Time-Varying Competition

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Time-Varying CompetitionK. Sudhir, Pradeep K. Chintagunta, Vrinda Kadiyali,

To cite this article:K. Sudhir, Pradeep K. Chintagunta, Vrinda Kadiyali, (2005) Time-Varying Competition. Marketing Science 24(1):96-109.http://dx.doi.org/10.1287/mksc.1040.0074

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Vol. 24, No. 1, Winter 2005, pp. 96–109issn 0732-2399 �eissn 1526-548X �05 �2401 �0096

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Time-Varying Competition

K. SudhirYale School of Management, 135 Prospect Street, Post Office Box 208200, New Haven, Connecticut 06520,

[email protected]

Pradeep K. ChintaguntaGraduate School of Business, University of Chicago, 1101 E. 58th Street, Chicago, Illinois 60637,

[email protected]

Vrinda KadiyaliJohnson Graduate School of Management, Cornell University, Sage Hall, Ithaca, New York 14853,

[email protected]

Normative models typically suggest that prices rise in periods of high demand and cost. However, in manymarkets, prices fall when demand or costs rise. This inconsistency occurs because the normative models

assume that competitive intensity does not change with demand and cost conditions over time. We thereforeintroduce the notion of time-varying competition by suggesting that it is important not only to account for thedirect effect of demand and cost on prices (e.g., higher demand means higher prices), but also the indirect effectof demand and cost changes on competition (e.g., higher demand could cause more competition and, hence,lower prices).We develop a general, unified framework to empirically model the direct and indirect effects of demand and

cost shifts on pricing in differentiated product markets. Our approach allows us to measure the indirect effectof multiple demand and cost drivers on competitive intensity and test predictions from alternative theories ofrepeated games. The empirical application is to the U.S. photographic film industry, where there are two mainplayers, Kodak and Fuji.We find that the indirect effects are highly significant and comparable in magnitude to the direct effects.

Competitive intensity is greater in periods of high demand and lower cost and is moderated by whether demandor costs are expected to grow or decline. Interestingly, we find asymmetries in the competitive responses ofKodak and Fuji. While Kodak is sensitive to demand factors, Fuji is sensitive to costs. Our results suggestthat market characteristics such as observability of competitor prices can be an important determinant of howcompetitive intensity is affected by demand and cost conditions.

Key words : pricing research; competition; competitive strategy; game theory; estimation and other statisticaltechniques

History : This paper was received September 5, 2001, and was with the authors 10 months for 5 revisions;processed by William Boulding.

1. IntroductionClassical microeconomics suggests that when facedwith a decline in demand or in their costs, firms in anindustry cut prices. The conventional wisdom of lowprices in the face of low demand or costs is, however,not always consistent with market reality. In manyconsumer markets, the holiday season tends to havethe highest volumes; yet consumers expect low pricesduring this period. Further, high-volume periods neednot necessarily translate to higher profits for firms. Asan article about the highway construction market inthe Engineering News-Record (June 26, 2000), tellinglytitled “Margins Miss Out on Boom” indicates:

The nation’s bold investment in rehabilitating andexpanding its highways promises to keep this one ofthe hottest construction markets for years to come � � � �Despite this bright picture, highway contractors are

facing a major dilemma—recession-like margins inthe face of record levels of volume. “The robustmarket hasn’t produced more comfortable margins,”says Mazey (president of Trumbull Corp, a Pittsburghbased company), “We’re not seeing that yet, and I’mbaffled as to why that’s the case.”

Similarly, cost declines do not always lead to pricedeclines. In the well-documented case of Procter &Gamble’s value pricing strategy, the firm loweredtrade promotion incentives and was able to lower itsmanufacturing costs by smoothing out its productionruns. At the same time, the net price paid by con-sumers in these categories increased by approximately20% for P&G’s products and by 8.4% for its competi-tors’ products (Ailawadi et al. 2001).In the last two examples, the predictions of classi-

cal microeconomic theories are violated empirically.

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Sudhir, Chintagunta, and Kadiyali: Time-Varying CompetitionMarketing Science 24(1), pp. 96–109, © 2005 INFORMS 97

Clearly, the theory is missing an important element.Consider the following example from the Wall StreetJournal (April 2, 2001) on the data storage systemsindustry:

For years, providers of data storage systems en-joyed fat profit margins and seemingly insatiabledemand � � � � But the reality of the economic slowdownis beginning to take its toll, just as competition amongstorage vendors has ratcheted up (p. B.4).

This example suggests the possibility that a declinein demand is accompanied by greater competition.Thus, prices fell due to two effects: (1) a direct effectof a decline in demand and (2) an indirect effect ofthe decline in demand due to greater competition.In this case, the direct and indirect effects reinforcedeach other, and both reduced prices and profitability.It is also possible that the direct and indirect effectscounteract each other. For example, when aggregatedemand increased in the highway construction mar-ket, it is possible that firms began to compete morefor market share because shares are worth more inhigh-demand periods. While the direct effect of anincrease in demand raises prices, the indirect effect ofincreased competition counteracts this effect, leadingto “recession-like margins” in high-volume periods.Similarly, a decline in cost could cause an increasein prices, as in the P&G case, if the cost decline ledto softer competition. Models typically used in mar-keting, based on static microeconomic models, onlyaccount for the direct effect, not the indirect effect,and hence are unable to account for the range ofempirical realities illustrated in the examples above.Hence, a full understanding of, and an accurate fore-cast of how prices change in response to changes indemand and cost conditions is possible only if wemodel the indirect effect of how competition varies overtime as a function of demand and cost conditions. We pro-pose a unified model that measures both these directand indirect effects.The empirical economics literature has paid lim-

ited attention to indirect effects. Its attention hasfocused only on homogeneous product markets. Fur-ther, it looks only at the effect of one variable (demandlevel) on competition and assumes that competitivebehavior is symmetric across firms. Our paper dis-tinguishes itself from the extant economics litera-ture in all these dimensions. (1) Our model of directand indirect effects is for differentiated product mar-kets, which are typically of interest to marketers.(2) Our model accounts for multiple drivers of theindirect effect simultaneously. There is a lively debatein the repeated-games literature about how currentand future demand and cost affect price competi-tion. By including all of these effects simultaneously,we are able to obtain a more comprehensive picture

of time-varying competition than has been possiblebefore. (3) Finally, our model allows each firm in anindustry to respond differently to changes in demandand cost conditions, making our competitive modelmore realistic.We provide an empirical analysis of the U.S. pho-

tographic film market, where Kodak and Fuji are thetwo major players. We find that indirect effects aresignificant and the magnitude of the indirect effect isas large as the direct effect in the film market. Thelarge magnitude of the indirect effect can potentiallyexplain the “baffling behavior” of recession-like mar-gins in booms. We also find that incorporating time-varying competition through the indirect effects canhelp substantially in improving model fit, thereforeproviding a better model for pricing decision supportsystems. Our results suggest that institutional detailssuch as the observability of competitor actions can bean important determinant of how competitive inten-sity is affected by demand and cost conditions.

