Influence of Geographical Indications on Wine Prices
Explore the impact of geographical indications on wine prices, specifically focusing on Hungarian geographical indications. This study delves into the potential price premiums producers can obtain through the use of GIs, addressing research questions, motivations, and implications for the wine market. Discover how Geographical Indications can serve as a differentiation factor for wines and influence consumer perceptions, ultimately affecting market positioning and pricing strategies.
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Presentation Transcript
How can geographical indications influence wine prices? Estimating price premiums for Hungarian geographical indications P ter G l
Research questions, motivation Geographical indications can act as a base of distinction between wines A vehicle for producers to obtain price premiums Can producers obtain price premiums by using GIs? Pragmatic motivaions: discussions/disputes on GI rules policy implications How can geographical indications influence wine prices? Estimating price premiums for Hungarian geographical indications 11th Annual AAWE Conference Padua, 2017
Wine as an experience good CONSUMER PRODUCER information information asymmetry asymmetry Products of low quality level supersede high quality products Akerlof, 1970 How can geographical indications influence wine prices? Estimating price premiums for Hungarian geographical indications 11th Annual AAWE Conference Padua, 2017
Monopolistic competition unique product homogenous products close substitute Homog n term kek remote substitute heterogenous products higher price Carlton and Perloff (2003) How can geographical indications influence wine prices? Estimating price premiums for Hungarian geographical indications 11th Annual AAWE Conference Padua, 2017
Markets P position A : generic products position B : GI products Q Botos, 1995 (completed) How can geographical indications influence wine prices? Estimating price premiums for Hungarian geographical indications 11th Annual AAWE Conference Padua, 2017
Wines and their origin Four groups of factors that influence wine quality: the place of origin (including physiographic, edafic, climatic and biotic dimensions), vintage year, grape varietal and technology Place of origin =/= terroir Labelling geographical names on wines has a long tradition (potential) abuse government action How can geographical indications influence wine prices? Estimating price premiums for Hungarian geographical indications 11th Annual AAWE Conference Padua, 2017
Economic nature of GIs Experience good, information asymmetry, monopolistic competition Collective entitlement collective nature collective action Club good (Theidig and Sylvander, 2000) vs. common pool resource (Ostrom, 2003, Megyesi and Mike, 2016) Collective vs. individual reputation (Tirole, 1996; Patchell, 2008; Castriota and Delmastro, 2012) Free-riders, controls (Winfree and McCluskey, 2005) How can geographical indications influence wine prices? Estimating price premiums for Hungarian geographical indications 11th Annual AAWE Conference Padua, 2017
Modelling framework hedonic price index: Rosen (1974): goods are an aggregate of their characteristics. Therefore, differences in prices reflect differences in the set of features lnP = 0+ qlnQ + iCi+ jGIj+ i tackling endogenity How can geographical indications influence wine prices? Estimating price premiums for Hungarian geographical indications 11th Annual AAWE Conference Padua, 2017
