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Determinants of price elasticities for private labels andnational brands of cheeseMin-Hsin Huang a , Eugene Jones b & David E. Hahn ba Department of Asia-Pacific Industrial and Business Management , National University ofKaohsiung , 700, Kaohsiung University Road, Kaohsiung 811, Taiwanb Environmental and Development Economics, Department of Agricultural , The Ohio StateUniversity , 2120 Fyffe Road Columbus, Ohio 43210-1067Published online: 30 Oct 2009.

To cite this article: Min-Hsin Huang , Eugene Jones & David E. Hahn (2007) Determinants of price elasticities for privatelabels and national brands of cheese, Applied Economics, 39:5, 553-563, DOI: 10.1080/00036840500439069

To link to this article: http://dx.doi.org/10.1080/00036840500439069

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Applied Economics, 2007, 39, 553–563

Determinants of price elasticities for

private labels and national brands

of cheese

Min-Hsin Huanga, Eugene Jonesb,* and David E. Hahnb

aDepartment of Asia-Pacific Industrial and Business Management, National

University of Kaohsiung, 700, Kaohsiung University Road, Kaohsiung 811,

TaiwanbEnvironmental and Development Economics, Department of Agricultural,

The Ohio State University, 2120 Fyffe Road Columbus, Ohio 43210-1067

An Almost Ideal Demand System model is developed and used to estimate

price elasticities for US cheese sold at retail. Growing consumption of

cheese coupled with fierce competition between private labels and national

brands serves as motivating factors for this study. Per capita consumption

of cheese grew by 75% during 1980–2004 and private labels captured a

rising share of this growth. Private labels today account for 35% of market

share; national brands, for the remaining 65%. Kraft accounts for 45% of

national brands, but price increases for Kraft brands led to a sizeable price

gap between its brands and private labels. This gap helped to stimulate

growth of private labels. Marketing managers seek to capitalize on both

growing cheese sales and price gaps for brands. Relevant information for

marketing managers is consumer sensitivity to price changes. This study

uses 69 weeks of scanner data, with consumers segmented by income levels

to derive price elasticities for both lower-and higher-income consumers.

Results show lower-income consumers to be more price sensitive. If large

price gaps are maintained, the results suggest continued growth of private

labels. Yet, meta-analyses for this study suggest that Kraft could lower the

price gap and regain market share.

I. Introduction

An issue of paramount importance to the long-turn

growth and profitability of the US dairy industry is

the growing influence of private labels and/or store

brands of dairy products. These products have long

dominated fluid milk sales and, over the past two

decades, have gained prominence in the cheese, butter

and ice cream categories. This study focuses on the

dairy product category with the largest economic

value: cheese. Cheese overtook fluid milk as the

largest user of raw milk in the late 1990s and,

by 2001, it accounted for $19.56 billion in sales

(US Census Bureau, 2004).The retail cheese market is best characterized as an

amalgam of leading brands: store brands account for

35% of total market share; and national brands

account for the remaining 65%, but Kraft alone

*Corresponding author. E-mail: Jones.73@osu.edu

Applied Economics ISSN 0003–6846 print/ISSN 1466–4283 online � 2007 Taylor & Francis 553http://www.tandf.co.uk/journalsDOI: 10.1080/00036840500439069

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accounts for 45% of this total (Cropp, 2001).

Together, store brands and Kraft account for 80%

of the retail cheese market. Observations on prices

show that store brands have lower prices and this is

generally attributed to lower manufacturing, adver-

tising and overhead costs. As distributors and

retailers seek to expand cheese sales, they are

naturally interested in consumers’ sensitivity to

price changes for store and national brands.An understanding of factors influencing consumer

sensitivity to prices is critical to profitable production

and marketing decisions. Marketing managers at the

retail level must make pricing decisions for promo-

tional and nonpromotional brands at fairly regular

intervals. Critical to these decisions is the knowledge

of price elasticities and the interrelationship of these

elasticities among brands. As managers seek to

maximize cheese revenue and profit, they must (1)

identify products that have inelastic or near inelastic

demands and can sustain price increases; (2) select

products that can best stimulate total category sales;

and (3) identify appropriate discount levels that will

maximize sales while simultaneously providing

desired levels of profit.The marketing literature is ripe with studies

addressing determinants of price elasticities, and

most of these studies address broad product cate-

gories with considerable focus on alternative func-

tional forms. Examples include a study by Hoch

et al. (1995) in which the authors used a log-linear

function to estimate store-level elasticities for 18

product categories, including dairy products.

