland (Etherdge et al., 997); and () that enforcement of current laws prohbtng lethal control has been neffectve (Redpath et al., 00). Another conc raptor speces, the Golden Eagle, s also found n heather moorlands. Golden Eagles have also been subject to llegal persecuton, partcularly on managed grouse moors (Watson et al., 989; Whtfeld et al., 007). To understand publc preferences over the conservaton of Hen Harrers on heather moorland, we desgned a stated preference Choce Experment (Hanley et al., 00). 6 The choce experment desgn conssted of four attrbutes. These were: Changes n the populaton of Hen Harrers on heather moorlands n Scotland. The levels for ths attrbute were a 0% declne (used as the status quo), mantanng current populatons, and a 0% ncrease n the current populaton. Changes n the populaton of Golden Eagles on heather moorlands n Scotland. The levels for ths attrbute were a 0% declne (used as the status quo), mantanng current populatons, and a 0% ncrease n the current populaton. Management optons. These ncluded the current stuaton, movng Hen Harrers ( MOVE ), dversonary feedng ( FEED ) and tougher law enforcement ( LAW ). These levels were 6 Ths dataset has been used n the context of combnng datasets wth dfferent scale n the context of new nformaton (Czajkowsk et. al. 0). That study does not account for experence, does not explctly model the theoretcal mplcatons of Bayesan updatng to nform the reduced form econometrc specfcaton developed here, nor does t allow for the precse nterpretaton of the results so as to support Bayesan updatng. 4

16 ncluded as labeled choces. That s, n each choce card, 4 optons were avalable. One represented the status quo, and then 3 choce columns showed varatons n other attrbute levels gven a partcular, labeled management strategy. Cost of the polcy. We told respondents that the cost level ndcated s the amount of extra tax whch a household lke yours mght have to pay f the government went ahead wth that opton. The levels used were 0 (the status quo), 0, 0, 5, and 50. Cost levels were chosen based on the results of a plot survey. Fgure gves an example of a choce card. Respondents were asked to carefully consder ther budgets and current expendtures n makng ther choces, and that they should not worry f they dd not feel that they had expert knowledge on the ssues, but that ther opnon was mportant to government polcy makng. Sx choce cards were gven to each respondent. Those respondents who chose the status quo, zero cost opton n each choce card were asked why ths was, n order to separate out protest bdders from people who dd not value Hen Harrer or Golden Eagle conservaton n moorlands. Havng completed ther choces, respondents were asked to read back carefully through these to make sure they were happy wth how they had completed these tasks. Fnally, a seres of soco economc and behavoral questons were asked, for example ncludng household ncome, and whether the respondent was a hunter or had ever been huntng. The choce experment was desgned to mnmze the determnant of the AVC matrx of the parameters (D error) gven the prors on the parameters of a representatve respondent s utlty functon usng a Bayesan effcent desgn (Scarpa et al., 008). The parameters of ths dstrbuton were derved from a prelmnary model estmated on data avalable from a plot study. 7 The fnal desgn conssted of 8 questonnare versons, each wth 6 choce cards per respondent. 7 The desgn for the plot study was also generated for D effcency, usng expert judgment prors. 5

17 Two samples were obtaned from a random selecton of households n Scotland. The samples dffered only n the nformaton provded to respondents, and each respondent receved only one set of nformaton. The frst survey, reported n Hanley et al. (00), used an nformaton pack developed solely by the research team, based on exstng research fndngs. The second survey used an nformaton pack whch was re wrtten by a group of stakeholders engaged n moorland ownershp, management and grouse shootng. In each case, the nformaton pack covered the followng tems: A descrpton of what we meant by the uplands n the UK how some uplands areas are managed as grouse moors the contrbuton that grouse shootng makes to the Scottsh economy the contrbuton of grouse management to mantanng heather moorlands, rather than allowng moorlands to be converted to rough grassland or plantaton forestry. A descrpton of the Hen Harrer, ncludng conservaton status and threats from llegal persecuton. A descrpton of Golden Eagles, ther conservaton status and current threats to the speces. The three alternatves for moorland management amed at Hen Harrers. The publc good beng valued n ths choce experment s thus the condton of heather moorlands n the Scottsh uplands n terms of () populatons of Hen harrers and () populatons of Golden Eagles. Responses were obtaned from a random selecton of households n Scotland, usng a seres of mal shots. Households were contacted by letter (addressed from the Unversty of Strlng), and a 3 stage Dllman procedure followed n terms of remnder letters and new copes of the survey nstrument. We obtaned 557 responses from,700 mal outs. Snce the nformaton provded to respondents vared 6

