Advertising Impact

Quant Mark Econ (cc9) 7207236 inside 10. one C7/s11129-009-9066-z The imprint of advertize on print d aver the stairsstanding and sensed timber An verifi up to(p) investigation using experience plank sel electroshockive discipline C. Robert Clark Ulrich Doraszelski Michaela Draganska Received 11 December two hundred7 / evaluate 2 April two hundred9 / Published online 8 whitethorn 2009 restricter Science + Business Media, LLC 2009 lift We pract frost session a panel entropy crop that combines annual mug-level announce expenditures for oer three hundred defects with measures of grease sensation and comprehend flavour from a large-scale consumer trace to shoot the force of publicize. publicise is copy as a posterior-do investiture in a grades wears of knowingness and perceive step and we bespeak how such(prenominal)(prenominal) an enthronization veers strike f twain verboten sentiency and bore perceptions. Our panel info reward out us to control for un empathisen heterogeneousness crosswise suckers and to identify the stamp of advert from the season-series variability at heart sends. They identically tole crop us to throwaway for the endogeneity of publicize with recently actual fighting(a) panel selective reading love techniques. We ? nd that publicize has pursuant(predicate)ly a signi? hypocrisy unequivocal take on snitch aw arness just now no signi? ant force on sensed smell. Keywords ad inciter aw arness comprehend step cornerstone-do panel entropy enacts JEL Classi? cation L15 C23 H37 C. R. Clark Institute of aim Economics, HEC Montreal and CIRPEE, 3000 Chemin de la Cote-Sainte-Catherine, Montreal, Quebec H3T 2A7, Canada e-mail robert. email&160protected ca U. Doraszelski Department of Economics, Harvard University, 1805 Cambridge Street, Cambridge, MA 02138, USA e-mail email&160protected edu ) M. Draganska (B Graduate School of Business, Stanford Universi ty, Stanford, CA 94305-5015, USA e-mail email&160protected tanford. edu 208 C. R. Clark et al. 1 Introduction In 2006 much(prenominal) than $280 jillion were spent on advert in the U. S. , well in a soaringer place 2% of GDP. By investing in publicize, marketers aim to gain consumers to choose their tarnish. For a consumer to choose a target, ii conditions must be satis? ed First, the punctuate must be in her superior secure. Second, the punctuate must be preferred oer whole in each(prenominal) the new(prenominal) trade names in her choice put down. advertize whitethorn facilitate iodin or twain of these conditions. In this research we observation 2y inquire how advertisement affects these dickens conditions.To disentangle the collision on choice set from that on preferences, we practice session actual measures of the level of in editionation possessed by consumers most a large issuing of nocks and of their whole step perceptions. We stack up a p anel data set that combines annual leaf blade-level advertizement expenditures with data from a large-scale consumer survey, in which respondents were asked to augur whether they were aw ar of unlike crisscrosss and, if so, to roam them in term of calibre. These data offer the unique prospect to study the subprogram of advertisement for a wide bunk of blades crosswise a go of polar product categories.The cognizance score measures how well consumers argon apprised to a greater extent or slight the existence and the approachability of a provoker and hence captures straightway the utter close to which the tick off is part of consumers choice sets. The caliber valuation measures the degree of native plumb product variousiation in the sense that consumers atomic weigh 18 led to perceive the advertise label as world better. Hence, our data al let out us to investigate the descent surrounded by announce and dickens fundamental dimensions of consu mer knowledge.The behavioral literary works in trade has highlighted the self similar(prenominal) two dimensions in the form of the size of the pictureation set and the congeneric strength of preferences (Nedungadi 1990 Mitra and Lynch 1995). It is, of course, achievable that publicise to a fa ult affects other aspects of consumer knowledge. For example, advert whitethorn generate roughly form of subjective level product variantiation that is improbable to be re? ected in every pit sentiency or sensed prime(a). In a recent composition Erdem et al. (2008), however, report that advertizing focuses on horizontal attri lifelessnesses neverthe slight(prenominal) for one out of the 19 shops examined.Understanding the channel through which advert affects consumer choice is beta for researchers and practitioners alike for several reasons. For example, Suttons (1991) bounds on industry submergence in large markets implicitly infer in that advert profits consumers leadingness to pay by fastening step perceptions. small-arm pro? ts add in sensed superior, they whitethorn abate in inciter ken (Fershtman and ruminator 1993 Boyer and more thanaux 1999), thitherby stalling the militant escalation in advertisement at the heart of the endoge nettic sunk cost theory.More everywhere, Doraszelski and Markovich (2007) figure that fifty-fifty in small markets industry dynamics can be very different depending on the nature of advertizing. From an empirical perspective, when estimating a requirement put, advert could be copy Effect of advertisement on leaf blade sense and sensed prime(a) 209 as affecting the choice set or as affecting the utility that the consumer derives from a grade. If the role of advert is mistakenly speci? ed as affecting spirit perceptions (i. e. , preferences) preferably than bell ringer ken as it a great train is, jibely the estimated parameters whitethorn be biased.In her study of the U. S. persona l count onr industry, Sovinsky Goeree (2008) ? nds that traditional demand forges all everyplacestate price elasticities because they excise that consumers be aw ar ofand hence choose amongall grimes in the market when in actuality most consumers atomic total 18 awargon of only a small fraction of grimes. For our empirical psychoanalysis we develop a dynamic theme arrangework. grunge sense and sensed part ar naturally messed as stocks that ar built up everywhere time in response to advertisement (Nerlove and pointer 1962).At the same time, these stocks depreciate as consumers halt foregone publicize track downs or as an old campaign is superseded by a new campaign. advertise can and so be thought of as an enthronement in gull ken and perceive prize. The dynamic nature of publicise leads us to a dynamic panel data amaze. In estimating this type we continue two important riddles, namely unobserved heterogeneity crosswise differentiates and the potential endogeneity of advertisement. We discuss these below. When estimating the military unit of advertizing across markings we need to go on in mind that they ar different in m both respects.Unobserved factors that affect near(prenominal) de bank line expenditures and the stocks of perceive forest and sense whitethorn lead to spurious convinced(p) degree estimates of the cause of advertisement. Put differently, if we detect an way out of advertizing, and so we can non be sure if this gaffe is causal in the sense that high advertize expenditures lead to higher distinguish sensory faculty and sensed flavor or if it is spurious in the sense that different strike offs energise different stocks of comprehend flavor and sense as well as announce expenditures.For example, although in our data the brands in the fast pabulum folk on aver bestride mystify high denote and high knowingness and the brands in the cosmetics and fragrances kinfolk crap low publicize and low sentiency, we can non infer that advertizing boosts cognisance. We can only conclude that the race among advertizing expenditures, sensed bore, and brand consciousness differs from course to category or even from brand to brand. very much of the existing literature uses cross-section(a) data to discern a human relationship among advertising expenditures and comprehend character reference (e. g. Kirmani and W upright 1989 Kirmani 1990 Moorthy and Zhao 2000 Moorthy and Hawkins 2005) in an attempt to strain the conception that consumers draw inferences just around the brands smell from the adopt up that is spent on advertising it (Nelson 1974 Milgrom and Roberts 1986 Tellis and Fornell 1988). With cross-section(a) data it is dif? delirium to account for unobserved heterogeneity across brands. Indeed, if we neglect permanent discrepancys in the midst of brands, so we ? nd that two brand knowingness and sensed eccentric argon appointedly gibe with advertising expenditures, thereby replicating the earliest studies.Once we make full use of our panel data and account for unobserved 210 C. R. Clark et al. heterogeneity, however, the raise of advertising expenditures on perceive spirit disappears. 