Willingness to Pay for Health Risk Reductions: Differences by Type of Illness

Transcription

1 Wllngness to Pay for Health Rsk Reductons: Dfferences by Type of Illness Workng Paper: Prelmnary and Incomplete Trudy nn Cameron Department of Economcs Unversty of Oregon J.R. DeShazo UCL Lews Center for Regonal Polcy Studes Unversty of Calforna, Los ngeles Erca H. Johnson* School of Busness dmnstraton Gonzaga Unversty 502 E. Boone ve., D Box 9 Spokane, W johnsone@jepson.gonzaga.edu June 2009 Keywords: value of a statstcal lfe (VSL), value of a statstcal llness profle, stated preference, wllngness to pay, health rsk reducton, health threat JEL Classfcatons: Q51, I18 cknowledgements: Ths research has been supported by a grant from the Natonal Scence Foundaton (SES ) to the Unversty of Oregon (PI: Trudy nn Cameron). It employs orgnal survey data from an earler project supported by the US Envronmental Protecton gency (R829485) and Health Canada (Contract H /001/SS). ddtonal support has been provded by the Raymond F. Mkesell Foundaton at the Unversty of Oregon. Offce of Human Subjects Complance approval fled as Protocol #C F at the Unversty of Oregon. Ths work has not been formally revewed by any of the sponsorng agences. ny remanng errors are our own. *Correspondng uthor

2 Wllngness to Pay for Health Rsk Reductons: Dfferences by Type of Illness BSTRCT In ths paper, we examne how ndvdual wllngness to pay (WTP) for health rsk reductons vares wth the type of health threat n queston. Our research focuses on systematc dfferences n WTP for health rsk reductons across dfferent types of major health threats, ncludng fve types of cancers (breast cancer, prostate cancer, colon cancer, lung cancer, skn cancer), chronc heart dsease (as well as sudden heart attacks), respratory dsease, strokes, dabetes, lzhemer s dsease and traffc accdents. Our emprcal results suggest that the margnal dsutlty from each type of health state dffers across categores of llness (or njury). Ths suggests that models whch constran the estmated margnal utlty parameters for dfferent health states to be the same across all llnesses may be too restrctve and may cause us to lose nformaton that may be very valuable from a polcy perspectve. The man contrbuton of ths paper s to renforce the case for why t may not be reasonable to assume one common value for WTP for rsk reductons across all types of health rsks.

3 1. Introducton When t s necessary to conduct a formal beneft-cost analyss for a polcy whch protects human health or human lves, government agences typcally monetze the benefts from health rsk reductons usng a measure known as the Value of a Statstcal Lfe (VSL). The VSL s an ex ante measure of ndvduals wllngness to pay (WTP) to acheve a change n the rsk of premature death. The same VSL estmate s typcally used whether the program n queston leads to a reducton n the rsk of lung cancer, heart dsease, or any other llness. The U.S. Envronmental Protecton gency (EP) uses a VSL on the order of $6-7 mllon (2003 $) n ts polcy evaluatons. In contrast, the Department of Transportaton uses a VSL closer to $3-4 mllon, where the prmary cause of death under consderaton s often motor-vehcle accdents. The contrbuton of ths paper s to demonstrate, wthn the context of a sngle study, that wllngness to pay for health rsk reductons vares systematcally and sgnfcantly by dsease type. WTP s smply a measure of nverse demand, and reductons n dfferent types of health rsks consttute dfferentated products. It should not be surprsng that smlar rsk reductons for dfferent types of llnesses and njures should be valued dfferently. Our results suggest that the EP and other government agences may wsh to consder usng dfferent VSL values for dfferent types of health threats to accurately represent more accurately the WTP of the populaton affected by a partcular polcy. In order to average the emprcal evdence of these tradeoffs across a varety of emprcal studes whch have consdered dfferent-szed rsk reductons, t s conventonal practce to scale each estmated WTP amount, proportonately, to a common (huge) rsk change of 1.0. Whle t s temptng to nterpret ths scaled amount (the VSL) for a 1.0 rsk change as WTP to avod certan death, ths s napproprate. The evdence concernng WTP measures s all based on very tny 3

4 rsk dfferences. The VSL value that reflects the average relatonshp between WTP and rsk changes s never used to place a dollar value on preventon of one partcular person s death wth certanty. Instead, the VSL s scaled back down to ts mpled WTP for some typcally tny rsk change that would be produced by a gven polcy, and the correspondng small WTP amount s summed across the affected populaton. There are two typcal ways of estmatng a VSL: revealed preference methods, most commonly wage-rsk studes, and stated preference methods, manly contngent valuaton and conjont choce experments. Typcal VSL estmates from all of these methods range between about $1 mllon and $10 mllon. Wage-rsk studes are the most common way of estmatng a VSL. The methodology uses a hedonc regresson that uses employee-specfc characterstcs, job characterstcs and the actuaral job fatalty rsk as the explanatory varables. The method s descrbed n an overvew of revealed preference VSL estmates n ldy and Vscus (2007). Ths estmaton strategy allows an estmate of the trade-off between rsks and wages made n the labor market and provdes a WTP to reduce mortalty rsk. The average partcpant n wage-rsk studes s years old and the rsk nvolved n ths decson s typcally a sudden, accdental death that occurs at the workplace (Robnson (2007)). Unfortunately for envronmental economsts, ths measure of a VSL may not be a good ft when t s transferred to the populaton affected by many envronmental polces. Chldren, the elderly and the nfrm, who are not generally represented by a sample of ndvduals from the actve labor force, are lkely to enjoy a large share of the benefts from mproved envronmental qualty. The trade-offs between rsk and ncome evdenced wthn the workng-age populaton may not match the trade-offs wllngly made by members of the groups most affected by mpared envronmental qualty. In addton to the dfferences between the relevant populatons, 4

