product proliferation strategies and firm performance: the moderating role of product space...

Strategic Management JournalStrat. Mgmt. J., 34: 1435–1452 (2013)

Published online EarlyView 2 April 2013 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/smj.2079

Received 17 May 2012 ; Final revision received 15 October 2012

PRODUCT PROLIFERATION STRATEGIES AND FIRMPERFORMANCE: THE MODERATING ROLEOF PRODUCT SPACE COMPLEXITY

ALICIA BARROSO1* and MARCO S. GIARRATANA2

1 Department of Business Administration, Universidad Carlos III de Madrid, Madrid,Spain2 Department of Management and Technology, Bocconi University, Milan, Italy

In the Spanish automobile market between 1990 and 2000, significant reductions in tariff andnontariff protections increased the complexity of the product space, through the penetration ofnew car brands and models. Acknowledging these environmental dynamics, this study detailsconditions in which across-niche (product breadth or intraindustry diversification) and within-niche (product depth or versioning) product proliferation exerts a positive relationship on firmperformance, as well as how key relationships change according to the complexity of the productspace in the industry. Copyright 2013 John Wiley & Sons, Ltd.

Rather than succumbing to product prolifera-tion, think about how to best use your menuto drive competitive advantage Rod Shaich(CEO, Panera Bread, in Nation’s RestaurantNews , 2010).

INTRODUCTION

Consider a market as a group of products thatsatisfy the same needs; a submarket in turn is asubgroup of products with homogenous tangiblecharacteristics (Klepper and Thompson, 2006;Sutton, 1998). When firms release variations oftheir products within a market, they may beable to build a competitive advantage based ondifferentiation (Kotha, 1995; Li and Greenwood,

Keywords: product proliferation; firm performance; auto-mobile industry; complexity; product portfolio*Correspondence to: Alicia Barroso, Department of BusinessAdministration, Universidad Carlos III de Madrid, c. Madrid126, 28903 Getafe, Madrid, Spain.E-mail: [email protected]

Copyright 2013 John Wiley & Sons, Ltd.

2004). Yet years of empirical research still have notresolved exactly how product proliferation affectsfirm performance: Some scholars indicate a nullor negative relationship (Bayus and Putsis, 1999;Kekre and Srinivasan, 1990; Li and Greenwood,2004; Stern and Henderson, 2004), whereas otherssupport a positive link (Bayus and Agarwal, 2007;Sorenson, 2000; Tanriverdi and Lee, 2008).

To solve this puzzle, recent research suggestsanalyzing the composition of the overall productportfolio of a firm to assess the different productstrategies that firms pursue simultaneously. Fromthis key idea, two types of product proliferationstrategies emerge (Dowell, 2006; Eggers, 2012;Ramdas, 2003; Sorenson, 2000): across-niche andwithin-niche. Across-niche product proliferation(also referred to as breadth, niche width, variega-tion, or intraindustry diversification) arises whena firm increases the submarket niches in which itsells products. Within-niche product proliferation(i.e., depth or product versioning) instead impliesaugmenting the quantity of different variants to sellin one submarket niche. Because firms might per-form either strategy, measurement errors and thus

1436 A. Barroso and M. S. Giarratana

spurious results could arise if research failed toconsider both strategies.

Important advances on this line of research havebeen proposed by resource partitioning scholars.To explain organizational survival, niche widththeory highlights the link between niche special-ization and identity from one side and betweenniche breadth and efficiency from the other (Hsu,2006; Hsu, Hannan, and Kocak, 2009; Kuilmanand Li, 2009; Negro, Hannan, and Rao, 2010; Ruefand Patterson, 2009). Thus, organizations naturallyevolve to become either specialists or generalists(Carroll, 1985; Dobrev, Kim, and Carroll, 2002;Hannan and Freeman, 1977). A generalist drawson a broad resource space and implements a prod-uct strategy in an attempt to maintain a diversi-fied, multi-niche product offer that appeals to abroad range of customer tastes. These firms tendto compete on efficiency, using scale and scopeeconomies. A specialist instead relies on a narrowand focused resource space and applies a productstrategy that specializes in a single product niche,such that it targets a particular range of customers.

From these foundations, this study details thedifferent effects of the two-product proliferationstrategies on firm performance, while also assess-ing the contingencies of these relationships.

In so doing, we complement prior researchthat typically focuses only on producer factors,such as economies of scale and scope or learningcurves (Anderson, 1995; MacDuffie, Sethuraman,and Fisher, 1996). We build on the work ofseveral scholars who have begun to shift theirattention to the role of demand factors. Forexample, Ye, Priem, and Alshwer (2012) showhow customer-based synergies arise from productdiversification; Hui (2004) notes customers whotreat different products of the same brand asclose substitutes; and Siggelkow (2003) suggeststhat the link between product proliferation andperformance depends on how customers addoptions to their consideration set.

In particular, we consider the complexity ofthe product space in an industry. Economicagents often apply simple decision routines tominimize the costs associated with solving com-plex problems (Knudsen and Levinthal, 2007);for customers, these costs are fuelled by thecomplexity of the purchasing process (Keller andStaelin, 1987), such that they tend to be signif-icant in contexts characterized by high productheterogeneity or high uncertainty (Chakravarti

and Janiszewski, 2003; Johnson and Payne, 1985).Thus, we refer to the level of complexity inproduct spaces as the degree of heterogeneity inthe attributes of products marketed in a particularindustry (Lenk et al., 1996), which is a norm inseveral industries (The Economist , 2010). Becauseproduct space complexity influences customers’buying processes, we argue that it moderatesthe relationship between performance and thetwo-product proliferation strategies.

To test this argument, we draw on data fromthe automobile industry, a market that offers sig-nificant heterogeneity in terms of firms’ productproliferation strategies, as well as many compet-ing brands, each of which markets multiple prod-uct models with different features. Therefore, eachfirm competes with a heterogeneous portfolio ofproducts, often sold to different submarkets. In par-ticular, we focus on the Spanish automobile marketbetween 1990 and 2000, in which setting we canexploit the presence of external conditions that per-turb product space complexity. Since the end of the1980s, the Spanish automobile industry has beensubject to gradual but significant reductions in tar-iff and nontariff protections, because of Spain’sintegration into the European Union. Through newforeign entries, the number of car models offeredincreased fivefold during the 1990s. To addressdemand and production drivers and evaluate theproduct proliferation–performance relationship,we use the number of products sold, multiplied byproduct margins estimated by a supply–demandstructural model (Kadiyali, Vilcassim, and Chinta-gunta, 1999), as a measure of performance.

We find a U-shaped effect between performanceand across-niche product proliferation. In contrast,we uncover an inverted U-shaped effect betweenperformance and within-niche product prolifera-tion. We also offer evidence that the complexityof the product space moderates the relationshipbetween performance and product proliferationstrategies in such a fashion that the positive andnegative effects are stronger when complexity isgreater.

