Survival of the Fittest: The Impact of Eco-certification on the Performance of German Wineries. Patrizia Fanasch University of Paderborn, Germany

Transcription

1 Survival of the Fittest: The Impact of Eco-certification on the Performance of German Wineries University of Paderborn, Germany

2 Motivation Demand (Customer) Rising awareness and interest in organic products (Wiedmann et al. 2014) Eco-labeled wines are perceived as low quality (Peattie/Crane 2005) Supply (Winemaker) Different ways: Self-declared, certification, labeling Drawbacks: Crop shortfall, lower perceived quality But do they also demand organic wine or wine made from organically grown grapes? Uncertainty whether a winery can benefit from eco-friendly practices in the long run 2

3 Literature Review Reputation Reputation is essential in the absence of perfect information on product quality (Landon/Smith 1998) Reputation might be even more important than wine quality (Benfratello et al. 2009) Individual Firm reputation increases bottle price (Roma et al. 2013) Reputation significantly increases wine prices (Haeger/Storchmann 2006) Collective Collective reputation, such as membership in a specific association (e.g. VDP) leads to a price premium (Frick/Simmons 2013) Eco-certification Certifications as quality signals that reduce information asymmetries (Lohr 1998; Delmas/Lessem 2014) Induce customers to pay a premium (Delmas/Grant 2013) Performance 3

4 Hypotheses Individual Reputation (Gault Millau-Rating) H1a (+) H2a (+) Eco-Certification (Collective Reputation) H3 (+) Firm Performance (Years listed in Gault Millau) VDP Membership (Collective Reputation) H2b (+) H1b (+) H1a: The higher a firm s individual reputation, the better its performance H1b: Membership in VDP is associated with a better performance H2a: Especially firms with a high individual reputation profit from eco-certification in terms of performance H2b: Especially VDP members profit from eco-certification in terms of performance H3: Eco-certification leads to a superior firm performance 4

5 Data Wine Guide Gault Millau First edition appeared in 1994 Period: Wineries can enter or exit the Gault Millau each year (unbalanced panel) 1,572 wineries and 17,143 winery-year-observations The rating system: Five grapes is the highest rating for the best wineries (based on the wine quality of each winery in the last 5-10 years with focus on the recent development and the actual wine quality) Wines are rated on a 100 point scale, with 100 highest (based on quality) 5

6 Number of Wineries in Gault Millau 6

7 Nonparametric Estimation (I) 8

8 Nonparametric Estimation (II) 10

9 Nonparametric Estimation (III) 12

10 Nonparametric Estimation (IV) 14

11 Survival Analysis - Model Selection Model / Distribution AIC Parametric: Exponential Parametric: Gompertz Parametric: Loglogistic Parametric: Lognormal Parametric: Weibull Best model Weibull is the most commonly applied parametric model in survival analysis (Lawless 2003) It is both, a proportional hazards model as well as an accelerated failure time model (Cefis/Marsili 2005) 15

12 The Model Accelerated failure time model: log(fs) = β 0 + β 1 REP1 + β 2 REP2 +β 3 REP345 + β 4 Ecocertification + β 5 Eco REP + β 6 VDP + β 7 VDP Eco + β 8 Reentry + β RD + β Year + σϵ FS Firm survival (number of years listed in wine guide, Min. 1; Max. 24) REP Reputation (measured by wine guide, Min. 0.5; Max. 5.0) Eco-certification Eco REP VDP VDP Eco-certification Re-entry RD Individual membership in eco-certified and/or biodynamic wine association (0 = no; 1 = yes) Interaction of eco-certification and reputation dummies REP1, REP2, REP345 Membership in Association of German Quality Wine Estates (0 = no; 1 = yes) Interaction of VDP membership and eco-certification Dummy variable indicating if winery re-entered Gault Millau (0 = no; 1 = yes) Vector of 13 region dummies (reference: Württemberg) Year Publication years of the wine guide (reference: 2016) 16

13 Results Survival Model - Weibull Variable Hazard Ratio Robust Std. Err. z REP *** REP *** REP *** Eco-certification ** Eco x Rep Eco x Rep Eco x Rep *** VDP ** VDP x Eco Re-entry ** Region Dummies Included Year Dummies Included Constant *** N of Wineries 1,572 N of Failures 924 N of Observations 17,143 LL Null Model LL Full Model Wald Chi *** *p < 0.10; ** p < 0.05; ***p <

