Quantity vs. Quality: An Analysis of Wine

Export Strategies into the U.S. Market

Guenter Schamel

Guenter Schamel guenter.schamel@unibz.it Free University of Bozen–Bolzano School of Economics and Management Via Sernesi 1, I-39100 Bozen–Bolzano, Italy

January 2010

Paper for the pre-AARES conference workshop on The World’s Wine Markets by 2030: Terroir, Climate Change, R&D and Globalization, Adelaide Convention Centre, Adelaide, South Australia, 7-9 February 2010.

Quantity vs. Quality:

An Analysis of Wine Export Strategies into the U.S. Market

Abstract:

The six largest wine exporters to the US are Italy, Australia, France, Argentina, Chile, and Spain which cover 87% of all wine exports by volume and 88% by value. Each of these countries follows quite different paths in terms of export volumes vs. export values for the ten years 1999-2008. French exports to the US for example have hardly moved in terms of quantity (+1%) during these years but they managed a 5% growth p.a. in terms of export value. In contrast, Australian exports to the US have increased by 20% p.a. in terms of volume but only by 16% p.a. in terms of value. Italian exports to the US have increased by 6% in terms of volume and by more than 10% in terms of value. In this paper, we attempt to analyze and interpret these macro trends using micro-level price-quality relations on an exporting country by country basis. For this purpose, we estimate separate hedonic regressions to determine the impact of quality indicators on consumer willingness to pay. Our analysis suggests that relative to Italy and France, Australian wine achieves a lower quality premium and a higher discount for labels exported in large quantities. This illustrates that the tremendous growth rates in Australian exports to the US has created a generic reputation problem for the country. On the other hand, the high quality wines from Australia struggle to find their market in the U.S. because consumers are not familiar with them and thus will only pay a lower quality premium relative to their European competitors. While rapid export growth has made Australian wine a household name in the US, it also has made it more difficult for high quality producers to differentiate themselves from high volume export brands.

Key Words: wine exports, reputation.

Guenter Schamel Free University of Bozen–Bolzano School of Economics and Management Via Sernesi 1, I-39100 Bozen–Bolzano, Italy E-mail: guenter.schamel@unibz.it

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1. Introduction

In this paper, we analyze recent price trends in the US wine market which is widely

regarded as the most important international export market for wine. Italy, Australia, France,

Argentina, Chile, and Spain are the six largest wine importers in the US market. In the year

2008, they together accounted for around 87% of all wine imports in the US by volume and of

about 88% by value. Other importers in the US are Germany, New Zealand, South Africa, and

Portugal with market shares between 1% and 3.5%. During the ten years between 1999 and

2008, each of the six major players followed quite different paths in terms of import volume vs.

import value (as shown in Tables 1a/b and Figures 1a/b). France for example exported more or

less the same quantity to the US over these years but it managed a 4.5% average growth rate p.a.

in terms of export value. In contrast to France, Italy’s exports to the US have both increased in

terms of volume (+6%) and in terms value (+10%). Australian exports to the US during these

years have increased on average by more than 20% p.a. in volume terms and by an average of

16% p.a. in value terms. Argentina is a rising star on the US market and has now a volume share

of more than 9% and a value share of around 4 percent with growth rates around 28% p.a. on

average in both volume and value terms. It has recently surpassed Chile in terms of export

volume. In 2008 Chile had US wine market shares of about 8% and 5%, respectively, in volume

and value terms. Spanish exports to the US are also growing rapidly at 9% on average in volume

and close to 11% in value terms. Finally, Germany and New Zealand both have market shares

around 3 percent in value terms and are also growing rapidly in value terms (17% and 33%,

respectively). In this paper, we attempt to analyze and interpret these macro trends using micro-

level price-quality relations for the six largest exporters into the US market.

Another way to look at U.S. import data is to calculate the difference between the volume

and value market shares of the six large importers to the US market (as shown in Figure 1c).

France imports high-quality high-priced wines into the US such that the difference between the

volume and value share is strongly negative ranging between –15% and –21%. Except Spain, all

other major importers have a larger volume share. For Australia and less so for Argentina, the

difference between volume and value share is rising in recent years. Australia in particular

achieved very high growth rates in terms of volume especially between 2001 and 2006 through

lower priced wines widening the gap between its volume and value share of total imports into the

US. Since high priced wines are inextricably linked to high quality, we post the hypothesis that

the reputation of Australian wine for quality is declining, i.e. that American consumers are

willing to pay less for quality of Australian relative to other wine origins. A similar hypothesis

may be posted for Argentina which lags Australia in terms of market penetration in the US. Italy

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on the other hand, seems to replace most of its lower priced imports strengthening its reputation

for quality wines in the US.

In order to support these assertions, we estimate both a full (unrestricted) hedonic model

of the US wine market as well as several restricted models to determine the impact of important

quality indicators on consumer willingness to pay on a country by country basis. Moreover, we

analyze two distinct market segments (above and below $17) in order to differentiate higher and

lower priced wines. Our analysis suggests that relative to Italy and France, Australian wine

achieves a much lower quality premium in particular within the higher price segment above $17,

a relatively high discount for labels exported in large quantities and a relatively low age

premium. Moreover, also in the lower price segment the Australian quality premium is lower

than that of Chile and Spain. This illustrates that the tremendous growth rates in Australian

exports to the US has created a generic reputation problem for the country. High quality wines

from Australia struggle to find their market in the U.S. because consumers are not familiar with

them and thus will only pay a lower quality premium relative to their competitors.

