Decomposing Market Channel Profits: The Case of Ready-to-Eat Cereals in Boston

Benaissa Chidmi, Rigoberto A. Lopez, and Ronald W. Cotterill

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 PCMs accruing to RTEC manufacturers and retailers. For instance, Stop & Shop, the leading supermarket chain in Boston, gets more than 50% of the channel profits, while RTEC manufacturers get more than 56% of the channel profits when dealing with smaller supermarket chains (Shaws, DeMoulas and Star Market). The results attest to possible volume discounts and efficiencies as the smaller supermarkets, especially those with urban location, charge higher prices in spite of smaller dollar PCMs, due in part to higher retailing costs. Among the manufacturers, General Mills brands command the highest PCMs while Post brands command the lowest ones.

Department of Agricultural and Resource Economics The University of Connecticut Storrs, CT February 2005

Decomposing Market Channel Profits: The Case of Ready-to-Eat Cereals in Boston

1. Introduction The 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). In these studies, the quantities sold and the prices at retail and wholesale levels are treated as equilibrium outcomes of a two-stage pricing game that assumes horizontal competition at each stage. This class of structural models allows one to examine the manufacturer-retailer relationship without observing wholesale prices and marginal costs. The fundamental inquiry of this research is to estimate and decompose the channel profits for the ready-to-eat cereal (RTEC) market in Boston. This will shed light on the power manufacturers and retailers have to set the price of RTECs in the Boston market. This research contributes to the existing literature in several ways. It is the first study to estimate a discrete choice random coefficient demand system for branded products at the chain as opposed to market level.1 Moreover, the study decomposes channel profits accruing to manufacturers and retailers respectively not attributing all profits to manufacturers as in Nevos work (2001). Villas-Boas (2002) has estimated a similar model for yogurt at the store level for a few stores. The advantage of this study is that it pursues on chain wide strategic pricing in a relevant regional market area, Boston Information Resources Incorporated (IRI) market. This study uses four-week data while
1

Cotterill and Dhar (2002) is the only prior chain level demand study and it uses a nested logit model.

prior brand demand analysis uses quarterly (Nevo, 2001); Hausman et al., 1994) or weekly (Kadiyali et al., 1999; Cotterill and Haller, 1997) observations.2 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. Since we have quarterly analyses (Ma, 1997; Nevo, 2001) and weekly analyses (Cotterill and Haller, 1997) of RTEC, this study at the four-week level may shed light at the importance of time aggregation in this particular industry. Comparison, however, will be difficult because of the differences in the methods across these studies. 2. Methodology The methodology used consists of two steps. First, the retail demand for differentiated brands of RTEC 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. As a byproduct, the wholesale to retail price transmission rate is obtained. The model is sufficiently flexible to allow for transmission above or bellow 100%.

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.

2.1. Demand Side Consider the case where consumers choose a brand of a product that maximizes their utility. More specifically, the indirect utility3 of consumer i from buying the brand j is given by
U ij = j + x j i i p j + j + ij , i = 1,...n j = 1,..., J

(1)

where 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 , j are the unobserved (by the researcher) product characteristics, and i j represents the distribution of consumer preferences about the unobserved product characteristics, with a density f ( ) . The parameters to be estimated are i and i . Note that those parameters are allowed to vary across consumers, therefore taking into account the heterogeneity taste of consumer. 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:

i = + Di + vi , i = + Di + vi ,

(2) (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 = j + x j + Di x j + vi x j p j Di p j vi p j + j + ij .

(4)

The indirect utility comes from a quasi-linear utility function.

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 j = j + x j p j + j and a deviation from that mean, which is a function of the interaction between the observed and unobserved consumers characteristics and the price and observed brand characteristics, given by

ij = Di x j Di p j + vi x j vi p j + 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 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 , j ; ) = {( Di , vi , ij ) : U ijt U ik k = 0,1,... N } ,

(6)

where 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 the jth brand is chosen. That is,
s j = I {( Di , vi , ij ) : U ij U ik k = 0,1,...N }dH (D ) dG (v ) dF ( ).

(7)

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.

