ISSN 1835-9728 Environmenal Economics Research Hub Research Repors Inducing Sraegic Bias: and is implicaions for Choice Modelling design Michael Buron Research Repor No. 61 May 2010 Abou he auhors Michael Buron is a Professor a he School of Agriculural and Resource Economics, Universiy of Wesern Ausralia E-mail: michael.buron@uwa.edu.au * This research is par of he Environmenal Economics Research Hub Projec 8: Divergence beween communiy and exper valuaions of ecosysems, which is funded by he Ausralian Commonwealh Environmenal Research Faciliies (CERF) programme. Valuable commens from Jonelle Cleland, Ben Whie and Dan Rigby are graefully acknowledged 1
Environmenal Economics Research Hub Research Repors are published by The Crawford School of Economics and Governmen, Ausralian Naional Universiy, Canberra 0200 Ausralia. These Repors presen work in progress being underaken by projec eams wihin he Environmenal Economics Research Hub (EERH). The EERH is funded by he Deparmen of Environmen and Waer Heriage and he Ars under he Commonwealh Environmen Research Faciliy. The views and inerpreaions expressed in hese Repors are hose of he auhor(s) and should no be aribued o any organisaion associaed wih he EERH. Because hese repors presen he resuls of work in progress, hey should no be reproduced in par or in whole wihou he auhorisaion of he EERH Direcor, Professor Jeff Benne (jeff.benne@anu.edu.au) Crawford School of Economics and Governmen THE AUSTRALIAN NATIONAL UNIVERSITY hp://www.crawford.anu.edu.au 2
Table of Conens Absrac 4 1. Inroducion 5 2. Concepual Focus 5 3. Sudy Design 7 4. Treamens 9 5. Descripive Analysis 13 6. Summary of Descripive Analysis 19 7. Esimaion 20 8. Idenificaion of Bias 25 9. Conclusions 27 References 29 Appendix 1 30 3
Absrac: I has been suggesed ha he naure of he ask wihin a muli-aribue muli-alernaive choice experimen may be sufficienly complex o make i difficul for individuals o develop response sraegies o sraegically bias heir answers. This experimen esed ha hypohesis by seing experimenal condiions ha provide incenives for sraegic bias. By changing design parameers one can invesigae wheher he sraegic bias can be reduced. The answer is effecively no: under mos circumsances, respondens could find a sraegy ha achieved significan bias in inferred preferences. The circumsances where his did no occur (involving ranking alernaives, raher han selecing a single preferred alernaive) he inferred preferences refleced neiher he inended bias, nor heir original preferences, making he answers useless o boh responden and researcher. Keywords: Sraegic bias, choice modeling, complexiy. JEL classificaion: Q51, C91. 4
1. Inroducion Valuaion of non-marke environmenal goods has relied upon hypoheical, saed preference mehods. One issue wih he coningen valuaion mehod is is exposure o sraegic bias, whereby someone wih a paricular ineres can inflae he perceived value hey place upon an oucome, beyond he level hey would give if hey acually had o pay for ha oucome. There is an unsuppored asserion made in some of he environmenal valuaion lieraure ha he choice modelling (CM) approach o experimenal design can reduce he abiliy of respondens o sysemaically selec choices ha are sraegically biased, as compared o he more radiional dichoomous choice echniques used in coningen valuaion. Surprisingly, in he conex of designing choice experimens, echnical guidelines for minimising sraegic bias are lacking, and here appears o be lile empirical research exploring his issue. This is a paricular issue for he CERF projec, as i will be working wih environmenal scieniss from a range of disciplines, and here is a concern ha heir responses may be biased by heir disciplinary perspecive. The objecive of he proposed experimen is o invesigae he implicaions of choice experimen design on he possibiliy of a responden achieving a sraegic bias in he oucome. I uses sudens as he respondens, and gives hem a srong financial incenive o simulae sraegic bias. 2. Concepual focus The issue of sraegic bias is linked o he concep of incenive compaibiliy, which is receiving increasing aenion wihin he choice modelling lieraure. For incenive compaibiliy, a survey quesion needs o fulfil wo crieria (Carson and Groves, 2007). Firsly i has o be consequenial in so far as he respondens answer has o have an influence on some decision, and ha he responden cares abou he oucome of ha decision. Wihou consequenialiy i is claimed ha i no possible for economic heory o predic respondens behaviour (Carson and Groves, 2007), alhough Mazar e al (2007) sugges ha ruh elling may be he oucome as a resul of individuals preference o see hemselves as hones. Assuming ha an experimen is consequenial does no necessarily imply ha answers will be ruhful if he design of he quesion is such ha here are incenives o misrepresen one s preferences, if ha increases one s personal benefis. An experimen is incenive compaible if i is designed so ha here are no incenives o give anyhing bu answers ha reflec he rue preferences of he responden. The requiremens for incenive compaibiliy wihin he conex of a ypical choice modelling experimen are quie severe: apar from he issue of consequenialiy, single sho binary-choice quesions are he only forma ha can ensure incenive compaibiliy wihin he conex of public good provision. This is no he forma ha is currenly mos ypically used (muliple quesions, wih muliple alernaives wihin each) and no he conex wihin which he argumens abou he benefis of choice experimens are made. In fac, 5
argumens abou he benefis of choice experimens (CE) have been based only informally around he issue of incenive compaibiliy, o he exen ha he forma may make i harder for respondens o work ou how o behave sraegically. We now urn o consider hese argumens. Alhough he suggesion is made, he lieraure is ofen vague on he exac reasons as o why he choice experimen 1 should be less prone o sraegic bias. Thus: choice modelling generally avoids an explici eliciaion of respondens willingness o pay by relying insead on raings, rankings or choices amongs a series of alernaive packages of characerisics from where willingness o pay can be indirecly inferred. As such, CE may minimise some of he response difficulies found in CVM (proes bids, sraegic behaviour, yeah saying). Bu his poin has ye o be demonsraed. Hanley e all (2001) p448. Similarly Adamowicz e al, (1999) sae Sraegic Behaviour should be minimal in Saed Preference asks since he choices are made from descripions of aribues and i is no clear which choice will over- or under represen a valuaion (p467). Lu e al (2008) sugges here may be wo processes a work wihin he CM environmen: By adding complexiy o he SP ask, respondens may exhibi less bias. This may be parly occur because of he exra effor required o complee he exercise wih bias, bu i is more likely o occur because of respondens failing o see any clear single purpose o he exercise (p128). Two possible issues emerge from hese