In Vino Veritas: Wine Prices in Slovenia
AnˇzeBurger and AljaˇzKunˇciˇc∗
University of Ljubljana, Faculty of Social Sciences
PRELIMINARY DRAFT
July 2010
Abstract Wine market reveals some interesting truths about the pattern of international trade in differentiated goods. We examine a large sample of domestic and foreign wines sold in Slovenian supermarkets and specialized wine shops in 2009. In our sample, imported wine is on average more expensive and of better quality than domestic wine. Higher quality wines sell at higher prices and quality-adjusted prices increase in quality. We present a het- erogeneous firms model with endogenous quality choice and non-constant mark-ups that matches the above mentioned stylized facts. The model incorporates quality competition among the differentiated products into the Melitz and Ottaviano (2008) framework and predicts that more productive wine producers choose higher quality upgrade and charge higher prices because they are able to raise their mark-up by more than their marginal cost advantage. Several implications of the theoretical model are empirically tested on the data, using product-, firm-, and country-level information and employing hedonic price function analysis for complete market and predetermined segments of the market. Empir- ical analysis confirms the existing evidence in the literature that quality, objective factors as well as reputation factors affect wine prices. Segmentations of the wine market on the basis of origin, variety and price are examined as well, and we confirm the findings of Costanigro et al. (2007) that price segmentation seems to be the most appropriate judging on explanatory power.
Keywords: Wine market, hedonic regression, market segmentation, firm heterogene- ity, quality choice.
JEL classifications: L66, Q13, F12, F14.
∗We would like to thank Mr. TomaˇzSrˇsenfor kindly providing the database. Our thanks also goes to undergraduate students Anja Novak and Sebastian Horvat for technical assistance. Contact: Centre for Inter- national Relations, Faculty of Social Sciences, University of Ljubljana, Kardeljeva ploˇsˇcad5, 1000 Ljubljana, Slovenia. Tel: +386-1-580-51-89; Fax: +386-1-580-51-09. Email: [email protected].
1 1 Introduction
Slovenian wine market has never been properly analyzed. There exist commercial guide- books such as Robinson (1999) or Johnson (1985) where general entries on Slovenian wine market and wines can be found, or aggregated statistical data on production and consumption from the Statistical office of the republic of Slovenia, Eurostat, European Commission and the Ministry of agriculture, forestry and food, but no effort has been made yet to examine the interaction of demand and supply on the market for wines and the role of international trade in Slovenian wine market. This paper aims at bridging this gap, using a unique dataset of 925 both domestic and imported varieties of wine available on the market in Slovenia. The aim of the paper is to analyze empirically the relationship between prices and quality, objective and country-specific reputation characteristics. We study a sample of domestic and foreign wine on Slovenian wine market. Being a textbook example of monopolistic compe- tition, wine market allows us to study a product that exhibits horizontal as well as vertical differentiation. The lack of direct measures of quality in other industries has hindered the precise quantification of the role of quality in explaining trade outcomes, forcing researchers to use imperfect proxies such as unit values. Unlike other goods, wine quality has a long and established tradition of being evaluated in a systematic and standardized manner, notwith- standing the subjective nature of wine tasting. The advantage of wine is that no other physical characteristic but its quality varies across varieties. In contrast, industrial products such as cars, phones and medicines bundle together different sets of components and functions so that they do not differ only with regards to quality in a narrow sense.1 As a consequence, the questions about the definition and the boundaries of relevant market arise in many settings, whereas wine market remains immune to such reservations. In terms of theoretical contribution to the literature, we present a heterogeneous firms model with endogenous quality choice and non-constant mark-ups that explains the above mentioned stylized facts. The model incorporates quality competition among the differen- tiated products into the Melitz and Ottaviano (2008) framework and predicts that more productive wine producers choose higher quality upgrade and charge higher prices because they are able to raise their mark-up by more than their marginal cost advantage. Only the most productive wineries export and their prices and wine quality depend on domestic and trading partner’s characteristics. We find some empirical support for our model when the entire sample is studied, namely that the costs of production in the country of origin affect prices positively, market size negatively, trade costs positively and wine quality positively. Theoretical model of heterogenous wineries in international trade is combined with a body of literature on hedonic price functions in order to disentangle the determinants of wine prices in Slovenia. Empirically, this paper contributes to the literature by analyzing the hedonic wine price function and adding to the evidence on wine quality and objective characteristics such as age and cellaring potential being significant determinants of wine prices. The results sug- gest that wine from more distant countries is more expensive relative to wine from nearby countries and domestic producers, controlling for other relevant factors. Larger export coun- tries provide lower wine prices while more developed countries export more expensive wine than less developed exporters. These findings are in line with the existing empirical evidence
1In that sense, wine can be considered as a one-purpose good, whereas more complex goods such as cars, phones, etc. can be considered multipurpose goods. For example, phone can also double for an mp3 player, photo camera or personal organizer.
2 on price-quality relationship and confirm the Alchian and Allen (1964) effect: ”shipping the good apples out”. We also discover a country specific reputation effect, with some countries having a price premium and some a price discount compared to domestic wines. We pursue the interesting question of Costanigro et al. (2007) on whether the market for wines should be segmented according to price, rather than the more usual segmentation on variety. We test segmentation on prices versus alternative segmentations based on origin and variety and confirm that statistically, price segmentation trumps the others. The paper is organized as follows: in section 2 we present some facts about the wine industry in Slovenia, section 3 develops the theoretical framework, section 4 outlines the data and discusses the results. Section 5 concludes.
2 Wine market in Slovenia in numbers and comparison
There were 17 thousand hectares of vineyards registered in Slovenia in 2008 (although the aerial footage reveals more – about 22 thousand), which is only about 0.5% of total vineyard area in the EU-27 (Ministry of agriculture, forestry and food (2009c), Eurostat (2008)). The prevailing modus operandi is fragmented production: as much as 88.9% of producers utilize an area of 1 hectare or less, yet they represent only 35% of total vineyard area. The largest vineyards of more than 5 hectares belong to 1.4% of producers, but their combined acreage is 5,756 hectares or 34% of total vineyard area (Ministry of agriculture, forestry and food, 2009c). Despite its apparent small size, the wine industry in the last few years (all from 2004- 2007) produced over 800,000 hectoliters of wine annually (70% being quality wine, and less than 30% being table wine)which represents a healthy 8% of Slovenian agricultural production value (Ministry of agriculture, forestry and food, 2009b). This amount corresponds to barely 0.5% of EU-27 production, however, at 40.1 liters per capita exceeds the EU-27 per capita production of 35.4 or EU-15 of 32.9 (Eurostat, 2009). Wine producers cultivate over 50 different varieties of wines, but the prevailing sorts at over 70% are white wines (Ministry of agriculture, forestry and food, 2010). On the consumption side, similarly as domestic production per capita, domestic consump- tion of wine per capita exceeds EU average. Best consumption estimation take into account both registered and unregistered production of wine, as well as stocks, imports and exports. The estimate of around 40.5 liters of wine per capita2 consumed ranks Slovenia consistently near the top of EU-273 and heavily exceeds the EU-27 average of 26.5 liters (European Com- mission, 2008a). Interestingly enough, comparing the Harmonized indices of consumer prices item weights, which imply the average share of income spent on a particular good, reveals that the wine share for Slovenia in 2010 is 0.552% while it is higher for both the EU-27 and EU-15 at 0.719% and 0.865%, respectively4. With wine production practically matching consumption in the last few years, the total wine trade balance is never far from equilibrium. In the period 2004-2007, the average yearly trade was more than 100,000 hectoliters or over 10% of total domestic wine production. Average wine imports on a yearly basis were 58,500 hectoliters, exports 46,250 hectoliters,
2Average of years 2000-2003 due to data availability 3It seems that only people in France, Luxembourg, Portugal and Italy are keener on wine than Slovenians. 4Taking the average weights for the last 10 years instead of 2000-2003 reveals a different image, with values 0.745% for EU-27, 0.803% for EU-15 and 0.788% for Slovenia.
3 yielding a trade balance deficit of 12,250 hectoliters. In terms of monetary value, average yearly imports were 5.5 millions EUR, exports 6.7 millions EUR and the trade balance deficit 1.2 million EUR. Table 1 and Table 2 offer a more detailed look at Slovenian wine trade.5 In all years but 2002, the unit price of exports was higher than the unit price of imports. From 2004-2007, the average unit value per liter of imports was 0.95 EUR and for exports 1.46, which is consistent with the fact that because of consumption and production structure, Slovenia imports mostly lower quality of wine such as table wine (almost 50% of all imports is IGP or table wine, a bit under 30% quality wine and the rest sparkling wine) and exports mostly quality wine (70% of all exports is quality wine, 24% IGP or table wine and the rest sparkling wine) (European Commission (2008b), Ministry of agriculture, forestry and food (2009c), Ministry of agriculture, forestry and food (2009a)). Sparkling wine is much more important as an import than as an export. Amongst the still wines, red wine dominates the imports and white wine is more important for exports. This trade pattern is consistent with climatic conditions in Slovenia which are less favorable for growing red varieties of wine.