5 the types of health rsks from envronmental exposures are dfferent from rsks faced n the labor market. Often the rsks from poor envronmental qualty do not cause sudden death, but nstead may have a long latency perod before an ndvdual develops symptoms. Other examples where a VSL has been estmated va revealed preference data nclude analyss of ndvduals wllngness to exceed mandated speed lmts, (shenfelter and Greenstone (2004)), and through purchases of safety equpment (Hakes and Vscus (2007) and tknson and Halvorsen (1990)). Stated preference surveys are another tool for measurng VSL. Surveys can be targeted to represent the general populaton or a sub-populaton may be dentfed for whch ndvduals are partcularly affected by a specfc health threat. There are dfferent types of stated preference survey formats. In contngent valuaton methods, preferences tend to be elcted for a partcular program consstng of a fxed bundle of attrbutes at varyng prces. In conjont choce experments, choce sets are often desgned to ensure that all specfed attrbutes of the alternatves, ncludng prce, are vared ndependently. The man characterstcs of a good stated preference study, accordng to Krupnck (2007), ncludes havng a large sample sze, passng scope and construct valdty tests, and askng debrefng questons n the survey to dentfy potental problems such as scenaro rejecton. lthough most researchers would prefer to use revealed preference data n assessments of wllngness to pay, such preference data are not always avalable. In the health rsk reducton context, t wll typcally be necessary to control for dfferences n dsease latency (.e. tme-toonset), the duraton of the llness or njury, whether or not the afflcton s fatal, and the number of lfe-years that may be lost as a result of havng suffered from ths llness or njury. Hedonc wage studes may be good at capturng current perod actuaral rsks of sudden death, but they 5

6 are ll-suted to modelng llnesses whch may have long latency perods, and those whch may be chronc namely, wth long perods of pre-mortalty morbdty. The standard one-sze-fts-all VSL s mperfect for varous reasons. It s convenent to have a standardzed measure of an nverse demand (a WTP) for health rsk reductons. However, a constant VSL mples that the demand does not vary wth ndvdual characterstcs lke the demand for most commodtes would. It s smply a fxed value and does not vary wth ncome, gender, or household characterstcs such as the presence of young chldren. Nether does t vary wth the type of llness or accdent whch s addressed by the rsk reducton polcy n queston. Crtcs of the constant VSL also pont out that t fals to acknowledge any dfference accordng to age t attaches the same value to premature death by one year as t does to premature death by eghty years. Consequently, a construct called the Value of a Statstcal Lfe- Year (VSLY) s sometmes used to quantfy WTP n addton to or n place of the VSL. The VSLY s calculated by dvdng the VSL by average remanng lfe expectancy. typcal VSLY thus assumes that each year s equally valuable. Much emprcal work has been done recently to show that the VSL does vary by ndvdual characterstcs, such as age, (e.g. ldy and Vscus (2007), Krupnck (2007) and Robnson (2007)). There s stll much research to be done to explan how VSL vares wth ncome, gender, lfe expectancy, and current health status as descrbed n Hammtt (2007). Ths present paper attempts to tackle one more aspect of how WTP vares across health rsks by lookng at WTP to reduce the rsks of sufferng specfc dseases. In ths paper based upon a large general-populaton stated preference survey we examne how ndvdual wllngness to pay (WTP) for health rsk reductons vares systematcally across dfferent types of major health problems, ncludng fve types of cancers (breast cancer, 6

7 prostate cancer, colon cancer, lung cancer and skn cancer), chronc heart dsease (as well as sudden heart attacks), respratory dsease, strokes, dabetes, lzhemer s dsease and traffc accdents. There are eleven dfferent health threats total, ncludng one gender-specfc llness that s desgnated as breast cancer f the respondent s female, and prostate cancer f the respondent s male. In ths conjont choce study by Cameron and DeShazo (2008), respondents are shown fve (ndependent) choce scenaros each nvolvng three alternatves: two dfferent rsk-reducton programs and the status quo. For each respondent, ten of the eleven possble major health threats were used (two n each of the fve choce sets). We then estmate a choce model that allows us to smulate estmates of the WTP for mcrorsk reductons for an entre llness profle. n llness profle s a sequence of future health states ncludng pre-llness years, sck years, potental recovered years, and lost-lfe years. Ths WTP measure s more general than the conventonal VSL. There are many reasons why people may be wllng to pay dfferent amounts to avod smlar llness profles attrbuted to dfferent types of llness or njury. Health rsks may dffer n terms of the degree of dread assocated wth them or wth the perceved controllablty of the rsk (Slovc (1987)). There may be a cancer premum as noted n Savage (1993). Van Houtven et al. (2008) fnds strong evdence for a cancer premum and fnds that preferences dffer wth the length of the latency perod. WTP by dsease may also vary wth the respondent s personal belefs concernng ther ndvdual subjectve rsk of contractng a dsease, ther current healthrelated behavors, and even wth the extent to whch they may feel that they have room to mprove ther health-related behavors n order to reduce the rsks of dfferent dseases. Savage (1993) fnds that WTP dffers across four specfc causes of death: stomach cancer, plane crash, automoble crash and a home fre. WTP s postvely related to the perceved 7