Depending on the assumptions regarding D, v , and , 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

i and 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

that equates the predicted market shares with observed market shares.
Given starting values for 2 (parameters that enter non-linearly) in (4) and , and the draws from the distributions of D and , 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 ( ) to integrate the s analytically. The predicted market shares are approximated by

5 6

The integral in (7) is solved using the simulation technique proposed by Pakes (1986). The starting values for the mean utility value come from the Logit model estimation

s j ( p, x, , Pns ; 2 ) =

1 ns 1 ns s ji = ns ns i =1 i =1

exp( j + ij ) 1 + exp( m + im )
m =1 J

(8)

where ns is the number of draws from the distributions D and given by the distribution Pns . The above predicted market shares allow computing the mean utility valuation 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 ( .t ; 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 i s ji (1 s ji ), s j p k s j i =1 j = = p k s j p k ns s s , s j i ji ki i =1 if j = k ,

(9)
otherwise.

The random coefficients model offers several important advantages over the traditional discrete choice models (Logit and Nested Logit models). First, the own price elasticities depend now on the price sensitivity of the individual in the sample and not on the functional form. Instead of being determined by the single parameter , the own price elasticities are obtained by averaging the price sensitivity of the individuals in the sample. A second advantage is that the full model is not constrained by a-priori segmentation of
7

Berry, Levinsohn and Pakes (1995) suggest using the following contraction mapping

t +1 = t + ln(S ) ln(s( p, x, , Pns ; 2 ),

where
T

t = 0,..., T

s(.) is the predicted market shares computed by equation (8) and T is the smallest integer such that
is smaller than some tolerance level

T 1

the market allowing for flexible substitution patterns. Finally, the full model by taking into account the consumer heterogeneity taste, gives another explanation besides the price variation to the variation of market shares across markets. 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 retailers problem is to maximize profits, given by

r =

( p
jS r

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 retailers 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 +

mS r

(p

r wm c m )

s m = 0. p j

(11)

Repeat the procedure for each supermarket, stack the solutions and write them using an ownership matrix to obtain the retailers price-cost margins. 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.

p w c r = (Tr * r ) 1 s ( p) ,

(12)

where Tr is the retailers 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 manufacturers problem. Each manufacturer sets the wholesale price w in order to maximize profits, given by

w =

jS w

(w

c w ) s j ( p( w))M , j

(13)

where S w represents the set of brands produced by manufacturer m , and c w is the j manufacturers marginal cost for brand j . The first-order conditions are sj +

mS w

(w

w cm )

s m = 0. p j

(14)

Similarly, defining a matrix of manufacturers ownership Tw and a matrix of manufacturers response w , and stacking all the manufacturers first-order conditions one obtains the 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 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,

[
k =1

s j p k

+ (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) .
3. Data Sources and Management

(19)

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 RTEC sales for 37 brands at the four leading supermarkets in Boston (Stop & Shop, Shaws, 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 (Kelloggs, General Mills, Post-Kraft, Quaker, Ralston and Nabisco) sold in four supermarket channels (Stop & Shop, Shaws, 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 the heterogeneity of consumer taste. This study 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

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.

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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 three subcomponents. The first one consists of the retail price of the brand in other supermarkets in Boston and the percentage of RTEC sold under any kind of merchandizing (Promotion) in that particular chain. The second contains the brand dummy variables11. The third subcomponent is the interaction between the input prices and the supermarket dummy variables. Wages in the Boston area and the price indices of energy, grain and sugar were interacted with supermarket dummy variables. All the price instruments mentioned above are 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.
4. Empirical Results 4.1. Demand Estimation Results

The estimates of the RCM parameters are presented in Table 1. The parameter estimates for the price and promotion are the GMM estimates of equation (4) while the taste parameters are the minimum distance estimates. The results of the random
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 The use of brand dummies implies that the model does not identify the taste parameters given by the product characteristics in the indirect utility function in equation (4). To circumvent this shortcoming, the study uses the minimum distance (MD) procedure proposed by Chamberlain (1982).
10

11

coefficients model account for the consumer heterogeneity by allowing the coefficients on price, sugar, calories, fiber contents and 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 taste parameters obtained by the MD approach are all highly significant. The brand unobserved characteristics (constant) have a negative and significant effect on the indirect utility. For the average consumer, sugars as well as the dummy for kids cereals have a negative 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 and fibers have a positive marginal utility. For the calories, the positive sign maybe explained by the fact that consumer perceive the RTEC as a good sources of energy. For the fiber, the positive coefficient shows the nutrition component of the RTEC. The estimates of standard deviations of the taste parameters are all significant, meaning that the unobserved consumer characteristics play an important role in the deviation from the mean utility. The marginal utility from the price increases with the income and decreases with age. Marginal valuation of sugar increases with age and decreases with income, as does the variable calories, while the variable fiber interacts