Our study thus contributes to research into theperformance impact of a firm’s product prolif-eration in several ways (Eggers, 2012; Ramdas,2003). First, we jointly test the direct impact ofacross- and within-niche product proliferation onperformance in industries characterized by scaleeconomies due to production, scope economiesfrom demand, and customer heterogeneity, which

Copyright 2013 John Wiley & Sons, Ltd. Strat. Mgmt. J., 34: 1435–1452 (2013)DOI: 10.1002/smj

Product Proliferation Strategies and Firm Performance 1437

complements even the most advanced ecologyresearch1 (Dobrev et al., 2002). Second, thisresearch goes beyond product proliferation imple-mentation (Bayus and Agarwal, 2007; Chong, Ho,and Tang, 1998; Putsis and Bayus, 2001) and theefficiency versus identity dialectic (Carroll, 1985)to show that the relationship between product pro-liferation strategies and performance depends onmore than just resources, position, and cost deter-minants. In particular, we highlight the complexityof the industry product space as an importantmoderator. Third, we complement performanceresearch that has focused on survival (Dobrev,Kim, and Hannan, 2001; Hsu, 2006), productquality (Eggers, 2012), market (Tanriverdi andLee, 2008), or accounting (Kekre and Srinivasan,1990; Li and Greenwood, 2004) measures anduse a measure of performance that simultaneouslyaddresses supply and demand factors.

THEORY

Product space complexity in an industry

As a well-diffused phenomenon (The Economist ,2010), a complex product space indicates het-erogeneity in the product attributes marketed ina particular industry (Lenk et al., 1996). Thelevel of complexity of the product space directlyinfluences customers’ buying processes. Highersearch (Beatty and Smith, 1987), evaluation(Shugan, 1980), and opportunity (Schmalensee,1982) costs confront customers in their purchasedecision processes; they are especially significantin contexts characterized by many availablealternatives and product attribute heterogeneity(Chakravarti and Janiszewski, 2003; Johnson andPayne, 1985; Keller and Staelin, 1987). Behavioraltheories therefore suggest that economic agentsadopt simple decision rules to minimize searchcosts and cognitive effort (Knudsen and Levinthal,2007; Simon, 1955).

Consumer behavior literature (Hauser and Wern-erfelt, 1990) further argues that customers applyrelatively simple rules to screen and map alter-natives, especially when the complexity of thepurchase decision is high (Hauser et al., 2010;Roberts and Lattin, 1991). Customers simplify

1 Ecology theory assumes similar industries characteristics,including scale economies and customer heterogeneity (Carroll1985).

their decision making by using decision rules,anchored on a few, or even single, productattributes (Bettman, 1979; Johnson and Payne,1985; Wright and Barbour, 1977). Some customersscreen for brand names; others eliminate alterna-tives that do not meet a predetermined productattribute (e.g., price, Hauser et al., 2010).

Several tangible product characteristics andbrands offer common attributes that customers useas anchors to simplify their purchasing decisions(Gilbride and Allenby, 2004; Hauser et al.,2010; Lapersonne, Laurent, and Le Goff, 1995).To generalize our theory across industries, weintroduce the concept of submarket niches, whichcustomers determine on the basis of the similarproduct attributes they consider to simplify theirpurchasing processes. Thus, a submarket nichecan be defined as a collection of products withhomogenous tangible characteristics (Klepper andThompson, 2006; Sutton, 1998).

We anticipate two main types of purchasingrules that define two sets of customers: brandloyalists and submarket niche loyalists. The firsttype chooses a brand to which they show fidelityor attraction (e.g., Toyota, Honda, Ford, GM,VW); the second focuses on a particular rangeof product characteristics that determines theirpreferred submarket niche (e.g., compact car,minivan, sports car).

Product proliferation

There are two important types of product prolif-eration. In across-niche product proliferation, thefirm sells products simultaneously in various sub-market niches; within-niche product proliferationimplies that it augments the quantity of variantsthat it sells in a single submarket (Ramdas, 2003).Ulrich et al. (1998) and Sorenson (2000) inves-tigate within-niche product proliferation, whereasChong, Ho, and Tang (1998) and Siggelkow (2003)study across-niche product proliferation. Eggers(2012), focusing on product quality, and Dowell(2006), considering firm survival, try to assess thecombined effects of the two strategies and findgenerally positive effects in the mutual fund andbicycle industries, respectively.

The population ecology perspective also reflectson the across-niche product proliferation strategy,2

2 The most conclusive results to date come from the analysisof the automobile industry (Dobrev, Kim, and Hannan 2001,Dobrev, Kim, and Carroll 2002).



in the form of niche width theory (Carroll, 1985;Freeman and Hannan, 1983; Hannan and Freeman,1977) that distinguishes between generalist (broad-niche) and specialist (narrow-niche) organizations.This theory assumes a trade-off between the nichewidth of a firm and its capacity for performance(Levins, 1968). Specialists concentrate their abil-ities on performing one type of action that canincrease their perceived identity, whereas gener-alists divide their capacities across many differ-ent kinds of activities, reducing their potential foridentity formation (Hannan and Freeman, 1977).

Product proliferation thus is a strategic choice(Eggers, 2012; Ramdas, 2003; Sorenson, 2000),with both benefits and costs. The benefits includeeconomies of scale and scope from operating andmanagement synergies (Gimeno and Woo, 1999;Tanriverdi and Lee, 2008), learning effects (Sternand Henderson, 2004), entry barriers in the formof saturated product niches (Lancaster, 1990), andthe ability to exploit firm-specific assets, such astechnology or brand names (Li and Greenwood,2004). Furthermore, greater product proliferationcan enable firms to generate demand synergies,because they offer ‘one-stop shopping’ and thuscapture more customers (Fosfuri and Giarratana,2007; Siggelkow, 2003; Ye et al., 2012) withgreater willingness to pay for customized versions(Bayus and Putsis, 1999; Kekre and Srinivasan,1990).

However, product proliferation also entails coststhat could have negative performance implications(Anderson, 1995; MacDuffie et al., 1996). Productproliferation can induce backlash, in the formof control and coordination costs (Jones andHill, 1988), learning traps (Rivkin, 2000; Sternand Henderson, 2004), or costs associated withcognitive management capabilities (Simon, 1991).On the demand side, product proliferation couldlead to cannibalization (Hui, 2004).

HYPOTHESES

We anticipate that our theoretical key mechanismscould be safely extended to industries character-ized by economies of scales from production, byscope economies from demand, by the presence ofsignificant customers’ heterogeneity, and by seg-mentation in different submarket niches that areuniformly recognized by customers.