perspecives. The firs is ha he benefi of he CE approach is hrough masking inen. The implicaion is ha respondens may hold values for all aribues, bu would be prepared o oversae he value held for any of hem, on he assumpion ha i was he policy arge. Faced wih a number of possible aribues, and unable o idenify which of hese is he rue arge of ineres o he policy maker, hey resor o revealing he ruh. However, his argumen does no preclude he possibiliy of respondens oversaing he value of all aribues, presumably by downplaying he cos aribue. The second possibiliy is ha he responden wans o oversae he value placed on an aribue relaive o ohers i.e. hey genuinely hold a preference for one aribue, and wish o inflae his value compared o ohers in he design. The increased complexiy of he CE srucure is hypohesised o make i difficul o idenify which choices will lead o his resul. However, if his argumen is rue, hen i undermines he efficacy of using he CE srucure a all. Indeed, one could argue ha sraegic bias is simply an expression of a well-behaved bu consruced uiliy funcion, and i is unclear as o why he CE srucure should make i difficul o reveal his consruced uiliy funcion: and if i does, why i allows he revelaion of a normal uiliy funcion. As such, he asserions ha CE will lead o reducions in sraegic bias seem o be speculaive a bes. The sudy implemened is focused on he second of hese hypoheses: ha ask complexiy leads o a reduced abiliy o sraegically influence oucomes. 1 This working paper assumes ha he basic srucure and analyics of choice experimens is known: see Hensher e al (2005) for an overview. 6
3. Sudy Design The objecive of his sudy is o invesigae wheher complexiy in he repeaed choice experimen ask is sufficien o preven sraegic behaviour. I does no address he issues of incenive compaibiliy of he design iself, or issues of provision mechanisms: indeed i conrols for incenive compaibiliy by aemping o deliberaely induce incenives for sraegic behaviour. The vehicle for he valuaion sudy was he desirable characerisics of rened accommodaion. This was chosen as i is a familiar concep o sudens, and one could easily idenify a number of relevan characerisics. The fac ha his is a privae good raher han a public good (such as an environmenal oucome) is no seen as an issue, as he objecive of he sudy is o idenify he abiliy of respondens o influence he oucome of he design. Five aribues were seleced (signs in parenheses indicae he anicipaed impac of he aribue on uiliy) : Ren per week (-ve), Disance from UWA (-ve), Toal number of people sharing (?), Furnished/unfurnished (+ve) and Norh/Souh of he river (+ve). Furnished sae is 1 if furnished, 0 oherwise, and Norh/Souh of he Swan River: 1 if Norh, 0 if Souh. The laer aribue needs some explanaion wih respec o he geography of Perh: he Swan River splis he ciy in wo, wih limied vehicle access via 2 bridges from one side o he oher wihin he ciy. The raffic bole neck his imposes, and he need for changing on public ranspor if one ravels from he Souh means ha access o UWA is likely o be seen as less easy if one lives a similar geographical disance from UWA, bu Souh of he river. However, i may also ac as a proxy for oher characerisics of accommodaion, such as access o beaches or oher infrasrucure. Respondens were iniially asked o complee a se of 8 choice ses, having been given he informaion ha he sudy was ineresed in he issue of preferences for suden housing, given is shorage in Perh. Having compleed hose iniial choice ses, he rue purpose of he sudy, he invesigaion of bias, was revealed. Respondens were old ha he ype of survey hey had jus compleed may be subjec o sraegic behaviour and ha in he remaining pars of he sudy hey were going o be asked o deliberaely behave sraegically, and aemp o bias he oucomes of he research. The incenive mechanism for doing so was he reward sysem of he survey iself: all paricipans were enered ino a loery for a significan ($400) reward bu hose who managed o bias heir responses he mos, in he way required, would ge a greaer number of enries ino he loery, increasing heir chances of winning. The full ex of he insrucions given is in Box 1 below. Box 1: Insrucions o respondens, explaining he incenive mechanism o induce sraegic behaviour 7
The ype of quesions ha you have jus compleed are very commonly used in valuing new producs or environmenal asses. From he choices made, and he levels of he aribues ha are included in he alernaives, i is possible o idenify how, on average, he respondens o he survey are rading off he differen aribues of he accommodaion. One issue is he exen o which hey are open o manipulaion: ha people will no give heir rue answers o he quesion because hey wan o ry and influence he oucomes in a paricular way. I is unlikely ha you were doing his, bu i may be he case where people ry and oversae he imporance of some feaure, in an effor o change public policy, or change he ype of produc provided. In he nex secion of his survey, you will be asked o deliberaely change he way ha you answer he quesions, o mimic his ype of biased response. Please read he following informaion carefully. Undersanding i will have a srong impac on your chances of winning he $400. In he following secion, you will be presened wih an addiional se of quesions. When answering hese quesions you should behave as if you wan o give he impression ha Being CLOSE o UWA as he mos imporan aribue o you, You wan his o be idenified as more imporan han he oher aribues of he accommodaion. A group of 40 of you have been given he ask of rying o influence he value aached o being CLOSE o UWA Oher groups of respondens have been given he ask of biasing he imporance placed on oher aribues. The exen o which you, as a group, are more successful in manipulaing he oucomes of he sudy will change he likelihood ha you will win he $400. Everyone who complees he survey will be awarded 1 enry ino he draw. Each person in he group of 40 who manage o bias heir designaed aribue he mos will be awarded 3 enries each ino he draw, increasing heir chance of winning he $400. The winner of he $400 will be drawn a random, and he draw will be moniored, o ensure ha he process oulined above is followed The firs paragraph explains he rue issue being invesigaed, while he second esablishes he condiions for incenive compaibiliy for achieving bias. The survey was designed o preven a reurn o he earlier choice ses o change answers. The second se of choice quesions was an exac replicaion of he iniial se. Evaluaion of he exen of bias was made relaive o he iniial se in each case. One criicism of his approach is ha here may have been learning or faigue effecs, and an alernaive may have been o compare he bias oucomes wih a conrol group who compleed 16 choice ses, wihou incenives o bias. This was no underaken because i was 8
