Supermarket pricing

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Vol. 27, No. 5, September–October 2008, pp. 811–828issn 0732-2399  eissn 1526-548X  08  2705  0811informs®doi 10.1287/mksc.1080.0398©2008 INFORMSSupermarket Pricing StrategiesPaul B. EllicksonDepartment of Economics, Duke University, Durham, North Carolina 27708,paul.ellickson@duke.eduSanjog MisraWilliam E. Simon School of Business Administration, University of Rochester,Rochester, New York 14627, misra@simon.rochester.eduMost supermarket firms choose to position themselves by offering either everyday low prices (EDLP) acrossseveral items or offering temporary price reductions (promotions) on a limited range of items. Whilethis choice has been addressed from a theoretical perspective in both the marketing and economic literature,relatively little is known about how these decisions are made in practice, especially within a competitive envi-ronment. This paper exploits a unique store level data set consisting of every supermarket operating in theUnited States in 1998. For each of these stores, we observe the pricing strategy the firm has chosen to follow,as reported by the firm itself. Using a system of simultaneous discrete choice models, we estimate each store’schoice of pricing strategy as a static discrete game of incomplete information. In contrast to the predictions ofthe theoretical literature, we find strong evidence that firms cluster by strategy by choosing actions that agreewith those of its rivals. We also find a significant impact of various demographic and store/chain characteristics,providing some qualified support for several specific predictions from marketing theory.Key words: EDLP; promotional pricing; positioning strategies; supermarkets; discrete gamesHistory: Received: March 22, 2006; accepted: February 27, 2008; processed by David Bell.1. IntroductionWhile firms compete along many dimensions, pricingstrategy is clearly one of the most important. In manyretail industries, pricing strategy can be characterizedas a choice between offering relatively stable pricesacross a wide range of products (often called every-day low pricing) or emphasizing deep and frequentdiscounts on a smaller set of goods (referred to aspromotional or PROMO pricing). Although Wal-Martdid not invent the concept of everyday low pricing,the successful use of everyday low pricing (EDLP)was a primary factor in their rapid rise to the topof the Fortune 500, spawning a legion of followersselling everything from toys (ToysRUs) to buildingsupplies (Home Depot). In the 1980s, it appeared thatthe success and rapid diffusion of the EDLP strategycould spell the end of promotions throughout muchof retail. However, by the late 1990s, the penetrationof EDLP had slowed, leaving a healthy mix of firmsfollowing both strategies, and several others employ-ing a mixture of the two.Not surprisingly, pricing strategy has proven to bea fruitful area of research for marketers. Marketingscientists have provided both theoretical predictionsand empirical evidence concerning the types of con-sumers that different pricing policies are likely toattract (e.g. Lal and Rao 1997, Bell and Lattin 1998).While we now know quite a bit about where a personis likely to shop, we know relatively little about howpricing strategies are chosen by retailers. There aretwo primary reasons for this. First, these decisionsare quite complex: managers must balance the pref-erences of their customers and their firm’s own capa-bilities against the expected actions of their rivals.Empirically modeling these actions (and reactions)requires formulating and then estimating a complexdiscrete game, an exercise which has only recentlybecome computationally feasible. The second is thelack of appropriate data. While scanner data setshave proven useful for analyzing consumer behavior,they typically lack the breadth necessary for tack-ling the complex mechanics of inter-store competi-tion.1The goal of this paper is to combine newlydeveloped methods for estimating static games witha rich, national data set on store level pricing poli-cies to identify the primary factors that drive pricingbehavior in the supermarket industry.Exploiting the game theoretic structure of ourapproach, we aim to answer three questions thathave not been fully addressed in the existing liter-ature. First, to what extent do supermarket chainstailor their pricing strategies to local market condi-tions? Second, do certain types of chains or stores1Typical scanner data usually reflect decisions made by only a fewstores in a limited number of markets.811 Ellickson and Misra: Supermarket Pricing Strategies812Marketing Science 27(5), pp. 811–828, ©2008 INFORMShave advantages when it comes to particular pricingstrategies? Finally, how do firms react to the expectedactions of their rivals? We address each of these ques-tions in detail.The first question naturally invites a market pulldriven explanation in which consumer demographicsplay a key role in determining which pricing strategyfirms choose. In answering this question, we alsoaim to provide additional empirical evidence that willinform the growing theoretical literature on pricingrelated games. Since we are able to assess the impactof local demographics at a much broader level thanprevious studies, our results provide more conclusiveevidence regarding their empirical relevance.The second question concerns the match betweena firm’s strategy and its chain-specific capabilities.In particular, we examine whether particular pricingstrategies (e.g., EDLP) are more profitable when firmsmake complementary investments (e.g. larger storesand more sophisticated distribution systems). Theempirical evidence on this matter is scant—this is thefirst paper to address this issue on a broad scale. Fur-thermore, because our data set includes all existingsupermarkets, we are able to exploit variation bothwithin and across chains to assess the impact of storeand chain level differences on the choice of pricingstrategy.Finally, we address the role of competition posedin our third question by analyzing firms’ reactionsto the expected choices of their rivals. In particular,we ask whether firms face incentives to distinguishthemselves from their competitors (as in most modelsof product differentiation) or instead face pressuresto conform (as in network or switching cost mod-els)? This question is the primary focus of our paperand the feature