Vertical Relationships in the Ready-to-Eat Cereal Market
in Boston
Benaissa Chidmi, Rigoberto A. Lopez, and Ronald W. Cotterill
Selected paper submitted to the European Association of Research in Industrial
Economics Meeting, Porto, Portugal, September 1-4, 2005.
The authors are Ph.D. candidate and Professors in the Department of Agriculture and
Resource Economics, University of Connecticut, Storrs, CT 06269-4021, USA.
Contact: Rigoberto.Lopez@uconn.edu, (860)-486-1921.
Vertical Relationships and Competition in the Ready-to-Eat
Cereal Market in Boston
Abstract:
In this paper we estimate a discrete choice demand model with random coefficients for 37
brands of ready-to-eat cereals (RTECs) at the supermarket chain level in the Boston area.
Then assuming a manufacturer Stacklberg model for vertical pricing, we decompose the
market channel price-cost margins (PCMs) for individual brands at four supermarket
chains. The results shed light on the share of channel profits accruing to individual RTEC
manufacturers and retailers. For instance, Stop & Shop, the leading supermarket chain in
Boston, gets greater dollar profits than smaller supermarket chains. In spite of this,
smaller supermarkets, especially those with urban locations, charge higher prices due in
part to higher retailing costs. Finally, the results also show that both manufacturers and
retailers dropped their RTEC prices in 1996 under a regulatory threat provoked by a
scandal of supra-normal RTEC prices.
Key words: Vertical relationships, discrete choice, manufacturers/retailers, market
channel.
JEL codes: L110, L130, L660.
1
Vertical Relationships and Competition in the Ready-to-Eat
Cereal Market in Boston
1. Introduction
Recent empirical literature on vertical relationships between food manufacturers
and retailers has focused on contracts and vertical integration, providing structural
models to explain the relationships between manufacturers and retailers (Kadiyali et al.,
1999; Villas-Boas and Zhao, 2000; Villas-Boas, 2002; and Manuszak, 2001).
This paper estimates and decomposes the channel profits for the ready-to-eat
cereal (RTEC) market in Boston at the brand/supermarket chain level. It contributes to
the existing literature in several ways. First, it is the first study to estimate a discrete
choice random coefficient demand system for branded products at the chain as opposed
to city market level.1 Second, the study decomposes channel profits accruing to
manufacturers and retailers, respectively, instead of attributing all profits to
manufacturers as in Nevo’s work (2001). Third, this study pursues chain-wide strategic
pricing in a relevant regional market area, the Boston Information Resources
Incorporated (IRI) market. Villas-Boas (2002) has estimated a similar model for yogurt at
the store level for a few stores. Fourth, this study uses four-week data while prior RTEC
brand demand analysis uses either quarterly (Nevo, 2001); Hausman et al., 1994) or
weekly observations (Kadiyali et al., 1999; Cotterill and Haller, 1997),2 shedding light on
the importance of time aggregation for this particular industry.
1
Cotterill and Dhar (2002) is the only prior chain level demand study and it uses a nested logit model.
There is no consensus concerning which time unit is desirable. Quarterly may be too aggregate, while
weekly may be too disaggregate to measure strategic pricing moves in a static equilibrium model.
2
2
2. Methodology
The methodology used consists of two steps. First, the retail demand for
differentiated brands of RTECs is estimated using a random coefficient model. Second, a
two-stage pricing model is implemented assuming a Nash-Bertrand competition at each
stage. In the vertical market, we assume manufacturer Stacklberg conduct holds; i.e.,
retailers play Nash-Bertrand when evaluating wholesale prices and maximizing profits,
while manufacturers employ the retail reaction functions to their wholesale price change
when maximizing profits (Choi, 1991). The demand results are used to compute the total
channel price-cost margins and to decompose them into the price-cost margins at the
retail and manufacturer stages.
2.1. Demand Side
Consider the case where consumers choose a brand of RTEC that maximizes their
utility. More specifically, the indirect utility3 of consumer i from buying the brand j is
given by
U ij = β j + x j β i − α i p j + ζ j + ε ij ,
i = 1,...n
j = 1,..., J
(1)
where β j represents the store/brand fixed effects, x j are the observed product
characteristics of brand j, p j is the price of the brand j , ζ j are the unobserved (by the
researcher) product characteristics, and ε i j represents the distribution of consumer
preferences about the unobserved product characteristics, with a density f (ε ) . The
parameters to be estimated are α i and β i . Note that those parameters are allowed to vary
across consumers, therefore taking into account the heterogeneity taste of consumer.
3
The indirect utility comes from a quasi-linear utility function.
3
These coefficients can be decomposed into a fixed component and a variable
component (changing with consumers’ observed and unobserved characteristics). This
decomposition can be expressed as:
α i = α + λDi + γvi ,
(2)
β i = β + ϕDi + ρvi ,
(3)
where the Di represents the consumers’ observed characteristics such as demographics
variables (e.g., income), and vi denotes the unobserved consumers’ characteristics.
Substituting (2) and (3) in (1) yields
U ij = β j + x j β + λDi x j + γvi x j − αp j − λDi p j − γvi p j + ζ j + ε ij .
(4)
Unobserved consumer characteristics vi are assumed to be normally distributed N (0, I ) ,
where I is the identity matrix; and the observed consumer characteristics Di have an
empirical distribution h(D ) , not necessarily a normal distribution.
The indirect utility in (4) can be decomposed into two parts: a mean utility given
by δ j = β j + β x j − αp j + ζ j and a deviation from that mean, which is a function of the
interaction between the observed and unobserved consumer’s characteristics and the price
and observed brand characteristics, given by
µ ij = λDi x j − λDi p j + γvi x j − γvi p j + ε ij .