Because a firm engaged in across-niche productproliferation introduces new products in differentsubmarkets in which it has no prior experience butstill functions in the same industry, it can exploitoperational and management synergies (scale andscope) that save costs (Paine and Anderson,1983; Siggelkow, 2003) even while capturing newcustomers (Mahajan, Sharma, and Buzzel, 1993).This strategy is particularly effective in variableor unpredictable environments, because it doesnot tie the success of a company to only oneniche (Dobrev et al., 2001; Hannan and Freeman,1977). These firms should secure more value,especially from brand loyalists, by attaining thepositive effects of one-stop shopping (Fosfuriand Giarratana, 2007; Ye et al., 2012). One-stopshopping advantages particularly emerge when avast array of products offered under the sameumbrella brand increase customers’ attention. Forbrand loyalists, across-niche product proliferationnot only minimizes search costs (i.e., they justlook for the brand) but also meets their needsbetter (Sappington and Wernerfelt, 1985). Theseeffects might increase consumption frequency andwillingness to pay.

However, initial firm conditions matter andcould be responsible for a nonlinear relationshipbetween performance and across-niche productproliferation strategies. Firms that focus on a singleor few niches (low across-product proliferation)and engage in across-niche product proliferationface two important constraints. First, introducingproducts in niches in which the firm has no priorexperience requires operational and managementadjustments (Imai, Nonaka, and Takeuchi, 1984).According to learning literature, the firm’s abilityto adjust is a dynamic capability; the more expe-rience it has and the more significant its previousmodifications have been, the more flexible the firmis to adaptations (Danneels, 2002; Dobrev, Kim,and Carroll, 2003; King and Tucci, 2002). Thus,the costs associated with across-niche product pro-liferation should be higher for firms with a lowlevel of across-niche product proliferation, thoughthe cost decreases with this level.

Second, across-niche product proliferationmay weaken the link with submarket loyalists,who want the brand to be linked closely to thefocal submarket before they will add it to theirconsideration set (Anderson and Spellman, 1995;Posavac, Sanbonmatsu, and Fazi, 1997). Becausea submarket follower’s brand choice depends on



how strongly he or she associates that brand withthe product submarket (Punj and Moon, 2002;Urban, Hulland, and Weinberg, 1993), firms withstrong links to a specific submarket usually gaina superior image and reputation among thesecustomers. For example, Porsche likely appearsin the consideration set of sports car buyers,because Porsche primarily offers sport cars. Theassociation of a brand with a submarket niche inturn depends on the concentration of the brand’sproduct offer (Meyvis and Janiszewski, 2004).Increasing across-niche product proliferationinstead disrupts this brand–submarket associationand might lead to a loss of submarket loyalists.The stronger the brand’s initial submarket asso-ciation, the more it relies on submarket loyalists,and the greater its loss will be if it undertakesacross-niche product proliferation (Keller andAaker, 1992; Loken and John, 1993).

The same idea appears in research on the cat-egories in markets (Hannan, 2010): Associationswith multiple categories or niches cause firms tolose their identity, such that customers perceivepoorer fit with category schemas. Dobrev et al.(2001) and Dobrev et al. (2003) detail some of thedifficulties of managing niche width expansions,including negative customer reactions to the loss ofidentity. Conversely, specialized organizations canimprove their performance by creating a strongerorganizational identity that taps specific customerneeds through coherent product customization(Carroll, 1985; Hsu, 2006; Hsu et al., 2009; Negroet al., 2010; Zuckerman and Kim, 2003).

These two costs have decreasing impacts asthe level of across-product proliferation increases,such that the impact is stronger for specializedniche firms, and the relationship between this prod-uct strategy and performance should be nonlinear.We thus predict a positive link between across-niche product proliferation and performance forfirms that are not overly specialized, that is, forthose with at least a minimum threshold level ofacross-niche product proliferation. This predictionsuggests a disadvantage for firms stuck ‘in mid-dle,’ in line with Dobrev and Carroll’s (2003)and Hannan, Polos, and Carroll’s (2007) propos-als of a liability of middleness. That is, medium-sized generalists and near-periphery firms operateneither in the center, where demand is greatestand economies of scale and scope can be fullyexploited, nor on the periphery, where specializa-tion and identity formation are valuable assets.

Hypothesis 1: There exists a U-shaped relation-ship between across-niche product proliferationand firm performance.

As we argued previously, customers adopt sim-ple decision rules to minimize their search costsand cognitive effort during the purchasing processwhen faced with complex product spaces (Knud-sen and Levinthal, 2007; Simon, 1955). Thus, thelevel of complexity of the product space affectscustomers’ incentives to simplify their decisionmaking with heuristics (Bettman, 1979; Gensch,1987; Johnson and Payne, 1985; Wright and Bar-bour, 1977). All else being equal, higher lev-els of complexity in the product space shouldincrease the number of customers who anchortheir decisions primarily on either brands or sub-market niches. As complexity increases, morecustomers use simplifying heuristics, and cus-tomers grow more divided as brand or submarketniche loyalists. As a first consequence, more cus-tomers become strongly associated with a multi-product brand or a specialized producer. Becauseevery submarket niche is then populated by morehomogenous customers, the intensity of the iden-tity attached to a particular product choice fora single customer should increase (Negro et al.,2010); that is, the variance between groups of cus-tomers increases while the variance within eachgroup decreases.

This increased polarization should amplifythe effect of across-niche product proliferationon performance. On the right side of the curve,demand synergy due to one-stop shoppingbecomes stronger, because not only are theremore brand loyalists (scale effect), but they alsoattach more value to multi-product brands thatreduce their search costs. On the left side of thecurve, more identity-homogenous and populatedsubmarket niches increase the penalty associatedwith the disruption of the brand–submarket nicheassociation. This reasoning leads us to predict.

Hypothesis 2: The complexity of product spacemoderates the relationship between across-niche product proliferation and firm perfor-mance, such that the positive and negativeeffects are stronger when complexity is greater .

When a firm follows a within-niche productproliferation strategy and introduces new products



in niches in which it has prior knowledge, it expe-riences more efficiency in operational and manage-ment processes, due to learning-by-doing factors(Kim and Kogut, 1996; Kogut and Zander, 1992;Smith, Collins, and Clark, 2005). Eggers (2012)provides empirical evidence that more firm experi-ence in a niche increases the quality of new prod-ucts introduced in that niche. Moreover, by con-tinuously refining products within a niche, firmsdevelop versions that work better and more exactlymatch the needs of their submarket loyalists.

In addition to focusing primarily on productcharacteristics that are salient, submarket loyaliststend to demand that firms respond to theirneeds and feedback (Schmalensee, 2000; VonHippel, 1986). According to Shapiro and Varian(1998), within-niche product proliferation signalsa responsive market orientation, because thefirm adapts its product offering to the preferenceheterogeneity of customers within a particularsubmarket niche. This argument resonates with theecologist tradition, which stresses how producerswho focus on a particular niche increase their iden-tity and their bonds with a particular portion of thecustomer space, offering a better fit and a positiveimpact on the firms’ viability (Hsu et al., 2009).

However, within-niche product proliferationalso implies an important negative effect, namely,cannibalization across firm product versions. Thedangers of cannibalization increase with the num-ber of versions that the firm offers within the niche(Hui, 2004).