that most distinguishes it from earlierwork.Our results shed light on all three questions. First,we find that consumer demographics play a signifi-cant role in the choice of local pricing strategies: firmschoose the policy that their consumers demand. Fur-thermore, the impact of these demographic factorsis consistent with both the existing marketing liter-ature and conventional wisdom. For example, EDLPis favored in low income, racially diverse markets,while PROMO clearly targets the rich. However, a keyimplication of our analysis is that these demographicfactors act as a coordinating device for rival firms,helping shape the pricing landscape by defining anequilibrium correspondence. Second, we find thatcomplementary investments are key: larger storesand vertically integrated chains are significantly morelikely to adopt EDLP. Finally, and most surprisingly,we find that stores competing in a given market haveincentives to coordinate their actions. Rather thanchoosing a pricing strategy that distinguishes themfrom their rivals, stores choose strategies that match.This finding is in direct contrast to existing theoreticalmodels that view pricing strategy as a form of dif-ferentiation, providing a clear comparative static thatfuture pricing models must address.Our paper makes both substantive and method-ological contributions to the marketing literature. Onthe substantive front, our results offer an in-depthlook at the supermarket industry’s pricing practices,delineating the role of three key factors (demand,supply, and competition) on the choice of pricingstrategy. We provide novel, producer-side empiri-cal evidence that complements various consumer-sidemodels of pricing strategy. In particular, we find qual-ified support for several claims from the literatureon pricing demographics, including Bell and Lattin’s(1998) model of basket size and Lal and Rao’s (1997)positioning framework, while at the same time high-lighting the advantages of chain level investment.Our focus on competition also provides a structuralcomplement to Shankar and Bolton’s (2004) descrip-tive study of price variation in supermarket scannerdata, which emphasized the role of rival actions. Ourmost significant contribution, however, is demonstrat-ing that stores in a particular market do not use pric-ing strategy as a differentiation device but insteadcoordinate their actions. This result provides a directchallenge to the conventional view of retail compe-tition, opening up new and intriguing avenues forfuture theoretical research. Our econometric imple-mentation also contributes to the growing literature inmarketing and economics on the estimation of staticdiscrete games, as well as the growing literature onsocial interactions.2In particular, our incorporation ofmultiple sources of private information and our con-struction of competitive beliefs are novel additions tothese emerging literatures.The rest of the paper is organized as follows. Sec-tion 2 provides an overview of the pricing landscape,explicitly defining each strategy and illustrating theimportance of local factors in determining store leveldecisions. Section 3 introduces our formal model ofpricing strategy and briefly outlines our estimationapproach. Section 4 describes the data set. Section 5provides the details of how we implement the model,including the construction of distinct geographic mar-kets, the selection of covariates, our two-step estima-tion method, and our identification strategy. Section 62Recent applications of static games include technology adop-tion by internet service providers (Augereau et al. 2006), prod-uct variety in retail eyewear (Watson 2005), location of ATMbranches (Gowrisankaran and Krainer 2004), and spatial differenti-ation among supermarkets (Orhun 2005), discount stores (Zhu et al.2005), and video stores (Seim 2006). Structural estimation of socialinteractions is the focus of papers by Brock and Durlauf (2002),Bayer and Timmins (2006), and Bajari et al. (2005). Ellickson and Misra: Supermarket Pricing StrategiesMarketing Science 27(5), pp. 811–828, ©2008 INFORMS813provides our main empirical results and discussestheir implications. Section 7 concludes with directionsfor future research.2. The Supermarket PricingLandscape2.1. Pricing Strategy ChoicesCompetition in the supermarket industry is a complexphenomenon. Firms compete across the entire retailand marketing mix, enticing customers with an attrac-tive set of products, competitive prices, convenientlocations, and a host of other services, features, andpromotional activities. In equilibrium, firms choosethe bundle of services and features that maximizeprofits, conditional on the types of consumers theyexpect to serve and their beliefs about the actions oftheir rivals. A supermarket’s pricing strategy is a keyelement in this multidimensional bundle.The majority of both marketers and practitionersframe a store’s pricing decision as a choice betweenoffering everyday low prices or deep but tempo-rary discounts, labeling the first strategy EDLP andthe second PROMO (Table 1).3 4Not surprisingly,the simple EDLP-PROMO dichotomy is too narrowto adequately capture the full range of firm behav-ior. In practice, firms can choose a mixture of EDLPand PROMO, varying either the number of categoriesthey put on sale or changing the frequency of salesacross some or all categories of products. Practitionershave coined a term for these practices—hybrid pric-ing. What constitutes HYBRID pricing is necessarilysubjective, depending on an individual’s own beliefsregarding how much price variation constitutes adeparture from pure EDLP. Both the data and defini-tions used in this paper are based on a specific storelevel survey conducted by Trade Dimensions in 1998,3This is clearly a simplification—a supermarket’s pricing policyis closely tied to its overall positioning strategy. Pricing strategiesare typically chosen to leverage particular operational advantagesand often have implications for other aspects of the retail mix. Forexample, successful implementation of EDLP may involve offeringa deeper and narrower product line than PROMO, allowing firmsto exploit scale economies (in particular categories), reduce theirinventory carrying costs, and lower their advertising expenses. Onthe other hand, PROMO pricing gives firms greater flexibility inclearing overstock, allows them to quickly capitalize on deep man-ufacturer discounts, and facilitates the