anicipaed ha he inducemen o bias would overwhelm any oher learning or faigue effecs ha may be presen. No furher informaion was given on he proocols ha would be used o define he mos successful bias oucome. Analysing he responses o he choice quesions hen allows one o idenify he abiliy of he responden o influence responses, and he impac of quesion design on ha abiliy. The saisical design of he survey and he exac insrucions o respondens was varied, allowing some judgemen o be made on he exen o which respondens were able o bias oucomes relaive o reamens. To undersand ha process, he differen reamens need o be oulined. 4. Treamens As noed in he inroducion, one hypohesis is ha he complexiy of he repeaed choice experimen may make i difficul o idenify appropriae sraegic behaviour. Therefore in he design a number of differen aspecs of complexiy were included. Work on he impacs of complexiy on behaviour in choice experimens has idenified a number of poenial aspecs ha may impac on behaviour (e.g. Hensher, 2006). Two of hose are seleced here: a number of alernaives wihin a choice se and he number of levels wihin aribues. Wheher respondens were asked o choose he bes alernaive or o rank all was also used. I was unclear ex ane how his may affec behaviour: ranking is clearly a more complex process, and hence makes i harder o behave sraegically, bu i may appear o offer more suble opions for influencing oucomes. I is imporan o remember ha no informaion was given on how he choices made would be analysed, bu i is perhaps reasonable o assume ha respondens would infer ha he full ranking informaion would be used, given i was being requesed (bu see he saisical analysis below). The final issue is on he number of aribues ha he respondens were asked o reveal bias owards. A quesion ha has no received explici aenion wihin he incenive compaibiliy lieraure is wheher here are specific aribues wihin an alernaive ha respondens are concerned abou and rying o influence he valuaion of. Usually he discussion is phrased in erms of he alernaives as a whole (i.e. of hree candidaes, who do you voe for: of four possible environmenal inervenions, which is preferred). However, wihin mos choice experimens, here can be a diverse se of aribues, ranging over a variey of oucome ypes (for example, designs may include aribues ha reflec pure exisence values for he resource, recreaional use values and social values such as he impac of any policy change on employmen levels or rural populaions). Sraegic behaviour may be focussed on paricular aribues. Of course, is aribue levels ha lead o aggregae uiliy from each alernaive, and ha will ranslae ino choices over alernaives (sraegic or oherwise). As such, he resuls from he incenive compaibiliy lieraure remain: faced wih more han a binary choice, here may be incenives o selec an alernaive oher han he ruly mos preferred. However, he naure of he rules ha deliver opimal sraegic oucomes are likely o be more complex if one is aemping o influence he provision of more han one aribue. To invesigae his, some samples are asked o bias one aribue, while ohers are asked o bias 2. 9
In summary, he mea-design consiss of 4 variables, each wih 2 modes: Number of alernaives per choice se (i.e. 3 or 6) The number of levels for cos and disance aribue (i.e. 4 or 6) Wheher respondens had o selec he mos preferred alernaive or rank all alernaives Wheher respondens were aemping o bias he effec of one aribue, or wo. This gives a oal of 16 poenial reamens (2 4 ) in he mea-design. However, due o limied suden numbers (see below on recruimen mehods) only, only 11 reamens were implemened. Given he objecive of he sudy, he focus was on he more complex designs. Table 1 below shows he full se of 16 possible designs, wih he 11 implemened designs shaded in grey. Table 1 Experimenal design showing 16 poenial reamens and he 11 implemened reamens shaded in grey. Aribues being influenced 1 2 Aribue levels 4 6 4 6 3 Rank All D1 D6 D9 Number of Selec Bes D2 alernaives 6 Rank All D3 D4 D7 D10 Selec Bes D5 D8 D11 The aribues and heir levels are repored in Table 1 below. Table 2 The aribue levels Aribue Levels Ren per week ($) 4 levels: 75,125,150,200 6 levels: 75,100,125,150,175,200 Disance from UWA (km) 4 levels: 5,10,15,20 6 levels: 8,10,12,14,16,20 Toal number of people sharing 1,2,3,4 Furnished/unfurnished 1/0 Norh/Souh of river 1/0 10
Wih regards o he issue of wheher hey have o influence one or wo aribues, in he case of he laer, he insrucions on he arge for bias were adjused accordingly: Box 2: Alernaive wording for respondens required o influence 2 aribues. In he following secion, you will be presened wih an addiional se of quesions. When answering hese quesions you should behave as if you wan o give he impression ha Being CLOSE o UWA and being NORTH of he river are he mos imporan aribues o you, You wan hese o be idenified as more imporan han he oher aribues of he accommodaion. A group of 40 of you have been given he ask of rying o influence he value aached o being CLOSE o UWA and being NORTH of he river. Alhough he mea-design conains 16 poenial reamens, here are only 4 saisical designs required (as ranking versus selecing bes and influencing 1 or 2 aribues operaes independenly of he formal CE design). All designs were generaed using Ngene (Rose e al, 2009), assuming a linear uiliy funcion in he 5 aribues, using 8 choice ses per design. The relaive simpliciy of he aribue specificaion means ha designs could be compleed wih 8 choice ses, and hence here was no requiremen o block he design. An adapive design process was employed. A pre-es o check he funcionaliy of he on-line survey allowed an iniial sample o be colleced based on a 3 alernaive - 4 aribue level design, designed o minimise D error. This convenience sample was used o esimae priors for he parameers of he uiliy funcion. These priors were hen used o re-generae a revised design, bu his ime using an S efficiency crierion (i.e. minimising sample size required o esimae he models: see Scarpa and Rose, 2008). Given he limied number of respondens and he large number of poenial reamens, achieving efficiency on his crierion was seen as he mos valuable. This revised design was hen used as he basis for collecing a full se of daa for reamens D1 and D2 (see Table 2 above), and his daa used o esimae a furher revised se of priors. These priors were hen used o generae designs for he hree more complex cases (6 alernaives-4 aribue levels; 3 alernaives-6 aribue levels; 6 alernaives-6 aribue levels). In all cases S efficiency was used as he design crieria. The minimum number of respondens needed was esimaed a 9 for all designs, given he revised priors and assuming ha each suden would complee he full se of 8 choice quesions. 11
