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Submitted Article

Do Consumer Responses to Media Food SafetyInformation Last?

Robin Dillaway, Kent D. Messer*, John C. Bernard, andHarry M. Kaiser

Robin Dillaway is a graduate of the Department of Food and Resource Economicsat the University of Delaware. Kent D. Messer is an associate professor andJohn C. Bernard is a professor in the Department of Food and ResourceEconomics, and the Department of Economics, respectively, at the University ofDelaware. Harry M. Kaiser is the Gellert Family Professor of Applied Economicsand Management at Cornell University.

*Correspondence may be sent to: [email protected].

Submitted 18 June 2010; accepted 17 March 2011.

Abstract Using experimental methods with adult subjects from the mid-Atlantic region of the United States, this research examines both the short- andlonger-term impacts of media information on consumer purchasing behavior.Subjects in the treatment group were given food safety information about poultryfrom a popular consumer magazine. Willingness to pay (WTP) estimates werethen elicited for two types of chicken breasts: (1) a leading-brand that was identi-fied in the information treatment as having a high incidence of Campylobacterand Salmonella bacteria; and (2) a lesser known brand, which was reported asbeing relatively free of harmful bacteria. Results indicated that both negative andpositive food safety information significantly impacted consumers’ WTP for saferchicken compared to the reportedly less-safe leading-brand chicken. These changesin behavior persisted throughout the seven-week study period.

Key words: Consumer behavior, food safety, experimental economics,media information.

JEL Codes: Q13, D83, C91.

Introduction

The Centers for Disease Control and Prevention (CDC) estimates thatthere are approximately 76 million illnesses, 325,000 hospitalizations, and5,000 deaths annually caused by food-borne diseases in the United States(Mead et al. 1999). The U.S. Department of Agriculture’s EconomicResearch Service estimates that food-borne illnesses from the top fivepathogens affecting humans cost society $6.9 billion annually (Crutchfieldand Roberts, 2000). Moreover, a recent study estimates that food-borneillness has a societal cost of $357 billion annually (Roberts, 2007). Clearly,

# The Author(s) 2011. Published by Oxford University Press, on behalf of Agricultural and AppliedEconomics Association. All rights reserved. For permissions, please email:[email protected].

Applied Economic Perspectives and Policy (2011) volume 33, number 3, pp. 363–383.doi:10.1093/aepp/ppr019

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food-borne illnesses are a health risk as well as an economic burden onsociety, yet the effect of food-borne illnesses on consumer behavior is notwell understood. The issue of whether and how quickly consumers returnto their previous behavior after receiving media food safety information isimportant to consider when studying the effect of information on consum-ers’ purchasing behaviors. However, research on how quickly the impactof food safety information decays is scarce. Accordingly, the focus of thisresearch is to provide a better understanding of consumer reactions tomedia food safety information over time.

Media reports about food safety concerns are relatively common.Consider for example, the 2006 Escherichia coli outbreak in spinach, whichresulted in 204 illnesses, 104 hospitalizations, and 3 deaths (Calvin 2007).In September 2006, the Food and Drug Administration (FDA) advised con-sumers not to eat bagged spinach and expanded the warning the follow-ing day to fresh spinach. This resulted in a five-day period during whichno fresh spinach was sold in the United States, while California spinachremained off the market for ten days (Calvin 2007). In the longer term,sales of bagged spinach were depressed for months afterward. Comparedto the same period from the previous year, retail sales of bagged spinachwere down 27% five months after the outbreak (Calvin 2007).

Knowing more about the effects of food safety information on consumerbehavior is of particular importance to government agencies. Agenciessuch as the CDC and FDA issue numerous warnings about specific foodproducts. For instance, the CDC reports that in 2006, there were 1,247food-borne outbreaks (CDC 2008). Given the large number of reportedoutbreaks, their impact on consumers can be important.

Media reports are an important source of information about food safetyissues for consumers. The degree of coverage a food safety incidentreceives is likely to influence consumer decisions. However, little isknown about how consumers react to situations where there is an initialburst of media information about the safety of a food product and thenrelatively little information is provided as the media focuses its attentionon other topics, which is common practice, as food safety tends to receivesporadic coverage in most media outlets.

This study differs from most of the previous literature in that it seeks tounderstand both short- and longer-term consumer responses to food safetyconcerns by eliciting willingness to pay (WTP) for poultry products usingexperimental economics methods. This study expands upon previousexperiments on food safety with its multiple sessions, which allows for anexamination of the dynamic processes that exist in the real world. Mostprevious experiments, (for example, Hayes et al. 1995; Lusk and Schroeder2002; and Thomsen and McKenzie 2001,) which consist of a single, isolatedsession, are unable to show consumers’ changing attitudes and WTP overtime. To our knowledge, the seven-week study period used here is thelongest used by an experimental study of this kind. Other studies (forexample Saghaian 2007; Beach et al. 2008) of consumer response to foodsafety scares have traditionally relied upon aggregate market data and donot track specific individuals to see how their behavior changes over time,as the experimental framework here allows.

Our study involved 110 adult participants who were randomly assignedto either the control group or the treatment group. Each subject partici-pated in three sessions over seven weeks. Participants in the control

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treatment were given no food safety information. Participants in the foodsafety treatment were given information from a Consumer Reports maga-zine article, which stated that during the testing of leading brands ofchicken (identified specifically by Consumer Reports as Perdue, Tyson,Foster Farms, and Pilgrim’s Pride)1 high rates of contamination of harmfulbacteria were found. Ranger brand chicken, however, was relatively cleanof these bacteria (Consumer Reports 2007). Analysis of the WTP data indi-cated that participants in the treatment group were willing to pay morefor the Ranger chicken (a relatively safer product). Results from this studyindicated that consumers were strongly affected by food safety informa-tion both immediately and over time.

Relevant Literature

The impact of food safety information on consumers’ WTP has beenwell established in both experimental and empirical studies. In general,the experimental studies have used controls available in a laboratory tomeasure consumers’ WTP at a single moment in time, while the empiricalstudies have employed econometric techniques to examine the change inconsumer behavior over time. That food safety information affects con-sumer purchasing decisions is well established in the literature with somestudies focusing on the effect of general information alone, others focusingon positive information, others on negative information, and some using acombination of both. The experiments of Hayes et al. (1995) demonstratedthat the availability of information can change consumers’ purchasinghabits in response to perceived risk, as consumers in this study werewilling to pay a premium for safer food. The authors also found thatexperimental subjects tended to underestimate the probability of a food-borne illness.