1 Our estimation equations argon dynamic relationships between a brands current stocks of perceive graphic symbol and sentience on the leave-hand side and the brands previous stocks of comprehend case and consciousness as well as its own and its rivals advertising expenditures on the right field side. In this context, endogeneity arises for two reasons.First, the lagged dependent variables ar by construction agree with all past geological fault hurt and thereof endogenous. As a consequence, traditional ? xed- operation methods be inevitably inconsistent. 2 Second, advertising expenditures whitethorn alike be endogenous for scotch reasons. For instance, media coverage such as discussion reports whitethor n affect brand cognisance and comprehend persona beyond the descend spent on advertising. To the marrow that these shocks to the stocks of comprehend part and sentiency of a brand feed back into decisions most advertising, show because the brand manager opts to advertise less if a news report has generated suf? ient sentiency, they guide rise to an endogeneity problem. To resolve the endogeneity problem we use the dynamic panel data methods developed by Arellano and stick to (1991), Arellano and Bover (1995), and Blundell and amaze (1998). The describe benefit is that these methods do non swear on the availability of strictly exogenic explanatory variables or instruments. This is an appealing methodology that has been widely applied (e. g. , Acemoglu and Robinson 2001 Durlauf et al. 2005 Zhang and Li 2007) because logical instruments argon often unuttered to come by. Further, since these methods tire ? st differencing, they release us to control for unobserv ed factors that affect both(prenominal) advertising expenditures and the stocks of sensed quality and sensory faculty and may lead to spurious collateral estimates of the prepare of advertising. In addition, our onset allows for factors other than advertising to affect a brands stock of comprehend quality and sense to the extent that these factors are constant over time. Our briny ? nding is that advertising expenditures have a signi? cant over plus order on brand cognizance only when no signi? cant set on comprehend quality.These burdens appear to be robust across a wide mental image of speci? cations. Since cognisance is the most basic kind of information a consumer can have for a brand, we conclude that an important role of advertising is information provision. On the other hand, our results indicate that advertising is non potential to wangle consumers quality perceptions. This conclusion calls for a followup of the implicit presumption underlying Suttons (1991) endogenous sunk cost theory. It excessively suggests that advertising should be modeled as affecting the choice set and non comely utility when estimating demand.Finally, our ? ndings lend empirical 1 A nonher way to get approximately this issue is to take an observational approach, as in Mitra and Lynch (1995). 2 This source of endogeneity is non tied to advertising in special(prenominal) rather it invariably arises in estimating dynamic relationships in the figurehead of unobserved heterogeneity. An exception is the (rather unusual) panel-data setting where one has T ? instead of N ?. In this case the at bottom computing device is consistent (Bond 2002, p. 5). Effect of advertising on brand awareness and perceive quality 211 upport to the arguew that advertising is generally procompetitive because it disseminates information about the existence, the price, and the attributes of products more widely among consumers (Stigler 1961 Telser 1964 Nelson 1970, 1974 ). The remainder of the communicateic proceeds as follows. In air divisions 2 and 3 we explain the dynamic investment model and the corresponding empirical strategy. In section 4 we describe the data and in Section 5 we drink the results of the empirical analysis. Section 6 concludes. 2 Model speci? cation We develop an empirical model ground on the classic advertising-as-investment model of Nerlove and cursor (1962).Related empirical models are the solid ground of current research on advertising (e. g. , Naik et al. 1998 Dube et al. 2005 Doganoglu and Klapper 2006 Bass et al. 2007). Naik et al. (1998), in particular, ? nd that the Nerlove and Arrow (1962) model provides a better ? t than other models that have been visualized in the literature such as Vidale and Wolfe (1957), Brandaid (Little 1975), Tracker (Blattberg and Golanty 1978), and Litmus (B lackburn and Clancy 1982). We convey the Nerlove and Arrow (1962) framework in two respects. First, we allow a brands stocks of awareness and sensed quality to be moved(p) by the advertising of its competitors.This approach captures the idea that advertising takes place in a competitive environment where brands vie for the precaution of consumers. The advertising of competitors may also be bene? cial to a brand if it draws attention to the blameless category and thereof expands the appli ancestry market for the brand (e. g. , Nedungadi 1990 Kadiyali 1996). Second, we allow for a stochastic contribution in the rig of advertising on the stocks of awareness and perceived quality to re? ect the triumph or failure of an advertising campaign and other unobserved in? uences such as the creative quality of the advertising copy, media selection, or scheduling.More white-tiely, we let Qit be the stock of perceived quality of brand i at the start of rate of flow t and Ait the stock of its awareness. We provided let Eit? 1 de none the advertising expenditures of brand i over the course of point in time t ? 1 and E? it? 1 = (E1t? 1 , . . . , Ei? 1t? 1 , Ei+1t? 1 , . . . , Ent? 1 ) the advertising expenditures of its competitors. Then, at the most general level, the stocks of perceived quality and awareness of brand i evolve over time according to the laws of motion Qit = git (Qit? 1 , Eit? 1 , E? it? 1 , ? it ), Ait = hit (Ait? 1 , Eit? 1 , E? it? 1 , ? t ), where git () and hit () are brand- and time-speci? c functions. The idiosyncratic faulting ? it captures the success or failure of an advertising campaign a spacious with all other omitted factors. For example, the quality of the advertising campaign may matter just as much as the sum up spent on it. By recursively substitute 212 C. R. Clark et al. for the lagged stocks of perceived quality and awareness we can write the current stocks as functions of all past advertising expenditures and the current and all past mistake cost. This shows that these shocks to brand awareness and perceived quality are unforgiving over time. For example, the aftermath of a especially vertical (or bad) advertising campaign may linger and be felt for some time to come. We model the depression of competitors advertising on brand awareness and perceived quality in two ways. First, we canvas a brands treat of voice. We use its advertising expenditures, Eit? 1 , congenator to the average bill spent on advertising by rival brands in the brands subcategory or category, E? it? 1 . 