use of consumer loyalty pro-grams (e.g. frequent shopper cards). In other words, the choice ofpricing strategy is more than just how prices are set: it reflects theoverall positioning of the store. This paper focuses on the pricingdimension alone, taking the other aspects of the retail mix as given.While this is limiting, modeling the entire retail mix is beyond thescope of this paper.4Note that we focus on the choice of pricing strategy and abstractaway from issues related to more tactical decisions about how pricesare (or should be) set (see e.g., Kumar and Rao 2006).Table 1 Descriptive StatisticsVariable Obs Mean Std. dev. Min. MaxStrategyEDLP 17388 028 045 0 1HYBRID 17388 038 048 0 1PROMO 17388 034 047 0 1MSA characteristicsSize (sq. miles) 333 186831 194399 4640 112296Density (pop ’000 333 1042 962 091 4906per sq. mile)Avg. food expenditure 333 66364 120137 1604 958209($ ’000)Market variablesMedian household size 8000 266 035 132 569Median HH income 8000 3525559 975395 1810960 8195460Proportion Black 8000 008 014 000 097Proportion Hispanic 8000 006 013 000 098Median vehicles in HH 8000 212 033 056 337Chain/store characteristicsVertically integrated 17388 051 050 000 100Store size (sqft ’000) 17388 2899 1634 200 25000Independent store 17388 023 042 000 100Number of stores 804 39015 47845 100 139900in chainwhich asked individual store managers to choosewhich of the following categories best described theirstore’s pricing policy:• Everyday LowPrice (EDLP): Little reliance onpromotional pricing strategies such as temporaryprice cuts. Prices are consistently low across theboard, throughout all packaged food departments.• Promotional (Hi-Lo) Pricing: Heavy use of spe-cials, usually through manufacturer price breaks orspecial deals.• Hybrid EDLP/Hi-Lo: Combination of EDLP andHi-Lo pricing strategies.According to Trade Dimensions, the survey wasdesigned to allow for a broad interpretation of theHYBRID strategy, as they wanted it to capture devia-tions along either the temporal (i.e., number of salesper year) or category based dimensions (i.e., numberof categories on deal). We believe that pricing strat-egy is best viewed as a continuum, with pure EDLP(i.e., constant margins across all categories) on oneend and pure PROMO (i.e. frequent sales on all cate-gories) at the other. This data set represents a coarsediscretization of that continuum.2.2. Supermarket Pricing: A Closer LookWithout observing data on individual stores, it mightbe tempting to conclude that all pricing strategies aredetermined at the level of the chain. While there arecertainly incentives to choose a consistent policy, thedata reveals a remarkable degree of local heterogene-ity. To examine the issue more closely, we focus in ona single chain in a single market: the Pathmark chainin New Jersey. Figure 1 shows the spatial locations of Ellickson and Misra: Supermarket Pricing Strategies814Marketing Science 27(5), pp. 811–828, ©2008 INFORMSFigure 1 Pathmark Stores in New Jersey41.040.539.539.0–75.5 –75.0 –74.5 –74.0EDLPHYBRIDPROMO40.0every Pathmark store in New Jersey, along with itspricing strategy. Two features of the data are worthemphasizing. We address them in sequence.First, Pathmark does not follow a single strategyacross its stores: 42% of the stores use PROMO pric-ing, 33% follow EDLP, and the remaining 25% useHYBRID. The heterogeneity in pricing strategyobserved in the Pathmark case is not specific to thisparticular chain. Table 2 shows the store level strate-gies chosen by the top 15 U.S. supermarkets (bytotal volume) along with their total store counts. Aswith Pathmark, the major chains are also surprisinglyheterogeneous. While some firms do have a clearfocus (e.g. Wal-Mart, H.E. Butt, Stop & Shop), oth-ers are more evenly split (e.g. Lucky, Cub Foods).This pattern extends to the full set of firms. Table 3shows the pricing strategies chosen by large andTable 2 Pricing Strategies of the Top 15 SupermarketsFirm Stores % PROMO % HYBRID % EDLPKroger 1399 47 40 13Safeway 1165 52 43 5Albertson’s 922 11 41 48Winn-Dixie 1174 3 30 67Lucky 813 35 38 27Giant 711 29 60 11Fred Meyer 821 22 60 18Wal-Mart 487 1 26 73Publix 581 13 71 16Food Lion 1186 2 12 86A&P 698 55 30 15H.E. Butt 250 1 3 96Stop & Shop 189 50 43 7Cub foods 375 26 34 40Pathmark 135 42 25 33Table 3Pricing Strategy by Firm Type% EDLP % HYBRID % PROMO“Large” firms:Chain 33 37 30Vertically integrated 35 36 29Large store size 32 38 30Many checkouts 31 39 30“Small” firms:Independent 22 28 50Not vertically integrated 21 32 47Small store size 23 26 52Few checkouts 22 26 52small chains, using four alternative definitions of“large” and small.5While large chains seem evenlydistributed across the strategies and “small” chainsseem to favor PROMO, firm size is not the primarydeterminant of pricing strategy.The second noteworthy feature of the Pathmarkdata is that even geographically proximate storesadopt quite different pricing strategies. While there issome clustering at the broader spatial level (e.g. northversus south New Jersey), the extent to which thesestrategies are interlaced is striking. Again, lookingbeyond Pathmark and New Jersey confirms that thiswithin-chain spatial heterogeneity is not unique tothis particular example: while some chains clearlyfavor a consistent strategy, others appear quiteresponsive to local factors. Broadly speaking, thedata reveal only a weak relationship between geog-raphy and pricing strategy. While southern chainssuch as Food Lion are widely perceived to favorEDLP and Northeastern chains like Stop & Shop arethought to prefer PROMO, regional variation doesnot capture the full story. Table 4 shows the per-cent of stores that choose either EDLP, HYBRID, orPROMO pricing in eight geographic regions of theUnited States. While PROMO pricing is most popularin the Northeast, Great Lakes, and central Southernregions, it is far from dominant, as both the EDLP andHYBRID strategies enjoy healthy shares there as well.EDLP is certainly favored in the South and Southeast,but PROMO still draws double digit shares in bothregions. This heterogeneity in pricing strategy canbe illustrated using the spatial structure of our dataset. Figure 2 plots the geographic location of everystore in the United States, along with their pricing5The four definitions of firm size are: chain/independent, verticallyintegrated and not, large/small store, and many/few checkouts.A chain is defined as having 11 or more stores, while