Numéro DR 02011 The Champagne Wine Industry: An

CENTRE
DE RECHERCHE
RESEARCH CENTER
DOCUMENTS DE RECHERCHE
WORKING PAPERS
- Numéro DR 02011
The Champagne Wine Industry:
An Economic Dynamic Model of Production and Consumption
Francis DECLERCK *
Martin L. CLOUTIER **
Juin 2002
* DECLERCK F.
** CLOUTIER M.L.
ESSEC,
Université du Québec à Montréal BP 6192, Downtown Station, Montréal,
QC H3C 4R2 Canada
GROUPE ESSEC
CERNTRE DE RECHERCHE / RESEARCH CENTER
GROUPE ESSEC,
ÉTABLISSEMENTS PRIVÉS D'ENSEIGNEMENT SUPÉRIEUR,
AVENUE BERNARD HIRSCH - BP 105
ASSOCIATION LOI 1901,
95021 CERGY-PONTOISE CEDEX FRANCE
TÉL. : 33 (0) 1 34 43 30 91
ACCRÉDITÉ AACSB - THE INTERNATIONAL ASSOCIATION
FOR MANAGEMENT EDUCATION,
FAX : 33 (0) 1 34 43 30 01
AFFILIÉ A LA CHAMBRE DE COMMERCE ET D'INDUSTRIE
Mail : [email protected]
DE VERSAILLES VAL D'OISE - YVELINES.
WEB : WWW.ESSEC.FR
The Champagne Wine Industry:
An Economic Dynamic Model of Production and Consumption
Francis Declerck, Ph.D.
Professor adjunct to the department of finance
ESSEC Business School
Avenue Bernard Hirsch - B.P. 105
95021 CERGY-PONTOISE cedex - FRANCE
Tel. 33 1.34.43.32.66
Fax. 33 1.34.43.32.60
E-mail: [email protected]
L. Martin Cloutier, Ph.D.
Associate Professor
Department of Management and Technology
School of Management
University of Quebec at Montreal
P.O. Box 6192, Downtown Station
Montreal, QC H3C 4R2 CANADA
Tel. 514.987.3000, ext. 3732
Fax. 514.987.3343
E-mail: [email protected]
Abstract
This paper reports on the development of an economic system dynamics (SD) model designed
to simulate both the short term and the long term production dynamics of the Champagne
wine industry. The model captures the main structural details of the Champagne business
activity. The goal of the SD model is to study the nonlinear and dynamic behavior observed in
the Champagne wine industry, and the long feedback delays involved in fixed asset
investments supporting the production of grapes and wine. All industry operators have to face
production, price and stock risks. Because it takes about three years to sell Champagne wine
after the vintage of grapes, industry operators have to cope with the uncertainty associated to
long time delays in the formation of price expectations, in the adjustment of supply, seasonal
production and demand. Thus, the model integrates structural elements of price expectations,
short term and long term supply responses, demand substitution and inventory fluctuation.
Perspectives are open to link the model to forecast profitability and financial leverage.
Key Words: Champagne wine, system dynamics, price expectations, feedback
1.
Introduction
The Champagne wine industry of Maisons de Champagne is constrained on the supply side
not only, like for every industry, by consumer demand, but it also satisfies legal limitations in
input use both in terms of quantity (limited designated area of origin, maximum authorized
yield) and quality (varieties of vines, manual harvest…). In the early 1990s, the Champagne
wine processors have become more and more responsive to consumers through efforts such as
- professional agreement to increase quality in getting 1.02 hl of must out of 160 kg of
grapes instead of 150 kg,
- the implementation of qualitative reserves of Champagne wine (CIVC, 1992).
1
However, uncertainty still exists because of weather fluctuations from year to year, explaining
swings in production.
With respect to the demand, fluctuations in shipments of bottles form processors to retailers
vary from year to year because of swings in the economic growth in the main importing
countries (CIVC, 1992, 2001).
The objective of the paper is to report on the design of an economic system dynamics (SD)
model designed to simulate both the short term and the long term production dynamics of the
Champagne wine industry. This model integrates the structural elements of supply response,
demand substitution and inventory fluctuation in the production of Champagne wine in the
short term and in the long term. The nonlinear and cyclical dynamics of production make SD
an appropriate method to capture the dynamics of the system in the short term (annual
harvest) and in the long term because of the long feedback delays involved in the aging of
bottles in cellars, implying stock management, and fixed assets in production.
The remainder of the paper is structured as follows. In the next section, an overview of
stylized facts and economic concepts inherent to Champagne wine production are presented.
In section 3, the research methods and empirical results about the dynamics of the Champagne
wine industry is provided to shed light on the production system. In section 4, information is
employed to build a dynamic model under a dynamic hypothesis represented in an influence
diagram. A model is developed. Details and data used in the calibration process are provided.
In section 5, empirical findings are shown. A conclusion follows in Section 6, and future
extensions to the model are discussed.
2.
Stylized facts, and economic concepts in Champagne wine production and
consumption
Stylized facts
The vineyards of Champagne can produce more than 300 million bottles annually from a
designated area of origin encompassing 33,000 hectares east of Paris, while its product is
consumed around the world. About 35% of the Champagne wine bottled is consumed in
France, and the remainder goes for exports mainly in the US, the UK, and Italy.
During the 1980s, supply was not able to keep up with the rise in demand and by September
1990 stocks had fallen below the three-year usual reserve period required for aging. In the
early 1990s, a drastic change in demand conditions has induced fluctuations in the stock of
bottles leading to economic and financial imbalance (Declerck & Pichot, 1994; Declerck,
1996).
In 1990, French grapes growers and Champagne wine processors broke their 30 years old
contract of vertical coordination. They reacted to stock shortages by raising prices to curtail
the boom in sales, but the timing of their decision coincided with the slump in the major
export markets, the US and UK, that subsequently spread to the rest of the world. As sales
fell from the 1989 peak of 249 million bottles to 214 million in 1991 and 1992 many
processors suffered financial pressures. Banks squeezed credits, debt rose to about 100% of
turnover, and in some cases, suppliers went unpaid and prices plummeted to cover cash
requirements. However, the year 1992 was difficult for wine processors and some of them
were able to generate positive margins (Declerck & Pichot 1994; Declerck 1996). Finally,
2
bankruptcies forced the industry to restructure. Some Champagne processors were sold to the
ones that had maintained a stronger financial position. For instance:
- in 1993, Deutz was acquired by Louis-Roederer, the most profitable processor;
- in 1996 Heidsieck Monopole was sold by Rémy-Cointreau to Vranken.
The Champagne wine processors responded to consumers through additional coordination
efforts along the value chain. Some of these measures included
- setting up qualitative reserves of Champagne wine kept from good quality vintages to
face bad years and then make up yield to the legal limit;
- agreeing to a four-year contract to limit the price fluctuations of grapes, because
grapes constitute the largest component of production costs.
So, the industry has tried to reduce temporal uncertainty and financial risks due to the
perceived three to four year of bottle stocks necessary to maintain on average (Declerck &
Pichot, 1994).
Most Champagne wine processors have suffered in 1991 and 1992 because the price of
Champagne wine declined strongly, while Champagne was made with grapes purchased at
high price three years earlier. Value added to sales, equity and financial debt levels were
significant factors to explain commercial margin and profitability. In order to internalize
input price fluctuation, Declerck (1996) emphasizes vertical integration. Since the price of
grapes is by far the most expensive input, wine processors may purchase as much vineyard as
possible. However, French regulations about farm land acquisition require the agreement of
farmers’ organizations representatives, including vine growers, for any transaction. And vine
growers want to keep vineyard in their hands. They prevent wine processors from acquiring
vineyard so they keep their bargaining power on the grape market.
In order to meet the expected strong demand at the turn of the millennium, part of the grapes
harvested in 1992, 1993 and so on have been processed into Champagne wine kept as
qualitative reserves. In the late 1990s, as expected, demand for bottles and grapes have
become a lot stronger and reached a peak in 1999. The demand for Champagne bottles has
declined from 327 million bottles shipped by wine processors in 1999 to 262.6 million bottles
in 2001. The average prices of a bottle have fallen too from FF 76.96 in 2001 to FF 74.19 in
2001. However, again these bottles were made with grapes paid at a high price of FF 25
(€3.8) per kg in 1998, while the price was only FF 20.5 (€ 3.1) per kg in 1993. In 2001, the
price of grapes was still high at FF 26.23 (€4,00) per kg. The industry is still under pressure
to restructure. For example, in April 2002, Pommery was sold by LVMH to Vranken.