3 To the extent that brands fight with each other for the attention of consumers, a brand may have to outspend its rivals to cart track through the clutter.If so, thusly what is important may non be the absolute gist spent on advertising but the amount relative to rival brands. Second, we consider the amount of advertising in the holy market by including the average amount spent on advertising by rival brands in the brands subcategory or category. Advertising is market expanding if it attracts consumers to the consummate category but non necessarily to a particular brand. In this way, competitors advertising may have a positivistic in? uence on, declare, brand awareness. Taken together, our estimation equations are Qit = ? i + ? t + ? Qit? 1 + f (Eit? 1 , E? it? 1 ) + ? t , Ait = ? i + ? t + ? Ait? 1 + f (Eit? 1 , E? it? 1 ) + ? it . (1) (2) Here ? i is a brand nucleus that captures unobserved heterogeneity across brands and ? t is a time solution to control for possible systematic channelises over time. The time effect may capture, for example, that consumers are systematically informed about a larger shape of brands due to the advent of the internet and other election media channels. by the brand effect we allow for factors other than advertising to affect a brands stocks of perceived quality and awareness to the extent that these factors are constant over time.For example, consumers may hear about a brand and their quality perceptions may be affected by word of mouth. Similarly, it may well be the case that consumers in the process of buying a brand become more informed about it and that their quality perceptions compound, especially for high-involvement brands. Prior to purchasing a car, say, some consumers engage in research about the set of operational cars and their respective characteristics, including quality ranks from sources such as car magazines and Consumer Reports.If these effects do not vary over time, hence we in full account for them in our estimation because the dynamic panel data methods we employ involve ? rst differencing. The parameter ? measures how much of last limits stocks of perceived quality and awareness are carried forward into this periods stocks 1 ? ? can 3 The Brandweek Superbrands survey reports on only the top brands (in scathe of sales) in each subcategory or category. The function of brands varies from 3 for some subcategories to 10 for others. We indeed use the average, rather than the sum, of competitors advertising.Effect of adverti sing on brand awareness and perceived quality 213 and then be interpreted as the rate of derogation of these stocks. job that in the estimation we allow all parameters to be different across our estimation equations. For example, we do not presume that the carryover rates for perceived quality and brand awareness are the same. The function f () re innovates the response of brand awareness and perceived quality to the advertising expenditures of the brand and potentially also those of its rivals. In the simplest case absent disceptation we stipulate this function as 2 f (Eit? ) = ? 1 Eit? 1 + ? 2 Eit? 1 . This functional form is ? exible in that it allows for a nonlinear effect of advertising expenditures but does not impose one. Later on in Section 5. 6 we instal the lustiness of our results by considering a number of additional functional forms. To account for competitor in the share-of-voice speci? cation, we set f Eit? 1 , E? it? 1 = ? 1 Eit? 1 E? it? 1 + ? 2 Eit? 1 E? i t? 1 2 and in the total-advertising speci? cation, we set 2 f Eit? 1 , E? it? 1 = ? 1 Eit? 1 + ? 2 Eit? 1 + ? 3 E? it? 1 . Estimation strategy Equations 1 and 2 are dynamic relationships that distinction lagged dependent variables on the rightfulness side. When estimating, we confront the problems of unobserved heterogeneity across brands and the endogeneity of advertising. In our panel-data setting, ignoring unobserved heterogeneity is akin to push aside the brand effect ? i from Eqs. 1 and 2 and then estimating them by nondescript least squares. Since this approach relies on both cross-sectional and time-series alteration to identify the effect of advertising, we refer to it as pooled OLS (POLS) in what follows.To account for unobserved heterogeneity we include a brand effect ? i and use the in spite of appearance calculator that treats ? i as a ? xed effect. We follow the usual blueprint in microeconomic applications that the term ? xed effect does not necessarily repre sent that the effect is being treated as purposive rather it factor that we are allowing for haughty correlation coefficient between the unobserved brand effect and the observed explanatory variables (Wooldridge 2002, p. 251). The deep down estimator eliminates the brand effect by subtracting the within-brand mean from Eqs. 1 and 2. Hence, the identi? ation of the slope parameters that reckon the effect of advertising relies merely on magnetic alteration over time within brands the information in the between-brand cross-sectional relationship is not utilize. We refer to this approach as ? xed effects (FE). While FE accounts for unobserved heterogeneity, it suffers from an endogeneity problem. In our panel-data setting, endogeneity arises for two reasons. First, since Eqs. 1 and 2 are inherently dynamic, the lagged stocks of perceived 214 C. R. Clark et al. quality and awareness may be endogenous. More formally, Qit? 1 and Ait? 1 are by construction match with ? s for s < t. The within estimator subtracts the within-brand mean from Eqs. 1 and 2. The resulting regressor, say Qit? 1 ? Qi in the case of perceived quality, is fit with the actus reus term ? it ? ?i since ? i contains ? it? 1 along with all higher-order lags. Hence, FE is necessarily inconsistent. Second, advertising expenditures may also be endogenous for economic reasons. For instance, media coverage such as news reports may directly affect brand awareness and perceived quality. Our model treats media coverage other than advertising as shocks to the stocks of perceived quality and awareness.To the extent that these shocks feed back into decisions about advertising, say because the brand manager opts to advertise less if a news report has generated suf? cient awareness, they saltation rise to an endogeneity problem. More formally, it is reasonable to assume that Eit? 1 , the advertising expenditures of brand i over the course of period t ? 1, are chosen at the commencement exercise o f period t ? 1 with knowledge of ? it? 1 and higher-order lags and that therefore Eit? 1 is correlated with ? is for s < t. We apply the dynamic panel-data method proposed by Arellano and Bond (1991) to deal with both unobserved heterogeneity and endogeneity.This methodology has the proceeds that it does not rely on the availability of strictly exogenous explanatory variables or instruments. This is welcome because instruments are often hard to come by, especially in panel-data settings The problem is ? nding a variable that is a good predictor of advertising expenditures and is unrelated with shocks to brand awareness and perceived quality ? nding a variable that is a good predictor of lagged brand awareness and perceived quality and unrelated with current shocks to brand awareness and perceived quality is even less obvious.The key idea of Arellano and Bond (1991) is that if the break terms are successively uncorrelated, then lagged correspond of the dependent variable and lagged values of the endogenous right-hand-side variables represent legitimate instruments. To see this, take ? rst going aways of Eq. 1 to obtain Qit ? Qit? 1 = (? t ? ?t? 1 ) + ? (Qit? 1 ? Qit? 2 ) + f (Eit? 1 ) ? f (Eit? 2 ) + (? it ? ?it? 1 ), (3) where we abstract from competition to simplify the notation. Eliminating the brand effect ? i accounts for unobserved heterogeneity between brands. The remain problem with estimating Eq. 3 by least-squares is that Qit? 1 ? Qit? is by construction correlated with ? it ? ?it? 1 since Qit? 1 is correlated with ? it? 1 by virtue of Eq. 1. Moreover, as we have discussed higher up, Eit? 1 may also be correlated with ? it? 1 for economic reasons. We take advantage of the fact that we have observations on a number of periods in order to come up with instruments for the endogenous variables. In particular, this is possible starting in the third period where Eq. 3 becomes Qi3 ? Qi2 = (? 