Advertising Pass-Through: How Does Advertising Affect Retail Prices?

Transcription

1 Advertising Pass-Through: How Does Advertising Affect Retail Prices? Current version: July 2015 Michaela Draganska Maria Ana Vitorino We are grateful to Els Breugelmans, Uli Doraszelski, Elisabeth Honka, Sanjog Misra, Koen Pauwels, and Raphael Thomadsen for their helpful comments and suggestions. We also thank the participants of the 2015 UT Dallas FORMS conference, and seminar participants at ESMT, the University of Frankfurt, Özyeğin University, the University of Minnesota and KU Leuven for their valuable feedback. We specifically thank the discussant Linli Xu for her detailed comments on our paper. Marco Qin provided excellent research assistance. Maria Ana Vitorino gratefully acknowledges support from the Dean s Small Grants Program at the University of Minnesota Carlson School of Management. All errors are our own. Drexel University, draganska@drexel.edu. University of Minnesota, vitorino@umn.edu.

2 Abstract The diverging interests of manufacturers and retailers famously give rise to the double marginalization problem, but have consequences far beyond pricing. In this paper we analyze if and how a strategic retailer adjusts prices in the presence of manufacturer advertising. We refer to this adjustment as advertising pass-through. We present a simple model to build intuition about how a retailer strategically reacts to manufacturer advertising. Then we examine empirically the relationship between retail prices and manufacturer advertising for a large set of products across eleven product categories. We show that retail prices change over and above what is expected after accounting for the changes in wholesale prices. Moreover, the effect advertising has on sales through its impact on retail pricing is an order of magnitude larger than its direct effect on sales (as reflected in the advertising elasticities). The estimated advertising pass-though rates are not only of sizable magnitude but also vary considerably across products and categories. To explain this variation we relate the obtained pass-through rates to a number of product and category characteristics. Keywords: retailing, advertising, channel coordination

3 1 Introduction What happens to the retail price of a brand when it is being advertised? Does the investment of manufacturers translate fully into increased revenues or does the retailer benefit disproportionately from the increased awareness or heightened preferences for the advertised brand? The existing literature has extensively studied channel coordination issues in the context of pricing and trade promotions, yet little has been done to determine how the presence of a strategic retailer may affect the impact of manufacturer advertising on retail prices and, ultimately, channel profits both the total and the distribution across different product categories. Manufacturers and retailers exert control over different components of the marketing mix: the retailer selects an assortment to offer and sets the retail price, the manufacturer sets the wholesale price and decides on advertising. Channel members also pursue different objectives. While the retailer s objective is to maximize category profits (Raju, Sethuraman & Dhar 1995, Tellis & Zufryden 1995, Kadiyali, Chintagunta & Vilcassim 2000), the objective of the manufacturer is to maximize brand profits. A decision taken by the retailer with respect to any single brand in the store is therefore not necessarily in the best interest of the manufacturer who produces that brand. Standard models of the distribution channel like Manufacturer Stackelberg or Vertical Nash show that these diverging interests and domains of control give rise to distortions in price setting. Since the retailer adds a markup on top of the markup of the manufacturer, the resulting retail price is too high, adversely affecting channel profits the well-known double marginalization problem. Our goal is to broaden the issue of channel coordination to advertising and examine if and how the retailer reacts strategically by in-store price setting to manufacturer advertising. Advertising raises awareness for the product category and for the advertised brand, and may also increase the quality perception (perceived utility) of that brand. These effects - in an ideal case - translate into increased consumer demand for the advertised brand and/or increased willingness to pay for it. We remain agnostic to the exact channel through which advertising affects demand, and ask how the retailer may adjust retail prices to reflect the changes in wholesale prices and 1

4 demand when a brand is being advertised. The extent to which a profit-maximizing retailer would do this adjustment is what we refer to as advertising pass-through. Our analysis proceeds in two steps. First, we use a simple game-theoretic model with multiple manufacturers that sell through a common retailer to build intuition about how the retailer reacts strategically by in-store price setting to manufacturer advertising. We provide conditions under which the retailer raises price, thereby dampening the sales response to manufacturer advertising and reaping the benefits of the manufacturer s investment. At the same time, we show that the retailer may increase or decrease the retail prices of rival brands. The manufacturer anticipates the retailer s strategic price setting, but cannot fully offset it. Compared to a retailer who charges a constant markup over the wholesale price, the strategic behavior of the retailer therefore distorts the distribution channel in equilibrium. The change in retail price in response to manufacturer advertising alters both channel profits and their distribution among the channel members. Advertising may, however, affect consumer price sensitivities in important ways that are not reflected in the simple model, or there may be a more elaborate contracting relationship between the channel members. In addition, even in the simple setting we analyze, a number of very stringent assumptions are needed to provide any insight into the direction of the advertising pass-though effect we are interested in. Therefore, we turn to empirical analysis to investigate the effect of advertising on retail prices in the market. Using store panel data on retail prices, wholesale prices, and manufacturer advertising for eleven product categories over a period of 123 weeks, we test if there is systematic variation in a brand s retail price in response to national advertising while controlling for wholesales price. Building on Besanko, Dubé & Gupta (2005), we use a reduced-form approach that focuses directly on the relationship between retail prices on the one hand and wholesale prices and advertising expenditures on the other hand. This approach allows us to proceed without specific assumptions on the functional form of demand or on the nature of advertising. The empirical model includes wholesale prices and advertising expenditures of rival brands as well, and thus yields estimates of the own- and the cross-effect of advertising expenditures on 2