8 rsk of exposure to that partcular cause of death and the extent to whch they dread the cause, and negatvely related to the amount respondents know about that partcular cause of death. He also fnds that the WTP for stomach cancer s more than double the WTP for the other causes, whch he attrbutes to the lack of famlarty wth stomach cancer and the dread assocated wth t. Savage (1991) fnds smlar responses to dread n a prevous study about nuclear power plant accdents. 1 Sunsten (1997) nvestgates people s assessments of WTP for bad deaths deaths that seem unusually horrble for some reason. There are many reasons that the cause of death may be perceved as bad. The cause of death may seem less controllable, may affect certan demographc groups more than others, and may cause unusually long and severe bouts of sufferng (.e. pre-mortalty morbdty). In a study of 116 Unversty of Chcago law students, Sunsten found that about 40% of respondents felt that an avoded death from cancer was worth more than one avoded death from a heart attack. lthough respondents may harbor more dread concernng some dseases than others, Sunsten concludes that the major polcy consderatons need to be the number of lves saved, the numbers of lfe-years saved, the qualty of lfe durng those saved years, and the cost-effectveness of the programs (.e. he does not advocate makng polcy smply based on the amount of dread assocated wth a dsease). Hammtt and Lu (2004) study systematc dfferences n WTP based on a latency perod and the amount of dread assocated wth the dsease. They fnd WTP estmates are hgher to reduce the rsk of cancer than to reduce the rsk for other smlar chronc dseases. They also fnd that WTP estmates may vary greatly between WTP to avod a fatal accdent and to avod a dsease that comes after a latency perod or one that nvolves a large amount of dread. Ths 1 For more artcles on cancer, rsks, and dread, see Trumbo et al. (2007), Chlton et al. (2002) and Chlton et al. (2006). 8

9 suggests that typcal VSL estmates from wage-rsk studes, for example, may not be deal for valung the benefts due to envronmental polces. Other researchers have found varous results relatng to specfc health threats. Subramanan and Cropper (2000) compare envronmental and publc health programs and fnd that the serousness of the rsk and the number of lves saved matters for the rate of substtuton between envronmental and publc health programs, but that the lack of controllablty and the nvoluntary nature of envronmental rsks does not matter. lbern et al. (2004) fnd that ndvduals who already have a dsease appear not to have a lower overall WTP than those who do not. Vassanadumrongdee and Matsuoka (2005) fnd that perceved dfferences n dread, severty, controllablty, and personal exposure between ar polluton and traffc accdents have lttle effect on the VSL. In ths paper, we contrbute the lterature by lookng at systematc dfferences n WTP for measures to reduce an ndvdual s rsk of sufferng from one of eleven major health threats. The paper s structured as follows. Secton 2 descrbes the avalable survey data. Secton 3 descrbes our random utlty choce model based on dscounted expected utlty and Secton 4 descrbes ts emprcal mplementaton. Secton 5 revews our estmaton results and Secton 6 covers the mplcatons of our estmated models for estmates of WTP whch generalze conventonal VSL estmates. Secton 7 concludes, wth a number of relevant caveats. 2. valable Data The stated preference dataset from Cameron and DeShazo (2006) provdes suffcent nformaton to permt an analyss of dfferences n WTP by type of dsease. The survey was admnstered by Knowledge Networks, Inc. to a random sample of respondents n the Unted States. Respondents are members of randomly selected households n the Unted States who are 9

10 offered free nternet access n return for completng a few surveys every month. Snce these respondents are part of a standng consumer panel, a large quantty of demographc and background nformaton, such as health status and health hstory, s avalable for every member of the panel. The survey has fve parts. 2 The frst part asks respondents about ther personal health profle and ther subjectve rsks of gettng these dseases. The second part s a rsk tutoral where rsks are dsplayed n three dfferent ways and respondents are requred to answer rsk comprehenson questons. fter thorough preparaton, the thrd part of the survey asks the respondent to consder fve dfferent three-alternatve conjont choce sets. In each choce scenaro, respondents choose between Program, Program B, and the status quo (nether program) as seen n the sample choce matrx n Fgure 1. Each program reduces the rsk that the ndvdual wll suffer a partcular llness profle. The health rsk reducton programs, as descrbed to respondents, consst of a dagnostc pn-prck blood test gven by the ndvdual s doctor once per year that ndcates whether the ndvdual s at rsk for the llness. If the blood test ndcates the ndvdual s at rsk, then the doctor would prescrbe medcaton and lfe-style changes (such as det and exercse) and contnue to montor the ndvdual. 3 Each llness profle conssts of a bref descrpton whch ncludes the age of the ndvdual when the llness starts, the duraton of the llness, the symptoms and treatments, and the expected effects on lfe expectancy. The rsk reducton programs are characterzed n terms of the expected rsk reducton acheved by the 2 For more detal, see an annotated survey at: 3 For traffc accdents, the program s descrbed as new arbag, brakng, and mpact reducton technologes that are becomng avalable. These wll reduce your chance of njury or death due to auto accdents. These technologes can be bult nto new vehcles, or added to exstng vehcles. You wll probably pay the cost of these technologes all at once when you buy a new car or have the equpment nstalled n an older one. When we descrbe costs, we wll convert them to monthly costs and also annual costs to make them easer to compare across programs. 10

11 program, and the cost of the program (expressed n both monthly and annual terms). ll of the attrbutes are randomzed, subject to basc plausblty constrants. The fourth part of the survey conssts of debrefng questons whch follow up on each conjont choce task. The ffth part of the survey was taken separately by all panelsts and gathers soco-demographc nformaton that can be readly merged wth the data collected expressly for ths survey. The survey was admnstered to 2,439 respondents wth a 79% response rate among nvted panelsts. In certan cases, a respondent or a specfc choce set was dropped from the estmatng sample. Respondents were excluded from the estmatng sample f they faled to pass skll-testng questons about rsk comprehenson, f they rejected outrght the types of choce scenaros the survey posed, or because of a small error n the randomzed desgn of the survey. 4 fter these data excluson crtera are appled, 1,619 respondents reman. These respondents consdered 7,520 choce sets nvolvng 13,696 rsk reducton programs (and a total of 20,544 alternatves when the status quo s ncluded). Descrptve statstcs are shown n Table Utlty-Theoretc Choce Model Survey respondents choose from three alternatves n each choce set. There are two rsk reducton programs, Program and Program B, and the status quo (Nether Program), whch are denoted, B and N. Each program reduces the rsk of facng an llness profle attrbuted to one of eleven dfferent llness labels but each program has an assocated monetary cost. The program cost s assumed to apply only durng pre-llness years and recovered years, so the ndvdual would not pay for the program f he or she were to fall ll as descrbed n the llness profle. n llness profle s a sequence of future health states that ncludes a specfed 4 Due to a lack of rsk comprehenson 1,236 choces (4,887 alternatves) were dropped, due to scenaro rejecton (where the respondent only chose scenaro rejecton as the reason for choosng the nether program alternatve) 2,236 choces (6,708 alternatves) were dropped, and due to an error n the randomzaton of the survey 332 choces (996 alternatves) were dropped. 11