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negatively with the income and positively with the age. For the Kid dummy has a positive interaction with income and age. Turning now to the implied elasticities, Table 2 presents the own-price elasticities implied by the RCM. The RCM elasticities are higher than those implied by the Logit Model12. The own price elasticities range from -3.2301 Ralston Cookie Crisp in Stop & Shop to -1.1973 for Kelloggs corn Flakes in Stop & Shop.
4.2. Supply Results

This section presents the results of the estimation of the price-cost margins (i.e., the differences between the price and the marginal cost) for the manufacturers, the retailers and the whole channel, under a double marginalization scenario. Given the demand estimates from the previous section, the price-cost margins were computed. The results are given in Tables 3, 4 and 5 (in $/lb) for the four leading supermarkets, the manufacturers (at the brand level) and the entire channel respectively. For the retailers, the price-cost margins vary from $0.6450/lb for Ralston Rice Chex in DeMoulas to $1.0439/lb for Post Raisin Bran in Stop & Shop. The remaining supermarkets obtain smaller dollar margins. The highest margins are realized by Stop & Shop (the leading supermarket) with an average of $0.8706/lb. For the manufacturers, the margins are higher than the retailers PCMs, except Stop & Shop who splits the channel margins evenly with the manufacturers. The manufacturers PCMs vary from $0.1676/lb for Post Grape Nuts in Stop & Shop to $1.52/lb for Kelloggs Corn Pops in DeMoulas. For the whole channel, the price-cost margins vary from $1.0685/lb for Kelloggs Corn Frosted Flakes in Stop &Shop to

12

Logit estimates are available upon request.

13

$2.31/lb for Kelloggs Corn Pops in DeMoulas. However, on average, dollar market channel margins are virtually identical across all four supermarkets (around $1.74/lb). Table 6 shows the average 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). Given Star Markets relatively lower price-cost margins, one concludes that the higher prices are in part due to higher retailing cost. Table 7 presents the price-cost margins as a percentage of the retail price (i.e., the Lerner index) for the supermarket chains. Interestingly, the results show that the Lerner index is proportional to the size of the supermarket chains. In sum, Stop & Shop (the leading supermarket) is able to capture a larger dollar margin than other supermarkets while RTEC manufacturers obtain a lower dollar margin at Stop & Shop. Since they charge higher prices, the larger PCMs are may be due to efficiency in handling a larger volume as well as possible volume discounts. The shares of the price-cost margins accruing supermarkets are given in Table 8. On average, the share of the manufacturers in Stop & Shop, the leading supermarket in Boston, is the lowest among the four supermarkets. In Stop & Shop, the manufacturers share do not exceed 50% of the whole channel margins, while this share attains more than 58% in Star Market. Stop & Shop supermarkets get more profits from Post, Nabisco and Ralston than from Kellogg and general Mills. In the remaining supermarkets, the retailers shares do not exceed 44%. This may show the balance of power between the

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retailers and the manufacturers. Hence, in Stop & Shop, General Mills' brands get the highest share of the price-cost margins attaining more than 52% of the whole channel profit, while the Post brand did not reach 40% of the channel profit. In the remaining supermarkets, General Mills seem to exercise power over the retailer since more than 60% of the channel profits go to the GM manufacturers.
5. Conclusion

The purpose of this paper was to decompose the channel price-cost margins for ready-to-eat cereals in the Boston area. The study uses highly disaggregated (supermarket chain and brand level ) monthly data from Information Resources Inc (IRI) from 1995 to 1997. The random coefficients model is used to estimate the demand for 37 brands of RTECs in the top four supermarkets in the Boston area. The demand estimates are then used to compute the price-cost margins for retailers and manufacturers under double marginalization scenario. The RCM allows estimating 21,904 own- and cross-price elasticities along with the elasticities of the other variables. The RCM results show the importance of the price and the product characteristics in the consumer choice of RTECs brands. The results shed light on the share of channel PCMs accruing to RTEC manufacturers and retailers. For instance, Stop & Shop, the leading supermarket chain in Boston, gets more than 50% of the channel profits, while RTEC manufacturers get more than 56% of the channel profits when dealing with smaller supermarket chains (Shaws, DeMoulas and Star Market). The results attest to possible volume discounts and efficiencies as the smaller supermarkets, especially those with urban location, charge higher prices in spite of smaller dollar PCMs, due in part to higher retailing costs. Among

15

the manufacturers, General Mills brands command the highest PCMs while Post brands command the lowest ones.