18 across the two surveys, we nclude a dummy varable to control for these dfferences n estmaton (study). We used the reported number of vsts to Scottsh uplands n the last months vst, as an ndcator of respondents famlarty wth the good. 4.. Preferences for water qualty mprovements n Northern Ireland. Ths study consdered the economc value of potental mprovements to coastal water qualty such as may result from mplementaton of changes to the European Unon s Bathng Waters Drectve n 05 to people lvng n Northern Ireland. The focus s on potental benefts to recreatonal users of coastal waters, and how these vary accordng to the extent of exposure to rsks. The focus of ths Choce Experment was on the valuaton of changes n coastal water qualty to those who use beaches n Ireland for recreaton, prncpally actve recreatonalsts such as surfers, swmmers and sea kayakers. Ths group of respondents s lkely to be partcularly affected by changes planned under revsons to the Bathng Waters Drectve, snce many of the water qualty parameters whch ths drectve focuses on are those lnked to human health and the exposure of beach users to llness from contact wth waterborne pathogens such as fecal colforms. The current revsons to the Drectve relate to greater restrctons upon the standards for bathng water: the current good standard becomes the future mandatory standard, the current excellent standard becomes the future good standard and the future excellent standard s twce as strct as the current excellent standard. 8 The attrbutes chosen for the Choce Experment descrbe three relevant aspects of coastal water qualty: benthc health, human health rsks, and beach debrs. We now descrbe each n more detal. 8 bathng/summary.html 7

19 Benthc Health Measures taken as part of complyng wth the revsed drectve wll mpact upon the health of the seas through mprovements at the benthc level. However, the concept of benthc health s not lkely to be understandable to most members of the publc, and so was related here to probable outcomes on vertebrate populatons (brds, fsh and marne mammal speces). Levels selected were: No Improvement to the current stuaton whch wll mean no changes to the numbers or chance of seeng fsh, brds and mammals. A small mprovement n Benthc Health whch wll mean that there wll be more fsh, brds and mammals. Ths wll mean that endangered speces wll be less lkely to dsappear from the seas around Northern Ireland, although respondents were told that t s unlkely that they would see any more fsh, brds or mammals on an average vst to the beach. A large mprovement n Benthc Health whch wll mean that there wll be many more fsh, brds and mammals wth an ncreased chance of you seeng them on your average vst to the beach. Health Rsks Health rsk was ncluded as fecal colform and fecal streptococc bactera concentratons are expected to be reduced under the new drectve standards. The levels of fecal colforms under current and future were then related to the rsk of a stomach upset of ear nfecton, based upon dose response relatonshps. Attrbute levels selected were: 0% Rsk No Change to the current rsk of a stomach upset or ear nfecton from bathng n the sea (current rsk as assessed by the EU). 8

20 5% Rsk Good Water Qualty acheved wth a somewhat reduced rsk of stomach upsets and ear nfectons although rsks stll exst n partcular for vulnerable groups such as chldren. Very Lttle Rsk Excellent Water Qualty acheved wth a larger reducton n the rsk of stomach upsets and ear nfectons. Debrs Management In addton to the lkely drect mpacts of the upcomng changes to the bathng water drectve t was dentfed that management would mpact upon the amount of ltter and other debrs found on the beaches and coastal waters. Ths was related to the amount of debrs (such as cans, bottles, cotton buds, plastc bags, santary products etc.) on the beach and n the water. Three levels were selected: No Change current levels of debrs wll reman. Preventon more fltraton of storm water, more regular cleanng of flters and better polcng of fly tppng. Collecton and Preventon debrs collected from beaches more regularly n addton to fltraton and polcng. Fnally, n order to estmate measures of economc value of changes n the envronmental attrbutes lsted above, we needed to nclude a cost attrbute n the desgn. We used the per vst cost to the ndvdual of vstng a beach wth a gven set characterstcs (the costs of travel to the ste) as ths cost attrbute. Travel costs have been used before as the prce attrbute n several choce experments relatng envronmental qualty changes to recreatonal behavor (e.g., Hanley et al., 00; Chrste et al., 007). Sx levels of addtonal cost were selected: 0, 0.6,.6, 3, 6, and 9. 9