Several empirical studies have also shown food demand to be affectedby food safety concerns. For instance, Piggitt and Marsh (2004) found arelatively small downward demand shift in response to food safety con-cerns, while Marsh, Schroeder, and Mintert (2004) found a significantdownward demand shift in response to Food Safety Inspection Servicerecalls. Literature on food product recalls has also shown negative marketimpacts (for example, Lusk and Schroeder 2002; Thomsen and McKenzie2001).

Studies that have focused on either negative or positive information (forexample Lusk et al. 2001) have shown that negative information decreasesa consumers’ WTP, while positive information increases consumers’ WTP.For example, the experimental results of Lusk et al. (2001) showed apremium involving positive information regarding the tenderness ofsteak. That is, consumers were willing to pay a premium when given ataste test that included information concerning the tenderness of a steakcompared to a taste test with no information. Results of the study indi-cated that consumers value quality (as indicated by tenderness in thisstudy) in the beef market. In another experimental study concerning con-sumer WTP, Stenger (2000) showed that information resulted in a signifi-cant increase in WTP for vegetables grown without the use of sewage

1All brand names are described as they were reported in Consumer Reports. The authors make noclaims to the accuracy of this information.

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sludge as a fertilizer over their current spending levels for fruits and vege-tables, despite consumers’ perception that the health risk from such a fer-tilizer treatment was small. Saghaian’s (2007) observational studyconcerning BSE food safety shocks also shows that negative informationaffects prices downward over a ten-week period. In this study, retailprices lag from a week to ten days behind producer price drops and areless severe.

When consumers receive information about food safety, this informationis often mixed with both positive and negative messages. Several studieshave found that negative information has a stronger effect than positiveinformation. For example, Fox, Hayes, and Shogren’s (2002) study of irra-diated pork tested favorable versus unfavorable information to determinewhich had a stronger effect on consumers’ WTP at the time of purchase.Using three treatments (negative information, positive information, and abalanced treatment involving both positive and negative information), thestudy demonstrated that providing negative information had a muchstronger influence, even when the negative information comes from a non-scientific source. In a study using real-life case reports concerning geneti-cally modified foods, Hu, Zhong, and Ding (2006) found that positiveinformation did not significantly affect participants’ WTP, but negativeinformation significantly decreased WTP.

Several studies have also considered consumers’ response to food safetyinformation over time. Brown, Cranfield, and Henson (2005) found thatWTP drops markedly as participants become more tolerant of risk; theirresults from a one-session experiment indicated that the WTP of consum-ers who initially overvalued the risk of food-borne illness tended toincrease their WTP as their tolerance for risk increased. The study did notcontain a longer-term time component to determine if participantsreturned to previous levels of tolerance for risk over time. Hammitt andHaninger’s (2007) stated-interest survey results indicate that participants’WTP is insensitive to duration between one and seven days. Shogren, Listand Hayes (2000) conducted an experimental study involving consecutiveauctions eliciting participants’ WTP for food products over a two weekperiod. The authors found that participants’ WTP changed over time.

Given the strong effect that information has regarding immediate con-sumers’ food purchasing behavior, food safety concerns can be expectedto change behavior over time. Studies that have used observational meth-odologies have indicated that consumer behavior is affected by thepassage of time. Beach et al. (2008) demonstrated in their study of theinfluence of newspaper stories about avian influenza on Italian poultrysales that negative media information had a persistent effect lasting up tofive weeks. The reasons for these variations in the duration of impacts onconsumer behavior are difficult to unravel. Saghaian’s (2007) observationalstudy of the effect of BSE on U.S. markets found downward price effectsduring the ten-week study period, forecasted up to 15 weeks. Thomsen,Shiptsova, and Hamm (2006) found that sales of a recalled brandremained depressed for approximately eight to twelve weeks and did notfully recover for four to five months. These results were specific to aparticular brand that was affected by a recall and did not pertain tonon-branded food commodity markets.


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Experimental Design

In empirical studies, a key concern is whether all consumers have thesame access to information regarding the safety of food products.Additionally, little is known about how consumer behavior changes asinformation concerning the incident becomes less available over time. Tohelp control for these influences, this study used an experimental econom-ics lab setting. To date, essentially all experimental studies have looked atconsumer behavior response to food safety information as captured in asingle session, and have not conducted the more difficult task of repeat-edly bringing adult subjects to a lab setting to evaluate change in WTPover time.

Our experimental design used within-subject comparisons of WTP fortwo different types of chicken breasts and between-subject comparisons ofWTP for two different treatment groups – one with food safety informa-tion and one without this information. This design was structured to testthe following five primary research questions in order to understand howpositive and negative information affected participants in both the short-and longer-term:

(1) Did consumers have different preferences for the two chicken typeswhen food safety information was not provided?

(2) Did consumers have different preferences for the two chicken typeswhen food safety information was provided?

(3) Did brand-related food safety information decrease consumers’ WTPfor a relatively less safe product?

(4) Did brand-related food safety information increase consumers’ WTPfor a relatively safer product?

(5) Did consumers’ WTP for any type of chicken in either treatmentgroup change over time after no additional food safety informationwas provided?

In this study, 110 adults participated in the experiment.2 Participants wererecruited from a large northeast university’s lifelong learning campus (forstudents aged 50 and over), as well as from the university’s staff, andpublic attendees of an annual event open to the general public. The latterexperiments were conducted in the Experimental Economics Laboratoryfor Policy and Decision Research at the University of Delaware. The studywas widely advertised as an experiment in decision-making that wouldrequire participation in three sessions. These respective settings werechosen because they provided an easily accessible laboratory setting forparticipants that were available in the same building, at the same timeand day of the week for several weeks. All experimental sessions wereconducted between April and July 2008. The subject pool was not selectedto represent the entire United States or even a regional area.3As shownin table 1, to examine short- and longer-term impacts on WTP, the

2Initially, 119 subjects participated in the first session, nine of whom dropped out of the study.Therefore, 92.4% of the subjects who attended the first session attended all three sessions.3The sample population used in this study had many participants over the age of 50. The administra-tors made every effort to ensure that all participants were fully capable of participating through severalrounds of instructions. It was not within the scope of this study to determine if older participants wereless capable of operating computers, read more carefully, were more risk averse, were more likely to beexposed to food safety concerns, or were more rigid in their beliefs. These behaviors may be possiblewith an older population.


