measuring consumer benefits of food safety risk reductions

Journal of Agricultural and Applied Economics, 30,1 (July 1998):69–82

0 1998 Southern Agricultural Economics Association

Measuring Consumer Benefits of FoodSafety Risk Reductions

Jean C. Buzby, John A. Fox, Richard C. Ready, andStephen R. Crutchfield

ABSTRACT

Microbial pathogens and pesticide residues in food pose a financial burden to society whichcan be reduced by incurring costs to reduce these food safety risks. We explore threevaluation techniques that place a monetary value on food safety risk reductions, and wepresent a case study for each: a contingent valuation survey on pesticide residues, anexperimental auction market for a chicken sandwich with reduced risk of Salmonella, anda cost-of-illness analysis for seven foodborne pathogens. Estimates from these techniquescan be used in costhenefit analyses for policies that reduce food safety risks.

Key Words: contingent valuation, cost of illness, experimental auction market, food safety,risk reduction, Salmonella.

The food supply in the United States is gen-erally considered healthy, nutritious, and safe.However, the modern industrial food systemmay result in undesired or unanticipated out-comes that pose a health hazard for consum-ers. The Centers for Disease Control and Pre-vention (CDC) and the Food and DrugAdministration (FDA) estimate that, based onreported outbreaks and other epidemiologicdata, between 6.5 and 33 million people in theUnited States become ill from microbialpathogens in their food each year (i.e., bacte-ria, parasites, viruses, and fungi); of these, upto 9,000 die (Council for Agricultural Science

Buzby is an agricultural economist with the EconomicResearch Service, U.S. Department of Agriculture(ERSJUSDA); Fox is an agricultural economist at Kan-sas State University; Ready is an agricultural econo-mist with the Agricultural University of Norway; andCrutchfield is an agricultural economist with the ERS/USDA. The views expressed here are the authors’ anddo not necessarily represent the views of their employ-ers. The contingent valuation survey was conductedwhile Buzby and Ready were working at the Univer-sity of Kentucky.

and Technology). Archer and Kvenberg esti-mate that 2–3 % of these foodborne illnesscases develop secondary illnesses or compli-cations such as arthritis following some Sal-

monella infections. Pesticide and other farmchemical residues may remain on fruits andvegetables, and prolonged dietary exposure tosuch chemicals may pose a risk of cancer orother adverse health effects (particularly tochildren), although evidence suggests thatthese risks are fairly low (Kuchler et al.). Inrecent years, there have been some highlypublicized outbreaks of foodborne illnesseslinked to a variety of foods. In the summer of1997, 25 million pounds of hamburger poten-tially contaminated with E. coli O 157:H7 wererecalled. News stories about foodborne diseasehave been widespread, contributing to risingpublic concern about the problem.

Consumers make choices about the foodproducts they buy based on a number of fac-tors. In addition to the price of the product,factors such as appearance, convenience, tex-ture, smell, and perceived quality influence

70 Journal of Agricultural and Applied Economics, July 1998

choices made in the marketplace. In an idealworld, consumers would make consumptiondecisions with full information about productattributes, and so choose the foods that max-imize their well-being. In the real world, thereare numerous food-safety information prob-lems which complicate the consumer’s deci-sion making. For example, all raw meat andpoultry products contain some level of microo-rganisms, some of which may be pathogens.However, consumers generally do not knowthe level of foodborne-illness risk, sincepathogens are not visible to the naked eye.Aside from some rather obvious indications(e.g., unpleasant odor or discoloration, both ofwhich may be caused by nonpathogenic spoil-age microorganisms), there are, in many cases,no clear-cut ways for consumers to determineif there is a health risk from pathogens or othercauses (e.g., pesticide residues).

While producers have some informationabout how safe their product is, such as infor-mation about the chemicals used during pro-duction or the care taken during butchering ofmeat, there is no incentive to share that infor-mation with consumers. This is because it isdifficult to charge a premium for the unob-servable increase in safety. This asymmetry ininformation about food safety between pro-ducers and consumers leads to a market fail-ure. The workings of a nonregulated marketmay yield greater-than-optimal levels ofpathogens and farm chemicals in the food sup-ply and excessive human-health risk, whichcould in turn result in higher levels of illnessand mortality. In such a case, the public wel-fare could be enhanced if society regulated thefood industry to reduce the level of foodbornehealth risks and/or increase consumers’knowledge so they can take personal actionsto reduce their risk of exposure to foodborneillness.

The economic issue of concern is how bestto achieve the goal of a safer food supply. Al-though regulations governing food production,processing, distribution, and marketing maycreate benefits by increasing the safety levelof the nation’s food supply (i.e., reducing riskof illness), these regulations also can increaseproducers’ costs and potentially raise food

prices. The task is to ensure that the regula-tions maximize the net benefits of increasingfood safety, that is, equating the marginal ben-efits of safer food with the marginal costs ofachieving food safety goals.