Economic concepts in production and consumption
Economic cycles of production and consumption express phases of production expansion and
contraction that can also be observed in the food sector, even though they tend to be less
important than in most industries. But, the consumption of non-basic goods such as
Champagne wine is more sensitive to the fluctuations of economic cycles.
For wine processors, the fluctuation of business activity brings good prospects for profits or
fears for losses. Often, decreases in the sales of Champagne bottles occurs three or four years
after an economic boom, when grapes used to fill these bottles were purchased at a high price
and when operators expected continued expansion. Required time delays in production
“disturb” the adjustment of supply to fluctuating demand (Declerck & Pichot, 1994; Declerck,
1996).
3
Consumers expect quite constant prices and constant quality for Champagne wine. They are
troubled by swings in prices and are afraid that these price swings may reflect fluctuations in
product quality. So, operators along the processing chain share a common interest in
supplying standardized high quality products at a similar price from year-to-year, in order to:
- keep consumer confidence in the quality of products;
- control stable or steady markets.
Results from research about similar coordinating processes found that fluctuations at
processing stages, upstream a value chain, are the result of information feedback processing
with time delays, known as the “bullwhip effect” are known to impact the overall chain
performance (Lee et al., 1997). The underlying temporal uncertainty splits the vertical
coordination into different fragments and markets, favoring short term tactics rather than long
term strategies (Cloutier, 1999).
However, a long term strategy contributes to (a) a better performance in activity coordination,
(b) the maintenance of quality standards, (c) a steady financial situation, and (d) a sharing of
economic risks among operators in the value chain (Sterman, 2000). Long term strategies that
account for reaction time delays on the part of decision makers are in the general interest of
all operators in a given sector including producers and consumers.
One challenge is the many forecasts managers must anticipate about the demand and other
suppliers’ reaction to market incentives. Endogenous mechanisms of production and
marketing amplify economic business fluctuations and imbalance when an exogenous shock
occurs because of a lack of information and further time delays for supply to adjust to demand
(Ruth et al., 1998).
3.
Research Methods
Vine growers and wine processors - Maisons de Champagne - try to improve the conception
and implementation of their strategies to reduce risk and increase efficiency. They identify
coordination mechanisms and discover means of interaction in order to manage the
determinants of material, financial, and information stock and flow fluctuations through time.
Vine growers and wine processors’ expectations are not static. So, a dynamic model must take
into account the interaction between structural stock and flow interactions that generate these
fluctuations (Ruth et al., 1998). The principles of system dynamics (SD) provides a strong
basis for empirical work by modeling interaction among stocks, flows, material and
information time delays (Forrester, 1994; Morecroft, 1994). In this paper, this method is
employed to model the interaction among stocks of bottles of Champagne wine, flows of
grapes, material time delays due to grape production and wine aging in cellars, and
information management along the Champagne value chain.
The objective is to design a dynamic model to study the vine and production of Champagne in
order to understand the mechanisms of time adjustment delay in supply after the occurrence
of an exogenous event or shock. In order to meet our objectives, the research proceeds as
follows. First, empirical results are provided and then employed to develop an influence
diagram. An influence diagram is a blueprint for the development of a calibrated SD model. It
is the first step in elucidating the structural relationship within a system (Coyle, 1998). The
role of the influence diagram is analogous to the one of a hypothesis (Oliva, 1996; Sterman,
2000). It is termed, in the SD literature, a dynamic hypothesis, since its purpose is to describe
4
the micro-structure underlying the macro-behavior of the model to be developed. Second, the
model is developed in mathematical form. Third, the calibration process of the model is
documented and associated estimated parameters are presented.
4.
Model development and calibration
Empirical findings
Approximately three years elapse between the vintage of grapes and the sale of a bottle of
Champagne wine. In 1982, 1992 and 2001, the industry faced a “scissors effect” with strong
decline in price and in demand for bottles made with grapes harvested three years earlier and
purchased, by historic standards, at a high price. Hence, the mechanisms of vertical
coordination in use to reduce temporal uncertainty and decrease financial risks related to the
storage of bottles were not sufficient to adjust to short term pressures in demand.
Figure 1 presents world shipments of Champagne wine bottles by processors with-respect-to a
deflated price with a basis in year 1978. 1978 is chosen as the benchmark year because no
price listings are publicly available prior to that year. Shipments of Champagne wine bottles
follow two distinct channels (1) to the French market, as shown in figure 2, and (2) outside
France, as seen in figure 3.
Long time production and reaction time delays by decision makers in the industry have led to
a ten-year production and consumption cycle in the industry. As seen in figures 1, 2 , and 3,
over the past three decades the patterns of consumption exhibit three cycles, in the form of
loops, with:
- peaks of sales and price per bottles in 1979, 1989 and 1999,
- low values of sales and price per bottle in 1982, 1992, and all conditions are united to
expect bottom values in 2002.
In the short term, defined by approximately a three to four-year time horizon, these loops
show a nonlinear evolution of sales and of the average price of a Champagne wine bottle due
to time delays in the adjustment of production, price, and stocks to consumer demand.
Over 1978 – 2001 period, the average deflated price per bottle has remained generally flat
while global shipments have nearly doubled:
- shipments of Champagne wine bottles to domestic and export markets have increased
from about 170 000 bottles in the late 1970s to about 310 000 bottles in the late 1990s;
- shipments of Champagne wine bottles to the domestic market have grown from about
125 000 bottles in the late 1970s to about 185 000 bottles in the late 1990s;
- shipments of Champagne wine bottles to export markets have expanded from about
55 000 bottles in the late 1970s to about 120 000 bottles in the late 1990s.
Over nearly 30 years, the Maisons de Champagne were able to double the quantity sold while
maintaining prices at the same level. To accomplish this, production capacity was almost
doubled. The process of raising production capacity is managed on two fronts:
- (1) by increasing the yield per hectare (These yields are decided by means of an
agreement between representatives of vine growers and wine makers, subject to
authorization by a French Government representative.);
- (2) by an expansion of the hectares in production as vineyards (This must also be
authorized by the French Government and the European Commission).
5
Figure 1 World shipments of bottles of Champagne wine from 1978 to 2001 in function of deflated bottle price
Deflated price of Chapagne wine bottle in French Francs (base
1978)
29.000
28.000
27.000
26.000
25.000
24.000
23.000
22.000
21.000
20.000
19.000
140 000 000 160 000 000 180 000 000 200 000 000 220 000 000 240 000 000 260 000 000 280 000 000 300 000 000 320 000 000 340 000 000
Number of Champagne wine bottles shipped world wide
Figure 2 Shipments of bottles of Champagne wine on the French market from 1978 to 2001
in function of deflated bottle price
Deflated price of Chapagne wine bottle in French Francs
(base 1978)
28.000
27.000
26.000
25.000
24.000
23.000
22.000
21.000
20.000
19.000
18.000
100 000 000 110 000 000 120 000 000 130 000 000 140 000 000 150 000 000 160 000 000 170 000 000 180 000 000 190 000 000 200 000 000
Number of Champagne wine bottles shipped on the French market
6
Figure 3 Shipments of bottles of Champagne wine on the market outside France from 1978 to 2001
in function of deflated bottle price
Deflated price of Chapagne wine bottle in French Francs
(base 1978)
33.000
31.000
29.000
27.000
25.000
23.000
21.000
40 000 000
60 000 000
80 000 000
100 000 000
120 000 000
140 000 000
Number of Champagne wine bottles shipped on the market outside France
Dynamic hypothesis of the Champagne wine production behavior
The influence diagram presented in figure 4 consists of three balancing feedback loops
(denoted B1, B2 and B3) and of one reinforcing feedback loop (denoted R1). The balancing
loops B1 and B2 depict the short and long term supply response, respectively. The balancing
loop B3 shows the structural interaction amongst consumer demand components. Finally, the
reinforcing feedback loop R1 shows how input costs related to the purchase of grapes are
interacting with the supply and demand equilibrating process.
