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

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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
vintages of
publications of 1993 (vintages of )
publications of
publications of
vintages of
vintages of
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

10. 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

11. 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

12. 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

13. 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

14. 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

15. Descriptives Variable Mean Std.dev Min Max Price (HUF/.75 l) 2687.875
194.85
194330
Lot size
120
607568
Total alcohol (%vol)
9.77
35.73
Actual alcohol (%vol)
0.9
16.45
Sugar (g/l)
13.216
578
Sugar free extract (g/l)
15.6
124.6
Acidity (g/l, in tartaric acid)
3.7
21.1 pH
2.88
4.01
How can geographical indications influence wine prices?
Estimating price premiums for Hungarian geographical indications
11th Annual AAWE Conference – Padua, 2017

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

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

20. Results/1 Model A1 A2 B1 B2 Lot size (log) -0.2124 (0.000) -0.2114
Actual alcohol
0.2323
0.1747
Actual alcohol (quadratic)
0.0088
0.0038 pH
Sugar free extract
0.0235
0.0249
Sugar
0.0077
0.0076
Sugar (quadratic)
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

21. 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

22. 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

23. 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

24. 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
***
Regional Similarity Index
***
Maximum yield **
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

25. Thank you! peter.gal@stud.uni-corvinus.hu