A general call was made o sudens a he Universiy of Wesern Ausralia (UWA), Perh, o paricipae in a survey. This required an iniial sign-up. Sudens who expressed an ineres in compleing he sudy were randomly allocaed o a group of approximaely 40 sudens. Each group compleed a unique survey, ermed a reamen. I was iniially inended ha all 6 reamens would be covered bu here were only sufficien sudens o complee 11. Each reamen differed only in he srucure of he choice experimens presened o he responden. A oal of 40 inviaions were sen ou o sudens for each version of he survey. Alhough hey had voluneered o complee he survey, no all inviaions were aken up. Table 3 gives sample numbers for hose who compleed he enire survey by relevan reamens (and hence were eligible for reward). Table 3: Number of compleed surveys by reamen Aribues being influenced 1 2 Aribue levels 4 6 4 6 3 Rank All D1 27 D6 26 D9 21 Number of Selec 1 D2 29 alernaives 6 Rank All D3 24 D4 27 D7 21 D10 29 Selec 1 D5 30 D8 24 D11 24 12
5. Descripive analysis Before considering a more formal saisical analysis, i is informaive o look a he acual choices made, by quesion. Alhough here are 11 differen reamens considered wihin he overall experimen, in erms of he aribue srucures wihin he choice ses here are only four unique designs (using 3 or 6 alernaives, and 4 or 6 aribue levels): he oher elemens of he experimen (selecing 1 or ranking, and biasing 1 or 2 aribues) are independen of he choice se design. Each of he four will be considered in urn. Noe ha for hose who had o rank (as opposed o selec a single bes alernaive) have been re-scored so ha heir firs ranked alernaive only is considered. In he four ables ha follow, he percenage of respondens who selec an alernaive are repored, for boh he pre-bias and pos-bias condiion. Noe ha hese are he same people wihin each reamen. The aribue value for Disance and Norh/Souh are repored for each quesion and alernaive. For hose who are aemping o bias 1 aribue only, hen ha is disance, for hose influencing 2, i is boh. The alernaive which would appear o be he dominaing alernaive, given he bias condiion is indicaed wih wo sars (**). This will be he case where here is only one alernaive wih he minimum disance, or only one alernaive where here is boh a minimum disance and Norh=1. In he case where here are several alernaives wih he same minimal disance, or where i is no possible o find a single alernaive ha achieves boh minimal disance and Norh, all possible alernaives are marked wih one sar (*). The sar sysem gives a simplisic assessmen of he opions one migh expec he bias-condiion o selec. However, noe ha i does no consider any of he oher aribue levels (i.e. cos) and in he case of rankings, i does no consider wha was being seleced as 2 nd or 3 rd opions. 13
Table 4 Percenage of sample selecing an alernaive, pre-bias and pos-bias: Design wih 3 alernaives and 4 levels (Treamens D1,D2,D6), by quesion. Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Alernaive 1 Alernaive 2 Alernaive 3 Pre-bias Pos- bias Dis=15,Norh=0 Dis=20,Norh=0 Dis=10,Norh=0 D2 selec: bias 1 4 4 38 0 58 96 ** D1 rank: bias 1 28 8 28 88 44 4 ** D6 rank 2 28 16 36 76 36 8 ** Dis=10,Norh=0 Dis=10,Norh=1 Dis=20,Norh=0 D2 selec: bias 1 38 48 * 35 36 * 27 0 D1 rank: bias 1 36 56 * 28 27 * 36 0 D6 rank: bias 2 50 12 23 88 ** 27 0 Dis=20,Norh=0 Dis=8, Norh=0 Dis=8, Norh=1 D2 selec: bias 1 42 0 35 48 * 23 52 * D1 rank: bias 1 48 4 16 0 * 36 96 * D6 rank: bias 2 58 4 19 8 23 88 ** Dis=20,Norh=0 Dis=8, Norh=1 Dis=8, Norh=1 D2 selec: bias 1 8 8 42 56 * 50 36 * D1 rank: bias 1 8 4 4 0 * 88 96 * D6 rank: bias 2 8 4 0 0 * 92 96 * Dis=8, Norh=1 Dis=10, Norh=1 Dis=15, Norh=1 D2 selec: bias 1 19 88 ** 8 8 73 4 D1 rank: bias 1 28 84 ** 24 12 48 4 D6 rank: bias 2 12 84 ** 46 16 42 0 Dis=8, Norh=1 Dis=15, Norh=1 Dis=20, Norh=1 D2 selec: bias 1 35 100 ** 62 0 4 0 D1 rank: bias 1 36 88 ** 40 12 24 0 D6 rank: bias 2 19 80 ** 54 20 27 0 Dis=15, Norh=1 Dis=20, Norh=1 Dis=10, Norh=0 D2 selec: bias 1 81 13 4 0 15 87 ** D1 rank: bias 1 88 20 4 76 8 4 ** D6 rank: bias 2 65 92 * 23 8 11 0 * Dis=10, Norh=1 Dis=15, Norh=1 Dis=15, Norh=0 D2 selec: bias 1 31 88 ** 62 8 8 4 D1 rank: bias 1 28 84 ** 16 8 56 8 D6 rank: bias 2 23 84 ** 23 8 54 8 Treamen codes, wheher hey selec or rank, and wheher hey bias 1 or 2 aribues in Column 2. Aribue levels for he poenial arge aribues - Disance and Norh/Souh (1,0) are repored. In ranking designs, Alernaives ranked 1 recoded o seleced. % of sample selecing each Alernaive repored. Values in bold are he pos-bias reamen ** indicaes a single alernaive is idenified as dominan for ha condiion, * indicaes one of several alernaives ha may be considered dominan. In general, hese resuls are consisen wih wha one would expec: respondens selec he Alernaive wih he smalles disance, or make some radeoff when required o bias boh disance and N/S. The anomalies are: in Q1, he samples ha rank boh selec Alernaive 2, even hough i has he highes disance. Similarly, in Q7, hose 14
who rank, and bias disance alone, have a very low probabiliy of selecing Alernaive 3, which has he lowes disance. However, in Q8 his effec is gone, and i was no presen in Q2. In Q7, hose who in d6 (bias 2, full ranking) appear o be rading off he increased disance in Alernaive 1 for he presence of he Norh aribue, which is plausible. In summary, he anomalies seem o occur (selecively) wihin hose who are required o rank Table 5. Percenage of sample selecing an alernaive, pre-bias and pos-bias: Design wih 3 alernaives and 6 levels, by quesion. Alernaive 1 Alernaive 2 Alernaive 3 Dis=10,Norh=0 Dis=20,Norh=0 Dis=8,Norh=1 Q1 D9 selec: bias 2 9 5 19 94 71 5 ** Dis=12,Norh=0 Dis=10,Norh=1 Dis=10,Norh=0 Q2 D9 selec: bias 2 9 5 14 86 ** 76 10 Dis=16,Norh=1 Dis=8,Norh=0 Dis=12,Norh=1 Q3 D9 selec: bias 2 43 5 52 43 * 5 52 * Dis=8,Norh=0 Dis=12,Norh=0 Dis=16,Norh=1 Q4 D9 selec: bias 2 71 52 * 19 48 10 0 * Dis=20,Norh=1 Dis=16,Norh=1 Dis=8,Norh=0 Q5 D9 selec: bias 2 10 5 10 10 * 81 86 * Dis=10,Norh=1 Dis=14,Norh=0 Dis=14,Norh=0 Q6 D9 selec: bias 2 76 100 ** 24 0 0 0 Dis=8,Norh=0 Dis=8,Norh=1 Dis=20,Norh=0 Q7 D9 selec: bias 2 57 5 38 95 ** 5 0 Dis=14,Norh=0 Dis=10,Norh=1 Dis=10,Norh=1 Q8 D9 selec: bias 2 5 5 62 0 * 33 95 * Treamen codes, wheher hey selec or rank, and wheher hey bias 1 or 2 aribues in Column 2. Aribue levels for he poenial arge aribues - Disance and Norh/Souh (1,0) are repored. In ranking designs, Alernaives ranked 1 recoded o seleced. % of sample selecing each Alernaive repored. Values in bold are he pos-bias reamen ** indicaes a single alernaive is idenified as dominan for ha condiion, * indicaes one of several alernaives ha may be considered dominan. In general he resuls are consisen wih expecaions, given ha his sample is aemping o bias 2 aribues: he only anomaly appears o be in quesion 1, where Alernaive 3 is he dominan Alernaive, and ye does no ge seleced, compared o sraegy. 15