Faced with the task of estimating benefitswhere there is market failure or an externalityproblem, economists have developed severalmeasurement techniques for nonmarket goods,and these techniques fall into two generalclasses. The first class of techniques valueschanges in social welfare by using indirect ev-idence from the marketplace such as the in-dividual’s expenditures on related goods andservices. One such technique is the cost-of-illness approach. Estimates from this approachcan be used as a proxy for a change in expen-ditures and foregone wages associated with achange in ill-health episodes caused by a spe-cific health risk. Other market-based tech-niques rely on observed market choices thatreveal preferences for a nonmarket good, suchas the travel cost method for estimating rec-reation benefits and the averting expendituremethod for estimating values of health risksfrom environmental hazards. The second classof techniques consists of those that use statedpreferences about nonmarket goods. The mostwidely used of these is contingent valuation.Falling in between these two classes are ex-perimental market techniques, where individ-ual choices made in constructed market situ-ations reveal preferences for a good thatusually cannot be directly purchased in themarket, We characterize experimental marketsas intermediate between market-based tech-niques and stated preference techniques be-cause the choice situation is artificially creat-ed—but the choices are real, not hypothetical.

In this paper, we explore three valuationtechniques that are used to measure the ben-efits of food safety risk reductions, and wepresent a case study for each: (a) a contingentvaluation survey on pesticide residues, (b) anexperimental auction market for a chickensandwich with reduced risk of Salmonella, and(c) a cost-of-illness analysis for foodbornepathogens. Estimates from these techniquescan be used in cost/benefit analyses for poli-cies that reduce food safety risks.

Buzby et al.: Measuring Consumer Benefits

Contingent Valuation Methods

Contingent valuation (CV) methods includetelephone surveys, mail surveys, and personalinterviews that elicit consumers’ willingnessto pay (WTP) for nonmarket goods “contin-gent” on a given hypothetical scenario. In thishypothetical decision situation, the respondentis willing to pay some money to obtain a levelof provision of a nonmarket or public good.In the past few decades, over 1,600 contingentvaluation and related publications have mea-sured the benefits of a variety of nonmarketgoods (Carson et al.).

CV surveys have been increasingly used tomeasure consumers’ WTP for food safety riskreductions. CV surveys have elicited consum-ers’ WTP for reduced risk from: toxins inshellfish (Lin and Milon), nitrates in drinkingwater (Crutchfield, Cooper, and Hellerstein),and Salmonella in chicken and eggs (Henson).Another survey elicited consumers’ WTP forleaner pork produced with porcine somatotro-pin (pST), a naturally occurring protein whichsome consumers feel poses a food safety risk(Halbrendt et al.). CV surveys have focusedon risk reductions from pesticides in food andhave estimated the value of organic produce,certified pesticide residue-free produce, orproduce with other pesticide risk reductions.Of these pesticide surveys, several have esti-mated consumers’ WTP for food that is “ saf-er” but that does not have a specified risk re-duction (Weaver, Evans, and Luloffi Misra,Huang, and Ott; Ott). Other CV pesticide sur-veys have estimated WTP for specific risk re-ductions (Buzby, Ready, and Skees; van Rav-enswaay and Hoehn). The case studypresented here contributes to this foundationby eliciting consumers’ WTP for two differentspecified risk reductions.

A Contingent Valuation Study Valuing Food

Safety

The contingent choice scenario used in the CVsurvey was designed to reveal WTP for a re-duction in exposure to pesticide residues onfresh produce. The respondents were told thatthey must shop for groceries at either Store A

71

or Store B. These two stores were describedas being similar to the stores where the re-spondents currently shop, with the exceptionof the fresh produce. TWO survey versionswere used (I and II). In both survey versions,Store A does not test any of its fresh producefor pesticide residues. Store A therefore rep-resents the typical situation found in most U.S.grocery stores. In survey version I, Store Btests all of its fresh produce and rejects anythat does not meet government standards forpesticide residues (this store is denoted here asthe “government standards” store). In surveyversion II, Store B tests all of its fresh produceand rejects any with detectable pesticide resi-dues (this store is denoted here as the “pesti-cide-free” store). Thus, shopping at Store Breduces or eliminates the amount of pesticideresidues to which the consumer is exposed.Apart from this difference, and the associateddifference in price, Store A and Store B weredescribed as being identical in all respects, andthe produce sold was described as being iden-tical in appearance, freshness, and taste.

Prior to the description of the store-choicescenario, respondents were asked their ownsubjective belief about the mortality risk fromconsuming fresh produce from a store likeStore A. A visual risk ladder, similar to thatused by Loomis and Duvair, showed mortalityrisks from several more-familiar causes ofdeath, including for example car accidents, ac-cidental falls, lightning, and meteorites. Theposition of each risk on the ladder was deter-mined by the actual number of deaths causedby that risk each year, per 1 million individ-uals. These numerical risk estimates also werepresented to the respondents, next to the riskladder. Respondents were asked to rate themortality risk from pesticide residues on freshproduce as compared to these causes of death,with the answer coded as an index varyingfrom 1–19 (with 1 = lower than the risk ofdeath from a meteorite, and 19 = higher thanthe risk of death from a car accident). Later inthe survey, during description of the store-choice scenario, respondents were given ob-jective estimates of the mortality risk fromcancer caused by pesticide residues on freshuroduce. For Store A. this was given as one.


death per 1 million people per year. In surveyversion I, shopping at the “government stan-dards” store reduced that risk by one-half. Insurvey version II, shopping at the “pesticide-free” store eliminated the risk completely.