2. Literature

The fact that the global wine market has witnessed major changes in recent years is well

documented. New market entrants have increased their exports not only to traditional European

markets but to other importing regions as well, whereas Old World producers have experienced

declining market shares at least in volume terms. Anderson and Berger (1999) review the

developments in international wine market during the 1990s and conclude that Australia as the

leader in New World wine exports had lower growth rates that wine exports from other Southern

Hemisphere countries (including Argentina, Chile, and New Zealand) and did only moderately

well in North America. Moreover, they assert that in terms of quality of exports as reflected in

average export prices, Australia and New Zealand hugely improved their positions to rival the

quality dominance of France. In order to gain export prevalence, the Australian grape and wine

industry has invested heavily in research and promotion efforts. Domestic producers receive

most of the gains from such R&D and they also get a far larger share of the benefit from export

promotions relative to domestic promotions (Zhao, Anderson and Wittwer, 2003).

Labys and Cohen employ econometric methods to analyze the recent major shifts in

world wine market shares and explain whether these are more of a secular trend-setting nature or

of a temporary cyclical nature. They estimate comparative advantage indices which reveal that

the old countries, particularly France, have a strong advantage in exporting sparkling wine.

Australia and Chile have a comparative advantage in exporting bottled and bulk wine while

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South Africa may have an advantage for bulk wine only. A number of previous empirical studies

provide evidence that the wine market is differentiated into multiple segments (Costanigro,

McCluskey and Mittelhammer, 2007; Fogarty, 2006; Gallet, 2007) while studies of alcoholic

beverage demand have been aimed at determining the effects of price on consumption (Nelson,

1997; Nelson and Moran, 1995).

3. Data and Model

Our data covers six vintages (1999 − 2004) of wines above $5 with expert quality ratings

by “The Wine Spectator” being sold in the US market (total sample size: 54220 observations).

On a continuing basis, the magazine publishes sensory wine quality ratings along with release

prices, special expert designations (e.g. value selection BB, etc.) as well as production and/or

import data for premium wines sold in the US. Rabobank (2003) has divided the global wine

market into six segments according to specific price points of which the basic category with

prices below $5 is excluded from our data set and analysis. To ease of the expose of our results,

we grouped the remaining five segments into two larger groups with the lower priced wines

below $17 comprised of the popular premium, premium and super premium segments and the

higher priced wines above $17 comprised of the ultra premium and icon segments.

First, we estimate a full model including 24 major wine regions present in the US market.

Subsequently, we estimate separate restricted models to determine willingness to pay impacts on

a per country basis given that consumers have already made their region of origin decision, i.e.

whether to buy a specific foreign or domestic wine. Moreover, we analyze the market segments

above and below $17 in order to differentiate the effects on higher and lower priced wines. This

allows us to test whether and how quality reputation varies according to price. As explanatory

variables, we include sensory quality ratings based on the 100-point scale (WSP), the number of

cases produced or imported (Cases) to account for any quantity effects due to scarcity or

abundance and the age of the wine at time of sensory expert evaluation (Age). Further control

variables are expert value selections (BB), an indicator for specialty wines (Spec) as well as

categorical dummies for 16 varieties (Var) and 24 regions or countries (Reg).

We use a mixed log-linear functional form to estimate both the full model as well as the

restricted models. Similar models have been used in several papers employing data from Wine

Spectator including Ramirez (2008), Gokcekusa and Fargnoli (2007). Thus, the principal

characterization of the full model estimated in Table 2 is as follows:

log(P) = α + β log(WSP) + γ log(Cases) + δ Age + ω BB + μ Spec + ηj Var + θk Reg + ε

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where log(P) is the logarithm of the suggested release price in US$. Given the functional form,

this equation, β measures the price elasticity for the quality rating and referred to as the quality

premium while γ is an elasticity of product scarcity or abundance subsequently referred to as the

quantity discount. The δ coefficient is the age premium indicating the percentage premium paid

for older and maturing wine. Coefficients μ and ω are percentage premiums/discounts for

specialty wines and any expert value selection while η and θ measure percentage price effects for

regional origin and variety, respectively. Estimating the equation above yields implicit prices for

quality attributes relative to the contribution of the base control variable.

The restricted regional models for Australia, Italy, France, Spain, Argentina and Chile

presented in Tables 3–8 are all similar to the full model presented above, but may include

additional dummies to measure specific country.1

4. Results and Discussion

The results of the unrestricted full model are presented in Table 2 in the full sample

column. The coefficients ω, μ, ηj and θk for expert value selections, specialty wines, variety and

region are presented for completeness but not further discussed. In our subsequent discussion of

the full model we focus on the quality premium, quantity discount and the age premium. In the

full unrestricted model, the quality premium is equal to 4.65, i.e. a 1% increase in the quality

rating would result in a 4.65% increase in price other things equal. For the restricted full models

with prices P<17 and P≥17, this elasticity is 1.39 and 3.98 respectively. Thus, the quality

premium that consumers are willing to pay is almost 3 times larger for higher priced wines

assuming that consumers have not already made their region of origin decision, i.e. whether to

buy a specific foreign or domestic wine. Comparable results can be found for the quantity

discount and the age premium in the full model, which are both around 2.5 times larger for

higher priced wines.

Next, we present the results of restricted models for the six largest importers in the US

market. The regression coefficients may be interpreted as premiums or discounts given that

consumers have a priory made their region of origin buying decision, i.e. to buy a wine from

Italy, Australia, France, Argentina, Chile, or Spain. In Figures 2a/b/c, we present a graphical

analysis of the restricted model estimates discussed next.

1 Dummy variables affect the interpretation of the constant (intercept) term but allow for comparisons of other ordinal independent variables.