2. Modeling the Indirect Effect:Choice of Variables

In this section, we discuss theories from the repeated-games literature that provide the motivation formodeling indirect effects in our model. The theoret-ical literature on repeated games offers predictionson how demand and cost variables affect competi-tive intensity. In repeated-game models, firms main-tain tacit collusion by using threats of future price cutsto punish competitors who deviate from cooperativeequilibrium prices. As demand and cost conditionschange, gains from undercutting competitor pricesand losses from future punishment change. Hence,observed prices change, creating a link betweendemand and cost conditions and competitive inten-sity. We discuss three sets of variables: (1) demandand cost levels, (2) demand and cost trends, and(3) asymmetric cost reduction.

Demand and Cost LevelsGreen and Porter (1984) predict that competitiveintensity will be higher in periods of low demand,while Rotemberg and Saloner (1986) make the oppo-site prediction. The key difference in the two papersis the assumption about the observability of aggre-gate demand and competitor actions. Green andPorter assume that aggregate demand and competi-tor actions are unobserved, so when a firm faces lowdemand, it cannot distinguish whether this is due toprice-cutting by a competitor or due to low aggregatedemand. Hence, whenever there is low demand (evenif it might be due to low aggregate demand), all firmscut prices in equilibrium. The resulting low marginremoves the incentives for firms to secretly cut priceswhen there is high demand in the hopes of stealing

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Sudhir, Chintagunta, and Kadiyali: Time-Varying Competition98 Marketing Science 24(1), pp. 96–109, © 2005 INFORMS

market share. In equilibrium, no firm cheats, butprices are lower and competitive intensity is greaterin periods of low demand.Rotemberg and Saloner assume that firms observe

aggregate demand and competitor prices. Aggregatedemand is assumed to be independent over time.When firms face high aggregate demand, they aretempted to cut prices to gain short-term profits. Whilefirms know that this will lead to lower future pricesfrom rivals, they also recognize that the aggregatedemand in future will on average be less than thecurrent high-demand period. Therefore, the threat offuture punishments has less impact in a high-demandperiod. In equilibrium, firms charge lower prices inhigh-demand periods to eliminate the incentives forfirms to undercut the cooperative equilibrium. Thisresults in lower prices and greater competitive inten-sity in high-demand periods.There are two interesting issues to note about these

theories in the context of our empirical application.First, unlike the assumptions made in the above the-oretical models, the film market that we analyze isnot a homogeneous product market. The more dif-ferentiated firms in an industry are, the less likely itis that firms’ demand outcomes are correlated withone another. In this case, should a firm observe adrop in its own demand, it is less likely to attributethis to a drop in the rival’s price, and hence itmay not retaliate by lowering its price. Therefore,it is unlikely that we will observe any systematiccorrelations between industry demand conditions andcompetitive intensity (Raith 1996). In the film mar-ket, universal compatibility of 35mm film reduces dif-ferentiation; however, color, sharpness, and contrastenhance differentiation. Hence, the extent of differen-tiation in the film market and its effect on competitiveintensity is an empirical question.Second, Rotemberg’s and Saloner’s (1986) theory

assumes competitor prices and market shares areobserved, while Green’s and Porter’s (1984) theoryassumes that these are unobserved. In the film mar-ket, thanks to the widespread availability of com-petitive action data, managers are quickly able toassess the prices charged by competitors, and the mar-ket shares, so we expect support for the theory ofRotemberg, and Saloner in this market. This is in con-trast to results in Porter (1983) and Ellison (1994), whofound support for Green and Porter in their analysisof the railroad shipping market. However, unlike thefilm market, competitive prices and market shares areunobserved in the railroad shipping market.Borenstein and Shepard (1996) point out that these

theories can be modified for cost levels. Since anincrease in cost reduces the profits to firms (oppositeof the demand effect on profits), the effect of changesin cost levels will be the opposite of the effect of

demand levels. We summarize the predictions fromthe three theories below:

Relationship between Relationship betweencompetition and competition and

Theory demand level cost level

Green and Negative PositivePorter (1984)

Rotemberg and Positive NegativeSaloner (1986)

Raith (1996) Zero Zero

Demand and Cost TrendsHaltiwanger and Harrington (1991) relax Rotemberg’sand Saloner’s assumption that demand is indepen-dent over time by allowing for trends in demand.However, they maintain the assumption that competi-tor prices and market shares are observable. If firmsvalue future profits sufficiently (long-term outlook),the threat of future punishments is a stronger deter-rent to cheating if there is a growth trend rather thana decline. Hence, controlling for demand levels, firmscan sustain higher prices and (lower competitiveintensity) when the demand trend is positive. How-ever, if firms discount future profits heavily (short-term outlook), the result is reversed. The predictionsfor cost trends are the opposite for demand trends.These predictions are summarized below:1

Relationship between Relationship betweenInference about competition and future competition and futurefirm behavior demand growth cost growth

Short term Positive Negative

Long term Negative Positive

In their analysis of the gasoline market, Borensteinand Shepard (1996) dismiss the possibility that firmscan be extremely short-term oriented and consideronly Haltiwanger’s and Harrington’s (1991) predic-tion consistent with the long-term outlook. We alsobelieve that firms are not so short term in theiroutlook to heavily discount the future. Hence, ourexpectation is that if firms behave consistently withthe predictions of Haltiwanger and Harrington, itwould be consistent with a long-term outlook.

Asymmetric Marginal Cost ReductionFirms make investments in plants and technologies toreduce their marginal costs of production. The mostvisible examples of these investments are those byJapanese and European auto firms on plants in theUnited States. When a firm’s costs fall, it cuts prices.In addition, it may use its relative cost advantageto price more aggressively, and therefore cooperation

1 Kandori (1991) and Bagwell and Staiger (1997) make similarpredictions with slightly different models.

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between firms may break down. Besanko et al. (1996,p. 364) note that asymmetry among firms is a majorreason for breakdown of cooperative behavior andincreased competition. A reduction in the costs of onefirm (in our case, Fuji) is likely to have a negativeimpact on the ability to cooperate among firms.

