The demand model with the dummy variables for weeks following the two events is designed to provide evidence that would help answer whether consumers’ responses were different. If the results of estimation showed consumers responded to both, it might have been difficult to judge whether the responses were similar or not. Judging amounts to comparing the magnitudes of a group of param-eter estimates from one time period with the magnitudes of a group from another time period. It might not be immediately obvious which is larger or if there is a difference at all. Here, results are unambiguous—the response to Listeria is clearly larger.
For the Salmonella event, there is nothing to indicate that consumers’ purchase patterns changed. Of the 55 dummy variables12, coefficient estimates were not significantly different from zero at
conven-12 Adding-up conditions were used to derive coefficient estimates and standard errors for dummy variables from the
Results of melon demand estimation: Coefficient and elasticity estimates1
Coefficient t-Statistic Elasticity t-Statistic
Cantaloupe budget share equation
Constant 0.0758 0.67
ln cantaloupe price -0.2421 -17.89** -1.69 -43.93**
ln honeydew price 0.0269 4.79** 0.07 4.54**
ln mini price 0.0279 4.19** 0.07 3.93**
ln watermelon price 0.1818 15.67** 0.49 15.87**
ln all other melons price 0.0056 0.73 0.01 0.59
ln expenditures/Stone index 0.0140 2.11* 1.04 55.88**
Cos 0.0235 2.03*
Sin -0.0015 -0.20
Honeydew budget share equation
Constant 0.1106 2.22*
ln cantaloupe price 0.0269 4.79** 0.40 5.08**
ln honeydew price -0.0607 -7.41** -1.89 -15.50**
ln mini price 0.0040 0.78 0.06 0.80
ln watermelon price 0.0246 4.25** 0.37 4.81**
ln all other melons price 0.0052 1.55 0.08 1.57
ln expenditures/Stone index -0.0016 -0.54 0.98 22.25**
Cos 0.0113 2.56*
Sin -0.0060 -2.28*
Mini/seedless budget share equation
Constant 0.2372 3.62**
ln cantaloupe price 0.0279 4.19** 0.35 4.69**
ln honeydew price 0.0040 0.78 0.05 0.88
ln mini price -0.0651 -8.41** -1.72 -19.50**
ln watermelon price 0.0369 5.19** 0.45 6.12**
ln all other melons price -0.0038 -0.86 -0.04 -0.72
ln expenditures/Stone index -0.0079 -2.05* 0.91 20.92**
Cos -0.0203 -3.35**
Sin 0.0086 2.40*
Watermelon budget share equation
Constant -0.1136 -0.56
ln cantaloupe price 0.1818 15.67** 0.41 13.12**
ln honeydew price 0.0246 4.25** 0.05 3.82**
ln mini price 0.0369 5.19** 0.08 4.71**
ln watermelon price -0.2491 -17.50** -1.63 -47.32**
ln all other melons price 0.0059 0.78 0.01 0.47
ln expenditures/Stone index 0.0318 2.56* 1.08 35.92**
Cos -0.0293 -2.52*
Sin 0.0017 0.21
All other melons budget share equation
Constant 0.6901 3.63**
ln price cantaloupe 0.0056 0.73 0.26 2.02*
ln price honeydew 0.0052 1.55 0.11 2.20*
ln price mini -0.0038 -0.86 -0.01 -0.13
ln price watermelon 0.0059 0.78 0.29 2.56*
ln price all other melons -0.0129 -1.76 -1.14 -11.09**
ln expenditures/Stone index -0.0363 -3.16** 0.49 3.09**
Cos 0.0149 1.54
Sin -0.0028 -0.51
Rho 0.7899 33.71**
1 Statistical significance at 0.01 denoted by **, at 0.05 by *.
Source: Author’s calculations using IRI InfoScan data, 2009-12.
tional levels of significance (0.05) for 54 variables. Random chance alone might have yielded more variables meeting a test of statistical significance.
There is an indication that consumers responded to the Listeria warning, eventually cutting back purchases of cantaloupe and substituting honeydew and watermelon. Figure 4 plots the coefficients on the Listeria dummy variables (dark lines) over the 11-week period starting September 4, 2011, when the news first broke. Each of the coefficient estimates is surrounded by 95-percent confidence intervals (2 standard errors each side). The confidence intervals offer a systematic way of identifying weeks in which dummy variables are decidedly positive or negative. That is, confidence intervals in which upper and lower limits are above zero strongly suggest that budget shares were abnormally high—too high to be explained as the result of market prices and consumers’ income. Confidence intervals in which upper and lower limits are below zero point to abnormally low budget shares.
Confidence intervals on the budget share dummy variable coefficients (for cantaloupe) are entirely below zero for 4 weeks beginning October 9, 2011. In contrast, honeydew budget share dummy variables are higher than normal for 3 weeks beginning October 2, 2011.13 And watermelon budget shares are abnormally high over the same period that cantaloupe shares are low.