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The results for the restricted Australian model are presented in Table 3. The quality

premium estimated for the full Australian sample is 4.19, i.e. the percentage premium paid for

Australian wine in response to a 1% increase in the quality rating is 0.46% lower relative to the

unrestricted full model. Moreover, this quality premium is the lowest for Australia compared to

other large importers (comparisons are shown in Figure 2a). Only for lower priced Australian

wines (P<17), the quality premium is not the lowest, reinforcing its value for money reputation

especially relative to Italy and France. The elasticity of product scarcity or abundance or the

quantity discount for the full Australian sample is –0.14 and almost identical to the result for the

unrestricted full model. In comparison, Argentina and Spain fare worse while Chile and Italy do

better (comparisons are shown in Figure 2b). Differentiating between price segments, low priced

wines from Australia do better as they command the lowest quantity discount reinforcing its

value for money reputation while high priced wines do worse especially relative to Italy and

Chile. When it comes to the age premium, the results are more difficult to compare

internationally as the Australian tax system discourages domestic storage such that wines are

sold at a relatively young age. While this may skew the results, the estimated age premium for

Australian wine is also the lowest compared to other importers in the US market (comparisons

are shown in Figure 2c).

It is also very interesting to look at the regional and varietal coefficients in the Australian

model. For lower priced wines, there is hardly any differentiation with respect to varietals and

regions (most coefficients in the P<17 column in Table 3 are red). However, with exceptions for

France, Argentina, and Spain, this is also true for other countries. However, some regional

differentiation is starting to emerge in the lower priced Australian sample, with significant

discounts (–13% to –27%) for the generic appellations (Australia, New South Wales South

Eastern Australia, South Australia) and significant positive premiums for the Adelaide Hills,

Orange and Margaret River. In contrast, there is considerably more differentiation in the higher

priced sample (much less coefficients in the P≥17 column in Table 3 are red) with significant

varietal and regional discounts. However, the overall picture of Australian regional

differentiation is confusing as some of the generic appellations are insignificant relative to a

Barossa Valley Shiraz. This result seems to support the conclusion that high quality producers

have difficulties to differentiate themselves from high volume export brands.

Finally, we examine the regional and varietal coefficients for the other major importers in

the US (Tables 4-8). Italy exhibits very significant regional differentiation for higher priced

wines explaining much of its success gaining US market share in terms of import value relative

to volume (Figure 1c). Similar conclusions can be drawn from the French results. Thus, it is safe

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to claim that the overall picture of French and Italian regional differentiation is much less

confusing to consumers at least relative to Australia. Spain shows a considerable regional and

varietal differentiation for lower priced wines potentially explaining its balanced growth in terms

of import volume and value (Figures 1a/b/c). Argentina exhibits some regional differentiation for

both lower and higher priced wines but the picture is also somewhat confusing with a significant

generic appellation (Argentina relative to Mendoza). Chile also reveals a considerable regional

differentiation for lower priced wine and emerging regional differences for higher priced wines

also explaining its relatively balanced growth in terms of import volume vs. value (Figures 1c).

In conclusion, our analysis suggests that especially relative to Italy and France,

Australian wine exported to the US obtains a lower quality premium and a higher discount for

labels exported in large quantities. This reinforces the conclusion that the tremendous growth

rates in Australian exports to the US created a generic reputation problem for the country. On the

one hand, lower priced Australian wines are considered a value for money bargain by American

consumers while on the other hand high quality wines struggle to find their market in the U.S.

because consumers are not familiar with the overall picture of regional and varietal

differentiation and thus will only pay a lower quality premium relative to their European

competitors. Rapid export growth has made Australian wine a household name in the US, but it

has also made it more difficult for high quality producers to differentiate themselves from high

volume export brands.

Literature:

Anderson, K and Berger, N. (1999) Australia’s Re-Emergence as a Wine Exporter: The First Decade in International Perspective, CIES Wine Policy Brief No 5, October 1999.

Costanigro, M., McCluskey, J.J. and Mittelhammer, R.C. (2007) Segmenting the wine market based on price: hedonic regression when different prices mean different products. Journal of Agricultural Economics, 58, 454–466.

Fogarty, J.J. (2008). The demand for beer, wine, and spirits: insights from a meta-analysis approach. American Association of Wine Economists, Working Paper No. 31.

Gallet, G.C. (2007). The demand for alcohol: a meta-analysis of elasticities. Australian Journal of Agricultural and Resource Economics, 51(2), 121–135.

Gokcekus, O. and Fargnoli, A. (2007). Is globalization good for wine drinkers in the United States? Journal of Wine Economics, 2(2), 187–195.

Labys, W.C. and B.C. Cohen, (2006). Trends versus Cycles in Global Wine Export Shares. Australian Journal of Agricultural and Resource Economics (50):527-537).

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Nelson, J.P. (1997). Economic and demographic factors in U.S. alcohol demand: a growth-accounting analysis. Empirical Economics, 22, 83–102.

Nelson, J.P. and Moran, J.R. (1995). Advertising and U.S. alcoholic beverage demand: system-wide estimates. Applied Economics, 65, 1225–1236.

Rabobank (2003). Wine is business, shifting demand and distribution: major drivers reshaping the wine industry, Food & Agribusiness Research, Utrecht, Rabobank International, 2003.

Ramirez, C. (2008). Wine quality, wine prices, and the weather: is Napa “different”? Journal of Wine Economics, 3(2), 114–131.

Zhao, X., Anderson, K. and Wittwer, G. (2003), ‘Who gains from Australian generic wine pro-motion and R&D’, Australian Journal of Agricultural and Resource Economics 47(2): 181-209.