5 retail prices. Our estimates show that, even when wholesale prices do not change, for the majority of brands the retail price changes in response to manufacturer advertising. Moreover, the effect advertising has on sales through its impact on retail pricing is an order of magnitude larger than its direct effect as captured by the advertising elasticities. The empirical results support the prediction of the theoretical model that the retailer reacts strategically by in-store price setting to manufacturer advertising. Ex-ante, this is not obvious. For example, the retail price would not change if the retailer followed a constant markup rule or, for a strategic retailer, if manufacturer advertising did not change the elasticity of demand. In addition to providing a broad empirical analysis of advertising pass-through rates across a large number of products, our research also aims to identify factors that are systematically related (and can thus possibly explain) the differences in retailer behavior across products and categories. Although no research to date has examined advertising pass-through per se, the literature on factors affecting pricing and promotions in a retail setting provides some guidance (e.g. Ailawadi, Harlam, César & Trounce 2006, Pauwels 2007). We expect two categories of factors to have an effect on how retailers adjust their prices in the presence of advertising: the factors pertaining to the relative power of the retailer vis-à-vis the manufacturers, and the variables capturing the competitive intensity and degree of perceived product differentiation. Our analysis reveals that the price and advertising elasticities of demand along with the strength of the store brand are strongly associated with the extent to which a retailer changes retail prices when a product is being advertised. The remainder of the paper is organized as follows. We review the related literature in Section 2. Section 3 develops and analyzes a simple theoretical model, which contrasts the case of a category-profit-maximizing retailer with the case of a constant-markup retailer. Sections 4 and 5 present the data and our empirical model. Section 6 discusses the findings of the empirical analysis. Section 7 concludes. 3

6 2 Related Literature There are two streams of literature on which we build: the theoretical and empirical work on retail pass-through and the research on the relationship between advertising and retail margins. Ours is the first paper to examine empirically the relationship between manufacturer advertising and retail pricing across a large number of products in a variety of categories and to describe the determinants of retailers pricing behavior in the presence of advertising. A solid body of literature has investigated the relationship between manufacturer wholesale prices (including trade promotions) and retail prices. The central question is to what extent retailers support manufacturer efforts to stimulate demand by passing the discounts they receive onto the end consumers. Many manufacturers complain that retailers apply about half of the trade dollars to their bottom line rather than to provide lower prices to consumers, while retailers claim that they pass through a high percentage of the trade dollars they receive from manufacturers. Answering the question conclusively has proven rather elusive. Tyagi (2000) shows in a theoretical model that even for a single-product monopolist manufacturer that sells through a monopolist retailer, the pass-through rate depends on the specific properties of the demand function (for a more recent contribution see also Fabinger & Weyl 2013). It is therefore not surprising that applied work in this area offers a variety of theoretical predictions and empirical findings for own-brand and crossbrand pass-through rates (Besanko, Gupta & Jain 1998, Sudhir 2001, Shugan 2001, Moorthy 2005). Besanko et al. (2005) (see also McAlister 2007, Dubé & Gupta 2008) examine a large number of products across several categories and report own-brand pass-through rates of, on average, more than 60%, and cross-brand pass-through rates that are either positive or negative. Pauwels (2007) also finds pass-through rates ranging from 0 to 183% along with significant cross-brand effects. In the most comprehensive study to date, Nijs, Misra, Anderson, Hansen& Krishnamurthi(2010) investigate how pass-through rates vary across more than 1000 retailers in over 30 states and relate the rates to measures of cost and competition. These authors also find great variability in the pass-through rates that cannot be explained by market 4