12 combnaton of pre-llness years, sck years, post-llness (recovered) years and lost-lfe years. Respondents are assumed to choose the alternatve that gves them the hghest level of utlty. Ths utlty-theoretc choce model s descrbed n detal n Cameron and DeShazo (2006), but we offer a bref explanaton of the model n ths paper. For smplcty, consder just the parwse choce between Program and Nether Program. 5 We assume that the utlty of an ndvdual,, at tme, t, depends upon net ncome n that perod, Y t mnus the cost of any program, and the health state they experence n that perod. The survey consders only sngle spells of any gven llness. In any gven perod, the ndvdual wll be n one of four possble health states. These are recorded va four ndcator varables: 1( pre t ) for pre-llness years, 1( ll t ) for llness-years, 1( rcv t ) for recovered or post-llness years, and 1( lyl t ) for lost-lfe years. We can wrte the ndvdual s ndrect utlty functon n each tme perod, t, as: V = β Y + β Y + α 1( pre ) + α 1( ll ) + α 1( rcv ) + α 1( lyl ) + η (1) 2 t 0 t 1 t 0 t 1 t 2 t 3 t t The health states are mutually exclusve and they are also exhaustve, meanng that the ndvdual experences one, and only one, of these four health states at a tme. There s uncertanty about whether the ndvdual wll actually fall sck from the dsease, so we model each choce as dependng upon expected ndrect utlty, wth the expectaton taken across the sck (S) and healthy (H) outcomes. Partcpaton n Program nstead of the status quo s descrbed as alterng the probablty of gettng sck from NS Π to S Π. Furthermore, each llness profle extends through the remander of the ndvdual s lfe expectancy, so we dscount t future tme perods usng dscount rate r and dscount factor δ ( 1 r) t = + to get the ndrect utlty n terms of present value, whch we denote as PDV. The ndvdual s assumed to choose 5 The three-way choce between two programs and nether program s analogous. 12

13 Program over the status quo alternatve (Program N) f hs or her dscounted expected utlty s greater under Program : ( ( ) ) ( ) ( ) S S S H NS NS NS NH PDV Π V + 1 Π V PDV Π V + 1 Π V > 0 (2) The present dscounted number of years makng up the remander of the ndvdual s nomnal lfe expectancy, T, s gven by pdvc T t = δ. Other relevant dscounted spells, also t= 1 summed from t = 1 to pdvr t = T nclude pdve t 1( δ pret ) = δ 1( rcvt ), and pdvl t 1( δ lylt ) t =, pdv t 1( = δ llt ), =. Snce the dfferent health states exhaust the ndvdual s nomnal lfe expectancy, pdve + pdv + pdvr + pdvl = pdvc. Fnally, to accommodate the assumpton that each ndvdual expects to pay program costs only durng the pre-llness or recovered post-llness perods, pdvp = pdve + pdvr, s defned as the present dscounted tme over whch payments must be made. To further smplfy notaton, let ( 1 ) cterm = Π pdvc + Π pdvp S S. Let S NS yterm = pdvc + Π pdv + Π pdvl and S pterm = Π α1 pdv + α2 pdvr + α3 pdvl. Then the expected utlty-dfference that drves the ndvdual s choce between Program and the status quo can then be defned as follows (where there wll be an analogous term for the utlty dfference between Program B and the status quo n our three-alternatve model): (, [ ]) β0 ( ) { } PDV E V = Y c cterm + Y yterm S H β1 {( Y c ) cterm + Y yterm } { pdv } + { pdvr } + { pdvl } + α Π α Π α Π + ε S S S (3) 13

14 The opton prce, n the sense of Graham (1981), s the common certan maxmum payment that makes an ndvdual ndfferent between payng for the program and havng the rsk reducton, or not payng for the program and not havng the rsk reducton. In the context of the detaled model from Cameron and DeShazo (2008), we can solve the dscounted expected ndrect utlty-dfference for the value of ths common certan payment: ( 0 1 ) ( β + β ) 1 β β Y yterm pterm ε cˆ = Y f Y cterm (4) 2 1 where f Y = ( β + β Y ) Y = β Y + β Y and f ( ) ( ) s the soluton to a quadratc form. The expected present value of the certan payment can then be calculated for the ndvdual s remanng lfetme and can be wrtten as: ( ) E ˆ ˆ S, H PV c cterm c = (5) Next, we normalze ths expected present value of the certan payment by a rsk change of one-n-one-mllon n order to allow comparson between dfferent rsk changes. We dvde E ( ) PV c ˆ S, H by the absolute sze of the rsk reducton and multply by to get: ( ) E ˆ S, H PV c WTP = Π (6) The WTP s a margnal rate of substtuton (wth the margnal utlty of the sequence of health states n the numerator and the margnal utlty of ncome n the denomnator) for a mcrorsk reducton. Snce the margnal utlty of an adverse llness profle s n the numerator of the WTP, an ncrease n the margnal (ds)utlty of any component of an llness/njury profle of health states (llness years, recovered years, and lost lfe-years) wll ncrease the WTP. Snce the margnal utlty of ncome s n the denomnator, an ncrease n the margnal utlty of ncome wll decrease the WTP. 14