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experiment consisted of three sessions. In the first session, which lastedone hour, participants’ WTP was elicited for each of four products. Thiswas followed by a second 15-minute session held at the same time andplace one week after the first session. No further food safety informationwas given to the participants at the second session. Participants repeatedpart B of the experiment, which involved bidding on the four products.The third session was held 28 days later for half of the participants and 49days later for the other half.4 This division was necessary to accommodate

Table 1 Experiment structure

Session 1 (1 hour)Part A

1. Participants read instructions

2. Verbal instructions

3. Practice round; WTP bid for pen

Part B



3. Presentation of food safety information if session is Treatment group

4. Presentation of products to participants

5. WTP bids for leading-brand chicken, Ranger brand chicken, fettuccine pasta,and one dozen eggs

Session 2 (7 days later, 15 minutes)Part B only, no additional information




Session 3 (28 days or 49 days later, 15 minutes)Part B only, no additional information




4A limiting factor for the length of the study was the semester length at the lifelong learning facility,which ended eight weeks after the beginning of this study. Thus, extending the study beyond sevenweeks would have likely meant a much lower percentage of participants completing all three sessions.


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scheduling conflicts, such as a national holiday. Both treatments wereequally represented in the different lengths of time for the third sessions.In both treatments, the third session lasted fifteen minutes, and Part B wasrepeated with no further food safety information presented.

While 119 subjects initially participated in the first session, nine of thesesubjects subsequently dropped out of the study. Therefore, 92.4% of thesubjects who attended the first session attended all three sessions.Participants earned $60 in cash and/or products for the experiment. Thepayments for all participants were $11 in cash and/or products insession 1, $10 in cash and/or products in session 2, and the remainingpayment in cash and/or products in session 3.

A modification of the sealed-bid English auction mechanism was used toelicit WTP estimates due to its ease of explanation and its demand-revealing and incentive-compatible properties (Davis and Holt 1993;Bernard 2006). Using this auction mechanism, subjects’ incentives were tobid their highest WTP and therefore reveal their true demand preferences.Compared to second-price auctions, traditional English auctions are betterable to measure participants’ WTP, but have the disadvantage of havingbids visible to everyone in the experiment session, which can lead to poten-tial group-effects on individual behavior. Lusk and Shogren’s (2007) reviewof different auction methods also point out the merits of the Englishauction, as well as the random nth price auction. The sealed-bid Englishauction retains the benefit of the increasing bid clock while keeping bidshidden (Bernard, 2006).

In this study, we used a modification of the traditional English Auction.We refer to this auction as a sealed, random nth bid English Auction. All ofthe bids were submitted confidentially so that participants were unaware ofwhat other participants in their experimental session were bidding. Thisresearch used $10 as the maximum bid since this was the amount of theinitial balance given to all participants and was significantly higher thanthe market price for 1.5 pounds of chicken breasts. In these experiments,the highest bid submitted was $9.90 – for Ranger chicken.

Participants were informed using both written and oral instructions thatthe optimal strategy was to bid their actual highest WTP for each product.Therefore, they needed to determine their personal highest WTP for theproduct being auctioned and submit that amount as their bid. A computerprogram that employed Excel spreadsheets and programmed using VisualBasic for Applications was used to confidentially record participants’WTP (Messer, Kaiser, and Schulze, 2008). Participants were instructed tostop the program when the displayed price reached her/his maximumWTP. The program initially showed a $0.00 price. Subjects wanting tosubmit a bid of $0 were provided a “Withdraw” button at this initialscreen. Participants wanting to submit bids greater than zero wereinstructed to start by clicking the “Start” button. When they did so, theprogram began increasing the price in one-cent increments at a uniformtime interval until the participant clicked a button marked “Withdraw.” Ifthe “Withdraw” button was never clicked, the program would record themaximum bid allowed ($1 for the training round and $10 for the fourproducts). The maximum price was always equal to the participants’initial balance of funds. To help ensure that participants did not get asense of what other participants were bidding, it was necessary to allowthe clock to run up to the maximum bid of $10.


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The price for the item was determined by the highest rejected bid. At theend of each auction, bids were arranged by the administrator’s computerprogram from highest to lowest and the number of purchasers was ran-domly determined. The subject with the highest bid was the first pur-chaser, and so on. The number of purchasers was determined randomlyby having a volunteer roll a six-sided die with the number of purchaserstherefore ranging from one to six for sessions with seven or moreparticipants.5 For example, if the number of participants in a session wasten, and the result of the die roll was five, the five highest bidders wouldpurchase the product and pay a price equal to the bid of the sixth highestbidder.

Prior to conducting the experiments, participants were randomlyassigned to either a treatment group (n ¼ 56) that received media-basedfood safety information, or a control group (n ¼ 54) that received nomedia information. The average number of participants in a session wasnine, with a range from three to sixteen. Verbal and oral instructions wereprovided to improve the understanding of experiment procedures (see theReview Appendix). Questions were encouraged during all stages of theexperiment. Subjects were seated at individual computer terminalsequipped with privacy screens so that all decisions would be madeconfidentially.

The first session was divided into two parts (table 1). Following Messeret al. (2011), Part A consisted of a practice round where participants bidon a pen. This practice round helped ensure that participants understoodhow the bidding process worked and how to stop the computer programat the desired WTP. For the pen, the initial balance provided to eachsubject was $1. Participants were instructed to bid zero if they valued theproduct at $0 or less, and were instructed to bid $1 if they valued theproduct at $1 or greater. If their value for the pen was between $0 and $1,they were instructed to stop the computer program at the price that repre-sented their highest WTP. During the training round, questions wereanswered to ensure that participants understood the program andprocedure.

In part B, the initial balance was $10 for each product. Participants werepermitted to bid between $0 and $10. In part B, four products wereauctioned:

1) Frozen boneless skinless chicken breasts from a leading-brand – suchas Foster Farms, Perdue, Pilgrim’s Pride, or Tyson (approximately112 pounds).

2) Frozen boneless skinless chicken breasts from Ranger (approximately112 pounds).

6

5For sessions with less than seven participants, the maximum number of purchasers was k – 1, wherek was the number of participants. In some cases, a four-sided die was used. The number of purchaseswas determined randomly using an nth-price auction because of the repeated nature of these experi-ments. For instance, if a standard second-price auction was used, then after the first session, most sub-jects would have a good sense of whether their bid for the selected product in the subsequent sessionswould be close to the highest bid in their group. The use of an nth-price auction helped ensure thatessentially none of the bidders would be “off-margin.”6Ranger brand chicken is only available in the Pacific Northwest. To avoid deceiving subjects, over 70pounds (nearly 50 packages) of Ranger brand frozen boneless skinless chicken breast were shipped over-night express from Bellingham, Washington, packaged in dry ice and in freezer containers, at a cost ofover a thousand dollars.


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3) Eggs (one dozen, size large).4) Fettuccine pasta (one pound).