7 structure. We contribute to this literature by showing that channel coordination issues extend beyond the classic retail pass-through and documenting how retailers adjust their prices in view of the changed demand due to manufacturer advertising. Our research is also related to studies on the relationship between advertising and retail margins, which provide us with some intuition as to the expected effects. For example, it is argued that, in the presence of manufacturer advertising, the role of the retailer as demand generator is diminished, and thus their margins suffer. That is, while manufacturers may raise wholesale prices when they advertise, retailers will not necessarily raise their prices (Albion & Farris 1987). Lal & Narasimhan (1996) explore theoretically the impact of manufacturer advertising on wholesale and retail margins. Formalizing an intuitive argument of Steiner (1973, 1978), they provide a set of conditions under which manufacturer advertising can decrease the retail margin while simultaneously increasing the wholesale margin. In this case, retailers earn lower margins on advertised products but higher margins on unadvertised products. We can thus expect to observe not only an effect of advertising on retail prices of the advertised brand but also on rival products. Using a structural model of the laundry market, Chan, Narasimhan & Yoon (2015) investigate how the presence of a strategic retailer affects advertising and pricing competition at the manufacturer level. They show that retailers mitigate pricing competition but intensify advertising competition between manufacturers and that under the assumptions of their model when manufacturers compete on advertising in addition to price, retail prices are lower but profits for all players are higher. In another related paper, Sethuraman & Tellis (2002) examine the implications of information and differentiation theories of advertising on retail price promotions. Their model predicts that, if advertising equals information (differentiation) and thus increases (decreases) the price sensitivity of consumers, then categories with higher advertising levels have larger (smaller) discounts than categories with lower advertising levels. Using a cross-section of categories, they find a positive relationship between category-wide aggregate advertising expenditures and the size and frequency of retail price discounts. The obvious difficulty for the empirical analysis is to control for the nature of advertising, which is inherently unobservable. Our empirical analysis 5

8 does not require us to make any assumptions on the nature of advertising. It also relies entirely on time-series variation for a given product in a given category to identify the impact of manufacturer advertising on retail prices, thus precluding unobserved heterogeneity across brands from biasing the estimates. 3 A Simple Model of Advertising Pass-through To study if and how the retailer reacts strategically by in-store price setting to manufacturer advertising, we develop a simple model of the distribution channel where advertising acts as a demand shifter. There are two manufacturers who sell through a retailer to consumers. 1 We use a Manufacturer Stackelberg model to describe the vertical strategic interaction between the manufacturers and the retailer. The manufacturers choose wholesale prices and advertising expenditures to maximize the profits of their own brands. For the horizontal strategic interaction between the manufacturers, we use a Bertrand competition model where manufacturers choose their wholesale prices and advertising expenditures simultaneously. The equilibrium concept is subgame perfect Nash equilibrium. The retailer observes the wholesale prices and the advertising expenditures of the manufacturers and then sets retail prices for all brands to maximize category profits. Most studies on retailer pricing behavior in the marketing literature assume that retailers set prices for different brands in a given product category to maximize total category profits (Raju et al. 1995, Tellis & Zufryden 1995, Kadiyali et al. 2000). Among these, Kadiyali et al.(2000) have shown explicitly that the retailer s categoryprofit-maximizing markups are brand specific, and simplified pricing rules such as an across-the-board constant markup are generally suboptimal. 1 We do not model retail competition in this paper. There is evidence of limited retail competition in many categories. For example, Slade (1995) interviewed grocery-chain managers who reported that over 90% of households do not engage in comparison shopping across stores to seek out the lowest-price item. Walters & MacKenzie (1988) find that price specials and double-coupon promotions had no significant effect on store traffic and only one of eight loss-leader categories examined significantly influenced store traffic. 6

9 In what follows we compare the category-profit-maximizing retailer model to a constant-markup retailer model. In this way, we gain insight into how the presence of a strategic retailer may affect the impact of advertising as compared to a retailer who uses a simplified pricing rule. This difference is what we define as the advertising pass-through and what we will empirically assess later. 3.1 Demand Assume that there are two differentiated brands i and j. The quantity q i demanded for brand i is: q i = α i b i p i +β i p j +g i A i γ i A j, (1) where p i and p j denotes the retail prices for brands i and j and A i and A j are the consumer advertising exposure levels for brand i and rival brand j, respectively. The parameters b i > 0 and g i > 0 capture the demand response to the manufacturer s own price and advertising, respectively, whereas the parameters β i > 0 and γ i > 0 capture the demand response to its rival s price and advertising, respectively. 3.2 Retailer Category-profit-maximizing retailer. The retailer sets retail prices for both brands to maximize category profits: Π r = (p 1 w 1 )q 1 +(p 2 w 2 )q 2, where w 1 and w 2 are the wholesale prices of products 1 and 2, and q 1 and q 2 are the quantities sold for products 1 and 2 as defined in equation (1). Given our functional form assumptions, the first-order conditions for prices are α 1 b 1 p 1 +β 1 p 2 +g 1 A 1 γ 1 A 2 b 1 (p 1 w 1 )+β 2 (p 2 w 2 ) = 0, (2) α 2 b 2 p 2 +β 2p 1 +g 2A 2 γ 2 A 1 +β 1 (p 1 w 1) b 2 (p 2 w 2) = 0. (3) 7