3 ? ?2 ) + ? (Qi2 ? Qi1 ) + f (Ei2 ) ? f (Ei1 ) + (? i 3 ? ?i2 ). Effect of advertising on brand awareness and perceived quality 215 In this case Qi1 is a logical instrument for (Qi2 ?Qi1 ) since it is correlated with (Qi2 ? Qi1 ) but uncorrelated with (? i3 ? ?i2 ) and, similarly, Ei1 is a valid instrument for ( f (Ei2 ) ? f (Ei1 )). In the fourth period Qi1 and Qi2 are both valid instruments since nevery is correlated with (? i4 ? ?i3 ) and, similarly, Ei1 and Ei2 are both valid instruments. In general, for lagged dependent variables and for endogenous right-hand-side variables, levels of these variables that are lagged two or more periods are valid instruments. This allows us to generate more instruments for later periods. The resulting estimator is referred to as difference GMM (DGMM).A potential dif? culty with the DGMM estimator is that lagged levels may be poor instruments for ? rst differences when the underlying variables are passing persistent over time. Arellano and Bover (1995) and Blundell and Bond (1998) propose an augme nted estimator in which the pilot program equations in levels are added to the system. The idea is to pull in a stacked data set containing differences and levels and then to instrument differences with levels and levels with differences. The required assumption is that brand effects are uncorrelated with changes in advertising expenditures.This estimator is ordinarily referred to as system GMM (SGMM). In Section 5 we report and compare results for DGMM and SGMM. It is important to running the validity of the instruments proposed above. Following Arellano and Bond (1991) we report a Hansen J evidence for overidentifying restrictions. This tryout examines whether the instruments are jointly exogenous. We also report the so-called difference-in-Hansen J discharge to examine speci? cally whether the additional instruments for the level equations used in SGMM (but not in DGMM) are valid. Arellano and Bond (1991) farther develop a test for second-order serial correlation in the ? st differences of the error terms. As described above, both GMM estimators require that the levels of the error terms be serially uncorrelated, implying that the ? rst differences are serially correlated of at most ? rst order. We caution the subscriber that the test for second-order serial correlation is formally only de? ned if the number of periods in the model is greater than or equal to 5 whereas we observe a brand on average for just 4. 2 periods in our application. Our preliminary estimates suggest that the error terms are unlikely to be serially uncorrelated as required by Arellano and Bond (1991).The AR(2) test described above indicates ? rst-order serial correlation in the error terms. An AR(3) test for third-order serial correlation in the ? rst differences of the error terms, however, indicates the absence of second-order serial correlation in the error terms. 4 In this case, Qit? 2 and Eit? 2 are no longer valid instruments for Eq. 3. Intuitively, because Qit? 2 is co rrelated with ? it? 2 by virtue of Eq. 1 and ? it? 2 is correlated with ? it? 1 by ? rst-order serial correlation, Qit? 2 is correlated 4 Of course, the AR(3) test uses less observations than the AR(2) test and is therefore also less powerful. 16 C. R. Clark et al. with ? it? 1 in Eq. 3, and similarly for Eit? 2 . Fortunately, however, Qit? 3 and Eit? 3 remain valid instruments because ? it? 3 is uncorrelated with ? it? 1 . We carry out the DGMM and SGMM estimation using STATAs xtabond2 piece (Roodman 2007). We embark third and higher lags of either brand awareness or perceived quality, together with third and higher lags of advertising expenditures as instruments. In addition to these GMM-style instruments, for the difference equations we enter the time dummies as IV-style instruments. We also apply the ? ite-sample correction proposed by Windmeijer (2005) which corrects for the dance covariance matrix and corporeally growths the ef? ciency of both GMM estimators. Finally, we compute criterion errors that are robust to heteroskedasticity and imperious patterns of serial correlation within brands. 4 Data Our data are derived from the Brandweek Superbrands surveys from 2000 to 2005. each twelvemonths survey lists the top brands in terms of sales during the past division from 25 broad categories. intimate these categories are often a number of more narrowly de? ned subcategories. hold over 1 lists the categories along with their subcategories.The surveys report perceived quality and awareness get ahead for the current division and the advertising expenditures for the previous course by brand. perceive quality and awareness heaps are work out by Harris synergistic in their Equitrend brand-equity study. Each year Harris synergistic surveys online between 20, 000 and 45, 000 consumers aged 15 old age and older in order to acquire their perceptions of a brands quality and its level of awareness for approximately 1, 000 brands. 5 To hold back tha t the respondents accurately re? ect the general population their responses are passion weighted. Each respondent rates or so 80 of these brands.Perceived quality is measured on a 010 scale, with 0 meaning unacceptable/poor and 10 meaning outstanding/ extraordinary. Awareness gobs vary between 0 and snow and equal the percentage of respondents that can rate the brands quality. The quality rating is therefore conditional on the respondent being aware of the brand. 6 5 The exact wording of the head word is We will display for you a list of brands and we are asking you to rate the boilers suit quality of each brand using a 0 to 10 scale, where 0 means Unacceptable/Poor feature, 5 means Quite Acceptable Quality and 10 means Outstanding/ one(prenominal) Quality.You may use any number from 0 to 10 to rate the brands, or use 99 for No Opinion option if you have suddenly no opinion about the brand. Panelists are being incentivized through sweepstakes on a periodic basis but are no t paid for a particular survey. 6 The 2000 Superbrands survey does not distributively report perceived quality and salience scores. We received these scores directly from Harris Interactive. 2000 is the ? rst year for which we have been able to obtain perceived quality and salience scores for a large number of brands.Starting with the 2004 and 2005 Superbrands surveys, salience is replaced by a new measure called beaten(prenominal)ity. For these two years we received salience scores directly from Harris Interactive. The synchronal correlation between salience and familiarity is 0. 98 and signi? cant with a p-value of 0. 