(5)
To complete the model, an outside good is included to give the consumer the
possibility not to buy any one of the J brands included in the choice set.4 The utility of
4
The inclusion of the outside good is necessary in order to accomplish with the exhaustiveness of
alternatives of the discrete choice model. For a detailed discussion, see Train (2002). For the case at hand,
the outside good can include all other brands, or the residual brands not included in the study.
4
the outside good is normalized to be constant over time and equal zero. Given the
observed and unobserved consumer characteristics define the set of choice by
S ( x j , p j , ζ j ;θ ) = {( Di , vi , ε ij ) : U ijt ≥ U ik ∀k = 0,1,... N } ,
(6)
where θ is a vector that includes all the parameters of the model.
The consumer purchases one unit of the brand that yields the highest utility. The
global market share of the jth brand corresponds to the probability that the jth brand is
chosen. That is,
s j = ∫ I {( Di , vi , ε ij ) : U ij ≥ U ik ∀k = 0,1,...N }dH (D ) dG (v ) dF (ε ).
(7)
Depending on the assumptions regarding D, v , and ε , the integral in (7) can have
or not a closed formula. In a general setting, the integral in (7) does not have a closed
formula and should be solved numerically (BLP, 1995; Nevo, 2000, Villas-Boas, 2002).
The Random coefficients model (RCM) allows for consumer heterogeneity
α i and β i as described in (2) and (3). That is, each consumer is different from another
consumer in their response to price and brand characteristics. However, the RCM poses
two challenges. First, the integral in equation (7) has no closed formula and should be
solved numerically.5 Second, information on the distribution of demographics is needed
to compute the individual market shares. Intuitively, the integral in (7) is solved based on
the choice of the parameters that minimize the distance between the predicted market
shares given by equation (7) and the observed market shares. This paper follows Berry
(1994) inversion of the market share function that obtains the mean utility valuation
δ that equates the predicted market shares with observed market shares.
5
The integral in (7) is solved using the simulation technique proposed by Pakes (1986).
5
Given starting values for θ 2 (parameters that enter non-linearly) in (4) and δ , and
the draws from the distributions of D and ν , the integral in (7) is estimated numerically.6
Nevo (2000) proposes to use the smooth estimator that makes use of the extreme value
distribution on f (ε ) to integrate the ε s analytically. The predicted market shares are
approximated by
s j ( p, x, δ , Pns ;θ 2 ) =
1 ns
1 ns
s ji = ∑
∑
ns i =1
ns i =1
exp(δ j + µ ij )
J
1 + ∑ exp(δ m + µ im )
,
(8)
m =1
where ns is the number of draws from the distributions D andν given by the
distribution Pns . The above predicted market shares allow computing the mean utility
valuation δ that equates the predicted market shares with observed market shares. This is
an iterative step and is solved numerically due to the non-linearity of the inversion of the
equation s.t (δ .t ;θ 2 ) = S .t .7 The errors are then computed and interacted with the
instruments to form the objective function to be minimized using the General Method of
Moments (GMM) estimation technique.
The elasticities of the random coefficients model are given by
⎧ p j ns
⎪ ∑ α i s ji (1 − s ji ),
∂s j p k ⎪ s j i =1
ηj =
=⎨
∂p k s j ⎪ p k ns
α i s ji s ki ,
−
⎪ sj ∑
i =1
⎩
6
7
if j = k ,
(9)
otherwise.
The starting values for the mean utility value δ come from the Logit model estimation
Berry, Levinsohn and Pakes (1995) suggest using the following contraction mapping
δ t +1 = δ t + ln(S ) − ln(s( p, x, δ , Pns ;θ 2 ),
where
s(.) is the predicted market shares computed by equation (8) and T is the smallest integer such that
δ − δ T −1
T
t = 0,..., T
is smaller than some tolerance level
6
A major advantage of RCM over the traditional discrete choice models (Logit and
Nested Logit models) is that by taking into account the consumer heterogeneity taste, it
gives another explanation besides the price variation to the variation of market shares
across markets, allowing for flexible substitution patterns.
2.2. Supply Side
Consider the case where a manufacturer chooses the wholesale price for each
brand it sells. Then, each chain retailer chooses the retail price for each brand to
maximize his own profits in a horizontal Nash –Bertrand model of competition. The
game is solved using backward induction starting from the retailers and going back to the
manufacturers’ equilibrium.
The rth retailer’s problem is to maximize profits, given by
πr =
∑( p
j∈S r
j
− w j − c rj ) s j ( p) M ,
(10)
where S r is the set of brands sold by the rth supermarket, p j is the retail price for brand
j, w j is the wholesale price the rth retailer pays for brand j , c rj is the retailer’s marginal
cost for brand j , s j ( p ) is the share of brand j , and M is a measure of the market size.
The first-order conditions are given by
sj +
∑(p
m∈S r
m
− wm − c mr )
∂s m
= 0.
∂p j
(11)
7
Repeat the procedure for each supermarket, stack the solutions and write them using an
ownership matrix to obtain the RTEC retailers’ PCMs. 8
p − w − c r = −(Tr * ∆ r ) −1 s ( p) ,
(12)
where Tr is the retailer’s ownership matrix with the general element Tr (m, j ) and ∆ r is a
matrix of first derivatives of all the shares with respect to all retail prices. The matrix
(Tr * ∆ rt ) is the element-by-element multiplication of the two matrices.