Table 6. Percenage of sample selecing an alernaive, pre-bias and pos-bias: Design wih 6 alernaives and 4 levels, by quesion. Alernaive 1 Alernaive 2 Alernaive 3 Alernaive 4 Alernaive 5 Alernaive 6 Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Treamen Dis=8,N =0 Dis=10,N=0 Dis=15, N=1 Dis=10, N=1 Dis=8,N =0 Dis=10, N=1 D3 rank: bias 1 7 76 * 16 24 18 0 19 0 20 0 * 20 0 D7 rank: bias 2 52 12 * 24 6 19 18 0 18 * 0 41 * 5 6 * D8 selec: bias 2 33 5 * 8 0 4 5 8 9 * 8 0 * 28 82 * Dis=20, N=0 Dis=15, N=0 Dis=10,N=0 Dis=8,N =1 Dis=20, N=1 Dis=20, N=1 D3 rank: bias 1 23 10 14 0 9 0 45 81 ** 9 0 0 10 D7 rank: bias 2 17 12 11 0 6 0 28 0 ** 22 6 17 81 D8 selec: bias 2 25 9 4 5 17 0 50 86 ** 4 0 0 0 Dis=10,N=0 Dis=8,N =1 Dis=8,N =1 Dis=15, N=1 Dis=10,N=0 Dis=15, N=0 D3 rank: bias 1 18 10 36 5 * 9 14 * 18 67 5 0 14 5 D7 rank: bias 2 32 6 5 0 * 16 0 * 11 44 16 31 21 19 D8 selec: bias 2 17 0 17 55 * 30 41 * 9 5 22 0 4 0 Dis=15, N=1 Dis=20, N=0 Dis=8,N =1 Dis=20, N=0 Dis=20, N=1 Dis=10,N=0 D3 rank: bias 1 5 5 0 0 32 86 ** 37 0 27 5 0 5 D7 rank: bias 2 16 6 16 69 26 19 ** 11 0 21 0 11 6 D8 selec: bias 2 4 0 26 9 39 82 ** 13 5 13 0 4 5 Dis=20, N=1 Dis=15, N=0 Dis=10,N=0 Dis=8,N =0 Dis=15, N=1 Dis=8, N=1 D3 rank: bias 1 5 5 0 0 18 0 14 0 * 9 5 55 90 * D7 rank: bias 2 5 6 0 0 0 38 11 6 21 19 63 31 ** D8 selec: bias 2 0 0 13 0 9 5 35 9 35 5 9 82 ** Dis=10,N=0 Dis=20, N=1 Dis=15, N=0 Dis=15, N=1 Dis=8, N=0 Dis=15, N=1 D3 rank: bias 1 27 14 18 81 9 0 36 5 9 0 ** 0 0 D7 rank: bias 2 16 19 0 12 0 0 58 19 * 5 44 * 21 6 * D8 selec: bias 2 23 9 5 5 18 0 5 27 * 0 9 * 50 50 * Dis=15, N=1 Dis=10,N=1 Dis=20, N=0 Dis=20, N=0 Dis=10,N=1 Dis=8, N=0 D3 rank: bias 1 5 5 0 0 9 5 45 86 32 0 9 5 ** D7 rank: bias 2 5 13 16 0 * 0 44 42 44 32 0 * 5 0 * D8 selec: bias 2 5 0 55 59 * 0 5 14 0 9 27 * 18 9 * Dis=8, N=1 Dis=8, N=1 Dis=20, N=1 Dis=10,N=0 Dis=15, N=0 Dis=20, N=0 D3 rank: bias 1 18 81 * 14 14 * 9 0 23 5 36 0 0 0 D7 rank: bias 2 37 88 * 26 6 * 5 0 11 0 21 0 0 6 D8 selec: bias 2 23 68 * 5 23 * 27 9 32 0 9 0 5 0 Treamen codes, wheher hey selec or rank, and wheher hey bias 1 or 2 aribues in Column 2. Aribue levels for he poenial arge aribues - Disance and Norh/Souh (1,0) are repored. In ranking designs, Alernaives ranked 1 recoded o seleced. % of sample selecing each Alernaive repored. Values in bold are he pos-bias reamen ** indicaes a single alernaive is idenified as dominan for ha condiion, * indicaes one of several alernaives ha may be considered dominan. In Q1, samples D3 and D8 perform as expeced, wih a concenraion on he lower disances, and D8 making a radeoff beween 8km and 10km based on he Norh aribue. However, D7 shows a considerable spread, wih a significan proporion selecing Alernaive 3, even hough i is dominaed by Alernaive 4 and 6. In Q2, D7 again generaes an anomaly selecing Alernaive 6, which appears o be inferior o Alernaive 4.In Q3, again D7 16
selecs Alernaives (4 & 5) which appear inferior o eiher 2 or 3. D3 also appears o be selecing dominaed Alernaive 4. In Q4, Q5 and Q7 D7 selecs Alernaives 2, 3 and 3-4 respecively wih greaer frequency han one migh expec given he objecives se. However, in quesion 8, all hree align, selecing Alernaives ha minimise disance or reward Norh. Alhough no presen everywhere, i would appear from his analysis ha when asked o rank 6 Alernaives, and aemp o achieve bias in 2 oucomes, respondens have difficuly. This issue will be reurned o in he saisical analysis laer. In Table 7 below, in Q1, boh D4 and D10 seem o over selec Alernaive 5, which has he highes disance. In Q2, boh ranking samples again seem o favour high disances (one would have expeced ha Alernaives 1, 5 and 6 would be seleced). In Q3, 93% of D4 (who rank) selec Alernaive 6, which has a higher disance, while 25% of D10 selec Alernaive3, which would appear o be dominaed by Alernaive 2. In Q4, again D4, who rank, selec Alernaive 6, which wih a disance of 10, is greaer han Alernaive 4. Quesion 5 seems o be consisen, bu in Q6 88% of D10 selec Alernaive 5, which has he smalles disance, bu Souh, while zero respondens selec Alernaive 2, which has boh he smalles disance plus Norh and would hence appear o dominae 5. In Q7, boh groups ha rank selec Alernaive 2, which has he highes disance, and is clearly dominaed by Alernaive 4. In Q8, D4 again selecs Alernaive 2, which would appear o be dominaed by eiher 1 or 3. 17
Table 7. Percenage of sample selecing an alernaive, pre-bias and pos-bias. Design wih 6 alernaives and 6 levels, by quesion. Alernaive 1 Alernaive 2 Alernaive 3 Alernaive 4 Alernaive 5 Alernaive 6 Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Pos- Pre-bias bias Treamen Dis=14, N=0 Dis=10,N=1 Dis=10,N=1 Dis=12, N=1 Dis=20, N=0 Dis=8, N=0 D4 rank: bias 1 4 4 15 0 11 0 15 0 33 96 22 0 ** D5 selc: bias 1 4 4 32 0 14 0 0 4 4 0 46 93 ** D10 rank: bias 2 4 0 0 0 * 29 8 * 22 0 33 84 11 8 * D11 selc: bias 2 8 9 22 48 * 9 39 * 0 0 9 0 52 4 * Dis=8, N=0 Dis=14, N=0 Dis=10,N=0 Dis=20, N=1 Dis=8, N=1 Dis=8, N=1 D4 rank: bias 1 19 35 * 19 46 19 15 26 0 15 0 * 4 4 * D5 selec: bias 1 18 29 * 32 14 4 0 4 0 36 32 * 7 25 * D10 rank: bias 2 36 0 44 8 4 40 16 48 0 4 * 0 0 * D11 selc: bias 2 17 4 26 4 0 0 4 0 39 61 * 13 30 * Dis=16, N=1 Dis=8, N=1 Dis=8, N=0 Dis=14, N=0 Dis=12, N=0 Dis=14, N=0 D4 rank: bias 1 15 7 35 0 * 12 0 * 8 0 23 0 8 93 D5 selec: bias 1 11 4 14 36 * 68 57 * 4 0 0 0 4 4 D10 rank: bias 2 4 0 23 48 ** 19 24 12 16 15 4 27 8 D11 selc: bias 2 9 4 9 87 ** 74 9 9 0 0 0 0 0 Dis=20, N=1 Dis=16, N=0 Dis=12, N=1 Dis=8, N=0 Dis=10,N=1 Dis=10,N=0 D4 rank: bias 1 12 8 12 0 15 0 38 0 * 12 0 12 92 D5 selec: bias 1 21 0 4 11 4 0 4 71 * 36 14 32 4 D10 rank: bias 2 15 0 12 4 8 36 35 4 * 12 44 * 19 12 D11 selc: bias 2 4 9 9 4 0 4 4 0 * 52 83 * 30 0 D=10,N=1 Dis=10,N=0 Dis=16, N=0 Dis=10,N=0 Dis=16, N=1 Dis=20, N=1 D4 rank: bias 1 27 15 * 23 65 * 31 15 15 0 * 4 4 0 0 D5 selec: bias 1 14 21 * 7 39 * 0 0 61 39 * 14 0 4 0 D10 rank: bias 2 15 88 ** 31 8 8 4 23 0 23 0 0 0 D11 selc: bias 2 17 91 ** 9 0 9 0 43 4 22 4 0 0 Dis=12, N=0 Dis=8, N=1 Dis=14, N=0 D=10,N=1 Dis=8, N=0 D=10,N=1 D4 rank: bias 1 4 4 12 0 * 19 4 23 12 38 76 * 4 4 D5 selec: bias 1 0 0 75 71 * 14 4 4 0 7 25 * 0 0 D10 rank: bias 2 4 0 12 0 ** 15 4 27 4 31 88 12 4 D11 selc: bias 2 0 4 70 83 ** 22 4 9 4 0 0 0 4 D=10,N=0 Dis=20, N=1 Dis=20, N=1 Dis=8, N=1 Dis=14, N=0 Dis=16, N=0 D4 rank: bias 1 4 4 12 92 12 0 27 0 ** 42 0 4 4 D5 selec: bias 1 4 7 0 4 21 4 57 86 ** 0 0 18 0 D10 rank: bias 2 8 0 12 48 8 0 15 44 ** 54 8 4 0 D11 selc: bias 2 0 4 4 0 9 0 70 96 ** 0 0 17 0 Dis=8, N=1 Dis=12, N=0 Dis=8, N=0 Dis=16, N=0 D=10,N=1 Dis=12, N=1 D4 rank: bias 1 27 58 * 42 42 12 0 * 12 0 4 0 4 0 D5 selec: bias 1 32 32 * 4 4 46 57 * 18 0 0 4 0 4 D10 rank: bias 2 32 76 ** 32 20 24 0 8 0 0 4 4 0 D11 selc: bias 2 17 57 ** 9 0 48 39 22 0 4 4 0 0 Treamen codes, wheher hey selec or rank, and wheher hey bias 1 or 2 aribues in Column 2. Aribue levels for he poenial arge aribues - Disance and Norh/Souh (1,0) are repored. In ranking designs, Alernaives ranked 1 recoded o seleced. % of sample selecing each Alernaive repored. Values in bold are he pos-bias reamen ** indicaes a single alernaive is idenified as dominan for ha condiion, * indicaes one of several alernaives ha may be considered dominan. 18