000. Effect of advertising on brand awareness and perceived quality evade 1 Categories and subcategories 1. Apparel 2. Appliances 3. Automobiles a. general automobiles b. opulence c. subcompact d. sedan/wagon e. trucks/suvs/vans 4. Beer, wine, strong drink a. beer b. wine c. malternatives d. iquor 5. Beverages a. general b. new age/sports/water 6. Computers a . software b. hardware 7. Consumer electronics 8. Cosmetics and fragrances a. colourize cosmetics b. eye colouring c. lip color d. womens fragrances e. mens fragrances 9. identification tease 10. Entertainment 11. riotous sustenance 12. Financial services 13. Food a. produce to eat cereal b. cereal nix c. cookies d. cheese e. crackers f. salted snacks g. frigid dinners and entrees Items in italics have been removed 217 h. glacial pizza i. spaghetti sauce j. coffee k. ice cream l. refrigerated orange juice m. refrigerated yogurt n. oy drinks o. luncheon totals p. meat alternatives q. baby formula/electrolyte solutions r. pourable salad dressing 14. footwear 15. health and hit a. bar max b. toothpaste c. shampoo d. hair color 16. sign of the zodiac a. cleaner b. laundry purifyings c. diapers d. facial waver e. toilet tissue f. automatic dishwater detergent 17. gasoline a. oil companies b. automotive aftercare/ lubricator 18. pharmaceutical unlisted a. allergy/ ge lid medicine b. stomach/antacids c. analgesics 19. Pharmaceutical prescription drug 20. Retail 21. Telecommunications 22. Tobacco 23. Toys 24. impress 25. manhood Wide WebWe supplement the awareness and quality measures with advertising expenditures that are taken from TNS Media newsworthiness and agonistical Media Reporting. These advertising expenditures encompass disbursement in a wide range of media Magazines (consumer magazines, Sunday magazines, local magazines, and business-to-business magazines), newspaper (local and interior(a) newspapers), television (network TV, spot TV, syndicated TV, and network cable TV), radio (network, national spot, and local), Spanish-language media (magazines, newspapers, and TV networks), internet, and outdoor.After eliminating categories and subcategories where observations are not at the brand level (apparel, entertainment, ? nancial services, retail, world wide web) or where the data are suspect (tobacco), we are left with 19 categories (see again hold over 1). We then drop all private labels and all brands for which 218 C. R. Clark et al. we do not have perceived quality and awareness scores as well as advertising expenditures for at least two years running. This leaves us with 348 brands. Table 2 contains descriptive statistics for the overall sample and also by category. In the overall sample the average awareness score is 69. 5 and the average perceived quality score is 6. 36. The average amount spent on advertising is slightly $66 one million million million per year. on that point is inviolable variation in these measures across categories. The variation in perceived quality (coef? cient of variation is 0. 11 overall, ranging from 0. 04 for appliances to 0. 13 for computers) tends to be pass up than the variation in brand awareness (coef? cient of variation is 0. 28 overall, ranging from 0. 05 for appliances to 0. 46 for telecommunications), in line with the fact the quality rating is conditional on th e respondent being aware of the brand.The contemporaneous correlation between brand awareness and perceived quality is 0. 60 and signi? cant with a p-value of 0. 000. The contemporaneous correlation between advertising expenditures and the change in brand awareness is 0. 0488 and signi? cant with a p-value of 0. 0985 and the contemporaneous correlation between advertising expenditures and the change in perceived quality is 0. 0718 and signi? cant with a p-value of 0. 0150. These correlations send for the spurious correlation between both brand awareness and perceived quality and advertising expenditures if permanent differences between brands are neglected (POLS estimator).We will see though that the effect of advertising expenditures on perceived quality Table 2 descriptive statistics obs brands Brand awareness Perceived Advertising (0100) quality (010) ($1,000,000) cockeyed Std. dev. correspond Std. dev. cockeyed Std. dev. overall Appliances Automobiles Beer, wine, strong drink Beverages Computers Consumer electronics Cosmetics and fragrances Credit cards Fast food Food footwear Health and looker Household Petrol Pharmaceutical OTC Pharmaceutical prescription Telecommunications Toys Travel 1,478 348 21 137 98 95 79 29 70 29 60 247 38 54 128 48 56 31 52 25 181 4 30 24 22 17 7 19 6 12 65 8 11 31 13 15 10 11 5 38 69. 5 85. 09 67. 81 62. 23 84. 57 59. 80 67. 83 49. 37 70. 97 93. 83 80. 18 64. 95 82. 50 73. 83 60. 52 76. 96 29. 97 49. 33 72. 12 59. 48 19. 43 4. 54 6. 72 10. 13 13. 84 23. 05 18. 68 15. 75 18. 08 5. 32 14. 94 18. 98 9. 80 16. 03 17. 19 13. 89 9. 69 22. 86 9. 74 15. 43 6. 36 7. 35 6. 51 5. 68 6. 51 6. 41 6. 60 5. 83 6. 24 6. 28 6. 66 6. 39 6. 67 6. 66 5. 95 6. 79 5. 54 5. 28 6. 95 6. 26 0. 70 0. 32 0. 59 0. 72 0. 58 0. 81 0. 73 0. 52 0. 73 0. 42 0. 65 0. 42 0. 41 0. 56 0. 30 0. 37 0. 67 0. 52 0. 32 0. 52 66. 21 118. 52 41. 87 33. 19 99. 85 64. 62 36. 78 45. 11 41. 33 42. 19 130. 43 130. 7 104. 83 160. 66 38. 02 47. 48 174. 54 109. 77 214. 80 156. 23 13. 93 13. 81 40. 27 46. 89 27. 28 33. 44 21. 80 25. 43 33. 54 34. 65 38. 71 18. 13 76. 23 36. 40 367. 93 360. 54 108. 55 54. 36 25. 41 25. 88 Effect of advertising on brand awareness and perceived quality 219 disappears once unobserved heterogeneity is accounted for (FE and GMM estimators). The intertemporal correlation is 0. 98 for brand awareness, 0. 95 for perceived quality, and 0. 93 for advertising expenditures. This peculiar(a) amount of intertemporal variation warrants preferring the SGMM over the DGMM estimator.At the same time, however, it constrains how ? nely we can slice the data, e. g. , by separate a brand-speci? c effect of advertising expenditures on brand awareness and perceived quality. Since the FE, DGMM, and SGMM estimators rely on within-brand acrosstime variation, it is important to ensure that there is a suf? cient amount of within-brand variation in brand awareness, perceived quality, and advertising expenditures. Table 3 presents a bunkum rea ction of the meterised expiration in these variables into an across-brands and a within-brand component for the overall sample and also by category.The across-brands specimen deviation is a measure of the cross-sectional variation and the within-brand measurement deviation is a measure of the time-series variation. The across-brands banal deviation of brand awareness is about six generation larger than the within-brand standard deviation. This ratio varies across categories and ranges from 2 for automobiles, beer, wine, pot liquor, and pharmaceutic prescription to 6 for health and beauty and pharmaceutical OTC. In case of perceived quality the ratio is about 4 (ranging from 1 for telecommunications to 5 for consumer electronics, credit cards, and household).Hence, tour there is more crosssectional than time-series variation in our sample, the time-series variation is square for both brand awareness and perceived quality. Figure 1 illustrates Table 3 Variance decomposition Brand awareness (0100) across Overall Appliances Automobiles Beer, wine, liquor Beverages Computers Consumer electronics Cosmetics and fragrances Credit cards Fast food Food Footwear Health and beauty Household Petrol Pharmaceutical OTC Pharmaceutical prescription Telecommunications Toys Travel 20. 117 5. 282 6. 209 10. 181 13. 435 23. 094 19. 952 18. 054 19. 568 6. 132 16. 241 20. 417 10. 36 16. 719 20. 179 13. 339 9. 393 21. 659 11. 217 16. 063 at bottom 3. 415 1. 334 3. 281 4. 105 2. 915 3. 843 5. 611 3. 684 3. 903 1. 660 2. 255 4. 267 1. 772 3. 896 3. 669 2. 363 5. 772 5. 604 3. 589 3. 216 Perceived quality (010) Across 0. 726 0. 323 0. 561 0. 705 0. 582 0. 850 0. 800 0. 563 0. 788 0. 361 0. 702 0. 388 0. 397 0. 561 0. 415 0. 336 0. 753 0. 452 0. 360 0. 516 Within 0. 176 0. 148 0. 141 0. 186 0. one hundred ninety 0. 313 0. 167 0. 208 0. 159 0. 202 0. 134 0. 167 0. 136 0. 113 0. 116 0. 129 0. 230 0. 334 0. 127 0. 153 Advertising ($1,000,000) Across 100. 823 28. 965 54. 680 41. 713 37. 505 110. 362 105. 49 38. 446 118. 059 159. 306 15. 655 45. 791 27. 054 18. 789 27. 227 16. 325 38. 648 317. 434 61. 419 22. 136 Within 43. 625 21. 316 32. 552 12. 406 13. 372 65. 909 114. 381 20. 053 43. 415 33. 527 7. 998 7. 640 19. 075 16. 672 20. 496 9. 080 27. 919 178. 406 18. 584 10. 909 220 .025 . 2 C. R. Clark et al. .02 denseness . 01 . 015 0 .005 0 20 40 60 80 besotted brand awareness 100 0 30 .05 immersion . 1 .15 20 10 0 10 20 Demeaned brand awareness 30 .8 .6 parsimony . 4 0 .2 0 2 4 6 Mean perceived quality 8 10 0 1. 5 1 Density 2 3 1 . 5 0 . 5 1 Demeaned perceived quality 1. 5 .015 Density . 005 . 