10 To simplify the exposition, we assume symmetry between the two brands so that α 1 = α 2 = α, b 1 = b 2 = b, β 1 = β 2 = β, g 1 = g 2 = g, and γ 1 = γ 2 = γ. That is, we assume that consumers have the same price sensitivity for both brands and the impact of advertising is also the same. The optimal retail prices for the categoryprofit-maximizing retailer are functions of wholesale prices and advertising levels and are obtained by solving equations (2) and (3) for p 1 and p 2: p i = 1 2 w i + bg βγ 2(b 2 β 2 ) A i + βg bγ 2(b 2 β 2 ) A j + α 2(b β). (4) Constant-markup retailer. A constant-markup retailer behaves non-strategically according to the pricing rule p i = w i (1+m), (5) where m is a fixed percentage rate, and w i m is the retail margin for brand i. Own and cross- advertising pass-through. Comparing equations (4) and (5), we see that, while the strategic retailer reacts directly to manufacturer advertising through in-store price setting, the constant-markup retailer does not react to manufacturer advertising. In order to sign the reaction of the retailer to advertising in equation (4), we need to make some assumptions about the parameters. From the demand equation (1), we have already assumed that the own price coefficients will be negative, and that the cross price coefficients will be positive. Similarly, we expect that the own advertising coefficients will be positive, and that the cross advertising coefficients will be negative, (Kadiyali et al. 2000, Chintagunta, Kadiyali & Vilcassim 2006). The signs of the strategic retailer s reactions to advertising thus depend on the relative magnitudes of b and β, g and γ. If we assume that the own price effect is larger than cross price effect, that is, b > β (Kadiyali etal.2000,chintagunta etal.2006), andtheownadvertising effect is larger than the cross advertising effect, that is, g > γ (Chintagunta et al. 2006), then theretailpriceofabrandincreaseswithitsownadvertising, thatis, f = p 1 A 1 = p 2 A 2 = 8

11 bg βγ 2(b 2 β 2 ) > 0. Thus, inthecontext ofourstylized model, acategory-profit-maximizing retailer has an incentive to increase the retail price of the advertised product. The intuition is that by advertising the manufacturer boosts consumers willingness to pay for the brand. The retailer captures part of this increased willingness to pay by increasing the retail price. Thisimplicationofourmodelisrelatedto andextends thewell-known doublemarginalization problem. Since the retailer s profit depends on the difference between retail and wholesale price whereas the manufacturer s profit depend on the difference between wholesale price and cost, the retailer maximizes a different objective function than the wholesaler and thus, in general, fails to set the retail price that is optimal from the perspective of the manufacturer. Our model additionally shows that the retailer changes the price of a brand in response to advertising in a way that is undesirable from the manufacturer s point of view. The effect of rival advertising on retail price is less straightforward than that of own advertising. In particular, inspection of equation (4) makes clear that the retail price p i does not necessarily increase in the level of rival advertising A j. More formally, we have p i A j > 0 only if βg > bγ or, equivalently, only if q i / A i q i / A j < q i/ p i q i / p j. (6) Condition (6) implies that whether the retail price p i increases in the level of rival advertising A j depends on how exactly demand responds to own and rival retail prices and advertising levels. This ambiguous result is in line with our intuition. Since a brand s advertising expands the demand for the category, the retailer has an incentive to adjust its prices in order to steer consumers to the most profitable brand and not necessarily the advertised brand. It is important to warn here against overinterpretation of our model as it represents a very specific form of demand and assumes a specific role of advertising in shifting demand. Generally, when advertising leads to a more elastic demand, we would expect a downward adjustment of own prices, and an upward adjustment when advertising renders demand less elastic. 9

12 In the remainder of this section, we show that this difference between a categoryprofit-maximizing and a constant-markup retailer persists once wholesale prices and advertising levels are endogenized. Hence, manufacturers cannot fully offset strategic retail behavior. 3.3 Manufacturers The objective function of the manufacturer selling brand i is given by: Π m i = (w i c i )q i f i (A i ), where c i is the marginal cost for brand i and f i (A i ) is a function that maps consumer advertising exposure levels for brand i into the manufacturer s advertising dollar expenditures. Following Chintagunta et al. (2006), we use a flexible specification of the advertising cost function that permits it to be linear, concave, or convex: f i (A i ) = θ 1i A i θ 2i A 2 i. Continuing with our symmetry assumption we take c 1 = c 2 = c, θ 11 = θ 12 = θ 1, and θ 21 = θ 22 = θ 2. The first-order condition with respect to wholesale price is dπ m i dw i = Πm i w i + Πm i q i q i w i = q i +(w i c) q i w i = 0. (7) The first-order condition with respect to advertising is dπ m i da i = Πm i A i + Πm i q i q i A i = (w i c) q i A i θ 1 +2θ 2 A i = 0. (8) Crucially, the sales response to a change in wholesale price ( q i w i ) or advertising level ( q i A i )dependsonthepricesthattheretailersetsandhencewillbedifferentdepending on whether the retailer maximizes category profits or applies a constant markup. 10