21 The desgn of the experment was generated usng effcent desgn prncples. Wth three blocks, ths meant that each ndvdual responded to 8 choce cards. In each choce card, respondents were asked to choose the opton they preferred from three choces. A sample choce card n ncluded as Fgure 3.Some 558 respondents were surveyed on ste at a range of beaches around the Northern Irsh coast n autumn 0. In ths study, the ndcator of respondents famlarty wth the good coastal water qualty whch we used was the reported number of days spent at the beach each year bdays. It should be noted that n both studes, our measures of famlarty, namely number of vsts to the uplands and the beach, are not exogenous. These are lkely to be correlated wth preferences for amentes assocated wth each publc good. Indeed, fndng an nstrument for experence or famlarty can always be an ssue n emprcal work on experence goods. As a result ths study cannot dentfy a causal lnk between experence and scale nor scale varance. We can, however, stll construct and estmate a model whch s theoretcally consstent wth Bayesan updatng of preferences and test whether the theoretcal predctons of the model are correct. 5. Results We now turn to the analyss of data collected n the two emprcal studes descrbed n secton IV. For each dataset we estmated the augmented G MNL model descrbed n secton III, whch allows us to account for possble effects of experence on respondents preferences, assumng all taste parameters to be random, normally dstrbuted, and possbly correlated. The ndcators of respondents experence or famlarty wth the goods were ncluded as covarates of scale and ts varance. The estmaton was performed n MatLab usng 000 shuffled Halton draws to smulate dstrbutons of random parameters. Snce the log lkelhood functon descrbed n secton III s not necessarly convex we used multple 0

22 startng ponts to ensure convergence at the global maxmum. Standard errors of coeffcents assocated wth standard devatons of random parameters were smulated usng Krnsky and Robb method wth 0 6 draws (Krnsky et al., 986). The estmaton results for the two studes are presented n Tables and. The attrbutes related to the choce varables of the raptor conservaton model (Table ) nclude alternatve specfc constants assocated wth dfferent protecton programs ( LAW, FEED, MOVE ), dummy coded levels of mprovement of hen harrers ( HH, HH ) and golden eagles ( GE, GE ), and the contnuously coded cost ( FEE ). The parameters were allowed to be study specfc (superscrpts on varable names ndcate the two dfferent samples), except for cost FEE, whch was constraned to be equal n both studes. 9 As a result, the vector of the attrbutes was: X LAW, FEED, MOVE, HH, HH, GE, GE, LAW, FEED, MOVE, HH, HH, GE, GE, FEE. (6) The ndcator of experence and famlarty wth the analyzed goods whch we decded to use n ths study was vst the reported number of vsts to Scottsh uplands n the last months (mean.9). In addton, a bnary varable study enterng as a covarate of scale and ts varance, whch allows us to control for possble scale dfferences between the two jontly estmated samples. In the case of the water qualty study the followng dummy coded choce attrbutes were used: SQ an alternatve specfc constant assocated wth the no change alternatve, mprovements n benthc health 9 The model allows for correlatons between all random parameters wthn each study only. Ths means that we constraned some elements of the Cholesky matrx to equal 0, to rule out correlatons between varables assocated wth dfferent studes. For example, t would make no sense for harrers n study ) to be correlated wth appeared together. HH (partal mprovement of hen HH (analogous attrbute for study ), as these attrbutes never

23 and populaton ( BH small ncrease, BH large ncrease) wth no mprovement as a reference level, reductons of health rsks ( HR reducton to 5% rsk, HR reducton to very lttle rsk ) wth the current 0% rsk as a reference level, and mprovements n debrs management ( DM preventon, DM collecton and preventon). In addton, the lnearly coded varable FEE represented the addtonal cost of travellng to each beach. The resultng vector of choce specfc varables was: X SQ, BH, BH, HR, HR, DM, DM, FEE. (6) We used bdays reported number of days spent at the beach each year (mean 74.89) as a proxy of respondents experence or famlarty wth coastal water qualty. In the model for the raptor conservaton study all taste parameters are hghly sgnfcant and of expected sgn. Statstcal sgnfcance of coeffcents assocated wth standard devatons of normally dstrbuted parameters ndcates that there s substantal unobserved preference heterogenety wth respect to all taste parameters. The alternatve specfc constants assocated wth each protecton program ( LAW, FEED, MOVE ) are relatvely hgh. Coeffcents assocated wth mprovements n hen harrers ( HH ) and golden eagle ( GE ) populatons show that overall respondents were more concerned wth the latter, but n both cases dsplayed only lmted senstvty to the scale of mprovement. The hgh and statstcally sgnfcant value of ndcates the presence of hgh unobserved scale heterogenety respondents were dfferent from one another n terms of how determnstc or how random ther choces were. In addton, we found that ntroducng a dataset specfc dummy varable study as a covarate of scale and ts varance proved to be an effcent way of controllng for the dfferences n scale and ts varance between the two samples. Put another way, the nformaton treatment sgnfcantly affects the average relatve magntude of the error component versus the determnstc component of utlty for respondents (scale) n addton to sgnfcantly affectng the