3. Methodological PositioningRelative to the Literature

In recent years, the empirical industrial organization(EIO)-based approach has been widely used by mar-keters to infer the competitive behavior of firms (seeKadiyali et al. 2001 for a review). In these models,price levels in the market depend upon demand andcost conditions and the nature of interfirm interac-tions in that market. By estimating a demand anda supply (price) function, this approach allows pricelevels to be decomposed into the effects of demand,cost, and competitive behavior. These models typi-cally assume “steady-state” competitive behavior andestimate an average measure of competition over the entireperiod of analysis. In these models, firms interact ina Nash-Bertrand manner over the entire duration ofthe observed data. Such analyses have been done onboth homogeneous (e.g., Genesove and Mullin 1998)and differentiated (e.g., Gasmi et al. 1992, Roy et al.1994) product markets. By making the assumptionof “steady-state” or time-invariant competition, thesemodels account for the direct effects of demand andcost, but ignore the indirect effects.

Market Symmetric/ Structural/ Variables modelinghomogenous/ asymmetric reduced Competition time-varyingdifferentiated strategies form model competition

Genesove andMullin (1998)

Sugar refining(homogenous)

Symmetric Structural Time invariant NA

Gasmi et al. (1992) Cola(differentiated)

Asymmetric Structural Time invariant NA

Roy et al. (1994) Auto(differentiated)


Kadiyali (1996) Film(differentiated)


Sudhir (2001) Auto(differentiated)


Porter (1983) Railroad shipping(homogenous)

Symmetric Structural Time varying Demand level (supportsGreen and Porter 1984)

Ellison (1994) Railroad shipping(homogenous)

Symmetric Structural Time varying Demand level (supportsGreen and Porter 1984)

Borenstein andShepard (1996)

Gasoline(homogenous)

Symmetric Reduced form Time varying Demand, cost trends (sup-ports long term behavior inHaltiwanger and Harrington1991)

This paper Film(differentiated)

Asymmetric acrossfirms

Structural Time varying Demand and Cost Levels

Demand and Cost TrendsAsymmetric Marginal Cost

Reduction

Next we consider papers that model time-varyingcompetition. Two papers by Porter (1983) and Ellison(1994) are similar in spirit to our model, in that theystructurally separate out direct and indirect effects.However, both of these papers analyze the railroadshipping market, which is a market for homogeneousgoods. They only model the impact of demand levelin the indirect effect. They also do not allow for asym-metries in firm behavior. Both papers find support forGreen and Porter (1984).Borenstein and Shepard (1996) use a reduced-form

approach to test the relationship between competition(price-cost margins) and demand and cost trends (notlevels), using gasoline price data. They find supportfor the predictions in Haltiwanger’s and Harrington’s(1991) theory where firms take a long-term perspec-tive. Because they use a reduced-form approach, theyare not able to separate the direct effects from the indi-rect effects. They also do not allow for asymmetriesin firm behavior.In contrast, our proposed approach estimates

demand and pricing equations at the firm level,accounting for product differentiation and asymme-try in firms’ competitive strategies. Ours is the firstpaper to investigate demand and cost levels and trendssimultaneously. This is important because predictionsabout trends are conditional on controlling for lev-els. We summarize the positioning of the currentpaper with respect to some select (but representative)papers.

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4. Model and EstimationOur goal in this paper is to model both the directand indirect effects (through changes in competitiveintensity) of changes in demand and cost conditionson prices. We now describe our models of demand,cost, and competitive intensity.

DemandEven though we use aggregate data, we modeldemand using a logit model with heterogeneity mod-eled across consumers (e.g., Sudhir 2001, Chintagunta2001). For a more detailed discussion of the benefitsof using the logit model with aggregate data, seeDubé et al. (2002). The probability Pijt that con-sumer i chooses brand j �j = 1�2� � � � � J in time t �t =1�2� � � � � T is:

Pijt =exp��ij +�ipjt +�iXjt +�jt

1+∑Jk=1 exp��ik+�ipkt +�iXkt +�kt

� (1)

where �ij is consumer i’s intrinsic brand preferencefor brand j , �i is consumer i’s price sensitivity param-eter, pjt is brand j’s price at time t, �i is consumer i’ssensitivity to other marketing activities in time t, Xjt .The term �jt in the above equation denotes brandand time-specific factors that are common across con-sumers that could influence consumer i’s utility forbrand j in period t. While these factors are importantto the consumer (and consumers observe these levels),the researcher does not observe them. These factorsare referred to as “demand shocks” or “unobservedattributes” of the brands. By their very nature, thesefactors could be correlated with the marketing activ-ities of firms (prices, advertising levels, etc.) that areincluded in the utility function above. This has conse-quences for the estimation of the model parameters—an issue that we return to subsequently. Note that weallow for category expansion via the inclusion of an“outside good” alternative. This results in the “1” inthe denominator of the expression in Equation (1).We allow for consumer preferences to be hetero-

geneous. Define the household preference coefficientsas a market-level mean and household-specific devi-ations: �ij = �j + ��ij , �i = � + ��i, �i = � + ��i,the utility of consumer i for brand j is decomposedinto household-invariant means �Yjt and household-specific terms ��Yijt.

�ij +�ipjt +�iXjt +�jt= �j +�pjt +�Xjt +�jt︸︷︷︸

Yjt

+��ij +��ipjt +��iXjt︸︷︷︸�Yijt

�

Let the household-specific parameters be �i = ��ij�j = 1�2� � � � � J ��i��i�, and let the distribution of �across consumers be denoted as F ��. F �· has meanzero and unknown covariance matrix, �. For example,

F �· can denote the CDF of a �J + 2-variate normaldistribution where Xjt has only one variable in it. Theaggregate share Sjt of brand j at time t across all con-sumers is obtained by integrating the consumer-levelprobabilities in Equation (1) over the region of the sup-port Ai of � that yields brand choice j .

Sjt =∫Ai

[exp��j +�pjt +�Xjt +�jt +��j+��pjt +��Xjt

]

·(1+

J∑k=1exp��k+�pkt +�Xkt +�kt +��k

+ ��pkt +��Xkt dF ��)−1

(2)

or

Sjt =∫Ai

exp�Yjt +�Yjt1+∑J

k=1 exp�Ykt +�YktdF ��

An important feature of the share expression in (2)is that the elasticities are not subject to the IIA restric-tion of the homogeneous logit model.

CostWe model costs as a function of factor inputs intothe production process, such as material, labor, etc.Specifically, we parameterize the cost to manufac-turer j at time t �cjt as follows:

cjt =Zjt�j + �jt� (3)

Zjt is a matrix of factor inputs (including an intercept),�j is a vector of unknown parameters for brand j tobe estimated, and �jt is the error in measuring costsusing observed factor inputs.