13 3.4 Equilibrium Category-profit-maximizing retailer. Substituting the optimal retail price from equation (4) and solving simultaneously the first-order conditions (7) and (8) yields the optimal wholesale prices and advertising levels of manufacturers who anticipate strategic retail behavior: wi = 2(2bc+2α)θ 2 +(g γ)(gc+2θ 1 ), 4(2b β)θ 2 +g(g γ) (9) A i = (b β)gc+2(2b β)θ 1 αg. 4(2b β)θ 2 +g(g γ) (10) Constant-markup retailer. For ease of exposition, set M = 1 + m. Plugging in the retail price for the constant-markup retailer (5) and solving simultaneously the first-order conditions (7) and (8) we obtain the optimal wholesale prices and advertising levels for manufacturers who expect constant-markup retail behavior: wi = 2(Mbc+α)θ 2 +(g γ)(gc+θ 1 ), 2M(2b β)θ 2 +g(g γ) (11) A i = M(b β)gc+m(2b β)θ 1 αg. 2M(2b β)θ 2 +g(g γ) (12) Comparison between category-profit-maximizing and constant-markup retailers. Comparing equation (9) to (11) and equation (10) to (12), we see that the wholesale price and advertising levels are very different depending on whether manufacturers anticipate profit-maximizing retailer behavior or not. This indicates that, even though manufacturers anticipate the retailer to behave strategically, they cannot offset this. To further illustrate the differences between strategic and constant-markup retailers behavior, we take a set of plausible parameter values, that is we select values which imply price and advertising elasticities consistent with empirical findings in the literature (see Table 1), and solve for the equilibrium. The implied retail margin is close to 50%. Figure 1 plots equilibrium wholesale prices, advertising levels, and retail prices for different values of the markup M. It is evident that wholesale prices, 11

14 advertising levels, and retail prices differ between the strategic and the constantmarkup retailers. Specifically, in the situation with a strategic retailer, wholesale and retail prices tend to be higher for most plausible values of M. The level of advertising is lower for lower values of M and higher for higher values of M. Table 1: Parameter values Parameter α b β g γ θ 1 θ 2 c Values Wholesale price Category-profit-maximizing Constant markup Advertising 0.4 Category-profit-maximizing Constant markup Markup: M Markup: M Category-profit-maximizing Constant markup Retail price Markup: M Figure 1: Comparison of Equilibrium Prices and Advertising under a strategic and a constant-markup retailer 12

15 4 Data Product categories included in the analysis. We merge two data sources a store panel data set providing weekly sales data and a data set with daily advertising expenditures. The sales data come from Dominick s Finer Foods (DFF), the secondlargest supermarket chain in Chicago, and covers retail prices, wholesale prices, and promotional activities from 81 stores in 123 weeks from September 1989 to May Data from eleven product categories are analyzed: bathroom tissue, carbonated soft drinks (CSD), ready-to-eat cereal, frozen dinners (specifically, the healthy/diet brands), dishdetergents (liquid), laundrydetergents(liquid), oatmeal, papertowels 2, softeners (liquid), sports drinks, and toothpaste. Although the original data are available at the UPC level, analysis of UPC-level data is difficult because there is a large number of UPCs per brand and because the advertising data is mostly available at the brand level. Aggregating up to the brand level presents the challenge of aggregating across different prices and possibly across UPCs that are uncorrelated in prices. We thus conduct the analysis at the product level, where products are defined by combining UPCs that have the same price level and a very high (larger than 0.85) correlation in their prices. For each category, we include the top brands (defined in terms of market share) and corresponding products, for which we have advertising data for a sufficient number of weeks. Further, we also include the Dominick s brand as long as it had a market share greater than 1%. As can be seen from Table 2, the number of brands considered per category varies from three to six, and the number of products varies from 7 in sports drinks to 66 in the very fragmented cereals category. The categories we consider also vary widely in the share of the store brand - from nonexistent in sports drinks to 18% in paper towels. Overall, the selected products cover at least 70% of the market for most categories. 2 We focus on the 1-ct size, which is what consumers typically purchase at supermarket chains like Dominick s. 13

16 Table 2: Description of product categories included in the analysis category # brands # products total share DOM share Bathroom tissue CSD Cereals Dinners Dish detergent Laundry detergent Oatmeal Paper towels Softeners Sports drinks Toothpaste Note: This table describes the product categories included in the analysis. # brands and # products are the number of brands and products considered, respectively. total share is the share of the category that is covered by the brands and products included in the analysis. total share includes DOM share which is the share of the supermarket brand (Dominick s) included in the analysis. Due to its low share in the Dinners category and to its non-existent presence in the Sports Drinks category, the Dominick s brand is not included in the analysis of these categories. Shares are calculated based on total sales in dollars. 14