Similarly, a study by Mulhern, et al. (1998) used a

negative exponential function to estimate price

elasticities for liquor purchases across 35 stores.

These studies, with considerable emphasis on

functional forms, were less concerned about the

development of a comprehensive framework that

incorporated economics fundamentals (Baltas, 2002).

Further, emphasis on broad product categories,

such as soft drinks, paper towels and toothpaste,

increased the probability of aggregation bias in the

estimation procedures. This article avoids such

aggregation bias by focusing on a single product:

cheese. Specifically, cheese is disaggregated into

product classes by brands and package sizes to

estimate price elasticities at a narrow and more

refined level.The purpose of this study is to estimate brand

demand elasticities by using plausible economic

theory within a consumer demand framework. This

framework, provided in the next section, incorporates

factors that determine price elasticities for store and

national brands of cheese. Subsequent sections of this

article provide the empirical models, data, results andconclusions/implications.

II. Theoretical Framework

With neoclassic economic theory as its foundation,applied demand analysis, as formulated in this study,focuses on the optimal allocation of consumerexpenditure among different products and services.A demand system is derived from the utilitymaximization problem and its parameters are esti-mated on the basis of observations on price andexpenditure. Assuming weak separability of prefer-ences, a brand demand system is defined as a set ofdemand equations that determine the utility-maximizing allocation of category expenditureamong competing brands (Baltas, 2002).

Applied economists have utilized several econo-metric models or functional forms for estimatingconsumer demand (Gould, 1997; Gould and Dang,2000; Maynard, 2000; Maynard and Lin, 1999).A major goal of this study is to derive demandelasticities for store and national brands of cheese byestimating theoretically plausible demand systems.Furthermore, demographic and marketing effects areknown to impact brand level demand and thus aflexible functional form that incorporates demo-graphic and marketing variables is utilized. Fromthis viewpoint, the Almost Ideal Demand System(AIDS) (Deaton and Muellbauer, 1980) providesmore efficient estimates than other common demandsystems, such as Linear Expenditure System (LES)(Stone, 1954), Rotterdam model (Barten, 1964; Theil,1965) and Translog model (Christensen et al., 1975).

Another factor suggesting the AIDS model is thatit satisfies the axioms of choice exactly, therebyallowing for testing and for imposing homogeneityand symmetry conditions. Further, this model per-mits some forms of aggregation and it is mathe-matically integrable. Such desirable theoreticalproperties and flexibility of the AIDS model facilitatethe incorporation of marketing and demographicvariables into the model. In particular, the ‘priceindependent generalized log’ (PIGLOG) class ofexpenditure functions in the AIDS model fulfills theconditions required for exact nonlinear aggregation.That is, the share equations and the expenditurefunction derived from the AIDS model can be seen ascoming from a single representative household. Thus,the parameters of a household’s expenditure functioncan be (approximately) recovered even though theshare equations are estimated using aggregate data.This advantage of the AIDS model is extremelyimportant when using store-level, supermarket

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scanner data, because demand equations in this studyrepresent retail-level market demand.

Utilizing these principles, the economic form of theAIDS budget share demand function for a cheeseproduct category can be written as:

wi ¼ �i þXj

�ij log pj þ �i logx

P

� �ð1Þ

where wi is average expenditure share for a specificproduct class i (e.g. store brands of small packagesize); �i,�i, gij, are parameters of the system;

x ¼Xni¼1

piqi

is total expenditure in the cheese product category; pjrepresent the price of the jth product class; pi and qirepresent the price and quantity, respectively, of theith product class; and P is a price index defined as

logP ¼ �0 þXk

�k log pk þ1

2

Xk

Xj

�kj log pk log pj

ð2Þ

The parameter restrictions derived from utility theoryrequire that the following conditions be satisfied:

Xni¼1

�i ¼ 1;Xni¼1

�ij ¼ 0;Xni¼1

�i ¼ 0 ðadding-upÞ

ð3ÞXj

�ij ¼ 0 ðhomogeneityÞ ð4Þ

�ij ¼ �ji ðsymmetryÞ ð5Þ

It should be noted that within the same brand,small and large package sizes provide different levelsof utility to consumers. For example, Folkes et al.(1993) showed that large packages, as compared tosmall ones, encourage greater use because consumersare less concerned about running out of the product.The greater the supply of a product (e.g. largepackage), the lower the transaction (replacement)costs for using the product and greater the volumepeople are willing to use (Lynn 1992). Wansink (1996)further indicated that packaging influences purchasebehaviour and usage behaviour. Building on thesetheoretical arguments, small and large package sizesare incorporated into a brand demand system.