3 Theoretical framework and existing research
The relationship between quality and prices has received a lot of attention in international trade theory as recently available highly disaggregated firm-product-destination datasets al- low the analysis of different aspects of firm heterogeneity at a finer resolution. Confronted with actual data on firm prices and productivity, theoretical models building on the workhorse model of Melitz (2003) and CES preferences failed to explain quality- and price-related em- pirical regularities in international trade flows. Namely, Melitz (2003) predicts that firms self-select into exporting based on their productivity level and that more distant markets de- mand higher productivity and lower prices. The model therefore predicts lower export prices on smaller and more distant markets, which is contrary to recent empirical findings described below. Bernard et al. (2006) report that capital and skill abundant countries produce vertically superior varieties and set higher prices. Bernard et al. (2007) examine three margins of US exports (number of exporting firms, number of exported products, and exported product unit value) and find that prices increase with distance. Their argumentation is that if cost of exporting depends on quantity and weight, rather than export value, then distance may influ- ence the quality composition of goods. Hallak (2006) shows that quality of products produced and consumed varies systematically with country income level. Hummels and Klenow (2005) observe that in developed countries a considerable portion of export growth occurs through quality upgrades. Baldwin and Harrigan (2007) analyze US product-level trade data in order to study the relationship between export unit values and distance and find a strong positive association. Hallak and Sivadasan (2008) provide evidence for higher quality and prices of goods sold abroad relative to those produced for the domestic market in Chile, Colombia, and India. They also report that firm level unit prices increase with firm size. Manova and Zhang (2009) use firm-product-destination data for China and find that firms charge higher prices in more distant markets and that larger number of firms export to larger and closer export
5In the tables, AOP or Appellation d’Origine Prot´eg´ee denotes the highest quality level on the French-based 4-level quality scale, and IGP or Indication Geographique Proteg´ee for the second-lowest quality level, the last level being VDT or Vin de Table or table wine.
4 Table 1: Intra and extra EU wine imports: Unit Value in EUR/litre
1999 2000 2001 2002 2003 2004 2005 2006 2007 Average Average % of total % of total 1999-2007 2004-2007 value (EUR) quantity (l) All wines 0.58 0.56 0.66 0.91 0.75 0.72 0.98 0.95 1.08 0.80 0.93 100% 100% Champagnes& 1.83 2.38 2.27 2.69 2.79 3.06 2.13 3.58 3.50 2.69 3.07 25% 8% Sparkling -Champagne: ------13% - -Sparkling: 1.26 1.68 1.55 1.66 1.71 1.84 1.34 1.87 1.73 1.63 1.70 12% 8% Still Wines 0.49 0.51 0.60 0.79 0.67 0.62 0.85 0.80 0.94 0.70 0.80 75% 90% -Quality- 1.53 1.86 1.40 1.45 1.60 0.83 3.66 2.48 1.56 1.82 2.13 29% 20% AOP: -IGP,Others: 0.42 0.45 0.49 0.57 0.53 0.55 0.71 0.66 0.81 0.58 0.68 46% 71% -White: 0.37 0.42 0.41 1.73 0.54 1.22 0.90 0.77 0.84 0.80 0.93 24% 33% -Red: 0.59 0.66 0.69 0.72 0.68 0.58 0.83 0.84 0.98 0.73 0.81 49% 55% Source: EU Commision (2008b) 5
Table 2: Intra and extra EU wine exports: Unit Value in EUR/litre
1999 2000 2001 2002 2003 2004 2005 2006 2007 Average Average % of total % of total 1999-2007 2004-2007 value (EUR) quantity (l) All wines 0.62 1.11 0.76 0.75 1.36 1.30 1.46 1.36 1.70 1.16 1.46 100% 100% Champagnes& - - - - 3.55 2.95 2.15 7.29 7.93 4.77 5.08 8% 0% Sparkling -Champagne: ------0% 0% -Sparkling: - - - - 3.54 2.85 2.03 5.66 6.73 4.16 4.32 8% 0% Still wines 0.60 1.08 0.75 0.74 1.32 1.26 1.42 1.25 1.55 1.11 1.37 94% 100% -Quality- 0.63 1.08 0.73 0.73 1.35 1.30 1.38 1.21 1.56 1.11 1.36 70% 71% AOP: -IGP,Others: 0.29 1.23 1.41 - 0.69 1.11 1.57 2.72 1.55 1.32 1.74 24% 29% -White: 0.50 0.97 0.62 0.62 1.26 1.19 1.27 1.02 1.35 0.98 1.21 54% 71% -Red: 1.04 1.26 1.66 1.32 1.36 1.46 1.56 2.37 2.18 1.58 1.89 39% 29% Source: EU Commision (2008b) markets. In contrast to G¨orget al. (2010) who study export unit values using Hungarian firm-product-destination data, Manova and Zhang find that firms ask higher prices in larger markets. G¨orget al. (2010) also show that unit values are positively related to GDP p.c. and that there is a weak negative relationship between unit values and market size, which is in line with the empirical results in this study. More in line with our subject of study, Crozet et al. (2009) find that high quality wine producers export to more markets, charge higher prices and exhibit higher revenues in each market. Our theoretical model builds upon Antoniades (2008) but makes several extensions that provide a larger set of price determinants, yet makes the open economy version of the model analytically intractable. This impediment, however, does not reduce the applicability of the model in any way since we are able to derive structural expressions for optimal wine prices. With these in hand, we test the theoretical predictions of price determinants on a sample of wines on Slovenian wine market. The model is based on Melitz and Ottaviano (2008) model of heterogeneous firms in a monopolistically competitive industry that face linear demand functions. Such a demand structure allows for endogeneous markups and responsiveness of markups and prices on the size of the market through competition effect. Unlike MO, our model allows firms to make deliberate investment in product quality which induces additional costs but is rewarded from consumers with higher demand. In contrast to standard heterogeneous models (e.g. Melitz (2003) and Melitz and Ottaviano (2008)) our model allows prices to increase with firm productivity (lower marginal costs), because better firms that produce goods of higher quality can also charge higher markups. In the following sections we first present the closed economy version of the model, followed by two country version that allows trade in differentiated goods between the countries. Finally, we present structural equations for optimum prices on domestic market that allow us to identify and study the marginal impact of model parameters on price levels.
3.1 International trade model with heterogenous wineries 3.1.1 Preferences and demand All consumers share the same preferences over a homogeneous good and a continuum of differentiated varieties of wines indexed by i ∈ Ω:
2 Z Z Z Z Z Z c c 1 c 2 1 2 c 1 c 1 U = x + α x di + α qi di − γ (x ) di − γ (qi) di + γ (x qi) di − η (x − qi) di (1) 0 i 2 i 2 i 2 i 2 i∈Ω i∈Ω i∈Ω i∈Ω i∈Ω i∈Ω
c c where x0 and xi represent the individual consumption levels of the numeraire good and each wine variety i, respectively. The demand parameters α and η index the substitution between each variety and the homogeneous good. The parameter γ captures the degree of product differentiation between the varieties of wine. All demand parameters are assumed to be positive. The quality for each variety is represented by qi. If none of the wine producers choose quality upgrade (qi = 0, ∀i) the utility function collapses to that in Melitz and Ottaviano (2008). Setting the price of the numeraire good equal to 1 (p0 = 1), utility function (1) then c yields the following inverse demand for each wine variety i with positive demand (xi > 0):
c pi = α − γxi + γqi − ηX (2)
6 R c 1 ∗ 6 where X = (xi − qi) di. Letting Ω ⊂ Ω be the subset of varieties consumed , Equation i∈Ω 2 (2) can be inverted to yield the linear market demand system for these varieties:
αL L ηNL 1 ηNL x ≡ Lxc = − p + Lq + p − q (3) i i ηN + γ γ i i γ(ηN + γ) 2 ηN + γ where L is the number of consumers (size of the country), N is the measure of consumed ∗ 1 R 1 R wine varieties in Ω , p = N i∈Ω∗ pi di is average price and q = N i∈Ω∗ qi di is average quality level. The demand for wine i is linear in its own price and quality but also depends on aggregate quantities. Lower price or higher quality induce higher demand. At a given price and quality, higher average wine price makes the particular variety relatively cheaper, so consumers buy more of it. Similarly, higher average wine quality reduces relative quality appeal of the individual wine and reduces the demand for it. Like in Melitz and Ottaviano (2008) the price elasticity of demand, i ≡ |(∂xi/∂pi)(pi/xi)| = −1 [(pmax/pi)−1] , is not determined solely by the level of product differentiation γ but depends also onp ¯ and N. Lower average wine price and higher number of competitors decrease the price bound pmax and hence increase the toughness of competition (higher i).