Hedonic price indices for wines Oczkowski (1994) Landon s Smith (1997) Combris, Lecocq s Visser (2000) Schamel s Anderson (2003) Schamel s Anderson (2003) Lecocq s Visser (2006) San Martin, Troncoso s Br mmer (2008) USA Schamel (2009) Market Australia USA France Australia New Zealand Bordeaux en primeur Bordeaux USA Producer countires Australia Bordeaux Bourgogne Australia New Zealand Argentina 27 regions, 11 countriens, 16 varietals OLS Method OLS OLS OLS OLS OLS panel 2SLS Specificatio n Sample size Right-hand side variables log-lin reciprocal square root lin-lin log-lin log-lin log-lin log-lin log-log (some variables lin) 2,033 196 613 255-2,154 53-362 1,615 1,102 56,661 sensory score, ageing potential, grape varietal, wine district, vintage year, producer size sensory scores, quantity, GI, varietal, Parker s classification, the 1855 classification, second label detailed sensory score, vintage, GI level sensory scores, varietal, regions sensory scores, varietal, regions weather data sensory scores, IV: age, labelling elements, wine district, varietal, Wines of Argentina membership, quantity produced and available cross-section vintages of 1977- 2005 individual brands (grouped), sensory scores, WS highlights, age of wine, place of origin, varietal Model years of vintages/ newspaper publication Sample type cross-section publications of 1988-1990 cross-section vintages of 1987- 1991 cross-section publications of 1993 (vintages of 1990-92) cross-section (9) publications of 1992-2000 cross-section (7) publications of 1993-1999 panel vintages of 1994- 2003 cross-section vintages from 1999 to 2004 database of a newspaper database of a newspaper database of a newspaper database of a newspaper database of a newspaper database of a newspaper database of a newspaper database of a newspaper Source Winestate Wine Spectator data bank Institut National de la Consomnation James Halliday (AUS) and Winestate (AUS) James Halliday (AUS) and Winestate (AUS) en primeur sales data, weather data Wine Spectator data bank Wine Spectator data bank
Sample N = 2,682 observation unit: a batch of wine sample: off-trade sector source: Ministry of Agriculture National Food Chain Safety Office How can geographical indications influence wine prices? Estimating price premiums for Hungarian geographical indications 11th Annual AAWE Conference Padua, 2017
4 models segmented GIs (two or three quality levels e.g. Eger Superior or Vill ny Pr mium) two approaches: (A) these GIs considered as a single name (B) these GIs considered as two or three separate names regression models: (1) robust standard errors (2) quantile regressions (for medians) How can geographical indications influence wine prices? Estimating price premiums for Hungarian geographical indications 11th Annual AAWE Conference Padua, 2017
Compulsory analytical tests total and actual alcohol content, free and bound sulphites, pH value, acidity (titratable, volatile), density, extract content (total and sugar free), sugar content How can geographical indications influence wine prices? Estimating price premiums for Hungarian geographical indications 11th Annual AAWE Conference Padua, 2017
Compulsory analytical tests total and actual alcohol content, free and bound sulphites, pH value, acidity (titratable, volatile), density, extract content (total and sugar free), sugar content How can geographical indications influence wine prices? Estimating price premiums for Hungarian geographical indications 11th Annual AAWE Conference Padua, 2017
Dummies Geographical indications Badacsony Balaton Balatonbogl r Balaton-felvid k Balatonf red-Csopak Balatonmell ki B kk Csongr d Debr i H rslevel * Duna Dun nt l Duna-Tisza k zi Eger (cl/sup/grand) Etyek-Buda Fels -Magyarorsz g Haj s-Baja K li* Kuns g M tra M r Nagy-Soml Neszm ly Pannon Pannonhalma P cs Sopron/ denburg* Szeksz rd* Tihany Tokaj (basic/special) Tolna Vill ny (cl/pr m/szp) Zala Zempl n single vineyard Individual brands 1st group 2nd group How can geographical indications influence wine prices? Estimating price premiums for Hungarian geographical indications 11th Annual AAWE Conference Padua, 2017