7
Figure 4 Influence diagram of the Champagne wine production/consumption dynamics
Production of
aging bottles
in cellars
+
+
Stocks of
Champagne
Harvest
+
Authorized
yields
Shipments
B1
+
+
B3
+
Real stocks to
expected stock ratio
B2
Hectares
+
-
+
Demand
Short term
supply response
-
-
+
Long term
supply response
Price of
Champagne
+
Profit
+
+
R1
Price of
grapes
-
The balancing loop B1 comprises seven elements. The relationship between the price and the
short term supply response follows basic economic principles as it relates to supply. As the
price of Champagne wine increases, there is an incentive for processors to increase the short
term supply response. Over time, the supply response can create an upward pressure on
authorized yields. As authorized production yields are increased, more aging wine in cellars
will be produced, in turn leading to greater stocks of Champagne wine.
However, this short terms supply response would be incomplete without its interaction with
the long term supply response detailed in the feedback loop B2. As seen, in the feedback loop
B2, an increase in the price of Champagne leads to greater profits. Higher profits lead, over a
long period of time, to a long term adjustment in hectares. As discussed earlier, a
representative from the French Government is ruling upon recommendations of vine growers
and wine producers representatives on the number of hectares that can be used to produce
grapes. More hectares and higher authorized yields translate into a larger harvest. The larger
the harvest, the larger the aging wine in cellars that can be produced, leading with some time
delay, to more stocks in Champagne wine.
The balancing feedback loops B1 and B2 follow the same path in closing the loop. An
increase in the real stocks of Champagne wine, assuming a given expected stock level, will
8
lead to a higher real stock to expected stock ratio. As the stock of bottles begins to rise, a
downward pressure will be put on price. Conversely, if the real to expected stock ratio falls
below a certain range, there will be an upward pressure on price.
The reinforcing feedback loop R1, shows that profits are impacted by the price of grapes. An
increase in the price of a Champagne wine bottle, leads to an increase in the price of grapes,
as there is more competition for the input. But higher prices for grapes does work against the
long term expansion of acres, as depicted in the influence diagram.
It is important to insist on the fact that there are long time delays involved in both the short
and long term supply adjustments that are endogenous to the supply response. Even though
the production is annual, producers form price expectations over a three to four year period,
the time needed to elaborate the product in a form ready for the market. The time delay is very
long and subject to production quantity errors on the part of decision makers in the industry.
After resources (in the form of fixed assets) are committed to production capacity, these
resources could contribute to a lasting over-expansion in capacity, especially if markets are
not there, in the meantime, to absorb the additional output. Assuming over-expansion, fixed
production costs per unit will be higher, and in the case of a price drop, industry operators
might keep the production going for several seasons hoping for higher prices, and thus,
further exacerbating excess supply. This results in inventory build up. The supply expansion
can have lasting effects in the industry as fixed assets create inertia that makes it even more
difficult to withdraw them from productions as operators are trying to recoup on their
investment and to decrease their fixed per unit cost over time.
The balancing feedback loop B3 represents the structure for the demand dynamics. As
discussed, an increase in Champagne wine stocks leads to a decrease in price. Although this
sends an incentive for an upward sort term supply adjustment, consumers face a more costly
product. As a result, of higher prices less Champagne wine will be shipped, hence leading to a
higher than needed accumulation of the stock of bottles. On the other hand, when prices are
kept low (due to a higher real to expected stock ratio), consumers will demand the product
and shipments will flow. However, the industry is under intense pressure to keep the price
“higher”, as part of the brand image management it strives to maintain. The price expectations
and the management of the real to expected stock ratio are critical in the management of this
dynamics. If stocks increase, a higher price tag is imposed to operators due to additional
storage cost, but liquidating the stocks at a lower price is not the preferred option or a long
term sustainable solution for the industry, as it banks on the preservation of its brand image of
quality and prestige. Too low of a price would be counter productive in meeting the objective
of that policy.
Although in the above discussion, the notion that higher cost in stockpiling the Champagne
wine has been discussed, there is not yet an explicit recognition of this factor in the influence
diagram. The working capital and financial relationships related to debt management are
under development. But the model, however, does capture the effect of these factors in its
calibration process.
Dynamic model of Champagne wine production and consumption
The SD model as seen in figure 4 was designed and calibrated in Powersim©, a software
dedicated to dynamic modeling and simulation. The model developed makes use of the Euler
forward integration method to generate its results. The model structure is adapted from the
9
one developed by Meadows (1970). The commodity cycle model developed by Meadows
represents a particular type of “smooth” or regular cycle and has been applied to hog, beef,
and poultry productions. More recently, the model was recalibrated to a more recent period to
study hog production (Cloutier, 1999). It has been extended to capture less regular and more
asymmetric cycle patterns in maple syrup production (Cloutier, 2001). The situation of the
Champagne wine production bears many similarities to maple syrup production due to the fact
that producers have fixed investments in production assets and that a large component of
production goes to the export market (Cloutier, 2001; Declerck, 1996).
Adjustments in the Champagne stocks state variables, denoted F, changes as a result of the
stocking rate (r). The Champagne wine inventory is lowered by the consumption rate (c) on
the local market, and by exports (x). This relationship is represented in equation (1)
(1)
Φ = Φ 0 +∫ (r − c − x) dt .
t
0
Equation (2) calculates the consumption rate that results from the per capita consumption,
denoted ? , multiplied by the size of the local market (l),
(2)
c = Λ l.
Equation (3) calculates exports as a residual of the stocks of Champagne wine bottles minus
the consumption rate multiplied by ?, a demand shock,
(3)
x =( Φ − c)θ.
The production of bottles of wine aging in cellars, denoted G, changes as a result of the
harvest (of grapes) rate (h). After the fermentation process, that lasts typically three years, the
bottles are ready for market and become part of the Champagne wine inventory, and thus the
stocks of bottles of wine aging in cellars. Those stocks are lowered by the appropriate
stocking rate (r), as defined in equation 1. This relationship is represented in equation (4)
(4)
Γ=
t
Γ0 + ∫ (h − r ) dttt
0
The time delay for wine aging in cellars to transit from the harvest rate to the stocking rate of
the Champagne inventory is explicitly calculated as follows in (5)
(5)
db
= ( Γd − Γ ) / β d
dt
The harvest rate h, as calculated in equation (6) augments the stock of wine aging in cellars
and is the result of the production capacity (O) measured in hectares multiplied by the
realized yield (?)
(6)
h = Ωγ .
Fluctuations in the overall stocks of bottle aging in cellars and of Champagne wine, that is,
the total stocks (S) of bottles, influence the annual average stock coverage (v). The average
stock coverage is the amount of bottles that defines the long term equilibrium with expected
consumption (m). This is calculated below in equation (7)
(7)
v = Σ / m.
10
A change in the average stock coverage influences the real to expected stock ratio (w). The
real to expected stock ratio is calculated by dividing the average stock coverage by the desired
coverage (d) as in (8)
(8)
w= v /δ .
The average stock coverage is the variable that influences the Champagne wine price
movements. A change, in price, influences price expectations. Operators expectations are
calculated with the software using an exponential smoothing function, also known as the
‘adaptive’ price expectation model (Arrow & Nerlove, 1958; Nerlove, 1958). This method is
frequently employed in SD models to account for the time delay in the transmission of
information “until persistent or stable delays are detected” (Lyneis, 1980:435, see also
Sterman, 2000). Technically, the adaptive price expectation model assumes that recent
information has more influence on the formation of price expectations than does less recent
information. The time delay underlying the formation of price expectation for Champagne
wine (t = 1 / t) is a time span that vine growers and wine processors are considering for
making an adjustment production decision. As discussed previously, the formation of price
expectations take somewhere between three to four years. Thus the integral component in (9)
divides the difference between the current price (P) and the exponential smoothed
Champagne price in the previous period (t - 1), that is (P0 ), over a time span (t) necessary for
operators to build their Champagne price expectation (EP). The adaptive price expectation for
Champagne wine in the model is calculated as follows
(9)
E P = P0 + ∫ τ ( P − P0 ) dt..
t
0
The price expectation is linked to the short term authorized yield response (? a ), which is in
turn linked to desired production capacity, denoted (? d ) (number of hectares) by means of a
table function. The yield obtained in the table function is the short term supply response
compared by means of a logical function that selects the minimum of the two, since
authorized rates cannot be exceeded, and at times, short term compressions in yields are a
possibility due to, amongst others, climatic conditions. This logical function is stated in (10)
as follows
(10)
Ψ = min
{ Ψd
, Ψa } .