All original packaging information was removed from the products priorto the experiment to control for any reaction individuals might have to thepackaging. Both types of chicken were displayed in clear, one-gallonsealed freezer bags. Participants were informed that if they purchased thechicken, we would package the chicken in ice, if desired, to ensure thatthe chicken would remain frozen until they returned home.7 Before thebidding began, an administrator walked around the room and displayedeach of the products. Participants were asked not to touch the products,since the products would be distributed after the experiment to the pur-chasers, but they were permitted to visually inspect each of them asclosely as they wished. To prevent potential order effects, the order inwhich the products were displayed was determined using a Latin squaresdesign.8

Participants bid on all four products, but only one product was actuallypurchased similar to the procedures designed by Bernard, Zhang, andGifford (2006). The purchased product was randomly predeterminedusing a four-sided die and was written on an index card. The card wassealed inside an envelope, which was opened by a volunteer after all ofthe auctions were completed. Prior studies have shown that, in amultiple-round auction such as this one, randomly determining theproduct that is purchased helps to elicit WTP among participants (seeLusk, Feldkamp, and Schroeder (2004); Hayes et al. (1995); and Messeret al. (2010)). This procedure compensates for the potential that a partici-pant purchased a product in one session and therefore decreased her orhis WTP in subsequent sessions. It was possible for the same product tobe binding in more than one session. The binding product in this experi-ment was randomly determined for each session in an effort to reduce thepotential effects across sessions caused by a subject receiving a product inone session and having to bid again for the same product in subsequentsessions.

These procedures were used since this experiment involved multiplesessions with the same products. In such cases, there is a potential thatparticipants who purchase a product in one session will bid lower thantheir true WTP in subsequent sessions since they already have theproduct. However, this effect was minimized since the actual quantity ofchicken sold during the experiments was small. Therefore, it was impor-tant to make the binding auctions random in order to minimize this typeof effect on participant WTP.

After bids for all four products were collected, the predeterminedproduct was distributed to purchasers and used to calculate cash earnings.

7All of the chicken was frozen so that consumers would have less concern about potential food safetyissues related to the administrator’s handling of the chicken, and to minimize concerns that the chickenwould need to be eaten immediately. This latter reason was important in the case that participantswere not returning to their homes immediately after the research, as well as to ensure that the potentialuseful life of this product was as long as possible, so that consumers WTP would not be significantlyaffected by the amount of chicken that they might have just recently purchased at a grocery store priorto the session.8A Latin Squares design is used here as a method of varying the order of products. For instance, if inthe first session, products A, B, and C are introduced in that order, in the next session, they will beintroduced in the order B, C, A. In the following session they will be in the order C, A, B, and so on.


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At the end of the experiment a short survey was then distributed in eachsession to collect demographic data, as well as information about subjects’beliefs about the safety of food products, and whether subjects receivedadditional food safety information between sessions of the experiment.The information provided to the treatment group about the two chickenproducts is included in appendix 1.

Results

The participants’ WTP bids for chicken are displayed in the four panelsof figure 1, which shows the demand for each chicken type in each sessionand in each treatment group. The figures plot WTP on the y-axis againstthe percentage of participants willing to pay at a given price on the x-axis.

Figure 1 WTP for approximately 1 12 pounds of chicken breasts, by type and treatment


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The legends of each figure provide the mean and standard deviationrelated to each demand curve.

To answer the research questions outlined in the experimental designsection, the participants’ responses were analyzed in several differentways. Normality tests were performed on WTP data for the leading-brandchicken and the Ranger brand of chicken using STATA’s sktest, whichmeasures skewness and kurtosis to test for normality. The results of theskewness and kurtosis tests indicated that the data were not normally dis-tributed. Thus, the WTP bids were analyzed with nonparametricWilcoxon rank-sum tests to analyze differences between the two treatmentgroups (table 2). Wilcoxon signed-rank tests were used to test for statisticalsignificance within experiment sessions and within chicken types.

Additionally, two-limit Tobit regression models were also used toexplain participants’ WTP for chicken. A two-limit Tobit was required,since participants’ bids were constrained at $0 and $10 in the experiment.The model included data from all three sessions, for both chicken typesfrom both the control and treatment groups. This model thus included sixobservations from each of the 103 non-vegetarian subjects bidding threetimes each for two brands of chicken. A random-effects model was usedsince each participant had multiple observations in the panel data set.Marginal-effect coefficients were calculated to translate the regressioncoefficients into WTP. The model included an information variable meas-uring the impact of the food safety information in the study on partici-pants’ WTP. The variable info was also interacted with all otherindependent variables to find the effects of food safety information onsubgroups. Table 3 provides descriptive statistics on the variables in thefollowing model:

wtp_chicken ¼ b0 + b1(info) + b2(ranger) + b3(ranger*info) + b4(days) + b5(days2) +b6(days*info) + b7(days2*info) + b8(ranger*days) + b9(ranger*days2) +b10(ranger*info*days) + b11(ranger*info*days2) + b12(age) + b13(age*info) +b14(female) + b15(female*info) + b16(children) + b17(children*info) +b18(primary_shopper) + b19(primary_shopper*info) + b20(education) +b21(education*info) + b22(nonwhite)+ b23(nonwhite*info)+ b24(p_chickensafe) +b25(p_chickensafe*info) + b26(income)+ b27(income*info)+ b28(eat_chicken_often)+b29(eat_chicken_often*info) + 1.

The variables p_chickensafe and p_chickensafe*info were constructed usingsurvey responses from participants indicating whether they consideredchicken to be a safe product. A Hausman test for endogeneity indicatedthat the p_chickensafe variable was endogenous. The residual term was a sig-nificant predictor of the dependent variable (p ¼ 0.023). Therefore, instru-mental variables were constructed for p_chickensafe and p_chickensafe*infousing auxiliary regressions of these variables on all exogenous variables inmodel.

Tests of Research Questions

The first research question was, “Did consumers have different preferencesfor the two chicken types when food safety information was not provided?” Avisual inspection of panels A and C in figure 1 suggests that the demandcurves for leading-brand chicken were slightly higher than the demandcurves for the Ranger brand. As can be seen in table 2, panel A, onaverage in the three sessions, participants were willing to pay between


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Table 2 Sets of tests of differences in WTP with Wilcoxon significance tests.