Now consider the RTEC manufacturer’s problem. Each manufacturer sets the
wholesale price w in order to maximize profits, given by
πw =
∑ (w
j∈S w
j
− c wj ) s j ( p( w))M ,
(13)
where S w represents the set of brands produced by manufacturer m , and c wj is the
manufacturer’s marginal cost for brand j . The first-order conditions are
sj +
∑ (w
m∈S w
m
− c mw )
∂s m
= 0.
∂p j
(14)
Similarly, defining a matrix of manufacturers’ ownership Tw and a matrix of
manufacturer’s response ∆ w , and stacking all the manufacturers’ first-order conditions
one obtains the RTEC manufacturers’ PCMs:
w − c w = −(Tw * ∆ w ) −1 s ( p) .
(15)
The matrix ∆ w is more complicated to compute than the matrix ∆ r due to the chain rule
8
Tr , the matrix of ownership, is introduced to facilitate the matrix notation of equation (12). It is a matrix
∂sm
of 1 and 0. The Tr elements are 1when brands m and j, in
in equation (11), are sold by the same
∂p j
supermarket and 0 otherwise.
8
effect of wholesale prices on market shares given by
∂s j ( p ( w))
∂w j
=
∂s j ∂p j
∂p j ∂w j
. In matrix
notation the manufacturers’ response matrix can be written as ∆ w = ∆' p ∆ r , where ∆ p is a
matrix of derivatives of all the retail prices with respect to all the wholesale prices. The
difficulty lies in estimating ∆ p . Following Villas-Boas (2000), this matrix can be derived
by totally differentiating for a given equation j in (11) with respect to all prices and
wholesale prices, and solving for the derivatives of all prices with respect to the
wholesale prices. That is,
N
∂s j
k =1
∂p k
∑[
N
+ ∑ (Tr (i, j )
i =1
∂s f
∂ 2 si
∂s
( pi − wi − cir )) + Tr (k , j ) k ]dp k − Tr ( f , j )
dw f = 0 .
∂p j ∂p k
∂p j
∂p j
(16)
In matrix notation, (16) becomes
Gdp − H f dw f = 0 .
(17)
Solving for the derivatives of all prices with respect to wholesale prices yields
∆ p = G −1 H f .
(18)
The market channel price-cost margin is the sum of the supermarkets’ and the
manufacturers’ price-cost margins given by equations (12) and (15) respectively.
p − c r − c w = −(Tr * ∆ r ) −1 s ( p) − (Tw * ∆ w ) −1 s ( p) .
(19)
3. Data Sources and Management
The data used in the above analysis consists of two kinds of variables: retail sales
variables and demographic variables.
The sales data were obtained from the Information Resource, Inc. (IRI) Infoscan
database at the Food Policy Marketing Center of University of Connecticut. It covers
9
RTEC sales for 37 brands at the four leading supermarkets in Boston (Stop & Shop,
Shaw’s, DeMoulas and Star Market) for four-weekly periods between April 1995 and
December 1997. One important feature of this period is that it covers significant price
drops in the 1990s when the RTEC industry was being questioned on market power
(Cotterill, 1999, and Connor, 1999). The sales data collected consists of the following
variables: dollar sales, volume (in pounds) sales, and the percent volume sold with any
feature.
From the RTEC sales data, the market shares and the retail prices were computed
for each brand and supermarket. Market shares are obtained by converting volume sales
into number of servings sold and dividing by the potential market size. This is done by
using the serving weight found on the box of cereals. The potential market size is
assumed to be one serving per capita and per day as in Nevo (2001). The real retail prices
were computed by dividing the dollar sales of each brand by the number of servings sold
and then deflated using the urban consumers CPI for Boston (with CPI=100 for 1981).
The analysis is conducted using a set of 37 RTEC brands produced by six
manufacturers (Kellogg’s, General Mills, Post-Kraft, Quaker, Ralston and Nabisco) sold
in four supermarket channels (Stop & Shop, Shaw’s, DeMoulas and Star market) in
Boston market from April 1995 to December 1997 for 5180 observations.
Primary data on product characteristics were collected by examining the cereal
boxes. The variables collected were the sugar content, the fiber content and the total
calories. A dummy variable was created to classify the branded cereals into “Kid cereal”
or not. It is assumed that those characteristics did not change since between 1995 and
1997. Besides the sales data, the analysis uses the demographic data to take account of
10
the heterogeneity of consumer taste. This paper uses two demographic variables: the
natural logarithm of age and income. Further it is assumed that those variables are jointly
normally distributed with mean given by the grocery data and variance-covariance matrix
given by the CPS data at Boston level.9
The demand model presented above implies endogeneity of RTEC prices, and,
hence, can lead to biased parameter estimates10. This implies that prices are correlated
with product characteristics. This study uses a set of instrumental variables to control for
retail price endogeneity in a particular supermarket. The set has two subcomponents. The
first one consists of the interaction between input prices and brand dummy variables,
where input prices included wages in the Boston area and the price of gas, the price of
industrial and commercial electricity at the location of manufacturers, the Federal Funds
Effective interest rate, and the 3-month Commercial Paper interest rate were interacted
with brand dummy variables. The second subcomponent consists of time dummy
variables describing the jawboning campaign events that induced price drops (change in
conduct) by RTEC manufacturers, as described by Cotterill (1999) and Connor (1999).
All the price instruments mentioned above were interacted with the error terms
when applying the GMM estimation procedure. The use of GMM technique implies the
need for an optimal weighting matrix. This paper follows Hansen (1982) who shows that
setting the weighting matrix equal the inverse of an asymptotic covariance matrix is
optimal in the sense that it gives parameter estimates with the smallest asymptotic
variance.