15 In order to get a smulated WTP usng our choce data, we need an approxmate jont dstrbuton for the llness profle of a partcular llness, whch comes from epdemologcal studes. We also need a jont dstrbuton of age, gender, and ncome level. Then, we make a large number of draws from these two jont dstrbutons and smulate the WTP values. The mean of the dstrbuton of WTP estmates can be nterpreted as the model s predcton of the average WTP of ths partcular llness and for ths partcular populaton. s descrbed n Cameron and DeShazo (2008), the data suggest that the basc fveparameter, homogeneous-preferences model gven n equaton (3) s domnated by a specfcaton that s not merely lnear n the terms nvolvng present dscounted health-state years. Rewrtng the fnal term n equaton (3) gves: { Π js j } + { js j } + { js j pdv Π pdvr Π pdvl } α α α js j j j = Π α1 pdv + α2 pdvr + α3 pdvl (7) N Where j =, B, N, and pdvx = 0 for X =, r, l. Ths smple lnear specfcaton fals to explan respondents observed choces as well as a model that employs shfted logarthms of the j pdvx terms. Startng from a form that s fully translog (ncludng all squares and parwse nteracton terms for the three log terms), and retanng only those terms where the α coeffcents are statstcally sgnfcantly dfferent from zero, ths fnal term becomes: ( pdv + ) + ( pdvr + ) + ( pdvl + ) 2 ( pdvl ) α pdv pdvl α1 log 1 α2 log 1 α3 log 1 S Π + α4 { log + 1 } + 5 { log ( + 1) log ( + 1) } (8) Fnally, because the opportunty for longer duratons n each health state s correlated wth the youth of the respondent, the α coeffcents must be allowed to dffer systematcally wth the respondent s current age wherever ths generalzaton s warranted by the data. Ths 15

16 2 leads to a model where α3 = α30 + α31age + α31age, and analogously for α 4 and α 5. Ths quadratc-n-age systematc varaton n parameters permts non-constant age profles for the model s WTP estmates, and ths sample tends to produce the usual hgher values durng mddle age and lower values for younger and older respondents. One fnal parameter stems from a correcton for dfferng samplng propenstes, whch appears to be relevant only for the ( ) log pdv + 1 term. 4. Emprcal Specfcaton In ths paper, we buld on ths basc thrteen-parameter specfcaton descrbed n Cameron and DeShazo (2008) by generalzng the margnal utlty parameters so that they vary systematcally by dsease type. There s no theory to recommend specfc functonal forms whereby these ndvdual parameters should be expected to vary, so we ntroduce dsease types and ther nteractons wth other relevant ndvdual atttudes and characterstcs n an effort to buld an understandng of how the mplct nformaton contaned n the dsease labels can nfluence respondents stated preferences over the alternatve health-rsk reducton programs proposed n our survey. ll of the specfcatons we consder retan the two basc ncome terms n the orgnal thrteen-parameter model. The two parameters to be estmated correspond to the lnear- and quadratc-n-net ncome terms n the underlyng assumed ndrect utlty functon. However, because the tme profle of ncome and program costs wll depend on the sequence of health states f the ndvdual suffers from the dsease n queston, t s necessary to construct the present dscounted expected net ncome terms under each rsk-reducton program and under the status quo. Fortunately, the underlyng lnear- and quadratc-term coeffcents persst as modfers n the constructed dscounted expected net ncome varables, whch we dentfy n the tables as 16

17 lnear net ncome term and quadratc net ncome term. The estmated coeffcent on the lnear term has the expected postve sgn and the sgn for the coeffcent on the quadratc term (whch s present to allow for dmnshng margnal utlty of net ncome) has the expected negatve sgn n all models. The ncome terms n the model enter nto the denomnator of the WTP formula for health rsk reductons (whch s a nonlnear functon of the estmated parameters). In the analyses descrbed n ths paper, we mantan the assumpton that the ndvdual s margnal utlty of net ncome s unaffected by any mplct or mputed characterstcs conveyed by llness label used for each program. Dfferentatng each parameter by type of llness expands the number of parameters by a factor of twelve. There s no evdence of any heterogenety by dsease type n the coeffcent on the quadratc-n-net-ncome term, although n smple models there s some suggeston that the lnear coeffcent n the margnal utlty of ncome may be lower when the llness n the program s descrbed as beng respratory dsease. In rcher models, however, that effect dsappears and a dfferent one, for dabetes, materalzes. s usual, t s dffcult to allow every parameter n a model to vary systematcally wth the same long lst of shfters. The queston of where to ntroduce the heterogenety appears to hnge on whch placement seems to produce the most robust results. Our focus n ths paper s the relevance of any other unspecfed mplct attrbutes of each llness conveyed by the arbtrarly assgned llness labels, yet not captured merely by the tme perods n dfferent health states (latency, sck-years, and lost lfe-years). It seems plausble that the effects of llness labels could act by shftng the margnal ex ante (ds)utlty of sckyears from that afflcton, or even the margnal ex ante (ds)utlty of lost-lfe years. In the latter case, however, there s the possblty that dead s dead, and that the afflcton from whch you 17

18 de has much more to do wth the margnal (ds)utlty of the sck-years leadng up to death than t wll wth the dsutlty of beng (prospectvely) prematurely dead for some number of years. lternatvely, llness label effects could enter smply as a lump of addtonal ndrect utlty affectng preferences for each alternatve n the choce set. The results from specfcatons that analyze each of these affects ndvdually and the full model wth all shfters are descrbed n ppendx II, but we focus here only on the preferred parsmonous model. In our specfcatons, we use heart dsease as the base case and all other margnal utltes are dfferentals relatve to the (ds)utlty from a dscounted sck-year wth heart dsease. It s worth emphaszng that the llness labels used n our survey were assgned randomly to dfferent llness profles. Some combnatons were mplausble (such as sudden death from dabetes or lzhemer s dsease), so these combnatons were removed. However, t would be possble for two dentcal llness profles to appear n the study, but wth dfferent llness labels. Thus we can be confdent that the effects we fnd for the mpacts of labels are not merely pckng up attrbutes of the llness profles. In all models the llness effects on the margnal (ds)utlty of sck-years are permtted to vary wth fve addtonal varables (as well as an nteracton between them): confdence, vulnerablty, controllablty, subjectve rsk, and smoker. 4.1 Confdence Confdence s the respondent s answer to the general queston Imagne you experence one of the major llnesses descrbed n ths survey. How confdent are you that your dagnoss and treatment by your current health care provder would be both tmely and of hgh qualty? Possble responses nclude -1= not at all confdent, 0= somewhat confdent, and +1=hghly confdent. Note that we normalze each atttudnal varable on the medan value n the sample 18