Competitive IntensityIntuitively, competitive intensity is lower when pricesare higher, but prices can also rise due to low demandelasticity and high costs. To measure competitiveintensity, we therefore need to control for demandand cost factors. Because the Bertrand price takes intoaccount demand elasticity and costs, it serves as auseful benchmark. Thus, the extent of deviation fromthe Bertrand price can serve as a measure of com-petitive intensity (relative to the Bertrand price). Thisis also a useful benchmark because previous researchhas imposed the Bertrand assumption on the data (e.g.,Berry et al. 1995, Besanko et al. 1998).Consider the situation in which firm j prices

according to the Bertrand equilibrium. In this case,brand j’s objective function can be written as (givenour empirical application, we assume single-productfirms):2

maxpjt jt =Mt�pjt − cjtSjt� (4)

2 Given that camera and film brands can be used interchangeably,and given the high level of competition in the camera market,

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Mt is the potential size of the market at time t. Solvingthe first-order conditions for profit maximizationunder the assumption of the Nash-Bertrand equilib-rium, we have:

pjt = cjt +−SjtSjjt

� where Sjjt ="Sjt

"pjt�

Hence, the Bertrand margin

mBertrandjt =−Sjt/Sjjt (5)

or pjt = cjt +mBertrandjt .Because our interest is in measuring the indi-

rect effect of changes in competitive intensity, weallow the margins to vary from the Bertrand mar-gin. We thus capture the indirect effect by introducinga multiplier %jt on the Bertrand margin. The pricingequation can then be specified as follows:

pjt = cjt +%jtmBertrandjt � (6)

Villas-Boas and Zhao (2005) use similar specifica-tions, but with time-invariant multipliers, thus mea-suring average competition over the entire period ofanalysis. Because our objective is to understand howcompetitive intensity varies with demand and costconditions, we parameterize %jt as follows:

%jt =Wjt'j � (7)

Wjt is the matrix of observed predictor variables affect-ing the competitive behavior of firm j in period t,and 'j is a vector of unknown parameters for brand jto be estimated. We discuss the predictor variables ingreater detail in a subsequent section. The interpre-tation of %jt is as follows: When %jt > �<1, firm jis pricing cooperatively (competitively) relative to theBertrand equilibrium. At %jt = 0, firm j prices atmarginal cost.3 Combining Equations (3), (6), and (7),

pjt =Zjt�j +Wjt'jmBertrandjt + �jt� (8)

In the above equation, �jt is independent of the factorinputs Zjt and the competition predictor variablesWjt .

two-part pricing strategies using either film or cameras as a lossleader cannot be sustained. Industry experts confirmed this intu-ition. Therefore, analyzing the film market separately is a reason-able assumption for our purpose.3 Corts (1999) suggests, using simulated data, that estimating com-petitive behavior using “conjectural variations” or conduct param-eters of the type we are estimating may be problematic in somesituations. In practice, this may not be a serious problem. Genesoveand Mullin (1998), in their analysis of the sugar-refining indus-try, show that they get reasonable competitive conduct parametersconsistent with the market structure of the industry. Further, Cortsshows that bias is strong when there is strong negative autocorre-lation in sales. In our empirical application, this is not an issue, aswe find positive autocorrelation in sales.

As we do not a priori impose any restrictions on the'j parameters, our specification does not favor anyparticular behavioral outcome from the data. Depend-ing upon the estimated values of parameters, wecould find a wide range of competitive interactions—from the firms pricing at marginal cost to the firmsbehaving in a cooperative manner.To see how Equation (8) captures the direct and

indirect effects of demand and cost factors on prices,we note the following: (1) The direct effects of costfactors are reflected in the first term on the right-hand side of (8), Zjt�j , which allows costs to varyover time. (2) The direct effects of demand factorsare embodied in the term mBertrandjt . From Equation (5),we know the factors that influence demand influencethe markup term, mBertrandjt . (3) The indirect effects ofdemand and cost factors, as well as the deviationsfrom Nash-Bertrand pricing, are reflected in the multi-plier term, %jt . The average value of %jt over the dura-tion of analysis will represent the average deviationof pricing in the market from the benchmark Bertrandprice level, whereas the variation in %jt over time cap-tures the dynamics of competitive interactions. Wenote that because the price outcomes in the theoret-ical repeated-games model range from the Bertrandequilibrium prices to the collusive equilibrium prices,our empirical model nests all the theoretically pos-sible price outcomes. Note that the indirect effectsof demand and cost factors have been introduced ina reduced-form manner in our model. Our empiri-cal approach of modeling indirect effects is similar toParker’s and Roller’s (1997) approach of analyzing theeffects of multimarket contact on competition.The parameters of the demand model and the

pricing equation are estimated simultaneously as inBerry et al. (1995) using a Generalized Method ofMoments (GMM) estimation procedure. To identifythe parameters of a model using GMM, we need atleast as many identifying restrictions as the numberof parameters in the model. As discussed earlier, wehave exogenous variables for the intercepts in thedemand and cost equations, theX variables (except foradvertising which we treat as endogenous), the Z vari-ables, and the W variables. Therefore, the coefficientsassociated with these variables will be identified.We need six additional identifying restrictions: twoparameters associated with the endogenous variables(price and advertising) and four nonlinear param-eters associated with individual-level heterogeneity.The four nonlinear heterogeneity parameters are threeassociated with the covariance matrix of the demandintercepts and one associated with the standard devi-ation in price.For advertising, we use both quarterly and annu-

ally lagged advertising as instruments. We use costvariables as instruments for price. Because we have

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three cost variables, we use them as instruments. Wealso interact them with the three quarter variables,thus obtaining another nine instruments. In all, wehave fourteen additional restrictions compared to thesix restrictions we need, and therefore our model isoveridentified.4

5. DataOur empirical illustration uses data from the U.S.photographic film market for the period 1981–1998.These data are on firm-level prices, units sold, adver-tising, and demand and cost variables. We obtainedprice data from Popular Photography, a monthly mag-azine where mail-order firms advertise photography-related products. Prices are for 35mm 24-exposure200 ASA color rolls—the most popular speed andsize of film (see the Wolfman Report on the Photo-graphic and Imaging Industry in the U.S. for furtherdetails). Quarterly advertising expenditures are fromthe Class/Brand YTD Report of Leading National Adver-tisers. We obtain quarterly market shares of firms (interms of quantities) and industry-level sales from var-ious sources, including Study of Media and Markets bySimmons Market Research Bureau, the Photo Mar-keting Association and Product Data Series by MediaMark Research. We obtain data on quarterly con-sumer incomes from Survey of Current Business.The wage data for Kodak is obtained from Monthly