As in the case of an across-niche product strat-egy, the initial conditions matter, and the strengthof these effects change with the level of within-niche product proliferation strategy. We thus pre-dict a nonlinear relationship with performance.Learning-by-doing effects are marginally decreas-ing at higher levels of specialization (Henderson,1984). Similarly, we safely assume concave shapesfor brand association or identity in case of profitcurves. Higher firm specialization also implies thatthe firm is more closely associated with a particu-lar submarket niche, such that it conveys a specificidentity (Hsu, 2006). However, excessive levels ofwithin-niche specialization could lead to increas-ing levels of product cannibalization (Garud andKumaraswamy, 1993), in that too many versionscould compromise perceived identity. The higherthe number of product versions in a submarketniche, the higher the probability that a particu-lar product does not conform with the perceived

identity. Negro et al. (2010) show that a nichehas sharp boundaries if audience members sensehigh membership grades, reflecting the degree towhich the niche features fit their schemas. Forexample, Ducati is famous for producing motor-bikes with a knee-down design; it would not intro-duce new designs with a more comfortable drivingposition because doing so would initiate identityclashes for customers. These contrasting forcesgenerate a threshold level for the negative rela-tionship between within-niche product prolifera-tion and performance.

Hypothesis 3: There exists an inverted U-shapedrelationship between within-niche product pro-liferation and firm performance.

Our complexity arguments in relation to across-niche product proliferation naturally extend to thewithin-niche form too. The greater the polarizationof submarket and brand loyalists due to highcomplexity, the more returns the firm can accruefrom its within-niche product proliferation, andthe more detrimental an excess application of thisstrategy will be. The higher the complexity, thehigher the homogeneity of submarket loyalistswithin a niche, and thus the more specializedfirms could exploit learning mechanisms anduse a product versioning strategy to adapt tousers’ requests. Moreover, the firm captures moresegment loyalists when it increases the levelof within-niche product proliferation, becausehigher identity conveys a clearer signal and morevisibility in the market.

However, as the complexity of product spaceincreases, excessive product versioning couldgenerate more cannibalization effects, becausedemand within a submarket niche grows moredefined (i.e., lower customer heterogeneity withinthe niche), so the identity attached to a partic-ular type of product becomes more relevant forcustomers. In this scenario, additional versionsof products might not conform to the particularschemas attached to a submarket niche identity.Therefore, we hypothesize.

Hypothesis 4: The complexity of product spacemoderates the relationship between within-nicheproduct proliferation and firm performance,such that the positive and negative effects arestronger when complexity is greater .



Table 1. Means of main attributes for Spanish automobile niches

Small Compact Intermediate Lux-Interm Luxury Sport Minivan

Number of models 45 48 28 42 40 24 30Cars sold 968.6 1,004.3 728.2 518 112.9 97.1 140.6Price (¤) 7,285 11,307.7 13,752.3 17,552.7 2,7746.4 2,5681.3 1,5644.8Horsepower 95.8 97.1 105.6 126.4 165.9 167 118.6Size (m2) 5.8 7 7.5 7.7 8.5 7.5 7.8Gas (l/km) 5.1 5.7 5.9 6.4 7.1 6.7 7.3Max. speed (km/h) 153.3 180.8 185.2 199 214.2 215.9 174.7

EMPIRICAL ANALYSIS

Empirical setting

The automobile industry is characterized by manycompeting brands and multiple product modelsthat appeal to different market niches. Therefore,this industry is appropriate for studying anddisentangling the effects of product proliferationon performance. Within the industry, our empiricalanalysis focuses on the Spanish automobile marketbetween 1990 and 2000. Each model is classifiedby ANFAC (The Spanish Association of Car andTruck Producers) within one of the seven majorsubmarkets (small, compact, intermediate, luxuryintermediate, luxury, sport, and minivan) that aredefined by the industry according to mechanical,design, and equipment characteristics, such thatall models in a submarket have homogenouscharacteristics, as we detail in Table 1.

The ANFAC classification has been stable overtime and matches the car attributes that customersuse during their purchasing processes (Lapersonneet al., 1995; Urban et al., 1993). Furthermore,and specific to the Spanish automobile market,a gradual but significant reduction of tariff andnontariff protections began after Spain joinedthe European Union in the late 1980s. In 1987,these tariffs reached 34.3 percent for non-Europeanproducers; but by 1993, they had fallen to10 percent. The tariff reductions led to a fivefoldincrease in the number of models offered by non-European foreign producers in Spain during the1990s, from 14 models in 1990 to almost 60 in2000. This proliferation also sparked the arrivalof six new brands, such that customers who oncesearched among 97 car models in 1990 couldconsider 169 models in 2000.

This market thus reveals important heterogene-ity over time in terms of firm product portfolios

and the complexity of the product space; it alsooffers a good setting for identifying the relation-ship among product proliferation, performance andproduct space complexity. We isolate a sampleof firms for which this dynamic is exogenous;therefore, we select European incumbents beforethe tariff reduction, that is, all European automo-bile brands present in Spain before 1990. Withthis approach, we are confident that the relation-ship between performance and product prolifera-tion strategies results from environmental condi-tions over which the sample firms had no con-trol. Because these firms are multinational corpo-rations that make product proliferation decisions ata global level, product strategies likely are inde-pendent of the dynamics of the Spanish market.

We employ quarterly panel data from 1990to 2000 (33 quarters), with car brand as theunit of analysis. We use the brand as the unitof analysis for two main reasons. Customers’fidelity and identity are usually associated withthe brand, not the parent company, as confirmedby several studies that highlight the importanceof brand advertising in car industry (Kwoka,1993). For example, customers who embracethe Alfa brand as a sort of community donot usually consider models made by its parentcompany Fiat (Baltas and Saridakis, 2010). Inour data set, subsidiaries constitute 30 percent ofthe sample, according to ANFAC and the SpainGuide of Car Buyers , a Spanish magazine.3 Thedata cover all 31 brands sold in the Spanishautomobile market during the sample period,including 19 incumbents with 621 brand/timeobservations, as well as seven brands that entered

3 We extend the panel with advertising data provided byInfoadex, a Spanish firm that computes expenditures bymonitoring communication markets and data on total firm assets,provided by Bureau van Dijk’s OSIRIS.



and two that exited the Spanish automobileindustry during the observation period.4 Thegathered information about each brand includessize and advertising expenditures, as well as theprice, sales, submarkets, and characteristics of itsoffered models.

Dependent variable

Various performance measures might reflect theimpact of product proliferation (e.g., product qual-ity [Eggers, 2012]; firm survival [Bayus and Agar-wal, 2007; Dobrev et al., 2002; Dowell, 2006; Fos-furi and Giarratana, 2007; Sorenson, 2000]; returnon investments [Kekre and Srinivasan, 1990]; mar-ket shares [Bayus and Putsis, 1999; Chong et al.,1998; Putsis and Bayus, 2001]). Our theoreticalpredictions are based on variations in demand(q) and margins (p − mc). Therefore, we followKadiyali et al. (1999), who use a supply–demandstructural model to estimate firm margins. With asystem of simultaneous equations, we can derivefor each model the equilibrium profit-maximizingprice, after specifying its demand functions. Thisdependent variable has the additional advantageof accounting for consumer behavior, in thatit embeds a model that takes consumer utilitydirectly into account and estimates it (Hui, 2004).The final dependent variable is the aggregation,at the firm level, of the profits accrued for eachmodel.