The long term supply response for the production capacity is the result of upward adjustments
as a response to incentives. These incentives are called profits, denoted ?. These incentives
are characterized in the model by means of an econometric estimate of profit expectations (E
? ), taking into account the price of grapes with the appropriate lag. This profit expectation is
used to calculate the long term or desired supply response (Od ) by means of another table
function. The expansion of hectares in production can occur, albeit at a very slow rate, when
market growth conditions are assumed favorable for the long haul. In the event that economic
conditions take a downturn, it is fair to assume that production will continue because assets, in
the form of equipment, are specific, and hectares are adjusted over a long period of time. The
rate by which hectares are expanded for production (z) in (11) incorporates the time delay
associated with the adjustment (?d ) associated with the econometrically estimated path that
calculates the difference between the long term desired hectare levels (Od ) and the current
level (O). The long term supply adjustment delay (?d ), measured in hectares, explicitly takes
into account the time necessary for the decision making process associated with the
determination of the assumed appropriate level, given by
11
dz
= (Ωd − Ω) / ξ d .
dt
The adjustment in the level of hectares for grape production is stated in equation (12)
(11)
Ω = Ω 0 + ∫ ( z −( y / t )) dt,
t
(12)
0
where ? is the adjustment in the number of hectares as calculated in (11).
Thus, the overall stocking rate (r) is calculated in (13) as follows
r =Ω Ψ.
(13)
The price of Champagne wine also is used to determine consumer demand on the domestic
market using a table function. This table function is based on a statistically calibrated
relationship between the price of a Champagne wine bottle and the per capita consumption.
This table function calculates the consumption rate (c) as seen in (2).
Data sources and model calibration
The data used in the process of model calibration were obtained from publicly available
statistics published by the Comité interprofessionnel du vin de Champagne (CIVC) for the
period from 1978 to 2000. They include time series data, technical coefficients, and industry
expertise in the form of commentaries that makes explicit the industry decision process on
many economic issues.
There are three state variables in the model, namely Champagne stocks, production of wine
aging in cellars, and production capacity. These initial levels constitute baseline figures for
the model starting in 1978. Table 1 displays the variable parameter name, the value, and the
reference for individual state variables, and parameters in the model.
Table 1
Symbol
F
O
G
Model state variable for the baseline specification
State variables
Specification
Initial Champagne wine stocks (M bottles
190.6
/ year)
Initial production capacity (ha)
24,254
Initial wine aging in cellars (M bottles /
381
year)
Reference
CIVC
CIVC
Calibrated
Table 2 contains the list of parameters that are included in the model, their calibrated
specifications, and their sources.
Table 2
Symbol
?
v
t
ad
ßd
?d
?
Model parameter specifications
Parameters
Realized yield (kg / ha)
Desired stock coverage (years)
Price expectation delay (years)
Short term supply adjustment delay (years)
Delay for the fermentation of wine
Long term supply adjustment delay (years)
Local population (individuals in thousands)
Specification
See table x1
3
3
3
2
9
See table x1
Reference
CIVC
Declerck (1996)
Calibrated
Declerck (1996)
Calibrated
Calibrated
EuroStat
12
Five table functions are specified in the model. For each function table corresponds an
econometric equation. These equations and their statistical properties are summarized in
Table 3. For each structural equation, F-statistics show that all coefficients of the dependent
variables are found to be significantly different from zero at the 5% level of significance or
greater. The significance of each coefficient is also tested by a traditional two tailed t-test.
Table 3 – Statistical estimates for the function tables employed to calibrate the dynamic
model1
Dependent variables
Independant variables
Price of Champagne
wine (P)
Short term supply
response (? )
Expected profits (? )
Long term supply
response (O)
Demand (? )
equation (14)
equation (15)
equation (16)
equation (17)
equation (18)
60.9**
-920,047.5**
-0.775
15,764.6**
5.9**
(258,153.1)
(1.67)
(3,702)
(0.50)
Intercept
(17.74)
Real to expected stock ratio ( w )
2
Real to expected stock ratio squared ( w )
3
Real to expected stock ratio cubed ( w )
2
-100.3
(52.92)
90.6
(50.30)
-27
(15.28)
Price of Champagne wine per bottle ( P )
-0.147**
(0.02)
Log price of Champagne wine per bottle (ln P )
0.57**
(0.06)
Price of Champagne wine per bottle lagged one year ( P t-1)
111,218.4**
(32,331.7)
Price of Champagne wine per bottle lagged two years ( P t-2 )
-4,414.5**
(1,342.4)
Price of Champagne wine per bottle lagged three years ( P t-3)
58.2**
(18.46)
Expected profits lagged by three years (Ept-3)
F -statistic
R2
R2(adjusted)
N
d.f.
5327**
(1496.9)
10.86**
14.67**
69.46**
12.66**
35.4**
0.76
0.69
0.81
0.76
0.82
0.81
0.59
0.54
0.73
0.71
14
10
14
10
17
15
11
9
15
13
1 ** indicates significance for p > 0.01
2 number in parentheses indicate the standard error of the coefficient estimate
First in figure 5, the average stock coverage in the model is a means to approximate the
relative scarcity of Champagne wine and wine aging in cellars with respect to the expected
equilibrium consumption amount in a given year. This variable determines the real to
expected stock ratio relative to the period of relative coverage. Variations in the real to
expected stock ratio influence the price of Champagne wine directly in the model. Using
deflated data based on year 1978, that is constant FF, it was determined that the equilibrium
price in the model for the historical data available was FF 24 per bottle of Champagne wine. It
13
is an equivalent to FF 71.23 current in 2002, it that is €10.86 per bottle. The graph showing
the estimated equation to arrive at these estimates is shown in figure 5.
The equilibrium “real to expected ratio” (w) falls between 1 and 1.2, and the resulting price is
about FF 24 per bottle (see figure 5). In such a case, real stocks are just above the desired
coverage and provide a buffer as a precautionary measure. Within that range, the price of
Champagne remains stable. When the real to expected stock ratio falls towards 0.7 or 0.6, it
means that the average stock coverage becomes a lot smaller than the desired coverage. So,
stocks are lower, leading to a higher price per bottle. As a result, it would limit an increase in
Champagne consumption. By contrast, when the real to expected stock ratio is greater than
1.2, stocks are lying heavier than desired level. So, the Champagne price may be lowered to
stimulate consumption.
The first table function shows the relationship between the real to expected stock price ratio
and the price of Champagne wine per bottle. The price movement as a function of the relative
scarcity in stocks was estimated using the econometric equation (14)
P = 60.9 – 100.3w + 90.6W2 – 27W3 ; R2 = 0.76 ; R2 (adjusted) : 0.69
(14)
Figure 5 Table function of the Real to Expected Stock Ratio and the Price of Champagne Wine
28.00
27.00
)
P
Pri
ce
of
26.00
ch
am
pa
gn
e ( 25.00
24.00
23.00
0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
1.4
Real to expected stock ratio ( w )
The second table function is the desired short term supply response shown in figure 6. This
econometric equation is given in (15). This equation is quadratic in form. The desired short
term response yield is a quadratic equation as a function of the price lagged by one period.
When a stronger short term supply is desired by wine processors, the situation become such
that given the possibility of grape shortages to meet demand, they will accept both an increase
in grape yield and an increase in grape price:
- First, some days before the harvest time, the representatives of vine growers and wine
processor meet in order to agree on a maximum yield level that will become the
official maximum yield authorized and enforced by the French Government. Wine
growers accept higher yields to get the volumes of grapes they need.
14
-
Second, any price increase in grapes gives incentives to vine growers to sell their
grapes to wine processors. And wine processors typically pass on higher input costs to
consumers by raising the price of Champagne wine.
In conclusion, any increase in short term yields is associated with higher expected
Champagne wine price and vice versa as mentioned in equation (15).
(15) ? d = -920,047.5 + 111,218.4 Pt-1 – 4,414.5Pt-12 + 58.2P t-1 3 ;
R2 = 0.81 ; R2 (adjusted) = 0.76
Figure 6 Table Function for the Short Term Supply Response
Desired Short Term Yields (?)
( Kg / hectares )
20000
16000
12000
8000
4000
0
20
22
24
26
28
30
32
Expected Champagne Wine Price (EP) (FF / bottle )
The third table function, seen in figure 7, represents the price expected profit relationship that
is used as the first step towards determining the long-term supply response. The econometric
equation (16) is represented as follows
(16)
? = -77.5 + 0.57 ln(P) ; R2 = 0.82 ; R2 (adjusted) = 0.81
15
Figure 7 Table function of the Price of Champagne Wine and Expected Profits
18
17
Expected profits (?)