19 to facltate nterpretaton of the base effect, whch wll apply for the ndvdual wth medan values of these shfters. Whle we could allow for greater generalty by capturng ths factor wth a par of dummy varables, we treat t as an approxmately cardnal varable to conserve on parameters. If a respondent does not have a hgh level of confdence n ther access to, or qualty of, health care, they may be more wllng to pay for a preventatve program. 4.2 Vulnerablty We also allow health state terms to vary by perceved health vulnerablty, whch s the respondent s answer to the queston What s the chance that you wll experence, ether for the frst tme or as a recurrence, one of the major llnesses we dscussed wthn the next 20 years? The response optons are coded as -2= very unlkely, -1= somewhat unlkely, 0= somewhat lkely, and 1= very lkely. If some respondents feel that they have a hgher chance of sufferng from a major llness over ths tme horzon, we expect that ther prospectve (ds)utlty wll be greater and they wll have a hgher WTP for health rsk reducton programs. Snce the percepton of health vulnerablty s lkely to be correlated wth age, however, we allow the effect of perceved vulnerablty to vary wth the respondent s current age (and we also allow for age to drectly shft the nfluence of each llness label on the margnal (ds)utlty of a sck-year), Older respondents are more lkely to face at least one of these named dseases n the next twenty years, so f vulnerablty were alone n the model, t mght merely be pckng up ths age effect. The relatonshp between age and subjectve future health vulnerablty s shown n Fgure 2. Indeed, many of our respondents experences and perceptons of health problems vary markedly wth the ndvdual s current age. ge s lkely to proxy for a number of factors whch wll affect the salence of specfc llnesses. 19

20 4.3 Controllablty We also allow for heterogenety n the margnal (ds)utlty of sck-year terms wth respect to the extent to whch the ndvdual feels the dsease s controllable, and how much the ndvdual feels he or she s at rsk for the dsease. Controllablty s the respondent s answer to an auxlary survey queston worded as follows: How much do you thnk that mprovng your lfestyle or habts would reduce your rsk of [each class of health rsk]. Response optons ranged from -2= very lttle, to +2= a lot. gan, the varable s treated as approxmately contnuous and we allow t to enter lnearly. (Ths varable has been normed on the neutral category.) The antcpated effect of ths atttude on demand for health-rsk reductons programs cannot be sgned n advance. If respondents feel that a dsease s more controllable, they mght express a greater demand for a program to prevent t, snce the program would be more lkely to work. 6 On the other hand, f they feel the dsease s controllable, perhaps they do not need a specal preventon program snce they feel they wll be able to control the rsk of the dsease on ther own Subjectve Rsk Fnally, we expect that ndvduals who feel more at rsk for gettng a partcular dsease would have a hgher WTP for a rsk reducton program for that dsease. Our survey asked respondents to Thnk about your health, your famly hstory, and hazards to whch you are exposed. Whch llnesses or njures do you feel most at rsk of experencng over your lfetme? Response optons ranged from -2= low rsk, to +2= hgh rsk. We expect that for 6 The health-rsk reducton programs descrbed n the survey nvolve a smple dagnostc test. Respondents are told: If a test says that you have a problem, your doctor could prescrbe medcaton and lfe-style changes that reduce your rsk of gettng the llness. You would contnue to be montored. 7 It s not possble to nteract the controllablty and subjectve rsk varables wth the dsease ndcators alone because they are specfc to each dsease. 20

21 llness labels correspondng to specfc health threats for whch the respondent feels partcularly at rsk should have a greater effect on the magntudes of the coeffcents. 4.5 Smoker We allow nteractons between the dsease labels and confdence, vulnerablty, age and age and vulnerablty. We also control for whether ndvduals reveal that there s room for them to reduce ther health rsks by mprovng ther lfestyle or habts f they qut smokng. We use ths acknowledgement to dentfy each ndvdual as a current smoker or non-smoker. We then allow for an nteracton between current smoker and two of the health threats, respratory dsease and lung cancer. If the respondent could mprove ther health f they smoked less, whch means they smoke, they may feel more vulnerable to experencng respratory dsease and lung cancer. We consdered the possbltes that heterogenety n demand by llness label may affect the margnal (ds)utlty of dscounted sck-years, the basc ndrect utlty assocated wth each program regardless of the tme profle of the llness, and the margnal (ds)utlty of dscounted lost lfe-years. Results are shown n ppendx II, but we should note here that we fnd numerous dfferences by llness label n all three types of generalzatons. The overwhelmng number of coeffcents n the most extensve model needs to be reduced, so we focus on the results from preferred parsmonous model n the next secton. Gven the randomzed desgn of the program attrbutes, there s much less rsk that multcollnearty wll complcate the process of dentfyng stable and statstcally sgnfcant llness label effects by prunng away persstently nsgnfcant varables. Of course, the varables whch we use to control further, for respondent atttudes, are correlated to a certan extent, but we wll be zerong out the nfluence of these varables n our smulatons. These are merely control varables, ncluded where necessary to help us better dscern the tendences n the data 21