3.1.2 Production Wineries are heterogeneous in productivity and employ labour that is inelastically supplied in a competitive market. Let the numeraire good be produced under constant returns to scale at unit cost and sold in a competitive market. These assumptions imply a unit wage. Prior to entry wineries incur fixed start-up costs, fE, to cover the cost of land purchase, wine making equipment, grapevines and similar production start-up costs. Subsequent production exhibits constant returns to scale at marginal cost Cjc units of labour, where Cj is country-specific cost parameter and c is firm-specific marginal cost.7 Only after making the irreversible investment fE, each winery discovers its true cost level. This is modelled as a draw from a common and known distribution G(c) with support on [0, cm]. Wineries with productivity high enough to cover marginal cost survive and produce, others exit the market. Surviving firms maximize their profits using the residual demand function (3), taking the number of producers on the market (N), average price (p), and average wine quality (q) as given. The decision entails the choice of optimal price and optimal wine quality. The cost function of a surviving winery in country j comprises of production cost and quality upgrade cost:
j j 2 TCij = C cixi + φ qi (4)
Cj is country-specific component of the firm’s cost that reflects general conditions for wine production such as climate, soil, type of terrain and labour cost. φj is a marginal quality cost shifter in country j and represents the cost of undertaking wine quality upgrades. Depending on country-specific factors, such as physical conditions, technology and marketing cost, quality level qi can thus be achieved at different cost across countries. Furthermore, wineries need to
6 ∗ 1 1 The set Ω is the largest subset of Ω that satisfies pi − γqi ≤ ηN+γ γα + ηNp − 2 ηNγq ≡ pmax, where price bound pmax represents the price at which the demand for a variety is driven down to 0. 7Bounded marginal utility in our model makes sure that high-cost wineries will not survive even in the absence of overhead production costs that would significantly reduce the tractability of the model.
7 pay increasing cost for further quality increases as this requires ever more sophisticated and expensive production technology and techniques. By backward induction, a winery first sets price that maximizes its profits at a given level of quality upgrade. In the next step the firm determines the optimal level of wine quality upgrade. The profit maximizing price p(c) and output level x(c) of a winery with cost c and L j quality level q must satisfy x(c) = γ p(c) − C c . Using the price bound pmax from the above, we can deduce the break-even cost cD at which a winery is indifferent about remaining in the industry. This producer earns zero profit since the optimum price implied by cD is set at pmax and therefore driven down to cD. All wineries with cost cD < c < cM exit the industry while producers with c < cD earn positive profits (gross of the entry cost) and remain active. Let r(c) = p(c)x(c), π(c) = r(c) − x(c)c, µ(c) = p(c) − c denote the revenue, profit, and nominal mark-up of a winery with cost c. Optimum price, output and these performance measures can then be expressed as a function of c, cD, and z.
1 γ p(c, q) = (c + Cjc) + q (5a) 2 D 2 1 γ µ(c, q) = (c − Cjc) + q (5b) 2 D 2 L L x(c, q) = (c − Cjc) + q (5c) 2γ D 2 L L Lγ r(c, q) = (c2 − (Cj)2c2) + qc + q2 (5d) 4γ D 2 D 4 L L Lγ π(c, q) = (c − Cjc)2 + q(c − Cjc) + − φj q2 (5e) 4γ D 2 D 4
In the second stage, the winery determines the level of wine quality that maximizes profit. Differentiating (5e) with respect to quality level q yields the optimum level of wine quality upgrade:
∗ j q = λ(cD − C c) (6) where λ = L/(4φj − Lγ) and we assume 4φj > Lγ to allow only positive quality upgrades. j More productive wineries (lower C c) or wineries further away from the cost threshold cD invest more in wine quality. Furthermore, larger countries (larger L), countries with more favorable wine quality conditions (lower φj)and more differentiated wine markets (higher γ) warrant higher positive quality upgrade. The performance measures can now be rewritten using only c and cD:
8 1 + γλ 1 − γλ p(c, q) = c + Cjc (7a) 2 D 2 1 + γλ µ(c, q) = (c − Cjc) (7b) 2 D L(1 + γλ) x(c, q) = (c − Cjc) (7c) 2γ D j j L(1 + γλ) (1 + γλ)cD + (1 − γλ)C c (cD − C c) r(c, q) = (7d) 4γ L(1 + γλ)(c − Cjc)2 π(c, q) = D (7e) 4γ An interesting feature of the model is that it allows prices to increase with higher produc- tivity. More productive wineries choose higher wine quality and hence charge higher prices. A number of empirical studies using firm-level data on productivity and prices have recently confirmed these predictions. Iacovone and Javorcik (2008) find that Mexican exporters charge higher prices in the domestic market than non-exporting firms. Using data from Colombia, Kugler and Verhoogen (2008) show that input prices (indicative of higher product quality) and output prices increase with firm size and export status. The study by Hallak and Sivadasan (2008) on fims from the U.S. and India find that larger firms within the same industry charge higher unit prices and that exporters set prices higher than non-exporters. In models with- out product quality heterogeneity across firms (e.g. Melitz (2003)) more productive firms set lower prices, which contradicts empirical findings. Even in Melitz and Ottaviano (2008) where mark-ups are endogenous and rise with productivity, unit prices still fall. In this model, more productive wineries have even higher mark-ups8 which may more than neutralize the effect of a lower marginal cost. From (7a) can be deduced that wine prices increase with productivity if L > 2φj/γ. If country is large enough, country-specific quality upgrading cost not too high, and consumers value variety the wineries with higher productivity set higher prices alongside with higher wine quality levels and mark-ups. We can define the quality-adjusted price for wine variety i and express it in terms of the parameters of the model:
j j j pi 2φ (4φ − γL) C c p˜i ≡ = + j (8) qi L L (cD − C c) Better price-quality ratio is available in larger countries and countries with lower cost of upgrading wine quality. Higher degree of product differentiation between the varieties of wine encourage quality upgrading, loweringp ˜. Lower country-specific production costs also lower quality adjusted price through lower unit value prices as well as through higher investment in quality upgrading. Finally, higher break-even cost, reflecting tougher competition, also brings about lower price-quality ratio.
3.1.3 Free entry equilibrium
R cD The expected profit of a potential entrant is 0 π(c)dG(c) − fE. New wineries enter the industry as long as the expected profit is non-negative. Using (7e), this yields the equilibrium
8 1+γλ j 1 j Compare µ(c, q) = 2 (µ(c) = cD −C c) in our setting with 2 (cD −C c) in Melitz and Ottaviano (2008).
9 free-entry condition
Z cD Z cD L(1 + γλ) j 2 π(c)dG(c) = (cD − C c) dG(c) = fE (9) 0 4γ 0 which determines the cost cut-off cD. Since cD = p(cD) is also equal to pmax,we have:
1 1 c = γα + ηNp − ηNγq D ηN + γ 2 which yields the zero cut-off profit condition
2γ α − cD N = j (10) η cD − C c¯ R cD 9 wherec ¯ = 0 cdG(c) /G(cD) is the average cost of surviving wineries. According to free entry condition (9), the cost cut-off cD will be lower and hence average productivity of active wineries higher (lowerc ¯) when sunk costs (fE) are lower, when wine varieties are closer substitutes (lower γ), in countries with more favorable conditions for wine quality upgrading (lower φ), and in larger markets (more consumers L). Competition is tougher in larger markets in which more wineries compete and push down average prices j p¯ = (cD + C c¯)/2. A winery with production conditions c responds to tougher competition by setting lower mark-ups (see (7b)).
3.1.4 Open economy We now examine the impact of trade in a world composed of two countries, H and F , with LH and LF consumers (workers) in each country. If cross-border trade were costless, integration between the countries could be studied as an increase of market size. Number of wine producers in each country would increase, whereas average prices and mark-ups would fall. In reality, wine exporters incur considerable trade costs: per-unit costs (such as transport costs and tariffs) and also some fixed costs that do not vary with export volume.10 Because trade is costly, integration creates changes that must be studied in a two-country general equilibrium setting. Consumers in both countries share the same preferences. However, controlling for price and quality they prefer domestic over foreign wine. We model this by imposing the following inequality on the demand shifter α: αD > αF . The preference towards domestic wines increases the upper cost bound for wine exporters from foreign country to domestic market and works independently of trade costs. Wineries can produce in one country and sell in the other, incurring a per-unit trade cost. They are modeled in the standard iceberg formulation: the delivered cost of a unit with cost c to country j (j=H, F ) is τ jc where τ j > 1. We allow countries to differ in four dimensions: market size Lj, trade costs τ jc, country-specific production costs Cj and country-specific wine quality upgrading cost φj.
9 We used the definition ofp ¯ and the equation (7a) to obtain the expression for average price:p ¯ = (cD + Cj c¯)/2. Similarly, we inserted the expression (6) into the definition ofq ¯ to specify average quality level: j q¯ = λ(cD − C c¯). 10To make our model as tractable as possible, we refrain from modeling any fixed export costs. Due to bounded marginal utility, marginal costs alone are enough to create selection of more productive wineries into exporting.