17 Table 3: Pricing zones and stores Number Sales Zone of stores $ % ,039, ,530, ,783, ,840, ,564, ,981, ,966, ,572, ,622, ,508, ,759, ,767, ,936, ,847, ,913, Total ,636, Note: This table reports the number of stores and sales, over the period studied, for each of the zones that belong to the Dominick s chain. Retail and wholesale price variables. Dominick s practices zone pricing, whereby everyday prices vary across stores in different zones (Besanko, Dubé & Gupta 2003). The data contain an index that classifies the 81 stores into 15 pricing zones. Table 3 gives an overview. Our analysis of the data indicated, consistent with previous research using this data set, that retail prices varied across stores in different zones within a week, while the variation in prices within each zone was very small. Wholesale prices are identical for all stores within a zone and week. Accordingly, we further aggregate retail and wholesale prices over the stores within a pricing zone. The DFF data contains information about the profit margin on retail price for each UPC and week and thus allows us to calculate the wholesale prices. Because retail pass-through of wholesale prices to consumers is not the main focus of our 15

18 paper, we refer the reader to Besanko et al. (2005) for a detailed discussion of the construction and potential issues with this variable. The main caveat is that the wholesale prices are based on average acquisition cost, which could pose problems if there is sluggish inventory adjustment. This does not appear to be the case with DFF. Advertising data. We supplement the Dominick s data with advertising expenditures from TNS 3 over the period from December 1994 to May There are seven different sources of advertising in the TNS data: cable TV, magazines, national newspapers, network TV, spot TV, Sunday magazine, and syndication. We use the total advertising expenditures from all sources and, if advertising expenditures are available at the sub-brand level, we aggregate up to the brand level. The total advertising spend includes both national and Chicago-DMA ads. Descriptive statistics of the variables at the category level are shown in Table 4. The set of eleven categories includes some categories with a relatively low unit price, such as paper towels and CSD, and some categories with a higher average price, such as laundry detergent and fabric softeners. Carbonated soft drinks is by far the largest category in terms of sales, followed by cereals and laundry detergent. There is a fair amount of variation across categories in the amounts spent on advertising: cereals and carbonated soft drinks are the most advertised, frozen dinners and oatmeal are the least advertised ones. Categories differ also in the degree of promotional activity - carbonated soft drinks, sports drinks, and frozen dinners are on promotion over 25% of the time, as opposed to oatmeal, dish detergent, and paper towels, which are rarely on sale. Price and Advertising Elasticities of Demand. Because the nature of demand is likely to affect channel behavior and it also greatly varies by category, we estimate an aggregate nonlinear demand model for each category in order to obtain estimates 3 Now replaced by Kantar Media s Ad$pender database. Kantar tracks the number of advertisements and advertising expenditures in national media as well as both measures of advertising in local media at the Designated Media Area (DMA) level. 16

19 Table 4: Category descriptives: Total sales and ad expenditures in dollars (000). Average prices and promotions. category sales adspend ret. price wh. price promos Bathroom tissue 18, CSD 119, Cereals 38, Dinners 16, Dish detergent 3, Laundry detergent 22, Oatmeal 7, Paper towels 7, Softeners 5, Sports drinks 3, Toothpaste 5, Note: This table shows total sales, advertising expenditures and average retail prices, wholesale prices and promotions for each category over the period studied. Average retail and wholesale prices and promotions are simple averages of prices and promotions calculated across brands, weeks, and zones. Brand prices for a given week and zone were constructed by aggregating from the UPC level using UPC sales as weights. Promotions, prior to averaging, is defined as a binary variable that indicates whether an UPC is on sale in a given week, store and zone. Only products used in the analysis are considered in these calculations. 17

20 of the price and advertising elasticities of demand. We use the logarithm of the ratio of the share of the top brands 4 in each category at the week-zone level to the share of the outside good (sales for the all other brands in the category) as the dependent variable, and brand fixed effects, retail price, zone dummies, holiday dummies, and total advertising at the brand-week level as independent variables. Following common practice, we used wholesale prices as instruments for retail prices. Table 5 presents the results obtained for the price and advertising elasticities in the two right-most columns. As we can see, in most categories, demand is quite elastic, while advertising has a smaller in magnitude effect, consistent with the findings in previous research (Kaul & Wittink 1995). Relationship between retail, wholesale prices, and advertising. As may be expected, retail and wholesale prices of a given brand are closely related. As an example, we show in Figure 2 the weekly retail and wholesale prices for one brand Charmin bathroom tissue in pricing zone 2. The graph indicates that wholesale price is a main driver of retail price. Figure 3 shows the weekly retail price for Charmin, along with its promotional intensity and advertising expenditures. Note that promotions are controlled by the retailer, whereas advertising is controlled by the manufacturer. The graph also shows that, most of the time, when advertising expenditures increase, so do retail prices. In line with our model in Section 3 however, there are exceptions to this pattern (around weeks 25 and 45, for example). In what follows we therefore turn to regression analysis in order to separate out the signal from the noise in the relationship between retail prices and advertising expenditures. 4 Top brands are the brands with the largest shares in each product category. Note that Dominick s does not always belong to the set of top brands and as such it is excluded from the demand estimation for some of the categories (which explains the missing values in the second right-most column of Table 5). 18