In both versions, respondents were told thatStore B was more expensive than Store A,with the weekly difference in cost between thetwo stores set at one of seven different levels:

$1, $3, $5, $8, $10, $15, and $25. Respon-dents were reminded that this decision wouldhave no impact on other mortality risks, andwere told that cancer caused by pesticide res-idues on fresh produce probably accounts forless than 1Yo of all cancer cases. Respondentswere asked two valuation questions in se-quence. First, they were asked at which storethey would shop, given the price difference.Next they were asked the maximum amountthey would pay to shop at Store B. Thus, botha dichotomous-choice and an open-endedWTP response were elicited from each re-spondent.

To answer the dichotomous-choice valua-tion question, respondents are assumed tochoose the store that yields higher utility. Wemodel this utility as:

and

where X, is a vector of variables describingboth individual Z and Store B, P, is the pricedifferential between the two stores faced byindividual Z, and e? and # are individual-spe-cific random errors, which we assume followtype I extreme value distribution, so that theparameters @ and y can be estimated using alogit regression. Two forms of f(P,) were in-

vestigated empirically, a linear form and thetranslocated log form, ln(Pi + 1). The trans-

location was set at one so that the price term

would vanish as the price differential goes to

zero.

Because E; and e? are not observable, the

price that sets U: and U: equal is not knownfor an individual. However, it is possible to

calculate the price that sets expected utilityfrom the two stores equal. For the linear formof f(Pi), that price is –X,~/y. For the trans-

located log form, that price is exp(–Xi~/y) –

1. This price also corresponds to the median

compensating variation for the risk reduction

across a population of similar individuals.

The Contingent Valuation Survey and Results

The Survey

The survey was administered by mail. A totalof 1,800 surveys were mailed out in threewaves of 600 each. Addresses were purchasedfrom a commercial mailing list vender. Thewaves went out in November 1994, February1995, and May 1995. Each wave followed theDillman total design method, with a notifica-tion letter, a survey packet, a reminder post-card, and a second survey packet to nonres-pondents. For the first two waves, all mailingswere sent by first-class post. Of these 1,200surveys, 161 (or 13.4%) were returned as un-deliverable or deceased. Of the remaining, 439were returned, giving an effective responserate of 42.3%. Due to a miscommunication,the third wave of 600 surveys was mailed bybulk-rate post. These had a similar rate of un-deliverable addresses (13.390), but a lower re-sponse rate. A total of 188 surveys were re-turned from the third-wave mailing, for aneffective response rate of 36.1 Yo. While thelow response rate in the third wave is unfor-tunate, the incidence of additional nonres-ponse due to the use of bulk rate did not ap-pear to be correlated with preferences for foodsafety. A dummy variable for survey wave(wave 3 versus waves 1 and 2) was not sig-nificantly related to the response to the valu-ation question (CY> 0.30).

There was wide variability in subjectivebelief about the danger posed by pesticide res-idues on fresh produce. Four percent of re-spondents thought that pesticide residuescause more deaths each year than do auto-mobile accidents, while 17?Z0thought that theycaused fewer deaths than do meteorites. Themedian risk index value was 5, which corre-sponds to a numerical risk of three deaths per

Buzby et al.: Measuring Consumer Benejits ’73

million people per year. The mean risk indexvalue was 13.2, which roughly translates to 14deaths per million people per year. However,translating the risk index values to numericalrisks, and then averaging gives roughly 43deaths per million per year. The skewed natureof the risk index values, and the nonlinear re-lationship between the risk index and numer-ical risk estimates make it difficult to charac-terize the central tendency of subjectivebeliefs. Nevertheless, it can be stated that mostrespondents believe the risk to be larger thanexpert assessments.

Of the 627 returned surveys from the threemailing waves, 22 did not contain a usablevaluation response. An additional nine werejudged to be protest responses, based on a stat-ed zero WTP for safer food, along with agree-ment with the statement, “I am concernedabout pesticides, but I refuse to pay more forfood to avoid them.” This left 596 usable sur-veys for estimation purposes: 289 version I,and 307 version II. The rate of usable re-sponses did not differ significantly across thetwo survey versions.

Contingent Valuation Survey Results

In order to avoid the effects of possible yea-saying, responses to the valuation questionswere interpreted conservatively. Ready, Buz-by, and Hu observed that mail survey respon-dents sometimes contradict themselves whenasked both a dichotomous-choice and an open-ended question, and viewed these contradic-tions as possible evidence of yea-saying be-havior. For this study, a respondent wasjudged to favor Store B only if she or he chosethat store in the first valuation question, andthen did not state an open-ended WTP lessthan the price differential described in the con-tingent-choice scenario. Thus, the elicitationprocedure is not a pure dichotomous choice,but includes a consistency check. A total of366 respondents (61.4%) chose Store B in theinitial valuation question. Of these, 101 con-tradicted themselves in the open-ended follow-up question.