14 Robustness Check REP1 REP2 REP345 Eco-certification Eco x Rep1 Eco x Rep2 Eco x Rep345 VDP VDP x Eco Re-entry Region Dummies Year Dummies Constant Variable Weibul Lognormal Loglogistic 1.950*** 2.012*** 1.962*** (0.105) (0.087) (0.088) 2.728*** 2.550*** 2.601*** (0.177) (0.127) (0.134) 4.016*** 3.433*** 3.560*** (0.423) (0.241) (0.297) 0.270** 0.300** 0.347** (0.122) (0.148) (0.158) (0.541) (0.361) (0.403) (0.809) (0.541) (0.650) 7.713*** 2.576*** 5.794*** (0.594) (0.481) (0.544) 0.349** * (0.138) (0.146) (0.153) (0.468) (0.483) (0.562) 0.182** (0.072) (0.119) (0.128) Included Included 2.049*** 1.514*** 1.522*** (0.127) (0.157) (0.158) N of Observations 17,143 LL Null Model LL Full Model Wald Chi *** *** ***

15 Conclusions, Implications, and Next Step H1a (+) Individual Reputation Performance H1b (+) VDP Membership Performance H2a (+) Eco-certification x Individual Reputation Performance H2b (+) Eco-certification x VDP Membership Performance X H3 (+) Eco-certification Performance Implications Apart from increasing individual reputation, wineries should strive for VDP membership and eco-certification Eco-certification is especially important for wineries with a high reputation Next Step Twofold differentiation of the eco-certification effect: Membership and nonmembership in eco association 19

16 References Benfratello, L.; M. Piacenza; S. Sacchetto (2009): Taste or Reputation: What Drives Market Prices in the Wine Industry? Estimation of a Hedonic Model for Italian Premium wines. Applied Economics, 41(17), Cefis, E.; O. Marsili (2005): A Matter of Life and Death: Innovation and Firm Survival. Industrial and Corporate Change, 14(6), Delmas, M. A.; L. E. Grant (2013): Eco-Labeling Strategies and Price-Premium: The Wine Industry Puzzle. Business & Society, 53(1), Delmas, M. A.; N. Lessem (2014): Saving Power to Conserve your Reputation? The Effectiveness of Private versus Public Information. Journal of Environmental Economics and Management, 67(3), Frick, B.; R. Simmons (2013): The impact of individual and collective reputation on wine prices: empirical evidence from the Mosel valley. Journal of Business Economics, 83(2), Haeger, J. W.; K. Storchmann (2006): Prices of American Pinot Noir wines: climate, craftsmanship, critics. Agricultural Economics, 35(1), Landon, S.; C. E. Smith (1998): Quality Expectations, Reputation, and Price. Southern Economic Journal, 64(3), Lawless, J. F. (2003): Statistical Models and Methods for Lifetime Data, 2nd ed. Wiley-Interscience, Hoboken, N.J. Lohr, L. (1998): Implications of Organic Certification for Market Structure and Trade. American Journal of Agricultural Economics, 80(5), Peattie, K.; A. Crane (2005): Green marketing: legend, myth, farce or prophesy?. Qualitative Market Research: An International Journal, 8(4), Roma, P.; G. Di Martino; G. Perrone (2013): What to show on the wine labels: a hedonic analysis of price drivers of Sicilian wines. Applied Economics, 45(19), Wiedmann, K.-P.; N. Hennigs; S. H. Behrens; C. Klarmann (2014): Tasting Green: An Experimental Design for Investigating Consumer Perception of Organic Wine. British Food Journal, 116(2),

17 APPENDIX 21

18 Descriptive Statistics (I/II) Variables N Mean SD Min Max Dependent Variable Firm Survival 17, Independent Variables REP1 17, REP2 17, REP345 17, Eco-certification 17, Eco x Rep1 17, Eco x Rep2 17, Eco x Rep345 17, VDP 17, VDP x Eco 17, Re-entry 17, Year Dummies , , , , , , , , , , , , ,

19 Descriptive Statistics (II/II) Variables N Mean SD Min Max , , , , , , , , , , Region Dummies Ahr 17, Baden 17, Franken 17, Hessische Bergstraße 17, Mittelrhein 17, Mosel-Saar-Ruwer 17, Nahe 17, Pfalz 17, Rheingau 17, Rheinhessen 17, Saale-Unstrut 17, Sachsen 17, Württemberg 17,

20 Eco-certification by Reputation Ecocertification Reputation Total 0 4,762 3,979 3,443 2, , ,092 Total 5,124 4,258 3,705 2, ,143 24