III. Estimation Procedure

The estimation procedure involves a two-stagemodelling process. First, the empirical AIDS

model is derived and used to estimate price

elasticities. Second, a meta-analysis procedure that

uses price elasticities as starting data points is used

to estimate the impact of independent factors on

these elasticities. That is, estimated price elasticities

from the AIDS model for both store and national

brands of cheese are regressed on a set of

independent factors.

The empirical AIDS model

As currently expressed, Equation 1 is void of

marketing variables. Given the influence of market-

ing activity on consumer shopping behaviour, it is

natural to extend the AIDS model to incorporate

these marketing variables. This study employs the

linear demographic translating method to incorpo-

rate marketing variables as discussed in Pollak and

Wales (1978, 1980). That is, the intercept term, �i inEquation 1, is assumed to be a linear function

of marketing attributes such as price promotion,

customer counts, holidays and seasonal effects.

More specifically,

�i ¼ ��i þXj

�ijPRþ �iCCþ �iHDþX3k

�ikSE ð6Þ

where ��i is the intercept net of marketing effects; PR

is price promotion, representing the number of items

on price discount within a product class during a

given week; CC is customer count that is the number

of customers shopping each week and this variable is

specified to capture the effect of store traffic on

particular product sales; HD is a zero-one dummy

variable that captures the effect of calendar holidays;

and SE is a seasonal effect, expressed as quarterly

dummies to capture seasonal effects of cheese

purchase. Substituting Equation 6 into the AIDS

model presented in Equation 1, the empirical AIDS

model incorporating marketing variables used in this

study can be derived as

wi ¼��i þ

Xj

�ijPRþ �iCCþ�iHDþX3k

�ikSE

þXj

�ij logpjþ�i

� logx��0�X

��i þ

Xj

�ijPRþ �iCCþ�iHD

þX3k

�ikSE

�� logpk�

1

2

Xk

Xj

�kj logpk logpj

!

ð7Þ

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where w denotes expenditure share, p represents price,

x is total expenditure in the cheese product category;

�,�, �, �, �, �, �, are model parameters to be estimated.Theoretical restrictions placed on the parameters

can be summarized as

Xni¼1

��i ¼ 1;

Xni¼1

�ij ¼ 0;Xni¼1

�i ¼ 0

Xni¼1

�ij ¼ 0;Xni¼1

�i ¼ 0

Xni¼1

�i ¼ 0;Xni¼1

�ik ¼ 0 ðadding-upÞ

ð8Þ

Xj

�ij ¼ 0 ðhomogeneityÞ ð9Þ

�ij ¼ �ji ðsymmetryÞ ð10Þ

Once the parameters have been estimated, the price

elasticities can be calculated as follows (Green and

Alston, 1990):

eii ¼ �1þ�ij � �ið�i þ

Pj �ij log pjÞ

wi

� ðown-price elasticityÞ ð11Þ

eij ¼�ij � �ið�j þ

Pj �ij log pjÞ

wi

� ðcross-price elasticityÞ ð12Þ

The linear approximation AIDS (LA/AIDS) model

is also estimated to compare the results with those

of the original nonlinear AIDS model. The linear

approximation AIDS model involves the replacement

of logP with a simpler index as suggested by

Moschini (1995). That is, the simpler price index

can be represented as

lnP� ¼Xni¼1

w0i lnðpitÞ ð13Þ

where w0i is the expenditure share of good i in the base

period. Consequently, the empirical LA/AIDS model

used in this study can be represented as

wi ¼ ��i þXj

�ijPRþ �iCCþ �iHD

þX3k

�ikSEþXj

�ij log pj þ �i logx

P�

� �ð14Þ

and the price elasticities can be calculated as follows

eii ¼ �1þ�iiwi

� �� �i ðown-price elasticityÞ ð15Þ

eij ¼�ijwi

� ��

�i

wi

� �wj ðcross-price elasticityÞ ð16Þ

The literature supports the hypothesis that higher-and lower-income consumers exhibit different shop-ping behaviour and sensitivity to price changes (e.g.Jones and Mustiful, 1996; Mulhern et al., 1998).Anticipating different behaviour across products andincome groups, cheese is classified into five productcategories based on product forms: shredded, sliced,chunk, snack and miscellaneous. Given five productcategories and two income groups, a total of 20demand systems are estimated for the two AIDSmodels (Equations 7 and 14).