Descriptives Variable Mean 2687.875 20096.39 Std.dev 5846.232 39179.09 Min Max Price (HUF/.75 l) Lot size Total alcohol (%vol) Actual alcohol (%vol) Sugar (g/l) Sugar free extract (g/l) Acidity (g/l, in tartaric acid) pH 194.85 120 194330 607568 13.27315 1.964198 9.77 35.73 12.53663 1.228177 0.9 16.45 13.216 37.61819 0 578 25.57576 6.88524 15.6 124.6 5.74461 1.024969 3.7 21.1 3.471966 .1593945 2.88 4.01 How can geographical indications influence wine prices? Estimating price premiums for Hungarian geographical indications 11th Annual AAWE Conference Padua, 2017
7000 6000 5000 4000 3000 2000 1000 0 r medi nja (Ft) tlag r (Ft) How can geographical indications influence wine prices? Estimating price premiums for Hungarian geographical indications 11th Annual AAWE Conference Padua, 2017
18000 16000 14000 12000 10000 8000 6000 4000 2000 0 Tokaj nem bork l nlegess g Tokaji Eger nem superior Eger Grand Superior Vill ny classicus Vill ny pr mium Eger Eger Superior Tokaj bork l nlegess g Vill ny r medi nja (Ft) 1699 1499 4305 4980 2990 2349 8999 1947 1597 4945 tlag r (Ft) 2638 2065 4806 7874 6832 3458 16877 2895 1812 5966 r medi nja (Ft) tlag r (Ft) How can geographical indications influence wine prices? Estimating price premiums for Hungarian geographical indications 11th Annual AAWE Conference Padua, 2017
Results/1 Model A1 A2 B1 B2 -0.2133 (0.000) 0.1747 (0.000) -0.2212 (0.000) -0.2124 (0.000) 0.2323 (0.000) -0.2114 (0.000) Lot size (log) Actual alcohol 0.0038 (0.000) -0.1501 (0.000) Actual alcohol (quadratic) 0.0088 (0.000) -0.3039 (0.000) -0.3224 (0.000) pH 0.0249 (0.000) 0.0235 (0.000) Sugar free extract 0.0076 (0.000) 0.0077 (0.000) Sugar 0.0000123 (0.000) 6.7921 (0.000) 0.7324 0.0000524 (0.000) 7.6338 (0.000) 0.4310 Sugar (quadratic) 7.7908 (0.000) 0.4594 2.95 6.6419 (0.000) 0.7304 1.46 Constant R2/pseudo R2 VIF 1.35 3.02
Results/2 Model A1 A2 B1 B2 Badacsony Balaton Balatonf red-Csopak Balatonmell ki Duna-Tisza k zi Eger Eger classicus Eger superior Eger grand superior Etyek-Buda K li M tra Nagy-Soml 27% 23% 14% -25% -35% 20% 25% 28% 14% -22% -40% 21% 29% 22% 13% -24% -38% 24% 17% 11% -33% -43% 15% 56% 53% 22% 89% -9% 34% 19% 52% 51% 23% 61% -11% 46% 25% 66% -9% 38% 20% 52% -10% 44% How can geographical indications influence wine prices? Estimating price premiums for Hungarian geographical indications 11th Annual AAWE Conference Padua, 2017
Results/3 Model A1 A2 B1 B2 Pannonhalma Sopron Szeksz rd Tokaj Tokaj nem bork l. Tokaji bork l nl. Vill ny Vill ny classicus Vill ny pr mium Zala single vineyard 1st group 2nd group 19% 22% 14% 18% 21% 20% 12% 21% 13% 38% 23% 16% 37% 155% 18% 132% 29% 29% 13% 82% -24% 40% 47% 32% 19% 86% -25% 46% 49% 33% 43% 46% 33% 40% 50% 35% How can geographical indications influence wine prices? Estimating price premiums for Hungarian geographical indications 11th Annual AAWE Conference Padua, 2017
Conclusions geographical indications may influence wine prices only some geographical indications have a price premium in the market the impact may be negative as well low positioning individual brands have a price premium as well segmentation based on quality level within geographical indications makes sense control variables (quantity, content in alcohol sugar and sugar-free extract, pH) are related to the price How can geographical indications influence wine prices? Estimating price premiums for Hungarian geographical indications 11th Annual AAWE Conference Padua, 2017
Further research: factors behind differences of prices Model (1) (2) (3) (4) (5) Dependent variable Reputation mean price (log) 0.0162*** Barriers to entry 2.2133*** Producer group heterogeneity Regional Similarity Index Maximum yield Constant R2 -0.0001*** -1.4074*** -0.0188** 9.4399*** 0.1575 7.3288*** 0.3899 7.0334*** 0.3823 7.6338*** 0.2537 8.3312*** 0.2529 How can geographical indications influence wine prices? Estimating price premiums for Hungarian geographical indications 11th Annual AAWE Conference Padua, 2017
Thank you! peter.gal@stud.uni-corvinus.hu
