supermarket responses to wal-mart supercenter expansion: a structural approach

Empir EconDOI 10.1007/s00181-013-0767-5

Supermarket responses to Wal-Mart Supercenterexpansion: a structural approach

Rebecca L. O. Cleary · Rigoberto A. Lopez

Received: 3 December 2012 / Accepted: 29 August 2013© Springer-Verlag Berlin Heidelberg 2013

Abstract The impact of Wal-Mart in lowering incumbents’ retail prices has beenwell documented by previous studies using reduced form models. This article uses astructural model to examine the pricing behavior and promotion responses of incum-bent supermarkets to a rapid expansion of Wal-Mart Supercenters (WMS) using theDallas–Fort Worth milk market as a case study. Empirical results verify that WMSexpansion disciplines incumbent supermarkets by decreasing oligopoly power andnumbing consumer responsiveness to promotion. In addition, WMS expansion luresaway price-sensitive consumers, leaving incumbent supermarkets to face more price-inelastic but lower demands for milk.

Keywords Wal-Mart · Competition · Promotions

1 Introduction

Wal-Mart revolutionized the retailing sector with its expert logistics system and low-cost business strategy. Competing with the behemoth has challenged retailers fromK-Mart to mom and pop stores, and in 1988 Wal-Mart brought its innovative systemsand business strategy to food-retailing via its Supercenter format, thus influencingfood markets, which are of critical importance to consumers.

The business strategy of Wal-Mart Supercenters (WMS) is every day low pricing(EDLP) which is very different from the Hi–Lo promotion-oriented pricing scheme

R. L. O. Cleary (B)Analysis Group, Boston, MA, USAe-mail: [email protected]

R. A. LopezUniversity of Connecticut, Storrs, CT, USAe-mail: [email protected]

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R. L. O. Cleary, R. A. Lopez

that most traditional supermarkets use (Hoch et al. 1994). Traditional supermarketsoffer a relatively high regular price and discount this periodically through promotions.Hi–Lo pricing is costly; EDLP needed a 3 % cost advantage to become feasible. Wal-Mart gained this cost advantage through “a business model dramatically at odds withindustry dogma and with orthodox ‘principles of management”’ (Goddard 1997).Thus, the entry of WMS may affect not only the demand facing incumbents, as iswell established in the literature, but also the way in which incumbent supermarketscompete in both price and promotions. Direct evidence of the association between pro-motions and entry of any new store is somewhat mixed (Bagwell 2007), and Richards(2007) suggests that if supermarkets behave oligopolistically, then price promotionsmay be used strategically, that is, in response to other stores’ price promotions ratherthan as tools of price discrimination or loss leadership.

The purpose of this article is to provide a unified and integrated analysis, using astructural model, of the impacts of a large, low-cost entrant on demand and pricingbehavior of incumbent supermarkets. Thus, we examine the role played by changes indemand and incumbent pricing and promotion behavior resulting from WMS expan-sion, providing insight into the underlying reasons for incumbent price changes aswell as demand and behavioral impacts. In order to understand the WMS effect in aworld without WMS price and quantity data, we model only the market for incumbent-supermarket milk. As WMS expand in the area, we allow it to lure away supermarketconsumers, thus shifting the demand facing the incumbents. In a similar way, we studythe effects of WMS on the level of competition among the incumbents. We are not ableto understand the strategic interactions of supermarkets and WMS, but we are able tounderstand the strategic interactions of incumbent supermarkets among themselves.

This article follows the case study approach. In this regard, the Dallas/Fort Worthmilk market is suitable to analyze the impact of WMS expansion on prices and pro-motions of incumbent supermarkets for a number of reasons. First, regarding thecommodity in question, milk is often used as a strategic product in retail competitionsince it is an important item in many consumer budgets and is perishable, so it mustbe replaced often, implying that the retailer can recoup losses from promotion withhigh prices in subsequent periods (Green and Park 1998). Second, as in much of theUnited States, Dallas–Fort Worth experienced a rapid WMS expansion in the 1980sand early 1990s. Third, unlike their Northern counterparts, this area experienced anoncompetitive food retail environment before the expansion of Wal-Mart (Barnes etal. 1996) whereby retail milk prices were well above the national average. In supportof this observation, this market was serviced by the top two national supermarketchains (Kroger and Albertsons) during Wal-Mart’s rapid expansion. Finally, becausethe effects of WMS presence and entry have been studied extensively (e.g., Baskerand Noel 2009; Volpe and Lavoie 2008), the Dallas–Fort Worth milk market casestudy allows for the comparison of impacts for other commodities, locations, and timeperiods.

Our findings suggest that the expansion of WMS mitigate oligopolistic pricingbehavior—during the period of analysis, incumbents went from a Cournot oligopolysituation to a more competitive one—and that WMS lure away price-sensitive con-sumers, thereby inducing a decrease in both demand and the price elasticity of demand

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Wal-Mart Supercenter expansion

facing supermarkets while decreasing the response of incumbents to each others’ pro-motions and their overall effectiveness.

2 Supercenters, supermarkets, and strategic promotions

WMS present a threat to competing supermarkets through their innovative formatand low-cost logistics system. Consisting of 185,000 square-foot stores that sell over100,000 different products ranging from general merchandise to a full line of groceries,WMS offer one-stop shopping convenience. Moreover, Wal-Mart’s expert logisticssystem, low cost of labor, and EDLP strategy have allowed WMS to offer products ata generally lower price than competitors, attracting consumers whose main concern isprice (Basker 2007). Their success in doing so is reflected in the growth of nontradi-tional food retailers from a 17.1 % share of all at-home-use grocery sales nationwidein 1994 to 31.6 % in 2005 (Martinez 2007), and in 2003 Wal-Mart surpassed Krogeras the nation’s largest grocery retailer (Market Scope).