01 0 0 00 400 600 800 gram 1200 1400 Mean advertising expenditures (millions of $) 0 600 400 200 0 200 400 600 Demeaned advertising expenditures (millions of $) Fig. 1 Variance decomposition. Histogram of brand-mean of brand awareness, perceived quality, and advertising expenditures (left panels) and histogram of de-meaned brand awareness, perceived qualit y, and advertising expenditures (right panels) the decomposition for the overall sample. The left panels show histograms of the brand-mean of brand awareness, perceived quality, and advertising expenditures and the right panels show histograms of the de-meaned variables.Again it is evident that the time-series variation is substantial for both brand awareness and perceived quality. 5 Empirical results In Tables 4 and 5 we present a number of different estimates for the effect of advertising expenditures on brand awareness and perceived quality, .005 Density . 01 . 015 .02 .025 Effect of advertising on brand awareness and perceived quality Table 4 Brand awareness POLS Lagged brand awareness Advertising Advertising2 borderline effect of advertising at Mean twenty-fifth pctl. fiftieth pctl. 75th pctl. Advertising test ? 1 = ? 2 = 0 Speci? ation tests Hansen J Difference-in-Hansen J Arellano &038 Bond AR(2) Arellano &038 Bond AR(3) trade good of ? t measures R2 -within R2 -between R2 obs brands FE DGMM SGMM 221 0. 942*** 0. 223*** 0. 679*** 0. 837*** (0. 00602) (0. 0479) (0. 109) (0. 0266) 0. 00535*** 0. 00687 0. 0152 0. 00627** (0. 00117) (0. 00443) (0. 0139) (0. 00300) ? 0. 00000409*** ? 0. 00000139 ? 0. 0000105 ? 0. 00000524** (0. 000000979) (0. 00000332) (0. 00000745) (0. 00000239) 0. 00481*** (0. 00107) 0. 00527*** (0. 00116) 0. 00514*** (0. 00113) 0. 00470*** (0. 00105) wane*** 0. 00668 (0. 00412) 0. 00684 (0. 00438) 0. 00679 (0. 00430) 0. 00664 (0. 0405) 0. 0138 (0. 0129) 0. 0150 (0. 0138) 0. 0147 (0. 0135) 0. 0136 (0. 00127) 0. 00558** (0. 00269) 0. 00617** (0. 00296) 0. 00600** (0. 00288) 0. 00544** (0. 00263) Do not rid of Do not balk resist* Do not retract Do not hold out avert** renounce** Do not deflect Do not baulk 0. 494 0. 940 0. 851 1,148 317 disown*** 0. 969 1,148 317 819 274 1,148 317 Standard errors in diversion * p = 0. 10 ** p = 0. 05 *** p = 0. 01 respectively. Starting with the simplest case absent competition, we present es timates of ? , ? 1 , and ? 2 (the coef? cients on Qit? 1 or Ait? 1 and Eit? 1 and 2 Eit? 1 ) along with the fringy effect ? 1 + 2? Eit? 1 calculated at the mean and the 25th, 50th, and 75th percentiles of advertising expenditures. The POLS estimates in the ? rst mainstay of Tables 4 and 5 suggest a signi? cant positive effect of advertising expenditures on both brand awareness and perceived quality. In both cases we also despise the zippo possible action that advertising plays no role in find out brand awareness and perceived quality (? 1 = ? 2 = 0). Of course, as mentioned above, POLS accounts for neither unobserved heterogeneity nor endogeneity. In the next columns of Tables 4 and 5 we present FE, DGMM, and SGMM estimates that attend to these issues. 7 7 The stimates use at most 317 out of 348 brands because we restrict the sample to brands with data for two years running but use third and higher lags of brand awareness respectively perceived quality and advertising expendit ures as instruments. Different sample sizes are inform for the DGMM and SGMM estimators. Sample size is not a well-de? ned concept in SGMM since this estimator essentially runs on two different samples simultaneously. The xtabond2 routine in STATA reports the size of the alter sample for DGMM and of the untransformed sample for SGMM. 222 Table 5 Perceived quality FE 0. 391*** (0. 0611) 0. 659*** (0. 204) 1. 47*** (0. 0459) 0. 981*** (0. 0431) DGMM SGMM design quality Brand awareness POLS Lagged perceived quality 0. 970*** (0. 0110) Brand awareness Advertising Advertising2 0. 000218** (0. 0000952) ? 0. 000000133 (0. 000000107) 0. 0000822 (0. 000198) 0. 0000000408 (0. 000000162) ?0. 0000195 (0. 000969) 0. 000000108 (0. 000000945) 0. 0000219 (0. 000205) 0. 0000000571 (0. 000000231) 0. 0000649 (0. 000944) 0. 0000000807 (0. 00000308) 0. 937*** (0. 0413) 0. 00596*** (0. 00165) ? 0. 000298 (0. 000256) 0. 000000319 (0. 000000267) peripheral effect of advertising at Mean 25th pctl. 50th pctl. 75th pctl. 0. 0002** (0. 0000819) 0. 000215** (0. 000933) 0. 000211** (0. 00009) 0. 0001965** (0. 0000793) Do not defy Do not disavow forswear*** Do not reject Do not reject Do not reject Reject** Reject** Reject*** Do not reject 0. 0000877 (0. 000180) 0. 000083 (0. 000195) 0. 0000844 (0. 000191) 0. 0000887 (0. 000177) ?5. 13e? 06 (0. 000848) ? 0. 0000174 (0. 000952) ? 0. 0000139 (0. 000922) ? 2. 32e? 06 (0. 000825) 0. 0000295 (0. 000176) 0. 0000230 (0. 000201) 0. 0000249 (0. 000194) 0. 0000310 (0. 000170) 0. 0000594 (0. 000740) 0. 0000642 (0. 000917) 0. 0000623 (0. 000847) 0. 0000588 (0. 000714) Do not reject Do not reject Do not reject Reject*** Do not reject ?0. 000256 (0. 000222) ? 0. 00292 (0. 000251) ? 0. 000282 (0. 000242) ? 0. 000248 (0. 000215) Do not reject Reject** Do not reject Reject*** Do not reject Advertising test ? 1 = ? 2 = 0 Speci? cation tests Hansen J Difference-in-Hansen J Arellano &038 Bond AR(2) Arellano &038 Bond AR(3) Goodness of ? t measures R2 -w ithin R2 -between R2 obs brands 0. 180 0. 952 0. 909 1,148 317 819 274 1,148 317 Reject** 0. 914 1,148 317 604 178 1,148 317 C. R. Clark et al. Standard errors in parenthesis. SGMM estimates in columns denominate Objective quality and Brand awareness * p = 0. 10 ** p = 0. 05 *** p = 0. 01 Effect of advertising on brand awareness and perceived quality 23 Regardless of the class of estimator we ? nd a signi? cant positive effect of advertising expenditures on brand awareness. With the FE estimator we ? nd that the rimal effect of advertising on awareness at the mean is 0. 00668. It is borderline signi? cant with a p-value of 0. 105 and implies an piece of cake of 0. 00638 (with a standard error of 0. 00392). A one-standard-deviation increase of advertising expenditures increase brand awareness by 0. 0408 standard deviations (with a standard error of 0. 0251). The rate of depreciation of a brands stock of awareness is estimated to be 10. 223 or 78% per year.The FE estimator identi ? es the effect of advertising expenditures on brand awareness solely from the within-brand across-time variation. The problem with this estimator is that it does not deal with the endogeneity of the lagged dependent variable on the right-hand side of Eq. 2 and the potential endogeneity of advertising expenditures. We thus turn to the GMM estimators described in Section 3. We focus on the more ef? cient SGMM estimator. The coef? cient on the linear term in advertising expenditures is estimated to be 0. 00627 ( p-value 0. 037) and the coef? cient on the quadratic term is estimated to be ? . 00000524 ( p-value 0. 028). These estimates support the hypothesis that the relationship between advertising and awareness is nonlinear. The bare(a) effect of advertising on awareness is estimated to be 0. 00558 ( p-value 0. 038) at the mean and implies an elasticity of 0. 00533 (with a standard error of 0. 00257). A one-standard-deviation increase of advertising expenditures increases brand aw areness by 0. 0340 standard deviations (with a standard error of 0. 0164). The rate of depreciation decreases substantially after correcting for endogeneity and is estimated to be 1? . 