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Wine Clusters

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Title: Wine Clusters


1
Wine Clusters
  • New Products, Marketing and Rural Development
  • June 20, 2005, Sonoma California
  • Daniel A. Sumner
  • University of California Agricultural Issues
    Center and UC Davis Department of
  • Agricultural and Resource Economics

2
Analysis of the Price of Winegrapes Regression
Price of Grapes P (Year, Variety, District)
3
What Determines the Price of Wine? Regression
Price of Wine P (Appellations, Variety, Score,
Age, Vintage, Vineyard, Reserve)
4
Sample of Grape Growing Costs Definition /
Example
  • Operating Cost all the expenses that are
    assigned to a particular operation during the
    year (planting, cultural, and harvest costs).
  • Example Chardonnay in SonomaOperating Cost
    2,900 /acre.Source UC Cooperative
    Extension, Karen Klonsky

5
Sample of Grape Growing Costs Definition /
Example
  • Cash Overhead Cost various cash expenses paid
    out during the year that are assigned to the
    whole farm (property taxes, interest on operating
    capital, office expense, liability and property
    insurance, and equipment repairs).
  • Example Chardonnay in Sonoma Cash Overhead Cost
    1,635 /acre.Source UC Cooperative Extension

6
Sample of Grape Growing Costs Definition /
Example
  • Non-Cash Overhead Cost capital recovery cost
    for land, equipment and other farm investments.
  • Example Chardonnay in SonomaInterest rate
    assumed 7.40Non-Cash Overhead Cost 4,660
    /acre.Land Recovery 2,590 /acre.
  • Important the land price is assumed as given in
    the Non-Cash Overhead Cost, thus in the Total
    Cost.Source UC Cooperative Extension

7
Sample of Grape Growing Costs/Revenue
8
Sample of Grape Growing Costs/Revenue
  • The revenue for Chardonnay grapes in Sonoma
    covers largely the Total Cost of Production, and
    even more there is a rent of 3,000 /acre. Why
    isnt the price of land in Sonoma higher?
  • The price of land in Lodi cannot support the
    costs of production of Cabernet grapes in the
    area. Either the price of land is too high, or
    the price of grapes is too low.

9
Illustration of Value - Added Up Down the
Marketing ChainCompare 3 illustrative
examplesCabernet Napa, Chardonnay Sonoma,
Cabernet Lodi
10
Illustration of Value-added Up Down the
Marketing Chain
11
Illustration of Value-added Up Down the
Marketing Chain
12
Effect on Wine Prices of a Land Price
IncreaseExample Chardonnay Sonoma, land price
increase of 10,000 /acre
13
Another Way to Estimate theEffect on Wine Prices
of a Land Price IncreaseExample Chardonnay
Sonoma, land price increase of 10,000 /acre
14
Price Endogeneity and Price Elasticity?
Price of wine
Supply of wine
P0
P1
Returns to Land
D0
D1
Quantity of wine
15
Effect on Land Prices of a Wine Price
DecreaseExample Chardonnay Sonoma, wholesale
price decrease of 0.10/bottle
16
Substitution Effects
  • We did the calculations, assuming that there is
    no substitution effects between inputs.
  • However, there may be some adjustments happening
    along the chain that would allow for less
    rigidity in the results.
  • If for example we assume that a decrease in wine
    prices would affect not only land but also other
    inputs supply and demand (oak use, cork and glass
    prices, etc), the impact on land prices would be
    smaller.

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20
Agricultural location and agglomeration
  • Agronomic basics determine location, terroir does
    matter for quality wine grapes.
  • Transport costs matter for heavy and cheap
    agricultural raw materials.
  • Processing depends on raw materials and vice
    versa so the location of processing is not a
    mystery either.
  • So does the cluster idea make any contribution?
  • Knowledge and information positive spillovers are
    easier within regions (space still matters, we
    are meeting here even though we are all on the
    web
  • Local input supplies depend on some local scale
    economies

21
Agricultural location and agglomeration
  • Hypothesis
  • Higher quality high prices raw material can be
    economically shipped farther for processing.
  • High value per of transport cost.
  • If transport costs of raw materials is the major
    issue clustering would matter more for cheap wine
    and less for fancy wine.
  • Will this allow a test of the importance of
  • local input suppliers
  • Local knowledge networks?
  • But terroir probably matters more for fancy wine.

22
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23
Networks in the wine industries of Italy Chile
Case study by Giuliani
24
Network density actual no. links / max. possible
no. of links
Note The positioning of nodes does not refer to
geographical distances between firms. The
network draws on a directed 32x32 matrix.
Linkages represent the existence technical advice
knowledge flows between any two nodes considered.
The direction of arrows indicate the direction of
knowledge flows.
25
Note The positioning of nodes does not refer to
geographical distances between firms. Linkages
represent the existence technical advice
knowledge flows valued 3 between any two nodes
considered. The direction of arrows indicate the
direction of knowledge flows.
26
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27
Wine clusters in Italy and Chile
  • Most empirical evidence shows that only a small
    subset of the firms actively participate in the
    knowledge diffusion process at intra-cluster
    level and in the absorption of extra-cluster
    knowledge, while the bulk of firms are either
    peripheral or totally disconnected from the
    intra-cluster knowledge system.

28
Sources
  • Guiliani, E. 2004. Laggard clusters as slow
    learners, emerging clusters as locus of knowledge
    cohesion (and exclusion) A comparative study in
    the wine industry. LEM Working Paper No.
    2004/09, Laboratory of Economics and Management,
    Sant'Anna School of Advances Studies, Pisa,
    Italy.
  • Giuliani, E. and Bell, M. 2005. The
    micro-determinants of meso-level learning and
    innovation evidence from a Chilean wine
    cluster. Research Policy 3447-68.
  • Porter, M. 1990. The competitive advantage of
    nations. London Macmillan.
  • von Hippel, E. 1987. Cooperation between rivals
    informal know-how trading. Research Policy
    16291-302.
  • von Hippel, E. 1994. Sticky information and
    the locus of problem solving implications for
    innovation. Management Science 40(4)429-439.
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