Low prices have been key in fueling WMS expansion. Previous studies finds pricediscounts ranging from 3 to 8 % for private label products (Volpe and Lavoie 2008) to30 % for an array of branded products (Hausman and Leibtag 2007). Basker and Noel(2009) find that WMS price goods 10 % lower than their competitors.

Large incumbent supermarket chains in particular respond aggressively to Wal-Mart’s entry (Khanna and Tice 2000). Lower prices at competitors’ stores are not onlycorrelated with WMS entry (e.g., Capps and Griffin 1998; Currie and Jain 2002), butWMS also caused competitors’ prices to decrease (Basker 2007; Hausman and Leib-tag 2007). This seems to be in contrast to incumbent airlines’ reactions to low-costentrants; Boguslaski et al. (2004) find that incumbents are generally accommodating,aligning their price to that of the entrant rather than undercutting it. Similarly, Bam-berger and Carlton (2006) find that most low-fare entries in airlines are successful, butincumbents’ fares do not significantly increase or decrease after their entry. It shouldbe noted that the ability of incumbent supermarkets to lower prices may be limitedif they face higher unit costs than WMS, particularly if they increase the quality ofservice and other nonprice forms of competition, which Stone (1995) suggests as away for incumbents to effectively combat the Wal-Mart effect.

Wal-Mart’s entry provides a natural experimental setting from which we can betterunderstand incumbents’ behavior; specifically WMS entry in the traditionally Hi–Lopricing food-retailing sector provides an opportunity to better understand incumbents’use of price promotions under threat of WMS entry. Promotions increase store salesand store traffic by stealing market share from other incumbents or limiting entry (e.g.,Gupta 1988; Orr 1974), increasing overall demand (e.g., Vilcassim and Chintagunta1995), or through consumer purchase acceleration, and/or stockpiling behavior (Blat-tberg et al. 1995). As milk is a perishable good, we are most interested in the first ofthese effects. The evidence on the effect of promotions and/or advertising 1 on entry ismixed. Bagwell (2007) gives a thorough survey of the topic. One group of studies sug-

1 Promotions and advertising are very closely related and, as Scott Morton (2000) points out, in someindustries, it is difficult to distinguish them. We will use promotions as an umbrella term.

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gests that promotions do indeed deter entry in consumer goods (e.g., Orr 1974; Duetsch1975; Shapiro and Khemani 1987), whereas another group of studies suggests that itactually facilitates entry (e.g., Alemson 1970; MacDonald 1986; Ferguson 1967). Thelatter finding may be due to endogeneity between promotions and product differen-tiation. Controlling for this endogeneity, Scott Morton (2000) finds that promotionsare not used strategically as a barrier to entry by generic firms in the US pharmaceu-tical market. Thomas (1999), in the ready-to-eat cereal market, finds that incumbentsuse price promotions to limit the scale of entry, after controlling for endogeneity ofadvertising and market share and price using a reduced-form approach. Regardingfood-retailing specifically, Zellner (1989), using a reduced form approach, claims thatsupermarkets use promotions strategically to deter entry. Similarly, Richards (2007),via a structural model that leads to the estimation of conjectural variations in promo-tions, finds that supermarkets use fresh fruit promotions strategically in response torivals. Volpe (2013) suggests that oligopolistic competition plays a significant role indeterming the timing and extent of supermarket promotions. Moreover, evidence fromprice and promotional elasticities suggests that promotions may work differently fromthe way a general price reduction does (e.g., Blattberg and Wisniewski 1989; Lat-tin and Bucklin 1989). We control for the endogeneity of promotion responses usinginstrumental variables and a structural framework that treats promotions as distinctfrom price changes.

From an economic standpoint, an important effect of WMS expansion is the ero-sion of demand facing incumbent supermarkets. The post-WMS-entry decrease intraditional supermarket sales in metropolitan areas (Artz and Stone 2006) and in aspecific store’s sales in Dallas/Fort Worth by 14 % (Capps and Griffin 1998) impliesthat Wal-Mart entry is a threat to traditional supermarkets’ sales. However, it is impor-tant to assess which types of consumers defect to WMS after their entry into a localmarket in order to determine the nature of the impact on incumbents’ demand. Thereis a surprising paucity of research investigating the type of consumer that defects toWMS. One notable exception is Singh et al. (2006), who examine the effect of WMSon one incumbent supermarket. They find that defection occurs mostly among house-holds with the largest pre-entry expenditures at the incumbent store, resulting in theincumbent supermarket losing 17 % of its sales volume.

In this article, the Dallas/Fort Worth milk market provides a case study to deter-mine WMS’ impact on the strategic pricing and promotion decisions of incumbentsupermarkets. Overall, southern retailers act less competitively than their northerncounterparts (Barnes et al. 1996); thus, the potential impact of WMS is likely to bemore significant in southern areas. Also, given the hub-and-spoke expansion of Wal-Mart, traditional supermarkets in states closer to Missouri, the site of the first WMSin 1988, may have had more time to make long-term adjustments to the expansion ofWal-Mart into the food retailing sector (Khanna and Tice 2000). Another interestingfeature of this market is that the top two players in Dallas/Fort Worth (Kroger andAlbertsons) were also the top two supermarket chains nationally during the periodconsidered.

Five main supermarket chains existed in Dallas/Fort Worth during the period ofanalysis: Albertsons, Kroger, Minyard, Tom Thumb, and Winn Dixie. It is clear fromFig. 1 that before April 1999, supermarket milk prices were uncommonly high, reach-

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Fig. 1 Supermarket milk prices, promotions, and number of Wal-Mart Supercenters in Dallas-Fort Worth

ing $3.41/gallon (Dallas/Fort Worth exhibited some of the highest milk prices nation-ally).2 After April 1999 supermarkets significantly reduced prices at a time whenWMS were expanding rapidly. Wal-Mart had been growing slowly in this marketsince 1995, but seems to enter a new phase of growth in the area around April 1999when it opened its twelfth Supercenter and, within 7 months, opened its twenty-first inDallas/Fort Worth. Promotions of milk throughout this period were also rampant, withnews noting extremely low milk prices, some as low as 79 cents a gallon (Wren 1999).