A model predicting store choice was esti-mated using logit regression. Explanatory

Table 1. Logit Regression Results of CV Re-sponses

Variable Coefficient t-Statistic

ln(l%-ice + 1) –1.441 9.49Intercept 1.398 2.02Pesticide-Free 0.1245 0.65Risk Index 0,0742 3.72Household Income 0.786E-05 1.88Education 0.544E-03 0.02Caucasian –0.212 0.72Female 0.451 2.27Birth Year 0,0061 0.88No. in Household –0.0254 0.26Children in Household 0.487 1.63

variables in the regression included householdincome, years of schooling completed by therespondent, respondent’s sex (female = 1),ethnicity (Caucasian = 1), year of birth (with1900 = O), number of people living in thehousehold, whether the household containschildren under the age of 18, and the respon-dent’s subjective rating of the mortality riskfrom pesticide residues on fresh produce, asmeasured by an index scaled from 1 to 19.Missing data for individual variables were setequal to sample means. A dummy variable forsurvey version (version II = 1) was also in-cluded in the regression.

The translocated log transformation ofprice outperformed a linear-price model asmeasured by model log-likelihood. Table 1presents the parameter estimates for the trans-located log model. Price differential was a sig-nificant determinant of store choice, with high-er price differentials favoring Store A, thecheaper store. The regression results alsoshowed that respondents giving a higher sub-jective risk rating, higher income respondents,and females were more likely to choose StoreB. The first two findings would be expected;the last is interesting but not required by the-ory. The other demographic variables were notsignificant, though there were some interestinginteractions, which we explore further below.The coefficient on the dummy variable for sur-vey version II was positive, as would be ex-pected, but was not significant.

Model estimates of median and mean WTP


Table 2. CV Estimates of Willingness to Pay per Week for Safer Produce ($)

%VO Confidence 95% ConfidenceStore Type Median Interval Mean Interval

Government Standards 5.31 4.30 I 6.63 8.36 7.141 9.62Pesticide-Free 5.88 4.81 I 7.24 8.91 7.69/ 10.20

for safer food are presented in table 2, alongwith 95 Yo confidence intervals constructed bythe simulation method described by Krinskyand Robb. We focus here on median WTP, asit represents the price differential at which ex-pected utility for the two stores is equal. Foran average individual (with Xi set at samplemeans), that value is $5.31 per household perweek for the government standards store, and

$5.88 for the pesticide-free store. These twovalues are not significantly different (t =0.65). It is also of interest to investigate how

this value varies across individuals with dif-

ferent characteristics. For example, for a re-

spondent born in 1940, with no children at

home, the estimated WTP to shop at a store

that meets government standards is $4.49 per

week. For a respondent born in 1960, who

does have children at home, the estimated

WTP is $7.38, and the difference between

these two estimates is statistically significant

(t = 2.15). Thus, even though neither year of

birth nor presence of children is individually

significant, their joint contribution can be.

Discussion of the CV Results

Two related issues are of concern with the CVresults. First, the valuation responses are sta-tistically insensitive to the size of the risk re-duction associated with shopping at Store B,although the direction of the difference fol-lows the prediction of theory. Second, theWTP values seem large relative to other em-pirical estimates of the value of a mortalityrisk reduction. Both of these results may berelated to respondents paying more attentionto the qualitative aspects of the scenario thanto the quantitative aspects.

Regarding the issue of the scope of thesafety improvement, it seems clear that re-spondents either were unable to interpret and

process the quantitative information about therisk reduction, or ignored that information. Inthe two survey versions, switching to the storethat meets all government standards reducesrisks by one-half. Switching to the store withno detectable residues completely eliminatesthe risk. However, these risk estimates are con-jectural. We were unable to locate a toxicol-ogist who was willing to give such policy-spe-cific estimates. It may well be that meeting allgovernment standards reduces the risk essen-tially to zero. If respondents believe this, thenthey may not see any benefit from going to astore that employs even stricter criteria.

With regard to the size of the WTP values,some simple math shows that these numbersare big relative to the risks. From these num-bers, the value of a premature death is calcu-lated at either $204 million (using the valuefor the store that meets government standards)or $113 million (using the value for the storethat has no detectable residues). Either esti-mate far exceeds common estimates fromstudies that look at wage differentials associ-ated with job risk (e.g., $3–$7 million, ac-cording to Viscusi). However, it may be thatrespondents did not believe our posited mor-tality risk of one out of 1 million persons peryear. The mean subjective risk given by re-spondents was 43 deaths per 1 million peopleper year. Using this risk estimate gives an es-timated value of a premature death of either

$4.8 or $2.6 million. These figures are moreconsistent with previous estimates of the valueof a premature death.