21 Table 5: Brand Characteristics and Demand Elasticities category brand adspend share avg.rp avg.wp promos price elast ad elast Bathroom tissue Charmin Bathroom tissue Cottonelle Bathroom tissue Dom Bathroom tissue Northern Bathroom tissue Scott CSD 7Up CSD Coke CSD Dom CSD Pepper CSD Pepsi CSD Rite Cereals Dom Cereals Genmills Cereals Kelloggs Cereals Post Cereals Quaker Dinners HC Dinners LC Dinners WW Dish detergent Dawn Dish detergent Dom Dish detergent Ivory Dish detergent Joy Dish detergent Palmolive Laundry detergent All Laundry detergent Cheer Laundry detergent Dom Laundry detergent Tide Laundry detergent Wisk Oatmeal Dom Oatmeal Nabisco Oatmeal Quaker Paper towels Bounty Paper towels Brawny Paper towels Dom Paper towels Scott Paper towels Viva Softeners Dom Softeners Downy Softeners Snuggle Sports drinks Allsport Sports drinks Gatorade Sports drinks Powerade Toothpaste Colgate Toothpaste Crest Toothpaste Dom Toothpaste Mentadent Note: This table reports a set of descriptive statistics for the brands included in the analysis and select results from demand estimations. adspend are total advertising expenditures in dollars (000) over the period studied. share is the share of each brand in each category calculated based on total sales in dollars. avg.rp, avg.wp, and promos are simple averages of brand prices and promotions calculated across weeks and zones. Brand prices for a given week and zone were constructed by aggregating from the UPC level using UPC sales as weights. Promotions, prior to averaging, is defined as a binary variable that indicates whether an UPC is on sale in a given week, store and zone. Only products used in the analysis are considered in these calculations. price elast and ad elast were obtained based on parameters estimated for non-linear demand models at the category-level. In the second left-most column in the table Dom stands for Dominick s.

22 6 Average wholesale price Average retail price 5 4 Price Week Figure 2: Time series of retail and wholesale prices for Charmin brand of bathroom tissue. 6 Advertising Average retail price Average promotions 5 4 Value Week Figure 3: Time series of retail prices, promotions, and advertising for Charmin brand of bathroom tissue. 20

23 5 Empirical specification Inspection of equation (4) shows that, in equilibrium, the correlation between retail price and manufacturer advertising comes from two sources: the retailer s strategic reaction to manufacturer advertising and the correlation between wholesale price and advertising at the level of the manufacturer. In contrast, as equation (5) shows, for a constant-markup retailer, the correlation between retail price and manufacturer advertising comes entirely from the second source. We are interested in the retailer s reaction to manufacturer advertising, that is, the first source, not in the inter-relation of the manufacturer s actions, that is, the second source. We further do not wish to impose any assumptions on the way advertising may affect demand, thus remaining agnostic regarding the role of advertising (information or persuasion). Building on Besanko et al.(2005), we therefore estimate the retail price of product i as a function of its own and its rivals wholesale prices and advertising levels: p i = f i (w i,w j,a i,a j ). (13) Note that, in specifying a reduced-form model, we remain agnostic as to the nature of strategic interactions. We approximate the pricing equation (13) with linear and log-linear models. Since our conclusions across these different specifications remain largely unchanged, we focus here on the linear model. Results for the log-linear model are available upon request. The empirical specification is as follows: p ib zt = a+dw ib zt +e w j b zt +fa bt +hā bt +kx ib zt +ǫ ib zt i j, (14) where the subscripts i and j index products, b indexes brands, z indexes pricing zones, and t indexes weeks. Here, p ib zt is the retail price of product i (belonging to brand b) in zone z and week t. w ib zt is the own wholesale price, w j b zt is the average across rival wholesale prices, A bt is the own advertising level, Ā bt is the average rival advertising level, X ib zt is a set of further control variables we describe below, and ǫ ib zt is a mean-zero disturbance. Note that, by construction, advertising levels do not 21