3 A structural model of supermarket responses

In this section, we describe a simple model to illustrate how WMS can impact thepricing and promotion decisions of incumbent supermarkets. We examine these deci-sions through a single, homogeneous product: milk gallons. We consider the entrydecision of WMS as an exogenous event,3 since consumers most likely do not switchto WMS based significantly on milk prices alone. Moreover, it seems reasonable thatconsumers switch to Wal-Mart based on distance to the store and lower prices for allgoods, as argued by Singh et al. (2006).

There are N incumbent firms that face demand for supermarket milk gallons, givenby

Q = f (P, S,m, Z), (1)

where P is price, S is a measure of WMS expansion, m is a measure of promotionalexpenditures, and Z is a vector of incumbent demand shifters.

2 Note that supermarkets in the area had been exposed to a failed Wal-Mart concept: Hypermart USA.Conversion of Hypermarts to Supercenters may have facilitated their rapid growth.3 However, we do control for the endogeneity of WMS with milk gallons sold by the incumbents, whichwe discuss in Sect. 4.

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The strategic decisions of the incumbents have two components: how much to selland how much to sell on promotion. Thus, incumbent firm i behaves as though itchooses quantity, qi , and promotion expenditures, mi , to solve 4

Maxqi ,miπi = P · qi − c(qi , w)− mi , (2)

where πi is the profit of the ith incumbent, c(·) is the retail cost function, w is a vectorof specific retailing cost components, and the cost of promotion is assumed to be setregardless of the number of gallons sold under promotion. Since one of our objectivesis to determine the effect of WMS on incumbents’ conduct, we solve (2) using thenecessary conditions in terms of the quantity response of supermarkets to each other,θi = ∂Q

∂qi

qiQ , and the promotional response of supermarkets to each other, ψ = ∂m

∂mimi ,

to yield

∂πi

∂qi= θi

η+ P − ∂c

∂qi= 0 (3)

and

∂πi

∂mi= ψi · ε

η

mi

qi+ εψi

θiP

mi

qi− 1 = 0, (4)

where η = ∂Q∂P

1Q is the semi-elasticity of demand and ε = ∂Q

∂m1Q is a measure of

consumer response to promotion. In equilibrium, θi = θ j = � and ψi = ψ j = ,where, following Bresnahan (1989),� is the average incumbent supermarket responseto quantity and is restricted to range from 0 to 1, and is the average incumbent super-market response to promotions. Moreover, we follow Appelbaum (1982) and assumec(qi ,w) is of the Gorman polar form, resulting in all firms having the same marginalcosts, c′(w). Since we would like a test of the impact of WMS on incumbents’ pricingconduct, we allow both� and to vary with S. Similarly, consumers’ responses couldalso be affected by the expansion of WMS, so η is also modeled to be a function of S,leading to Eqs. 5 and 6 presented below, which are the industry-wide analogues of 3and 4, respectively, allowing for the Wal-Mart effect:

P = − �(S)

η(S,m)+ c′(w) (5)

4 Following Cabral (2000), Cournot (c.f. Bertrand) competition seems an appropriate model assumptionfor several reasons: (1) milk is a commodity, thus on aggregate, the milk market is a homogeneous productmarket; (2) milk is a perishable (i.e., non-storable in its fluid form) product, thus capacity constraintsare unlikely to play a role in strategic decisions; and (3) marginal cost pricing does not seem to be anappropriate assumption given that the market is characterized by few, large incumbents in a location wheresupermarkets have been found less likely to be competitive (Barnes et al. 1996). Under Bertrand competitionwith homogeneous products and no capacity constraints, price falls to marginal cost and the lowest-pricedmilk captures the entire market. The model described herein treats the degree of competitive behavior asan empirical issue. Later, we allow for the possibility of supermarket price-cutting strategies generated byresidual demand and collusion shocks generated by Wal-Mart’s expansion.

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and

m

Q= (S) · ε

(1

η(S,m)+ P

�(S)

). (6)

Market equilibrium is reached when P and Q fulfill Eqs. 1, 5, and 6 simultane-ously. Notice that in this context the Lerner index of oligopoly power is given byL = − �(S)

η(S)·P . Thus, our model allows WMS to impact supermarket oligopoly powerby impacting the quantity responsiveness of incumbent supermarkets as well as theprice sensitivity of the consumers who continue to shop at supermarkets as Wal-Martincreases the number of Supercenters.

4 Data and empirical implementation

The main data to operationalize the model came from the Information ResourcesIncorporated-Infoscan (IRI) database provided by the Zwick Center for Food andResource Policy (formerly the Food Marketing Policy Center) at the University ofConnecticut. It includes 58 four-week-ending observations covering the period fromMarch 1996 to July 2000. The IRI data provide the supermarket values and volumes ofmilk sales for the market area as well as the percentage of milk sold under promotion.The retail price ($/gallon) was computed by dividing total dollar sales by total volume.Expenditures on promotion were proxied using a weighted average of value per unitfor milk gallons sold under promotion. Data on Wal-Mart milk quantity and pricingwere not available. Summary statistics are presented in Table 1.

The number of WMS, used to measure Wal-Mart expansion in the Dallas/FortWorth area, was obtained from Market Scope on a yearly basis and extrapolated usingthe entry strategy that Sam Walton describes in his autobiography (Walton and Huey1992). Neumark et al. (2008) and Hicks (2009) use time and distance from BentonCounty, Arkansas, to instrumentalize for WMS entry. Since all WMS in this studywere approximately at the same distance from Benton County, time became the onlyviable instrument. Marginal cost components include the Dallas county co-op class Ifluid milk (CCFM) price and supermarket square-footage, which comes from SpectraMarketing 5 and matches the IRI data.