Labor Review and International Comparisons of Man-ufacturing Productivity and Labor Cost Trends, bothpublished by the Bureau of Labor Statistics. Theprice of capital (proxied by annual interest rates)is obtained from International Financial Statistics. ForKodak, the monthly price of silver is obtainedfrom annual wholesale price indexes/producer priceindexes issued by the Bureau of Labor Statistics. ForFuji, the price for corresponding materials (wages,capital, silver) is obtained by using monthly indexesfrom Bank of Japan’s Price Index Annual. To standard-ize Japanese costs (for wages, capital, and silver) toU.S. prices, we use the monthly exchange data fromAnnual Statistical Digest (1980–1988) and Survey of Cur-rent Business (1989–1998). We use annual tariff num-bers from the Hamonized Tariff Schedule to obtain effec-tive U.S. costs. To convert the indices to real prices, weneed at least the prices of silver and labor for one timeperiod. For the United States, we obtain these datafrom the Producer Price Indexes and Employment and

4 We also estimated our models with instruments such as consumerconfidence on the demand side and quarterly interactions with con-sumer confidence. Our results are robust to these other instruments.We recognize that lagged advertising is a problematic instrumentfor advertising due to serial correlation, but given the difficultyin finding other instruments for advertising, we use this in ouranalysis.

Table 1. Descriptive Statistics—1981–1998

Variable Mean Std dev

Kodak quantity (no. of rolls) 112�6M 19�7MFuji quantity (no. of rolls) 18�8M 8�3MKodak price (1990 $/roll) 2�39 0�07Fuji price (1990 $/roll) 2�04 0�05Kodak advertising (1990 $) 11�1M 0�7MFuji advertising (1990 $) 1�5M 2�1MPer-capita income (1990 $ quarterly) 1�275�47 16�55Price of capital (U.S.)(interest rate %) 6�39 0�34Price of labor (U.S.) (1990 $ per hr) 10�58 0�07Price of silver (U.S.) (1990 $ per gram) 673�23 62�90Price of capital (Japan) (interest rate %) 4�90 0�44Price of labor (Japan) (1990 $ per hr) 8�70 0�14Consumer confidence index (U.S.) 87�28 1�37

Earnings Report of the Bureau of Labor Statistics. ForJapan, we obtain these numbers from the Yearbook ofMinerals and Nonferrous Metal Statistics, published byMITI and the Yearbook of Labor Statistics, published byJapan’s Ministry of Labor. We use the Consumer PriceIndex (base year 1990) for the United States for con-sumer prices of film and the Wholesale Price Index(base year 1990) for the United States for the UnitedStates and Japan for costs. These indices are obtainedfrom International Financial Statistics.

Operationalizing the TestsWe now discuss the predictor variables, Wjt , to beincluded in the indirect effect multiplier function %jt .For demand levels, we need some exogenous vari-ables that are unambiguously related to the level ofdemand. From Table 2, we see that sales in the firstquarter are substantially lower than in other quarters.Hence, we use quarter dummies to measure demandlevels. A second variable we found useful to mea-sure demand levels is the index of consumer confi-dence. A regression of sales against quarter dummiesand consumer confidence showed that consumer con-fidence explained 22% of the variance and is posi-tively related to sales.For cost levels, there are three potential factor costs:

labor, silver, and cost of capital. From Table 1, we seethat variation in silver prices (both in absolute termsand relative to the mean) is substantially greater thanfor other cost factors. Also, because silver is an impor-tant input into the cost of producing film and it isan internationally traded commodity (so there is a

Table 2. Mean Quantities and Prices

Kodak quantity Fuji quantity Kodak price Fuji price

Quarter 1 89�5M 15�1M $2�57 $2�08Quarter 2 121�8M 20�5M 2�31 2�02Quarter 3 117�6M 19�2M 2�36 2�04Quarter 4 121�6M 20�3M 2�34 2�04

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common price), Kodak and Fuji closely monitor theircosts (Kadiyali 1996). To limit the number of variables(to conserve degrees of freedom), we use only thecost of silver to measure the effect of cost levels oncompetition.Demand and cost trend variables are harder to

observe. We tried several methods of classifying peri-ods into growth or decline trend periods and usedthe following strategy. We fit a polynomial regressionequation on consumer confidence and silver priceswith time as the independent measure. Based on thefitted polynomial, upward-sloping periods are classi-fied as growth periods and downward-sloping peri-ods are classified as decline periods. We found that afifth-order polynomial regression (with t� t2� t3� t4� t5

as variables), tracked both the index of consumerconfidence and the silver prices well and gave usa smooth curve, which helps us to classify peri-ods into the growth or decline phase. We also fittedhigher-order polynomials, and the results we reportare robust to classifications based on alternative fits.We discuss other methods of classifying quarters intogrowth and decline periods when discussing robust-ness checks.Fuji started production in Greenwood, South Car-

olina in 1995, which lowered its cost to serve the U.S.market. Due to domestic production, Fuji no longerhas to pay import duties of 3.7% that it had previ-ously paid for film imported from the Netherlandsand Japan. We allow the factory to affect marginalcosts as well as competitive intensity by introducingdummy variables in the set of variables in the costequation (U.S. Factory in Zjt and the indirect effects(Kodak and Fuji U.S. factory in Wjt for the period1995–1998.5

Control for Possible Correlation in DemandElasticity and Demand LevelWe have argued that a decline in prices in periodsof high demand could be due to the indirect effectof greater competition. However, it is possible thatthis effect is due to consumers being more price elas-tic in high-demand periods (Bils 1989, Warner andBarsky 1995). For example, Wolfram (1999), in heranalysis of the British electricity market, finds thatprice elasticities tend to be different during weekdaysand weekends, summers and winters. Bils’ modelfor durable goods may be inappropriate for the filmmarket. Warner and Barsky suggest that consumerswill search more for lower prices in periods of highdemand due to economies of search leading to greater

5 The effective marginal cost for Fuji could also appear to be lowerdue to excess capacity and the need for factory managers to exploitpotential economies of scale. We thank a reviewer for suggestingthis possibility.

elasticity of demand. To test the indirect effect ofdemand and competition, we need to control for pos-sible correlation in demand sensitivity and demandlevels. In our empirical analysis, we include price-quarter interactions in the demand function to cap-ture the differences in price sensitivity in differentquarters.

6. ResultsThe results of the estimation are reported in Tables3A–3C below. Table 3A contains the demand esti-mates, 3B the cost estimates, and 3C the estimates ofcompetitive interactions.

DemandTo have a modest number of parameters, we allowfor heterogeneity only in the intrinsic preferencesand price sensitivities of consumers. Quarter 1 has a

Table 3. Model Estimates

A. Demand Parameters

Kodak intercept n.s.Fuji intercept n.s.Quarter 1 2�37∗

Quarter 2 1�23∗∗

Quarter 3 n.s.Advertising (log) n.s.Price-mean −1�89∗∗

Price-SD 0�68∗

Price X Quarter 1 −1�28∗∗

Price X Quarter 2 −0�63∗

Price X Quarter 3 −0�39∗∗

Log income (Kodak) −8�00∗

Log income (Fuji) n.s.L11 n.s.L22 n.s.L21 n.s.