an indepen-dent has 10 of fewer. Vertically integrated means the firm operatesits own distribution centers. Large versus small store size and manyversus few checkouts are defined by the upper and lower quartilesof the full store level census. Ellickson and Misra: Supermarket Pricing StrategiesMarketing Science 27(5), pp. 811–828, ©2008 INFORMS815Table 4 Pricing Strategies by RegionRegion % PROMO % HYBRID % EDLPWest Coast 39 39 22Northwest 32 51 17South West 20 48 32South 32 25 43Southern Central 45 27 28Great Lakes 54 29 17North East 40 37 23South East 23 37 40strategy. As is clear from the three panels correspond-ing to each pricing strategy, there is no obvious pat-tern: all three strategies exhibit quite uniform cover-age. Taken together, these observations suggest look-ing elsewhere for the primary determinants of pricingFigure 2 Spatial Distribution of Store Pricing StrategyHYBRID storesEDLP storesPROMO storesTable 5 Local FactorsEDLP HYBRID PROMOLocal demographicsMedian household 284 (0.331) 281 (0.337) 280 (0.329)sizeMedian household 34,247 (14,121) 36,194 (15,121) 36,560 (16,401)incomeMedian vehicles 212 (0.302) 213 (0.303) 209 (0.373)in HHMedian age 354 (4.59) 358 (4.98) 357 (4.25)Proportion Black 0128 (0.182) 0092 (0.158) 0110 (0.185)Proportion Hispanic 0078 (0.159) 0073 (0.137) 0070 (0.135)Strategies of rivalsPercent of rivals using 49 (31) 49 (25) 52 (23)same strategyNote. The main numbers in each cell are means, standard deviations are inparentheses.strategy. We turn next to the role of market demo-graphics and then to the nature and degree of com-petition.Table 5 contains the average demographic char-acteristics of the local market served by stores ofeach type.6PROMO pricing is associated with smallerhouseholds, higher income, fewer automobiles percapita, and less racial diversity, providing some ini-tial support for Bell and Lattin’s (1998) influen-tial model of basket size.7However, the differencesin demography, while intuitive, are not especiallystrong. This does not mean that demographics areirrelevant, but rather that the aggregate level patternslinking pricing strategy and demographics are notoverwhelming. Isolating the pure impact of demo-graphic factors will require a formal model, which weprovide below.The final row of Table 5 contains the share of rivalstores in the competing market that employ the samestrategy as the store being analyzed. Here we find astriking result: 50% of a store’s rivals in a given loca-tion employ the same pricing strategy as the focalstore. Competitor factors also played a lead role inthe work of Shankar and Bolton (2004), which ana-lyzed pricing variability in supermarket scanner data.In particular, they note that “what is most striking,however, is that the competitor factors are the mostdominant determinants of retailer pricing in a broadframework that included several other factors” (p. 43).Even at this rather coarse level of analysis, the data6Roughly corresponding to areas the size of a ZipCode, these “localmarkets” are defined explicitly in §5.2.7Bell and Lattin (1998) find that the most important features ofshopping behavior can be captured by two interrelated choices:basket size (how much you buy) and shopping frequency (howoften you go). They suggest that large or fixed basket shoppers(i.e. those who buy more and shop less) will more sensitive tothe overall basket price than those who shop frequently and willtherefore prefer EDLP pricing to PROMO. They present empiricalevidence that is consistent with this prediction. Ellickson and Misra: Supermarket Pricing Strategies816Marketing Science 27(5), pp. 811–828, ©2008 INFORMSreveal that most stores choose similar pricing strate-gies to their rivals. This pattern clearly warrants amore detailed investigation and is the focus of ourstructural model.Stepping back, three key findings emerge. First, su-permarket chains often adopt heterogeneous pricingstrategies, suggesting that demand related forces cansometimes outweigh the advantages of chain levelspecialization. Second, local market factors play a keyrole in shaping demand characteristics. Finally, anyempirical analysis of pricing strategy must addressthe role of competition. While investigating the roleof market demographics and firm characteristics isnot conceptually difficult, quantifying the structuralimpact of rival pricing strategies on firm behaviorrequires a formal game theoretic model of pricingbehavior that accounts for the simultaneity of choices.In the following section, we embed pricing strategyin a discrete game that accommodates both localdemographics and the strategies of rival firms. Wethen estimate this model using the two-step approachdeveloped by Bajari et al. (2005).3. A Strategic Model ofSupermarket PricingA supermarket’s choice of pricing strategy is natu-rally framed as a discrete game between a finite setof players. Each firm’s optimal choice is determinedby the underlying market conditions, its own charac-teristics and relative strengths, as well as its expecta-tions regarding the actions of its rivals. Ignoring strate-gic expectations, pricing strategy could be modeled asa straightforward discrete choice problem. However,since firms condition their strategies on their beliefsregarding rivals’ actions, this discrete choice must bemodeled as a system of simultaneous equations. Inour framework, firms (i.e., supermarket chains8) makea discrete choice of pricing strategy, selecting amongthree alternatives: everyday low pricing, promotionalpricing, and a hybrid strategy. While there is clearlya role for dynamics in determining an optimal pric-ing policy, we assume that firms act simultaneously ina static environment, taking entry decisions as given.This static treatment of competition is not altogetherunrealistic since these pricing strategies involve sub-stantial store level investments in communication andpositioning related costs that are not easily reversed.9We assume that competition takes place in “local”markets, each contained in a global market (here, an8Henceforth, we will use chains and firms interchangeably.9As discussed above, pricing decisions are relatively sunk, due tothe positioning costs associated with conveying a consistent store-level message to a group of repeat customers. Furthermore, sincethis is not an entry game, we are not particularly concerned aboutthe possibility of ex post regret that can sometimes arise in staticgames (Einav 