Demand shifters include median per capita consumer income and average house-hold size collected from Market Scope; the number of WMS in the Dallas/Fort Wortharea; the average price of orange juice; seasonal indicator variables; and expenditureson promotions. The retail price of milk and income were deflated by the ConsumerPrice Index to impose homogeneity on the demand equation.

Retail price in the demand equation is likely to be correlated with errors in thedemand equation because these errors contain unobservable factors that managers takeinto account when determining quantity to stock and merchandising expenditures. Inorder to address possible endogeneity issues resulting from the simultaneity of supplierdecisions and consumer purchases, we implement a standard identification strategy

5 Spectra Marketing is a sister company of the Nielsen Company. All marketing data were obtained fromthe Zwick Center for Food and Resource Policy at the the University of Connecticut.

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Table 1 Summary statistics

Variable Mean Minimum Maximum Std. dev.

Supermarket price 2.55 1.85 3.21 0.327

Log of gallons sold 15.377 15.287 15.497 0.051

Promotion per gallon 0.170 0.032 0.601 0.126

Promotion expendituresa 0.819 0.151 3.057 0.630

Number of WMS 11.603 6 24 5.681

Cost variables

Co-op class I fluid milk 1.429 1.178 1.787 0.146

Total square footageb 12.951 10.738 14.979 1.309

Energy index 45.070 42.785 47.345 1.331

Packaging index 1.052 0.971 1.235 0.073

Retail wage rate 8.028 7.364 8.695 0.387

Demographic variables

Average total incomec 20.668 16.407 30.388 3.978

Average household size 2.626 2.589 2.713 0.044

Average age 32.823 31.921 33.331 0.386a In $1,000,000b This is the sum of square-footage across stores, capturing both number and size of stores and measuredin 1 billion square feet)c This is total income in Dallas/Fort Worth, measured in $10 billion, reflecting both income and population

that uses cost shifters and functions of cost shifters to identify demand and exogenousdemand characteristics and functions of those characteristics to identify the quantitysupplied by incumbent retailers.

To instrumentalize cost, the retail wage rate for Texas6 to control for the price oflabor, the energy price index for Dallas/Fort Worth since fluid milk is refrigerated, andtwo functions of out-of-market merger dummies that are suspected to influence costcoefficients through changes in management were used.

To identify demand, we used second-order polynomials of the following cost com-ponents: CCFM price, supermarket square-footage, retail wage rate and packagingindex to add richer variation.7 In order to identify the supply decisions of the incum-bents, we implement a similar strategy using the average age of the population sincechildren are expected to drink more milk. Since higher-order variations may likewisebe important on the demand side, we also used second-order polynomials of averageage and the price of orange juice. The instruments also included two functions oftime, which, with the seasonal indicators introduced above, should capture all time-

6 The retail wage rate for the Dallas/Fort Worth Metropolitan Statistical Area at the monthly level wasunavailable.7 Instrumentalization is theoretically based on small variations of the estimated parameters around the“true” value of those parameters, and in this context the variations need only be of the first order. For smallsample sizes, like the one in this study, however, higher-order variations may assume greater importance(Bowden and Turkington 1990).

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related demand shocks. Note that for these to be viable instruments they must be bothexogenous and relevant.

By virtue of Eqs. 5 and 6, the empirical analogue of Eq. 1 takes a semi-logarithmicform. Since the primary goal of this article is to investigate the effect of WMS onincumbent conduct, we allow for a second-order approximation of the Wal-Mart effecton the conduct parameter while capturing first-order effects on other parameters thatmay be affected by WMS. The empirical specification assumes that η(S), (S), andmarginal cost are linear while allowing for quadratic effects of S on�. Therefore, thesystem of equations to be estimated is

lnQ = (η0 + η1 · S) · P + γ · S + ε · m + ∑Kk=0 δk · zk + μ1, (7)

P = −�0+�1·S+�2·S2

η0+η1·S + ∑Rr=0 βr · wr + μ2, (8)

and

m

Q= (0 +1 · S) · ε

(1

η0 + η1 · S+ P

�0 +�1 · S +�2 · S2

)+ μ3, (9)

where ln is the natural log operator, zk denotes demand shifters, wr denotes marginalcost shifters, and our measure of WMS expansion, S, is the number of WMS inDallas/Fort Worth. The error associated with each equation, μl , l = 1, 2, 3, includesmeasurement error and unobservables (to the econometrician) that may be correlatedwith elements of the pricing or promotion equation, rendering estimates by ordinaryleast squares inconsistent. To correct for this potential endogeneity of prices (P),the number of WMS (S), and promotional expenditures (m), instrumental variableswere used. The system of Eqs. 7–9 is recursive, nonlinear in parameters, and has sixcross-equation restrictions (η0, η1,�0,�1,�2, ε).

In order for our analysis of the GMM-estimated parameters to be meaningful,our instruments should be both exogenous and relevant. The results of hypothesestests of instrument exogeneity and relevance are reported in Table 2. In the caseof the GMM estimator, exogeneity implies the identification of additional momentsand is tested using Hansen’s J-test (1982). For our analysis, Hansen’s J-stat with ap value of 0.998 does not reject the validity of the 54 over-identifying restrictions.The relevance of the instruments is tested based on a ‘first-stage’ regression analy-sis (Staiger and Stock 1997). A rule of thumb for this test is that the F-statistic begreater than 10. The hypothesis that all coefficients on the variables of the reduced-form model are zero is rejected by an F-statistic of 17.96 and a p value of 0.0001.Therefore, there is statistical evidence to support the claim that the instruments usedin this analysis are both exogenous and relevant, rendering meaningful the parameterestimates.