9
Romeo (2005) shows that knowing the joint distribution for demographics at the city level is sufficient to
infer the distribution at the county or zip code levels.
10
This endogeneity comes from the fact that retail prices depend on observed and unobserved product
characteristics. Any variation in those characteristics induces a variation in retail prices.
11
4. Empirical Results
4.1. Demand Estimation Results
The estimates of the RCM parameters are presented in Table 1. The results of the
random coefficients model account for the consumer heterogeneity by allowing the
coefficients of RTEC brand prices, sugar, calories, fiber contents and a kid-cereal dummy
variable to vary across consumers as a function of the natural logarithm of their age and
income, and the unobserved consumer characteristics.
As expected, the price coefficient is negative and highly significant, meaning that
the mean valuation utility decreases when price increases. The promotion coefficient is
positive and highly significant, implying that the promotion increases the mean valuation
utility. The brand unobserved characteristics (constant) have a negative and significant
effect on the indirect utility. For the average consumer, sugars, fiber as well as the
dummy for kids’ cereals have a negative but not significant effect on marginal utility.
Stanley and Tschirthart (1991) and Nevo (2000) find a positive sugar coefficient. The
negative sign of the coefficient of sugar may be explained by the increased worry of
consumer on the effect of sugar consumption on weight gain. The calories have a
negative effect on marginal utility, illustrating the increased awareness of consumers on
their calories intake.
The interaction between the unobserved consumer characteristics and the sugar
content has a positive and significant effect on the mean utility, while the effect is
negative and significant for the calories. The results also show that the price sensitivity
increases with the age and decreases with income. Further, the sensitivity for the fiber
content increases with age.
12
Table 2 presents the own-price elasticities implied by the RCM. The own price
elasticities range from –9.8183 for Ralston Cookie Crisp in Star Market to –3.2811 for
Kellogg’s corn Flakes in Stop & Shop. The elasticities were highly significant and were
deemed suitable for further results.
4.2. Supply Results
This section presents the retailers’ and manufacturers’ PCMs under a double
marginalization scenario, given the demand estimates from the previous section. The
results are given in Tables 3 and 4 (in $/lb) for the four leading supermarkets and the
manufacturers (at the brand level) respectively. For the retailers, the price-cost margins
vary from $0.6220/lb for Kellogg Corn Flakes in DeMoulas to $0.8139/lb for Kellogg
Corn Pops in Stop & Shop. The remaining supermarkets obtain smaller dollar margins.
The highest margins are realized by Star market with an average of $0.7143/lb.
For the manufacturers, the margins are higher than the retailers’ PCMs. The
manufacturers’ PCMs vary from $1.1621/lb for Ralston Cookie Crisp in DeMoulas to
$1.5740/lb for Kellogg’s Raisin Bran in DeMoulas. Figure 1 shows the retail RTEC
prices charged to consumer at various supermarkets. Stop & Shop, in spite of obtaining
the largest dollar margins, charged prices close to the overall average price ($3.34/lb). On
the other hand, Star Market, mostly an urban supermarket with generally small store size,
charged the highest prices that were approximately 10% higher than those of other
supermarkets ($3.61/lb). Shaw’s supermarket chain charged the lowest prices during the
period of this study.
The shares of the PCMs accruing to supermarkets are given in Table 5. On
average, the share of the manufacturers in Stop & Shop, the leading supermarket in
13
Boston, is the lowest among the four supermarkets. In Stop & Shop, the manufacturer’s
share varies between 62.63% for Nabisco and 67.86% for Kellogg. Table 5 also shows
that the shares of the price-cost margins accruing to supermarkets are negatively related
to the size of the manufacturer. Four Stop & Shop, Shaw’s and DeMoulas these share
increase in the following order: Kellogg, General Mills, Post , Quaker, Ralston and
Nabisco, while for Star Market the order is Kellogg, General Mill, Post, Ralston, Nabisco
and Quaker. In the other hand, manufacturers concede more to bigger supermarkets such
as Stop & Shop than to smaller ones.
Finally, figures 1, 2 and 3 show share-weighted RTEC prices, PCMs and marginal
cost over time. They illustrate several important points. First, supermarket RTEC prices
dropped after the jawboning campaign that exposed high RTECs price in the media.
Second, supermarket PCMs actually increased after the jawboning campaign, meaning
that the price drops by RTEC manufacturers were not fully passed on to the consumers.
Last, supermarket marginal cost decreased due to lower RTEC wholesale prices after the
jawboning campaign.
5. Conclusion
This paper decomposed the channel price-cost margins for ready-to-eat cereals in
the Boston area at the supermarket chain and brand level. The random coefficients model
is used to estimate the demand for 37 RTEC brands which were used to compute the
PCMs for retailers and manufacturers under double marginalization scenario.
Overall, the results show that RTEC manufacturers capture most of the channel
profits (two-third). The leading supermarket chain captures the highest profits among
14
supermarkets even though they charge one of the lowest prices. Further results show that
any cut in RTEC wholesale prices are not fully passed on to consumers.
15
Table 1. RTEC Demand Parameter Estimates and Related Statistics, Boston Market.