2003).MSA). Before proceeding further, we must introducesome additional notation. Stores belonging to a givenchain c = 1C, that are located in a local mar-ket lm= 1Lm,inanMSAm = 1M, will beindexed using ilmc= 1Nlmc. The total numberof stores in a particular chain in a given MSA isNmc=Lmlm=1Nlmc, while the total number of storesin that chain across all MSAs is Nc=Mm=1Nmc.Ineach local market, chains select a pricing strategy(action) a from the three element set K = EHP,where E ≡ EDLP, H ≡ HYBRID, and P ≡ PROMO.If we observe a market lmcontaining Nlm=Cc=1Nlmcplayers for example, the set of possible action pro-files is then Alm= EHPNlmcwith generic elementalm= a1a2ailmcaNlmc. The vector of actions ofstore ilmc’s competitors is denoted a−ilmc= a1ailmc−1ailmc+1aNlmc.In a given market, a particular chain’s state vec-tor is denoted smc∈ Smc, while the state vector for themarket as a whole is sm= sm1smNc ∈Nmcc=1Smc. Thestate vector smis known to all firms and observed bythe econometrician. It describes features of the mar-ket and characteristics of the firms that we assumeare determined exogenously. For each firm, there arealso three unobserved state variables (correspondingto the three pricing strategies) that are treated asprivate information of the firm. These unobservedstate variables are denoted ilmcailmc, or more com-pactly ilmc, and represent firm specific shocks to theprofitability of each strategy. The private informa-tion assumption makes this a game of incompleteor asymmetric information (e.g. Harsanyi 1973) andthe appropriate equilibrium concept one of BayesianNash Equilibrium (BNE). For any given market, theilmc’s are assumed to be i.i.d. across firms and actions,and drawn from a distribution filmc that is knownto everyone, including the econometrician.Firms maximize store-level profits, choose pricingstrategies independently across stores. In market lm,the profit earned by store icis given byilmc= ilmcsmailmca−ilmc+ ilmc (1)where ilmcis a known and deterministic function ofstates and actions (both own and rival’s). Since the’s are private information, each firm’s decision ruleailmc= dilmcsmilmc is a function of the common statevector and its own , but not the private informationof its rivals. From the perspective of both its rivalsand the econometrician, the probability that a givenfirm chooses action k conditional on the common statevector is then given byPilmcailmc= k=1dilmcsmilmc= kfilmcdilmc (2)where 1dilmcs ilmc = k is an indicator function equalto 1 if store ilmcchooses action k and 0 otherwise. Ellickson and Misra: Supermarket Pricing StrategiesMarketing Science 27(5), pp. 811–828, ©2008 INFORMS817We let Plmdenote the set of these probabilities for agiven local market. Since the firm does not observe itscompetitors actions prior to choosing its own action,it makes decisions based on its expectations. Theexpected profit for firm ilmcfrom choosing action ailmcis thenilmcailmcsmi Plm=ilmcailmcsm+ ilmc(3)=a−ilmcilmcsmailmca−ilmcP−ilmc+ ilmc (4)where P−ilmc=j=ilmcPjaj sm. Given these expectedprofits, the optimal action for a store is thenilmc= Prilmcailmcsm+ ilmcailmc> ilmcailmcsm+ ilmcailmc∀ ailmc= ailmc (5)which is the system of equations that define the (purestrategy) BNE of the game. Because a firm’s optimalaction is unique by construction, there is no need toconsider mixed strategies.If the ’s are drawn from a Type I Extreme Valuedistribution, this BNE must satisfy a system of logitequations (i.e. best response probability functions).The general framework described above has beenapplied in several economic settings and its propertiesare well understood. Existence of equilibrium followsdirectly from Brouwer’s fixed point theorem.To proceed further, we need to choose a particularspecification for the expected profit functions. We willassume that the profit that accrues to store ilmcfromchoosing strategy k in location lmis given byilmcailmc=ksmiPlm= smk+E−ilmck1+P−ilmck2+mck+ck+ilmck (6)where smis the common state vector of both market(local and MSA) and firm characteristics (chain andstore level). The E−ilmcand P−ilmcterms represent theexpected proportion of a store’s competitors in mar-ket lmthat choose EDLP and PROMO strategies,respectivelyk−ilmc=1Nlmj=ilmcPjaj= kNote that we have assumed that payoffs are a lin-ear function of the share of stores that choose EDLPand PROMO, which simplifies the estimation prob-lem and eliminates the need to consider the sharewho choose HYBRID H. We further normalize theaverage profit from the PROMO strategy to zero, oneof three assumptions required for identification (wediscuss our identification strategy in detail in §5.7).In addition, we have assumed that the private infor-mation available to store ilmc(i.e. ilmc) can be decom-posed into three additive stochastic componentsilmck = mck + ck + ilmck (7)where ilmck represents local market level privateinformation, mck is the private information thata chain possesses about a particular global market(MSA), and ck is a nonspatial component of pri-vate information that is chain specific. Following ourearlier discussion, we assume that ilmck is an i.i.d.Gumbel error. We further assume that the two remain-ing components are jointly distributed with distribu-tion function Fmck ck , where  is a set ofparameters associated with F . Denoting the parametervector  =  and letting ilmck be an indicatorfunction such thatilmck =1ifailmc= k0ifailmc= k(8)the optimal choice probabilities (conditional onmck ck) for a given store can be written asilmcailmc=k  PlmXmckck=expsmk+E−ilmck1+P−ilmck2+mck+ckk∈EHPexpsmk+E−ilmck1+P−ilmck2+mck+ck(9)while the likelihood can be constructed asc∈Cckm∈Mmcklm∈Lmilmc∈Nlmcilmcailmc= k   Plm smck ckilmckdF mck ck s.t. Plm=  lm Plm smck ck (10)Note that the construction of the likelihood involvesa system of discrete choice equations that must sat-isfy a fixed point constraint Plm= lm. There are twomain approaches for dealing with the recursive struc-ture of this system, both based on methods originallyapplied to dynamic discrete choice problems. The first,based on Rust’s (1987) Nested Fixed Point (NFXP)algorithm, involves solving for the fixed point of thesystem at every candidate parameter vector and thenusing these fixed point probabilities to evaluate thelikelihood. However, the NFXP approach is both com-putationally demanding and straightforward to apply Ellickson and Misra: Supermarket Pricing Strategies818Marketing Science 27(5), pp. 