( FF / Bottle)
16
15
14
13
12
11
10
20
22
24
26
28
30
Price of champagne wine (P )
( FF / Bottle )
The cost of grapes is the most expensive input expense in Champagne wine processing
(Declerck & Pichot, 1994). Recall that most processors produce less than 30% of the grapes
they need and must purchase more than 70% of the grapes they process. For processors, that
do not own any vineyard, the cost of grapes is a major direct variable cost since the
processing of one bottle of Champagne wine requires 1.2 kg of grapes. So, wine processors’
expected profits are a log-linear function of the cost of grapes as shown in equation (16).
The fourth table function is the econometric estimation of the desired long term supply
response is shown in figure 8. This desired long term supply response is estimated using the
following equation (17)
(17)
Od = 15,764.6+ 5327 Ep t-3 ; R2 = 0.585 , R2 (adjusted) = 0.538.
This equation is estimated using the expected profit variable lagged by three periods. This is
because the operators are considering three years as the expected long term horizon between
the time they realize the full benefit from the harvest of three years ago. Bottles of
Champagne sold today were processed with grapes harvested at least three years ago.
In the long term, that is, over several decades and “cycles” mentioned above (see figures 1, 2
and 3), processors were able to sell more and more bottles. Shipments of bottles have roughly
doubled from the 1970s to the 1990s. So, wine processors needed a higher supply of grapes
from vine growers. Wine processors and vine growers agreed to ask the French Government
and European Commission for an expansion of the Champagne designated area of origin.
The fifth table function is the demand curve for Champagne wine consumption as seen in
Figure 9. This equation was estimated using a univariate econometric model. The per capita
consumption is the dependent variable regressed on the price of Champagne wine. The model
16
equation showing estimates is given in (18), and the fitted plot is displayed in Figure A2 in
the Appendix,
(18)
? = 5.9 – 0.147 P ; R2 = 0.73, R2 (adjusted) = 0.71.
Figure 8 Table Function of the Long Term Supply response
Long Term Supply Response ( O )
( hectares )
32000
30000
28000
26000
24000
22000
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Expected Profits (E ? t-3) ( FF / Bottle )
The adjusted goodness of fit measure (R2 ) is 0.71. This means that roughly 71% of the
variance within the econometric model is explained by the variation in the price of
Champagne wine alone. Other methods could be employed to estimate the demand as a
function of the price of Champagne wine. However, the results obtained are adequate within
the context of this work. T The demand for Champagne wine is a curve with negative slope
as it is for any normal good (Declerck and Pichot, 1994).
17
Figure 9 Table Function for Consumer Demand
Per capital consumption
( bottle / capita )
2.55
2.15
1.75
1.35
21
23
25
27
29
31
Champagne wine price (P) ( FF / bottle )
5. Results
The stock and flow diagram of the Champagne wine model is presented in figure 10
below.
18
Figure 10 Stock and Flow Diagram of the Champagne Wine Model
Growth_in_demand
Demand_forecast
Grape_bottle_conversation_factor
Initial_production
Export_rate
Initial_stocks
Local_market_population
Wine_on_lattes_production Production_delay
Champagne_stocks
Harvest_rate
Stocking_rate
French_consumption_rate
Grape_production
Total_stocks
Expected_consumption
Authorized_yields
Initial_production_capactiy
Average_stock_coverage
Consumption_per_capita
Desired_stock_coverage
Realized_yield
Adjustment_delay
Real_to_expected_stock_ratio
Short_term_desired_yield_response
Champagne_price
Production_capacity
Production_adjustment_rate
Expected_Champagne_price
Long_term_supply_response
espérence_du_profit_du_champagne
Expected_Champagne_profits
Expected_profit_including_grape_prices
In figure 11, there is an example of a calibrated result obtained from the model to illustrate
how the model relates to historical data. Beyond establishing the relationship between
variables in the model it is important to check how the model performs. As can be seen in the
figure below, the model performs quite well relative to its core dynamic relationship, that is,
the model specification for managing stocks. This relationship links the total stocks of wine
aging in cellars as well as Champagne wine in bottles ready for market to production. These
stocks follow quite closely historical values. This shows the robustness of the long and short
term supply response estimated in the model.
19
Figure 11 Historical versus simulated Champagne wine stocks, 1978 - 2000
1 200 000 000
(Champagne wine bottles)
1 000 000 000
800 000 000
600 000 000
400 000 000
200 000 000
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
(year)
Simulated stocks
Historical stocks
In figure 12 below the historical versus the simulated relationship of the long term supply
response are presented. As can be seen, the two series are highly correlated. The variations
between the series are minimal.
Figure 12 Historical versus simulated long term supply response - 1978 - 2000
35 000
30 000
(hectares)
25 000
20 000
15 000
10 000
5 000
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
(year)
Simulated hectares in production
Historical hectares in production
In figure 13, the simulated price is compared to the historical price. While this result does
show a general parallel pattern over time, the price within the model does not seem to
sensitive to the variation in the real to expected stock ratio. This means that further calibration
20
work needs to be conducted in this general area. Most likely the statistically estimated
relationship needs to be further investigated. One does know however, that the calibration is
within the general accepted range since the long term supply response, to which the price is
indirectly connected, performs as expected. The model is in the process of being further
developed to account for the financial relationships that relate to liquidity management, debt
and credit as a result of production and consumption dynamics. When these additional
relationships are added to the model, it will be subjected to the full battery of structural and
behavioral tests according to standard practice in the SD literature.
Figure 13 Historical versus simulated price for Champagne wine - 1978 - 2000
30
25
(FF per bottle)
20
15
10
5
0
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
(year)
Simulated price
6.
Historical price
Conclusion and Perspectives
This paper reports on the development of an economic system dynamics (SD) model designed
to simulate both the short term and the long term production dynamics of the Champagne
wine industry.
Long time production and reaction time delays by decision makers in the industry have led to
a ten-year production and consumption cycle in the industry. From 1978 to 2001, the patterns
of consumption exhibit three cycles, in the form of loops, with:
- peaks of sales and price per bottles in 1979, 1989 and 1999,
- low values of sales and price per bottle in 1982, 1992, and all conditions are united to
expect bottom values in 2002.
In the short term, defined by approximately a three to four-year time horizon, these loops
show a nonlinear evolution of sales and of the average price of a Champagne wine bottle due
to time delays in the adjustment of production, price, and stocks to consumer demand.
21
Over 1978 – 2001 period, the average deflated price per bottle has remained generally flat
while global shipments have nearly doubled:
- shipments of Champagne wine bottles to domestic and export markets have increased
from about 170 000 bottles in the late 1970s to about 310 000 bottles in the late 1990s;
- shipments of Champagne wine bottles to the domestic market have grown from about
125 000 bottles in the late 1970s to about 185 000 bottles in the late 1990s;
- shipments of Champagne wine bottles to export markets have expanded from about
55 000 bottles in the late 1970s to about 120 000 bottles in the late 1990s.
Over nearly 30 years, the Maisons de Champagne were able to double the quantity sold while
maintaining prices at the same level. To accomplish this, production capacity was almost
doubled. The process of raising production capacity is managed on two fronts:
- (1) by increasing the yield per hectare (These yields are decided by means of an
agreement between representatives of vine growers and wine makers, subject to
authorization by a French Government representative.);
- (2) by an expansion of the hectares in production as vineyards (This must also be
authorized by the French Government and the European Commission).
The model captures the main structural details of the Champagne business activity. The goal
of the SD model is to study the nonlinear and dynamic behavior observed in the Champagne
wine industry, and the long feedback delays involved in fixed asset investments supporting
the production of grapes and wine. All industry operators have to face production, price and
stock risks. Because it takes about three years to sell Champagne wine after the vintage of
grapes, industry operators have to cope with the uncertainty associated to long time delays in
the formation of price expectations, in the adjustment of supply, seasonal production and
demand. Thus, the model integrates structural elements of price expectations, short term and
long term supply responses, demand substitution and inventory fluctuation.
The simulated price is compared to the historical price. This result does show a general
parallel pattern over time, but the price within the model does not seem to sensitive to the
variation in the real to expected stock ratio. So, further calibration work is needed to improve
the model.