10 Wineries that find it profitable to export set different prices at home and abroad. However, they do not differentiate in wine quality across markets. Since wineries produce under constant returns to scale, each can independently maximize profits on both markets. The operating profit net of fixed cost and wine quality upgrade cost from domestic and export sales is given j j j j j j k j j by πD(c, q) = (pD(c, q) − C c)xD(c, q) and πX (c, q) = (pX (c, q) − τ C c)xX (c, q), respectively. As before, profit maximizing prices and quantities set in each market must satisfy the following j Lj j j j Lk j k j conditions: xD(c, q) = γ (pD(c, q) − C c) and xX (c, q) = γ (pX (c, q) − τ C c), where k 6= j. Wineries remain present only in the market (domestic or foreign) where they earn non-negative profits. This condition determines the cut-off cost for firms from country j selling in domestic, j j cD, and foreign (k) market, cX :
j j j cD = sup{c : πD(c) > 0} = pmax (11a) pk cj = sup{c : πj (c) > 0} = max (11b) X X τ k Foreign wine producers find it more difficult to break even relative to domestic wineries k j j since the cut-off conditions above imply cX = cD/τ . Optimal prices and quantities can now be expressed in terms of the cut-offs:
1 γ pj (c, q) = (cj + Cjc) + q (12a) D 2 D 2 τ k γ pj (c, q) = (cj + Cjc) + q (12b) X 2 X 2 Lj Lj xj (c, q) = (cj − Cjc) + q (12c) D 2γ D 2 Lk Lk xj (c, q) = τ k(cj − Cjc) + q (12d) X 2γ X 2 which yield the following operating profit levels in each market:
Lj Lj γLj πj (c, q) = (cj − Cjc)2 + q(cj − Cjc) + q2 (13a) D 4γ D 2 D 4 Lk Lk γLk πj (c, q) = (τ k)2(cj − Cjc)2 + τ kq(cj − Cjc) + q2 (13b) X 4γ X 2 X 4
j j j k k We will assume that cX < cD (or, equivalently, that pmax > pmax/τ ) in each country so that the cost threshold for exporting wine is to the left of the upper bound cost for wineries selling in their domestic market. No winery therefore finds it optimal to produce at home and only sell abroad. Knowing their productivity levels, wineries now choose optimal level of wine quality upgrade that maximizes the combined profits from both markets net of the cost j j j j 2 of quality upgrade, π (c, q) = πD(c, q) + max{πX (c, q), 0} − φ q . j j Country j’s winery with cost level cX < c < cD sells its wine only on domestic market, so its optimal level of wine quality must satisfy
∗ j j qA(c) = λA(cD − C c) (14)
11 j j j where λA = L /(4φ − γL ). Total profits for domestic non-exporting wineries can now be written as Lj πj (c) = (1 + γλ )(cj − Cjc)2 (15) A 4γ A D Better wineries also export part of their produce. Optimal level of quality upgrade in an j open economy for a country j’s winery with cost level c < cX is given by
∗ j j k j j qEX (c) = λD(cD − C c) + λX τ (cX − C c) (16) j j k j j k where λD = L /(4φ − γL), λX = L /(4φ − γL), and L = L + L . Using (16), exporting wineries’ total profits from domestic and foreign sales minus quality upgrading cost can be expressed as follows:
Lj Lk Ljλ πj (c) = (1+γλ )(cj −Cjc)2 + (τ k)2(1+γλ )(cj −Cjc)2 + X (cj −Cjc)(cj −Cjc) EX 4γ D D 4γ X X 2 D X (17) Comparing (14) and (16) reveals that exporters are able to invest more in wine quality upgrading. Part of the investment increase (second term on the right-hand side of equation (16)) comes from the export-driven expansion of revenues, allowing for higher cost of quality upgrade to be covered by larger sales. The second part (stemming from the fact that λD > λA) arises because higher quality of wine further increases sales in domestic market, creating resources for additional quality improvements. Free entry of wineries in country j implies zero expected profits in equilibrium, so that
j j Z cX Z cD πEX (c)dG(c) + πA(c)dG(c) = fE (18) j 0 cX where we assumed that both countries face identical entry sunk cost fE and cost distribution G(c). Combining (18) with its equivalent for country j and expressing export cut-off conditions k j j j in terms of partner country’s non-exporting productivity cut-offs (cX = cD/τ and cX = k k j k cD/τ ) yields a system of two equations from which the cost thresholds cD and cD can be deduced. These thresholds determine the rest of the variables in the model, such as number of varieties, average cost, average price, and average wine quality.
3.1.5 Optimal prices on domestic market Since the open economy model above cannot be solved analytically even with a simple productivity distribution like Pareto, our approach in the following subsection is to express prices with a combination of parameters and endogenous variables. Instead of providing structural equations for optimal prices, each of the model’s parameters will be analyzed in terms of its impact on equilibrium prices set on domestic wine market. Combining the expression for optimal prices on domestic market (12a) and optimal level of wine quality for domestic non-exporters (14) and domestic exporters (16) we obtain home H H market wine prices for domestic non-exporters, pA (c), and exporters, pD (c). Similarly, by combining (12) and (16) we can derive optimal prices for foreign (F ) wineries exporting wine
12 F to domestic (H) market, pX (c). We can write optimal prices for all three types of wineries present on domestic market as follows: 1 pH (c) = cH + CH c + γλ (cH − CH c) (19a) A 2 D A D 1 pH (c) = cH + CH c + γλ (cH − CH c) + γλ τ F (cH − CH c) (19b) D 2 D D D X X 1 pF (c) = cH + τ H CF c + γλF (cH − τ H CF c) + γλF (τ F cH − CH c) , (19c) X 2 D D D X X
F F F F H F H where λD = L /(4φ − γL), λX = L /(4φ − γL) and τ are trade costs for shipping wine from country F to country H. Comparing (19a) with (19b) reveals that domestic wineries that export part of their produce set higher prices than domestically bound wineries because export revenues allow wineries to invest more in wine quality upgrading and set higher H H markups. It should be noticed, however, that through cost cut-offs cD and cX , even domestic producers’ prices depend on foreign market characteristics. A larger foreign country with more favorable wine growing conditions increases competition on domestic market through import penetration that effectively shifts the threshold costs. Foreign wine prices, in turn, depend on conditions in both countries indirectly (as was the case for home producers) as well as directly. As noted above, our model allows more productive wineries (lower c) to charge higher prices if certain conditions about parameter values are met (2φ/γ < L < 4φ/γ in a closed economy). Alternatively, productivity may as well be inversely associated with the price level as in the models without vertical differentiation. The sign of the relationship is hence an empirical question, but unfortunately lack of information on productivity in the data does not allow its identification. The same ambiguity arises in case of country-specific costs (CH and CF ) since they lower production costs on one hand, but may induce wineries to charge higher markups for upgraded wine quality on the other hand. More intense competition due to higher substitutability between wine varieties (lower γ) decreases prices, while more pronounced demand for wine in general (higher demand shifter α) inflates prices through higher cost cut-offs. Stronger preference for country j’s wine therefore increases their wine prices. Next, higher cost of quality upgrading (higher φ) shifts total production costs for all three types of wine producers on home market directly through higher investment cost, yet at the same time it lowers threshold productivity level (higher cD), so the net effect is ambiguous in the general notation of optimum prices in (19). Higher fixed entry cost (fE) warrants lower share of entering firms to break even, effectively increasing the cost cut-off cD and hence market prices. In our model, larger markets (large L) increase market competition and shift productivity thresholds upwards (lower cD and cX ), leading to lower market prices. Competition is tougher in larger markets since more firms compete and average prices are lower. Wineries respond to tougher competition by setting a lower markup (see Equation 7b). Finally, transport costs (τ) increase prices directly through more expensive imports and indirectly through higher cost cut-offs. The determinants and their expected marginal effect on the optimal prices can be condensely reported as follows:
H F p = f(c,C ,C , γ, α, φ, fE,L, τ ) (20) ± ± ± + + ± + − +
13 3.2 Hedonic wine price function and existing research The hedonic approach in economics, as first used in Rosen (1974), is common especially in housing and environmental valuation. It is a way to determine the market value of goods otherwise not sold on the market. Specifically, the hedonic approach relates the price of a product to its determining characteristics, and thus effectively tries to determine what is the market price of product characteristics embedded in the product (the characteristics themselves usually not being on the market on their own). An environmental application is determining the price of clean air or noise-free environment through the price analysis of the housing market, where both environmental factors influence the price. Hedonic analysis is especially appropriate for heterogenous goods which can be considered to be aggregates of some particular characteristics of interest. A hedonic price function thus takes into account both demand and supply factors on the market, as it models the price of the product in equilibrium. Wine as an exemplary heterogenous good with defining characteristics is a good example of a good well placed for hedonic analysis, as shown by an increasing body of literature on hedonic wine price functions discussed below. The market price of wine in equilibrium or the hedonic wine price function can be written as being determined by a set of characteristics z = z1, ..., zj, ..., zJ : p = f(z). (21) It follows that unbiased estimates of such a regression yield for each characteristic the marginal willingness to pay for a unit increase of the characteristic as follows from Equation (22). Since the estimation is in equilibrium this partial can be considered to be the estimated unit price of the untradeable characteristic. In the case of a multiple linear regression, this is simply the partial regression coefficient as in Equation (23).