IV. Data Discussion and Issues

The data used in this study are store-level scannerdata provided by a national supermarket chain in theColumbus, Ohio Metropolitan Area (COMA). Thedata represent weekly observations and they includeUniversal Product Codes (UPCs), prices, packagesizes and dollar and quantity sales. These datarepresent 69 weeks of consumer purchases, coveringthe period 31 December 2000 to 21 April 2002.

Six stores are included in the data set, and thesestores represent two broadly defined store groups:higher-and lower-income stores. These groups areidentified from socioeconomic information providedby the chain for all residents within a 3-mile radius ofeach store. The lower-income stores (1, 2 and 3) arelocated in areas that have large proportions of lower-income shoppers, while the higher-income stores (4, 5and 6) are located in areas that have large propor-tions of higher-income shoppers. The three lower-income stores are within the inner city of Columbusand the three higher-income stores are located inColumbus suburbs. As shown in Table 1, an averageof 4.2% of the residents in higher-income areas hashousehold incomes less than $10 000 annually. At theopposite end of the income spectrum, an averageof 34.1% of residents in higher-income areas haveannual household incomes exceeding $75 000.In contrast, 12% of lower-income residents havehousehold incomes below $10 000 and just 11.8%have incomes above $75 000.

Differences between the two groups exist not onlyfor income, but also for education and race. Asshown in Table 1, only 10% of the prospectiveshoppers in areas identified as lower-income stores(stores 1, 2 and 3) are college graduates, as comparedto 38% of prospective shoppers in higher-incomeareas (stores 4, 5 and 6). The lower-income areas arealso characterized by populations that are moreheterogeneous than the homogeneous populationsof the higher-income areas.

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Stationarity is an important issue when usingtime series data in econometric analysis. Thetraditional test for stationarity of time-series datais called the unit-root test. In the current study, theAugmented Dickey–Fuller (ADF) tests are con-ducted to identify whether each individual time-series variable in the AIDS model is stationary ornonstationary. The results of ADF test indicatethat absolute values of the estimated t-statistics arelarger than corresponding asymptotic critical valuesfor most variables in the AIDS model, with someexceptions. In other words, the time-series variablesused in the present study are stationary, and onecan reject the null hypothesis that the data containa unit root.

V. Results of AIDS Models

A total of 20 demand systems are estimated for thenonlinear original AIDS as well as for the linearapproximation AIDS models. These 20 demandsystems led to a total of 84 equations and 964parameters, and therefore these numerous para-meters are not reported in this article. Derivedelasticities provide interesting perspectives onconsumer behaviour, and therefore own-price elas-ticities from both the linear and nonlinear AIDSmodels for store and national brands of cheese aresummarized in Table 2. These are Marshallian(uncompensated) elasticities and they are shown by

store location, product category and package size.Hicksian or compensated elasticities are generallysmaller (absolute value) than uncompensated elasti-cities and that pattern is observed for the estimatesof this study.

As economic theory would predict, shoppersresiding in lower-income areas are shown to bemore price sensitive than those residing in higher-income areas. Shoppers of the lower-income storesare shown to be more price sensitive for all products,except store brands of sliced cheese, national brandsof sliced cheese (large package size) and nationalbrands of chunk cheese (small package size).Of course, it must be recognized that the lower-andhigher-income stores of this study are not patronizedsolely by lower- or higher-income shoppers. Yet,income differences are sharp enough to warrant thedescribed classification. Indeed 24 of the 32 own-priceelasticities for each of the AIDS models in Table 2suggest that the income classification is appropriateand meaningful.

Since cheese purchases represent a major expendi-ture and national brands of cheese have higher pricesthan store brands, economic theory would predicthigher price sensitivity for these brands on the part oflower-income consumers. Yet, given the large andoften sustained advertising campaigns for nationalbrands, theory would also predict lower pricesensitivity for national brands relative to storebrands. Indeed one of the objectives of brandadvertising is to diminish consumers’ price sensitivityfor national brands relative to store and/or other

Table 1. Household demographic data for six stores (by percentage)

Lower-income stores Higher-income stores

Demographic informationa Store 1 Store 2 Store 3 Average Store 4 Store 5 Store 6 Average

Household incomeUnder $10 000 13.8 12.9 9.3 12.0 3.8 5.0 3.8 4.2$10 000–$49 999 57.6 58.3 54.1 56.7 32.8 41.8 37.7 37.4$50 000–$74 999 18.5 18.2 22.4 19.7 27.4 20.9 24.6 24.3$75 000–$99 999 6.5 6.3 8.4 7.1 17.5 12.1 15.3 15.0$100 000þ 3.8 4.3 5.9 4.7 18.8 20.2 18.2 19.1

