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Willingness to Pay for Sensory Properties in Washington State Red Wines*

Published online by Cambridge University Press:  08 June 2012

Nan Yang ,
Jill J. McCluskey  and
Carolyn Ross
Nan Yang
Affiliation:
School of Economic Sciences, Washington State University, Pullman, WA 99164–6210, email: [email protected]
Jill J. McCluskey
Affiliation:
School of Economic Sciences, Washington State University, Pullman, WA 99164–6210, Tel. 509–335–2835, email: [email protected] (corresponding author)
Carolyn Ross
Affiliation:
School of Food Science, Washington State University, Pullman, WA 99164–6210, email: [email protected]
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Abstract

In this article, we evaluate how sensory qualities of wine, such as astringency, bitterness, aroma, and flavor, affect consumers' willingness to pay for wine. In order to accomplish this objective, we utilize data collected from untrained consumers, a trained panel, and laboratory measurements of tannin intensity. From this data, a consumer-preference model, a consumer-intensity model, a trained-panel model, and an instrumental-measurement model are estimated and compared. Overall, the consumer-preference model is the most accurate in predicting consumers' willingness to pay. As expected, the closer a wine is to a consumer's ideal, the more they are willing to pay. Astringency has a mostly positive effect, and bitterness has a negative effect. Comparing the accuracy of the other models, the instrumental-measurement model is the next best, followed by trained-panel model, and the consumer-intensity model. This suggests that the instrumental measurements can be used as an effective alternative to trained panels. This is important because trained panels may be less practical to use on an ongoing basis. (JEL Classification: Q13, M31)

Type
Articles
Information
Journal of Wine Economics , Volume 4 , Issue 1 , Spring 2009 , pp. 81 - 93
Copyright
Copyright © American Association of Wine Economists 2009

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References

Brennan, C.S. and Kuri, V. (2002). Relationship between sensory attributes, hidden attributes and price in influencing consumer perception of organic foods. In Powell, J. et al. (eds.), UK Organic Research 2002: Proceedings of the COR Conference, Aberystwyth, 65–68.Google Scholar
Cardebat, J.-M. and Fiquet, J.-M. (2004). What explains Bordeaux wine prices? Applied Economic Letters, 11, 293–296.CrossRefGoogle Scholar
Cameron, T. and Quiggin, J. (1994). Estimation using contingent valuation data from a ‘dichotomous choice with follow up’ questionnaire. Journal of Environmental Economics and Management, 27, 218–234.CrossRefGoogle Scholar
Combris, P., Lecoq, S. and Visser, M. (1997). Estimation of a hedonic price equation for Bordeaux wine: does quality matter? The Economic Journal, 107, 309–402.CrossRefGoogle Scholar
Flachaire, E. and Hollard, G. (2006). Controlling starting-point bias in double-bounded contingent valuation surveys. Land Economics, 82, 103–111.CrossRefGoogle Scholar
Goldstein, R., Almenberg, J., Dreber, A., Emerson, J.W., Herschkowitsch, A. and Katz, J. (2008). Do more expensive wines taste better? Journal of Wine Economics, 3(1), 1–9.CrossRefGoogle Scholar
Hanemann, M.W., Loomis, J. and Kanninen, B.J. (1991). Statistical efficiency of double-bounded dichotomous choice contingent valuation. American Journal of Agricultural Economics, 73, 1255–1263.CrossRefGoogle Scholar
Hanemann, W.M. and Kanninen, B. (1999). The statistical analysis of discrete-response CV data. In Bateman, I.J. and Willis, K.G. (eds.). Valuing environmental preferences: theory and practice of the contingent valuation method in the US, EU, and developing countries. New York: Oxford University Press, 302–442.Google Scholar
Harbertson, J.F., Picciotto, E.A. and Adams, D.O. (2003). Measurement of polymeric pigments in grape berry extracts and wines using a protein precipitation assay combined with bisulfite bleaching. American Journal of Enology and Viticulture, 54, 301–306.CrossRefGoogle Scholar
Kanninen, B.J. and Khawaja, M.S. (1995). Measuring goodness of fit for the double-bounded logit model. American Journal of Agricultural Economics, 77(4), 885–890.CrossRefGoogle Scholar
Veale, R. and Quester, P. (2008). Consumer sensory evaluations of wine quality: the respective influence of price and country of origin. Journal of Wine Economics, 3(1), 10–29.CrossRefGoogle Scholar