Variable
Price
Promotion
Constant
Sugar
Calories
Fiber
Kid Dummy
Estimate
-26.3720***
1.2251***
-8.1029***
-0.1336
-3.6308***
-0.0552
-0.0331
t-Statistic
14.6840
7.8179
11.3300
1.3100
13.7010
1.3597
0.1885
Price
Constant
Sugar
Calories
Fiber
Kid Dummy
-1.0394
0.0810
0.7642***
-2.1721***
0.0281
-0.8062*
0.4504
0.1303
7.6546
11.9030
0.5226
1.9070
Interaction with Age Price
Constant
Sugar
Calories
Fiber
Kid Dummy
2.7514
-7.2706**
0.0315
0.7189
1.5242***
1.0390
0.1409
1.9675
0.0419
0.3449
5.7497
1.4241
Interaction with Age Price
Constant
Sugar
Calories
Fiber
Kid Dummy
-33.4760***
3.3782***
-0.4749**
-2.6385***
-0.1437
-0.1702
10.2980
4.0534
2.0142
5.0415
1.1657
0.6685
Means ( β ’s)
Standard Deviation
One, two and three asterisks indicate significance at 10%, 5% and 1% levels,
respectively. The sample consisted of 6,475 observations.
16
Table 2. Own-Price Elasticity Estimates for RTECs in Boston Supermarkets
RTEC Brand
KApple Jacks
KComplete Bran
KCorn Flakes
KCorn Pops
Kcrispix
Kfroot Loops
Kfrosted Flakes
Kfrosted Mini Wheats
Kraisin Bran
Krice Krispies
Kspecial K
GMcheerios
GMCinammon Crunch
GMCoco Puffs
GMGolden Grahams
GMHoney Nut Cheerios
GMKix
GMLucky Charms
GMMulti Gain Cheerios
GMTotal
GMTotal Raisin Bran
GMWheaties
GMApple Cinnamon
Pbanana Nut Crunch
Pcocoa Pebbles
Pfruit Pebbles
Pgrape Nuts
Phoney Comb
Praisin Bran
Qcap N Crunch
Qoat
QToasted
N Frosted Wheat Bites
N Spoon Size
R Cookie Crisp
R Corn Chex
R Rice Chex
Stop &
Shop
-6.6405
-5.3655
-3.2811
-5.6708
-6.1444
-6.1206
-4.6327
-4.5705
-3.9920
-5.6942
-6.4419
-5.0058
-6.1692
-6.0719
-6.6767
-5.5582
-6.4051
-6.2279
-6.9412
-6.5692
-5.1984
-4.5544
-5.6728
-5.3212
-6.4396
-6.2363
-3.7112
-5.8955
-4.1247
-5.4188
-5.5556
-5.9123
-5.3109
-5.0452
-9.0245
-6.5584
-6.5440
Shaw’s
DeMoulas
-6.2749
-5.2406
-3.4651
-5.5357
-5.9071
-6.4301
-4.8169
-4.5036
-4.335
-5.5619
-6.5383
-4.6451
-5.7531
-5.9009
-6.1482
-5.4489
-6.1278
-5.7973
-6.8014
-5.9602
-5.4267
-4.4745
-5.8485
-4.6780
-5.7366
-5.5027
-3.7289
-5.3765
-3.8369
-5.1140
-5.0589
-5.5711
-5.0439
-4.6056
-7.9934
-5.8920
-5.9151
-6.1003
-5.2411
-3.6698
-6.2584
-6.7430
-5.7442
-4.5219
-4.1984
-3.9813
-5.7505
-7.0539
-5.6274
-6.4313
-6.4782
-6.6970
-5.3830
-7.4510
-6.5970
-6.6132
-6.8404
-5.2357
-4.9020
-5.7377
-5.6519
-6.2701
-6.1688
-3.9438
-5.8610
-3.9477
-5.3418
-4.3969
-5.8291
-5.2597
-4.9987
-7.6441
-6.8352
-6.8656
17
Star
Market
-6.9988
-5.7329
-3.8211
-6.3551
-6.9214
-6.2090
-4.9636
-4.9135
-4.5659
-5.9265
-6.9246
-5.7413
-6.4880
-6.4021
-7.3264
-5.7054
-7.4505
-6.4199
-7.6776
-7.1392
-5.1982
-5.4208
-6.2264
-5.7781
-6.9037
-6.9147
-3.8775
-6.4101
-4.4766
-6.3534
-6.0631
-6.8210
-5.5465
-5.1703
-9.8183
-7.3917
-7.3653
Simple
Average
-6.4132
-5.3731
-3.5408
-5.9380
-6.4600
-6.0292
-4.6814
-4.5460
-4.1693
-5.7088
-6.7311
-5.2300
-6.2106
-6.1986
-6.6719
-5.4832
-6.8292
-6.2419
-6.9933
-6.6610
-5.2701
-4.8597
-5.8140
-5.2986
-6.2581
-6.1531
-3.8183
-5.8425
-4.0501
-5.5108
-5.2996
-6.0016
-5.2750
-4.9670
-8.5863
-6.6881
-6.6977
Table 3: Price-Cost Margins for Supermarkets in Boston ($/lb)
RTEC Brand
KApple Jacks
KComplete Bran
KCorn Flakes
KCorn Pops
Kcrispix
Kfroot Loops
Kfrosted Flakes
Kfrosted Mini Wheats
Kraisin Bran
Krice Krispies
Kspecial K
GMcheerios
GMCinammon Crunch
GMCoco Puffs
GMGolden Grahams
GMHoney Nut Cheerios
GMKix
GMLucky Charms
GMMulti Gain Cheerios
GMTotal
GMTotal Raisin Bran
GMWheaties
GMApple Cinnamon