6. Summary of descripive analysis This analysis is based on he assumpion ha respondens would have responded o he reques o bias heir answers by selecing alernaives ha conain he required oucomes: he shores disance and/or Norh of he river. Where hey are required o bias boh, and i is no possible o opimise on boh simulaneously one would expec o see some dispersion across alernaives, which one does see. However, here are some cases, especially in he samples who are asked o rank all Alernaives where hey appear o no be following his sraegy. I is possible ha he abiliy o rank inroduced a more suble sraegy in he case where 2 aribues are being opimised for: ieraively favouring one alernaive in he firs selecion, and hen he oher in he second, bu is no clear ha his will raionalise all anomalies. Alernaively hey may be considering oher aribues joinly i.e. avoiding alernaives wih low cos (even if hey have he lowes disance and Norh) so as o avoid appearing o selec raionally on ha basis. Unforunaely he designs are no full facorials, so ha revealed behaviours canno be compared wih all possible sraegies. Table 8 provides a summary of he previous descripive analysis, by idenifying he percenage of respondens who seleced wha has been idenified as a dominaed alernaive as heir preferred opion wihin each choice se quesion i.e. who make a wrong choice. They are grouped by saisical design, as per he previous 4 Tables. Overall, he resuls indicae ha when asked o selec a single bes alernaive, he choices conform o expecaions, or if hey rank over 3 alernaives. When ranking, wih 6 alernaives, hey do no. The only couner example o his is D8, which has a reasonable good performance. In he following secion we will consider comparisons of economeric models. 19
Table 8. Percenage of respondens selecing a dominaed alernaive wihin a choice se, by reamen and quesion. q1 q2 q3 q4 q5 q6 q7 q8 3 alernaives and 4 levels D2 Selec: Bias 1 4 0 0 8 12 0 13 12 D1 Rank: Bias 1 96 0 4 4 16 12 96 16 D6 Rank: Bias 2 92 12 12 4 16 20 8 16 3 alernaives 6 levels D9 Selec: Bias 2 99 15 5 48 5 0 5 5 6 alernaives and 4 levels D3 Selec: Bias 1 24 20 82 15 10 100 96 5 D7 Rank: Bias 1 24 99 100 81 69 31 101 6 D8 Rank: Bias 2 5 14 5 19 19 14 5 9 6 alernaives and 6 levels D4 Rank: Bias 1 100 61 100 100 19 24 100 42 D5 Selec: Bias 1 8 14 8 29 0 4 15 12 D10 Rank: Bias 2 84 96 52 52 12 100 56 24 D11 Selec: Bias 2 22 8 13 17 8 16 4 43 7. Esimaion An issue wih he analysis of he daa is how o idenify he exen of any change in behaviour beween he firs and second rounds, i.e. wihou and wih an explici incenive o bias he oucome. The insrucions simply said (for he case of influencing a single aribue): In he following secion, you will be presened wih an addiional se of quesions. When answering hese quesions you should behave as if you wan o give he impression ha Being CLOSE o UWA as he mos imporan aribue o you, You wan his o be idenified as more imporan han he oher aribues of he accommodaion. 20
There was no guidance given o he responden as o how he daa would be analysed, or how he measure of more imporance will be calculaed. This is probably consisen wih he process in mos choice experimens: respondens are asked o make choices, and he subsequen manipulaion of he daa is no made explici (he excepion o his would be one sho CEs where an individual only answers one choice quesion, and which explicily deal wih incenive compaibiliy by saing ha he alernaive ha is seleced mos frequenly will be implemened). As such, his lack of clariy in process may be considered as a conribuing facor owards he abiliy of CEs o hwar sraegic bias, bu in he curren conex i makes modelling he sraegies ha he respondens used when compleing he quesions under he bias incenive clear. An obvious echnical inerpreaion of preference is ha an aribue is preferred if he parworh of he aribue is larges, compared o he oher aribues. Thus bias in preferences would imply bias in parworhs. However, he parworh depends on a measure of he marginal uiliy of cos (i s he (negaive) raio of aribue parameer o cos parameer). If cos is ignored by he responden, he esimaed coefficien will end o zero, and hence he parworh ges inflaed. Three issues hen arise: all parworhs will be inflaed, including hose for non arge aribues, all parworhs will end o become insignifican if he marginal uiliy of cos becomes posiive (which in he esimaion i may) hen he implicaion will be ha he aribue is NOT preferred a all, if one akes he convenional inerpreaion of he parworh. Given ha a feasible response is o lexicographically place weigh upon only he arge aribues and ignore all ohers, he possibiliy of a zero (or insignifican) coefficien on cos is high. Below we presen a mehod of evaluaing effeciveness of he biasing aciviy, based on he probabiliy of selecing an alernaive. The iniial ask is o esimae models over he iniial 8 quesions. Table 9 repors condiional logi resuls: in hose cases where he reamen asked for rankings, he alernaive wih he highes rank is used as he seleced alernaive, so ha he esimaion can use a common basis. In addiion, Table 10 repors he Rank Ordered Logi (Hair e al., 2010, Hausman & Ruud, 1987) resuls for hose samples ha were required o rank alernaives. All esimaion has been underaken wih Saa Version 11 (SaaCorp. 2009) In general, he parameers are well defined, wih expeced signs on cos and disance o UWA. The resuls wih 6 alernaives which require ranking have relaively few significan, suggesing ha a his level of complexiy some cogniive dissonance is seing in. The second column of esimaes in each cell represen he esimaes of he pos-bias daa for he same models. 21
Table 9. Resuls for boh pre-bias and pos-bias daa, using condiional logi models, by design. Number of alernaives 3 6 Selecion mehod Rank Selec Rank D1 Aribues being influenced Disance Disance and NS Aribue levels 4 6 4 6 D6 D9 cos -0.013*** 0.006** dis -0.069*** -0.052** num -0.187** 0.061 fur -0.123-0.255 n Ns 0.084 1.414*** D2 cos -0.009*** 0.013*** dis -0.076*** -0.319*** num -0.498*** 0.002 fur 0.339* -0.305 n Ns 0.456 0.259 D3 cos -0.001-0.013*** dis -0.051*** -0.099*** num -0.197** 0.141 fur 0.037 0.382** n ns 0.025 1.080*** D4 cos 0.003 -.002 dis -0.019 0.091*** num -0.019 0.772*** fur -0.271* 0.928*** n Ns -0.313** -1.641*** cos -0.012*** -0.014*** dis -0.039** -0.150*** num -0.161** 0.031 fur -0.168-0.716*** n Ns -0.252 1.827*** D7 cos -0.001 0.009*** dis -0.044** 0.067*** num -0.113 0.261** fur -0.101-0.837*** n Ns 0.374** -0.397** cos -0.009*** -0.026*** dis -0.169*** -0.241*** num -0.112 0.385*** fur -0.252 2.469*** n ns 0.133 0.667** D10 cos 0.001-0.006** dis -0.024-0.20 num 0.070 0.539*** furn 0.026 0.316 ns -0.486*** -0.047 Selec D5 cos -0.022*** -0.006** dis -0.183*** -0.548*** num -0.451*** -0.127 fur 0.374** 0.412*** n ns 0.516*** -0.167 D8 cos -0.009*** -0.001 dis -0.112*** -0.380*** num -0.665*** -0.412*** fur 0.214 0.048 n ns 0.360** 2.597*** D11 cos -0.022*** -0.018*** dis -0.183*** -0.367*** num -0.316*** -0.011 furn 0.682*** 0.025 ns 0.345** 2.871*** 22