Unfortunately, we cannot give guidance onwhich set of values is more correct for ourrespondents, or whether their value lies some-where in between. At a minimum, we mustquestion our ability to impose a detailed hy-pothetical scenario that differs quantitativelyfrom the beliefs held by respondents. Our re-

Buzby et al.: Measuring Consumer Benejits 75

suits are consistent with respondents who be-lieve that: (a) therisk from pesticide residueson fresh produce sold in atypical store is fair-ly large, and (b) if all government regulationsare met, that risk is effectively eliminated. Un-der those assumptions, the estimated value ofa premature death is $2.6 million, which fallswithin the range of available estimates fromother studies.

Experimental Auction Markets

Food safety is, as described above, a nonmar-ket good primarily because of high informa-tion costs and/or asymmetry. Contingent val-uation of food safety overcomes theinformation problem by providing objectiveassessments of health risk. Valuation of foodsafety in experimental markets attempts to goone step further-eliminating the information-al deficiency and placing the good in some-thing akin to a market situation where moneychanges hands. Application of experimentalvaluation to food safety is relatively new. Thefirst published studies originate from authorsat Iowa State University in the 1990s (Shin etal.; Shogren et al.; Fox et al. 1995; Hayes etal.). The technique has been applied to a num-ber of other food characteristics includingpork attributes (Melton et al.), reductions inpesticide risk (Roosen et al.), and the use ofbovine growth hormone in milk production(Fox).

Advantages of Experimental Markets

Regardless of how well a contingent valuationsurvey is designed and executed, participantsare still aware that they are valuing a hypo-thetical scenario. The absence of market dis-cipline (i.e., a budget constraint) can create anenvironment conducive to questionable re-sponses, and the literature contains several ex-amples of inconsistencies such as lack of re-sponsiveness to the scope and scale ofbenefits. In our contingent valuation casestudy, we found some evidence of scope in-sensitivity and some inconsistencies betweenthe discrete-choice and open-ended values.

Valuation in the lab offers some advantages

in valuing food safety risk reductions. First,experimental markets feature real monetarypayments, forcing respondents to considertheir budget constraints. Second, revelation oftruthful values is encouraged through a re-quirement to consume one of the “risky”products being valued and through the use ofan incentive-compatible auction mechanism.The auction mechanism typically used is theVickrey second-price auction in which thehighest bidder obtains the product being auc-tioned for a price equal to the second-highestbid. By first participating in a candy bar auc-tion, participants are familiarized with the pro-cedure and learn that truthful revelation of val-ue is the dominant strategy. A third potentialadvantage is that selection bias directly relatedto the good can be minimized by recruitingfor a “generic consumer study.”

There is some debate regarding the use ofsingle- or repeated-trial experimental auctions.With repeated trials, participants are informedup front that only one of a number of roundsof bidding will be binding. Repeated trialswith market feedback allow individuals theopportunity to appraise their own preferencesand beliefs in light of the information gener-ated by the market. However, proponents of aone-shot design argue that repetition may af-fect the incentive-compatible properties of thesecond-price auction because it allows for thedevelopment of intra-group competition. Anadvantage of repetition is that it allows for theinjection of new information (e.g., about risk),and examination of its effect on values. Onestudy suggests that repetition does not resultin significant bias (Roosen et al.) but, at thispoint, no compelling evidence exists to preferone approach over the other. The laboratorysetting accommodates both approaches, but arepeated valuation exercise would be unreal-istic in a survey setting.

Limitations of Experimental Markets

Obvious limitations on experimental valuationare the higher costs per respondent and thenecessary geographic restrictions on samples.The time commitment required of subjects issubstantially greater than that for a survey,


thereby requiring some level of financial com-pensation to reduce sample selection effects.It is possible, of course, that participation pay-ments may affect revealed values—an issuewe address below. Another limitation com-pared to surveys is that, at least up until now,experiments have been restricted to valuingdeliverable goods.

Finally, the experimental environment isartificial and contrived, but no more so thanthe hypothetical scenario presented in a sur-vey. In fact, since external distractions areeliminated in the laboratory, respondents cangive undivided attention to the valuation issue.Given the potential benefits and limitations, itappears that experimental valuation can bestbe used as a complement to the survey meth-od. In this context, Fox et al. (1998) describethe use of an experiment as a means of cali-brating hypothetical survey values.

An Experimental Auction Market Valuing

Food Safety

Methodology

In this study, we elicit willingness-to-pay val-ues for reductions in Salmonella risk from asample of 50 undergraduate students at KansasState University. The procedure replicates theexperiments described in Fox et al. (1995),and is identical in all respects except for: (a)

the level of payment to participants; (b) thesimultaneous valuation, in one treatment, of asecond good; and (c) the use of 10 repeatedtrials instead of 20. Subjects were enrolled inan agricultural marketing class, and the exper-iment was conducted during a class period tofacilitate reduced payment of subjects withoutinvolving significant participation bias effects.