5 Empirical results

The parameter estimates for the system of Eqs. 7–9 are reported in Table 3 along withtheir standard errors and corresponding p values. All parameters of interest, excludingthose relevant to promotions, are significant at the one-percent level. The promotions

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Table 2 Model diagnostics

Test description Nullhypothesis

Test-statistic Test-value Degrees offreedom

p value Decision

Instrumentexogeneity

Over-identifyingrestrictions arevalid

Hansen’sJ-statistic

28.89 54 0.998 Do not reject

Instrumentrelevance

Instruments areuncorrelatedwithendogenousvariables

Staiger andstock’sF-statistic

17.96 20 0.0001 Reject

parameters, excluding promotion expenditures, which are not statistically significant,are significant at the five-percent level.

5.1 Milk sales response

The number of WMS enters the demand equation as a shift (γ ) in supermarket milksales and a shift in semi-elasticity (η1). The parameter estimates given in Table 3show that WMS can impact supermarket sales by luring away customers (i.e., shiftingdemand) and attracting a specific type of consumer, changing the customer-base oftraditional supermarkets (i.e., rotating demand). The coefficient on the number ofWMS is −0.022—that is, each additional Supercenter shifts supermarket milk salesdown by 2.2 %. However, this overstates the Wal-Mart effect on demand as the overallmarginal WMS effect on demand should take into consideration the rotation of thedemand curve as well.

If WMS lure away a proportion of supermarket sales, then traditional supermarketsface a reduction in demand for their product at all prices. However, WMS lure awayconsumers that are sensitive to price changes, thus rotating the demand curve inward:this can be seen from the coefficient on the interaction of price and number of WMS.At the bottom of Table 3, the marginal effect of WMS on the own-price elasticity ofdemand (calculated at the means) is 0.212 and significant at the one-percent level,suggesting that WMS increase the own-price elasticity of demand that incumbentsupermarkets face. Moreover, Table 4 contains measures of the own-price elasticity atvarious numbers of WMS. At the minimum number of WMS in our sample (6), theown-price elasticity of demand is −0.335, which is significant at the one-percent level,and is negative, inelastic, and consistent with others’ findings (e.g., Chidmi et al. 2005).As WMS expand to their mean 8 of 12 Supercenters, this elasticity increases to −0.222,which is significant at the one-percent level; at the maximum number of WMS in thesample (24), the elasticity becomes −0.0123, which again is significant at the one-percent level. This suggests that consumers who remain shopping at traditional retailersduring the expansion of WMS are not as price-sensitive as the overall consumer. Thatis, consumers that continue shopping at traditional retailers may do so for reasons

8 The mean number of WMS in our sample is 11.6. We instead use 12 as the rounded mean for calculations,as the interpretation of 11.6 WMS is unclear.

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Table 3 Estimation results of the demand, pricing, and promotions equations and relevant calculations

Variable Parameter Estimate Standard error p value

Demand equationConstant δ0 8.262 0.975 <0.0001

Total income δ1 −0.0031 0.0008 0.0012

Household size δ2 2.715 0.419 <0.0001

Summer δ3 −0.042 0.005 <0.0001

Spring δ4 −0.018 0.005 0.0016

Winter δ5 0.010 0.005 0.0406

Price of substitute (orange juice) δ6 0.415 0.069 <0.0001

Number of WMS in D/FW γ −0.022 0.005 0.0002

Price η0 −0.173 0.038 <0.0001

Price*number of WMS in D/FW η1 0.007 0.002 <0.0001

Promotion expenditures ε 0.000 0.000 0.1598

Pricing equation

Supermarket quantity response �0 0.127 0.030 0.0001

Supermarket qty resp*WMS �1 −0.014 0.003 0.0001

Supermarket qty resp*WMS2 �2 0.0004 0.0001 0.0001

Supermarket promotion response 0 −1.1532 0.55179 0.0415

Supermarket merch resp*WMS 1 0.0611 0.0029 0.0445

Co-op class I fluid milk price β1 1.131 0.094 <0.0001

Total square-footage of supermarkets β2 0.058 0.009 <0.0001

Additional calculations

Marginal cost 2.35 0.021 <0.0001

WMS marginal effect on demand −0.003 0.0019 0.061

WMS marginal effect on the Lerner index −0.018 0.0011 <0.0001

WMS marginal effect on own-price elasticity 0.212 0.047 <0.0001

WMS marginal effect on quantity response −0.005 0.00124 <0.0001

WMS marginal effect on promotion response 0.14 0.0132 <0.0001

Table 4 The effect of WMS on market power indicators

Simulation of no WMS Sample minimum Sample mean Sample maximum

Elasticity −0.44 −0.335 −0.227 −0.012

(0.096) (0.073) (0.05) (0.009)

Lerner index 0.289 0.1713 0.056 0.116

(0.021) (0.011) (0.002) (0.04)

Quantity response 0.127 0.057 0.012 0.001

(0.030) (0.013) (0.003) (0.0006)

Promotion response −0.0861 −0.0587 −0.0313 0.0234

(0.041) (0.0279) (0.014) (0.0123)

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other than price.9 Moreover, a simulated elasticity in the case of no WMS is −0.44,which is more elastic than even at the minimum number of WMS in our sample andsignificant at the one-percent level, suggesting that consumers that remain shoppingat a traditional supermarket as WMS expand are less price-sensitive, on average, thanthe consumers in the overall milk market in Dallas/Fort Worth. These findings suggestthat price-promotions may not be the best way for traditional supermarkets to retaincustomers in the face of WMS expansion.

Combining WMS effect on the shift and rotation of demand yields the overallmarginal demand effect of WMS, which is to lure away 0.3 % of milk gallon purchasesfrom incumbent supermarkets. This is economically quite significant, especially giventhat the supermarkets were, during the sample period, offering aggressive temporarypromotions of milk, with some retailers offering a gallon of milk for as little as 79cents (Wren 1999). The strength of the result in such a necessary product as milksales during promotional periods highly suggests that supermarkets lost at least thismuch in overall product sales, as consumers who chose to buy milk at WMS insteadof a traditional supermarket most likely chose to purchase their entire basket of goodsthere (Singh et al. 2006).