H0: WTP for Ranger brand - WTP for Leading-brand 5 0

Panel Control Prob> |z| Panel Treatment Prob> |z|

A Session 1 2.57 – 2.86 ¼ 20.29 0.1018 B 3.15 – 2.21 ¼ 0.94 0.000Session 2 2.37 – 2.61 ¼ 20.24 0.2059 2.41 – 2.05 ¼ 0.36 0.036Session 3 2.38 – 2.53 ¼ 20.15 0.3040 2.32 – 1.95 ¼ 0.37 0.012All Sessions 2.44 – 2.67 ¼ 20.23 NA 2.62 – 2.07 ¼ 0.55 NA

H0: WTP for Treatment group – WTP for Control group ¼ 0Leading-brand Prob. |z| Ranger brand Prob. |z|

C Session 1 2.21 – 2.86 ¼ 20.65 0.0219 D 3.15 – 2.57 ¼ 0.58 0.2821Session 2 2.05 – 2.61 ¼ 20.56 0.0929 2.41 – 2.37 ¼ 0.03 0.8146Session 3 1.95 – 2.53 ¼ 20.58 0.0691 2.32 – 2.38 ¼ 20.06 0.8247All Sessions 2.07 – 2.67 ¼ 20.60 NA 2.62 – 2.44 ¼ 0.18 NA

H0: WTP for Session 1 – WTP for Session 2 ¼ 0Leading-brand Prob. |z| Ranger brand Prob. |z|

E Control 2.86 – 2.61 ¼ 0.25 0.1253 F 2.57 – 2.37 ¼ 0.20 0.2159Treatment 2.21 – 2.05 ¼ 0.16 0.6146 3.15 – 2.41 ¼ 0.76 0.0018


G Control 2.61 – 2.53 ¼ 0.08 0.7252 H 2.37 – 2.38 ¼ 20.01 0.8256Treatment 2.05 – 1.95 ¼ 0.10 0.8156 2.41 – 2.32 ¼ 0.09 0.9818


I Control 2.86 – 2.53 ¼ 0.33 0.1424 J 2.57 – 2.38 ¼ 0.21 0.2432Treatment 2.21 – 1.95 ¼ 0.26 0.8982 3.15 – 2.32 ¼ 0.83 0.0171

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$0.15 and $0.29 less for a pound and a half of the chicken breasts.However, these differences were not statistically significant in any session.For the pooled data used in the two-limit Tobit analysis, the coefficient onthe variable ranger was again negative (-0.273) and not statistically signifi-cant (p ¼ 0.216) (table 4).

Recall that participants were given information which stated thatRanger brand was safer. The second question, “Did consumers have different

Table 3 Variables in the Tobit model.

Variable MeanStandarddeviation Minimum Maximum

wtp_chicken - WTP for bothleading-brand and Rangerchicken.

2.430 1.787 0.0 9.9

info - 1 for the treatment group and0 otherwise.

0.509 0.500 0.0 1.0

ranger - 1 for WTP for Ranger and 0otherwise.

0.500 0.500 0.0 1.0

days - Number of days since thefirst session.

15.024 17.745 0.0 49.0

days2- Number of days since thefirst session squared.

540.083 860.511 0.0 2401.0

age- 19 if , 20; 24.5 if between 20and 29; 34.5 if between 30 and39, . . .; 84.5 if between 80 and 89.

47.211 16.233 19.0 84.5

female- 1 for female and 0otherwise.

0.668 0.471 0.0 1.0

children- 1 if participant haschildren under 18 living at homeand 0 otherwise.

0.187 0.390 0.0 1.0

primary_shopper - 1 if participant isprimary shopper in householdand 0 otherwise.

0.730 0.444 0.0 1.0

education- 0 if less than collegedegree, 1 if college degree, and 2if more than a college degree.

1.332 0.812 0.0 2.0

nonwhite - 1 if nonwhite racialdesignation and 0 otherwise.

0.104 0.305 0.0 1.0

p_chickensafe - Instrumental variableconstructed from participantresponses whether they considerchicken safe.

2.746 0.574 1.0 4.2

income- Categorical householdannual income variable rangingfrom 0 ($0 - $39,999) to 10 (over$200,000) by $40,000 increments.

73.772 54.500 10.0 240.0

eat_chicken_often - 0 if participanteats chicken never; 1 if rarely; 2 ifonce a month; 3 if several times amonth; 4 if once a week; and 5 ifseveral times a week.

2.713 1.205 0.0 4.0


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preferences for the two chicken types when food safety information was provided,”contrasted WTP in the information treatment for the two chicken types.Inspection of figure 1 shows a marked increase in WTP betweenleading-brand chicken (panel D) and Ranger chicken (panel B). These dif-ferences in average WTP ranged from $0.36 to $0.94, which were statisti-cally significant in session 1 (p ¼ 0.000), session 2 (p ¼ 0.036), and session3 (p ¼ 0.012) (table 2, panel B). This result was further supported by theTobit model results where the increase in WTP for Ranger from the foodsafety information was $1.24 when all other factors were held constant

Table 4 Two-limit Tobit results, WTP for chicken.

Marginal effect Standard error P> |z|

constant 21.526 1.676 0.362info 1.236 2.079 0.552ranger 20.273 0.221 0.216ranger*info 1.234 0.318 0.000days 20.037 0.023 0.116days2 0.001 0.001 0.161days*info 20.050 0.036 0.170days2*info 0.001 0.001 0.137ranger*days 20.008 0.031 0.797ranger*days2 0.000 0.001 0.696ranger*info*days 0.010 0.048 0.843ranger*info*days2 20.001 0.001 0.578age 0.015 0.016 0.370age*info 20.012 0.022 0.595female 20.061 0.505 0.904female*info 0.169 0.674 0.802children 0.926 0.513 0.071children*info 22.551 0.854 0.003primary_shopper 1.053 0.587 0.073primary_shopper*info 20.103 0.777 0.894education 0.216 0.293 0.461education*info 20.648 0.392 0.098nonwhite 20.066 0.575 0.908nonwhite*info 21.113 1.014 0.272p_chickensafe 0.956 0.383 0.013p_chickensafe*info 20.967 0.496 0.051income 0.007 0.004 0.095income*info 20.006 0.006 0.360eat_chicken_often 20.309 0.196 0.116eat_chicken_often*info 1.157 0.266 0.000Sum of all info coefficients 21.639F test statistic 8.550Wald chi2 ¼ 118.820Prob. chi2 ¼ 0.000Log likelihood ¼ 2863.063Left-censored observations 79Uncensored observations 455Right-censored observations 0

Note: Since some of the survey questions were left blank by subjects, some observations were notincluded in the final model, reducing the total number of observations to 534.


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(p ¼ 0.000). All of this evidence suggested that the null hypothesis can berejected, as participants in the treatment group were willing to pay morefor Ranger chicken than for leading-brand chicken. This response to thepositive information for Ranger chicken9 corroborated results found inLusk et al. (2001) and Stenger (2000).