The budget share dummy variables indicate changes in budget shares and can be used to solve for changes in quantities demanded. As weekly budget shares for a melon variety are defined as
𝐸𝐸 , under the assumption that prices and melon expenditures are exogenous, the changes in budget shares are reflected in quantity changes: ∆𝑆𝑆𝑖𝑖 =𝑃𝑃𝑖𝑖∆𝑄𝑄𝑖𝑖
𝐸𝐸 . This equation can be solved for changes in quantity demanded, given exogenous weekly prices and expenditures along with changes in budget shares. The total for the 4-week period left U.S. cantaloupe purchases 6.2 million pounds below normal. Honeydew sales were up for 3 weeks, for a total of 1.0 million pounds. And water-melon purchases were up 3.7 million pounds over a 4-week period.
Expenditures on melon varieties, Pi
Cantaloupe purchases fell $3.9 million, and honeydew and watermelon increased $0.9 million and
$3.2 million, respectively, over the 4-week period. The average (2009-10) weekly expenditures on cantaloupe during October were $14.4 million. The $3.9-million dropoff represents a 6.7-percent reduction over the 4-week period.
The pattern of dummy variable magnitudes suggests that consumers understood the message from Federal health and safety officials and reduced purchases of cantaloupe. At the same time, consumers apparently parsed the warning closely and recognized that other melons were safe to consume. The reaction was relatively modest and temporary, in keeping with Sandman’s character-ization of adjustment to new risks.14
13 The first major spike in the newspaper report series occurred a few days before the melon demand model shows the cantaloupe budget share dummy variable becoming statistically significant.
14 Entirely different conclusions might be drawn if there was evidence that consumers fled from melons. Here, consum-ers are assumed to make two-stage budgeting decisions, first choosing to allocate expenditures among groups of foods, one of which is melons. Then, expenditures on melons are allocated among the varieties. If consumers reduced expen-ditures on the melons group, the impact of the warnings would not be confined to allocating expenexpen-ditures among melon varieties. Unfortunately, there is not enough data to judge whether melon expenditures during 11 weeks in the fall of 2011 were unusually low. As the data display annual cycles and the first data available come from 2009, there are effectively two observations that might be used to establish a baseline for comparison with expenditures in 2011. More data cycles would have to be observed before 2011 to credibly establish a baseline for comparison with 2011 expenditures.
Source: Authors’ calculations using IRI InfoScan data, 2009-12.
Weekly changes in budget shares (dark lines) associated with the 2011 listeriosis outbreak
Cantaloupe budget share changes
Empirical evidence points to consumers reacting when Federal health and safety officials recalled cantaloupe in 2011 because of contamination with Listeria monyocytogenes. Consumers temporarily reduced purchases of cantaloupes, even after accounting for the influence of prices and income.
And they seemed to receive the message that other melons were safe: purchases of watermelon and honeydew increased. A year later, Federal health and safety officials again recalled some canta-loupe, this time for Salmonella contamination. There is no evidence that consumers responded to the second recall. News media appear to be associated with the differing consumer responses: there was television and newspaper coverage of both outbreaks, but the Listeria outbreak received substantially more coverage than the Salmonella.
Some attributes of the listeriosis outbreak were unique. A pathogen had found a new way into the food supply, after being linked only to meat before. The pathogen had a high fatality rate and was especially hazardous for the elderly and pregnant women. This event received media attention, and consumers took advantage of the new information to protect themselves. Further, by the time the salmonellosis outbreak occurred, several earlier salmonellosis outbreaks had alerted consumers and reduced their demand for the implicated foods, with few fatalities. Under those conditions, alarm or a dramatic swing in purchases by consumers seems unlikely.
If retail food demand does shift in response to the magnitude of foodborne disease risks, those shifts might result in greater food safety. When consumers are alerted to a food safety problem and they decide to avoid the risk and spend their money on other goods, they exhibit more control over food safety than do food safety regulators. Unlike regulatory actions that might be slowed by legal challenges, withholding purchases can quickly put firms out of business; all the resources put into building a brand can be wiped out. If information given to consumers precisely identifies products that are unsafe, consumers’ individual choices might rapidly punish firms that were not vigilant about food safety. Consumers’ choices would create strong incentives for suppliers to make food safe.
Here, however, the demand shifts were slightly mistimed to accomplish that task perfectly. Melon demands shifted a month after the recall. By that time, supplies were sourced outside Colorado and the costs imposed by consumers’ choices accrued to suppliers who had nothing to do with compro-mised food safety. That is, there are still welfare gains possible from better communication between health/safety officials and consumers.
Arnade, C., F. Kuchler, and L. Calvin. 2013. “Consumers’ Response When Regulators Are Uncertain About the Source of Foodborne Illness,” Journal of Consumer Policy 36(1): 17-36.
Arnade, C., D. Pick, and M. Gehlar. 2005. “Testing and Incorporating Seasonal Structures into Demand Models for Fruit,” Agricultural Economics 33 supplement: 527-532.