RaceWhite 59.2 83.6 85.7 76.2 95.4 92.4 93.1 93.6Black 38.6 14.4 12.1 21.7 2.3 3.2 5.0 3.5Others 2.1 2.0 1.8 2.0 2.6 4.6 1.9 3.0

EducationGrade school 7.3 10.0 11.1 9.5 4.1 2.0 2.5 2.9Some high school 21.3 25.4 25.8 24.2 11.6 5.0 8.6 8.4High school gradate 33.5 36.7 37.6 35.9 28.2 16.2 27.0 23.8Some college 24.3 19.2 17.8 20.4 26.2 26.6 28.2 27.0College graduate 13.8 8.8 7.5 10.0 29.9 50.6 33.5 38.0

Source: Spectra, 2001.Note: aNumbers may not add to 100.0 because of rounding.

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competing brands. Advertising expenditures wereunavailable for this study, so an advertising proxyof price promotion (observed price reductions) wasused. Price promotions are registered immediately inconsumer purchases and therefore these capturedeffects are short-term and may be larger than those ofadvertising expenditures that seek to generate positiveand long-term product differentiation. Stated differ-ently, unlike the short-term elasticities estimated inthis study, long-term price elasticities would likelycapture the effects of advertising expenditures andshow lower-income shoppers to be more pricesensitive than higher-income shoppers for all productclasses. Further, relative to store brands, they wouldlikely show national brands to be less price-sensitiveto a larger extent than what is shown in this study.

Although contrary to economic expectation, theempirical results of Table 2 show both lower- andhigher-income consumers to be more price sensitivefor national brands than for store brands. Thispattern is observed for five of the eight productclasses from each set of AIDS results. Again, withprice promotion being a proactive tool to moveproduct through the marketing channels, these

elasticities are likely to reflect the extent to whichconsumers have responded to price promotions fornational brands. Observations on price promotionsshow that promotion for branded products is mosteffective when the price gap between national andstore brands falls to consumers’ threshold purchaselevel. Indeed studies show that a promotion price fora national brand that is equal to that of a store brandleads to practically zero sales for the store brand(Wedel and Zhang, 2004). Such price promotionswould not continue indefinitely if media advertising isalso used to convey persuasive messages about thecharacteristics of national brands. Thus, the own-price elasticities of this study should be viewed asshort-term elasticities that show consumer reactionsto price promotions.

Although the results of Table 2 show that lower-income consumers have greater price sensitivity thanhigher-income ones, and that national brands havehigher elasticities than store brands, the results inTable 3 are intended to provide a comparison of allstore brands elasticities with those of national brands.The un-weighted values of both the AIDS and LA/AIDS models show that national brands not only

Table 2. The estimated own-price elasticities of store brands and national brands*

Lower-income stores Higher-income stores

Store brands National brands Store brands National brands

AIDS model

Shredded cheeseSmall sizesa �1.655 �3.453 �1.520 �2.704Large sizes �2.165 �1.865 �1.653 �1.573

Sliced cheeseSmall sizesb �1.935 �3.454 �1.988 �2.114Large sizes �1.778 �3.049 �2.943 �3.149

Chunk cheeseSmall sizesa �2.568 �1.235 �2.306 �2.275Large sizes �2.014 �2.331 �1.874 �2.232

Snack cheese �2.397 �1.530 �2.161 �0.538Miscellaneous cheese �1.350 �1.979 �0.526 �1.649

LA/AIDS model

Shredded cheeseSmall sizes �1.691 �3.407 �1.508 �2.686

Large sizes �2.170 �1.764 �1.601 �1.426Sliced cheese

Small sizes �1.851 �3.470 �1.901 �2.070

Large sizes �1.700 �3.011 �2.936 �3.137

Chunk cheeseSmall sizes �2.452 �1.187 �2.182 �2.695Large sizes �2.121 �2.341 �1.837 �2.326

Snack cheese �2.256 �1.262 �1.981 �0.363Miscellaneous cheese �1.477 �1.956 �0.553 �1.704

Notes: aPackage size at 8-oz or below; bPackage size at 12-oz or below.*All own-price elasticities are derived from statistically significant parameters as shown in Equation 11. Each elasticity isstatistically significant at the 0.10 level or better. Numbers in bold show national brands to have more elastic demands thanprivate labels.