Pbanana Nut Crunch
Pcocoa Pebbles
Pfruit Pebbles
Pgrape Nuts
Phoney Comb
Praisin Bran
Qcap N Crunch
Qoat
QToasted
N Frosted Wheat Bites
N Spoon Size
R Cookie Crisp
R Corn Chex
R Rice Chex
Simple Average
Stop &
Shop
0.7133
0.6665
0.6572
0.6865
0.6859
0.6880
0.6842
0.7204
0.7203
0.6908
0.6888
0.6825
0.7010
0.6939
0.6966
0.6917
0.7011
0.6995
0.6963
0.6911
0.7145
0.6731
0.6903
0.7524
0.7018
0.6916
0.6736
0.6883
0.7208
0.6849
0.7318
0.7348
0.7279
0.7370
0.7188
0.6969
0.6966
0.6998
Shaw’s
DeMoulas
0.6431
0.6213
0.6200
0.6339
0.6320
0.6359
0.6346
0.6512
0.6508
0.6375
0.6340
0.6299
0.6393
0.6385
0.6364
0.6359
0.6388
0.6378
0.6368
0.6310
0.6475
0.6265
0.6367
0.6676
0.6377
0.6325
0.6276
0.6322
0.6488
0.6318
0.6564
0.6570
0.6531
0.6592
0.6448
0.6349
0.6350
0.6391
0.6433
0.6226
0.6220
0.6382
0.6364
0.6336
0.6347
0.6509
0.6514
0.6379
0.6380
0.6341
0.6436
0.6402
0.6399
0.6368
0.6450
0.6413
0.6372
0.6367
0.6488
0.6292
0.6373
0.6664
0.6405
0.6360
0.6287
0.6354
0.6505
0.6339
0.6555
0.6587
0.6535
0.6591
0.6435
0.6409
0.6411
0.6412
18
Star
Market
0.7548
0.7057
0.6880
0.8138
0.6665
0.6753
0.6980
0.7432
0.7892
0.6680
0.6733
0.7055
0.6701
0.7294
0.7112
0.6886
0.6736
0.7042
0.6859
0.6791
0.7448
0.7002
0.6979
0.7373
0.7278
0.7344
0.7384
0.6953
0.7997
0.6882
0.7235
0.7194
0.7234
0.7344
0.7992
0.6700
0.6707
0.6488
Simple
Average
0.6886
0.6540
0.6468
0.6931
0.6552
0.6582
0.6629
0.6914
0.7029
0.6586
0.6585
0.6630
0.6635
0.6755
0.6710
0.6633
0.6646
0.6707
0.6641
0.6595
0.6889
0.6573
0.6655
0.7059
0.6769
0.6736
0.6671
0.6628
0.7050
0.6597
0.6918
0.6925
0.6895
0.6974
0.7016
0.6607
0.6608
0.6736
Table 4. Price-Cost Margins for RTEC Manufacturers ($/lb)
RTEC Brand
KApple Jacks
KComplete Bran
KCorn Flakes
KCorn Pops
Kcrispix
Kfroot Loops
Kfrosted Flakes
Kfrosted Mini Wheats
Kraisin Bran
Krice Krispies
Kspecial K
GMcheerios
GMCinammon Crunch
GMCoco Puffs
GMGolden Grahams
GMHoney Nut Cheerios
GMKix
GMLucky Charms
GMMulti Gain Cheerios
GMTotal
GMTotal Raisin Bran
GMWheaties
GMApple Cinnamon
Pbanana Nut Crunch
Pcocoa Pebbles
Pfruit Pebbles
Pgrape Nuts
Phoney Comb
Praisin Bran
Qcap N Crunch
Qoat
QToasted
N Frosted Wheat Bites
N Spoon Size
R Cookie Crisp
R Corn Chex
R Rice Chex
Simple Average
Stop &
Shop
1.4831
1.3910
1.3824
1.4609
1.4728
1.4457
1.4242
1.5451
1.5517
1.4511
1.4492
1.4050
1.4148
1.4118
1.4057
1.4199
1.4342
1.4407
1.4358
1.4010
1.4023
1.3734
1.4017
1.3213
1.2400
1.2312
1.2363
1.2283
1.2984
1.1842
1.2629
1.2155
1.2047
1.2505
1.1974
1.1743
1.1749
1.3574
Shaw’s
DeMoulas
1.4604
1.3769
1.3853
1.4796
1.4768
1.4409
1.4211
1.5147
1.5345
1.413
1.4328
1.3911
1.3992
1.3844
1.3834
1.3949
1.4338
1.4088
1.4215
1.3727
1.3956
1.3774
1.402
1.327
1.229
1.2227
1.2261
1.2221
1.2894
1.1706
1.243
1.2192
1.2011
1.2193
1.1912
1.2123
1.2115
1.3483
1.5071
1.3822
1.3986
1.4504
1.4276
1.4298
1.4262
1.5275
1.574
1.4221
1.4452
1.4000
1.411
1.4052
1.4442
1.3858
1.4481
1.4174
1.4158
1.3843
1.3865
1.3562
1.3914
1.3234
1.2329
1.2263
1.2370
1.2244
1.3146
1.1889
1.2098
1.2355
1.2011
1.2043
1.1621
1.1867
1.1858
1.3505
19
Star
Market
1.4633
1.3821
1.4017
1.5190
1.4763
1.4673
1.4152
1.5162
1.5272
1.4417
1.4325
1.4124
1.4230
1.4115
1.4418
1.4081
1.5107
1.4160
1.4474
1.4299
1.4029
1.3789
1.4085
1.3279
1.2482
1.2418
1.2245
1.2258
1.2902
1.1767
1.2486
1.2303
1.2095
1.2512
1.1853
1.2468
1.2362
1.3642
Simple
Average
1.4785
1.3831
1.3920
1.4775
1.4634
1.4459
1.4217
1.5259
1.5468