The model is in the process of being further developed to account for the financial
relationships that relate to liquidity management, debt and credit as a result of production and
consumption dynamics. Perspectives are open to link the model to forecast profitability and
financial leverage.
7.
References
Arrow, K.J., & M. Nerlove (1958). A note on expectations and stability. Econometrica, 26:
297-305.
Comité interprofessionel du vin de Champagne (CIVC), economic data from 1978 to 2001.
Cloutier, L.M. (1999). Economic and Strategic Implications of Coordination Mechanisms in
Value Chains: A Nonlinear and Dynamic Synthesis. Unpublished Ph.D. Thesis, University of
Illinois at Urbana-Champaign.
22
Cloutier, L.M. (2001). The maple sap products industry in Quebec: An economic and
production system dynamics model. Proceedings 19th International Conference of the System
Dynamics Society, 19:44 and CD-ROM.
Coyle, R.G. (1998). The practice of system dynamics : Milestones, lessons and ideas from 30
years of experience. System Dynamics Review, 14: 343-365.
Declerck, F. & O. Pichot (1994). Strategy et performances dans le Champagne: marges de
manoeuvre. IGIA, December 1994.
Declerck F., "Chain management and performance relationships in the Champagne wine
sector", Proceedings from the 2nd International Conference on Chain Management,
Wageningen Agricultural University & the Foundation for Agri-Chain Competence, pages
511-538, at Ede, the Netherlands, 30-31 May 1996.
Forrester, J.W. (1994). Policies, decisions, and information sources for modeling. In
Morecroft, J.W.D., & Sterman, J.D. (eds.), Modeling for Learning Organizations. Portland,
OR: Productivity Press, pp. 51-84.
Lee, H.L., Padmanabhan, V., & S. Whang (1997). Information distortion in a supply chain:
The bullwhip effect. Management Science, 43: 546-558.
Lyneis, J.M. (1980). Corporate Planning and Policy Design: A System Dynamics Approach.
Cambridge, MA: Pugh-Robert Associates.
Meadows, D.L. (1970). Dynamics of Commodity Production Cycles. Cambridge, MA:
Wright-Allen Press.
Morecroft, J.W.D. (1994). Executive knowledge, models, and learning. In Morecroft, J.W.D.,
& Sterman, J.D. (eds.), Modeling for Learning Organizations. Portland, OR: Productivity
Press, pp. 3-28.
Nerlove, M. (1958). Adaptative expectations and cobweb phenomena. Quarterly Journal of
Economics, 72: 227-240.
Oliva, P.R. (1996). A Dynamic Theory of Service Delivery: Implications for Managing
Service Quality. Unpublished Ph.D. Thesis, Sloan School of Management, The
Massachussetts Institute of Technology.
Ruth, M., Cloutier, L.M., & P. Garcia (1998). A nonlinear model of information and
coordination in hog production: Testing the Coasian-Fowlerian dynamic hypotheses. Paper
presented at the annual meeting of the American Agricultural Economics Association, Salt
Lake City, UT.
Sterman, J.D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex
World. Boston, MA: Irwin/McGraw-Hill.
23
ESSEC
CE NTRE
DE RECHERCHE
LISTE DES DOCUMENTS DE RECHERCHE DU CENTRE DE RECHERCHE DE L’ESSEC
(Pour se procurer ces documents, s’adresser au CENTRE DE RECHERCHE DE L’ESSEC)
LISTE OF ESSEC RESEARCH CENTER WORKING PAPERS
(Contact the ESSEC RESEARCH CENTER for information on how to obtain copies of these papers)
[email protected]
1997
97001 BESANCENOT D., VRANCEANU Radu
Reputation in a Model of Economy-wide Privatization.
97002 GURVIEZ P.
The Trust Concept in the Brand-consumers Relationship.
97003 POTULNY S.
L’utilitarisme cognitif de John Stuart Mill.
97004 LONGIN François
From Value at Risk to Stress Testing: The Extreme Value Approach.
97005 BIBARD Laurent, PRORIOL G.
Machiavel : entre pensée du pouvoir et philosophie de la modernité.
97006 LONGIN François
Value at Risk: une nouvelle méthode fondée sur la théorie des valeurs extrêmes.
97007 CONTENSOU François, VRANCEANU Radu
Effects of Working Time Constraints on Employment: A Two-sector Model.
97008 BESANCENOT D., VRANCEANU Radu
Reputation in a Model of Exchange Rate Policy with Incomplete Information.
97009 AKOKA Jacky, BRIOLAT Dominique, WATTIAU Isabelle
La reconfiguration des processus inter-organisationnels.
97010 NGUYEN. P
Bank Regulation by Capital Adequacy and Cash Reserves Requirements.
97011 LONGIN François
Beyond the VaR.
97012 LONGIN François
Optimal Margin Level in Futures Markets: A Method Based on Extreme Price Movements.
97013 GROUT DE BEAUFORT Viviane
Maastricth II ou la copie à réviser.
97014 ALBIGOT J.G., GROUT DE BEAUFORT V., BONFILLON P.O., RIEGER B .
Perspectives communautaires et européennes sur la réduction du temps de travail.
97015 DEMEESTERE René, LORINO Philippe, MOTTIS Nicolas
Business Process Management: Case Studies of Different Companies and Hypotheses for Further
Research.
Page 1
97016 PERETTI Jean-Marie, HOURQUET P.G., ALIS D.
Hétérogénéité de la perception des déterminants de l’équité dans un contexte international.
97017 NYECK Simon, ROUX Elyette
WWW as a Communication Tool for Luxury Brands: Compared Perceptions of Consumers and
Managers.
97018 NAPPI-CHOULET Ingrid
L’analyse économique du fonctionnement des marchés immobiliers.
97019 BESANCENOT D., ROCHETEAU G., VRANCEANU Radu
Effects of Currency Unit Substitution in a Search Equilibrium Model.
97020 BOUCHIKHI Hamid
Living with and Building on Complexity: A Constructivist Perspective on Organizations.
97021 GROUT DE BEAUFORT V., GRENOT S., TIXIER A . TSE K.L
Essai sur le Parlement Européen.
97022 BOULIER J.F., DALAUD R., LONGIN François
Application de la théorie des valeurs extrêmes aux marchés financiers.
97023 LORINO Philippe
Théorie stratégique : des approches fondées sur les ressources aux approches fondées sur les processus.
97024 VRANCEANU Radu
Investment through Retained Earnings and Employment in Transitional Economies.
97025 INGHAM M., XUEREB Jean-Marc
The Evolution of Market Knowledge in New High Technology Firms: An Organizational Learning
Perspective.
97026 KOENING Christian
Les alliances inter-entreprises et la coopération émergente.
97027 LEMPEREUR Alain
Retour sur la négociation de positions : pourquoi intégrer l’autre dans mon équation personnelle ?
97028 GATTO Riccardo
Hypothesis Testing by Symbolic Computation.
97029 GATTO Riccardo , JAMMALAMADAKA S. Rao
A conditional Saddlepoint Approximation for Testing Problems.
97030 ROSSI (de) F.X., GATTO Riccardo
High-order Asymptotic Expansions for Robust Tests.
97031 LEMPEREUR Alain
Negotiation and Mediation in France: The Challenge of Skill-based Learnings and Interdisciplinary
Research in Legal Education.
97032 LEMPEREUR Alain
Pédagogie de la négociation : allier théorie et pratique.
97033 WARIN T.
Crédibilité des politiques monétaires en économie ouverte.
97034 FRANCOIS P.
Bond Evaluation with Default Risk: A Review of the Continuous Time Approach.
97035 FOURCANS André, VRANCEANU Radu
Fiscal Coordination in the EMU: A Theoretical and Policy Perspective.
97036 AKOKA Jacky, WATTIAU Isabelle
MeRCI: An Expert System for Software Reverse Engineering.
97037 MNOOKIN R. (traduit par LEMPEREUR Alain)
Page 2
Surmonter les obstacles dans la résolution des conflits.
97038 LARDINOIT Thierry, DERBAIX D.
An Experimental Study of the Effectiveness of Sport Sponsorship Stimuli.
97039 LONGIN François, SOLNIK B.
Dependences Structure of International Equity Markets during Extremely Volatile Periods.
97040 LONGIN François
Stress Testing : application de la théorie des valeurs extrêmes aux marchés des changes.