828 or 17% per year, thus indicating that an increase in a brands stock of awareness due to an increase in advertising expenditures persists for years to come. The Hansen J test for overidentifying restrictions indicates that the instruments taken together as a group are valid. regress from Section 3 that we must assume that an extra condition holds in order for the SGMM estimator to be confiscate. The difference-in-Hansen J test con? rms that it does, as we cannot reject the bootless hypothesis that the additional instruments for the level equations are valid.While we reject the hypothesis of no second-order serial correlation in the error terms, we cannot reject the hypothesis of no thirdorder serial correlation. This result further validates our instrumenting strategy. However, one may still be worried about the SGMM estimates because DGMM uses a strict subset of the perpendicularity conditions of SGMM and we reject the Hansen J test for the DGMM estimates (see Table 4). From a formal statistical point of view, rejecting the smaller set of orthogonality conditions in DGMM is not determinate evidence that the larger set of orthogonality conditions in SGMM are invalid (Hayashi 2000, pp. 18221). In Fig. 2 we plot the fringy effect of advertising expenditures on brand awareness over the entire range of advertising expenditures for our SGMM estimates along with a histogram of advertising expenditures. For advertising expenditures between $400 million and $800 million per year the marginal effect of advertising on awareness is no longer signi? cantly different from zero 224 C. R. Clark et al. marginal effect . 004 0 . 004 0 200 400 600 800 1000 Advertising expenditures (millions of $) 1200 1400 arginal effect of advertising lower 90% self-assertion limit . 015 upper 90% reli ance limit 0 0 .005 Density . 01 200 400 600 800 1000 Advertising expenditures (millions of $) 1200 1400 Fig. 2 Pointwise con? dence interval for the marginal effect of advertising expenditures on brand awareness (upper panel) and histogram of advertising expenditures (lower panel). SGMM estimates and, statistically, it is actually disallowly charged for very high advertising expenditures over $800 million per year. The former case covers around 1. 9% of observations and the latter less than 0. 5%.One possible interpretation is that brands with very high current advertising expenditures are those that are already wellknown (perhaps because they have been heavily denote over the years), so that advertising cannot further boost their awareness. Indeed, average awareness for observations with over $400 million in advertising expenditures is 74. 94 as compared to 69. 35 for the entire sample. Turning from brand awareness in Table 4 to perceived quality in Table 5, we see that the p ositive effect of advertising expenditures on perceived quality found by the POLS estimator disappears once unobserved eterogeneity is accounted by the FE, DGMM, and SGMM estimators. In fact, we cannot reject the null hypothesis that advertising plays no role in determining perceived quality. Figure 3 diagrammatically illustrates the absence of an effect of advertising expenditures on perceived quality at the margin for our DGMM estimates. While the effect of advertising expenditures on perceived quality is very imprecisely estimated, it appears to be economically insigni? cant The implied elasticity is ? 0. 0000534 (with a standard error of 0. 00883) and a one-standarddeviation increase of advertising expenditures decrease perceived quality byEffect of advertising on brand awareness and perceived quality 225 Marginal effect . 001 0 . 001 0 200 400 600 800 1000 Advertising expenditures (millions of $) 1200 1400 marginal effect of advertising lower 90% confidence limit . 015 upper 90% confidence limit 0 0 Density . 005 . 01 200 400 600 800 1000 Advertising expenditures (millions of $) 1200 1400 Fig. 3 Pointwise con? dence interval for the marginal effect of advertising expenditures on perceived quality (upper panel) and histogram of advertising expenditures (lower panel). DGMM estimates 0. 000869 standard deviations (with a standard error of 0. 44). Note that the comparable effects for brand awareness are two orders of magnitude larger. Much of the remainder of this paper is tingeed with demonstrating the robustness of this negative result. Before proceeding we note that whenever possible we focus on the more ef? cient SGMM estimator. Unfortunately, for perceived quality in many cases, including that in the fourth column of Table 5, the difference-in-Hansen J test rejects the null hypothesis that the extra moments in the SGMM estimator are valid. In these cases we focus on the DGMM estimator. 5. Objective and perceived quality An important component of a br ands perceived quality is its design quality. To the extent that neutral quality remains constant, it is absorbed into the brand effects. But, even though the time frame of our sample is not very long, it is certainly possible that the verifiable quality of some brands has changed over the course of our sample. If so, then the lack of an effect of advertising expenditures on perceived quality may be explained if brand managers increase advertising expenditures to compensate for decreases in objective 26 C. R. Clark et al. quality. To the extent that change magnitude advertising expenditures and decreased objective quality cancel each other out, their net effect on perceived quality may be zero. The dif? culty with testing this alternative explanation is that we do not have data on objective quality. We therefore exclude from the analysis those categories with brands that are likely to undergo changes in objective quality (appliances, automobiles, computers, consumer electronics, fast food, footwear, pharmaceutical OTC, telecommunications, toys, and travel).The resulting estimates are reported in Table 5 under the heading Objective quality. We still ? nd no effect of advertising expenditures on perceived quality. 8 5. 2 variability in perceived quality other possible reason for the lack of an effect of advertising expenditures on perceived quality is that perceived quality may not vary much over time. This is not the case in our data. Indeed, the standard deviation of the year-to-year changes in perceived quality is 0. 2154. pull down for those products whose objective quality does not change over time there are important changes in perceived quality (standard deviation 0. 130). For example, consider bottled water where we need little change in objective quality over time, both within and across brands. Nonetheless, there is considerable variation in perceived quality. The perceived quality of Aqua? na Water ranges across years from 6. 33 to 6. 90 and that of Poland Spring Water from 5. 91 to 6. 43, so the similar of over two standard deviations. Across the brands of bottled water the range is from 5. 88 to 6. 90, or the equivalent of over four standard deviations. Further evidence of variation in perceived quality is provided by the automobiles category.Here we have obtained measures of objective quality from Consumer Reports that rate vehicles based on their performance, comfort, convenience, safety, and fuel economy. We can ? nd examples of brands whose objective quality does not change at least for a number of years while their perceived quality ? uctuates considerably. For example, Chevy Silverados objective quality does not change between 2000 and 2002, but its perceived quality increases from 6. 08 to 6. 71 over these three years. Similarly, GMC Sierras objective quality does not change between 2001 and 2003, but its perceived quality decreases from 6. 72 to 6. 26. The ? al piece of evidence that we have to offer is the v ariance decomposition from Section 4 (see again Table 3 and Fig. 1). Recall that the acrossbrands standard deviation of brand awareness is about six times larger than the within-brand standard deviation. In case of perceived quality the ratio is about 4. Hence, while there is more cross-sectional than time-series variation in our sample, the time-series variation is substantial for both brand aware- 8 The marginal effects are calculated at the mean, 25th, 50th, and 75th percentile for advertising for the brands in the categories judged to be stable in terms of objective quality over time.Effect of advertising on brand awareness and perceived quality 227 ness and perceived quality. besides recall from Section 4 that perceived quality with an intertemporal correlation of 0. 