***= p<0.01, **= p<0.05, *= p<0.1 23
Table 10. Resuls for boh pre-bias and pos-bias daa, using Rank Ordered Logi models, by design. Number of alernaives 3 6 Selecion mehod Rank Rank D1 cos -0.013*** 0.005** dis -0.063*** -0.063*** num -0.113** 0.098* fur -0.436*** 0.081 n Ns 0.055 0.226 D3 cos -0.009*** -0.011*** dis -0.028*** -0.114*** num -0.016 0.054 fur -0.028 0.164** n ns 0.014 0.684*** Aribues being influenced Disance Disance and NS Aribue levels 4 6 4 6 D6 D9 D4 cos -0.001 0.006*** dis -0.001 0.002 num -0.001-0.237*** fur 0.235*** -0.210*** n Ns 0.069 0.144* cos -0.010*** -0.006*** dis -0.036*** -0.126*** num -0.093* -0.151*** fur -0.418*** -0.537*** n Ns -0.261** 1.356*** D7 cos -0.002** 0.002** dis -0.008-0.046*** num 0.089** 0.079 fur -0.127-0.353*** n Ns 0.112 0.346*** cos -0.011*** -0.016*** dis -0.122*** -0.107*** num -0.192** 0.307*** fur -0.097 0.875*** n ns 0.231 1.223*** D10 cos -0.002** -0.000 dis -0.012-0.030*** num -0.004 0.071* furn 0.214*** -0.009 ns 0.020 0.440*** 24
8. Idenificaion of Bias Ideally one would like o idenify a measure of bias ha is holisic, and does no require a dependence on a significan (and negaive) cos parameer for inerpreaion. Here we propose a measure based on he simulaed probabiliy of choosing an alernaive. We do his by simulaing a 2 alernaive choice siuaion based on he parameers esimaed from he pre-bias condiion and he pos-bias condiion. Consider he case where only disance is being argeed. Assume here are wo alernaives (i=1,2), and ha he parameers of he uiliy funcion are hose obained using he unbiased responses wihin reamen. Assume ha he aribue levels for alernaives 1 and 2 are given by: Alernaive 1 Alernaive 2 Norh/Souh 0 0 Furnished 0 0 Disance 8 12 Cos 100 100+ PW d4 Then i is possible o idenify a value for PW d4 such ha 2 P( y = 1) = f( A, A, β ) = 0.5 ˆ 1 2 where A i are he aribue levels, and βˆ he pre-bias parameer esimaes for reamen. Thus, he value of PW d4 is idenified ha resuls in he responden being indifferen beween he wo alernaives. The measure of success in achieving bias is hen idenified by calculaing: P( b y = 1) = f( A1, A ˆ 2, βb) where ˆb β is he parameer vecor esimaed for reamen in he incenive o bias phase. If he design is such ha i has no been possible o induce bias, hen P( b y = 1) = 0.5 i.e. prediced choice is unchanged. A successful bias oucome would increase he value beyond 0.5. I is necessary o calibrae he iniial probabiliy a 0.5 (as opposed o comparing he probabiliies associaed wih he acual design aribues), because i would be possible 2 Noe ha he value of PW d4 needed o achieve equaliy in he probabiliy of selecion will, in fac, be he parworh associaed wih a 4 uni change in disance. 25
for some configuraion of aribues and parameers o lead o a high probabiliy of selecing he firs alernaive wihou bias. Because of he non-linear naure of he logi probabiliy funcion, ha would hen reduce he exen o which one could idenify biasing behaviour. In he case where he 2 aribues are arges for bias, in addiion he Norh/Souh variable is se o 1 in he aribue se for Alernaive 1, and zero for Alernaive 2, and he calibraing level of cos is deermined by PW d4-ns i.e. he parworh associaed wih a 4 uni change in disance and a change in locaion from Souh o Norh. There are wo alernaive vecors of parameers for hose reamens where respondens have o rank alernaives. They can eiher be esimaed using a Ranked Ordered Logi (ROL) model o ake advanage of he full ranking daa, or he alernaive ranked 1 can be used wihin a convenional CL model. As a resul one can idenify wo alernaive measures of sraegic bias, based on which of hese approaches is used. Resuls for boh are repored in Table 11 below (he value in parenhesis in each cell is he ROL resul). Table 11: Implied probabiliy of selecing he arge alernaive Aribues being influenced 1 2 Aribue levels 4 6 4 6 3 Rank All D1 0.73 (0.74) D6 0.97 (0.94) D9 0.08 (0.53) Number of Selec 1 D2 0.99 alernaives 6 Rank All D3 0.01 (0.53) D4 0.28 (0.52) D7 0.99 (0.75) D10 0.21 (0.69) Selec 1 D5 0.99 D8 0.99 D11 0.99 Thus, he resuls for reamen D1 show ha he simulaed probabiliy of selecing alernaive 1 (he alernaive which has he desirable level of disance) is increased from 0.5 under he pre-bias condiion, up o 0.73 under he incenive o bias condiion. Thus, one could conclude ha his group have achieved a reasonable degree of success in conveying heir sraegic preferences. The conclusion is very similar (0.74) if he parameers esimaes obained from he ranked ordered logi resuls are used. However, his success is dwarfed by he success of hose in reamen D2, who shif he probabiliy of selecing he arge alernaive from 0.5 up o 0.99. In reamen D3, on he oher hand, where respondens had o rank across a 6-alernaive choice se, he conclusions from he simulaion are quie differen. Using jus he firs ranked as he seleced opion as he basis for idenifying parameers, he probabiliy of selecing he arge alernaive drops from 0.5 o 0.01 i.e. heir observed behaviour in he incenive-o-bias seing gives absoluely no indicaion of he preferred aribue hey were argeing. Alhough he resuls improve if he parameers from he ROL are used (he probabiliy rises o 0.53) his is effecively no differen o he baseline probabiliy of 0.5. To he exen ha here is any consisen paern emerging across hese resuls, i would appear o be ha when asked o selec jus one alernaive, consisenly high levels of bias can be induced in he resuls, irrespecive of he number of aribues levels or alernaives, or he number of aribues argeed. When asked o rank all 26
alernaives his capaciy is reduced (he excepion is when ranking wih 4 aribue levels and influencing one aribue: is no clear why ha is he case). I is possible ha he possibiliy of ranking alernaives offers he opporuniy for a more complex heurisic ha is hen no being capured in he simple linear uiliy funcion represenaion i.e. ha selecion sraegies vary across he sequence of choices, while he ROL model assumes he same process is being used in each sep. 9. Conclusions This sudy is he firs aemp we are aware of ha aemps o acively induce sraegic bias wihin a choice experimen seing. The resuls from his simple exercise sugges ha a moivaed responden can influence he oucomes from a Choice Experimen o some considerable degree. This is paricularly rue wihin he conex of selecing a single, mos preferred, alernaive which is he dominae eliciaion mehod wihin he CE lieraure. These resuls are no affeced by oher elemens of he design. The exen of he bias may be reduced if he respondens are required o rank he full se of alernaives. Wheher he laer conclusion arises because he ac of ranking makes he decision harder, or wheher i is because he saisical model ha has been esimaed does no capure he heurisic he individuals have adoped is open o quesion. To link hese resuls o he ideas raised in he lieraure on CM and bias: As such, CE may minimise some of he response difficulies found in CVM (proes bids, sraegic behaviour, yeah saying). Bu his poin has ye o be demonsraed. Hanley e all (2001) p448. If anyhing, he resuls from his sudy demonsrae he opposie. Sraegic Behaviour should be minimal in Saed Preference asks since he choices are made from descripions of aribues and i is no clear which choice will over- or under represen a valuaion Adamowicz e al, (1999) (p467). Again, if he inenion is o exhibi a sraegic bias in favour of one (or more) of he aribues, hen he resuls would appear o conradic his. By adding complexiy o he SP ask, respondens may exhibi less bias. This may be parly occur because of he exra effor required o complee he exercise wih bias, bu i is more likely o occur because of respondens failing o see any clear single purpose o he exercise Lu e al (2008) (p128). 27