5.2 Price response

WMS not only have a significant impact on sales of their supermarket competitors,but also impact their pricing. The overall effect on the price of milk, shown in Fig. 2averaged over time, is a decrease of 53 cents a gallon, or about 21 % of the averageoverall price. This finding is consistent with other research in this area. It is alsointeresting to note that when S = 6 (the smallest number of WMS observed in thedata), the price reduction is 30 cents, or approximately 11 % of the price; near the endof our data, when S = 21, the price reduction reaches its peak at 44 % of the price.However, by the end of the sample, when S = 24 the price reduction becomes 17 %.This suggests that the supermarkets were using price reductions to compete with WMSin an unsustainable manner. However, note that the price reduction at 24 WMS is stilllarger than it was at 6 WMS, indicating that the long-run price-effects of WMS maybe different than the short-run impacts. While the fact that WMS decrease the price oftheir competitors has been well-researched in previous studies, the way in which thisprice reduction is achieved has not heretofore been investigated.

5.3 Quantity response

In order to understand if WMS are able to decrease competitors’ prices by decreas-ing noncompetitive behavior, we measure the responsiveness of supermarkets to eachothers’ quantities. The quantity-responsiveness of supermarkets is measured by theconjectural variations elasticity based on how incumbents react to each other in terms

9 There are significant other possible reasons for consumers to remain shopping at traditional retail formats.For example, distance from the home or workplace, lack of public transportation, avoidance of the sheersize of WMS (Bonanno and Lopez (2012)), or even socio-political bias against Wal-Mart (Ingram et al.2010). These are beyond the scope of this study.

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Fig. 2 Percentage price decrease due to WMS expansion

of quantity adjustments, which in part depends on WMS expansion. In the empiricalestimation, the conjectural quantity-variations elasticity is allowed to vary quadrati-cally with S to provide more flexibility in capturing the responses. The baseline value ofthe conjectural quantity-variations elasticity (�0), which can also be interpreted as thesimulated price-responsiveness had WMS never entered, was estimated to be 0.127,and this decreased by −0.014 for each additional WMS (�1), while the quadratic effectincreased it by 0.0004 (�2). Together, these yield the marginal effect of WMS on theconjectural quantity-variations elasticity of supermarkets (calculated at the mean) of−0.005, which is significant at the 1 % level and indicates that WMS has an overalldecreasing effect on the response of supermarkets to each others’ price changes (seeTable 3). At the minimum number of WMS in the sample, the conjectural quantity-variations elasticity is 0.057 (see Table 4), which is already quite small although itis statistically different from zero. We can see the impact attenuate, for at the meannumber of WMS it is 0.0129 and at the maximum 0.001, which are still statistically pos-itive. However, comparing this to the baseline measure reported above, it is clear thatWMS decrease the responsiveness of supermarkets to each others’ quantities. Whilethe conjectural quantity-variations elasticity is always decreasing with the increasingnumber of WMS, initially it decreases slowly and then begins to decrease more rapidlyas the frequency of WMS entry increases. While the conjectural quantity-variationselasticity decreases at an increasing rate, the number of WMS does not increase inDallas/Fort Worth at a constant rate, so that the impact of WMS is felt most whenthey begin to enter more frequently (from April 1999 on). The initial impact is small(even though each Supercenter has a greater impact in earlier periods) and leads to adramatic decrease as WMS begin to enter more frequently.

5.4 Promotion response

In a perishable goods market, promotions work by increasing market share at theexpense of rivals (e.g., Gupta 1988; Orr 1974) or increasing demand (e.g., Vilcassim

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Fig. 3 Lerner index of supermarket oligopoly power

and Chintagunta 1995). From the discussion in the previous section, the latter effect isnot occurring—during this period of increased promotional activity, supermarkets donot gain sales but actually lose sales to WMS. Further, their response to other super-markets’ promotional activity is mitigated. While the quantity response is estimateddirectly via the conjectural quantity-variations elasticity, the promotional responseis measured through a semi-elasticity of conjectural promotion-variations. Dividingthis by the aggregate percentage price reduction yields the conjectural promotion-variations elasticity. The baseline estimate of supermarkets’ responsiveness to eachothers’ promotions (0) is −1.153, which is negative, elastic, and statistically signif-icant at the five-percent level. This suggests that supermarkets offer promotions whenothers are offering the regular price and that they do not hold promotions simultane-ously, which may be indicative of market power. The effect of WMS on supermarkets’promotion response (1) is 0.061, so that the marginal effect of WMS on supermarkets’promotion response (calculated at the mean) is 0.14, which is significant at the 1 %level; an additional Supercenter increases the promotional response of supermarkets,which, since the baseline is negative, decreases the responsiveness of supermarkets toeach others’ promotions. At the minimum number of WMS in the sample, the semi-elasticity of conjectural promotion-variations of −0.058, which is negative, inelastic,and significant at the five-percent level. If we view the baseline estimate (0) as asimulation of the promotion response if WMS had never entered, then it seems thatthe WMS effect is to render supermarkets less responsive to others’ promotions. Cal-culated at the mean number of WMS, the promotion response is −0.03 and significantat the five-percent level, which is less elastic than at 6 WMS, still indicating that WMSmay induce supermarkets to be less responsive to each others’ promotions. Moreover,at the maximum number of WMS in the sample, the promotion response is 0.023,which is positive, inelastic, and significant at the ten-percent level, suggesting thatWMS have a disciplining effect on the promotional responses of supermarkets.

