Wine Investment

AMERICAN ASSOCIATION OF WINE ECONOMISTS
AAWE WORKING PAPER No. 1
Editor Victor Ginsburgh
THE IMPACT OF GURUS: PARKER GRADES AND EN PRIMEUR WINE PRICES

Héla Hadj Ali Sébastien Lecocq Michael Visser

April 2007

www.wine-economics.org

The impact of gurus: Parker grades and en primeur wine prices
H´la Hadj Ali† S´bastien Lecocq‡ Michael Visser§ e , e , September 2005 ∗

Abstract The purpose of this paper is to measure the impact of Robert Parker’s oenological grades on Bordeaux wine prices. We study their impact on the so-called en primeur wine prices, i.e., the prices determined by the chˆteau owners when the wines are still extremely young. The Parker a grades are usually published in the spring of each year, before the wine prices are established. However, the wine grades attributed in 2003 have been published much later, in the autumn, after the determination of the prices. This unusual reversal is exploited to estimate a Parker effect. We find that, on average, the effect is equal to 2.80 euros per bottle of wine. We also estimate grade-specific effects, and use these estimates to predict what the prices would have been had Parker attended the spring tasting in 2003.

Keywords: Expert opinion, natural experiment, treatment effect, Bordeaux wine price. JEL codes: C21, D89, L15.
∗ We

are grateful to Alan Duncan, Fabrice Etil´, Victor Ginsburgh, Sylvie Lambert, and participants at the VDQS e

conference (Dijon, 2004), the RES conference (Nottingham, 2005), the JMA meeting (Hammamet, 2005), and the EEA congress (Amsterdam, 2005), for their helpful comments and suggestions.
† INRA,

Chemin de Borde Rouge, Auzeville, BP 27, 31326 Castanet Tolosan, France.

Email:

had-

[e-mail].
‡ INRA, § INRA,

65 boulevard de Brandebourg, 94205 Ivry, France. Email: [e-mail]. 48 boulevard Jourdan, 75014 Paris, France. Email: [e-mail].

1

1

Introduction

The judgment of experts and gurus matters in many

References: [1] Abadie, A. (2005), “Semiparametric difference-in-difference estimators,” Review of Economic Studies, 72, 1-19. [2] Avery, C. and J. Chevalier (1999), “Identifying investor sentiment from price paths: the case of football betting,” Journal of Business, 72, 493-521. [3] Bauwens, L. and V. Ginsburgh (2000), “Art experts and auctions. Are pre-sale estimates unbiased and fully informative?,” Louvain Economic Review, 66, 131-144. [4] Chossat, V. and O. Gergaud (2003), “Expert opinion and gastronomy: the recipe for success,” Journal of Cultural Economics, 27, 127-141. [5] Combris, P., S. Lecocq and M. Visser (1997), “Estimation of a hedonic price equation for Bordeaux wine: does quality matter?,” Economic Journal, 107, 390-402. [6] Di Vittorio, A. and V. Ginsburgh (1996), “Des ench`res comme r´v´lateurs du classement des e e e vins,” Journal de la Soci´t´ Statistique de Paris, 137, 19-49. ee [7] Dubois, P. and C. Nauges (2005), “Identifying the effect of unobserved quality and experts’ reviews in the pricing of experience goods: empirical application on Bordeaux wine,” Working Paper, University of Toulouse. [8] F´vrier, P., W. Roos and M. Visser (2003), “The buyer’s option in multi-unit ascending auce tions: the case of wine auctions at Drouot,” Working Paper #2003-03, CREST, forthcoming in Journal of Economics & Management Strategy. [9] Ginsburgh, V. (2003), “Awards, success and aesthetic quality in the arts,” Journal of Economic Perspectives, 17, 99-111. [10] Ginsburgh, V., M. Monzak and A. Monzak (1994) “Red wines of Medoc: what is wine tasting worth,” Verona: Vineyard Data Quantification Society. 22 [11] Hadj Ali, H. (2002), “La commercialisation des vins en primeur,” PhD Thesis, University of Toulouse. [12] Hadj Ali, H. and C. Nauges (2004), “Reputation and quality effects in the pricing of experience goods: the case of en primeur wine,” Working Paper, INRA-LEERNA. [13] H¨rdle, W. (1990), Applied Nonparametric Regression. Cambridge, Massachussets: Cambridge a University Press. [14] Heckman, J.J. (1990), “Varieties of selection bias,” American Economic Review, Papers and Proceedings, 80, 313-318. [15] Heckman, J.J., H. Ichimura and P.E. Todd (1997), “Matching as an econometric evaluation estimator: evidence from evaluating a job training programme,” Review of Economic Studies, 64, 605-654. [16] Heckman, J.J., L. Lochner and C. Taber (1998), “General-equilibrium treatment effects: a study of tuition policy,” American Economic Review, Papers and Proceedings, 88, 381-386. [17] Jones, G.V. and K.-H. Storchmann (2001), “Wine market prices and investment under uncertainty: an econometric model for Bordeaux crus class´s,” Agricultural Economics, 26, 115-133. e [18] Rubin, D.B. (1974), “Estimating causal effects of treatments in randomized and nonrandomized studies,” Journal of Educational Psychology, 66, 688-701. [19] Rubin, D.B. (1980), “Discussion of paper by D. Basu,” Journal of the American Statistical Association, 75, 591-593. [20] Shleifer, A. (1986), “Do demand curves for stocks slope down?,” Journal of Finance, 41, 579590. [21] Silverman, B.W. (1986), Density Estimation for Statistics and Data Analysis. London: Chapman and Hall. 23 [22] Wooldridge, J.M. (2002), Econometric Analysis of Cross Section and Panel Data. Cambridge, Massachussets: The MIT Press. 24 Table 1. Descriptive statistics Ranking 1er Cru Class´ e 2`me Cru Class´ e e 3`me Cru Class´ e e 4`me Cru Class´ e e 5`me Cru Class´ e e Cru Bourgeois 1er Grand Cru Class´ A e 1er Grand Cru Class´ B e Grand Cru Class´ e Cru Class´ e Cru Non Class´ e Total Appellation Barsac Bordeaux Blanc Bordeaux Sup´rieur e Cˆtes de Castillon o Fronsac Graves Blanc Graves Rouge 1.29 2.15 0.43 2.58 1.29 1.29 0.86 5.15 8.58 4.29 3.86 6.44 14.16 0.43 2.15 9.44 6.01 39.48 100.00 Appellation Haut-M´doc e Lalande-de-Pomerol Listrac Margaux M´doc e Montagne-St-Emilion Moulis Pauillac Pessac-L´ognan e Pomerol 1`res Cˆtes de Blaye e o 1`res Cˆtes de Bordeaux e o Puisseguin-St-Emilion St-Emilion Grand Cru St-Estephe St-Georges St-Emilion St-Julien Sauternes Total 6.87 2.58 1.29 10.30 0.43 1.72 1.72 8.15 11.16 6.44 0.86 0.43 0.86 21.89 3.86 0.43 5.58 5.58 100.00 Note: The descriptive statistics are based on the sample of 233 chˆteaux and give for each variable the percentage a of observations that is equal to one. 25