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Table 1a: Import Volume: US Market Share and Growth

Growth Average Annual Market Share (hl) 1999 vs. 2008 Growth Rate Country 1999 2007 2008

60% 5.6% Italy 35.2% 29.5% 28.3% 368% 20.2% Australia 9.6% 23.4% 22.5%

4% 0.9% France 24.8% 14.6% 13.0% 756% 28.6% Argentina 2.2% 7.6% 9.2% 49% 4.8% Chile 10.4% 6.9% 7.8% 109% 9.0% Spain 5.7% 5.8% 6.0% 168% 12.0% Germany 2.5% 3.6% 3.4% 928% 31.6% New Zealand 0.4% 2.3% 2.3% 739% 27.7% South Africa 0.5% 1.2% 1.9% 34% 3.5% Portugal 1.9% 1.3% 1.3% 30% 5.4% Rest of World 6.9% 3.9% 4.5% 99% 8.1% Total Imports 100% 100% 100%

Source: www.fas.usda.gov Table 1b: Import Value: US Market Share and Growth

Growth Average Annual Importing Market Share (US$) 1999 vs. 2008 Growth Rate Country 1999 2007 2008

140% 10.4% Italy 24.7% 27.6% 28.1% 241% 15.9% Australia 9.2% 17.0% 14.9% 40% 4.5% France 45.6% 30.8% 30.4% 746% 28.1% Argentina 1.0% 2.8% 3.9% 92% 7.8% Chile 5.3% 4.4% 4.8% 139% 10.6% Spain 5.4% 5.8% 6.1% 286% 16.8% Germany 1.7% 3.1% 3.2% 1101% 33.1% New Zealand 0.5% 3.1% 3.1% 435% 22.0% South Africa 0.4% 0.9% 1.0% 18% 2.7% Portugal 2.6% 1.5% 1.5% 73% 7.0% Rest of World 3.6% 2.8% 2.9% 111% 8.8% Total Imports 100% 100% 100%

Source: www.fas.usda.gov

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Figure 1a: US Market Shares by Import Volume

Italy

Australia

France

Argentina

Chile

Spain

Rest of World

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

Italy Australia France Argentina Chile SpainGermany New Zealand South Africa Portugal Rest of World

Ger- many

NZ

Figure 1b: US Market Shares by Import Value

25%

26%

27%

28%

28%

28%

28%

28%

28%

28%

9%

12%

15%

17%

19%

21%

20%

18%

17%

15%

46%

40%

35%

33%

34%

30%

29%

31%

31%

30%

5.3%

5.9%

5.9%

5.0%

4.2%

4.3%

4.3%

4.0%

4.4%

4.8%

5.4%

4.7%

4.8%

4.9%

4.8%

5.4%

5.6%

5.7%

5.8%

6.1%3.9%

2.8%

3.2%

3.1%

3.1%

3.1%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

Italy Australia France Argentina Chile SpainGermany New Zealand South Africa Portugal Rest of World

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Figure 1c: US Wine Imports (Volume−Value Share)

ItalyAustralia

France

ArgentinaChile

Spain

-21%

-18%

-15%

-12%

-9%

-6%

-3%

0%

3%

6%

9%

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

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Table 2: All Major Regions Dependent variable log(Price)

Full Sample Price<17 Price≥17Parameter estimate t-stat p-value estimate t-stat p-value estimate t-stat p-valueCONSTANT -16.50 -90.10 0 -3.265 -17.72 0 -13.52 -63.93 0log(WSP) 4.653 114.6 0 1.394 33.91 0 3.985 84.88 0log(Cases) -0.138 -103.9 0 -0.047 -39.37 0 -0.112 -66.70 0Age 0.184 69.34 0 0.060 23.75 0 0.144 45.92 0BB -0.273 -18.14 0 -0.106 -12.80 0 -0.338 -10.90 0Spec 0.795 35.01 0 -0.833 -4.16 0 0.774 34.55 0CabBlend 0.014 1.14 0.26 0.042 3.35 0 -0.007 -0.52 0.61Chardonnay -0.141 -14.07 0 -0.005 -0.64 0.52 -0.188 -15.90 0Merlot -0.115 -9.71 0 -0.024 -2.47 0.01 -0.146 -10.40 0OtherRed -0.202 -18.00 0 0.005 0.53 0.60 -0.245 -18.15 0OtherWhite -0.257 -19.13 0 0.001 0.13 0.90 -0.298 -17.26 0PinotGris -0.149 -8.47 0 0.067 5.10 0 -0.185 -7.81 0PinotNoir 0.052 4.72 0 0.048 3.81 0 -0.030 -2.42 0.02RedBlend -0.158 -14.38 0 0.014 1.40 0.16 -0.193 -15.08 0Riesling -0.281 -16.39 0 -0.060 -4.74 0 -0.284 -12.31 0Sangiovese -0.274 -17.94 0 0.010 0.61 0.54 -0.312 -18.56 0SauvBlanc -0.256 -19.74 0 -0.014 -1.41 0.16 -0.293 -16.45 0Shiraz -0.072 -7.05 0 0.003 0.29 0.77 -0.131 -11.27 0Viognier -0.116 -5.50 0 0.068 3.48 0 -0.217 -9.13 0WhiteBlend -0.198 -13.92 0 -0.021 -1.94 0.05 -0.218 -12.15 0Zinfandel -0.212 -14.88 0 0.021 1.34 0.18 -0.292 -18.86 0Argentina -0.542 -34.74 0 -0.259 -16.36 0 -0.290 -12.87 0Australia -0.468 -44.95 0 -0.167 -11.47 0 -0.373 -32.37 0Chile -0.581 -43.21 0 -0.253 -17.11 0 -0.372 -16.58 0New Zealand -0.391 -27.44 0 -0.007 -0.44 0.66 -0.376 -22.95 0Bay Central Coast -0.282 -16.31 0 -0.083 -3.98 0 -0.226 -12.18 0Carneros -0.170 -9.03 0 0.074 1.47 0.14 -0.194 -10.32 0Oregon -0.493 -35.94 0 -0.185 -10.29 0 -0.396 -26.84 0Rest North America -0.578 -39.26 0 -0.156 -9.76 0 -0.484 -25.80 0Rest California -0.458 -36.52 0 -0.174 -11.90 0 -0.301 -18.49 0Sonoma -0.223 -20.36 0 0.025 1.45 0.15 -0.213 -18.87 0South Coast -0.355 -26.61 0 -0.034 -1.69 0.09 -0.333 -24.10 0Washington -0.564 -46.90 0 -0.247 -15.56 0 -0.479 -36.64 0Bordeaux -0.176 -11.73 0 -0.129 -6.27 0 -0.109 -6.89 0Burgundy -0.079 -6.76 0 -0.066 -3.44 0 -0.014 -1.13 0.26Languedoc-Rouss -0.771 -49.45 0 -0.314 -20.22 0 -0.557 -22.18 0Rest France -0.409 -31.19 0 -0.133 -8.55 0 -0.316 -19.67 0Rhone -0.129 -10.06 0 -0.154 -9.17 0 -0.005 -0.32 0.75Northern Italy -0.404 -29.87 0 -0.114 -7.12 0 -0.345 -22.02 0Piedmont -0.101 -7.86 0 -0.037 -2.19 0.03 -0.013 -0.88 0.38Tuscany -0.058 -4.08 0 -0.044 -2.35 0.02 -0.034 -2.25 0.02Rest Italy -0.299 -21.06 0 -0.163 -10.32 0 -0.127 -7.28 0Spain -0.408 -29.81 0 -0.235 -15.14 0 -0.160 -9.29 0Germany -0.352 -18.05 0 -0.066 -3.31 0 -0.298 -12.13 0Adj. R2 / F-Ratio 62.99 1988 36.36 185.2 45.30 702.4 Colors indicate Significance at the 1%, 5%, 10% and <10% level, respectively. Base Category: Napa Valley Cabernet Sauvignon; Source: Own Calculation