The third research question, “Did brand-related food safety informationdecrease consumers’ WTP for a relatively less safe product,” related to whetherinformation affects WTP for chicken. Since all variables were interactedwith the information variable in the Tobit analysis, the derivative of infowas calculated by adding the coefficient for info and all variables inter-acted with info. A model-specification F-test indicated that the info variablehad a coefficient of -1.639 and was significant at the p , 0.01 significancelevel (table 4). The effect of information was most pronounced in thelowering of WTP for leading-brand chicken. For instance, as shown inpanel C of table 2, participants who were given food safety informationfrom Consumer Reports were willing to pay, on average, $0.65 less in thefirst session for leading-brand chicken than participants who were notgiven this information. This difference was statistically significant(p ¼ 0.0219) (table 2, panel C). The $0.56 and $0.58 average differences inthe second (p ¼ 0.0929) and third (p ¼ 0.0691) sessions, respectively, werenot significant at the p ≤ .05 significance level, but were significant at the0.10 level. Taken together, this suggested that the answer to the thirdresearch question was that consumers do reduce their WTP for the lesssafe product after receiving negative food safety news. The results forleading-brand chicken were consistent with the literature (see Hayes et al.1995 and Messer et al. 2011) in that negative information decreased WTP.

Research question 4, “Did brand-related food safety information increase con-sumers’ WTP for a relatively safer product,” reveals the result that food safetyinformation for Ranger chicken had a significant positive effect. Table 4indicates that the Tobit model results for the variable ranger*info arehighly significant with a coefficient of 1.234. All else held constant,average WTP for Ranger chicken was $1.23 higher. Table 2 (panel B) alsoindicated that participants who received food safety information werewilling to pay a premium for Ranger chicken when compared to leading-brand chicken in all three sessions. In contrast, the information treatmentdid not increase participants’ WTP for Ranger chicken by session, as noneof the differences between any of the three sessions were statistically sig-nificant (table 2, panel D). Thus, consumers will pay more for a saferproduct after learning of positive food safety information. Furthermore,consumers’ WTP for this product will not decrease like it does for the lesssafe brand of chicken. However, this result is not differentiable by session,which suggests that time is not a factor in consumer willingness to pay apremium for a safer product.

The fifth research question, “Did consumers’ WTP for any type of chickenin either treatment group change over time after no additional food safety infor-mation was provided,” tested the longer-term effect on WTP. As shown infigure 1, average WTP for both types of chicken in both treatment groupswas highest in the first session. However, tests of these differences

9While the information about Ranger brand chicken was not universally positive, as some level of bac-teria contamination was detected, the Consumer Reports article clearly presented Ranger as the rela-tively safer alternative for chicken consumers.


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(table 2, panels E-J), show that the only differences that were statisticallysignificant were the $0.76 and $0.83 decrease in WTP for Ranger chickenin the information treatment (p ¼ 0.0018 and p ¼ 0.0171, respectively).The two-limit Tobit model indicates that none of the eight variables thatincluded days were statistically significant. The variable, days, measuredthe number of days that had elapsed since the first session. This passageof time could potentially have affected participants in several ways: partic-ipants may have forgotten about the information they learned over time;participants’ desire for chicken may also have changed over time based onwhether they had recently purchased chicken; participants may also havechanged their mind or had second thoughts since the previous sessions.The insignificance of the days variables demonstrates that there was no sig-nificant change in participants’ WTP over time for leading-brand orRanger brand chicken in either treatment group.10

A more differentiated analysis is available from the Wilcoxon signifi-cance tests shown in table 2. The results of these tests indicate that WTPfor Ranger was significantly different between sessions 1 and 2 in thetreatment group (panel F). Session 1 WTP for Ranger is also significantlydifferent from session 3 for the treatment group (panel J). However, WTPfor Ranger is not significantly different between sessions 2 and 3 in thetreatment group (panel H). None of the differences in WTP for Rangerbetween any of the sessions is significantly different in the control group.WTP for leading-brand is not significantly different from session tosession for either the treatment or the control group (panels E, G, and I).These results indicate that the premium for Ranger is short-lived and dis-appears by session 2, while the negative effect on WTP for leading-brandremains over all sessions.

In addition to treatment dummy variables, several demographic varia-bles were also included in the Tobit regression model. As is common inexperimental studies, gender, age, income, and education variables wereincluded as explanatory variables of WTP (Bernard, Zhang, and Gifford2006; Fox, Hayes, and Shogren 2002; Hobbs et al. 2005; Hu, Zhong, andDing 2006; Lusk et al. 2001; Lusk, Feldkamp, and Schroeder 2004). Sincesome research has found the presence of children in participants’ house-holds to influence WTP, the number of children in each household wasincluded as an explanatory variable (Bernard, Zhang, and Gifford 2006;Hu, Zhong, and Ding 2006; Kanter, Messer, and Kaiser 2009; Lusk et al.2001; Messer et al. 2011). Following previous studies, race (Bernard,Zhang, and Gifford 2006), whether a subject considers themselves theprimary shopper in the household (Kanter, Messer, and Kaiser 2009), and

10Survey results indicate that 24 subjects (21.8% of the total sample) reported receiving outside infor-mation between experimental sessions that affected their bidding during the course of the experiment.Of these 24 subjects, 9 were in the control group and 15 were in the treatment group. While subjectswere not given any information about Ranger brand chicken other than the safety information providedin the Consumer Reports article, simple internet searches about this brand would reveal to consum-ers that Ranger is also free range and the chickens are raised without the use of hormones or antibiot-ics. To test whether subjects researched Ranger brand chicken and subsequently bid higher insubsequent sessions, any positive responses were compared with bids that were at least 10% higher forRanger brand chicken. Of the subjects who responded that they were affected by outside information,only 4.6% of subjects bid at least 10% higher for Ranger in session two than in session one, 6.4% ofsubjects bid at least 10% higher in session three than in session two, and 5.5% bid at least 10%higher in session three than in session one. Therefore, the potential impact of information gainedoutside of the experiment is likely minimal.


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the frequency which subjects reported that they eat chicken (Messer et al.2011) were also included as explanatory variables.