B. Cost Parameters

Kodak intercept 0�96∗∗∗

Fuji intercept 0�81∗∗∗

Labor 0�65∗∗

Capital 0�48∗

Silver 0�59∗∗

U.S. factory −0�18∗∗

C. Competition Parameters

Kodak Fuji

Intercept n.s. 0�42∗

Quarter 1 0�30∗ n.s.Consumer −0�59∗∗ n.s.

confidenceSilver price n.s. 0�80∗∗

Consumer 0�21∗∗ n.s.confidence growth

Silver price growth n.s. −0�20∗

U.S. factory −0�39∗∗ −0�35∗∗

∗ p < 0�1; ∗∗ p < 0�05; ∗∗∗ p < 0�01; n.s.: not significant.

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higher coefficient than other quarters, but the pricesensitivity in Quarter 1 is also higher than in otherquarters. Adjusting for the greater price sensitivityand the fact that prices are higher in the first quar-ter, we find the net utility for consumers is lower inthe first quarter than in other quarters. This is notsurprising given the lower average sales in Quarter 1relative to other quarters. The price sensitivity resultsrun counter to the hypothesis of Warner and Barsky(1995) because consumers are more price sensitivein the low-volume first quarter than in other quar-ters. Also, consumers have higher price sensitivityin Quarters 1–3 compared to Quarter 4, as indicatedby the negative price-quarter interactions. This couldbe because consumers who exclusively buy duringthe holiday season may not be as price sensitiveas others who buy throughout the year. The meanprice coefficient is −1�89 �p < 0�05. There is signif-icant unobserved heterogeneity in price sensitivity�p < 0�1, but not in preferences for Kodak and Fujifilm.Income has a negative and significant effect on

demand for Kodak. In the late 1980s and in the 1990s,higher-income households started substituting cam-corders and digital cameras for film. We conjecturethat the negative income effect for Kodak could bebecause higher-income people who otherwise pur-chase Kodak’s more expensive film substituted out ofthe film category more than Fuji consumers.

CostsThe cost estimates in Table 3B indicate that labor, cap-ital and silver prices are all significant.6 As expected,the coefficient of U.S. Factory is significant, indicatingthat the introduction of a U.S. factory lowered costsfor Fuji.

Competitive InteractionsSeveral predictors of competitive behavior are statis-tically significant (see Table 3C). The positive coeffi-cient on Quarter 1 for Kodak �p < 0�1 supports theprediction of Rotemberg and Saloner (1986) that com-petition will be less in periods of low demand. How-ever, this coefficient is positive, but not significant, forFuji. We find support for this result in the trade press.Kodak typically raised prices for the first quarter andFuji followed many times (though not always) withits own price increases (USA Today 1989). The Kodakcoefficient on consumer confidence is negative andsignificant, indicating that Kodak prices more compet-itively in periods of high demand, supporting Rotem-berg and Saloner. In contrast, Fuji does not respondto changes in consumer confidence. With respect to

6 We also allowed separate coefficients on the factor inputs forKodak and Fuji in the cost equation. Because the differences werenot significant, we constrained them to be equal.

costs, Fuji prices more cooperatively when costs rise,but Kodak’s behavior is not affected by changes incost levels. In sum, we conclude that there is broadsupport for the prediction of Rotemberg and Saloner.Given the institutional reality of observable aggre-gate demand and competitive actions, the Rotembergand Saloner model (1986) is more appropriate thanthe Green and Porter model (1984) for the film mar-ket. Our results also show that, contrary to Raith’s(1996) argument, the Rotemberg and Saloner predic-tions are robust to modest levels of differentiation inthe market.With respect to demand and cost trends, we find

that Kodak is more cooperative when there is apositive demand trend (the positive coefficient onconsumer confidence growth variable). Thus, Kodakbehaves in a manner consistent with the long-termoutlook in the Haltiwanger and Harrington (1991)model. Similarly, Fuji behaves in a manner consis-tent with a long-term outlook in the Haltiwangerand Harrington model with respect to trends in sil-ver prices. A consistent pattern across both level andtrend variables is that Kodak responds to demand fac-tors, while Fuji responds to cost factors.The consistent support for both Rotemberg and

Saloner (1986) and Haltiwanger and Harrington (1991)across the level and trend variables for demand andcosts suggests the important role of observability ofaggregate demand and competitor actions. By simul-taneously testing their impact, we are able to obtainan integrative picture of how level and trend vari-ables affect competitive intensity consistent with the-ories that assume observability. This would not havebeen possible with piecemeal approaches used in pre-vious research that focused only on a subset of thesevariables.As Fuji began production in its U.S. factory in 1995,

it became more competitive as indicated by the neg-ative coefficient on U.S. Factory. We evaluate this bycomputing the margins in the years around 1995. ForFuji, the average margin in 1995 went down to $0.83from $1.11 in 1994. The Lerner Index (Margin/Price)fell from 0.65 to 0.51. The Kodak response was moreaggressive. The average margins fell from $1.27 in1994 to $0.83 in 1995, and the Lerner Index fell from0.63 to 0.43. Thus, Fuji’s cost reduction made bothKodak and Fuji aggressive in their pricing.The estimated price-cost margins for Kodak from

1996–1998 are $1.36, $1.31, and $1.11, while the esti-mated price-cost margins for Fuji were $0.78, $0.78,and $0.77. Thus, Kodak did not change its competitivestrategy much between 1996 and 1997, but aggres-sively responded in 1998 even when Fuji’s pricereduction was very marginal. Our findings about thedifference in Kodak’s margins between 1997 and 1998appear to be consistent with a 1998 price war reportedin the Hamilton (August 2, 1998):

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In 1997, Kodak lost three to four percentage points ofU.S. market share to Fuji, which waded into the marketwith an aggressive and almost unchallenged programof promotion and price cutting � � � “The big differencethis year and last year is that they reacted slowly ordidn’t react at all to Fuji’s moves” said analyst � � � � Thisyear Kodak has undertaken major steps � � � includingprice promotions of its own (p. H.02).