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Table 3: Australia Dependent variable log(Price)

Full Sample Price<17 Price≥17Parameter estimate t-stat p-value estimate t-stat p-value estimate t-stat p-valueCONSTANT -14.64 -20.25 0 -4.283 -5.59 0 -12.45 -14.52 0Log(WSP) 4.187 26.10 0 1.593 9.26 0 3.715 19.55 0Log(Cases) -0.138 -34.81 0 -0.034 -9.77 0 -0.128 -23.32 0Age 0.124 15.55 0 0.046 6.17 0 0.094 9.51 0BB -0.185 -5.52 0 -0.093 -5.12 0 -0.286 -3.34 0CabBlend -0.071 -2.78 0.01 -0.012 -0.59 0.55 -0.036 -1.12 0.26CabSauv -0.064 -3.08 0 0.012 0.64 0.52 -0.081 -3.23 0Chardonnay -0.170 -8.66 0 -0.010 -0.69 0.49 -0.179 -6.00 0Merlot -0.154 -4.06 0 -0.026 -1.15 0.25 -0.099 -1.49 0.14OtherRed -0.155 -4.68 0 0.023 0.58 0.56 -0.167 -4.50 0OtherWhite -0.322 -8.05 0 0.029 1.02 0.31 -0.394 -6.63 0PinotGris -0.304 -2.58 0.01 0.168 1.28 0.20 -0.390 -2.90 0PinotNoir 0.057 1.41 0.16 0.085 2.12 0.03 0.027 0.55 0.58RedBlend -0.188 -5.81 0 -0.021 -0.66 0.51 -0.183 -4.83 0Riesling -0.378 -9.92 0 -0.031 -1.05 0.29 -0.402 -7.78 0SauvBlanc -0.259 -5.97 0 -0.026 -0.95 0.34 -0.201 -2.64 0.01Viognier -0.337 -4.26 0 -0.117 -1.26 0.21 -0.436 -4.84 0WhiteBlend -0.272 -7.29 0 -0.058 -2.66 0.01 -0.282 -3.78 0McLarenVale 0.006 0.30 0.76 -0.035 -1.37 0.17 -0.005 -0.22 0.82SouthEasternAustr -0.319 -11.87 0 -0.275 -13.04 0 -0.100 -1.57 0.12SouthAustralia -0.209 -7.97 0 -0.129 -6.03 0 0.026 0.66 0.51Victoria -0.202 -6.72 0 -0.062 -2.38 0.02 -0.095 -2.43 0.01ClareValley -0.253 -8.29 0 -0.020 -0.66 0.51 -0.248 -6.85 0MargaretRiver -0.083 -2.67 0.01 0.052 1.68 0.09 -0.097 -2.58 0.01AdelaideHills -0.133 -3.87 0 0.090 2.71 0.01 -0.156 -3.71 0WesternAustralia -0.191 -5.52 0 0.011 0.43 0.67 -0.158 -3.18 0Coonawarra -0.114 -3.17 0 -0.054 -1.36 0.17 -0.105 -2.58 0.01YarraValley -0.165 -4.00 0 0.041 0.95 0.34 -0.168 -3.43 0HunterValley -0.112 -2.84 0 -0.047 -1.51 0.13 -0.043 -0.83 0.41LanghorneCreek -0.179 -4.23 0 -0.014 -0.30 0.77 -0.184 -3.83 0EdenValley -0.060 -1.21 0.23 -0.039 -0.67 0.50 -0.030 -0.52 0.60Heathcote 0.097 2.13 0.03 0.028 0.38 0.71 0.069 1.39 0.16LimestoneCoast -0.246 -4.86 0 0.041 1.01 0.31 -0.228 -3.41 0MorningtonPeninsu -0.142 -2.47 0.01 -0.124 -1.23 0.22 -0.155 -2.44 0.01NewSouthWales -0.281 -5.11 0 -0.191 -5.15 0 -0.089 -1.03 0.30Australia -0.311 -5.16 0 -0.211 -5.90 0 -0.171 -1.28 0.20Padthaway 0.121 2.11 0.03 0.076 1.19 0.23 0.124 1.90 0.06Bendigo -0.337 -5.06 0 0.034 0.64 0.52 -0.368 -4.21 0Tasmania -0.038 -0.49 0.62 0.085 0.99 0.32 -0.037 -0.43 0.67Pyrenees 0.150 1.99 0.05 0.023 0.18 0.86 0.130 1.61 0.11Mudgee -0.140 -1.78 0.07 0.066 0.92 0.36 -0.180 -1.89 0.06Orange -0.154 -1.97 0.05 0.137 2.05 0.04 -0.206 -2.12 0.03Adj. R2 / F-Ratio 65.10 194.0 0 55.05 45.38 0 39.38 44.68 0 Colors indicate Significance at the 1%, 5%, 10% and <10% level, respectively. Base Category: Barossa Valley Shiraz; Source: Own Calculation