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Table 1. RTEC Demand Parameter Estimates and Related Statistics, Boston Market. Means ( s) Variable Price Promotion Constant1 Sugar1 Calories1 Fiber1 Kid Dummy1 Price Constant Sugar Calories Fiber Kid Dummy Estimate -12.2180*** 1.0362*** -8.1029*** -0.1186*** 2.9416*** 0.0927*** -0.3009*** -0.8715*** 0.5726*** 0.0117* 0.6619*** 0.3141*** -0.7157*** 2.5828*** -7.9903*** 0.1818 3.7094*** -0.2290 0.5502 -31.8100*** 6.1302*** -0.5275 -0.0708 2.0762** 1.8825* t-Statistic 69.9771 29.0571 11.3300 9.5312 7.3540 3.0123 50.1451 4.9321 9.3257 1.5811 3 4.8368 18.6964 17.4136 0.5097 3.1830 0.0861 12.6342 0.2209 2.6799 46.1793 29.8597 59.2697 0.4658 63.1064 22.1731

Standard Deviation

Interaction with Age Price Constant Sugar Calories Fiber Kid Dummy Interaction with Age Price Constant Sugar Calories Fiber Kid Dummy

Note A 1 indicates that the estimate comes from a minimum distance procedure. One, two and three asterisks indicate significance at 10%, 5% and 1% levels, respectively. The sample consisted of 5,180 observations.

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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 -2.3739 -2.0464 -1.1973 -1.8982 -2.2925 -2.2855 -1.6779 -1.7186 -1.4468 -2.1174 -2.3950 -1.7354 -2.3319 -2.1520 -2.4183 -2.0481 -2.4137 -2.2563 -2.6348 -2.4816 -1.8795 -1.6982 -2.0642 -2.0205 -2.3105 -2.2228 -1.3631 -2.1603 -1.4876 -2.0318 -1.9242 -2.1657 -1.9999 -1.8953 -3.2301 -2.4346 -2.4301 Shaws -2.2148 -2.0012 -1.2732 -1.9043 -2.1981 -2.4063 -1.7551 -1.6921 -1.5854 -2.0748 -2.4371 -1.6504 -2.1618 -2.0850 -2.2308 -2.0128 -2.3017 -2.1042 -2.5806 -2.2369 -1.9669 -1.6723 -2.1263 -1.7775 -2.0744 -1.9779 -1.3989 -1.9707 -1.4063 -1.9192 -1.6939 -2.0391 -1.8932 -1.7277 -2.8600 -2.1804 -2.1902 DeMoulas -2.1948 -1.9888 -1.3501 -2.0876 -2.5129 -2.1488 -1.6465 -1.5742 -1.4531 -2.1382 -2.6445 -2.0737 -2.4416 -2.3137 -2.4339 -1.9883 -2.8226 -2.4017 -2.5016 -2.5859 -1.8949 -1.8380 -2.0928 -2.1404 -2.2694 -2.2172 -1.4825 -2.1531 -1.4592 -2.0169 -1.5993 -2.1332 -1.9777 -1.8770 -2.7595 -2.5402 -2.5586 Star Market -2.0365 -2.0418 -1.0870 -1.8410 -2.0003 -2.7885 -1.4205 -1.4167 -1.3162 -2.1717 -2.0056 -1.6652 -2.2890 -2.3554 -2.133 -1.6510 -2.1767 -1.8628 -2.2399 -2.064 -1.4876 -1.5533 -2.1800 -2.6916 -2.9949 -2.1996 -1.2914 -1.8585 -1.288 -2.3833 -1.7101 -1.9859 -1.6027 -1.4994 -2.8939 -2.1586 -2.1502 Simple Average -2.205 -2.0196 -1.2269 -1.9328 -2.251 -2.4073 -1.625 -1.6004 -1.4504 -2.1255 -2.3706 -1.7812 -2.3061 -2.2265 -2.304 -1.925 -2.4287 -2.1563 -2.4892 -2.3421 -1.8072 -1.6905 -2.1158 -2.1575 -2.4123 -2.1544 -1.384 -2.0357 -1.4103 -2.0878 -1.7319 -2.081 -1.8684 -1.7498 -2.9359 -2.3285 -2.3323