811–828, ©2008 INFORMSonly when the equilibrium of the system is unique.10An alternate method, based on Hotz and Miller’s(1993) Conditional Choice Probability (CCP) estimator,involves using a two-step approach that is both com-putationally light and more robust to multiplicity.11The first step of this procedure involves obtaining con-sistent estimates of each firm’s beliefs regarding thestrategic actions of its rivals. These “expectations” arethen used in a second stage optimization procedure toobtain the structural parameters of interest. Given thecomplexity of our problem, we chose to adopt a two-step approach based on Bajari et al. (2005), who werethe first to apply these methods to static games.4. Data SetThe data for the supermarket industry are drawnfrom Trade Dimension’s 1998 Supermarkets PlusDatabase, while corresponding consumer demograph-ics are taken from the decennial Census of the UnitedStates. Descriptive statistics are presented in Table 1.Trade Dimensions collects store level data from everysupermarket operating in the United States for use intheir Marketing Guidebook and Market Scope publica-tions, as well as selected issues of Progressive Grocermagazine. The data are also sold to marketing firmsand food manufacturers for marketing purposes. The(establishment level) definition of a supermarket usedby Trade Dimensions is the government and industrystandard: a store selling a full line of food productsand generating at least $2 million in yearly revenues.Foodstores with less than $2 million in revenues areclassified as convenience stores and are not includedin the data set.12Information on pricing strategy, average weeklyvolume, store size, number of checkouts, and addi-tional store and chain level characteristics was gath-ered using a survey of each store manager, conductedby their principal food broker. With regard to pric-ing strategy, managers are asked to choose the strat-egy that is closest to what their store practices on10It is relatively simple to construct the likelihood function whenthere is a unique equilibrium, although solving for the fixed pointat each iteration can be computationally taxing. However, con-structing a proper likelihood (for the NFXP) is generally intractablein the event of multiplicity, since it involves both solving for allthe equilibria and specifying an appropriate selection mechanism.Simply using the first equilibrium you find will result in mispec-ification. A version of the NFXP that is robust to multiplicity hasyet to be developed.11Instead of requiring a unique equilibrium to the whole game,two-step estimators simply require a unique equilibrium be playedin the data. Futhermore, if the data can be partioned into distinctmarkets with sufficient observations (as is the case in our applica-tion), this requirement can be weakened further.12Firms in this segment operate very small stores and compete onlywith the smallest supermarkets (Ellickson 2006, Smith 2006).a general basis: either EDLP, PROMO or HYBRID.The HYBRID strategy is included to account for thefact that many practitioners and marketing theoristsview the spectrum of pricing strategies as more acontinuum than a simple EDLP-PROMO dichotomy(Shankar and Bolton 2004). The fact that just over athird of the respondents chose the HYBRID option isconsistent with this perception.5. Empirical ImplementationThe empirical implementation of our frameworkrequires three primary inputs. First, we need tochoose an appropriate set of state variables. Thesewill be the market, store and chain characteristicsthat are most relevant to pricing strategy. To deter-mine which specific variables to include, we drawheavily on the existing marketing literature. Second,we will need to define what we mean by a “mar-ket.” Finally, we need to estimate beliefs and con-struct the empirical likelihood. We outline each ofthese steps in the following subsections, concludingwith a discussion of unobserved heterogeneity andour strategy for identification.5.1. Determinants of Pricing StrategyThe focus of this paper is the impact of rival pricingpolicies on a firm’s own pricing strategy. However,there are clearly many other factors that influencepricing behavior. Researchers in both marketing andeconomics have identified several, including con-sumer demographics, rival pricing behavior, and mar-ket, chain, and store characteristics (Shankar andBolton 2004). Since we have already discussed the roleof rival firms, we now focus on the additional deter-minants of pricing strategy.Several marketing papers highlight the impact ofdemographics on pricing strategy (Ortmeyer et al.1991, Hoch et al. 1994, Lal and Rao 1997, Bell andLattin 1998). Of particular importance are consumerfactors such as income, family size, age, and accessto automobiles. In most strategic pricing models, thePROMO strategy is motivated by some form of spa-tial or temporal price discrimination. In the spatialmodels (e.g. Lal and Rao 1997, Varian 1980), PROMOpricing is aimed at consumers who are either will-ing or able to visit more than one store (i.e. thosewith low travel costs) or, more generally, those whoare more informed about prices. The EDLP strategyinstead targets consumers who have higher travelcosts or are less informed (perhaps due to hetero-geneity in the cost of acquiring price information). Inthe case of temporal discrimination (Bell and Lattin1998, Bliss 1988), PROMO pricing targets customerswho are willing to either delay purchase or stockpileproducts, while EDLP targets customers that preferto purchase their entire basket in a single trip or at a Ellickson and Misra: Supermarket Pricing StrategiesMarketing Science 27(5), pp. 811–828, ©2008 INFORMS819single store. Clearly, the ability to substitute over timeor across stores will depend on consumer characteris-tics. To account for these factors, we include measuresof family size, household income, median vehicleownership, and racial composition in our empiricalanalysis.Since alternative pricing strategies will require dif-fering levels of fixed investment (Lattin and Ortmeyer1991), it is important to control for both store andchain level characteristics. For example, large andsmall chains may differ in their ability to effi-ciently implement particular pricing strategies (Dharand Hoch 1997). Store level factors also play arole (Messinger and Narasimhan 1997). For example,EDLP stores may need to carry a larger inventory (tosatisfy large basket