Many results from the above mentioned variables are worth noting. In theTobit model, children*info, p_chickensafe, and eat_chicken_often*info were highlysignificant independent variables. The variables children, primary_shopper, edu-cation*info, p_chickensafe*info, and income were also significant. Participants inthe treatment group with children (children*info) under 18 living at home werewilling to pay $2.55 less on average for chicken. Those in the control group(children) were willing to pay $0.93 more, on average. Those participants in thetreatment group who considered chicken to be a safe product were willing topay $0.96 more on average. Interestingly, those participants in the treatmentgroup who ate chicken often (eat_chicken_often*info) were willing to pay $1.16more on average. This result was consistent with the results of Payne et al.(2009), who found frequent consumers of beef were less affected by negativefood safety information. Participants who consume chicken more frequentlyare also more likely to have been exposed to similar food safety warnings con-cerning chicken in the past. This may also be a factor in the lack of significanceof this variable for the control group and such a strongly significant result forthe treatment group. The significance of the variable primary_shopper indicatedthat participants in the control group who considered themselves to be theprimary shopper in the household were willing to pay $1.05 more on averagethan those who did not consider themselves to be the primary shopper. Thesignificance of the impact of product safety information and education (educa-tion*info) indicated that more highly educated participants in the treatmentgroup were willing to pay, on average, $0.43 less ($0.65 less + $0.22 from thecoefficient for the education variable). The significance of the income variable(income) indicated that for every dollar of household income that participantsin the control group reported, participants were willing to pay $0.007 more forchicken. The remaining demographic variables were statistically insignificant.

Conclusion

This experimental study demonstrated that a combination of negativeand positive information regarding food safety has a long-lasting impacton demand. One hundred ten adults participated in this research to testthe effects of negative and positive information over time on consumerpurchasing behavior (as measured by WTP). Participants were asked torepeat the experiment twice after the initial session to measure changes intheir WTP over time, extending out to seven weeks. During each session,WTP data were collected on both leading-brand and Ranger brandchicken. Demographic data were also obtained from the participants. Thefood safety information used in the study came from a 2007 ConsumerReports magazine article stating that leading-brand chicken, specificallyidentified as Perdue, Tyson, Foster Farms, and Pilgrim’s Pride, frequentlycontained harmful bacteria. The article also stated that another brand,Ranger, was relatively free of harmful bacteria.

Results from this study indicated that consumers are willing to changetheir purchasing behaviors to avoid unsafe products. Both positive andnegative information had an effect on consumers’ WTP. Consumers werewilling to pay less for the leading-brand chicken after they received nega-tive food safety information compared to a control group that did not


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receive this information. Participants that received positive food safetyinformation about Ranger brand chicken were consistently willing to paymore for this safer option than for leading-brand chicken. This suggeststhat when the information about which brands are safer is available, con-sumers are willing to alter their purchasing behavior to favor the saferalternative, even if it was a relatively unknown brand. Both of theseeffects appeared to last well beyond the initial exposure to mediainformation.

The results of this study provided limited guidance for food industriesaiming to avoid potential losses from media reports regarding food safetyconcerns. Like consumers, an industry may be affected severely by mediareports about food safety that lead to a long-lasting decrease in consumerwillingness to buy the affected product. Negative information about a spe-cific brand or specific brands can, over time, affect an entire industry. Thiscan be especially devastating to a seasonal agricultural industry, an indus-try that has many food substitutes, or in situations where the true sourceof the food contamination is incorrectly identified. Results from this studyindicated that introducing safer alternative products when feasible canhelp capture the premiums that are lost during an incident that raisesfood safety concerns. Unlike some of the event studies (see Thomsen andMcKenzie 2001; Lusk and Schroeder 2002), which focus on the observatio-nal effects of negative food safety information only, this study was able toobserve a measurable difference between a relatively more safe and rela-tively less safe product over time. The measurable difference betweenproducts is short-lived, while the negative effect of food safety informationon the less safe product persists in the longer term. The longer-termdecrease in consumer willingness to pay for leading-brand chicken sug-gests that negatively impacted food products will result in significantlylower consumer WTP for an extended time. This suggests that industryshould seek to prevent food safety problems, or face an extended decreasein consumer demand.

An obvious limitation of this study is its seven-week duration. Thislength was initially chosen to align with several empirical studies on con-sumer responses to food safety concerns, and also due to logistical con-straints on recruiting adult subjects for a repeated experiment. Given theresults suggesting that consumer WTP was still affected upwards of sevenweeks, investigations into a longer duration are warranted. However, thisseven-week study is to our knowledge the longest of its type to date.There is a need for longer-term experimental studies to overcome the pos-sibility that experimental studies merely capture a snapshot of subjects’behavior and are not an accurate portrayal of their true behavior overtime. Another potential limitation of the experimental setting over time inthis study is the possibility that subjects interacted and affected eachothers’ bidding behavior between sessions, or that subjects were influ-enced by other sources of information between sessions. This study triedto address these concerns by using a between-subject control and treat-ment design, and asked all participants to sign a confidentiality agree-ment11. Further investigation of these issues will help develop a better

11The confidentiality agreement stated, “I agree that by participating in this experiment I will notdiscuss or make known the experiment or any part thereof to any individual.”


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understanding of how long consumers’ responses are influenced by mediafood safety information.

Supplementary Material

Supplementary material is available at Applied Economic Perspectives andPolicy online (http://aepp.oxfordjournals.org/).

References

Beach, Robert H., Fred Kuchler, Ephraim Leibtag, and Chen Zhen. The Effects ofAvian Influenza News on Consumer Purchasing Behavior: A Case Study of ItalianConsumers’ Retail Purchases. USDA Economic Research Service, EconomicResearch Report 65. August 2008.

Bernard, John C. 2006. Finding and Retaining the Dominant Strategy: TheSecond-price, English, and ‘Sealed Offer’ English Auctions. Journal of EconomicBehavior and Organization 60: 553–562.

Bernard, J.C., and D. J. Bernard. 2009. What is it about Organic Milk? AnExperimental Analysis. American Journal of Agricultural Economics 91: 826–836.

Bernard, John C., Chao Zhang, and Katie Gifford. 2006. An ExperimentalInvestigation of Consumer Willingness to Pay for Non-GM Foods When anOrganic Option Is Present. Agricultural and Resource Economics Review 35(2):374–386.

Brown, Jennifer, John A.L. Cranfield, and Spencer Henson. 2005. RelatingConsumer Willingness-to-Pay for Food Safety to Risk Tolerance: AnExperimental Approach. Canadian Journal of Agricultural Economics 53: 249–264.

Calvin, L. 2007. Outbreak Linked to Spinach Forces Reassessment of Food SafetyPractices. Amber Waves 5(3): 24–32.

Centers for Disease Control. 2008. Outbreak of Salmonella Serotype SaintpaulInfections Associated with Multiple Raw Produce Items—United States.Morbidity and Mortality Weekly Report 57(34): 929–934.

Consumer Reports. 2007. Dirty Birds: Even ‘Premium’ Chickens Harbor DangerousBacteria. Consumer Reports: 20–24.

Crutchfield, Stephen R., and Tanya Roberts. 2000. Food Safety Efforts Accelerate inthe 1990s. FoodReview 23(3): 44–50.

Davis, Douglas D., and Charles A. Holt. 1993. Experimental Economics, Princeton,New Jersey: Princeton University Press.