Specifically, we model the demand function foreach car model, using a random coefficient logitmodel (Berry, Levinsohn, and Pakes, 1995; Nevo,2000); and we derive the first-order price conditionfor each model, assuming optimal multiproductfirm decisions in an oligopolistic market (i.e., firmsmake choices to maximize profits). To estimatethe equations simultaneously, we use a generalizedmethod of moments and control for possibleheterogeneity in firm resources, which might leadto different firm cost structures. To account foreconomies of scale and scope, the cost of eachmodel is regressed on model attributes, the totalnumber of overall models produced, the numberof models produced inside the corresponding

4 The sample brands are Citroen, Peugeot, Ford Europe, Opel,Renault, Seat, Volkswagen, Audi, Alfa-Romeo, BMW, Fiat,Jaguar, Lancia, Porsche, Rover, Saab, Skoda, Volvo, and Yugo.The nonincumbent brands are Honda, Mazda, Hyundai, Nissan,Toyota, Mitsubishi, Suzuki, Subaru, KIA, Galloper, Daewoo, andChrysler.

35000

30000

25000

20000

15000

10000

5000

0

Eur

os

20 40

Percentiles of Price

60 80 100

Price Marginal Cost

33%

37.6%

38.9%

41.6%

42.8%

Figure 1. Markup estimates. Notes: The estimates usethe parameters reported in Table A.1 of the Appendix.They correspond to the average markups of car modelswithin the percentile of the distribution of the observed

prices

niche, and the number of submarket niches inwhich the firm simultaneously offers models,5 aswell as firm, year, and month dummies (see theAppendix). Figure 1 contains the observed pricesand estimated margins for the car models.

Similar structural models appear in previousstudies of the automobile market (Berry, Levin-sohn, and Pakes, 1999; Goldberg, 1995). Themargins and demand-price elasticities are in linewith prior research, including a mean elasticityof approximately three and an inverse relation-ship between margins and elasticities (i.e., higherprice–cost margins for models with less demand-price elasticity). Therefore, we define the Perfor-mance dependent variable as margins multipliedby the number of model units sold at time t foreach brand i . Performance reflects the firm’s abil-ity to charge higher prices and increase its marketshares, while also controlling for the underlyingcost structure:

Performanceit =∑

rCFit

(prt –mcrt ) qrt ,

where Fit is the set of products offered by firm iat period t , and prt, mcrt, and qrt are the price,marginal cost, and units sold, respectively, ofproduct r during period t .

5 Our estimates confirm the economies of scale on the producerside, as previously identified (see the Appendix, Table A.1).



Variables of theoretical interest

To measure across-niche product proliferation(APP ), we use the Berry index of dispersion. Forfirm i at time t , it is defined as

APPit =[

1–∑

s

(Nist/Nit )2

]× 100,

where Nist is the number of products offered byfirm i in niche s at time t . The Berry indexvaries theoretically from 100 (maximum APP ) to0 (firm sells in only one niche); it offers a precise,standard measure of dispersion (see Fosfuri andGiarratana, 2007). In turn, to calculate within-niche proliferation (WPP ), we count the numberof models that a firm is selling in a given timein the niche with the highest density of productmodels for that same firm (Dowell, 2006). Thebasic statistics appear in Table 2.

We construct additional variables to determinethe conditions in which APP and WPP leadto better performance. Specifically, because wepredict a U-shaped effect for APP and an invertedU-shaped effect for WPP , we introduce theirsquare terms (APP2 and WPP2).

The complexity of a product space is a func-tion of the heterogeneity and interdependence ofits product characteristics. We follow McEvily andChakravarthy (2002) and measure complexity asthe dispersion of characteristics, keeping interde-pendence fixed. Thus, we build Complexity byevaluating the dispersion of the product character-istics (size, maximum speed, gasoline consump-tion, and engine displacement) offered in eachperiod. First, we identify 10 intervals of equal sizefor each attribute, starting with the minimal valueand ending with the maximum value observed inthe sample. Second, for each period we calculatethe proportion of models in each interval to com-pute a Berry index for each attribute. The Com-plexity variable ultimately equals the average ofthese (product attribute) Berry indexes for eachperiod (multiplied by 100 for scaling). Complexityuses the data for all competitors (European, U.S.,and Asian) in the Spanish automobile industry. Totest the mediation effect suggested in Hypotheses2 and 4, we also interact Complexity with our vari-ables of interest.

Control variables

We must rule out every possible effect on perfor-mance that can be attributed to competition. We Tabl

e2.

Des

crip

tive

stat

istic

san

dco

rrel

atio

nm

atri

x

Mea

nSD

Min

Max

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

(1)

Perf

orm

ance

71.9

73.8

0.01

353.

91

(2)

No.

firm

mod

els

4.82

2.61

112

0.46

1(3

)A

PP56

.527

.50

85.7

0.58

0.67

1(4

)W

PP1.

961.

081

50.

130.

790.

211

(5)

APP

23,

942.

22,

225.

50

7,34

6.9

0.65

0.63

0.97

−0.1

1(6

)W

PP2

4.98

5.47

125

0.62

0.76

0.15

0.98

0.04

1(7

)W

PP/A

PP1.

170.

810

3.54

0.39

0.95

0.60

0.88

0.52

0.83

1(8

)A

PPch

ange

0.34

0.47

01

0.18

0.29

0.35

0.16

0.35

0.10

0.30

1(9

)C

ompl

exity

77.7

0.80

76.3

79.1

0.23

0.10

0.09

0.07

0.08

0.07

0.13

−0.0

31

(10)

Com

petit

ion

I85

3.7

65.2

90.4

0.11

0.22

0.23

0.15

0.22

0.12

0.21

0.08

−0.4

0(1

1)C

ompe

titio

nII

17.9

18.

133

390.

260.

380.

700.

030.

690.

010.

310.

110.

170.

091

(12)

Com

petit

ion

III

124

2090

160

0.25

0.20

0.13

0.14

0.13

0.12

0.22

0.20

0.64

0.11

0.09

1(1

3)Si

ze(1

00$b

)65

.976

.527

.130

3.1

0.29

0.13

0.09

−0.0

40.

18−0

.03

0.05

0.04

0.09

0.17

−0.0

40.

171

(14)

Adv

ratio

5.1

5.2

022

.70.

720.

260.

51−0

.03

0.56

−0.1

00.

200.

170.

010.

020.

300.

020.