∂p ∂f(z) = (22) ∂zj ∂zj
∂f(z) = βj (23) ∂zj There exist a wide body of literature applying the hedonic price function to wine, amongst them being Steiner (2004) examining French wines on the British market, Jones and Storch- mann (2001) are looking at several Bordeaux wines in the Bordeaux region in France, Oczkowski (1994) looks at the Australian wine market using data from consumer wine guides (as we do in this paper), Cardebat and Figuet (2004) examines Bordeaux wine prices for the 1996–1999 vintages, while Cardebat and Figuet (2009) examines Alsace, Beaujolais and Provence wine prices. When reviewing the literature, three general questions with hedonic wine price function estimation arise. Firstly, what are the relevant factors influencing wine prices, secondly, how to determine and what are the appropriate market segments for estimation, and thirdly, what is the correct functional form. The first two questions are discussed below while the third one is addressed in the next section. Existing work examining hedonic wine price functions has emphasized various wine char- acteristics, broadly falling into either sensorial (such as aroma or color) or objective (such as label,vintage, region, winery, etc.) category. Combris et al. (1997, 2000) examine what
14 determines the Bordeaux and Burgondy wine prices on the market and conclude that the ob- jective wine characteristics are main price determinants, explaining this with the fact that ”it is expensive to obtain information about the sensory characteristics (only available through tasting, learning, and reading wine guides)” (Combris et al., 1997), while objective charac- teristics are readily available on the bottle label. Even more so, wine is an experience good, the sensory traits only being obvious after consumption, which in itself implies that objective characteristics should be the defining factors of the price. Lecocqa and Visserb (2006) find the same importance of objective characteristics, although their finding is contested by Thrane (2009). Additionally to objective characteristics, wine prices should depend on the quality of the wine, for which the expert grades in commercial wine guides act as a proxy, as discussed and estimated among others by Angulo et al. (2000) and Schamel and Anderson (2003). The significance of expert grades can be related to relative unimportance of sensory characteristics in hedonic regressions, since expert ratings serve as a signal to consumers, which can not ex ante experience the wine, and are thus a substitute for the sensory characteristics. Although in comparison, Ali and Nauges (2007) find that current quality is not of central importance comparing to reputation, when examining the market for wine still in barrels in the Bordeaux region. Although some collective regional reputation factors are examined in Schamel and Ander- son (2003) and Angulo et al. (2000), a more specific strand of literature relies especially on reputation effects in the hedonic wine price equation (Landon and Smith, 1997, 1998), using argumentation of imperfect information. Since more often than not the customers do not have information about a particular variety of wine readily available, they have to depend on the reputation of wineries, wine regions or wine countries, which are the same brand effects that are the topic of this paper and are implied by our theoretical model of monopolistic competition. Benfratello et al. (2009) segment the characteristics of wine in various hedonic wine price function application in four groups: objective, sensorial, reputation, and quality. They find that a ”...model including objective and reputation variables outperforms, on statisti- cal grounds, a model with objective and sensorial characteristics.”. In sum, the evidence on hedonic wine price equation is largely consistent on the importance of objective, quality (as proxied by expert grade) and reputation characteristics, and less on agreement when it comes to sensory characteristics11. A question arising in the studies with data on more than a couple of wine varieties is whether the market should be examined as a whole or segment by segment, making a different hedonic price model for each market segment such as for white wines, red wines, sparkling and fortified wines or even more disaggregated. Thrane (2004) emphasizes that different hedonic price functions must be used when explaining the price of white and red wine, since the impacts of wine characteristics on price may be different between red and white wine. Not only variety can determine a separate market, but also the price range, as argued by Costanigro et al. (2007). They argue for wine heterogeneity not based on varieties but on price ranges, as the consumer can firstly decide on a price range and than search for a specific wine variety within the chosen range. The authors statistically determine 3 structural breaks
11Although it must be noted that when examining the relevant factors for quality of wine as proxied by expert grade, the sensory characteristics are key determinants of quality (Combris et al., 1997)
15 in the data using the criterion of goodness of fit, minimizing sum of squared residuals across the four segments, where a separate hedonic price function is estimated for each segment. Due to the idiosyncratic demand side factors influencing different wine market segments differently, it is recommendable to choose the wine market segments for hedonic price function estimation based on the empirical evidence and move from less restrictive specifications to more general ones.
4 Empirical analysis and discussion
Hedonic wine price function is estimated with a multiple regression, where the dependent variable is some transformation of the price and the explanatory variables are wine character- istics. Hedonic price functions per se do not offer any guidance for the functional form, so the final functional form is either motivated from theory other than hedonic models and should be determined according to an empirical criterion of best fit. Since common problems in the hedonic wine price function estimation include multicollinearity and heteroskedasticity of the errors, because of the explanatory variables being largely binary, functional form should if possible be such that it avoids these problems. Costanigro et al. (2007) determine the functional form by estimating a series of possible transformations of the dependent variable, looking at the variance stabilization, normality of the residuals and misspecification properties of each. They use the inverse of square root of the price as the best transformation, similarly as Landon and Smith (1997). Jones and Storchmann (2001) use the untransformed price as the dependent variable, while most of the studies such as Oczkowski (1994), Combris et al. (1997),Cardebat and Figuet (2004), Ali and Nauges (2007), Combris et al. (2000), Cardebat and Figuet (2009) and others use the log linear form of the hedonic wince price function. Our data implies that there is an exponential relationship between the price and the expert grade, which together with the fact that the change of the natural logarithm of the price is practically identical to percentage change in price for small changes and has thus clear implications for interpretation, warrants for the use of the log linear form of the hedonic wine price function.
4.1 Summary statistics Our paper combines two main sources of data, wine-level information from Slovenian Wine Guide 2010 and various country-level information from different data sources. Wine Guide was released in 2010 and is written by TomaˇzSrˇsen,one of the leading Slovenian sommeliers and gastronomes, and Bruno Gaberˇsek,secretary of Slovenian Association of Family Wine- growers. The booklet provides information of around 1000 wines that were tasted during 2009 and that are as widely accessible to an average consumer as possible. To this end, the sample includes wines that are available in major specialized wine shops, supermarkets, web shops and wine importers. As is common in wine guidebooks, the sample excludes low-quality wines and therefore does not cover the whole wine market. It includes white wines, red wines, sparkling wines, and fortified wines from 18 countries that were tasted blindly in order to objectively assess the quality. The database which was kindly provided to us by the authors includes information on the cellar/wine-maker, name or sort of wine, vintage, country of origin, grade on a scale from 0 to 100, price in euros, written description of a wine, retail seller, and ageing
16 potential. In the latter category, wines were classified into one or more of the three classes: drink soon, store, and age. There are several difficulties with using guidebook ratings as quality measures. First, the ratings are subjective and may be unreliable in the sense that the authors may have idiosyn- cratic tastes or are influenced by non-taste considerations such as the environment in which wines are tasted. Second, the ratings may influence demand and hence prices by increasing customer awareness. Even though the prices collected preceded the publication date, the wine experts’ previous writings about the same wine-maker may have had a distorting advertising effect. Third, ratings are hard to interpret. Albeit the marks are on an ordinal scale, it is hard to compare different grades. Fourth, there may be a sample selection bias present if the authors of guidebook favor some wineries or sellers over the others or simply exclude some due to lack of contacts. Country-level variables from the theoretical model complement wine-level determinants in our empirical analysis. Trade costs are proxied by as-the-crow-fly distance from Slovenia to the export country of origin. GDP per capita in PPP current US $ serves as an indicator for country-specific production costs and is obtained from World Development Indicators Online Database from World Bank. The same source is used for country population size that captures the size of a country. Table 3 provides basic summary statistics of the variables included in the empirical anal- ysis. In the first part of the table, wines are broken down by country of origin, followed by classification by basic wine types, and finally dissected into four price quartile groups. Of 925 wines in the sample, 62% are from Slovenia and 38% foreign, most of them coming from France, Italy, Spain, and Portugal. Prices range from less than 2 to 288 euros. Average price of foreign wines at e29 considerably exceeds average price of Slovenian wines (e12), which is in line with the theoretical model that predicts only better foreign wineries to export wine at higher quality and higher prices. Since we do not have the data on individual wine consump- tion, these non-weighted average prices cannot be directly compared to the average import prices at the aggregate level in section 2. Given the fact that our sample excludes low-quality wine segment and that wine consumption generally decreases with price and quality, sample average prices of foreign wines logically exceed the aggregate consumption-weighted figures. Apart from prices, the data also confirms theoretical predictions about the quality of wine. Foreign wine surpasses domestic one by 5 points in terms of quality grades and quality varies less than in the group of domestic wines. In terms of quality-adjusted prices, foreign wine prices surpass domestic wine prices by a ratio of 1.24/0.80, where sample average price-quality ratio is by definition equal to 1. Conditional on quality grade, foreign wines overprice domes- tic substitutes by e5-e10. Foreign wines on Slovenian market are on average more than a year older than domestic wines. When sorted according to the types, fortified and sparkling wines stand out with regards to prices, price-quality ratios and vintage. In addition, most of the red and sparkling wines come from countries with higher GDP per capita, which is due to the leadership of France and Italy in this area. Segregation of wines into quartiles reveals some interesting regularities, too. Average quality steadily increases with higher price groups whereas variability remains roughly constant across all the quartiles. Quality adjusted prices similarly increase as we move upscale, yet this time variability increases as well. Not controlling for other relevant characteristics, wines from higher price ranges come on average from larger countries, while GDP p.c. does not seem to be related to this type of categorization of wines.