1.4320
1.4399
1.4021
1.4120
1.4032
1.4188
1.4022
1.4567
1.4207
1.4301
1.3970
1.3968
1.3715
1.4009
1.3249
1.2375
1.2305
1.2310
1.2252
1.2982
1.1801
1.2411
1.2251
1.2041
1.2313
1.1840
1.2050
1.2021
1.3551
Table 5. Supermarkets Shares of the Channel Profit
Stop & Shop
Shaw’s
DeMoulas
Star Market
Mean
Kellogg
32.14
30.68
30.49
30.58
30.97
General Mills
32.96
31.29
31.29
31.20
31.69
Post
35.88
33.86
33.79
34.03
34.39
Quaker
37.00
34.88
34.89
35.21
35.49
Nabisco
37.37
35.16
35.30
35.14
35.74
Ralston
37.32
34.63
35.27
35.02
35.56
Mean
35.45
33.42
33.51
33.53
33.97
20
Stop $ Shop
Shaws
21
DeMoulas
Star Market
Dec 7, 97
Nov 9, 97
Oct 12, 97
Sep 14, 97
Aug 17, 97
Jul 20, 97
Jun 22, 97
May 25, 97
Apr 27, 97
Mar 30, 97
Mar 2, 97
Feb 2, 97
Jan 5, 97
Dec 8, 96
Nov 10, 96
Oct 13, 96
Sep 15, 96
Aug 18, 96
Jul 21, 96
Jun 23, 96
May 26, 96
Apr 28, 96
Mar 31, 96
Mar 3, 96
Feb 4, 96
Jan 7, 96
Dec 10, 95
Nov 12, 95
Oct 15, 95
Sep 17, 95
Aug 20, 95
Jul 23, 95
Jun 25, 95
May 28, 95
Apr 30, 95
Prices ($/lb)
Figure 1: Share-Weighted Retail RTEC Prices at Boston Supermarkets, April 1995 to
December 1997
3.7
Jawboning Campaign
3.5
3.3
3.1
2.9
2.7
2.5
Stop & Shop
Shaws
22
DeMoulas
Star Market
Dec 7, 97
Nov 9, 97
Oct 12, 97
Sep 14, 97
Aug 17, 97
Jul 20, 97
Jun 22, 97
May 25, 97
Apr 27, 97
Mar 30, 97
Mar 2, 97
Feb 2, 97
Jan 5, 97
Dec 8, 96
Nov 10, 96
Oct 13, 96
Sep 15, 96
Aug 18, 96
Jul 21, 96
Jun 23, 96
May 26, 96
Apr 28, 96
Mar 31, 96
Mar 3, 96
Feb 4, 96
Jan 7, 96
Dec 10, 95
Nov 12, 95
Oct 15, 95
Sep 17, 95
Aug 20, 95
Jul 23, 95
Jun 25, 95
May 28, 95
Apr 30, 95
PCM ($/lb)
Figure 2: Estimated Share-Weighted Price-Cost Margins for RTEC at Boston Supermarkets,
April 1995 to December 1997
0.74
Jawboning Campaign
0.72
0.7
0.68
0.66
0.64
0.62
0.6
Apr 30, 95
Stop & Shop
23
Shaw's
DMoulas
Star Market
Dec 7, 97
Nov 9, 97
Oct 12, 97
Sep 14, 97
Aug 17, 97
Jul 20, 97
Jun 22, 97
May 25, 97
Apr 27, 97
Mar 30, 97
Mar 2, 97
Feb 2, 97
Jan 5, 97
Dec 8, 96
Nov 10, 96
Oct 13, 96
Sep 15, 96
Aug 18, 96
Jul 21, 96
Jun 23, 96
May 26, 96
Apr 28, 96
Mar 31, 96
Mar 3, 96
Feb 4, 96
Jan 7, 96
Dec 10, 95
Nov 12, 95
Oct 15, 95
Sep 17, 95
Aug 20, 95
Jul 23, 95
Jun 25, 95
May 28, 95
MC ($/lb)
Figure 3: Estimated Share-Weighted Marginal Cost for RTEC at Boston Supermarkets, April
1995 to December 1997
3.2
Jawboning Campaign
3
2.8
2.6
2.4
2.2
2
References
Azzam A.M. and E. Pagoulatos, 1997. “Vertical Relationships: Economic Theory and
Empirical Evidence,” Paper prepared for the June 12-13, 1999 International
Conference on Vertical Relationships and Coordination in the Food System.
Universita Cattolica Del Sacro Cuore, Piacenza, Italy.
Berry, Steven T., 1994. “Estimating Discrete-Choice Models of Product Differentiation,”
Rand Journal of Economics, 25 No. 2, pp.242-262.
Berry, S., J. Levinsohn and A. Pakes, 1995. “Automobile Prices in Market Equilibrium,”
Econometrica, 63, No. 4, pp.841-890.
Besanko, D. and M.K. Perry, 1993. “Equilibrium Incentives for Exclusive Dealing in a
Differentiated Products Oligopoly,” Rand Journal of Economics, 24, No. 4, pp.
646-667.
Bresnahan, T., 1987. “Empirical Studies of Industries with Market Power,” in
Schmalensee, R., R.D. Willig eds., Handbook of Industrial Organization, Volume II,
Amsterdam: North Holland, pp.1011-1057.
Choi, S.C., 1991. “Price Competition in a Channel Structure with a Common Retailer.”
Marketing Science, 10, No. 4, pp. 271-296.
