The principal finding from the hedonic regression analysis of ready-to-drink shelf stable cranberry flavor juices indicated that a statistically significant and positive coefficient exists for the presence of the labeling claim “No Sugar Added” on product packaging. This finding implies that consumers have been paying a significant premium for cranberry flavor juice products as a result of the inclusion of the allegedly
misbranded labeling claim. In this specific instance, consumers have overpaid by 25.35% for these products, attributed exclusively to the labeling claim “No Sugar Added”. With this premium, and considering the total sales during the class time period, damages associated with mislabeled Brand 1 products were in the range of $102.7 million to $117.4 million.
Going forward, this methodology could be employed to a wide range of food and beverage products to determine damages due as a result of mislabeled products. The hedonic regression analysis is a methodology employed to determine what price
premium consumers pay as a result of the questioned label “No Sugar Added”. But, this methodology could be applied to a variety of label claims such as “No Artificial
Flavors,” “All Natural,” “0 Grams Trans Fat,” and many others. A wide and complete body of economic and marketing research has shown that consumers are willing to pay a premium for products bearing many of these characteristics and labeling statements.
Additionally, manufacturers would not undertake the costs required in the
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manufacturing, advertising, and promotion of these products if the benefits of doing so did not outweigh these costs.
The hedonic regression analysis is used to quantify how much of a premium consumers pay for the aspects on the product packaging taking into account other factors that may also affect prices. Simply put, a hedonic analysis involves the specification of a regression with price as a function of a potential myriad of variables, including those accounting for the presence or absence of product attributes represented on labeling claims. Once this premium is determined, the economic damages in class action lawsuits can be calculated. This methodology is not just restricted to the “No Sugar Added”
claim, or even to this specific product category of ready-to-drink shelf stable juices featuring the flavor cranberry. It is widely applicable and can be used on different product packaging claims and to a range of product categories in the entire food and beverage industry.
As in any research project, limitations in this analysis are present. With the set of estimated coefficients, omitted variable bias likely was present as the brand explanatory variable had to be dropped to avoid near perfect multicollinearity. However, this issue does not reflect adversely on the model utilized or the subsequent methodology developed in this thesis. As the introduction of hedonic price analysis becomes more prevalent in labeling cases, courts will have to assess the applicability of the method.
Consequently, proper model specification will be paramount in the use of this technique in order to capture the premium associated with the labeling claim in question.
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Additionally, recall that price, the endogenous variable in the hedonic regression, is defined as the ratio of dollar expenditures divided by quantities sold. Hence by its very construction, volume as an explanatory variable could potentially be endogenous. As such, the estimated coefficients could then be biased. However, based on the Hausman test (1978), endogeneity of the volume variable was not evident in this particular analysis.
In subsequent analyses, the model and methodology are able to be applied if in the data collection process, certain steps are taken. Primarily, in order to avoid
collinearity issues, more brands should be included in the analysis. If more brands are utilized, then the high correlation between the brand dummy variables and the labeling claim variable likely would be reduced, permitting the set of brand variables to remain in the regression. Including brand variables will address the omitted variable bias, and more importantly, result in a more precise estimate of the coefficient on the labeling claim variable.
These limitations notwithstanding, as stated by Hartman and Doane (1987, p.
354), “clearly, any analytic technique will be useful if it can assist a court to certify the class by explicitly determining whether class members were commonly and uniformly damaged by Defendant's illegal actions. Hedonic regression analysis is such a technique.
Using it, the court can focus on the action(s) of the Defendant and measure the common damage to each putative class member. Depending on the specific statute violated, the damages will reveal themselves in such observable economic measures as product prices…” To this end, a hypothetical case study example involving ready to drink
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cranberry juice products was developed and presented illustrating the use of the hedonic regression technique.
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