24 varaton n the average relatve magntude of the error component versus the determnstc component of utlty across respondents (scale varance). Fnally, we note that ncreases n the measure of experence used here, namely the number of vsts to Scottsh uplands n the last months (vst ), decreased respondents scale parameters. Ths means that respondents who were more famlar wth uplands made, statstcally, more determnstc choces. At the same tme, vst sgnfcantly decreased scale varance, ndcatng that the scale parameters of respondents who had more experence became more smlar. Put another way, we fnd that addtonal experence decreases the average relatve magntude of the error component versus the determnstc component of utlty for respondents (scale) and sgnfcantly decreases the varaton n the average relatve magntude of the error component versus the determnstc component of utlty for respondents (scale varance). Both of these results are consstent wth the model of Bayesan updatng developed n Secton. The taste parameters of the coastal water qualty study are also very well behaved, all hghly sgnfcant and of expected sgn. As n the case of the raptor conservaton study, there s a consderable amount of unobserved preference (taste) heterogenety. The results ndcate that respondents perceved debrs management as the most mportant, followed by the mprovements n benthc health and health rsks. We used the number of days a respondent spent at the beach n the past year ( bday ) as a measure of experence wth the good. As n the other study, respondents who vsted beaches more often had a sgnfcantly hgher scale parameter (.e. lower magntude of the error component n ther random utlty functon), and sgnfcantly lower scale varance. Ths mrrors the results from the frst study and s agan consstent wth the model o Bayesan updatng developed n Secton. 3

25 6. Conclusons It s surprsng that the full mplcatons of experence on preference uncertanty have not receved more attenton n the lterature on the estmaton of demand for publc goods (lke conservaton programs) for whch market data does not exst. The key theoretcal result of ths paper, that experence sgnfcantly decreases the varance of a respondent s random utlty error term and the varance of that error term across respondents, s consstent wth a model of Bayesan updatng. Ths paper then develops a reduced form econometrc model of demand estmaton whch s consstent wth such a theoretcal framework. The man emprcal fndng s that these theoretcal predctons of the effects of more experence on the random component of utlty and how ths s dstrbuted across respondents are supported by two data sets relatng to two dfferent envronmental goods: a pure publc good (bodversty conservaton n the case of the moorland raptor study) and a quas publc good (coastal water qualty and amenty). 0 Ths econometrc model s also mplementable wth revealed preference data and, as shown n the Appendx A, can be ntegrated nto models whch preference parameters are allowed to be functons of prevous experence levels as well. There are several mplcatons of the results n ths paper. Frst s whether consumers do update as Bayesans or use some other updatng procedure. Ths paper shows that we cannot reject a Bayesan model of updatng. However, ths does not mply that the Bayesan model s the correct one. Other models posted by the lterature nclude behavoral models such as confrmatory bas and cogntve load (Rabn et al., 999; Gabax et al., 006). Second, t s unclear exactly how the dstrbuton of experence wthn the data can nteract wth the estmaton of random utlty parameters. Snce all parameters of the utlty functon can only be nterpreted relatve to scale, ths s a non trval pont. Thrd, and potentally most mportant for stated preference work, the results ndcate that the mportance of pror 0 We refer to coastal water qualty and amenty as a quas publc good snce ncreased partcpaton n beach recreaton due to an mprovement n water qualty could reduce the utlty of a trp to the beach due to crowdng. 4

26 experence, the nformaton presented to respondents, and the nteracton of the two has been largely omtted n theoretcally consstent emprcal work. Ths s especally troublng gven the nature of stated preference work: respondents are presented wth a large amount of nformaton and asked to thnk about t before statng ther preferences for varous polcy outcomes. The nteractng roles of experence and nformaton provson are thus partcularly mportant n ths feld. 5

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