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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 0.8779 0.9793 0.8320 0.9347 0.7822 0.7836 0.8195 0.9418 0.9923 0.7865 0.7925 1.0085 0.8015 0.8691 0.8237 0.8286 0.8061 0.8300 0.8555 0.8275 0.8883 0.8745 0.8182 0.9370 0.8561 0.8775 0.9899 0.8141 1.0439 0.8693 0.8678 0.8666 0.9418 0.9163 0.9376 0.7709 0.7704 0.8706 Shaws 0.7325 0.7548 0.6917 0.7680 0.6686 0.6706 0.7038 0.7397 0.7595 0.6803 0.6752 0.7853 0.6801 0.7344 0.7001 0.6943 0.6808 0.7042 0.6963 0.6940 0.7373 0.7176 0.7044 0.7542 0.7256 0.7234 0.7658 0.6964 0.7879 0.6997 0.7292 0.7330 0.7357 0.7286 0.7583 0.6683 0.6672 0.7175 DeMoulas 0.7221 0.7509 0.6857 0.7900 0.6595 0.6597 0.6868 0.7297 0.7439 0.6630 0.6488 0.7400 0.6499 0.7401 0.6936 0.6758 0.6487 0.6891 0.6777 0.6620 0.6930 0.7109 0.6804 0.7069 0.6980 0.7105 0.7486 0.6722 0.7750 0.7034 0.6901 0.6805 0.7099 0.7203 0.7341 0.6455 0.6450 0.6984 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 0.7143 Simple Average 0.7718 0.7977 0.7243 0.8266 0.6942 0.6973 0.7270 0.7886 0.8212 0.6994 0.6974 0.8098 0.7004 0.7682 0.7321 0.7218 0.7023 0.7319 0.7288 0.7156 0.7658 0.7508 0.7252 0.7839 0.7519 0.7614 0.8107 0.7195 0.8516 0.7401 0.7527 0.7499 0.7777 0.7749 0.8073 0.6887 0.6883 0.7502

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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 1.2035 1.0612 1.2937 1.3516 1.1184 1.1257 0.2490 0.3667 0.5563 1.1259 1.1269 0.6362 1.0574 1.0469 1.0179 1.0934 1.0990 1.0588 1.1471 1.0744 1.0397 0.3061 1.0624 0.8358 0.8905 0.9016 0.1676 0.8468 0.2419 0.7632 0.9837 0.9728 0.6259 0.6937 0.7876 0.7835 0.7832 0.8783 Shaws 1.3387 1.2768 0.6276 1.4803 1.2131 1.2139 1.2355 0.5604 0.6727 1.2113 1.2240 1.0045 1.1826 1.1589 1.1245 1.2098 1.2224 1.1710 1.3055 1.2360 1.1840 0.5075 1.1681 1.0412 1.0095 1.0119 0.3754 0.9476 0.5644 0.8682 1.0957 1.1021 0.8200 0.8606 0.9507 0.8791 0.8794 1.0253 DeMoulas 1.3806 1.3179 0.6100 1.5200 1.2535 1.2681 0.4044 0.6676 0.7891 1.2531 1.2453 0.6747 1.1744 1.1418 1.1281 1.2232 1.1906 1.1815 1.3385 1.2125 1.2117 0.4356 1.1636 1.0347 1.0016 1.0162 0.3663 0.9643 0.5538 0.9250 1.0998 1.1072 0.8483 0.8992 0.9213 0.8972 0.8966 1.0086 Star Market 1.3516 1.3506 0.5644 1.4825 1.2119 1.2358 1.2424 0.5104 0.6485 1.2185 1.2292 0.5921 1.1620 1.1118 1.1021 1.2018 1.1989 1.1580 1.2595 1.1904 1.1822 1.2828 1.1511 0.9939 0.9987 1.0124 1.0939 0.9660 0.3925 0.9372 1.1138 1.1092 0.8307 0.8650 0.9197 0.8904 0.8924 1.0447 Simple Average 1.3186 1.2516 0.7739 1.4586 1.1992 1.2108 0.7828 0.5263 0.6667 1.2022 1.2064 0.7269 1.1441 1.1149 1.0932 1.1821 1.1777 1.1423 1.2626 1.1783 1.1544 0.6330 1.1363 0.9764 0.9751 0.9855 0.5008 0.9312 0.4382 0.8734 1.0732 1.0728 0.7812 0.8296 0.8948 0.8626 0.8629 0.9892