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5.5 Effect on oligopoly power

Our main hypothesis is that WMS decrease supermarket price by decreasing supermar-ket oligopoly power. The Lerner index of market power (L = − �(S)

η(S)·P ) is composed oftwo effects: consumer response (via the demand elasticity,η·P) and supermarket quan-tity response (via the conjectural quantity variations elasticity,�). The responsivenessof consumption to price is allowed to vary with the number of WMS, as is the supermar-ket quantity response (the WMS effect on the price sensitivity of consumers has alreadybeen discussed in the context of supermarket sales effects) is also allowed to varywith the number of WMS. Through the effects on the conjectural quantity-variationselasticity, we know that supermarkets are less responsive to each others’ changes inquantity after the number of WMS increases rapidly; however, this does not translateinto a direct measure of market power. During this time, WMS were also luring awayprice-sensitive consumers and thereby decreasing the elasticity of demand faced byincumbents. Therefore, the two components of market power are moving in oppositedirections. WMS are luring away price-sensitive consumers, leaving the incumbentsto face a more inelastic demand (i.e., the denominator of the Lerner index is becom-ing less negative) while also disciplining the quantity response of the incumbents toeach other (i.e., the numerator of the Lerner index is decreasing). Since the consumerswho remain shopping at incumbent supermarkets as the number of WMS increasesare less price-sensitive than overall milk market consumers, the decreasing effect onmarket power of the decreasing conjectural quantity-variations elasticity is attenuated.The marginal effect of WMS on the Lerner index of the incumbents is -0.018 and issignificant at the 1 % level (see Table 3), indicating that the effect on the conjecturalquantity-variations elasticity overcomes the effect on price sensitivity in this case.

Since the fewest number of WMS we observe in our data is six, the above findingdoes not reveal the complete impact of WMS on incumbent supermarket oligopolypower. We simulate the Lerner index for zero WMS, and under the assumption of zeroWMS and Cournot behavior compared both to the Lerner index predicted by the modelin Fig. 3. Assuming no structural differences between 1995 (when WMS first enteredthis market) and 1996 (the beginning of our data), before WMS entry (i.e., when S = 0),the Lerner index of market power was 0.289, indicating that incumbent supermarketswere pricing 28.9 % above cost, and when there were 12 WMS supermarkets priced5.6 % above cost (see Table 4). Moreover, when there are no WMS, we find evidencein support of Cournot level market power. That is, with a p value of 0.631, we cannotreject the hypothesis that oligopoly power devoid of the Wal-Mart effect and Cournotlevel of power in the absence of WMS are equivalent.

6 Simulation of alternative pricing scenarios

The Lerner index shows oligopoly power decreasing with WMS expansion in theDallas/Fort Worth milk market: Wal-Mart is shown to cause the Dallas/Fort Worthmilk market to become competitive. However, in order to quantify the price reductionand determine what the price would have been under alternative incumbent behaviors,we performed simulations and compared them with the results already discussed.

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Fig. 4 Supermarket prices under alternative pricing scenarios

First, a baseline price was predicted by the model. Then prices were simulated forfour different scenarios of incumbent conduct:

1. marginal cost pricing (L = 0),2. oligopoly pricing if WMS had never entered (L = �0

η0·P ; �1 = �2 = η1= 0),

3. Cournot pricing if WMS had never entered (L =∑

i s2i

η0·P ; �1 = �2 = η1= 0), and4. perfect collusion (�(S) = 1 and η1 = 0).

Figure 4 plots the first three of these simulations against the estimated price fromEq. 8.

6.1 Marginal cost pricing

As is the case in all empirical studies that use equilibrium concepts to identify marginalcosts, the correct specification of c′(w) is essential. There are two possible consistencychecks: the predicted marginal cost should be (1) greater than the CCFM price (i.e.,the raw milk price set at the farm level) ,and (2) less than the predicted retail price.However, given that some of the supermarkets in the sample priced below the CCFMprice, the latter is not valid in this case, and we expect that for a limited period, thepredicted retail price will lie below the marginal cost. Figure 4 shows that marginalcost is less than price everywhere, whereas the data show some supermarkets pricingeven below the CCFM price (which is the cost of just one input in the productionof retail milk). The marginal cost components are the overall square-footage (whichtakes into account number of stores) and the CCFM price.10 The overall marginal cost,

10 The transmission of the CCFM price to the retail price is $1.31 and is significant at the 1 % level. Insome studies, the transmission of the primary input price can reflect market power. Here, we focus on othermeasures to infer the competitive nature of the market.

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evaluated at averages, is $2.35 and statistically different from zero at the one-percentlevel.

6.2 Oligopoly pricing without WMS

In order to simulate the retail pricing that would have occurred had WMS never enteredthe Dallas/Fort Worth milk market, we set �1 = �2 = 0 to simulate supermarkets’behavior without WMS disciplining presence and η1= 0 to allow supermarkets to priceaccording to the more elastic demand of the entire milk market. We find that withoutthe expansion of WMS, supermarkets would have charged, on average, $3.10/gallon.Thus, WMS cause a 20 % reduction in price on average throughout WMS expansion,and a maximum reduction in price of 44 %, which is consistent with the findings ofCurrie and Jain (2002).

6.3 Cournot pricing without WMS

From the model, the average Cournot price is predicted to be $3.07, with a mini-mum of $2.67 and a maximum of $3.54. Since we cannot reject that oligopoly powerdevoid of the Wal-Mart effect is equivalent to Cournot power, then the prices thetwo strategies yield must be statistically equivalent. Therefore, before WMS entry,assuming no structural difference between 1995 (when WMS first entered this mar-ket) and 1996 (the beginning of our data), supermarkets were pricing according toCournot behavior and may have continued to price that way if WMS had neverentered.

6.4 Collusive pricing

The perfectly collusive scenario was calculated setting the conjectural quantity-variations to one and ignoring WMS effect on the semi-elasticity of demand. Whilethe latter is not specified by the theoretical development of the model, it is moreconsistent with our market power findings. Moreover, allowing WMS to affect thesemi-elasticity in this simulation would lead to very high prices for milk (approxi-mately $8.67/gallon on average) since these colluders would be facing an extremelyinelastic residual demand. We are, therefore, assuming no change in semi-elasticityof demand due to the expansion of WMS. If the supermarkets had been colluding per-fectly, then the average price for milk would have been $4.67/gallon, with a minimumof $4.19 and a maximum of $5.50. Thus, while the supermarkets exhibited noncompet-itive behavior before the expansion of WMS, they did not approach perfectly collusivebehavior.