17 Table 3: Summary statistics
N N wineries Price Grade Price/quality Age L GDP p.c. Country: Argentina 12 5 13.04 84.17 0.80 4.08 39.88 14.31 8.68 6.41 0.48 1.78 Australia 17 3 15.82 83.00 0.97 4.29 21.43 38.78 13.02 5.20 0.78 1.47 Bosnia and 3 1 5.80 83.33 0.40 4.00 3.77 8.09 Herzegovina 1.39 3.79 0.09 1.73 Chile 19 8 12.41 84.74 0.76 3.16 16.80 14.44 9.91 4.79 0.56 1.57 Croatia 6 5 23.32 85.50 1.49 4.00 4.43 17.66 10.90 3.51 0.62 1.10 Macedonia, 6 2 2.80 74.33 0.22 3.50 2.04 9.34 FYR 0.60 4.27 0.08 1.22 France 112 66 45.64 85.48 1.48 5.37 64.18 33.06 52.63 7.33 1.44 2.84 Hungary 1 1 51.75 92.00 3.37 10.00 10.04 19.79 .... Italy 86 38 25.64 85.13 1.47 4.97 59.83 31.28 20.61 5.60 1.20 1.68 Lebanon 2 1 21.75 86.00 1.29 7.50 4.19 11.78 11.67 7.07 0.60 2.12 Montenegro 4 2 7.20 78.00 0.49 3.50 0.62 13.39 3.80 6.06 0.24 0.58 New Zealand 9 2 11.93 81.67 0.82 3.36 4.27 27.26 4.23 4.50 0.30 0.89 Portugal 30 14 30.83 87.70 0.96 6.16 10.62 23.25 30.44 3.58 0.71 5.00 Serbia 2 2 19.40 78.00 1.49 4.22 7.35 10.55 0 0 0 0 South Africa 6 2 17.03 83.83 1.06 4.50 48.69 10.12 13.12 4.96 0.72 1.38 Spain 31 21 18.74 86.45 1.07 4.94 45.56 31.67 15.46 5.45 0.93 1.70 USA 2 1 11.20 80.00 0.72 5.50 304.06 46.35 4.67 2.83 0.28 0.71 Foreign 348 174 28.89 84.99 1.24 4.95 46.70 28.39 35.77 6.22 1.15 2.59 29.89 7.80 Slovenia 577 124 12.31 79.79 0.80 3.81 2.02 27.87 15.00 6.84 0.99 1.88 Type of wine: White 397 154 13.47 80.59 0.75 3.44 9.80 27.87 18.72 6.58 0.97 1.54 19.46 3.48 Red 373 196 15.84 82.56 0.86 4.60 23.27 27.65 15.77 7.04 0.78 2.10 32.81 6.06 Rose 15 15 9.55 81.00 0.54 2.76 6.79 27.3 6.54 4.09 0.35 0.85 11.97 5.94 Fortified 19 7 39.9 87.95 2.07 6.72 10.62 23.25 34.90 3.58 1.76 6.08 0 0 Sparkling 121 48 41.30 82.12 2.16 5.53 37.54 30.83 49.51 8.50 2.37 2.48 29.62 2.60 Price range: p≤e7.2 240 97 5.61 77.21 0.41 3.04 9.35 26.96 1.21 5.89 0.11 1.21 18.8 4.97 e7.2
2 lnPiwc = β0 + β1gradeiwc + β2gradeiwc + β3Lc + β4GDP pcc + β5distancec + β6Argentinac + ... + β23USA(24)c 2 lnPiwc = β0 + β1gradeiwc + β2gradeiwc + β3Lc + β4GDP pcc + β5distancec + (25) + β6ageiwc + β7age × Dageiwc + β8Dnovintageiwc + β9Dstoreiwc + β10Dageiwc +
+ β11Ddrink × Dstoreiwc + β12Dstore × Dageiwc + β13Ddrink × Dstore × Dageiwc +
+ β14Dwhiteiwc + β15Droseiwc + β16Dfortiwc + β17Dsparklingiwc + β18Argentinac + ... + β35USAc
Table 4 shows the results of the estimation of Equations (24) and (25), the latter of which is additionally estimated for each of the three wine variety market segments: white, red(+rose+fortified)12, and sparkling wine. In specification (6) and (7), wine market is dis- aggregated on domestic wines and imports. Country dummies are included in all regressions but not reported in the interest of space. Next, we follow Costanigro et al. (2007) and segment the wine market into four price categories. Using a Stata programmed loop, we searched for three structural price breaks by setting a grid over the entire range of wine prices and estimating 4 models, one for each segment, with each possible price breakpoints combination. The increments of price break- points changes were set to 1. The statistical criterion for structural price breaks estimation is minimizing the sum of squared residuals (SSR) across all 4 models. The resulting structural price breaks which minimize SSR for the basic model in Equation 24 are e9, e19 and e62. Similarly, for Equation 25 they are e8, e18 and e62. Estimating the entire hedonic price function as in 25 for each of the market segments yields comparable results to those in Table 4, if we compare the market segmentation based on the criterion of minimizing SSR. Table 5 shows the results of market segmentation according to price. We name the categories as Costanigro et al. (2007). Due to only 32 observations in the Ultrapremium market segment, we estimate an additional model where the Premium and Ultrapremium segments are combined. We test the hypothesis that the shared coefficients13 across the different segmentations are the the same between the segments and in comparison to the whole market, for all three
12Rose wines and fortified wines are estimates with red wines, since there are only 19 fortified wines and 15 rose wines in the database. 13In some regressions some dummies are missing, such as a dummy for red wines with the market segment of white wines and similar in other regression.