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Table 5. Price-Cost Margins for the Entire Channel ($/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 2.0813 2.0406 2.1257 2.2863 1.9005 1.9093 1.0685 1.3085 1.5487 1.9123 1.9195 1.6447 1.8589 1.9161 1.8415 1.922 1.9052 1.8887 2.0025 1.9019 1.928 1.1806 1.8805 1.7729 1.7465 1.7791 1.1575 1.6609 1.2858 1.6324 1.8515 1.8395 1.5678 1.6099 1.7252 1.5545 1.5535 1.7489 Shaws 2.0712 2.0316 1.3192 2.2484 1.8817 1.8845 1.9394 1.3001 1.4322 1.8916 1.8993 1.7898 1.8627 1.8933 1.8246 1.9041 1.9031 1.8751 2.0018 1.9299 1.9213 1.2251 1.8725 1.7954 1.7351 1.7353 1.1412 1.644 1.3523 1.5679 1.8249 1.8351 1.5557 1.5893 1.709 1.5473 1.5467 1.7428 DeMoulas 2.1026 2.0687 1.2957 2.31 1.9129 1.9277 1.0912 1.3973 1.5329 1.9161 1.8941 1.4146 1.8244 1.882 1.8216 1.899 1.8393 1.8706 2.0161 1.8745 1.9047 1.1464 1.844 1.7416 1.6996 1.7266 1.1149 1.6366 1.3289 1.6285 1.7899 1.7878 1.5582 1.6195 1.6554 1.5427 1.5416 1.7070 Star Market 2.1063 2.0562 1.2524 2.2964 1.8784 1.911 1.9404 1.2536 1.4378 1.8864 1.9025 1.2976 1.832 1.8411 1.8132 1.8904 1.8725 1.8621 1.9454 1.8695 1.927 1.9829 1.8489 1.7311 1.7265 1.7468 1.8323 1.6613 1.1921 1.6254 1.8373 1.8286 1.5541 1.5994 1.7189 1.5604 1.5631 1.7590 Simple Average 2.0904 2.0493 1.4982 2.2853 1.8934 1.9081 1.5099 1.3149 1.4879 1.9016 1.9038 1.5367 1.8445 1.8831 1.8252 1.9039 1.8801 1.8742 1.9915 1.894 1.9202 1.3838 1.8615 1.7602 1.7269 1.7469 1.3115 1.6507 1.2898 1.6136 1.8259 1.8227 1.5589 1.6045 1.7021 1.5512 1.5512 1.7394