131

Not

es:

Perf

orm

ance

in10

mill

ion

(199

5)E

uros

and

cars

sold

inth

ousa

nds

ofun

its.



thus jointly consider three measures of competi-tion from prior literature. Competition I is canon-ical Berry index, that is, the weighted averageof the Berry indexes calculated using the mar-ket shares in the different niches in which thefirm competes. These weights represent the pro-portion of the firm’s total revenue earned in eachniche. Therefore, it varies by time and firm andcaptures not only the niches in which the firm com-petes but also how important each niche is for thefirm and the level of competition in each niche.The Berry indexes are multiplied by 100 for scal-ing. Competition II is equal to the number of(European and non-European) firms that special-ize in the same niches as a focal firm i . Thus, itmeasures the closest competition that firm i faces.Santalo and Becerra (2008) highlight the impor-tance of introducing this measure when special-ized firms compete against more diversified ones.A firm i is considered specialized in a niche j ifthe weight of the niche j in the firm’s productportfolio (i.e., number of models) is greater thanthe observed weight of the niche j in the industry.That is, firm i is specialized in niche j at time tif (N ijt /N it ) > (N jt /N t ). Finally, following ecologyliterature, we include the number of car modelssold in the market (Competition III ) to proxy forthe density of the product offer at any time t . AsTable 2 shows, these variables are not highly cor-related and capture different aspects of rivalry. Wealso control for the potential economies or dis-economies of pursuing the two-product strategiessimultaneously. We thus introduce WPP/APP , thatis, the interaction effect of WPP and APP .

We introduce firm- and other market-levelcontrols. Quarterly and annual dummies controlfor seasonal components and common marketshocks, such as variations in market demand orindustry production costs. We control for firm-fixed effects using brand dummies, which capturerelevant factors that determine firm performanceand explain heterogeneity across firms, such asthe vertical integration level (Dowell, 2006; Novakand Stern, 2009), firm reputation (Rao, 1994), orresource specificity (Dyer, 1996).

Variations of APP could generate both opera-tional and management costs and negative cos-tumer reactions due to changes in the firm’s fitwith category schemas. To evaluate this effect, weinclude APP Change, a dummy variable that takesthe value of 1 if there is variation in the level ofacross-product proliferation by time and by firm.

Finally, we include time-specific firm controls.Size equals total assets (obtained from the Bureauvan Dijk’s OSIRIS), and we introduce it in anonlinear fashion to control for economies of scale(i.e., Size and Size2). Advertising refers to thepercentage of the firm’s advertising expenditures,standardized by the industry total. With thisratio, we capture firm heterogeneity in advertisinginvestments and isolate the effect of commonvariations in advertising expenditures driven byindustry trends. We employ ordinary least squaresfor these assessments. Table 2 shows the basicstatistics and correlations for our variables. Notethat the correlation between our core covariatesWPP and APP is not excessively high (0.20).

RESULTS

Table 3 includes the estimation results for Model1, which represents the baseline model withonly the control variables; Models 2–6, whichprogressively add each variable of interest; andModels 7 and 8, introducing the interaction effectsof Complexity with the variables of interest.

Across-niche product proliferation shows aU-shaped effect on Performance: When firmsengage in low levels of across-niche product pro-liferation, the cost associated with entry into newniches, because they have low flexibility in theirprocesses and lose submarket loyalists, dominatesthe benefits of exploiting scale economies andattracting more brand loyalists. Independent of themodels, the threshold level at which greater across-niche product proliferation achieves higher perfor-mance is around 0.5.

We also model the interaction effects of Com-plexity together with our variables of interest, APPand APP2. In this case, we maintain all the con-trols from the previous estimation (Models 6–7).The results support Hypothesis 2: The multiplica-tive effects for APP and APP2 are significant andof the expected sign. We display in Figure 2 thelinear prediction of the estimated effects of across-niche product proliferation on firm performance,keeping all the other variables at their mean levels.

This figure depicts the influence of APP onPerformance when the level of product space com-plexity increases. Therefore, the forces that attractbrand loyalists or deflect submarket loyalists exertincreasing impacts when the complexity of theproduct space increases.



Table 3. Firm performance as dependent variable

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

APP −0.0245 −0.222*** −0.233*** −0.228*** −0.226*** 6.037*** 5.952***(0.0163) (0.0367) (0.0383) (0.0384) (0.0398) (1.141) (1.376)

APP2 0.0024*** 0.0025*** 0.0026*** 0.0026*** −0.109*** −0.153***(0.0004) (0.0004) (0.0004) (0.0004) (0.0155) (0.0201)

WPP 0.577*** 2.472*** 2.543*** 2.067*** −365.8***(0.218) (0.683) (0.791) (0.744) (64.49)

WPP2 −0.404*** −0.401*** −0.276** 28.58***(0.135) (0.135) (0.124) (9.158)

WPP/APP −0.0012 −0.0032 3.514***(0.0070) (0.0073) (0.779)

APP_Complexity −0.080*** −0.078***(0.015) (0.018)

APP2_Complexity 0.0014*** 0.0020***(0.0002) (0.0003)

WPP_Comp — 4.748***Lexity

(0.834)WPP2_Complexity −0.367***

(0.118)WPP/APP_Complexity −0.045***

(0.0101)APP change −0.289 −0.281 −0.329 −0.373 −0.373 −0.331 −0.388*

(0.248) (0.240) (0.241) (0.242) (0.242) (0.235) (0.232)Competition I −0.139*** −0.168*** −0.213*** −0.231*** −0.239*** −0.238*** −0.165*** −0.162***

(0.047) (0.051) (0.050) (0.051) (0.051) (0.052) (0.047) (0.046)Competition II 0.076 0.084 0.076 0.071 0.059 0.059 0.049 0.047

(0.076) (0.077) (0.075) (0.074) (0.075) (0.075) (0.069) (0.068)Competition III −0.016 0.0034 −0.032 −0.018 −0.005 −0.005 −0.042 −0.043

(0.024) (0.028) (0.028) (0.028) (0.028) (0.028) (0.027) (0.027)Advertising ratio 0.0490 0.0485 0.0717 0.0917** 0.0939** 0.0936** 0.141*** 0.138***

(0.047) (0.048) (0.046) (0.046) (0.046) (0.047) (0.042) (0.041)Size 4.356*** 4.909*** 0.968 0.165 2.132 2.239 0.570 5.676**

(1.423) (1.482) (1.523) (1.596) (1.801) (1.986) (1.786) (2.334)Size2 −0.982** −1.090** −0.318 −0.174 −0.525 −0.546 −0.387 −1.351**

(0.416) (0.427) (0.422) (0.434) (0.469) (0.504) (0.443) (0.530)Complexity −0.240 −0.266 −0.144 −0.176 −0.120 −0.122 −1.236** −5.613***

(0.506) (0.506) (0.489) (0.483) (0.482) (0.483) (0.493) (0.965)Firm effects YesSeasonal effects YesAnnual effects YesObservations 621 621 621 621 621 621 621 621R-squared 0.952 0.952 0.956 0.957 0.957 0.957 0.964 0.965

Notes: Heteroskedastic consistent standard errors are in parentheses.Significant at: ***0.01 level; **0.05 level; *0.1 level.