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Table 4: Italy Dependent variable log(Price)

Full Sample Price<17 Price≥17Parameter estimate t-stat p-value estimate t-stat p-value estimate t-stat p-valueCONSTANT -18.38 -41.47 0 -1.308 -3.39 0 -15.87 -30.2 0log(WSP) 4.946 49.97 0 0.948 10.9 0 4.367 37.2 0log(Cases) -0.102 -30.67 0 -0.046 -17.9 0 -0.063 -15.4 0Age 0.183 31.29 0 0.039 6.92 0 0.127 19.3 0BB -0.433 -10.89 0 -0.127 -5.99 0 -0.458 -6.09 0Spec 0.401 10.56 0 -0.780 -4.40 0 0.335 8.86 0CabBlend 0.325 12.94 0 0.038 0.95 0.34 0.302 12.1 0CabSauv 0.177 4.61 0 -0.160 -3.91 0 0.225 5.65 0Chardonnay -0.013 -0.48 0.63 -0.066 -3.14 0 0.017 0.57 0.57Merlot 0.228 7.72 0 -0.113 -4.01 0 0.319 10.0 0OtherRed -0.016 -0.73 0.47 0.027 1.44 0.15 -0.035 -1.45 0.15OtherWhite 0.009 0.42 0.67 0.003 0.20 0.84 -0.029 -1.09 0.27PinotGris 0.090 2.89 0 0.008 0.35 0.73 0.016 0.39 0.69PinotNoir 0.124 2.08 0.04 -0.134 -1.50 0.13 0.115 1.96 0.05RedBlend 0.146 6.98 0 -0.016 -0.85 0.40 0.177 7.80 0SauvBlanc 0.038 1.12 0.26 -0.017 -0.59 0.55 -0.004 -0.10 0.92Shiraz 0.132 3.02 0 -0.001 -0.02 0.98 0.159 3.53 0WhiteBlend -0.004 -0.17 0.86 -0.043 -2.37 0.02 0.026 0.91 0.36Zinfandel -0.132 -2.50 0.01 -0.014 -0.42 0.67 -0.188 -2.37 0.02Chianti -0.135 -8.82 0 0.017 1.24 0.21 -0.156 -9.29 0Brunello 0.158 5.11 0 0.259 8.39 0Barbera -0.006 -0.29 0.77 -0.030 -1.61 0.11 -0.023 -1.00 0.32Barolo 0.345 14.14 0 0.129 2.16 0.03 0.380 14.7 0Barbaresco 0.414 14.71 0 0.383 13.5 0Northern Italy -0.322 -17.41 0 -0.022 -1.35 0.18 -0.236 -11.0 0Piedmont -0.143 -7.26 0 0.005 0.29 0.77 -0.077 -3.53 0Rest Italy -0.255 -14.11 0 -0.102 -6.77 0 -0.083 -3.91 0Adj. R2 / F-Ratio 64.77 657.2 29.98 45.43 54.76 313.4 Colors indicate Significance at the 1%, 5%, 10% and <10% level, respectively. Base Category: Tuscany Sangiovese; Source: Own Calculation

15

Table 5: France Dependent variable log(Price)

Full Sample Price<17 Price≥17Parameter estimate t-stat p-value estimate t-stat p-value estimate t-stat p-valueCONSTANT -15.24 -38.6 0 -1.232 -3.46 0 -14.54 -31.8 0log(WSP) 4.372 49.9 0 0.903 11.3 0 4.229 41.7 0log(Cases) -0.137 -40.1 0 -0.037 -13.3 0 -0.123 -28.7 0Age 0.146 18.1 0 0.064 10.6 0 0.104 10.4 0BB -0.261 -5.46 0 -0.121 -5.44 0 -0.351 -3.43 0FirstGrowth 1.555 12.9 0 1.534 12.6 0Vin de Pays -0.199 -6.95 0 -0.120 -7.95 0 0.008 0.15 0.88CabBlend -0.339 -6.59 0 0.082 2.41 0.02 -0.265 -3.20 0CabSauv -0.269 -4.16 0 -0.065 -1.89 0.06 -0.340 -1.76 0.08Chardonnay -0.088 -5.09 0 -0.034 -1.27 0.20 -0.054 -2.91 0Merlot -0.149 -2.33 0.02 -0.077 -2.28 0.02 0.285 1.14 0.25OtherRed -0.225 -4.61 0 0.111 3.43 0 -0.325 -4.00 0OtherWhite -0.295 -6.49 0 -0.032 -1.02 0.31 -0.206 -2.86 0PinotGris -0.123 -2.08 0.04 0.116 2.36 0.02 -0.086 -1.04 0.30RedBlend -0.344 -8.06 0 0.006 0.22 0.83 -0.246 -3.28 0Riesling -0.267 -4.74 0 0.033 0.74 0.46 -0.223 -2.78 0.01SauvBlanc -0.115 -2.39 0.02 -0.055 -1.67 0.10 -0.020 -0.26 0.79Shiraz 0.011 0.25 0.80 0.052 1.65 0.10 0.031 0.40 0.69Viognier 0.273 4.78 0 0.126 2.92 0 0.268 3.04 0WhiteBlend -0.204 -4.60 0 -0.016 -0.52 0.60 -0.117 -1.56 0.12Bordeaux -0.188 -3.42 0 -0.127 -4.00 0 -0.120 -1.36 0.17Lang_Rous -0.515 -11.5 0 -0.184 -7.20 0 -0.453 -5.83 0Rhone -0.165 -3.78 0 -0.099 -3.89 0 -0.143 -1.88 0.06Alsace -0.174 -3.63 0 0.006 0.18 0.86 -0.219 -2.98 0RoFRA -0.461 -11.2 0 -0.114 -4.72 0 -0.439 -6.39 0Beaujolais -0.032 -0.53 0.60 -0.215 -8.32 0 0.088 0.26 0.79Côte_Rôtie 0.507 11.8 0 0.433 9.74 0Châteauneuf 0.589 24.3 0 0.078 0.42 0.68 0.397 14.1 0Meursault 0.105 3.44 0 0.036 1.16 0.25Gevrey Chambertin 0.093 2.64 0.01 0.075 2.10 0.04Chablis -0.033 -1.23 0.22 0.107 2.72 0.01 -0.115 -3.98 0Sancerre 0.208 4.33 0 0.323 7.42 0 0.048 0.79 0.43Pessac_Léognan 0.387 8.44 0 0.082 1.57 0.12 0.250 4.71 0St_Estèphe 0.413 7.07 0 0.122 1.28 0.20 0.231 3.62 0St_Julien 0.631 10.3 0 -0.080 -0.42 0.67 0.434 6.58 0Pomerol 0.768 18.9 0 0.065 0.48 0.63 0.589 12.7 0Pauillac 0.677 12.5 0 0.113 0.60 0.55 0.475 8.10 0St_Emilion 0.517 11.0 0 0.059 1.42 0.16 0.372 5.86 0Margaux 0.425 9.11 0 0.049 0.52 0.61 0.243 4.66 0Adj. R2 / F-Ratio 63.97 514.5 42.68 55.92 45.03 182.1 Colors indicate Significance at the 1%, 5%, 10% and <10% level, respectively. Base Category: Burgundy Pinot Noir; Source: Own Calculation