22 whch can be dentfed for a respondent wth neutral atttudes on all dmensons, for selected specfc age levels. 5. Estmaton Results Table 2 shows selected coeffcents on the basc terms, age, and smoker from the parsmonous verson of the extensve model that retans the most robust and persstently statstcally sgnfcant coeffcents. The full parsmonous model, showng the full set of the ncluded controls, s n ppendx I. Our emprcal results suggest that the dsutlty assocated wth the dsease labels of heart attacks, breast cancer (for female respondents), prostate cancer (for male respondents), colon cancer, strokes, and dabetes s larger than the dsutlty assocated wth one year of heart dsease. The dsutlty for the dsease labels of lung cancer, skn cancer and respratory dsease appears to be less than the dsutlty assocated wth heart dsease. For traffc accdents, dabetes and lzhemer s dsease, the dsutlty for these labels appears to decrease wth the age of the respondents. Smokers appear to have a greater dsutlty from lung cancer and respratory dsease than non-smokers. The estmated coeffcents suggest suggest that the margnal (ds)utlty of a sck-year for many dseases s statstcally ndstngushable from that assocated wth heart dsease. These results suggest that respondents vew a sck-year due to heart attacks, prostate cancer (for male respondents), and strokes as statstcally sgnfcantly dfferent from heart dsease. Lost-lfe years due to lzhemer s dsease are statstcally sgnfcantly dfferent from lost-lfe years due to heart dsease, but there appears to be more sgnfcance related to whether respondents thnk they wll receve good medcal treatment for that dsease and how controllable t s. 22

23 The more nterestng and ntutve results, that use these margnal utlty parameters, are the WTP estmates that are dscussed n the next secton. 6. Implcatons for WTP dfference by dsease We use ths parsmonous model when smulatng WTP values. We wll focus on varaton by dsease label, but also by age, smokng status, and two types of llness profles. We wll conduct these smulatons of WTP for an ndvdual who s assumed to have medan levels (.e. zero values) of the other control varables we employ namely, the varables for confdence n the qualty of future health care, subjectve vulnerablty to major health problems n the upcomng twenty years, the subjectve controllablty of each type of llness and the subjectve rsk of sufferng each type of llness. Table 3 shows examples of the predctons of the parsmonous model concernng the WTP for a mcrorsk reducton. The numbers n ths table can be nterpreted as the ftted wllngness to pay for a 1 n 1,000,000 reducton n the rsk of the specfed llness profle (ncludng latency, sck-years, and lost-lfe years from a partcular named llness) for an ndvdual wth an ncome of $42,000. These values are generated by drawng 1000 values from the asymptotcally normal jont dstrbuton of the maxmum lkelhood parameter estmates and usng each set of parameters to calculate the WTP from the basc formula n terms of a specfc set of values for the varables and that replcaton s set of parameters. cross ths smulated dstrbuton, we acknowledge that respondents were gven no opportunty to express negatve wllngness to pay. ll they could do was to not choose a less desrable program. Thus we convert all negatve calculated values to zero and report the mean of the resultng dstrbuton. We also report the 5 th and 95 th percentles of each dstrbuton. 23

24 Table 3 dsplays results for a 30-year-old, a 45-year-old, and a 60-year-old. Two dfferent llness profles are consdered. The frst llness profle s sudden death now (due to each of the health threats whch may be unlkely n some cases). The sudden death WTP estmates are most smlar to a conventonal VSL. We also offer another example of an llness profle, nvolvng ten years of latency, fve years of llness, followed by death. For a benchmark ndvdual, our hghest estmates of wllngness to pay to reduce the rsk of llness appear to be for smokers to reduce the rsk of lung cancer. cross our three representatve age groups, WTP estmates for these ndvduals are hghest for the 45-year-old, at just over $ Non-smokers, on the other hand, seem to have relatvely low wllngness to pay to reduce ther rsks of lung cancer. The WTP s less than $1.00 under the sudden death scenaro. Under the more realstc latent llness scenaro, the WTP s hgher, at $2.36 for the 30- year-old, but t falls dramatcally wth age, amountng to only $0.40 for the 60-year-old. For smokers, there s also a dramatc dfferental n wllngness to pay for reductons n the rsk of respratory dsease. The WTP for a mcrorsk reducton n sudden death from respratory dsease s on the order of $5.00 to $6.30 for smokers. For non-smokers, t ranges from only $0.38 down to $0.03. The latent llness scenaro, whch s probably more plausble, produces even hgher WTP estmates for smokers, but only for the 30-year-old s the non-smoker WTP greater than $1.00. The WTP appears to be dramatcally lower for the 45-year-old and essentally zero for a non-smoker who s already 60 years old. mong other cancers, both breast cancer and prostate cancer are of consderable concern to all age groups, whether there s a latency perod or not. These WTP values, on the order of $4.00 to about $8.70, when scaled up by one mllon to be compared wth a VSL, are n the ballpark of numbers used currently by the U.S. EP and the Department of Transportaton. 24