Berndt, E.R., and N.E. Savin. 1975. “Estimation and Hypothesis Testing in Singular Equation Systems with Autoregressive Disturbances," Econometrica 43(5-6): 937-958.
Bakhtavoryan, R., O. Capps, Jr., and V. Salin. 2012. “Impact of Food Contamination on Brands: A Demand Systems Estimation of Peanut Butter,” Agricultural and Resource Economics Review 41(3): 327-339.
Centers for Disease Control and Prevention. 2014a. “Salmonella.”. http://www.cdc.gov/salmonella/
Centers for Disease Control and Prevention. 2014b. “Reports of Selected Salmonella Outbreak Investigations.” Updated Aug. 21. http://www.cdc.gov/salmonella/outbreaks.html.
Centers for Disease Control and Prevention. 2012a. “Multistate Outbreak of Listeriosis Linked to Whole Cantaloupes from Jensen Farms, Colorado.” Aug. 27. http://www.cdc.gov/Listeria/
Centers for Disease Control and Prevention. 2012b. “Multistate Outbreak of Salmonella
Typhimurium and Salmonella Newport Infections Linked to Cantaloupe (Final Update).” Oct. 5.
Centers for Disease Control and Prevention. 2011. “Timeline of Events: Multistate Outbreak of Listeriosis Linked to Whole Cantaloupes from Jensen Farms, Colorado.” Updated Nov. 2. http://
Centers for Disease Control and Prevention. 2014. “Listeria (Listeriosis).” Updated July 29. http://
Deaton, A., and J. Muellbauer. 1980. “An Almost Ideal Demand System,” American Economic Review 70(3): 312-326.
Hoffmann, S., B. Maculloch, and M. Batz. 2015. Economic Burden of Major Foodborne Illnesses Acquired in the United States. USDA, Economic Research Service, EIB-140, May. http://www.
Jackson, K.A. 2011. “Multistate Outbreak of Listeriosis Associated with Jensen Farms Cantaloupe,”
Morbidity and Mortality Weekly Report, Centers for Disease Control and Prevention 60(39):
Lloyd, T.A., S. McCorriston, C.W. Morgan, and A.J. Rayner. 2006. “Food Scares, Market Power and Price Transmission: the UK BSE Crisis,” European Review of Agricultural Economics 33(2):
Media Insight Project. 2014. “The Personal News Cycle.” March. http://www.americanpressinstitute.
Minor, T., A. Lasher, K. Klontz, B. Brown, C. Nardinelli, and D. Zorn. 2015. “The Per Case and Total Annual costs of Foodborne Illness in the United States,” Risk Analysis, June. doi:10.1111/
Sandman, P.M. 2005. “Adjustment Reactions: The Teachable Moment in Crisis Communication.”
Risk = Hazard + Outrage. The Peter Sandman Risk Communication Website. http://www.
Schlenker, W., and S.B. Villas-Boas. 2009. “Consumer and Market Responses to Mad Cow Disease,”
American Journal of Agricultural Economics 91(4): 1140-1152.
Shimshack, J.P., M.B. Ward, and T.K.M. Beatty. 2007. “Mercury Advisories: Information,
Education, and Fish Consumption.” Journal of Environmental Economics and Management 53:
Smith, M.E., E.O. van Ravenswaay, and S.R. Thompson. 1988. “Sales Loss Determination in Food Contamination Incidents: An Application to Milk Bans in Hawaii,” American Journal of Agricultural Economics 70: 513-520.
Swartz, D.G., and I.E. Strand, Jr. 1981. “Avoidance Costs Associated with Imperfect Information:
The Case of Kepone,” Land Economics 57: 139-150.
Thornsbury, S., and L. Calvin. 2011. “Listeria Outbreak in Cantaloupe,” Vegetables and Melons Outlook. VGS-347. USDA, Economic Research Service, Oct. 27: 27-31. http://usda.mannlib.
U.S. Department of Agriculture, Agricultural Marketing Service. 2011. Fresh Fruit and Vegetable Shipments: By Commodities, States, and Months. FVAS-4 Calendar Year 2010. http://www.ams.
U.S. Department of Agriculture, Food Safety and Inspection Service. 2013. "FSIS Rule Designed To Reduce Listeria monocytogenes in Ready-to-Eat Meat & Poultry." http://www.fsis.usda.gov/
U.S. Food and Drug Administration. 2014. “Fish: What Pregnant Women and Parents Should Know.” Draft Updated Advice by FDA and EPA. June. http://www.fda.gov/Food/
Wittenberger, K., and E. Dohlman. 2010. “Peanut Outlook: Impacts of the 2008-09 Foodborne Illness Outbreak Linked to Salmonella in Peanuts.” USDA. Economic Research Service.
OCS-10a-01. Feb. http://www.ers.usda.gov/media/146487/ocs10a01_1_.pdf