shoppers), while PROMO storesmight need to advertise more heavily. Therefore, weinclude a measure of store size and an indicatorvariable for whether the store is part of a verticallyintegrated chain. Finally, since the effectiveness ofpricing strategies might vary by market size (e.g.urban versus rural), we include measures of geo-graphic size, population density, and average expen-ditures on food.5.2. Market DefinitionThe supermarket industry is composed of a largenumber of firms operating anywhere from 1 to1,200 outlets. We focus on the choice of pricing strat-egy at an individual store, abstracting away from themore complex issue of how decisions are made atthe level of the chain. This requires identifying theprimary trading area from which each store drawspotential customers. Without disaggregate, consumer-level information, the task of defining local marketsrequires some simplifying assumptions. In particular,we assume markets can be defined by spatial prox-imity alone, a strong assumption in some circum-stances (Bell et al. 1998). However, absent detailedconsumer level purchase information, we cannot relaxthis assumption further. Therefore, we will try to beas flexible as possible in defining spatial markets.Although there are many ways to group firmsusing existing geographic boundaries (e.g. ZipCodesor Counties), these pre-specified regions all share thesame drawback: they increase dramatically in sizefrom east to west, reflecting established patterns ofpopulation density.13Rather than imposing this struc-ture exogenously, we allow the data to sort itself byusing cluster analysis. In particular, we assume thata market is a contiguous geographic area, measur-able by geodesic distance and containing a set of13One exception is Census block groups, which are about half thesize of a typical ZipCode. However, we feel that these areas are toosmall to constitute reasonably distinct supermarket trading areas.competing stores. Intuitively, markets are groups ofstores that are located close to one another. To con-struct these markets, we used a statistical clusteringmethod (K-means) based on latitude, longitude, andZipCode information.14Our clustering approach pro-duced a large set of distinct clusters that we believeto be a good approximation of the actual markets inwhich supermarkets compete. These store clusters aresomewhat larger than a typical ZipCode, but signifi-cantly smaller than the average county. As robustnesschecks, we experimented with the number of clus-ters, broader and narrower definitions of the market(e.g. ZipCodes and MSAs), as well as nearest neigh-bor methods and found qualitatively similar results(see Appendix B.1).5.3. Estimation StrategyAs noted above, the system of discrete choice equa-tions presents a challenge for estimation. We adopt atwo stage approach based on Bajari et al. (2005). Thefirst step is to obtain a consistent estimate of Plm, theprobabilities that appear (implicitly) on the right handside of Equation (9).15These estimates Plm are usedto construct the −ilmc’s, which are then plugged intothe likelihood function. Maximization of this (pseudo)likelihood constitutes the second stage of the proce-dure. Consistency and asymptotic normality has beenestablished for a broad class of two-step estimatorsby Newey and McFadden (1994), while Bajari et al.(2005) provide formal results for the model estimatedhere. We note in passing that consistency of the esti-mator is maintained even with the inclusion of thetwo random effect terms  and , since these vari-ables are treated as private information of each store.A final comment relates to the construction of stan-dard errors. Because the two-step approach precludesusing the inverse information matrix, we use a boot-strap approach instead.165.4. The LikelihoodIn our econometric implementation, we will assumethat  and  are independent, mean zero normalerrors, so thatFmck ck = Fmck k × Fck k (11)14ZipCodes are required to ensure contiguity: without ZipCodeinformation, stores in Manhattan would be included in the samemarket as stores in New Jersey.15The −ilmc’s are functions of Plm.16In particular, we bootstrapped across markets (not individ-ual stores) and held the pseudorandom draws in the simulatedlikelihood fixed across bootstrap iterations. To save time we usedthe full data estimates as starting values in each bootstrap iteration. Ellickson and Misra: Supermarket Pricing Strategies820Marketing Science 27(5), pp. 811–828, ©2008 INFORMSwhere both Fand Fare mean zero normal dis-tribution functions with finite covariance matrices.For simplicity, we also assume that the covariancematrices are diagonal with elements 2k and 2k.For identification, consistent with our earlier inde-pendence and normalization assumptions, we assumethat mcP = cP = 0 ∀ c ∈ Cm ∈ M. We can thenuse a simulated maximum likelihood procedure thatreplaces (10) with its sample analog =c∈CR−1Rr=1m∈MR−1Rr=1lm∈Lmilmc∈Nlmcilmcailmc=k  Plmsmckckilmck(12)In the simulation procedure, mckrand ckrare drawn from mean zero normal densities with vari-ances 2k and 2k respectively. We use R= R=500 and maximize (12) to obtain estimates of the struc-tural parameters. Note that the fixed point restriction,Plm= lm, no longer appears since we have replacedPlmwithPlmin the formulae for E−ilmcand P−ilmc, whichare used in constructing ilmc(see (9)). We now turnto estimating beliefs.5.5. Estimating BeliefsIn an ideal setting, we could recover estimates ofeach store’s beliefs regarding the conditional choiceprobabilities of its competitors using fully flexiblenonparametric methods (e.g. kernel regressions orsieves). Unfortunately, our large state vector makesthis infeasible. Instead, we employ a parametricapproach for estimating ˆ−ilmc, using a mixed multi-nomial logit (MNL) specification to recover these firststage choice probabilities (Appendix B.4 provides asemi-parametric robustness analysis). This is essen-tially the same specification employed in the sec-ond stage procedure (outlined above), only the store’sbeliefs regarding rivals’ actions are excluded from thisreduced form. Note that we do not require an explicitexclusion restriction, since our specification alreadycontains natural exclusion restrictions due to the pres-ence of state variables that vary across stores andchains.We implement an estimator similar to (12), but withthe coefficients on the −ilmc’s (i.e. ’s) set to zero.Let the parameters in the first stage