Robustness ChecksWe perform several robustness checks. We included atime trend in the multiplier function for both Kodakand Fuji in addition to the other variables, but thesewere not significant.7 Directionally, the other variablescontinue to maintain the same pattern as in the mainmodel.We also estimated a benchmark model with no

indirect effects (only a time-invariant multiplier is esti-mated for the competition parameters) as is the cur-rent standard practice in the literature. The demandand cost estimates are fairly close to the estimatesin Table 3. This suggests that extant models that donot account for time-varying competition still obtainconsistent demand and cost estimates. The averagetime-invariant multiplier on the Bertrand price forKodak was 1.22 and that for Fuji was 1.61, indicatingthat both Kodak and Fuji were more cooperative thanthe Bertrand price-setting behavior. Thus, our resultsare consistent with Kadiyali (1996), who also findsthat Kodak and Fuji were cooperative in their pric-ing behavior. The higher multiplier for Fuji, however,does not necessarily mean that Fuji was more cooper-ative than Kodak, because the difference between theBertrand and cooperative prices may have been lowerfor Kodak than for Fuji. Given the large share of Kodakin the market, the difference between Bertrand pric-ing and cooperative pricing is possibly much lower.To check this, we compute the average ratio of coop-erative margins to Bertrand margins for Kodak andFuji. These ratios were 1.12 and 1.99, respectively. Thisimplies that the cooperative margins are only 12%higher than the Bertrand margins for Kodak, whilethey are 99% higher for Fuji. Therefore, Kodak hasmuch less leeway in terms of punishing Fuji by revert-ing to Bertrand pricing. This is consistent with the“fat cat” posture that has been adopted by Kodak inaccommodating Fuji’s entry, previously documentedin the literature (Kadiyali 1996).We checked the impact of our definition of growth

and decline periods for the trend variables. As an alter-native to the fitted curve obtained from a fifth-degreepolynomial, we used higher-degree polynomials, and

7 We thank the area editor for suggesting this specification to checkif the indirect effect could be explained by a simple downwardtrend in competitive intensity, given the systematic decline in pricesover the period of analysis.

the results are robust. We also used a different smooth-ing procedure to test robustness, by maintaining theperiod to be in the same state (growth or decline) as inthe previous period unless there was a change greaterthan a 10% threshold. So, if period t− 1 was a growthperiod, period t was classified as a decline period onlyif the consumer confidence fell by over 10%. Simi-larly, if period t − 1 was a decline period, period twas classified as a growth period only if the consumerconfidence increased by over 10%. This allowed usto smooth out small changes in consumer confidence,which may not be perceived as growth periods bymanagers. We also did this for costs. We found no sub-stantive changes in the results when we varied thethreshold percentages from 8–15%.8

Managerial ImplicationsOur approach and results are useful to managersoperating in turbulent markets in the face of fluctuat-ing demand and cost conditions. Typical models usedin decision support systems for setting prices assumethat competitive intensity will not vary as demandand cost conditions change. We propose and docu-ment that the indirect effect of demand and cost oncompetitive intensity have significant impact on theprices observed in the marketplace. Firms can use thisinformation about changes in competitive intensityto compute the optimal prices. We further assess thesubstantive usefulness of modeling indirect effects by(1) computing the relative improvement in the fit ofprices and (2) the relative magnitude of the direct andindirect effects.Table 4 shows the observed and fitted prices for the

time-variant and time-invariant competition modelsin the different quarters and before and after the U.S.factory was introduced. Clearly, the fit is better forthe time-variant model that models indirect effects.Further, we find that the Mean Absolute PercentageError (MAPE) between fitted and actual prices forKodak for the time-varying competition and time-invariant competition model are 8.3% and 21.8%. Thecorresponding numbers for Fuji are 5.6% and 11.9%,respectively. While it is not surprising that the sam-ple fit improves (given the additional model param-eters), the magnitude of the improvement in fit sug-gests that the indirect effect is substantively important

8 We also tested the robustness of the estimates to demand functionalform by estimating a linear model of demand (as in Kadiyali1996). However, the number of parameters in the linear modelincreases at double the rate compared to the logit model, as weneed two parameters for each variable to account for the differen-tial effects of Kodak and Fuji. It was not possible to meaningfullyestimate the price-quarter interaction effect with the linear model(the price coefficient became positive when we included this inter-action effect). We therefore tested the model with all the variableswe use in the logit model, but without the price-quarter interaction.For this model we found that the predictors of competition hadidentical effects (in terms of the signs) as the logit results.

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Table 4. Comparison of Observed Prices and Fitted Prices

Before AfterQuarter 1 Quarter 2 Quarter 3 Quarter 4 factory factory

Kodak Observed price $2�54 $2�25 $2�30 $2�29 $2�53 $1�69Time varyingCompetition price 2�53 2�26 2�29 2�26 2�52 1�70Time invariantCompetition price 1�84 2�53 2�55 2�85 2�55 2�07

Fuji Observed price 2�04 1�97 1�99 1�99 2�14 1�49Time varyingCompetition price 2�05 1�95 1�99 2�02 2�15 1�49Time invariantCompetition price 1�77 2�05 2�09 2�26 2�13 1�73

and should help decision support systems set morerealistic prices that are in tune with market condi-tions.Because Quarter 1 has the lowest demand and

Quarter 4 has the highest demand, firms shouldcharge a lower margin in Quarter 1 and higher marginin Quarter 4, if we only consider the direct effect, byholding competition constant. Accordingly, with time-invariant competition as the benchmark, the ratio ofKodak’s Quarter 1 margins to Quarter 4 margins is0.6; for Fuji it is 0.43. However, after we allow for theindirect effect, the same ratio becomes 1.14 and 0.97for Kodak and Fuji, respectively. Thus, the marginsin the high- and low-demand quarters become almostcomparable (i.e., closer to 1), indicating that the directeffect of higher prices in high-demand periods hasbeen effectively neutralized by the indirect effect. Thisresult is striking in that it is similar to the high-way contractors’ example cited in the introductionwhere management complained about a “baffling”decrease in margins in boom times. Our analysis pro-vides a rationale for such market outcomes that couldotherwise be puzzling.

