How can geographical indications influence wine prices How can geographical indications influence wine prices? Estimating price premiums for Hungarian geographical indications Péter Gál

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 information asymmetry PRODUCER CONSUMER 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 homogenousproducts close substitute Homogén termékek heterogenous products remote substitute 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 Q position „A”: generic products position „B”: GI products 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 = ß0 + ßq lnQ + Σ ßi Ci + Σ ßj GIj + εi 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 és Smith (1997) Combris, Lecocq és Visser (2000) Schamel és Anderson (2003) Lecocq és Visser (2006) San Martin, Troncoso és Brümmer (2008) Schamel (2009) Market Australia USA France New Zealand Bordeaux en primeur Producer countires Bordeaux Bourgogne Argentina 27 regions, 11 countriens, 16 varietals Method OLS panel 2SLS Specification log-lin reciprocal square root lin-lin log-log (some variables lin) Sample size 2,033 196 613 255-2,154 53-362 1,615 1,102 56,661 Right-hand side variables 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 weather data sensory scores, IV: age, labelling elements, wine district, varietal, Wines of Argentina membership, quantity produced and available individual brands (grouped), sensory scores, WS highlights, age of wine, place of origin, varietal Model cross-section cross-section (9) cross-section (7) years of vintages/ newspaper publication publications of 1988-1990 vintages of 1987-1991 publications of 1993 (vintages of 1990-92) publications of 1992-2000 publications of 1993-1999 vintages of 1994-2003 vintages of 1977-2005 vintages from 1999 to 2004 Sample type database of a newspaper Source Winestate Wine Spectator data bank Institut National de la Consomnation James Halliday (AUS) and Winestate (AUS) en primeur sales data, weather data

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 Villány Prémium)  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 Balatonboglár Balaton-felvidék Balatonfüred-Csopak Balatonmelléki Bükk Csongrád Debrői Hárslevelű* Duna Dunántúl Duna-Tisza közi Eger (cl/sup/grand) Etyek-Buda Felső-Magyarország Hajós-Baja Káli* Kunság Mátra Mór Nagy-Somló Neszmély Pannon Pannonhalma Pécs Sopron/Ödenburg* Szekszárd* Tihany Tokaj (basic/special) Tolna Villány (cl/prém/szp) Zala Zemplén 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 Std.dev Min Max Price (HUF/.75 l) 2687.875 5846.232 194.85 194330 Lot size 20096.39 39179.09 120 607568 Total alcohol (%vol) 13.27315 1.964198 9.77 35.73 Actual alcohol (%vol) 12.53663 1.228177 0.9 16.45 Sugar (g/l) 13.216 37.61819 578 Sugar free extract (g/l) 25.57576 6.88524 15.6 124.6 Acidity (g/l, in tartaric acid) 5.74461 1.024969 3.7 21.1 pH 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

How can geographical indications influence wine prices? Estimating price premiums for Hungarian geographical indications 11th Annual AAWE Conference – Padua, 2017

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 Lot size (log) -0.2124 (0.000) -0.2114 -0.2133 -0.2212 Actual alcohol 0.2323 0.1747 Actual alcohol (quadratic) 0.0088 0.0038 pH -0.3224 -0.3039 -0.1501 Sugar free extract 0.0235 0.0249 Sugar 0.0077 0.0076 Sugar (quadratic) 0.0000524 0.0000123 Constant 6.6419 7.6338 6.7921 7.7908 R2/pseudo R2 0.7304 0.4310 0.7324 0.4594 VIF 1.46 3.02 1.35 2.95

Results/2 Badacsony 27% 25% 29% 24% Balaton 23% 28% 22% 17% Model A1 A2 B1 B2 Badacsony 27% 25% 29% 24% Balaton 23% 28% 22% 17% Balatonfüred-Csopak 14% 13% 11% Balatonmelléki -25% -22% -24% -33% Duna-Tisza közi -35% -40% -38% -43% Eger 20% 21% Eger classicus 15% 19% Eger superior 56% 52% Eger grand superior 53% 51% Etyek-Buda Káli 66% 89% 61% Mátra -9% -10% -11% Nagy-Somló 38% 44% 34% 46% How can geographical indications influence wine prices? Estimating price premiums for Hungarian geographical indications 11th Annual AAWE Conference – Padua, 2017

Results/3 Pannonhalma 19% 21% Sopron 22% 20% 23% Szekszárd 14% 13% 12% Model A1 A2 B1 B2 Pannonhalma 19% 21% Sopron 22% 20% 23% Szekszárd 14% 13% 12% 16% Tokaj 18% 38% Tokaj nem borkül. 37% Tokaji borkülönl. 155% 132% Villány 29% Villány classicus Villány prémium 82% 86% Zala -25% -24% single vineyard 46% 43% 40% 1st group 49% 47% 50% 2nd group 33% 32% 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 mean price (log) Reputation 0.0162*** Barriers to entry 2.2133*** Producer group heterogeneity -0.0001*** Regional Similarity Index -1.4074*** Maximum yield -0.0188** Constant 7.3288*** 7.0334*** 7.6338*** 8.3312*** 9.4399*** R2 0.3899 0.3823 0.2537 0.2529 0.1575 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