16

Table 6: Spain Dependent variable log(Price)

Full Sample Price<17 Price≥17Parameter estimate t-stat p-value estimate t-stat p-value estimate t-stat p-valueCONSTANT -22.31 -17.6 0 -5.249 -5.83 0 -17.96 -9.11 0log(WSP) 5.906 21.1 0 1.781 8.79 0 5.051 11.6 0log(Cases) -0.158 -22.8 0 -0.037 -7.50 0 -0.165 -14.7 0Age 0.128 10.3 0 0.065 7.96 0 0.068 3.30 0BB -0.385 -5.14 0 -0.137 -3.44 0 -0.444 -2.67 0.01VinodelaTerra 0.056 0.84 0.40 -0.129 -3.19 0 0.078 0.71 0.48CabBlend 0.295 3.36 0 0.156 2.91 0 0.148 1.02 0.31CabSauv 0.076 0.80 0.42 0.158 3.02 0 -0.300 -1.63 0.10Chardonnay 0.240 3.02 0 0.097 2.22 0.03 0.230 1.42 0.16Merlot 0.121 1.31 0.19 0.137 2.83 0 -0.024 -0.12 0.91OtherRed 0.030 0.57 0.57 -0.024 -0.76 0.45 -0.011 -0.12 0.90OtherWhite 0.045 0.62 0.53 0.061 1.55 0.12 -0.343 -2.28 0.02RedBlend 0.085 2.22 0.03 0.007 0.29 0.77 -0.058 -0.85 0.39SauvBlanc 0.143 1.03 0.30 0.055 0.76 0.45 0.215 0.62 0.53Shiraz 0.123 1.66 0.10 0.074 1.38 0.17 -0.170 -1.56 0.12WhiteBlend -0.050 -0.93 0.35 -0.001 -0.03 0.98 -0.365 -3.70 0RoSpain -0.131 -2.48 0.01 -0.065 -2.02 0.04 -0.100 -1.17 0.24Murcia -0.459 -4.08 0 -0.140 -2.39 0.02 -0.435 -1.59 0.11Utiel_Requena -0.308 -2.96 0 -0.174 -3.05 0 -0.106 -0.50 0.62Bierzo -0.113 -1.31 0.19 0.128 1.84 0.07 -0.277 -2.31 0.02Montsant -0.317 -4.63 0 0.006 0.11 0.91 -0.309 -3.39 0Catalunya -0.203 -2.93 0 -0.041 -0.99 0.32 -0.185 -1.50 0.13CastillayLeón -0.208 -2.51 0.01 -0.024 -0.47 0.64 -0.037 -0.28 0.78Toro -0.033 -0.53 0.60 -0.091 -2.05 0.04 -0.108 -1.21 0.23Jumilla -0.371 -6.09 0 -0.114 -3.21 0 -0.299 -2.67 0.01Aragon -0.340 -5.27 0 -0.228 -6.32 0 -0.159 -1.16 0.25Navarra -0.421 -7.18 0 -0.152 -4.61 0 -0.406 -3.21 0Rueda -0.275 -3.30 0 -0.089 -1.97 0.05 0.024 0.12 0.91Baixas 0.078 0.94 0.35 0.186 3.68 0 0.162 1.05 0.29CastillaLaMancha -0.236 -3.63 0 -0.170 -4.64 0 -0.086 -0.70 0.48Penedès -0.258 -4.80 0 -0.098 -3.08 0 -0.111 -1.15 0.25Priorat 0.181 3.83 0 0.051 0.67 0.50 0.058 0.99 0.32RiberadelDuero 0.154 3.19 0 0.074 2.12 0.03 0.017 0.23 0.82Adj. R2 / F-Ratio 64.87 101.5 39.29 18.86 43.74 21.83 Colors indicate Significance at the 1%, 5%, 10% and <10% level, respectively. Base Category: Rioja Tempranillo; Source: Own Calculation

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Table 7: Argentina Dependent variable log(Price)