be denoted by1= 1117and the first stage likelihood for agiven store be denoted by ilmcmck ck. Usinga simulated maximum likelihood (SML) approach, we17The subscript 1 indicates that these are first stage estimates.obtainˆ1, the SML estimate of 1. Given these esti-mates, and applying Bayes’ rule, the posterior expec-tation of Pailmc= k  smck ck can be obtained viathe following computationilmcailmc=k ˆmckck·ilmcˆmckckdF mckck1k·ilmcˆmckckdF mckck1k−1(13)While this expression is difficult to evaluate analyt-ically, the vector of beliefs defined byPilmcailmc=k= ilmcailmc=k ˆmckck(14)can be approximated by its simulation analogPilmcailmc=kRr=1ilmcailmc=k ˆmckckrilmcˆmckckrRr=1ilmcˆmckckr(15)in which mck ckrare draws from a distributionFmck ck with similar properties to those de-scribed in §5.4. Again, we use R = 500 simulationdraws. Recalling that k ∈ K = EHP, we can nowdefine a consistent estimator of k−ilmcasˆk−ilmc=v=cNlmv−1j=ilmcPjailmc= k (16)5.6. Common UnobservablesWhile our data set is rich enough to include a largenumber of covariates upon which firms may condi-tion their actions, the strong emphasis we have placedon strategic interaction may raise concerns regardingthe role of unobserved heterogeneity. In particular,how can we be sure that firms are actually reacting tothe actions of their rivals, rather than simply optimiz-ing over some common features of the local marketthat we do not observe? Manski (1993) frames this asthe problem of distinguishing between endogenousand correlated effects. Although the presence of botheffects yields collinearity in the linear in means modelthat Manski analyzes (i.e. the reflection problem), thenonlinearity of the discrete choice framework elimi-nates this stark nonidentification result in our setting.However, the presence of correlated unobservablesremains a concern. In what follows, we outline twostrategies for handling this problem. The first incorpo-rates a fixed effect at the MSA level, while the second [...]... basket composition data for intelligent supermarket pricing Marketing Sci 25(2) 188–199 Lal, R., R Rao 1997 Supermarket competition: The case of everyday low pricing Marketing Sci 16(1) 60–81 Ellickson and Misra: Supermarket Pricing Strategies Marketing Science 27(5), pp 811–828, © 2008 INFORMS Lattin, J., G Ortmeyer 1991 A theoretical rationale for everyday low pricing by grocery retailers Working paper... differences in pricing behavior? We will address both issues in turn First, with regard to local pricing, we should note that supermarket firms clearly have the technological resources to set prices (and therefore pricing strategy) at a very local level Indeed, Montgomery (1997) provides a novel method for profitably customizing prices at the store level, using widely available scanner data.21 We contacted pricing. .. with regard to pricing strategy To the contrary, we find that rather than isolating themselves in strategy space, firms prefer to coordinate on a single pricing policy Pricing strategies are strategic complements This coordination result stands in sharp contrast to most formal models of pricing behavior, which (at least implicitly) assume that these strategies are vehicles for differentiation Pricing strategy... the details of their actual pricing strategies, but did acknowledge that they “certainly have the data and resources to do it.” Furthermore, a consultant who was involved in several recent supermarket mergers confirmed that the extent of local pricing was a key factor in the approval process.22 A related issue is whether firms face significant pressure to maintain a consistent (pricing) image across stores... cases, this means drawing expenditures away from the outside good 20 Note that PPROMO /PEDLP is now our object of interest Ellickson and Misra: Supermarket Pricing Strategies 825 Marketing Science 27(5), pp 811–828, © 2008 INFORMS In the context of supermarket pricing, this suggests that coordination may actually increase the amount consumers are willing to spend on groceries, perhaps by drawing them... Aguirregabiria, Pat Bajari, J P Dubé, Han Hong, Paul Nelson, and Ellickson and Misra: Supermarket Pricing Strategies 826 Chris Timmins for their comments All remaining errors are the authors’ responsibility Appendix A Survey Validity All of the variables in the Trade Dimensions data, including the information on pricing strategy, are self-reported This may raise some concerns regarding accuracy, especially... characteristics Store size (sqft ’000) Vertically integrated Chain characteristics Number of stores in chain Chain effect Chain/MSA effect Ellickson and Misra: Supermarket Pricing Strategies 823 Marketing Science 27(5), pp 811–828, © 2008 INFORMS choice of EDLP as a pricing strategy This is important from an econometric standpoint, since we use these very same factors to construct expectations in the first stage... Montgomery, A L 1997 Creating micro-marketing pricing strategies using supermarket scanner data Marketing Sci 16(4) 315–337 Newey, W K., D McFadden 1999 Large sample estimation and hypothesis testing D McFadden, R Engle, eds Handbook of Econometrics, Vol 4, Chap 36 Elsevier, North-Holland, Amsterdam, The Netherlands, 2113–2245 Orhun, A Y 2005 Spatial differentiation in the supermarket industry Working paper,... marketing and warrant further attention The final section outlines a research agenda for extending the results found in this paper 7 Conclusions and Directions for Future Research This paper analyzes supermarket pricing strategies as discrete game Using a system of simultaneous discrete choice models, we estimate a firm’s optimal choice conditional on the underlying features of the market, as well as each... demographics and firm characteristics are strong determinants of pricing strategy From a theoretical perspective, it is clear that we have yet to fully understand what drives consumer demand The fact that firms coordinate with their rivals suggests that consumers prefer to receive a consistent message While our results pertain most directly to supermarkets, it seems likely that other industries could behave . directionsfor future research.2. The Supermarket PricingLandscape2.1. Pricing Strategy ChoicesCompetition in the supermarket industry is a complexphenomenon.. approachdeveloped by Bajari et al. (2005).3. A Strategic Model ofSupermarket PricingA supermarket s choice of pricing strategy is natu-rally framed as a discrete game