Fox, John A., Dermot J. Hayes, and Jason F. Shogren. 2002. Consumer Preferencesfor Food Irradiation: How Favorable and Unfavorable Descriptions AffectPreferences for Irradiated Pork in Experimental Auctions. The Journal of Risk andUncertainty 24(1): 75–96.

Hammitt, James K., and Kevin Haninger. 2007. Willingness to Pay for Food Safety:Sensitivity to Duration and Severity of Illness. American Journal of AgriculturalEconomics 89(5): 1170–1175.

Hayes, D.J., J.A. Fox, and J.F. Shogren. 2002. Experts and Activists: HowInformation Affects the Demand for Food Irradiation. Food Policy 27: 185–193.

Hayes, Dermot J., Jason F. Shogren, SeungYoull Shin, and James B. Kliebenstein.1995. Valuing Food Safety in Experimental Auction Markets. American Journal ofAgricultural Economics 77(1): 40–54.

Hu, Wuyang, Funing Zhong, and Yulian Ding. 2006. Actual Media Reports on GMFoods and Chinese Consumers’ Willingness to Pay for GM Soybean Oil. Journalof Agricultural and Resource Economics 31(2): 376–391.

Kanter, Christopher, Kent D. Messer, and Harry M. Kaiser. 2009. Does ProductionLabeling Stigmatize Conventional Milk? American Journal of AgriculturalEconomics 91(4): 1097–1109.


381

http://aepp.oxfordjournals.org/http://aepp.oxfordjournals.org/http://aepp.oxfordjournals.org/http://aepp.oxfordjournals.org/http://aepp.oxfordjournals.org/

Lusk, Jayson L., John A. Fox, Ted C. Schroeder, James Mintert, and MohammadKoohmaraie. 2001. In-Store Valuation of Steak Tenderness. American Journal ofAgricultural Economics 83: 539–551.

Lusk, Jayson L., Ty Feldkamp, and Ted C. Schroeder. 2004. Experimental AuctionProcedure: Impact on Valuation of Quality Differentiated Goods. AmericanJournal of Agricultural Economics 86(2): 389–406.

Lusk, Jayson L., and Ted C. Schroeder. 2002. Effects of Meat Recalls on FuturesMarket Prices. Agricultural and Resource Economics Review 31/1 (April): 47–58.

Lusk, Jayson L., and Jason Shogren. Experimental Auctions: Methods and Applicationsin Economic and Marketing Research. Cambridge, UK: Cambridge UniversityPress, 2007.

Marsh, Thomas L., Ted C. Schroeder, and James Mintert. 2004. Impacts of MeatProduct Recalls on Consumer Demand in the USA. Applied Economics 36:897–909.

Mead, Paul S., Laurence Slutsker, Vance Dietz, Linda F. McCaig, Joseph S. Bresee,Craig Shapiro, Patricia M. Griffin, and Robert V. Tauxe. 1999. Food-RelatedIllness and Death in the United States. Emerging Infectious Diseases 5(5): 607–626.

Messer, Kent D., Harry M. Kaiser, Collin Payne, and Brian Wansink. 2011. “CanAdvertising Alleviate Consumer Concerns About Mad Cow Disease?” AppliedEconomics 43(12):1535–1549.

Messer, Kent D., Harry M. Kaiser, and William D. Schulze. 2008. “The Problem ofFree Riding in Voluntary Generic Advertising: Parallelism and PossibleSolutions from the Lab” American Journal of Agricultural Economics 90(2):540–552.

Messer, Kent D., Greg L. Poe, Daniel Rondeau, William D. Schulze, and ChristianVossler. 2010. “Social Preferences and Voting: An Exploration Using a NovelPreference Revealing Mechanism.” Journal of Public Economics 94(3–4): 308–317.

Payne, Collin, Kent D. Messer, and Harry M. Kaiser. 2009. Which Consumers AreMost Responsive to Media Induced Food Scares? Agricultural and ResourceEconomics Review 38(3): 295–310.

Piggott, Nicholas E., and Thomas L. Marsh. 2004. Does Food Safety InformationImpact U.S. Meat Demand? American Journal of Agricultural Economics 86(1):154–174.

Roberts, Tanya. 2007. WTP Estimates of the Societal Costs of U.S. Food-borneIllness. American Journal of Agricultural Economics 89(5): 1183–1189.

Saghaian, Sayed H. 2007. Beef Safety Shocks and Dynamics of Vertical PriceAdjustment: The Case of BSE Discovery in the U.S. Beef Sector. Agribusiness: AnInternational Journal 23: 333–348.

Shogren, Jason F., John A. List, and Dermot J. Hayes. 2000. Preference Learning inConsecutive Experimental Auctions. American Journal of Agricultural Economics82(4): 1016–1021.

Stenger, Anne. 2000. Experimental Valuation of Food Safety Application to SewageSludge. Food Policy 25: 211–219.

Thomsen, Michael R., and Andrew M. McKenzie. 2001. Market Incentives for SafeFoods: An Examination of Shareholder Losses from Meat and Poultry Recalls.American Journal of Agricultural Economics 82(3): 526–538.

Thomsen, Michael R., Rimma Shiptsova, and Sandra J. Hamm. 2006. SalesResponses to Recalls for Listeria Monocytogenes: Evidence from BrandedReady-to-Eat Meats. Review of Agricultural Economics 28(4): 482–494.

Appendix 1

Leading-brand chicken

According to Consumer Reports magazine, a recent study of broilerchicken revealed that “campylobacter was present in 81 percent of thechickens, salmonella in 15 percent; and both bacteria in 13 percent. . . .


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Both salmonella and campylobacter can cause intestinal distress, andcampylobacter can also lead to meningitis, arthritis, and Guillain-Barrésyndrome, a neurological disorder . . . Among all brands, 84 percent of thesalmonella and 67 percent of the campylobacter organisms (tested)showed resistance to one or more antibiotics. . . . The findings suggest thatsome people who are sickened by chicken might need to try several antibi-otics before finding one that works. . . . No major brand fared better thanothers overall. Foster Farms, Pilgrim’s Pride, and Tyson chickens werelower in salmonella incidence than Perdue, but they were higher incampylobacter.”

Consumer Reports concludes that their tests reveal that if you eat under-cooked chicken (less than 1658F) or have cross-contamination to otherfoods from mishandling the chicken, “you have a good chance of feelingmiserable.”

Ranger brand chicken

Consumer Reports magazine reports that “there was an exception to thepoor showing of most premium chickens. As in our previous tests, Ranger. . . was extremely clean . . . Of the ten samples analyzed, 0 percent hadsalmonella and only 20 percent had campylobacter.”


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