The experiment had three parts. In part one,identification numbers were assigned and de-mographic information collected. In part two,subjects participated in a candy bar auctionwith repeated trials in order to learn the pro-cedure and the incentive for truth revelation.In part three, participants were endowed witha Type I chicken sandwich and allowed to bidfor an upgrade to a Type II chicken sandwich.The following descriptions were provided:

● Type I: This food has a typical chance ofbeing contaminated with the foodbornepathogen Salmonella; i.e., ithas been pur-chased from a local source.

● Type II: This food has been subject to strin-gent screening for Salmonella. There is aone in 1 million chance of getting salmo-nellosis from consuming this food.

Following the fifth trial, additional infor-mation was provided that described symptomsof salmonellosis and informed participants thatthe odds of contracting salmonellosis fromtheir typical Type I sandwich was one in137,000. Following the tenth trial, a drawingdetermined the binding trial. The highest bid-der in the binding trial paid the appropriateprice to exchange his/her Type I sandwich forthe Type II—then all participants consumedtheir sandwiches.

Participants were allocated at random toone of four groups, each with 12 or 13 indi-viduals. Each group represented one replica-tion within a 2 X 2 treatment matrix. Thetreatments were: (a) level of payment—$0 or$3, and (b) the introduction of simultaneousbidding for a second good—an upgrade froma plain pencil to a KSU souvenir pencil. Here,we will focus on the sensitivity of values tothe participation payment. Note that in the ex-periments reported in Fox et al. (1995), allparticipants were paid $18.

Experimental Auction Results

Figure 1 shows the average bid at each of the10 trials for Groups 1 and 2 in which partic-ipants bid only for safer chicken. On average,the mean bid is 25c higher for the group re-ceiving the $3 payment. This difference is sig-nificant at the 10% level in trials 5, 7, and 8,and at the 1YO level in trials 9 and 10. Simi-larly, for Groups 3 and 4 in which participantssimultaneously bid for the pencil upgrade, theaverage bid was, on average, 17@ higher forthe group receiving the payment, but the dif-ference was significant only in trial 2 at the10% level. Differences in the mean bid werenot significant in the first trial. Using the trial1 bid as a dependent variable whose variation

Buzbyet al.: Measuring Consumer Benefits 77

0.5~

+! 0.4Q

1234567 8910

Trial

Figure 1. Comparison of average bids for a reduction in Salmonella risk

is explained by participant characteristics(gender, having had food poisoning) and treat-ment effects (payment, bidding on a secondgood), the coefficient on payment is positive(~ = O.11) but not significant (t = 1.18). How-ever, using the average bid over trials 8, 9, and10 as the dependent variables shows a positive(~ = 0.28) and statistically significant (t =2.56) effect for the $3 payment.

Since our sample is small and nonrandom,we cannot conclude with any reasonable de-gree of confidence that payment results in anupward bias. It is certainly possible that otherunobserved differences in participant charac-teristics account in part for the difference inbid levels. In the original study, Fox et al.(1995) reported bids for the same reduction inSalmonella risk for four groups of undergrad-uate students. In the final trials of those ex-periments, the average bid for groups in Ar-kansas and Massachusetts exceeded $1,significantly greater than the approximately

$0.55 average bids of groups in Iowa and Cal-ifornia. Those differences could not be attrib-uted to payment level since all participants re-ceived $18.

It is also important to note that, since ourexperiment was conducted during a class pe-riod, our subjects had zero opportunity cost forparticipating. Therefore, it seems reasonable tohypothesize that they may have been likely toregard their $3 payment as “found money. ”

Conversely, subjects recruitederal population may be more

from the gen-likely to view

payment as compensation for expenses in-curred. In that context, it remains an openquestion as to whether or not payment influ-ences bids. If future studies find that it does,researchers may need to rely on alternativemethods of compensation (e.g., charitable do-nations on the subject’s behalf) which may beless likely to affect values.

Focusing on our quantitative results, wenote that the average informed bid (over trials6–10) for Salmonella risk reduction for indi-viduals receiving the $3 payment was $0.54,very similar to that of the Iowa and Californiagroups mentioned above. For participants re-ceiving no payment, the average informed bidwas $0.32.

Cost-of-Illness Analysis Technique

The cost-of-illness technique has been used toobtain partial estimates of annual costs of acutefoodborne illnesses and select secondary com-plications by estimating the present value oflifetime medical costs and lost productivity(i.e., lost income and household productioncaused by the illness) (Buzby et al.). Althoughcost-of-illness analyses are less supported byeconomic theory than are contingent valuationand experimental auction techniques, they havebeen useful in food safety policy making. A


case in point is the Food Safety and Inspection

Service’s (FSIS’S) Pathogen Reduction/Hazard

Analysis and Critical Control Point (HACCP)

system regulation for federally inspected meat

and poultry slaughter and processing plants

(“Pathogen Reduction . . . ,“ Federal Register)

that used cost-of-illness estimates to representthe benefits of the rule, as did the seafoodHACCP rule (“Procedures for Safe and Sani-tary Processing . . . ,“ Federal Register).