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Table 6. Average Retail Prices ($/lb) for RTEC in Boston, April 1995 through December 1997. RTEC Brand Stop & Shaws DeMoulas Star Simple Shop Market Average 3.77 4.06 3.53 3.64 3.87 KApple Jacks 3.10 3.30 3.01 3.01 3.09 KComplete Bran 2.31 2.21 2.12 2.00 2.91 KCorn Flakes 3.44 3.67 3.62 3.20 3.28 KCorn Pops 3.72 4.01 3.90 3.41 3.56 Kcrispix 3.55 3.59 3.33 3.73 3.56 Kfroot Loops 2.74 2.87 2.62 2.78 2.70 Kfrosted Flakes 2.65 2.86 2.45 2.62 2.68 Kfrosted Mini Wheats 2.47 2.67 2.33 2.52 2.36 Kraisin Bran 3.32 3.43 3.33 3.22 3.31 Krice Krispies 3.92 4.03 4.10 3.80 3.76 Kspecial K 3.06 3.35 3.27 2.70 2.93 GMcheerios 3.61 3.77 3.74 3.34 3.59 GMCinammon Crunch 3.59 3.70 3.74 3.41 3.51 GMCoco Puffs 3.88 4.24 3.87 3.55 3.87 GMGolden Grahams 3.20 3.31 3.11 3.15 3.23 GMHoney Nut Cheerios 3.98 4.33 4.33 3.55 3.73 GMKix 3.63 3.72 3.82 3.35 3.62 GMLucky Charms 4.06 4.45 3.82 3.94 4.02 GMMulti Gain Cheerios 3.84 4.14 3.95 3.44 3.82 GMTotal 3.04 3.00 3.01 3.13 3.01 GMTotal Raisin Bran 2.79 3.13 2.82 2.57 2.63 GMWheaties 3.38 3.59 3.30 3.37 3.27 GMApple Cinnamon 3.15 3.40 3.30 2.75 3.14 Pbanana Nut Crunch 3.67 4.00 3.63 3.31 3.74 Pcocoa Pebbles 3.59 4.00 3.57 3.18 3.62 Pfruit Pebbles 2.29 2.61 2.27 2.15 2.15 Pgrape Nuts 3.40 3.71 3.38 3.10 3.41 Phoney Comb 2.38 2.61 2.28 2.23 2.42 Praisin Bran 3.21 3.68 3.09 2.95 3.14 Qcap N Crunch 3.00 3.47 2.55 2.78 3.19 Qoat 3.51 3.98 3.38 3.23 3.45 QToasted 3.07 3.22 3.05 2.93 3.09 N Frosted Wheat Bites 2.89 3.02 2.90 2.68 2.96 N Spoon Size 5.01 5.72 4.42 4.64 5.25 R Cookie Crisp 3.87 4.29 3.96 3.40 3.81 R Corn Chex 3.86 4.27 3.97 3.42 3.80 R Rice Chex Simple Average 3.34 3.14 3.32 3.61 3.35

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Table 7: Price-Cost Margins for Supermarkets in Boston (Percent of Retail Price)

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 22.68 31.70 43.55 28.47 21.99 22.02 30.40 35.16 41.97 23.75 21.05 34.42 22.30 24.75 21.30 25.64 21.62 22.92 21.28 21.69 29.50 33.31 24.99 29.83 22.91 24.27 46.13 23.89 43.08 27.68 27.23 25.11 30.52 30.90 17.87 20.26 20.29 27.47

Shaws 20.13 25.05 34.62 24.03 19.59 17.97 25.28 28.23 30.12 21.11 17.79 29.12 20.37 21.53 19.70 22.03 19.17 20.99 17.69 20.16 23.57 27.87 20.90 27.45 21.89 22.76 35.65 22.48 35.38 23.70 26.25 22.70 25.10 27.14 16.35 19.65 19.53 23.60

DeMoulas 20.45 24.93 32.29 21.81 16.92 19.82 26.22 29.82 31.91 19.90 15.83 22.61 17.38 19.79 17.92 21.70 14.98 18.04 17.76 16.74 23.00 25.18 20.60 21.42 19.24 19.92 32.95 19.87 33.93 22.78 27.11 20.11 23.31 24.81 16.63 16.31 16.23 21.90

Star Market 18.60 21.40 31.11 22.15 16.63 18.79 24.31 25.97 29.60 19.46 16.71 21.09 17.77 19.71 16.76 20.83 15.55 18.95 15.40 16.40 24.82 22.41 19.45 21.69 18.20 18.35 28.31 18.77 30.65 18.71 20.85 18.06 22.47 24.31 13.97 15.62 15.70 20.53

Simple Average 20.46 25.77 35.39 24.11 18.78 19.65 26.55 29.80 33.40 21.06 17.85 26.81 19.45 21.44 18.92 22.55 17.83 20.22 18.04 18.75 25.22 27.19 21.48 25.10 20.56 21.33 35.76 21.25 35.76 23.22 25.36 21.49 25.35 26.79 16.20 17.96 17.94 23.38

Note: The price-cost margins correspond to (p-MC)/p.

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Table 8. Supermarkets Shares of the Channel Profit Stop & Shop Kellogg General Mills Post Quaker Nabisco Ralston Mean 49.78 47.81 61.15 49.08 58.49 51.18 52.92 Shaws 40.72 39.39 48.87 41.51 46.57 43.57 43.44 DeMoulas 41.73 39.77 48.22 39.94 45.02 42.67 42.89 Star Market 41.12 38.61 46.00 40.35 46.23 44.11 42.74 Mean 43.34 41.40 51.06 42.72 49.08 45.38 45.50

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