Full Sample Price<17 Price≥17Parameter estimate t-stat p-value estimate t-stat p-value estimate t-stat p-valueCONSTANT -17.60 -16.38 0 -4.474 -5.65 0 -16.53 -7.10 0log(WSP) 4.806 20.47 0 1.620 9.18 0 4.617 9.00 0log(Cases) -0.172 -18.97 0 -0.057 -8.05 0 -0.159 -10.24 0Age 0.208 13.54 0 0.083 7.17 0 0.145 5.71 0BB -0.160 -2.78 0.01 -0.019 -0.54 0.59 -0.075 -0.52 0.60CabBlend 0.260 4.67 0 0.054 0.99 0.32 0.288 4.28 0CabSauv -0.061 -1.71 0.09 0.016 0.67 0.50 0.000 0.00 0.99Chardonnay -0.039 -0.89 0.37 -0.026 -0.87 0.39 0.012 0.14 0.89Merlot -0.090 -1.75 0.08 -0.039 -1.18 0.24 0.031 0.30 0.77OtherRed -0.269 -4.50 0 -0.110 -2.80 0.01 -0.171 -1.48 0.14OtherWhite -0.149 -1.59 0.11 -0.072 -1.31 0.19 0.062 0.17 0.86PinotGris 0.079 0.41 0.68 0.038 0.35 0.73PinotNoir -0.095 -0.50 0.62 -0.092 -0.73 0.46 0.659 1.77 0.08RedBlend 0.142 3.18 0 0.039 0.93 0.35 0.053 0.96 0.34Sangiovese -0.354 -2.79 0.01 -0.262 -3.67 0SauvBlanc 0.015 0.15 0.88 -0.024 -0.38 0.71 0.472 2.10 0.04Shiraz -0.173 -3.50 0 -0.024 -0.76 0.45 -0.176 -1.66 0.10Viognier -0.125 -1.07 0.28 0.048 0.73 0.47WhiteBlend -0.077 -0.60 0.55 -0.073 -1.02 0.31Salta 0.180 2.39 0.02 0.172 3.40 0 0.294 2.21 0.03Argentina 0.237 3.17 0 0.093 1.67 0.09 0.513 4.56 0SanJuanLaRioja 0.101 1.69 0.09 -0.013 -0.35 0.72 0.366 2.74 0.01UcoValley -0.171 -4.06 0 -0.092 -3.10 0 -0.069 -0.99 0.32SanRafael -0.045 -0.69 0.49 0.022 0.48 0.63 -0.070 -0.62 0.54Cuyo -0.078 -1.71 0.09 0.081 2.60 0.01 -0.105 -1.30 0.19Adj. R2 / F-Ratio 64.91 80.8 28.29 11.56 46.59 18.14 Colors indicate Significance at the 1%, 5%, 10% and <10% level, respectively. Base Category: Mendoza Malbec; Source: Own Calculation

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Table 8: Chile Dependent variable log(Price)

Full Sample Price<17 Price≥17Parameter estimate t-stat p-value estimate t-stat p-value estimate t-stat p-valueCONSTANT -19.55 -22.7 0 -6.518 -9.07 0 -18.0 -9.23 0log(WSP) 5.134 26.6 0 2.101 12.9 0 4.826 11.08 0log(Cases) -0.107 -15.3 0 -0.063 -11.7 0 -0.067 -4.20 0Age 0.169 14.6 0 0.104 11.9 0 0.089 3.26 0BB -0.194 -5.12 0 -0.041 -1.50 0.13 -0.349 -3.85 0CabBlend 0.222 6.12 0 0.070 2.16 0.03 0.240 4.09 0Chardonnay -0.017 -0.55 0.58 -0.012 -0.55 0.58 0.074 0.80 0.42Merlot -0.046 -1.61 0.11 0.007 0.34 0.73 -0.111 -1.29 0.20OtherRed -0.079 -2.56 0.01 -0.012 -0.55 0.58 -0.135 -1.97 0.05OtherWhite -0.196 -2.19 0.03 -0.115 -1.85 0.06 -0.272 -1.09 0.28PinotNoir 0.135 1.81 0.07 0.077 1.17 0.24 0.163 1.27 0.20RedBlend 0.230 4.67 0 0.081 1.57 0.12 0.144 2.01 0.05SauvBlanc -0.082 -2.10 0.04 -0.036 -1.35 0.18 0.011 0.07 0.94Shiraz 0.006 0.14 0.89 -0.035 -1.07 0.29 -0.064 -0.97 0.33SanAntonio 0.125 1.49 0.14 0.047 0.42 0.67 -0.102 -0.87 0.39Limarí 0.163 1.51 0.13 0.012 0.18 0.86Curicó 0.018 0.50 0.62 -0.010 -0.38 0.70 -0.098 -1.18 0.24Lontué 0.100 1.70 0.09 -0.030 -0.76 0.45 0.599 1.74 0.08Casablanca -0.016 -0.39 0.69 -0.010 -0.33 0.74 -0.218 -2.35 0.02Aconcagua 0.109 2.45 0.01 -0.192 -5.12 0 0.224 2.99 0Rapel -0.050 -1.56 0.12 -0.098 -4.20 0 -0.009 -0.12 0.91Central -0.035 -1.03 0.30 -0.131 -5.27 0 -0.127 -1.53 0.13Colchagua -0.031 -1.21 0.23 -0.046 -2.33 0.02 0.001 0.02 0.98Maule -0.002 -0.07 0.94 -0.051 -2.04 0.04 -0.039 -0.41 0.68Adj. R2 / F-Ratio 64.34 119.2 42.65 37.51 45.70 15.42 Colors indicate Significance at the 1%, 5%, 10% and <10% level, respectively. Base Category: Maipo Valley Cabernet Sauvignon; Source: Own Calculation

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Figure 2a: Quality Premium (Restricted Model)

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Figure 2b: Quantity Discount (Restricted Model)

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Figure 2c: Age Premium (Restricted Model)

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