19 Table 4: Estimation results with wine market segmentation on variety and origin Adj. R2(3 − 5)=0.6726 Adj. R2(6-7)=0.6659 Basewhole Whole White RedRosePort Sparkling Home Foreign (1) (2) (3) (4) (5) (6) (7) grade -.440 -.256 -.251 -.300 .046 -.222 -.061 (.061)∗∗∗ (.021)∗∗∗ (.040)∗∗∗ (.054)∗∗∗ (.212) (.056)∗∗∗ (.092) grade-2 .003 .002 .002 .002 .00008 .002 .0007 (.0004)∗∗∗ (.0001)∗∗∗ (.0003)∗∗∗ (.0003)∗∗∗ (.001) (.0004)∗∗∗ (.0005) L -.002 -.002 .008 -.0003 -.013 -.0002 (.00003)∗∗∗ (.0001)∗∗∗ (.0003)∗∗∗ (.00005)∗∗∗ (.005)∗∗ (.0007) gdppc .033 .028 .002 .002 .288 .00002 (.0009)∗∗∗ (.001)∗∗∗ (.002) (.001) (.108)∗∗∗ (.005) distance .009 .016 .024 .010 -.204 -.005 (.0003)∗∗∗ (.0005)∗∗∗ (.002)∗∗∗ (.002)∗∗∗ (.063)∗∗∗ (.015) age .104 .129 .108 .035 .101 .103 (.006)∗∗∗ (.017)∗∗∗ (.017)∗∗∗ (.028) (.011)∗∗∗ (.016)∗∗∗ age×Dage -.046 -.088 -.051 .070 -.086 -.030 (.022)∗∗ (.044)∗∗ (.021)∗∗ (.032)∗∗ (.043)∗∗ (.028) Dnonvintage -.077 -.440 .017 -.141 -.221 -.016 (.097) (.169)∗∗∗ (.241) (.060)∗∗ (.100)∗∗ (.095) Dstore .185 .198 .172 .132 .181 .192 (.020)∗∗∗ (.015)∗∗∗ (.035)∗∗∗ (.090) (.040)∗∗∗ (.053)∗∗∗ Dage .881 1.155 .804 .314 .865 1.120 (.172)∗∗∗ (.064)∗∗∗ (.368)∗∗ (.364) (.257)∗∗∗ (.394)∗∗∗ Ddrink-store .091 .196 .059 -.011 .171 -.004 (.058) (.019)∗∗∗ (.035)∗ (.200) (.066)∗∗∗ (.056) Dstore-age .459 .759 .440 -.015 .683 .364 (.108)∗∗∗ (.161)∗∗∗ (.132)∗∗∗ (.406) (.201)∗∗∗ (.144)∗∗ Ddrink-store-age .799 1.209 .743 -.325 1.368 .442 (.297)∗∗∗ (.421)∗∗∗ (.055)∗∗∗ (.325) (.323)∗∗∗ (.202)∗∗ Dwhite .057 -.034 .234 (.081) (.036) (.147) Drose .145 .097 .017 .269 (.092) (.111) (.080) (.235) Dport .394 .392 .541 (.098)∗∗∗ (.185)∗∗ (.065)∗∗∗ Dsparkling .587 .493 .726 (.132)∗∗∗ (.080)∗∗∗ (.186)∗∗∗ N 925 925 397 407 121 577 348 R2 .509 .66 .549 .66 .78 .531 .685 RSS 279.872 193.896 78.183 69.651 24.083 93.038 89.156 Country cluster robust standard errors are in parentheses (in (6) only robust) Significance level: ∗∗∗ at 1%, ∗∗ at 5%, ∗ at 10%
20 Table 5: Estimation results with wine market segmentation on price Adj. R2(1-4)=0.9107 Adj. R2(1,2,5)=0.8525 Commercial Semipremium Premium Ultrapremium PremiumUltrapremium (1) (2) (3) (4) (5) grade .067 -.057 -.172 .475 -.321 (.116) (.024)∗∗ (.062)∗∗∗ (.064)∗∗∗ (.154)∗∗ grade-2 -.0004 .0004 .001 -.003 .002 (.0007) (.0002)∗∗ (.0004)∗∗∗ (.0004)∗∗∗ (.0009)∗∗ L -.0008 .0009 .005 .021 .006 (.00006)∗∗∗ (.0001)∗∗∗ (.0002)∗∗∗ (.004)∗∗∗ (.001)∗∗∗ gdppc .018 -.0008 -.008 -.177 .007 (.0001)∗∗∗ (.0006) (.003)∗∗ (.015)∗∗∗ (.005) distance .005 -.001 .007 .009 .002 (.002)∗∗∗ (.002) (.003)∗∗ (.040) (.006) age .033 .060 .035 -.032 .045 (.004)∗∗∗ (.003)∗∗∗ (.009)∗∗∗ (.005)∗∗∗ (.011)∗∗∗ age×Dage -.067 -.005 -.019 .001 -.003 (.027)∗∗ (.013) (.026) (.003) (.009) Dnonvintage -.284 -.044 .139 -.644 .062 (.018)∗∗∗ (.081) (.077)∗ (.033)∗∗∗ (.121) Dstore .072 .010 -.019 .167 -.032 (.015)∗∗∗ (.015) (.052) (.102) (.050) Dage .400 .206 .184 -.121 .417 (.151)∗∗∗ (.089)∗∗ (.156) (.058)∗∗ (.257) Ddrink-store .001 .049 .040 -.390 -.050 (.065) (.040) (.081) (.031)∗∗∗ (.083) Dstore-age .340 .115 .122 -.246 -.014 (.105)∗∗∗ (.035)∗∗∗ (.155) (.059)∗∗∗ (.043) Ddrink-store-age .073 .193 .084 .270 (.087) (.153) (.115) (.171) Dwhite -.030 .054 -.054 .185 .068 (.016)∗ (.021)∗∗ (.064) (.142) (.138) Drose .097 -.018 .111 .073 (.019)∗∗∗ (.024) (.054)∗∗ (.196) Dport .228 .083 .238 (.098)∗∗ (.046)∗ (.048)∗∗∗ Dsparkling .227 .164 .098 .475 .212 (.139) (.038)∗∗∗ (.128) (.064)∗∗∗ (.132) N 309 363 221 32 253 R2 .331 .351 .416 .63 .467 RSS 15.669 13.559 13.666 1.786 45.764 Country cluster robust standard errors are in parentheses Significance level: ∗∗∗ at 1%, ∗∗ at 5%, ∗ at 10%
21 (variety, origin, price) market segmentations. We compare the seemingly unrelated estimates of all segments, which is basically a Wald test for composite linear hypothesis (or a Chow test). The results in Appendix B show that for all three segmentations, none of the submodels has the same regression coefficients as any of the other submodels or the entire sample. Which segmentation is best must be determined according to an empirical criterion. The combined SSR in the four or three price market segments from Table 5 is 44,680 or 74,992, respectively. The combined SSR from market segmentation according to variety is 171,917 and the SSR from market segmentation according to origin is 182,194. Comparing all the possible market segmentations with the unsegmented market SSR of 193,896 implies that market segmentation is better than no market segmentation. Comparing the three possible segmentations, the lowest combined SSR is achieved when the market is segmented according to price, implying that the explanation of Costanigro et al. (2007) holds in the Slovenian wine market and that different prices do in fact mean different product segments in the eyes of consumers. Comparing the R2 of the complete model with the segmentation taken into account14 reveals the same ranking. Even more so, the model of market segmentation according to 4 price ranges yields an R2 of 91% (or 85% for 3 price ranges), well above the R2 of 67% for variety segmentation and origin base segmentation. Segmentation according to price, statistically speaking, trumps all other segmentations. Examining the wine price function coefficients from Table 4, regressions (1) and (2), im- plies that both the theoretical trade model and the hedonic model have support in the data. From the theoretical model of heterogenous wineries it follows that country-specific produc- tion cost, captured by GDP p.c., has the expected positive sign, market size as proxied by population size has a negative sign and quality and trade costs captured by distance both have the expected positive signs. In the light of the hedonic price function, we are interested in the previously mentioned groups of characteristics: objective characteristics (age, cellar- ing potential, their interaction, variety), quality (proxied by grade) and reputation effects, of which all are confirmed to be relevant for price determination. Looking at the broadest specification, Model (2), we find objective characteristics to be significant and of the expected signs. If the wine can be stored or aged, this is reflected in a higher price, although in contrast to expectations, the interaction effect of a wine that must be aged and its age is negative. There is also a price premium for fortified and sparkling wine, comparing to red wine, which is not surprising as both varieties are used for special occasions and can as such command a higher price on the market. The quality has a non ∂lnP linear U-shape relationship with price. The partial effect of quality on price ( ∂grade ) becomes positive for grade higher than 64. There are only 16 wines graded at 64 or less, so we can say that the quality of wine has generally a positive (and increasingly so) effect on the price. The U-shape is not neglectable though, there are wines for which it seems that making them a little better would be associated not with a higher, but a lower price. The explanation for this is twofold. Firstly, there is a lack of lower quality cheaper wines in the wine guide, making the price-quality relation non-linear and U-shaped, and secondly, due to an overlap of consumer tastes, the wine sold in largest quantity is the one of medium quality, which allows wine producers to lower the price for medium quality wine (compared to lower and higher quality), because of the benefits of scale economies for those varieties.
14The R2 is calculated stacking the segmented datasets in a single (block diagonal) design matrix and estimating the segmented model all at once, with a single regression constant.