7 Conclusions

This article uses a structural model and data from the Dallas-Fort Worth milk marketfrom a 5-year period when the number of Wal-Mart Supercenters (WMS) quadrupled

123


from 6 to 24. As expected from previous study using reduced-form models, supermar-ket chains responded as they have elsewhere—by lowering prices. This contributesto the Wal-Mart literature by disentangling the responses of incumbent supermarkets.We focus on the two decisions of incumbent supermarkets that determine pricing:deciding the quantity to sell and the quantity to sell under promotion. Given data onthe milk market in Dallas/Fort Worth, we are able to identify marginal costs as wellas the noncompetitive behavior of incumbents in milk sales and promotions.

The empirical results indicate that WMS expansion disciplined the fairly collusiveconduct of incumbent supermarkets in the Dallas/Fort Worth milk market, causingthose incumbents to adopt behaviors more similar to perfectly competitive behavior.Over the length of this study, the cumulative effect of WMS reduces supermarkets’price–cost margin by 59.8 %. WMS lowered the price of Dallas/Fort Worth supermar-kets 17 % through their competition-inducing effect on behavior with a marginal effecton supermarkets’ price–cost margin of −1.8 %. Moreover, we find that WMS—large,low-cost, low-price entrants—did not just discipline the quantity decision of super-markets, but they also disciplined their quantity-to-sell-under-promotion decision. Theexpansion of WMS induced supermarkets to hold promotions simultaneously insteadof when their competitors were not doing so, which could also have had a loweringeffect on the price of the aggregate. That is, instead of stealing market share from rivalsby promoting milk when rivals’ prices were high, incumbents by holding promotionssimultaneously decreased their effectiveness.

WMS growth was also able to mitigate the effectiveness of promotions in increasingthe overall demand. An additional WMS was able to lure away 0.3 % of supermar-ket sales even during a period of aggressive price reductions by incumbents, whichshould lead to an increase in the overall demand; however, we find evidence thatsuggests that WMS do not lure away consumers in proportion to the overall market;that is, WMS shoppers may be different than supermarket shoppers. We examine thisin the dimension of price sensitivity and find that supermarkets retained less price-sensitive consumers after WMS expanded, leaving incumbent supermarkets to facemore inelastic demand for milk and lower demand levels, resulting in lower dollarsales.

These effects have important implications for management: namely, nonprice com-petition may be more effective than price competition to retain consumers when facedwith a large, low-cost, low-price entrant. This may require muting price competitionby attempting to match prices for necessities such as milk, leaving competition moreto location, service, public relations with local economic development entities, andstore atmospherics. That is, WMS may not only introduce a new pricing strategy withEDLP, but may render the traditional Hi–Lo pricing of supermarkets ineffective incompeting even among themselves.

While the case-study framework allows for the application of a structural model,it also has drawbacks for analyzing horizontal as well as vertical competition. Eventhough milk is an essential commodity and can therefore be used to gauge pricing andpromotion behavior of incumbents, horizontal retail competition goes well beyond asingle commodity. In this regard, it has been suggested that superstores such as WMSuse the entire grocery sector as a loss-leader to attract consumers to the store andthen compensate low profit margins by strategic pricing and placement of general

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merchandise. Moreover, while the findings of this study suggest that WMS are ableto discipline tacit collusion, the only legal form of collusive behavior, at the chainlevel, it does not speak to competition between brands within each store. WMS abilityto discipline horizontal behavior between supermarkets may hamper supermarkets’ability to compete with manufacturers horizontally via their private labels and maydecrease their vertical bargaining power as well.

Acknowledgments We are grateful to two anonymous journal referees for helpful comments thatimproved the manuscript and to the Zwick Center for Food and Resource Policy for providing the IRIscanner data used in this research. We would also like to thank Professors Jean-Paul Chavas, Kyle Stiegert,and Guanming Shi of the University of Wisconsin and Vardges Hovhannisyan for their insightful sugges-tions on an earlier draft. We are, however, solely responsible for any remaining errors. We acknowledgefunding from USDA NIFA grant 2010-34178-207066.

Appendix: Deriving the estimating equations

This appendix gives the derivation of the equations to be estimated. Each supermarket,i = 1, ..., N , chooses quantity, qi , and promotion expenditures, mi , to maximize

Maxqi ,ai {πi = P(Q)qi − c(qi )− mi : Q = f (m, S)} (10)

which has as necessary conditions for maximization,

∂πi

∂qi= ∂P

∂Q

∂Q

∂qiqi + P − ∂c

∂qi= 0

and

∂πi

∂mi= ∂P

∂Q

∂Q

∂m

∂m

∂miqi + ∂qi

∂Q

∂Q

∂m

∂m

∂miP − ∂g

∂mi= 0.

The derivation for the estimating equation of price is given by the first necessarycondition. Multiplying the first term by Q

Q yields

P = − ∂P

∂QQ∂Q

∂qi

qi

Q+ ∂c

∂qi

letting η = ∂Q∂P

1Q and θ j = θi = ∂Q

∂qi

qiQ , and aggregating across firms yields the

following equation to be estimated:

P = −�η

+ ∂c

∂Q. (11)

The derivation for the estimating equation of marketing mix is given by the secondfirst-order condtion. Multiplying the first term by Q

Qmimi

and the second term by mimi

qiqi

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yields

∂P

∂QQ∂Q

∂a

1

Q

∂m

∂mimi

qi

mi+ ∂qi

∂Q

Q

qi

∂Q

∂m

∂m

∂mimi

qi

miP = ∂g

∂mi

letting ε = ∂Q∂m

1Q andψ = ∂m

∂mimi , aggregating across firms, inverting, and normalizing

marginal cost of marketing to one yields the following equation to be estimated

m

Q= ψε

(1

η+ P

�

). (12)

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