With regard to WPP , the data also confirman inverted U-shaped relationship for firm per-formance. Higher within-niche product prolifera-tion exerts a significant positive impact on firmperformance up to a threshold level, after whichcannibalization costs starts to emerge. There-fore, Hypothesis 3 passes our econometric test.Hypothesis 4 also receives support: Complex-ity , interacted with WPP and WPP2, makes the

curve steeper, consistent with the results of theinteraction between Complexity and APP . Evenas product space complexity causes demand tobecome more polarized in different homogeneousgroups of customers, it increases the influenceof the different product strategies on perfor-mance. In Figure 3, we depict the estimatedeffect of within-niche product proliferation on firmperformance.



Figure 2. Effects of across-niche product proliferation (APP) on performance. Notes: This simulation uses theregression results of Table 3, Model 8

Figure 3. Effects of within-niche product proliferation (WPP) on performance. Notes: This simulation uses theregression results of Table 3, Model 8

The variable APP Change appears significantonly for the final model when we control forthe complexity level (Model 8). In this case,the variable exerts a negative significant sign, inaccordance with expectations. The results suggestthat APP Change needs some moderators, suchas complexity, to produce an effect; otherwise,due to the substantial across-time heterogeneity ofour data, APP and APP2 capture all the dynamiceffects.

For the control variables, the estimates are inthe expected directions, though their significancevaries across models. The negative coefficient ofthe competition term indicates that performancesuffers for firms that operate in more competitiveenvironments, especially if they function in com-petitive niches. The magnitude of the competitionterm significantly increases when the square termof APP is included (Model 2 versus Model 3 inTable 3). This result is in line with Dobrev et al.



(2001) study of automobile manufacturing firmsin France, Germany, and Great Britain; general-ists are more efficient for economies of scale andscope (the nonlinearity of the covariate) but sufferfrom more direct competition.

Firms with higher advertising expenditures,compared with their competitors, achieve betterperformance (significantly in Models 3–8). Thesize coefficient shows a nonlinear pattern that con-firms the presence of economies of scale, but itis not always significant. Across-product prolif-eration and its square could be highly correlatedwith size, especially in industries characterized bysunk costs (Boone, Brocheler, and Carroll, 2000),which might help explain the null effect of size,which is not a straightforward result for the auto-mobile industry. Size becomes significant whendemand synergies are introduced with Complexity(Model 7 and 8), meaning that economies of scaleare also present at marketing level (i.e., umbrellabranding). The direct effect of Complexity is sig-nificant only in the last two models that feature allthe interactions; we thus can interpret the effectof Complexity only in the general regression. Thesame argument applies for the interaction of thetwo strategies (WPP/APP), which is significantonly in the last regression. The differences in theR-square values between the baseline and the fullmodel are marginal. Firm-fixed effects consistentlycapture the average slopes and therefore most ofthe variance in the model.

CONCLUSIONS

To investigate the relationship between productproliferation strategies and firm performance, westudied the Spanish automobile market between1990 and 2000 and exploited the presence of anexternal trend (EU trade policy) that significantlyincreased the complexity of the product space.Our findings reveal conditions in which across-and within-niche product proliferation exert a pos-itive impact on firm performance in industriescharacterized by scale economies from produc-tion, customer heterogeneity, and scope economiesfrom demand. Accordingly, our study comple-ments a mainstream perspective in similar indus-tries (Bayus and Agarwal, 2007; Hannan and Free-man, 1977), which has focused more on the het-erogeneity of firm assets and resources to explain

the link between product proliferation and perfor-mance and proposes the complexity of the overallindustry product space as an important moderator.

This analysis offers several important man-agement implications. First, managers andresearchers should recognize that within- andacross-niche product proliferation are differentstrategies. Across-niche proliferation impliestypical intraindustry diversification (Siggelkow,2003); within-niche proliferation is associatedwith product versioning (Shapiro and Varian,1998). They entail different learning processes,different impacts on performance, and differentoutcomes, given the environmental conditions.Firms performing across-niche product prolifera-tion need to build up competences and routines toexploit economies of scale and scope at the levelof production and demand, whereas those focusedon within-niche product proliferation shouldappreciate the advantages they can accrue throughlearning effects and their ability to respond to cus-tomers’ feedback to different versions of the sameproduct. Therefore, managers are well advisedto establish organization designs that match theirchosen strategy, and researchers should assess thedirect and combined effects of these strategies indifferent environmental contexts.

Moreover, product proliferation strategies pro-duce benefits (e.g., differentiation, entry barriers,one-stop shopping), but managers cannot ignoretheir costs, which extend beyond production abil-ities to include a potential loss of the associationbetween a brand and a niche. Both strategies exerta nonlinear effect on performance. For across-product proliferation, we confirm the results ofthe ecology tradition that has identified a liabil-ity of middleness: Being stuck in the middle isdetrimental for performance (Hannan et al., 2007).As a novel result, we find that within-niche productproliferation generates learning curves and positivesynergies between a brand and a submarket niche,which expire after a certain level due to canni-balization (Eggers, 2012). Firms are well advisedto stay away from the tails of the within-productproliferation distribution.

This article shows how diverse purchasingprocesses of customers imply different impacts onthe performance of proliferation strategies. Themagnitude of costs and benefits depends on thecomplexity of the product space, which couldchange the distribution of customers, accordingto their different decision-making rules. A novel



finding is that the higher the complexity, the moresensitive are the relationships between productstrategies and performance. This finding highlightsthe importance of monitoring the dynamics of amarket and in particular the drivers that determinethe levels of complexity faced by customers; theymight change how firms calibrate their productproliferation strategies.

When complexity is high, it is more detrimentalfor firms to be distant from their optimal levels ofproduct proliferation. In the case of across-productproliferation, more complexity increases the firm’sincentives to push the strategy toward extremes,that is, to maximum (or minimum) diversifica-tion. For within-product proliferation, it suggeststhe ability to maintain an equilibrium on an opti-mal level. Calibration is not easy though. Man-aging a product portfolio is inherently a complextask, because it entails two opposing forces: theintroduction and the termination of products. Forexample, firms that specialize in one niche shoulddesign organizational processes to introduce dif-ferent versions of new products while also cullingand retiring older models to retain the optimallevel of within-niche product proliferation (Soren-son, 2000). The level of across-niche product pro-liferation also changes; for example, firms mightincrease the level of diversification by reducing thenumber of models they sell in their more populatedproduct niches. Further research should determinehow firms use product culling and introductiontactics in various fashions to shape their productstrategies.

Our findings highlight the importance of somedemand effects that have to be taken into accountto manage an optimal brand and product portfolio.It is worth noting that we find our effects at thelevel of brands. Parent firms should recognize thatbrands could combine differently to constitute theoverall performance of the group. For example,firms with low levels of across-niche product pro-liferation might opt to introduce new products innew segments using a new brand; in so doing, theycan exploit economies of scale and scope withoutlosing their initial association with the segment.This approach also could represent the startingpoint for new research that models the branddecision (one vs. multiple) of a firm in relation tothe level of complexity of the product space.