7. ConclusionContributionsResearchers typically assume that competitive inten-sity is invariant when the demand and cost condi-tions change. With this assumption, an increase indemand or cost will cause prices to increase. Exam-ples from the business press suggest that this predic-tion is not always valid. We argue that changes indemand and cost conditions may influence the com-petitive intensity, thereby causing an “indirect effect”on prices. Only the “direct effect” is accounted forwhen competition is maintained invariant.We develop a general approach to allow for both

direct and indirect effects. We test a number of alter-native predictions (developed based on the theoreticalliterature on repeated games) about how competitivebehavior changes as a function of demand and cost

variables. We test these predictions using data fromthe U.S. market for photographic film and demon-strate that our model with indirect effects does sub-stantially better at predicting prices than a model thatdoes not allow for such indirect effects. The key con-tributions of the paper are:(1) It introduces the notion of time-varying com-

petition to the marketing literature by modeling theindirect effects of demand and cost variables on pricesthrough its impact on competition.(2) It develops a unified framework to operational-

ize and measure direct and indirect effects in differen-tiated product markets—the kind of markets that aretypically of interest to marketers. Our framework rec-ognizes that firm strategies can be asymmetric, andhelps us to obtain more general insights relative toprevious research.(3) The framework allows us to test several pre-

dictions from the literature on repeated games in dif-ferentiated product markets. These have previouslyonly been tested in homogeneous product marketsand only on a piecemeal basis. This is the first paperwhere the effects of multiple demand and cost drivers(trends and levels) are assessed simultaneously.(4) Our results consistently support the predictions

from theoretical models, in which competitive actionsare assumed to be observed, an institutional charac-teristic of the market for photographic film. Previousresearch found support for alternative theories,applicable to markets where competitor actions areobserved. This suggests that the institutional detailssuch as the observability of competitor actions areimportant in understanding competitive behavior.9

(5) We show that there can be substantial gains infit by allowing for time-varying competition through

9 Domowitz et al. (1986) investigate the relationship betweenprice-cost margins using accounting costs in a cross-category anal-ysis across industries. They find that margins are greater in highdemand periods (as in Green and Porter) for concentrated indus-tries. Our study suggests that the cyclicality of margins may be dif-ferent in markets depending on observability of competitor actions.

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the indirect effects. In our empirical analysis, we findthat the indirect effect is large enough to neutralizethe direct effect. Thus, the indirect effect needs to beincorporated in pricing decision support systems.Decision support systems in marketing typically

calibrate demand and cost models, but assume thecompetitive equilibrium (Bertrand, Cournot, Stackel-berg) in making predictions about the optimal prices.Decision support systems usually do not test whetherthe assumption made about the competitive equilib-rium is appropriate, but this is required if we are toaccount for how competition will react to changes ina firm’s marketing mix in response to a change indemand and cost conditions. By estimating how firmsdeviate from the prices predicted by the Bertrandequilibrium model and allowing firms to react asym-metrically to changes in market conditions, we areable to provide a precise quantification of the likelycompetitive response.In constructing a decision support system, a

question that arises is: Is the estimated competitiveequilibrium optimal, or can firms improve upon thisequilibrium? Firms need to perform simulations toidentify whether they can increase profits by modify-ing their current reactions to changes in demand andcost conditions as well as to competitive reactions. Forexample, when P&G found that it would be profitableto move away from a prisoner’s dilemma high-lowpricing strategy to an EDLP pricing strategy, it sig-naled strongly to its competitors and the channels thatthis was a permanent move. While it took a tempo-rary cut in profits, it was able to get its competitors tomove to a higher profit equilibrium in the longer run.Most firms, however, may not be powerful enoughto change the market equilibrium and have to actas per the inferred equilibrium in the market place.Given the inferred equilibrium, decision support sys-tems can then automate the routine process of settingprices by using estimates of the calibrated model.A related question is: If the current price setting by

managers is optimal given the inferred equilibrium,what is the value of automating this decision? Russoand Shoemaker (1990, pp. 134–135) demonstrate thatdecisions made by a bootstrapped model fitted onratings provided by an expert “outperforms” eventhe same expert in new situations. This is becausethe rule-based model performs more consistently thanany (even expert) human being using the same set ofrules. In that spirit, we believe price setting based ona model of firm behavior calibrated using observedmarket outcomes can lead to more consistently prof-itable price setting by firms.10

10 We thank the area editor and the editors for encouraging us toaddress these questions.

Limitations and Future ResearchWhile we have tested the robustness of our key resultson a number of dimensions, we recognize that theresults may still be sensitive to certain specificationchoices that we make in the paper. The signifi-cance of some of the coefficients is marginal, so itwould be useful to check the robustness of our sub-stantive claims with alternate datasets and in dif-ferent markets. Our results suggest that the natureof time-varying competition is determined by theobservability of competitor actions. It would be par-ticularly useful to test the robustness of this find-ing by identifying markets that differ in observabilityand compare the support for the theory of Green andPorter versus that of Rotemberg and Saloner.In this paper, we focused on price as the strategic

variable of interest, but did not model other strate-gic variables such as advertising. By including adver-tising in the demand model and accounting for theendogeneity of advertising in the pricing equation, weobtain consistent inference about the effect of adver-tising on pricing. In future research, it also would beinsightful to study how firms compete with strategicinstruments such as advertising (e.g., Kadiyali 1996).However, specifying the equilibrium advertising lev-els over time is not a trivial task due to carryovereffects of advertising (Dubé and Manchanda 2005).We do not model time-varying competition in a

structural manner. As discussed earlier, it is impor-tant to understand what structure should be imposedon the model before estimating a structural model.Because our analysis is an early step in empiricallyunderstanding competitive dynamics, the flexibilityof our reduced-form approach enables us to gainexploratory insights. Nevertheless, it would be par-ticularly useful to incorporate structural dynamicsinto competitive models using Markov perfect equi-librium methods (e.g., Pakes and Ericson 1998, Pakesand McGuire 2001). However, estimation of realisticdynamic structural models of firms is computationallyprohibitive and restrictive at the present time. Sucha structural model should be able to endogenouslyaccount for asymmetries in firm responsiveness to dif-ferent demand and cost variables as well.Chevalier et al. (2003) provide reduced-form evi-

dence that retail prices (in contrast to our focus onmanufacturer prices) do not rise in periods of highdemand due to loss-leader pricing. We expect futurework to address differences in the responses of man-ufacturers and retailers to demand and cost con-ditions in understanding variations in pass-throughover time.We have taken the first steps in addressing the issue

that a change in demand or cost not only has a directeffect on the marketing mix, but also an indirect effect

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due to its impact on competitive intensity. As dis-cussed above, much more remains to be learned. Weexpect our research to serve as a beginning for afruitful stream of research investigating “time-varyingcompetition.”

AcknowledgmentsThe authors thank participants at the 1999 INFORMSConference at Philadelphia, the 2000 Marketing ScienceConference at Los Angeles, the 2001 MSI Conference onCompetitive Responsiveness at Boston, the 2001 ChoiceConference at Monterey, the 2001 Northeast Marketing Fac-ulty Colloquium at MIT, and the 2003 MSI Young ScholarsProgram. They also thank the Marketing Camp participantsat Catholic University at Leuven and Northwestern Uni-versity, as well as the seminar participants at Maryland,Stanford, UT Dallas, and SUNY Buffalo. The authors thankJean-Pierre Dubé and Gunter Hitsch for their comments andsuggestions, and they thank the editors of the special issue,the area editor, and the reviewers for their constructive com-ments and suggestions.

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Documents

Time-Varying Competition