Estimating the Costs of Foodborne Illness

and Premature Death

Cost-of-Illness Estimates

Cost-of-illness estimates are calculated fromthe number of annual foodborne-illness casesand deaths; the number of cases that developsecondary complications; and the correspond-ing medical costs, lost productivity costs, andother illness-specific costs, such as special ed-ucation and residential-care costs. In this casestudy, for each foodborne illness, cases weredivided into four severity groups: (a) thosewho did not visit a physician, (b) those whovisited a physician, (c) those who were hos-pitalized, and (d) those who died prematurelybecause of their illness. For some of the patho-gens, a fifth severity group was used for pa-tients who develop select secondary compli-cations from the acute illness. For eachseverity group, medical costs were estimatedfor physician and hospital services, supplies,medications, and special procedures unique totreating the particular foodborne illnesses.Such costs reflect the number of days/treat-ments of a medical service, the average costper service/treatment, and the number of pa-tients receiving such service/treatment.

Most people with foodborne illnesses missonly one or two days of work. This lost pro-ductivity is approximated by wage rates, pub-lished by the Bureau of Labor Statistics. How-ever, some patients die and some developcomplications that prevent them from ever re-turning to work. The total cost of lost produc-tivity is the sum for all individuals affected,including the patients and, in the case of illchildren, their parents or paid caretakers.

Calculating the Value of a Premature Death

We used two different approaches as proxiesfor the foregone earnings of someone who diesprematurely or who is unable to ever return towork because of his/her foodborne illness (ta-ble 3). The first approach focuses primarily onlost productivity using a combination of humancapital and WTP estimates developed by Lan-defeld and Seskin. Human-capital estimates arethe value in today’s dollars of an individual’slifetime stream of income if the illness had notoccurred. The human capital estimates are in-creased by a multiplier that captures people’sWTP to avoid death, as reflected in life insur-ance premiums. These estimates of the value ofa premature death range, depending on age,from roughly $15,000 to $2,037,000 (in 1996dollars). The major limitation of this approachis that it does not fully consider the value thatindividuals may place on (and pay for) feelinghealthy, avoiding pain and suffering, or usingtheir free time. Because the approach does notcover all of these valuable aspects of health, itis generally thought to understate the true so-cietal costs.

The second set of estimates uses less con-servative values based on the “risk premium”revealed in labor markets through the higherwages employers must offer to induce workersto take jobs with injury. Viscusi comparedwage differences in 24 labor market studiesand found that the extra wages associated withthe increased overall hazard of one death fromrisky jobs are between $3 million and $7 mil-lion (in 1990 dollars). Several regulatoryagencies, such as the FDA, use either Viscusi’srange of estimates or the $5 million midpointwhen analyzing the benefits of proposed pub-lic-safety rules. For our second set of cost es-timates (table 3), we used the $5 million es-timate, regardless of the age of the patient. Weused both approaches here because economistshave not reached a consensus on which esti-mates to use, though they may now be leaningtoward the labor market approach.

High Costs of Foodborne Illness: Analysis

Results

We performed cost-of-illness analyses on sev-en pathogens which are found on some meat


Table 3. Estimated Annual Costs of U.S. Foodborne Illness, 1996

Estimated Foodborne IllnessCosts, Assuming:

Pathogen, Acute Illness,Foodbome Illness Estimates

Human Capital Labor Marketand Complication No. Cases No. Deaths Approach’ Approachb

Bacteria

Campylobacter jejuni or coli

CampylobacteriosisGuillain-Barr6 syndrome

Subtotal

Clostridium per$ringens

C. per$ringens intoxications

,tk4terichia coli 0157:H7E. coli 0157:H7 diseaseHemolytic uremic syndromec

Subtotal

Listeria monocytogenes~

ListeriosisComplications

Subtotal

Salmonella (non-typhoid)Salmonellosis

Staphylococcus aureus

S. aureus intoxications

Parasite

Toxoplasma gondiie

ToxoplasmosisComplications

Subtotal

Total

1,100,000–7,000,000293–2,681

NIA

10,000

16,000–32,000800–1 ,600

N/A

928-1,76722–4 1NIA

696,000-3,840,000

1,513,000

2601>560NIA

3,300,000–12,400,000

110–5116-53

116-564

100

40–8023–4663-126

230-4850

230–485

870-1,920

454

400

40

1,900–3,700

------ ($ bil,, 1996) ----

0.7–4.40.1–1.30.8-5.7

0.1

0.05–0. 10.1-0.2

0.16-0.3

0.12-0.260.03-0.05

0.1–0.3

0.9–3.6

1.2

0.043.283.3

6.6–14.5

1.2–6.70.4-3.41.6–10.1

0.5

0.1–0.20.2-0.40.3-0.7

1.2–2.30.1-0.21.3–2.4

4.8–12.3

3.3

0.17.77.8

19.6–37,1

Notes: N/A = not applicable. Subtotals and totals may not add due to rounding; totals are rounded down to reflect theuncertainty of the estimates.‘ The Landefeld and Seskirr approach is basically a human capital approach, increased by a willingness-to-pay multiplier,and estimates the cost of a premature death, depending on age, to range from roughly $15,000 to $2,037,000 in 1996dollars.bThis labor market approach values the cost of a premature death at $5 million.‘ Hemolytic uremic syndrome (HUS) is characterized by kidney failure. HUS following foodborne E. coli O 157:H7infections causes 44–90 acute illness deaths and 33–62 chronic illness deaths.dLi.steria monocytogenes includes only hospitalized patients because of data limitations.c Toxoplasma gorrdii includes only toxoplasmosis cases related to fetuses and newborn children who may become blindor mentally retarded. Some cases do not have noticeable acute illness at birth, but develop complications by age 17.Does not include all other cases of toxoplasmosis. Another high-risk group for this parasite is the immunocompromised,such as patients with AIDS or cancer.