1998
98001 TISSOT (de) Olivier
Quelques observations sur les problèmes juridiques posés par la rémunération des artistes interprètes.
98002 MOTTIS Nicolas, PONSSARD J.P.
Incitations et création de valeur dans l’entreprise. Faut-il réinventer Taylor ?
98003 LIOUI A., PONCET Patrice
Trading on Interest Rate Derivatives and the Costs of Marking-to-market.
98004 DEMEESTERE René
La comptabilité de gestion : une modélisation de l’entreprise ?
98005 TISSOT (de) Olivier
La mise en œuvre du droit à rémunération d’un comédien ayant « doublé » une œuvre audiovisuelle
er
(film cinématographique ou fiction télévisée ) avant le 1 janvier 1986.
98006 KUESTER Sabine, HOMBURG C., ROBERTSON T.S.
Retaliatory Behavior to New Product Entry.
98007 MONTAGUTI E., KUESTER Sabine, ROBERTSON T.S.
Déterminants of « Take-off » Time for Emerging Technologies: A Conceptual Model and Propositional
Inventory.
98008 KUESTER Sabine, HOMBURG C .
An Economic Model of Organizational Buying Behavior.
98009 BOURGUIGNON Annick
Images of Performance: Accounting is not Enough.
98010 BESANCENOT D., VRANCEANU Radu
A model of Manager Corruption in Developing Countries with Macroeconomic Implications.
98011 VRANCEANU Radu, WARIN T.
Une étude théorique de la coordination budgétaire en union monétaire.
98012 BANDYOPADHYAU D. K.
A Multiple Criteria Decision Making Approach for Information System Project Section.
98013 NGUYEN P., PORTAIT Roland
Dynamic Mean-variance Efficiency and Strategic Asset Allocation with a Solvency Constraint.
98014 CONTENSOU François
Heures supplémentaires et captation du surplus des travailleurs.
98015 GOMEZ M.L.
De l’apprentissage organisationnel à la construction de connaissances organisationnelles.
98016 BOUYSSOU Denis
Using DEA as a Tool for MCDM: some Remarks.
98017 INDJEHAGOPIAN Jean-Pierre, LANTZ F., SIMON V.
Page 3
Dynamique des prix sur le marché des fiouls domestiques en Europe.
98019 PELISSIER-TANON Arnaud
La division du travail, une affaire de prudence.
98020 PELISSIER-TANON Arnaud
Prudence et qualité totale. L’apport de la philosophie morale classique à l’étude du ressort psychologique
par lequel les produits satisfont les besoins de leurs utilisateurs.
98021 BRIOLAT Dominique, AKOKA Jacky, WATTIAU Isabelle
Le commerce électronique sur Internet. Mythe ou réalité ?
98022 DARMON René
Equitable Pay for the Sales Force.
98023 CONTENSOU François, VRANCEANU Radu
Working Time in a Model of Wage-hours Negociation.
98024 BIBARD Laurent
La notion de démocratie.
98025 BIBARD Laurent
Recherche et expertise.
98026 LEMPEREUR Alain
Les étapes du processus de conciliation.
98027 INDJEHAGOPIAN Jean-Pierre, LANTZ F., SIMON V.
Exchange Rate and Medium Distillates Distribution Margins.
98028 LEMPEREUR Alain
Dialogue national pour l’Europe. Essai sur l’identité européenne des français.
98029 TIXIER Maud
What are the Implications of Differing Perceptions in Western, Central and Eastern Europe for Emerging
Management.
98030 TIXIER Maud
Internal Communication and Structural Change. The Case of the European Public Service: Privatisation
And Deregulation.
98031 NAPPI-CHOULET Ingrid
La crise des bureaux : retournement de cycle ou bulle ? Une revue internationale des recherches.
98032 DEMEESTERE René
La comptabilité de gestion dans le secteur public en France.
98033 LIOUI A., PONCET Patrice
The Minimum Variance Hedge Ratio Revisited with Stochastic Interest Rates.
98034 LIOUI A., PONCET Patrice
Is the Bernoulli Speculator always Myobic in a Complete Information Economy?
98035 LIOUI A., PONCET Patrice
More on the Optimal Portfolio Choice under Stochastic Interest Rates.
98036 FAUCHER Hubert
The Value of Dependency is Plant Breeding: A Game Theoretic Analysis.
98037 BOUCHIKHI Hamid, ROND (de) Mark., LEROUX V.
Alliances as Social Facts: A Constructivist of Inter-Organizational Collaboration.
98038 BOUCHIKHI Hamid, KIMBERLY John R.
In Search of Substance: Content and Dynamics of Organizational Identity.
98039 BRIOLAT Dominique, AKOKA Jacky, COMYN-WATTIAU Isabelle
Electronic Commerce on the Internet in France. An Explanatory Survey.
Page 4
98040 CONTENSOU François, VRANCEANU Radu
Réduction de la durée du travail et complémentarité des niveaux de qualification.
98041 TIXIER Daniel
La globalisation de la relation Producteurs-Distributeurs.
98042 BOURGUIGNON Annick
L’évaluation de la performance : un instrument de gestion éclaté.
98043 BOURGUIGNON Annick
Benchmarking: from Intentions to Perceptions.
98044 BOURGUIGNON Annick
Management Accounting and Value Creation: Value, Yes, but What Value?
98045 VRANCEANU Radu
A Simple Matching Model of Unemployment and Working Time Determination with Policy Implications.
98046 PORTAIT Roland, BAJEUX-BESNAINOU Isabelle
Pricing Contingent Claims in Incomplete Markets Using the Numeraire Portfolio.
98047 TAKAGI Junko
Changes in Institutional Logics in the US. Health Care Sector: A Discourse Analysis.
98048 TAKAGI Junko
Changing Policies and Professionals: A Symbolic Framework Approach to Organizational Effects on
Physician Autonomy.
98049 LORINO Philippe
L’apprentissage organisationnel bloquée (Groupe Bull 1986-1992) : du signe porteur d’apprentissage au
Piège de l’habitude et de la représentation-miroir.
98050 TAKAGI Junko, ALLES G.
Uncertainty, Symbolic Frameworks and Worker Discomfort with Change.
1999
99001 CHOFFRAY Jean-Marie
Innovation et entreprenariat : De l’idée… au Spin-Off.
99002 TAKAGI Junko
Physician Mobility and Attidudes across Organizational Work Settings between 1987 and 1991.
99003 GUYOT Marc, VRANCEANU Radu
La réduction des budgets de la défense en Europe : économie budgétaire ou concurrence budgétaire ?
99004 CONTENSOU François, LEE Janghyuk
Interactions on the Quality of Services in Franchise Chains: Externalities and Free-riding Incentives.
99005 LIOUI Abraham, PONCET Patrice
International Bond Portfolio Diversification.
99006 GUIOTTO Paolo, RONCORONI Andrea
Infinite Dimensional HJM Dynamics for the Term Structure of Interest Rates.
99007 GROUT de BEAUFORT Viviane, BERNET Anne-Cécile
Les OPA en Allemagne.
99008 GROUT de BEAUFORT Viviane, GENEST Elodie
Les OPA aux Pays-Bas.
99009 GROUT de BEAUFORT Viviane
Les OPA en Italie.
Page 5
99010 GROUT de BEAUFORT Viviane, LEVY M.
Les OPA au Royaume-Uni.
99011 GROUT de BEAUFORT Viviane, GENEST Elodie
Les OPA en Suède.
99012 BOUCHIKHI Hamid, KIMBERLY John R.
st
The Customized Workplace: A New Management Paradigm for the 21 Century.
99013 BOURGUIGNON Annick
The Perception of Performance Evaluation Criteria (1): Perception Styles
99014 BOURGUIGNON Annick
Performance et contrôle de gestion.
99015 BAJEUX-BESNAINOU Isabelle, JORDAN J., PORTAIT Roland
Dynamic Asset Allocation for Stocks, Bonds and Cash over Long Horizons.
99016 BAJEUX-BESNAINOU Isabelle, JORDAN J., PORTAIT Roland
On the Bonds-stock Asset Allocation Puzzle.
99017 TIXIER Daniel
La logistique est-elle l’avenir du Marketing ?
99018 FOURCANS André, WARIN Thierry
Euroland versus USA: A Theoretical Framework for Monetary Strategies.
99019 GATTO Riccardo, JAMMALAMADAKA S.R.
Saddlepoint Approximations and Inference for Wrapped α-stable Circular Models.