25 Wllngness to pay to reduce colon cancer rsk s somewhat lower, slghtly over half as great for the sudden death scenaros and about three-quarters as large for the scenaro wth ten years of latency and fve years of llness followed by death. In contrast, measures to reduce the rsk of skn cancer attract very lttle nterest. cross our sx age/latency cases, only the 30-year-old, n the latent case, has a WTP greater than $1.00. Sxty-year-olds have neglgble wllngness to pay to reduce ther rsks of skn cancer. mong the non-cancer llnesses, the largest WTP amounts are assocated wth heart dsease and heart attacks. The numbers tend to be vrtually dentcal n the sudden death case, whch s reassurng, because there s no requrement that the data yeld dentcal values for these two dfferent dsease labels, but logcally we mght expect them to be smlar. For the case wth latency, the WTP estmates are about $1.00 hgher for heart dsease than for heart attacks, although the dfference s smaller for the 60-year-old. Cerebrovascular llness (stroke) s only somewhat less of a concern, wth WTP amounts on the order of $6.40 for the 30-year-old down to $5.40 for the 60-year-old. Wth ten years of latency and 5 years of sck-tme, however, the WTP s somewhat lower, and t drops to only $2.70 for the 60-year-old. Despte the ncdence of Type II dabetes ncreasng wth age, as shown n Fgure 7, the WTP for dabetes (n the mprobable sudden death case) drops from $5.3 for the 30-year-old to only $300,000 for the 60-year-old. For the scenaro wth latency and fve years of llness, the WTP s hgher for the 30-year-old (at $7.15), but t drops to only $0.07 for the 60-year-old. Wllngness to ncur costs to lower the rsk of traffc accdents s surprsngly low. However, we suspect that our choce scenaros may have left some to be desred. Respondents were told that they could buy equpment wth ther new car, or retroft an older vehcle at a 25

26 specfed annualzed cost. For most of the llness profles, however, the njury was descrbed as occurrng more than seven years nto the future. It s possble that many people, assumng that they would not own ther current car for more than seven years, mght have been reluctant to pay for these measures. It was dffcult to get ths llness scenaro to conform to the others. There s no plausble age at whch one mght become more lkely to suffer an accdent. In fact, most people perceve themselves to be at relatvely lttle rsk of an accdent and feel that traffc accdent rsks are beyond ther control. Despte the potental for scenaro rejecton wth the traffc accdent rsk reducton programs, the mpled WTP for sudden death n the current year s stll over $1.00 for the 30- year-old, although t drops to only $.07 for the 60-year-old. Fgure 8 reveals that older respondents report lesser experence wth traffc accdents, ether for themselves or among ther famly and frends. For the case wth latency, however, the 30-year-old has a WTP of $2.69. If scaled to be comparable to a VSL, the nnety percent nterval contans the $3-4 mllon VSL amount used by the Department of Transportaton n Our data certanly support the noton that wllngness to pay to reduce hghway rsks s less than that for heart dsease, stroke, dabetes (at least among the young) and several types of cancers. The only dsease for whch there s evdence of dramatc ncreases wth age s lzhemer s dsease. The thrty-year-old and the 45-year-old are not wllng to pay much at all to reduce ther rsks of lzhemer s, ether n the current year or ten years down the road. Ths concdes wth the very much lower ncdence of lzhemer s among younger people. For the 60-year-old, however, wllngness to pay to reduce lzhemer s now s about $2.20. In the latent case for the 60-year-old, where lzhemer s wll not begn untl they are 70 and they wll not de untl they are 75, the WTP s lower, at just over $

27 7. Caveats and Conclusons Usng a stated preference survey concernng wllngness to pay for health rsk reductons, we look at systematc dfferences by dsease. We use a random utlty model framework for ths analyss and then allow the parameters to shft wth dsease labels n our emprcal analyss. We fnd systematc dfferences due to the dsease labels by themselves, but also a number of dfferences, by dsease, n the margnal (ds)utltes assocated wth prospectve future adverse health states (sck-years and lost lfe-years). Reductons n the rsk of breast and prostate cancers, especally n the near term, seem to be valued even somewhat above the VSL currently employed by the U.S. EP. Values for colon cancer reductons are somewhat lower, but the range of smulated values ncludes the roughly $6-7 mllon VSL used by the EP. Reductons n lung cancer rsks are of much lesser concern except to smokers, where the WTP s on the order of $11.00 n the near term and even hgher f some latency s nvolved. Non-smokers care relatvely lttle about reducng lung cancer rsks, and nobody seems to care very much about reducng skn cancer rsks. Wllngness to pay to reduce rsks from heart dsease and heart attacks are very smlar to each other (and to breast cancer and prostate cancer), the WTP to reduce mcrorsks are on the order of $7.00 to $8.00, whle strokes may be of somewhat lesser concern, perhaps smlar to colon cancer (at least when some latency s nvolved) and n lne wth current EP numbers. Smokers appear to be as much concerned about reducng ther rsk of respratory dsease as men are about reducng prostate cancer rsks f the rsk nvolves some latency (but somewhat less so n the case of sudden death n the current perod). The WTP values for a mcrorsk reducton are on the order of $ $8.00. Non-smokers, however, have very lttle nterest n payng to reduce ther rsks of respratory dsease. 27

28 Dabetes s more of a concern among the young than among older people, whereas the reverse s true for lzhemer s dsease. Traffc accdents are of surprsngly lttle concern among older people, perhaps because they see themselves to be less at rsk because they spend less tme on the road, or because they beleve themselves to be safer drvers. Reports of traffc accdents for respondents themselves, or among ther famly and frends, seem to declne wth age as shown n Fgure 8. Only for the youngest group does there appear to be a wllngness to pay to reduce serous traffc accdents that, when scaled, approaches the VSL used by the Department of Transportaton n Concernng envronmental threats to health, one mght thnk frst of respratory dsease and lung cancer from crtera pollutants and toxc ar pollutants. Some porton of the populaton may also be aware of the role of ar polluton n heart dsease. Our results suggest that there may be a huge dfference between the smokng and non-smokng populatons n demands for health rsk reductons va reductons n ar polluton. We plan to explore further whether there s a dfference accordng to the actual death rates from lung cancer and respratory dsease n the ndvdual s county of resdence (as a way to capture the nfluence of exstng stressors n the form of ar polluton). We may be able to recover from our database suffcent nformaton to place respondents nsde or outsde an urban area, whch may also help explan systematc dfferences n WTP for ar polluton reductons. One of the frst questons n our survey elcted, from each respondent, hs or her subjectve opnon about the degree to whch ar qualty and drnkng water qualty posed a threat to ther health. We have yet to fully explot ths nformaton to help explan varatons n WTP by dsease. 28