The firs of hese (he impac of effor) would appear o be confounded: even wih relaively complex srucures, a well moivaed responden seems o be able o apply he effor required o induce bias. The second is less clear, given he respondens were given very explici informaion abou he purpose of he exercise: hey were asked o oversae values for specific aribues. Bu wheher an individual wih a paricular moivaion owards an aribue would fail o idenify he appropriae response wihou promping would seem o be unlikely. Wheher in a more complex srucure, such as a ranking, he responden sruggles o idenify how o influence he (o hem) unknown saisical process of inferring values, would seem o be an open quesion. However, of more imporance is no wheher he responden has failed o bias he response in he way ha hey required, bu wheher hey have generaed choices which reveal useful informaion abou heir rue preferences. I would be a pyrrhic vicory if he complexiy of he design ha prevens sraegic bias leads o choices ha reveal lile abou rue preferences. 28
References Adamowicz, W. Boxall,P.C., Louviere,J.J..,and Swai,J. (1999) Saed preference mehods for evaluaing environmenal ameniies, in Baeman, IJ. and Willis, KG (eds) Valuing environmenal Preferences: Theory and Pracice of he Coningen Valuaion Mehods in he US, EU and Developing Counries: 460-482 Carson, R.T. & Groves,T. (2007) Incenive and informaional properies of preference quesions Environmenal and Resource Economics 37: 181-210 Hair Jr., J. F., W. C. Black, B. J. Babin, and R. E. Anderson. (2010). Mulivariae Daa Analysis. 7h ed. Upper Saddle River, NJ: Pearson. Hanley,N. Mourao,S. and Wrigh, R. (2001) Choice modelling approaches: a superior approach for environmenal valuaion? Journal of Economic Surveys 15(3): 435-462. Hausman, J. A., and P. A. Ruud. 1987. Specifying and esing economeric models for rank-ordered daa. Journal of Economerics 34: 83 104. Hensher, DA (2006) How do respondens process saed choice experimens? Aribue consideraion under varying informaion load Journal of Applied Economerics 21:6 pp861-878 Hensher, D.A, Rose,J. and Greene,W.H. (2005) Applied Choice Analysis: a primer Cambridge Universiy Press, New York. Lu,H., Fowkes,T. and Wardman,M. (2008) Amending he incenive for sraegic bias in saed preference sudies Transporaion Research Record 2049 :128-135. Mazar,N. Amir,O., Ariely,D. (2008) The Dishonesy of Hones People: A Theory of Self-Concep Mainenance Journal of Markeing Research, Volume 45, Issue 6, p. 633-644. Ngene v.1.0.0 (2009) Rose, J. Collins, A. Bliemer, M, Hensher, D. Scarpa, R. and Rose,J. (2008). Design efficiency for non-marke valuaion wih choice modelling: how o measure i, wha o repor and why Ausralian Journal of Agriculural and Resource Economics, vol. 52(3), pp 253-282 SaaCorp. 2009. Saa: Release 11. Saisical Sofware. College Saion, TX: SaaCorp LP. 29
Appendix 1 Tex version of survey. Separaors idenify page splis in he web-based survey Thank you for agreeing o ake par in his survey. Is purpose is o help undersand how people answer a paricular ype of survey quesion, where one of several opions has o be seleced. The conex ha is being used is rened accommodaion. As a reward for compleing he survey you will be offered an enry ino a prize draw for $400. To be eligible for he draw you will have o complee i fully, o he very end. I should only ake abou 5-10 minues. I is assumed ha compleing he survey implies you are giving consen o paricipae in his sudy. I will be possible o link your responses o your email address, as his is needed o allow us o ener you ino he draw, bu once he draw for he prize has been compleed, all links beween he survey and your personal deails will be deleed. Because of his, once submied o us, i may no be possible o wihdraw your daa from he sudy. If you have any quesions please feel free o conac me a he email address below. Yours ruly, Dr. Michael Buron michael.buron@uwa.edu.au The Human Research Ehics Commiee a he Universiy of Wesern Ausralia requires ha all paricipans are informed ha, if hey have any complain regarding he manner, in which a research projec is conduced, i may be given o he researcher or, alernaively o he Secreary, Human Research Ehics Commiee, Regisrar s Office, Universiy of Wesern Ausralia, 35 Sirling Highway, Crawley, Wa 6009 (elephone number 6488-3703). There is currenly a considerable shorage of rened accommodaion in Perh, making i difficul for people o idenify somewhere o live which has he righ characerisics, a an affordable price. In his survey you will be presened wih a number of descripions of hypoheical rened accommodaion, and asked o rank hem in order of preference. The aribues of he accommodaion will include: The ren per week (bills included) The oal number of people sharing he fla/house Wheher i is furnished/unfurnished Wheher is Norh or Souh of he river The disance from UWA When looking a he opions, and selecing he one ha you prefer, you should imagine ha you are in 30
he posiion of having no oher alernaive han o accep one of hese, or be homeless. There hen followed 8 choice quesions of he form: The ype of quesions ha you have jus compleed are very commonly used in valuing new producs or environmenal asses. From he choices made, and he levels of he aribues ha are included in he alernaives, i is possible o idenify how, on average, he respondens o he survey are rading off he differen aribues of he accommodaion. One issue is he exen o which hey are open o manipulaion: ha people will no give heir rue answers o he quesion because hey wan o ry and influence he oucomes in a paricular way. I is unlikely ha you were doing his, bu i may be he case where people ry and oversae he imporance of some feaure, in an effor o change public policy, or change he ype of produc provided. In he nex secion of his survey, you will be asked o deliberaely change he way ha you answer he quesions, o mimic his ype of biased response. Please read he following informaion carefully. Undersanding i will have a srong impac on your chances of winning he $400. In he following secion, you will be presened wih an addiional se of quesions. When answering hese quesions you should behave as if you wan o give he impression ha Being CLOSE o UWA as he mos imporan aribue o you, You wan his o be idenified as more imporan han he oher aribues of he accommodaion. 31