99020 MOTTIS Nicolas, PONSSARD Jean-Pierre
Création de valeur et politique de rémunération. Enjeux et pratiques.
99021 STOLOWY Nicole
Les aspects contemporains du droit processuel : règles communes à toutes les juridictions et procédures
devant le Tribunal de Grande Instance.
99022 STOLOWY Nicole
Les juridictions civiles d’exception et l’étude des processus dans le droit judiciaire privé.
99023 GATTO Riccardo
Multivariate Saddlepoint Test for Wrapped Normal Models.
99024 LORINO Philippe, PEYROLLE Jean-Claude
Enquête sur le facteur X. L’autonomie de l’activité pour le management des ressources humaines et pour
le contrôle de gestion.
99025 SALLEZ Alain
Les critères de métropolisation et les éléments de comparaison entre Lyon et d’autres métropoles
françaises.
99026 STOLOWY Nicole
Réflexions sur l’actualité des procédures pénales et administratives.
99027 MOTTIS Nicolas, THEVENET Maurice
Accréditation et Enseignement supérieur : certifier un service comme les autres…
99028 CERDIN Jean-Luc
International Adjustment of French Expatriate Managers.
99029 BEAUFORT Viviane, CARREY Eric
L’union européenne et la politique étrangère et de sécurité commune : la difficile voie de la construction
d’une identité de défense européenne.
Page 6
99030 STOLOWY Nicole
How French Law Treats Fraudulent Bankruptcy.
99031 CHEVALIER Anne, LONGIN François
Coût d’investissement à la bourse de Paris.
99032 LORINO Philippe
Les indicateurs de performance dans le pilotage organisationnel.
99033 LARDINOIT Thierry, QUESTER Pascale
Prominent vs Non Prominent Bands: Their Respective Effect on Sponsorship Effectiveness.
99034 CONTENSOU François, VRANCEANU Radu
Working Time and Unemployment in an Efficiency Wage Model.
99035 EL OUARDIGHI Fouad
La théorie statistique de la décision (I).
2000
00001
CHAU Minh, LIM Terence
The Dynamic Response of Stock Prices Under Asymetric Information and Inventory Costs: Theory and
Evidence
00002
BIBARD Laurent
Matérialisme et spiritualité
00003
BIBARD Laurent
La crise du monde moderne ou le divorce de l’occident.
00004
MATHE Hervé
Exploring the Role of Space and Architecture in Business Education.
00005
MATHE Hervé
Customer Service: Building Highly Innovative Organizations that Deliver Value.
00006
BEAUFORT (de) Viviane
L’Union Européenne et la question autrichienne, ses conséquences éventuelles sur le champ de révision
de la CIG.
00007
MOTTIS Nicolas, PONSSARD Jean-Pierre
Value Creation and Compensation Policy Implications and Practices.
00009
BOURGUIGNON Annick
The Perception of Performance Evaluation Criteria (2): Determinants of Perception Styles.
00010
EL OUARDIGHI Fouad
The Dynamics of Cooperation.
00011
CHOFFRAY Jean-Marie
Innovation et entrepreneuriat : De l’Idée…au Spin-Off. (Version révisée du DR 99001).
00012
LE BON Joël
De l’intelligence économique à la veille marketing et commerciale : vers une nécessaire mise au point
conceptuelle et théorique.
00013
ROND (de) Mark
Reviewer 198 and Next Generation Theories in Strategy.
00014
BIBARD Laurent
Amérique latine : identité, culture et management.
00016
BIBARD Laurent
Les sciences de gestion et l’action.
Page 7
00017
BEAUFORT (de) V.
Les OPA au Danemark.
00018
BEAUFORT (de) V.
Les OPA en Belgique.
00019
BEAUFORT (de) V.
Les OPA en Finlande.
00020
BEAUFORT (de) V.
Les OPA en Irlande.
00021
BEAUFORT (de) V.
Les OPA au Luxembourg.
00022
BEAUFORT (de) V.
Les OPA au Portugal.
00023
BEAUFORT (de) V.
Les OPA en Autriche.
00024
KORCHIA Mickael
Brand Image and Brand Associations.
00025
MOTTIS Nicolas, PONSSARD Jean-Pierre
L’impact des FIE sur les firmes françaises et allemandes : épiphénomène ou influence réelle ?
00026
BIBARD Laurent
Penser la paix entre hommes et femmes.
00027
BIBARD Laurent
Sciences et éthique (Notule pour une conférence).
00028
MARTEL Jocelyn, C.G. FISHER Timothy
Empirical Estimates of Filtering Failure in Court-supervised Reorganization.
00029
MARTEL Jocelyn
Faillite et réorganisation financière : comparaison internationale et évidence empirique.
00030
MARTEL Jocelyn, C.G. FISHER Timothy
The Effect of Bankruptcy Reform on the Number of Reorganization Proposals.
00031
MARTEL Jocelyn, C.G. FISHER Timothy
The Bankruptcy Decision: Empirical Evidence from Canada.
00032
CONTENSOU François
Profit-sharing Constraints, Efforts Output and Welfare.
00033
CHARLETY-LEPERS Patricia, SOUAM Saïd
Analyse économique des fusions horizontales.
00034
BOUYSSOU Denis, PIRLOT Marc
A Characterization of Asymmetric Concordance Relations.
00035
BOUYSSOU Denis, PIRLOT Marc
Nontransitive Decomposable Conjoint Measurement.
00036
MARTEL Jocelyn, C.G. FISHER Timothy
A Comparison of Business Bankruptcies across Industries in Canada, 1981-2000.
2001
01001
DEMEESTERE René
Pour une vue pragmatique de la comptabilité.
Page 8
01002
DECLERCK Francis
Non Disponible.
01003
EL OUARDIGHI Fouad, GANNON Frédéric
The Dynamics of Optimal Cooperation.
01004
DARMON René
Optimal Salesforce Quota Plans Under Salesperson Job Equity Constraints.
01005
BOURGUIGNON Annick, MALLERET Véronique, NORREKLIT Hanne
Balanced Scorecard versus French tableau de bord : Beyond Dispute, a Cultural and Ideological
Perspective.
01006
CERDIN Jean-Luc
Vers la collecte de données via Internet : Cas d’une recherche sur l’expatriation.
01012
VRANCEANU Radu, CERNAT Lucian
Globalization and Growth: New Evidence from Central and Eastern Europe.
01013
BIBARD Laurent
De quoi s’occupe la sociologie ?
01014
BIBARD Laurent
Introduction aux questions que posent les rapports entre éthique et entreprise.
01015
BIBARD Laurent
Quel XXIème siècle pour l’humanité ?
01016
MOTTIS Nicolas, PONSSARD Jean-Pierre
Value-based Management at the Profit Center Level.
01017
BESANCENOT Damien, HUYNH Kim, VRANCEANU Radu
Public Debt : From Insolvency to Illiquidity Default.
01018
BIBARD Laurent
Ethique de la vie bonne et théorie du sujet : nature et liberté, ou la question du corps.
01019
INDJEHAGOPIAN Jean-Pierre, JUAN S . LANTZ F., PHILIPPE F.
La pénétration du Diesel en France : tendances et ruptures.
01020
BARONI Michel, BARTHELEMY Fabrice, MOKRANE Mahdi
Physical Real Estates: Risk Factors and Investor Behaviour.
01021
01022
BESANCENOT Damien, VRANCEANU Radu
Quality Leaps and Price Distribution in an Equilibrium Search model
01023
BIBARD Laurent
Gestion et Politique
01024
BESANCENOT Damien, VRANCEANU Radu
Technological Change, Acquisition of Skills and Wages in a search Economy
01025
BESANCENOT Damien, VRANCEANU Radu
Quality Uncertainty and Welfare in a search Economy
01026
MOTTIS N. , PONSARD J.P.,
L’impact des FIE sur le pilotage de l’entreprise
01027
TAPIERO Charles, VALOIS Pierre
The inverse Range Process in a Random Volatibility Random Walk
01028
ZARLOWSKI Ph., MOTTIS N.
Making Managers into Owners An Experimental Research on the impact of Incentive Schemes on
Shareolder Value Creation
Page 9
01029
BESANCENOT Damien, VRANCEANU Radu
Incertitude, bien-être et distribution des salaires dans un modèle de recherche d’emploi
Page 10