24 vary across pricing zones, nor across products within a brand. Average retail prices, wholesale prices and rival advertising are all constructed as weighted averages (using UPC sales as weights in the case of prices, and brand sales in the case of advertising). We account for promotional activity that may affect pricing by including it in the set of X ib zt variables. 5 To control for systematic differences in prices across zones, we also include in X ib zt a full set of zone fixed effects. To the extent that demographics vary across zones, their impact is automatically controlled for by the zone fixed effects. Finally, we also include a full set of holiday fixed effects as the most flexible way to capture any systematic fluctuations in demand. We estimate equation(14) by OLS and compute standard errors that are robust to heteroskedasticity and autocorrelation. To proceed with a minimum of assumptions, we run the regression separately for each product in each category. Hence, we do not impose any restrictions on the estimated coefficients a, d, e, f, h, and k across products or across categories. This approach precludes unobserved heterogeneity across products from biasing the estimates. 6 Results Table 6 presents the coefficient estimates averaged across products within a brand, and Table 7 reports a summary of the number of positive and negative product-level advertising pass-through rates obtained from the regressions. The empirical model fits the data well. Across all 286 product-level regressions, the goodness of fit (R 2 ) is 0.72 on average and the F-tests for the overall models are highly significant. We organize the results around our central question about the effect of advertising on retail prices. First we look at the product-level estimates from the regression of retail price on own and rival advertising, thereby also comparing our estimates of wholesale price pass-through rates to the ones in the existing literature. We then assess the magnitude of the estimated advertising pass-through rates by decomposing 5 Similarly to prices, promotional activity is constructed as a weighted average using UPC sales as weights. Promotions, prior to averaging, is defined as a binary variable that indicates whether an UPC is on sale in a given week, store and zone. 22

25 the effect of advertising on sales into a direct effect (operating through the advertising elasticity of demand) and an indirect effect (operating through the retail price adjustment). Then, we investigate whether there are some systematic patterns across products/categories in the way the retailer responds to manufacturer advertising by relating the estimated advertising pass-through rates to a number of product and category characteristics. 23

26 Table 6: Impact of own and rival advertising on retail prices, controlling for wholesale prices (averages across products within each brand). category brand # products own ads cross ads own whsp cross whsp Bathroom tissue Charmin Bathroom tissue Cottonelle Bathroom tissue Dom Bathroom tissue Northern Bathroom tissue Scott CSD 7Up CSD Coke CSD Dom CSD Pepper CSD Pepsi CSD Rite Cereals Dom Cereals Genmills Cereals Kelloggs Cereals Post Cereals Quaker Dinners HC Dinners LC Dinners WW Dish detergent Dawn Dish detergent Dom Dish detergent Ivory Dish detergent Joy Dish detergent Palmolive Laundry detergent All Laundry detergent Cheer Laundry detergent Dom Laundry detergent Tide Laundry detergent Wisk Oatmeal Dom * Oatmeal Nabisco Oatmeal Quaker Paper towels Bounty Paper towels Brawny Paper towels Dom Paper towels Scott Paper towels Viva Softeners Dom Softeners Downy Softeners Snuggle Sports drinks Allsport Sports drinks Gatorade Sports drinks Powerade Toothpaste Colgate Toothpaste Crest Toothpaste Dom Toothpaste Mentadent * not enough wholesale price variation to estimate own wholesale coefficients for the products that belong to this brand. Note: This table reports select results from the product-level OLS regressions described in Section 5. own ads and cross ads are simple averages of the coefficients estimated for the variables own advertising level (also called own pass-through rates in the text) and average rival advertising level (also called rival pass-through rates in the text) taken across all products within a brand. own whsp and cross whsp are simple averages of the coefficients estimated for the variables own wholesale price and average rival wholesale price taken across all products within a brand. In the second left-most column in the table Dom stands for Dominick s.

27 Direction and Magnitude of Pass-through Rates Own-brand and cross-brand wholesale price pass-through. Our empirical strategy is an extension of Besanko et al. (2005), where we control for the passthrough of wholesale price (which was the main focus of their paper) in order to isolate the effect of advertising on retail prices. We can thus compare our results presented in Table 6, columns labeled own whsp and cross whsp, to the passthrough rates they report. Our estimates are very much in line with theirs and the estimates in subsequent empirical work (Nijs et al. 2010, Pauwels 2007): 89% of the estimated own wholesale pass-through rates and 66% of the estimated crossbrand pass-through rates are significantly different from zero. About 80% of the pass-through rates are between 50% and 75% and 20% are 76% or higher. These results give us confidence that our regression specifications adequately capture the relationships in the data. Own advertising pass-through. Now we turn to our main research question, namely whether retailers adjust their prices in response to manufacturer advertising. We investigate the empirical relationship between advertising and retail prices by estimating product-specific advertising pass-through rates. The own advertising pass-through rate can be interpreted as the proportion of a unit ($1,000) advertising expenditure change that is passed through as a change in the own retail price. Thus, an advertising pass-through rate of 0.50 means an advertising expenditure reduction of $1,000 results in a retail price reduction of $0.50. As a benchmark, for a constantmarkup retailer, we would have a pass-through of 0, as a constant-markup retailer would not react to manufacturer advertising. Because our model is specified at the product level and yields estimates of the effect of advertising on the own retail prices of 249 individual products (there are 37 product belonging to the store brand, Dominick s, which does not advertise). After controlling for the changes in wholesale price, promotional activity, and a rich set of fixed effects, we still obtain a large number of significant effects of manufacturer advertising on retail price. About 74% of the estimated own pass-through rates (185 25