Cotterill, R. W. 1999. “Jawboning Cereal: The Campaign to Lower Cereal Prices.”
Agribusiness: An International Journal, 15, No. 2, pp. 197-205.
Cotterill, Ronald W. and Haller, Lawrence E. 1997. An Economic Analysis of the
Demand for RTE Cereal: Product Market Definition and Unilateral Market
Power Effects. University of Connecticut Food Marketing Policy Center Research
Report No. 35 (September).
Connor, J.M., 1999. “Breakfast Cereals: The Extreme Food Industry,” Agribusiness: An
International Journal, 15, No. 2, pp. 257-259.
Gal-Or, E., 1991. “Vertical Restraints with Incomplete Information,” Journal of
Industrial Economics, pp. 503-516.
Gilligan, T.W., 1986. “The Competitive Effects of Resale Price Maintenance,” Rand
Journal of economics, 17, No. 4, pp.544-556.
Hausman, J., G. Leonard, and J.D. Zona, 1994. “Competitive Analysis with
Differentiated Products.” Annales D’Economie et de Statistique, 34: 159-180.
24
Hastings, J.S., 2002. “Vertical Relationships and Competition in Retail Gasoline
Markets: Empirical Evidence from Contract Changes in California,” University of
California Energy Institute Power, Working Paper No. 84.
Kadiyali, V., N. Vilcassim and P. Chintagunta, 1999. “Product Line Extensions and
Competitive Market Interactions: An Empirical Analysis,” Journal of Econometrics,
88, pp.339-363.
Klein, B. and K.M. Murphy, 1997. “Vertical Integration as a Self-Enforcing Contractual
Arrangement,” American Economic Review, 87, pp. 415-420.
Kuhn, K.U., 1997. “Nonlinear Pricing in Vertically Related Duopolies,” Rand Journal of
Economics, 28, No. 1, pp.37-62.
Ma, L.Y., 1997. “An Econometric Analysis of Competition in a Differentiated Product
Industry: The U.S. Ready-to-Eat Cereal Industry,” Ph.D. Dissertation. Department
Of Agricultural & Resource Economics, University of Connecticut.
Manuszak, M. D., 2001. “The impact of Upstream Mergers on Retail Gasoline Markets,”
Working Paper, Carnegie Mellon University.
Mathewson, G.F. and R.A. Winter, 1984. “An Economic Theory of Vertical Restraints,”
Rand Journal of Economics, 15, No. 1, pp.27-38.
McFadden, D., 1973. “Conditional Logit Analysis of Qualitative Choice Behavior,”
Frontiers of Econometrics, P. Zarembka, eds., New York, Academic Press, pp.105142.
McFadden, D. and K. Train, 2002. “Mixed MNL Models of Discrete Response,” Journal
of Applied Econometrics, 15, No. 5, pp.447-470.
McGuire, T.W. and R.Staelin, 1983. “An Industry Analysis of Downstream Vertical
Integration,” Marketing Science, 2, No. 2, pp.161.191.
Messinger, P.R. and C. Narashiman, 1995. “Has Power Shifted in the Grocery
Channel?,” Marketing Science, 14, No. 2 pp.189-223.
Nevo, A., 1998. “Identification of the Oligopoly Solution Concept in a Differentiated
Products Industry,” Economics Letters, 59, pp.391-395.
Nevo, A., 2000. “A Practitioner’s Guide to Estimation of random Coefficients Logit
Models of demand,” Journal of Economics and management Strategy, 9, No. 4,
pp.513-548.
25
Nevo, A., 2001. “Measuring Market Power in the Ready-To-Eat Cereal Industry,”
Econometrica, 69, No. 2, pp.307.342.
Rey, P. and J. Tirole, 1986. “The Logic of Vertical restraints,” American Economic
Review, 76, pp.921-939.
Romeo, C., 2005. “Estimating discrete joint probability distributions for demographic
characteristics at the store level given store level marginal distributions and a citywide joint distribution.” forthcoming Quantitative Marketing and Economics.
Scherer, F.M., 1979. “The Welfare Economics of Product Variety, An Application to the
Ready-To-Eat Cereals Industry,” Journal of Industrial Economics, 28 (December),
pp. 113-134.
Schmalensee, R., 1978. “Entry Deterrence in the Ready-to-Eat Breakfast Cereal
Industry,” Bell Journal of Economics, 9 No. 4, pp. 305-327.
Shaffer, G., 1991. “Slotting Allowances and resale Price Maintenance: A Comparison of
Facilitating Practices,” Rand Journal of Economics, 22, No. 1, pp.120-135.
Shaffer, G. and D. P. O’Brien, 1997. “Nonlinear Supply Contracts, Exclusive Dealing,
and Equilibrium Market Foreclosure,” Journal of Economics and Management
Strategy, 6, pp.755-785.
Sudhir, K., 2001. “Structural Analysis of Manufacturer Pricing in the Presence of a
Strategic Retailer,” Marketing Science, 20, No. 3, pp.244-264.
Tirole, J. 1988. The Theory of Industrial Organization, Cambridge: The MIT Press.
Villas-Boas, J.M. and Y. Zhao, 2000. “The Ketchup Marketplace: Retailer,
Manufacturers and Individual Consumers,” Working Paper, University of
California Berkeley.
Villas-Boas, B.S., 2002. “Vertical Contracts Between manufacturers and Retailers: An
Empirical Analysis,” Working Paper, University of California Berkeley.
Waterson, M., 1993. “Vertical Integration and Vertical Restraints,” Oxford Review of
Economic Policy, 9, No. 2, pp. 41-57.
26