We find a threshold level in the performance–product proliferation strategy relationship, thoughwe also acknowledge that our results could depend

on some well-defined industry assumptions.A potential weakness of our analysis is the defini-tion of the submarket niche. We consider submar-ket niches determined by customers according tothe similar product attributes they use to simplifytheir purchasing processes, so that a submarketniche can be defined as a collection of productswith homogenous tangible characteristics (Klepperand Thompson, 2006; Sutton, 1998). In our set-ting, niche segmentation is implicitly assumed tobe stable and uniformly recognized by customers.These assumptions might not hold in generalthough, because niche segmentation could changewith the level of complexity or other effects.

Thus, additional research should considerexplicitly when U- versus L-shaped forms arise,using data that include heterogeneity across indus-tries in terms of economies of scale and scopein production and demand, as well as the extentto which submarket niches are well established.What happens if these features do not apply? Forexample, for markets without scale economiesand with customers who only consider the brand(brand loyalists), a monotonic positive relationshipbetween across-niche product proliferation and thefirm’s performance likely operates independentlyof the initial firm levels of across-niche productproliferation. In summary, our results extend onlyto industries with characteristics similar to thoseof our empirical setting.

Finally, we treat complexity of the product spaceas an exogenous factor, but we recognize thatin highly concentrated industries the complexitycould even depend from the choices of a singlefirm. Further studies could analyze the complexityof product space as an endogenous strategicvariable when industries tend, for example, toduopolies.

ACKNOWLEDGEMENTS

We are grateful to Bruno Cassiman, MichaelCusumano, J. P. Eggers, Juan Santalo, OlavSorenson, editor Tomi Laamanen, two anonymousreviewers and seminar participants at ESMTBerlin, LMU Munich, Bocconi University, CarlosIII Madrid, AoM and SMS Conferences for theirvaluable comments and constructive suggestionson earlier drafts of this paper. In addition,we thank Marıa Jose Moral and Infoadex forproviding the data. This research was generously



supported by the Italian Ministry of Education(Project 2010H37KAW) and the Spanish Ministryof Science and Innovation (Project SEJ2011-0033-001).

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APPENDIX: PRICE—COST MARGINESTIMATES

We estimate a supply–demand structural modelto obtain price–cost margins. We specify thecustomer purchase decision (demand function ofproducts) and the first-order condition of pricefor a firm maximization profit problems. Weemploy monthly panel data, from January 1990to December 2000 (132 months), with car modelas the unit of analysis (16,362 observations)Table A.1.

To obtain the demand equation, we considera market with I consumers and J differentproducts. Each customer i obtains utility at periodt : U ijt =

∑k βk X jk –αipjt + ξ it + εijt , where the

parameters βk are the average tastes for observedproduct characteristic k , X jkt . Unobserved productcharacteristics are captured by a generic variableξ it . The price of the product pjt has a customer-specific effect αi. Following Berry et al. (1995),we assume this effect derives from differencesin customer income yi , so that αi =α/yi . Ifincome is log-normally distributed with mean myt

and variance σ y2, then αi =α exp(−myt + σ y v iy ),

where v iy is normally distributed with a mean of 0and variance of one. Finally, εijt captures customeri ’s idiosyncratic taste for product j at time t . Thisstochastic term is drawn from a Type-I extremevalue distribution with mean 0, independently andidentically distributed across products, consumers,and time.

The set of products includes an outsideoption (denoted as good 0) that correspondsto no purchasing behavior and with a utility;U i0t = σ 0v i0 + εi0t , where σ 0 is the averagetaste for the outside option, and v i0 is theunobserved customer taste for the outside good,normally distributed with mean 0 and variance 1.



Table A.1. Estimated parameters for the demand andsupply model

Demand-sideparameters

Dependentvariable Customer utility

Means (β) Constant −8.358*** (0.048)CC/W 0.164*** (0.007)Max. speed 1.432*** (0.009)km/l 0.234*** (0.015)Size 3.570*** (0.017)Advertising 1.275*** (0.016)

Term on price(α)

5.733*** (0.684)

Std. dev ofoutside good(σ0)

2.886*** (0.385)

Firm effects YesSeasonal

effectsYes

Cost-sideparameters

Dependentvariable

Marginal cost

Attributes (η) Constant 4.059*** (0.112)CC/W 0.592*** (0.031)Max. speed 0.862*** (0.014)km/l 0.196*** (0.042)Size 0.071 (0.103)Weight 1.019*** (0.073)

Number ofmodels

−0.016*** (0.003)

Number ofmodels inthe niche

−0.015*** (0.001)

Number ofniches

−0.013** (0.006)

Trend −0.003*** (0.001)Firm effects YesSeasonal

effectsYes

Notes: The standard errors (reported in parentheses) are robustto heteroskedasticity and serial correlation.Significant at: ***0.01 level; **0.05 level; *0.1 level.

The probability that product j maximizes customerutility, or purchase probability, in period t amongall the products offered in the market (ncC ), is

sjt =∫v

e�k βk xkjt +αi pjt +ξjt

eσ0vi0 +∑n∈C

e�k βk xknt +αi pnt +ξntf (v),

where the predicted market share of productj at time t , sjt, derives from the aggregationover the distribution of consumers’ characteristics,f (v ) = f (v i0,v iy ).

On the supply side, we consider multiproductfirms that choose, every period, the price for eachof their products. These firms behave as Bertrandcompetitors. The optimal price decision of firm ffor product j arises from the first-order conditionof a maximization profit problem

sjt +∑

rCFft

(prt –mcrt )(∂srt/∂pjt

) = 0,

where Fft is the set of products offered by the firm fat period t , and mcrt is the marginal cost of productr at period t . As is common in prior literature,we approximate the marginal cost using a hedonicapproach (Rosen, 1974), modeling it as a functionof the log of product attributes. The marginal costis decomposed into a subset of observed wjt andan unobserved component ζ jt . To capture potentialeconomies of scale, the number of products offeredby the firm in the market and in the niche of theproduct are included. We also include the numberof niches in which the firm operates to captureeconomies of scales from the flexibility of thefirm’s process.

Thus, the model consists of two equations,demand and price, estimated simultaneously usingthe generalized method of moments and follow-ing the technique proposed by Berry et al. (1995).As explanatory variables that approximate the util-ity function and marginal cost, we employ productattributes: size (m2), auto cubic capacity per kg(cm3/kg), gas mileage (km s covered at a constantspeed of 90 kph with a liter of gasoline), maxi-mum speed, and weight (Kg m). For the demandfunction, we also include advertising expendituresas an endogenous variable. The time-based con-trol variables capture common variation over time,and brand controls capture heterogeneity acrossfirms. The instruments employed in the reportedestimate are characteristics of the product, prod-ucts produced by the same multiproduct firm andproduced by rival firms, and price differences withrespect to the product mean lagged one year. Thisis a standard approach in econometric analyses ofthe automobile market (Berry et al., 1995; Gold-berg, 1995).


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