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have higher elasticities, but they have a wider range.As shown, own-price elasticities for national brandsrange from �0.36 to �3.47; those for store brandsrange from �0.53 to �2.94. These results suggest thatnational brands of cheese are quite responsive to pricechanges and that revenue maximization is bestachieved through price reductions.

The aforementioned elasticities shed some insighton the price competition struggle between Kraft andmakers of store brands of cheese. This struggle hasbeen addressed in several Wall Street Journal articlesas well as other publications. These sources include:‘Kraft Profit Misses Expectations, Hurt by Private-Label Brands’ (17 July 2003); ‘Kraft Loses 2 TopExecutives Amid Private-Label Struggle’ (10 July2003); ‘Food for Thought: Why Kraft Is StillFrowning’ (16 April 2003); ‘Kraft’s Private-LabelLesson’ in Reveries Magazine (Hoyt, 2004); and‘Commodity Prices Strain Marketing’ in AdvertisingAge (Thompson, 2004). Although much of theliterature (e.g. Bushman, 1993; Richardson et al.,1994) support the premise that store brands are lowerin quality than national brands, the price responsesof consumers shown in Table 3 call this premise intoquestion. This question is especially relevant whenconsidered against the evidence of large marketinginvestments to build brand loyalty. Brand loyaltycommits consumers to paying premium pricesfor national brands, and therefore this loyalty leadsto lower price sensitivity for national brands.Store brands are generally hypothesized to havefairly elastic price responses and therefore theresults of Table 3 do not support the theoreticalarguments of consumer demand. Of course, itmust be recognized that 71% of supermarketshoppers now consider private-labels to be of equalor better quality than national brands (Hoyt, 2004).Further, marketing analysts agree that Kraft allowedthe price gap between its brands of cheese

and private-label brands to grow too wide(Thompson, 2004).

VI. A Meta-Analysis

Meta-analysis is a regression technique that allowsfor comparison of similar, but not necessarilyidentical quantity estimates. These estimates aremost often derived from different empirical modelsand meta-analysis provides a way for synthesizing

and interpreting these research results (Farely andLehmann, 1986). Meta-analysis has been developedand widely used in the context of the social sciencessuch as marketing, economics and psychology andrefers to the statistical analysis of empirical research

results (Stanley, 2001). Meta-analysis has also beenestablished to synthesize empirical research results bymeans of an analysis of the variation in estimateddemand elasticities. For example, Tellis (1988)described a meta-analysis of econometric studies

that estimated the elasticity of selective sales ormarket share to price. Dalhuisen et al. (2003)presented a meta-analysis of variations in price andincome elasticities of residential water demand.

In the current study, meta-analysis is used tosynthesize the factors that determine the estimatedstore-level price elasticities for national brands and

store brands of cheese. More specifically, the meta-regression of price elasticity for store brands andnational brands, respectively can be written as:

Elasticity ¼ �0 þ �1 Shareþ �2 LowIncome

þ �3 ShredCheeseþ �4 SlicedCheese

þ �5 SnackCheeseþ �6 MiscelCheese

þ �7 SmallSizeþ �8 AIDSþ " ð17Þ

Table 3. The descriptive statistics of estimated own-price elasticities for store brands and national

brands

BrandsNumber ofobservations Mean SD

Minimumof magnitude

Maximumof magnitude

AIDSStore brands 16 �1.927 0.552 �0.526 �2.943National brands 16 �2.196 0.818 �0.538 �3.454

LA/AIDSStore brands 16 �1.889 0.519 �0.553 �2.936National brands 16 �2.175 0.876 �0.363 �3.470

POOLEDStore brands 32 �1.908 0.527 �0.526 �2.943National brands 32 �2.185 0.834 �0.363 �3.470

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where:

Elasticity is the absolute value of own-priceelasticity for a specific productbrand;

Share is the market share of the specificproduct brand;

LowIncome is 1 for lower-income stores; 0otherwise;

ShredCheese is 1 if shredded cheese; 0 otherwise;SlicedCheese is 1 if sliced cheese; 0 otherwise;SnackCheese is 1 if snack cheese; 0 otherwise;MiscelCheese is 1 if miscellaneous cheese; 0

otherwise;SmallSize is 1 if small package size; 0 otherwise;

AIDS is 1 if elasticity i is estimated bynonlinear AIDS model; 0 otherwise;and �’s are parameters to beestimated.