and poultry (i.e., Campylobacter jejuni, Clos- ciated deaths. Using the Landefeld and Seskintridium perjYingens, E. coli O 157:H7, Listeria human capital/WTP approach for the cost ofmonocytogenes, Salmonella, Staphylococcus a premature death, total annual costs for theaureus, and Toxoplasma gondii) (table 3). In seven foodborne illnesses and select second-1996, there were an estimated 3.3 to 12.4 roil- ary complications (in terms of medical costslion U.S. cases of the foodborne illnesses from and costs of lost productivity) in 1996 dollarsthese seven pathogens, and up to 3,700 asso- ranged between $6.6 billion and $14.5 billion.


Using the $5 million estimate of the cost of a

premature death from the labor market studies

increases total annual costs to between $19.6

billion and $37.1 billion.

Both sets of estimates undervalue the true

costs of foodborne illnesses to society, how-

ever, because the analyses cover only seven of

the more than 40 different foodborne patho-

gens believed to cause human illnesses. Esti-

mated costs would also increase if the costs

for all secondary complications linked to

foodborne illnesses were included. These es-

timates primarily include medical costs and

lost productivity. Total costs would increase if

we include other societal costs, such as painand suffering, travel to medical care, and lostleisure time.

The wide range of costs is largely due touncertainty about the true number of annualfoodborne illness cases and associated deaths.Many people sick with diarrhea do not visit adoctor, and even if they do, most will not havea stool culture taken—let alone have the spe-cific test necessary to identify the pathogenthat caused the illness. The lab test may notfind the pathogens. Even if a particular patho-gen is implicated, not all culture-confirmedfoodborne illnesses are reported to the CDC,and these illnesses may not be traced back toa particulm food source. Therefore, most food-borne illnesses go unrecorded. Better datacould help narrow the ranges of cases anddeaths, and could provide information to cal-culate the costs of other foodborne pathogens.Knowledge on the extent and severity of theseillnesses is still growing, and estimates needto be updated as better data become available.

Conclusion

This paper has presented examples of how dif-ferent techniques can be used to evaluate thecosts of foodborne illness and the benefits tosociety of increasing the safety of the foodsupply. To many economists, contingent val-uation or experimental economics are pre-ferred to cost-of-illness studies, since they are(theoretically) grounded in individual prefer-ences and measure changes in well-being di-rectly. However, cost-of-illness methods have

some appeal because they present economiccosts in a straightforward manner easily un-derstood by decision makers, and they repre-sent real costs to society (as opposed to hy-pothetical costs ginned up from syntheticmarkets. Both expenditure-based and prefer-ence-based valuation techniques can play arole in cost/benefit analyses to better allocatelimited government resources.

In some cases, the choice of valuationmethodology is irrelevant if benefits (or costs)dominate the costhenefit calculus. In theirstudy of the benefits of the new HACCPpathogen reduction rule, Crutchfield et al. useda sensitivity analysis which calculated the ben-efits of HACCP using the cost-of-illness ap-proach. They found the benefits of pathogenreduction associated with this rule to be be-tween $1.9 to $171.8 billion over 25 years,depending upon the choice of valuation for

premature death (Landefeld and Seskin versus

Viscusi) and upon the effectiveness of

HACCP (the degree to which the new rule

would reduce pathogen levels, illness, and

death). The costs of HACCP were put at $1. l–

$1.3 billion over 25 years. Not all public pol-

icy choices are as clear cut as this, of course.

There may be some situations where the ben-

efit/cost calculation will be sensitive to the

valuation methodology, and where choice of

one tool over another may yield a different

conclusion as to the desirability of some pub-

lic policy affecting food safety.

Economics has much to contribute to un-

derstanding food safety issues because of the

nonmarket nature of food safety, the wide ar-

ray of available analytic tools currently being

used by economists, and the need for partici-pation by economists in multidisciplinary andinteragency food safety research. Economicanalyses can reduce the misallocation of so-cietal resources by identifying data gaps, pri-oritizing food safety problems, and estimatingthe marginal costs and benefits of alternative

public and private control strategies. Econom-ic analyses also can identify the distributionalimpacts of foodborne illness and control mea-sures. The cost/benefit requirement ensuresthat economics is incorporated into political


decision making and, in turn, provides a

strong demand for economic research.

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