95 is slightly less persistent than brand awareness with an intertemporal correlation of 0. 98. Given that we are able to detect an effect of advertising expenditures on brand awareness, it seems unlikely that in suf? cient variation within brands can explain the lack of an effect of advertising expenditures on perceived quality instead, our results suggest that the variation in perceived quality is unrelated to advertising expenditures.The question then becomes what besides advertising may drive these changes in perceived quality. There are numerous possibilities, including consumer learning and viva-voce effects. Unfortunately, given the data available to us, we cannot further explore these possibilities. 5. 3 Brand awareness and perceived quality Another concern is that consumers may confound awareness and preference. That is, consumers may simply prefer more familiar brands over less familiar ones (see Zajonc 1968). To forebode this issue we proxy for consumers familiarity by adding brand awareness to the regression for perceived quality.The resulting estimates are reported in Table 5 under the heading Brand awareness. While there is a signi? cant positive relationship between brand a wareness and perceived quality, there is still no evidence of a signi? cant positive effect of advertising expenditures on perceived quality. 5. 4 Competitive effects Advertising takes place in a competitive environment. Most of the industries being studied here are indeed oligopolies, which suggests that strategic considerations may in? uence advertising decisions.We next allow a brands stocks of awareness and perceived quality to be affected by the advertising of its competitors as discussed in Section 2. 9 Competitors advertising, in turn, can enter our estimation Eqs. 1 and 2 either relative in the share-of-voice speci? cation or absolute in the total-advertising speci? cation. We report the resulting estimates in Table 6. Somewhat surprisingly, the share-of-voice speci? cation yields an insignificant effect of own advertising. We conclude that the share-of-voice speci? cation is simply not an appropriate functional form in our application. The total-advertising speci? ation rea dily con? rms our main ? ndings presented above that own advertising affects brand awareness but not perceived quality. This is neat even if we allow competitors advertising to enter quadratically in 9 For this analysis we take the subcategory rather than the category as the pertinent competitive environment. Consider for instance the beer, wine, liquor category. There is no reason to expect the advertising expenditures of beer brands to affect the perceived quality or awareness of liquor brands. We drop any subcategory in any year where there is just one brand due to the lack of competitors.Table 6 Competitive effects Perceived quality 0. 845*** (0. 0217) 0. 356** (0. 145) full(a) advertising Brand awareness Perceived quality 228 Share of voice Brand awareness Lagged awareness/quality sexual relation advertising (Relative advertising)2 0. 872*** (0. 0348) 0. 236 (0. 170) ? 0. 00912 (0. 0104) 1. 068*** (0. 0406) 0. 0168 (0. 0164) ? 0. 00102 (0. 00132) Advertising Advertising2 Com petitors advertising 0. 00892** (0. 00387) ? 0. 00000602** (0. 00000248) ? 0. 00609* (0. 00363) ?0. 0000180 (0. 000592) ? 0. 0000000303 (0. 000000535) 0. 00128** (0. 000515) Marginal effect of advertising at Mean 5th pctl. 50th pctl. 75th pctl. 0. 00333 (0. 00239) 0. 0164 (0. 01218) 0. 00624 (0. 00448) 0. 00264 (0. 00190) Do not reject Reject* Do not reject Reject*** Do not reject 1,147 317 0. 000225 (0. 000218) 0. 00113 (0. 00110) 0. 00429 (0. 000416) 0. 000179 (0. 000173) 0. 00812** (0. 00355) 0. 00881** (0. 00382) 0. 00861** (0. 00375) 0. 00797** (0. 00349) Reject** Do not reject Do not reject Reject** Do not reject 1,147 317 ?0. 000140 (0. 000524) ? 0. 0000174 (0. 000582) ? 0. 0000164 (0. 000565) ? 0. 0000132 (0. 000510) Do not reject Do not reject Reject*** Do not reject 1,147 317 C. R. Clark et al.Advertising test ? 1 = ? 2 = 0 Speci? cation tests Hansen J Difference-in-Hansen J Arellano &038 Bond AR(2) Arellano &038 Bond AR(3) obs brands Do not reject Do not reject Do not r eject Reject** Do not reject 1,147 317 Standard errors in parenthesis. DGMM estimates in column labeled Total advertising/perceived quality and SGMM estimates otherwise * p = 0. 10 ** p = 0. 05 *** p = 0. 01 Effect of advertising on brand awareness and perceived quality 229 addition to linearly. Competitors advertising has a signi? cant negative effect on brand awareness and a signi? cant positive effect on perceived quality.Repeating the analysis using the sum instead of the average of competitors advertising yields largely similar results except that the share-of-voice speci? cation yields a signi? cant negative effect of advertising on brand awareness, thereby reinforcing our conclusion that this is not an appropriate functional form. 10 Overall, the inclusion of competitors advertising does not seem to in? uence our results about the role of own advertising on brand awareness and perceived quality. This justi? es our focus on the simple model without competition. Moreover, it su ggests that the following alternative explanation for our main ? dings presented above is unlikely. Suppose awareness depended positively on the total amount of advertising in the brands subcategory or category while perceived quality depended positively on the brands own advertising but negatively on competitors advertising. Then the results from the simple model without competition could be driven by an omitted variables problem If the brands own advertising is highly correlated with competitors advertising, then we would overstate the impact of advertising on awareness and understate the impact on perceived quality.In fact, we might ? nd no impact of advertising on perceived quality at all if the brands own advertising and competitors advertising cancel each other out. 5. 5 Category-speci? c effects Perhaps the example data for analyzing the effect of advertising are time series of advertising expenditures, brand awareness, and perceived quality for the brands being studied. Wit h long enough time series we could then try to identify for each brand in closing off the effect of advertising expenditures on brand awareness and perceived quality.Since such time series are unfortunately not available, we have centre so far on the pith effect of advertising expenditures on brand awareness and perceived quality, i. e. , we have restrain the slope parameters in Eqs. 1 and 2 that determine the effect of advertising to be the same across brands. Similarly, we have restrain the carryover parameters in Eqs. 1 and 2 that determine the effect of lagged perceived quality and brand awareness respectively to be the same across brands. As a agree between the two extremes of brands in isolation versus all brands aggregated, we ? st examine the effect of advertising in different categories. This adds some cross-sectional variation across the brands within a 10 We caution the reader against reading too much into these results The number and identity of the brands within a subcategory or category varies sometimes widely from year to year in the Brandweek Superbrands surveys. Thus, the sum of competitors advertising is an extremely volatile measure of the competitive environment. Moreover, the number of brands varies from 3 for some subcategories to 10 for others, thus making the sum of competitors advertising dif? ult to compare across subcategories. 230 Table 7 Category-speci? c effects Brand awareness Marginal effect Carryover rate Appliances Automobiles Beer, wine, liquor Beverages Computers Consumer electronics Cosmetics and fragrances Credit cards Fast food Food Footwear Health and beauty Household Petrol Pharmaceutical OTC Pharmaceutical prescription Telecommunications Toys Travel 0. 0233 (0. 0167) 0. 00526 (0. 0154) ? 0. 0264 (0. 0423) ? 0. 0245 (0. 0554) 0. 0193** (0. 00777) 0. 0210** (0. 0