VII. Results of Meta-Analysis

The relatively large price elasticities shown inTables 2 and 3 together with the substantialvariability among these elasticities suggest a need toassess factors which are most influential in theirdetermination. This study estimates two meta-regres-sions. One set of regression involves the own-priceelasticities for store-brands of cheese; the otherinvolves own-price elasticities for national-brands ofcheese. The two-stage modelling process is utilized inthe current research. More specifically, in the firststage, the own-price elasticities for store brands andnational brands are estimated by AIDS models(original nonlinear AIDS and linear approximationAIDS). In the second stage, the estimated own-priceelasticities are regressed on the determinant factors.

This meta-analysis is used to reveal the factors thataffect store-level price elasticities of store brands andnational brands of cheese. For instance, �2 representsthe difference in price elasticity of store brands(national brands) cheese associated with a changefrom the higher-income group to the lower-incomegroup. A test of the null hypothesis that �2¼ 0provides a test of the hypothesis that there is nodifference between the price elasticity of store brands(national brands) of cheese associated with thehigher-income group and that associated with thelower-income group.

The meta-regression results for store brands areprovided in Table 4. The R2 and adjusted R2 are 62.3and 49.2%, respectively. The goodness of fit measuresshow reasonably good performance for the model.

It should be noted here that the dependent variable,

own-price elasticity, is expressed as an absolute value,

as the interest lies in determining the impact of

independent factors on the magnitude of change for

cheese price elasticities.Interpreting the estimated parameters, market

share has a negative impact on own-price elasticity

for store brands, but this coefficient is statistically

insignificant. This suggests that higher market shares

have not reduced the price elasticity for store brands.

In addition, lower-income stores, as compared to

higher-income ones, have larger own-price elasticities,

although statistically this difference is not significant.

Two parameter estimates that are statistically sig-

nificant are: shredded cheese and miscellaneous

cheese. Relative to the base of chunk cheese, these

two classes of cheese decrease the price elasticity of

store brand cheese.The results of meta-regression for national brands

are reported in Table 5. The R2 and adjusted R2 are

69.2 and 58.5%, respectively, which indicates that the

model explains a great deal of the variation in price

elasticities. Market share has a negative and statisti-

cally significant impact on the magnitude of price

elasticities for national brands. In other words, large

market shares convey market power and serve to

diminish consumers’ price sensitivity. With respect to

store location, lower-income stores have a higher

price elasticity for national brands, and this difference

is statistically significant with chunk cheese as the

base category. The results show that snack cheese

serves to decrease price elasticity for national brands.

In contrast, sliced and miscellaneous cheeses serve to

Table 4. Meta-analysis parameter estimates for

determinants of store brands price elasticities

VariableRegressioncoefficient SE Prob.> jtj

Intercept 2.447 0.321 0.000Share �1.190 1.014 0.253LowIncome 0.193 0.143 0.190HighIncome (base)ShredCheese �0.424 0.188 0.034SlicedCheese �0.132 0.204 0.523SnackCheese 0.295 0.303 0.341MiscelCheese �1.323 0.252 0.000ChunkCheese (base)SmallSize 0.005 0.179 0.977LargeSize (base)AIDS 0.038 0.133 0.775LA/AIDS (base)N 32Model F-statistic 4.75 0.002R-square 0.623Adjusted R-square 0.492

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increase price elasticity for national brands. Relative

to package size, small package sizes have a

positive impact on the price elasticity for national

brands. Results in both Tables 4 and 5 show no

statistically significantly difference between the

LA/AIDS and AIDS model for store or national

brands of cheese.Results from the meta-regressions for both store

and national brands provide informative information

for marketing managers in the cheese industry. First,

as discussed in the empirical results section, lower-

income shoppers are more price-sensitive than higher-

income shoppers and these meta-analysis results

provide additional information for examining differ-

ences in their shopping behaviour. For national

brands of cheese, lower-income shoppers are shown

to be considerably more price sensitive, as measured

by a statistically significant coefficient of 0.352. That

is, relative to the base of higher-income stores, the

lower-income stores significantly increase the magni-

tude of the own-price elasticity for national brands.

Similar results are also revealed for store brands.