22 Country-specific demand shifters can be interpreted as country reputation effects as some countries have a better reputation in the general public as wine countries. Examining the country dummies (with Slovenia as the base category) for the countries with 10 or more observations, we can see from Appendix 10 that the two countries with most observations, France and Italy, have a considerable price premium on Slovenian wines – 33% and 45%, respectively. We believe that such a price premium (controlling for quality and all the other factors) is consistent with country-specific reputation effects, for France and Italy do have somewhat of a reputation in the wine market. Spain has a slight price discount, while Chile has a healthy 15% premium. Interestingly, Australia, not having a particularly good street name for wines, is inflicted with an almost 30% price discount, compared to Slovenian wines. The results of the estimation on the entire sample in Table 4, regression (2), are further confirmed by a robustness check in Appendix 11, where we estimate the same model using winery-specific fixed effects. The comparison of coefficients across winery-specific effects or country-specific effects shows that the coefficients remain largely similar, keeping the same magnitudinous, signs and significance. Controlling for winery-specific effects does, however, change the estimation of the effect of wine being white or fortified, which become significantly negative, and the estimation of rose wines, which gain a slight, but significant price premium relative to red wines. Other objective characteristics as well as the quality confirm our original conclusions15, and in addition the explanatory power of the model is quite high at adjusted R2 = 0.7823. The estimation results in the segmented cases in both Table 4 and 5 are less clear and sometimes contradict the estimations of the whole model, but due to the fact that one of the goals of this paper is to determine which is the most suitable market segmentation as stated above, we discuss only the more interesting coefficients from the three segmented models. From the segmentation based on variety it follows that the quality does not have a significant effect on price for the segment of sparkling wines, which might be due to the fact that the champagne is already priced very high, because it is a special occasion wine. The average price of sparkling wines in the sample at e41 in comparison to the average price of the sample of e19 seems to confirm this notion. The segmentation based on origin does not offer any particulary new information, while the segmentation based on the price (for the 3 segment case) is more interesting. It shows that for the lowest segment of commercial wines, quality does not statistically significantly affect the price, but it is significant for all other segments. The coefficients on country size (L) and labour cost (GDP p.c.) in all segments but the commercial one are either not significant or do not have the expected sign, which casts legitimate doubts on the suitableness of L and GDPpc as market size and production costs proxies. It should be noted, however, that the segmentation based on price ranges is inappropriate for studying the effects of country size, production costs, and trade costs if these factors are correlated with prices in the first place. This is indeed the case, as our theoretical model predicted and several studies confirmed empirically. In other words, what we achieve with price-based market segmentation, was explained endogenously with the above mentioned country-specific variables in the complete sample. Next, trade costs as captured by the distance affect the price significantly only for the cheapest wines, which is understandable in the light that these costs present a larger share of total wine price for cheaper wines. Age
15Country reputation can not be included in the regression, because country dummies are perfectly collinear with producer dummies.
23 and cellaring potential both have the expected signs. As for variety, the price premium or discount seems to be haphazard, much dependent on the price range for the same variety.
5 Conclusion
Slovenian wine market has never been properly analyzed. This paper aims at bridging this gap, using a unique dataset of 925 both domestic and imported varieties of wine available on the market in Slovenia. A theoretical model of international trade is developed and combined with the hedonic price function, which is subsequently estimated. This paper tries do address three questions. Firstly, we try to find empirical support for the implications of the theoretical trade model of heterogenous wineries, namely that quality, market size, production costs and trade costs have an effect on the price of wines. Secondly, this paper aims at determining the relevant factors influencing wine prices in the tradition of hedonic wine price function estimations, namely within the groups of quality, objective and reputation characteristics. Thirdly, we determine what is the most appropriate market segmentation for wine. Using a log-linear functional form, we find support for the theoretical model of heteroge- nous wineries when the entire sample is estimated, while there are conflicting evidence when the sample is segmented. The data in the entire sample seems to support the implications of the theoretical model that costs of production in the country of origin affect prices positively, market size negatively, trade costs positively and wine quality positively. These results are in line with recent empirical findings that provide evidence for positive association between product prices and product quality on one hand and country income, distance, and market smallness on the other hand. Aggregated model supports the expanded hedonic price func- tion as well. We confirm the findings in the literature that quality is an important price determinant, as well as objective factors such as age and cellaring potential. Country-specific reputation effects are also found, with France and Italy, well known for their wine industry, having a considerable price premium, and the less known Australian wines exhibiting a price discount. However, the evidence is more unclear when we segment the market according to either wine variety, origin or price. This is somehow expected since the segmentations are based on the variables that themselves determine the wine prices and are correlated with other explanatory variables. Comparing the explanatory power of three possible market seg- mentation, we find that the most suitable market segmentation is the one based on price, which suggests that typical consumer compares alternatives within the chosen price range and values the same wine attributes differently across price categories.
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27 Appendices
A Variables
Table 6: Variables explained
Variable Description Source lnP Natural log of a 0.75l bottle of wine (or equivalent) Wine guide grade Expert grade Wine guide grade 2 Expert grade squared Wine guide L Number of inhabitants in a country in thousands for 2008 WB WDI GDPpc Gross domestic product per capita for 2008 in millions and WB WDI current prices distance As the crow flies distance between capital cities, in 1000 km http://www.chemical- ecology.net/java/capitals.htm age Age of the wine: 2010-Vintage Wine guide age×Dage Age of the wine times dummy variable if the wine can be aged Wine guide Dnovintage If vintage is not known, the average of the dataset is assigned Wine guide as the vintage and a dummy variable for those cases is added Dstore∗ Dummy variable, 1 if the wine can only be stored (1-3 years) Wine guide Dage Dummy variable, 1 if the wine can only be aged (3 years or Wine guide more) Ddrink store Dummy variable, 1 if the wine can be drunk or stored Wine guide Dstore age Dummy variable, 1 if the wine can be stored or aged Wine guide Ddrink store age Dummy variable, 1 if the wine can be drunk, stored or aged Wine guide Dwhite∗∗ Dummy variable, 1 if the wine is white Wine guide Drose Dummy variable, 1 if the wine is a rose Wine guide Dfort Dummy variable, 1 if the wine is a fortified wine Wine guide Dsparkling Dummy variable, 1 if the wine is sparkling Wine guide Country dummies∗∗∗ Country dummies ∗Baseline category is drink within one year. ∗∗Baseline category is red wine. ∗∗∗Baseline category is Slovenia.
B Coefficients in different segments
Table 7: Segmentation based on variety
White Red(+Rose+Fortified) Sparkling Whole sample 4 ∗ 107 4.3 ∗ 105 5.8 ∗ 106 0.000 0.000 0.000 White 3.3 ∗ 105 8.2 ∗ 105 0.000 0.000 Red(+Rose+Fortified) 4.3 ∗ 108 0.000 χ2 statistics and P values for testing the equality of common coefficients across segments
28 Table 8: Segmentation based on origin
Home Foreign Whole sample 4.9 ∗ 106 4.1 ∗ 105 0.000 0.000 Home 5431.17 0.000 χ2 statistics and P values for testing the equality of of common coefficients across segments
Table 9: Segmentation based on price
Commercial Semipremium Premium Ultrapremium PremiumUltrapremium Whole sample 1 ∗ 108 1.3 ∗ 106 4.3 ∗ 106 1.8 ∗ 108 1.5 ∗ 107 0.000 0.000 0.000 0.000 0.000 Commercial 3.3 ∗ 105 3.6 ∗ 105 1.1 ∗ 105 6414.77 0.000 0.000 0.000 0.000 Semipremium 1829.12 19892.21 3640.38 0.000 0.000 0.000 Premium 6432.51 9385.22 0.000 0.000 Ultrapremium 84751.63 0.000 χ2 statistics and P values for testing the equality of common coefficients across segments
C Price premium
Table 10: Reputation effects: price premium
Country Freq. Coefficient Average wine Price premium on Price premium on dummy price Slovenian wines with- Slovenian wines with out controls in %∗ controls in %∗∗ Argentina 12 not significant 13.042 / / Australia 17 -0.346 15.821 28.553 -29.249 Chile 19 0.145 12.405 0.796 15.604 France 112 0.282 45.639 270.838 32.578 Italy 86 0.375 25.638 108.320 45.499 Portugal 30 not significant 30.83 / / Spain 31 -0.069 18.743 52.295 -6.667 Slovenia 577 / 12.307 0 /
Pj ln ∗ Unconditional price premium is calculated as (e∆lnP − 1) ∗ 100 = (e PSI − 1) ∗ 100 ∗∗ Conditional price premium is calculated as (eCountrydummy − 1) ∗ 100
29 D Winery-specific effects
Table 11: Winery-specific effects Winery specific effects Whole model (country specific) (1) (2) grade -.163 -.256 (.041)∗∗∗ (.021)∗∗∗ grade-2 .001 .002 (.0003)∗∗∗ (.0001)∗∗∗ L -.002 (.0001)∗∗∗ gdppc .028 (.001)∗∗∗ distance .016 (.0005)∗∗∗ age .102 .104 (.008)∗∗∗ (.006)∗∗∗ age×Dage -.063 -.046 (.019)∗∗∗ (.022)∗∗ Dnonvintage -.018 -.077 (.124) (.097) Dstore .140 .185 (.034)∗∗∗ (.020)∗∗∗ Dage .827 .881 (.227)∗∗∗ (.172)∗∗∗ Ddrink-store .063 .091 (.087) (.058) Dstore-age .513 .459 (.152)∗∗∗ (.108)∗∗∗ Ddrink-store-age .882 .799 (.281)∗∗∗ (.297)∗∗∗ Dwhite -.031 .057 (.019)∗ (.081) Drose .067 .145 (.030)∗∗ (.092) Dfort -.844 .394 (.129)∗∗∗ (.098)∗∗∗ Dsparkling .334 .587 (.143)∗∗ (.132)∗∗∗ N 925 925 R2 .856 .66 RSS 82.375 193.896 Country cluster robust standard errors are in parentheses Significance level: ∗∗∗ at 1%, ∗∗ at 5%, ∗ at 10% L, gdppc and distance are dropped in (1) because they do not vary within category
30