That is, relative to the base of higher-income stores,

lower-income stores are shown to increase the

magnitude of own-price elasticity, although the

coefficient is not statistically significant. Differences

in consumers’ price sensitivity for store locations

(e.g. lower-and higher-income) suggest that retail

managers could utilize micro-marketing strategies

with different prices across stores or retail market

areas. That is, these findings suggest that there are

possible advantages to having store- or area-specific

pricing. A retailer might be able to set prices in a

more profit-maximizing manner by matching

prices to customers’ price sensitivity within a given

store or location.A second piece of information for marketing

managers is derived from the observation that

brands with higher market shares have lower levels

of price sensitivity. Meta-regression results show

market share coefficients to be negative for both

national and store brands, although estimates for

store brands are not statistically significant. Yet, the

negative signs lend support to the economic principle

that market share leads to market power and there-

fore less elastic price responses. For national brand

manufacturers, these findings suggest that consumer

price sensitivity might be reduced by adopting an

explicit strategy of increasing market share. For

manufacturers of store brands, this research suggests

that store brands can be competitive products,

particularly after they build up store loyalty. In the

long run, store brands are likely to experience growth

and narrow the price gap between store and national

brands and capture a larger percentage of store

profits.A third piece of informative information relates to

the fact that price elasticities for both store and

national brands of cheese vary by product types.

Meta-regression results for national brands indicate

that, relative to the base of chunk cheeses, snack

cheese has a coefficient of �0.897 and this measure is

statistically significant. That is, holding the sales

of chunk cheese constant, an increase in the sales of

snack cheese will reduce the price-sensitivity of

national brands of cheese. Stated differently, con-

sumers are less sensitive to price changes for national

brands and the coefficient estimate for snack

cheese suggests a willingness to pay premium prices

for value-added products, such as snack cheeses.

A plausible explanation for this observation is that

consumers may associate higher-levels of product

quality with some value-added products such as

snack cheese, but may associate lower-levels of

product quality with other value-added products.

These research findings suggest that manufacturers of

national brands can implement premium price

policies for some product categories, but utilize

competitive price policies for other product cate-

gories. For store brands, the meta-regression results

show that, relative to the base of chunk cheeses,

shredded and miscellaneous cheese significantly

decrease the magnitude of own-price elasticities.

This suggests that store managers may wish to

focus much of their advertising, merchandising and

promotion efforts on increasing the sales of shredded

and miscellaneous cheese.

Table 5. Meta-analysis parameter estimates for determi-

nants of national brands price elasticities

VariableRegressioncoefficient SE Prob.> jtj

Intercept 2.971 0.485 0.000Share �10.568 3.767 0.010LowIncome 0.352 0.191 0.079HighIncome (base)ShredCheese �0.325 0.345 0.356SlicedCheese 1.518 0.358 0.000SnackCheese �0.897 0.370 0.024MiscelCheese 1.364 0.641 0.044ChunkCheese (base)SmallSize 1.115 0.390 0.009LargeSize (base)AIDS 0.020 0.190 0.916LA/AIDS (base)N 32Model F-statistic 6.46 0.000R-square 0.692Adjusted R-square 0.585

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VIII. Conclusions and Implications

Marketing managers are very interested in gainingan understanding of consumers’ sensitivity to pricechanges. The most prevalent measure of consumerprice sensitivity is the price elasticity of demand,which represents the percentage change in quantitysold for a given percentage change in price. Byutilizing retailer-supplied scanner data, this studydeveloped and estimated a theoretically plausiblemodel to provide price elasticities for specific cheeseproducts. These elasticities were then used in a meta-regression model to reveal factors that determine priceelasticities for store and national brands of cheese.The findings of this study provide a rich knowledgebase for retail store managers and manufacturersto use for maximizing sales and profits.

With the results showing lower-income consumersto be more price sensitive, cheese manufacturers anddistributors may wish to provide retailers a differentset of price incentives for lower-income stores thanthose provided for higher-income ones. That is, agiven price reduction in lower-income stores is likelyto lead to a greater quantity response than the sameprice reduction would generate in higher-incomestores. While the meta-analyses show that marketshare gains tend to decrease consumer price elasticity,the higher elasticities for national brands relative toprivate labels suggest that Kraft, in the process ofgaining larger market shares, may have increased theprice gap between the brands beyond consumers’tolerance level for brand loyalty. In essence, alowering of the price gap between private labels andKraft’s brands could diminish price elasticities fornational brands to the extent that Kraft’s brandsbecome far more competitive with private labels.With Kraft having such a large share of the cheesemarket, its response to competition from privatelabels will determine the ultimate growth andstructure of the cheese market. If Kraft lowers theprice gap between the national and private-labelbrands and adopts a long-run strategy of regainingmarket share through advertising and competitivepricing, growth of private labels is likely to bearrested. In contrast, if the company focuses onmaximizing its short-run profits by relying onpurchases from those loyal to its brands, privatelabels are likely to continue their encroachment onthe market shares of Kraft and other brands.

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