Retail Pass-through Research Articles

Agency or Wholesale? The Role of Retail Pass-Through

With the rapid growth of e-commerce, agency selling is currently gaining popularity among online retailers (e-tailers). Prior research implicitly abstracts away cross-brand pass-through under traditional wholesale selling (i.e., how the retail price of another brand adjusts to changes in a given brand’s wholesale price) and suggests that a shift to agency selling benefits e-tailers but harms suppliers. As an important counterweight to this result, we discover that an e-tailer’s choice regarding selling format is critically moderated by retail pass-through behavior. On the one hand, agency selling can improve channel efficiency compared with wholesale selling. On the other hand, the relative intensity of supplier competition between these two selling formats is ambiguous. We show that the existing result applies only for a nonnegative cross-brand pass-through rate (e.g., under linear demands); otherwise (e.g., under multiplicative or exponential demands), the opposite may hold. Interestingly, we find that the conflict over the preference of selling format may not arise, and that all channel members could be better off with wholesale selling. Compared with the case of Bertrand competition, the e-tailer is even more likely to adopt wholesale selling under Cournot competition. Finally, under agency selling with an endogenous commission fee, we advise caution regarding the seemingly innocuous normalization of suppliers’ marginal costs. Surprisingly, suppliers may benefit from higher marginal costs. Overall, our findings not only shed light on the theory of agency selling but also provide a plausible explanation for the observation that wholesale selling continues to prevail in online markets. This paper was accepted by Eric Anderson, marketing.

Agency or Wholesale? The Role of Retail Pass-Through

With the rapid growth of e-commerce, agency selling is currently gaining popularity among online retailers (e-tailers). Prior research implicitly abstracts away cross-brand pass-through under traditional wholesale selling (i.e., how the retail price of another brand adjusts to changes in a given brand's wholesale price) and suggests that a shift to agency selling benefits e-tailers but harms suppliers. As an important counterweight to this result, we discover that an e-tailer's choice regarding selling format is critically moderated by retail pass-through behavior. On the one hand, agency selling can improve channel efficiency compared to wholesale selling. On the other hand, the relative intensity of supplier competition between these two selling formats is ambiguous. We show that the existing result applies only for a nonnegative cross-brand pass-through rate (e.g., under linear demands); otherwise (e.g., under multiplicative or exponential demands), the opposite may hold. Interestingly, we find that the conflict over the preference of selling format may not arise, and that all channel members could be better off with wholesale selling. Compared to the case of Bertrand competition, the e-tailer is even more likely to adopt wholesale selling under Cournot competition. Finally, under agency selling with an endogenous commission fee, we advise caution regarding the seemingly innocuous normalization of suppliers' marginal costs. Surprisingly, suppliers may benefit from higher marginal costs. Overall, our findings not only shed light on the theory of agency selling but also provide a plausible explanation for the observation that wholesale selling continues to prevail in online markets.

A structural model of cost pass-through: the case of the US yogurt retailing

We provide a structural framework for retail cost pass-through based on a neoclassical inverse demand model and retail profit maximization conditions. Unlike previous literature where game-theoretic structures are imposed on cost pass-through, our framework allows for estimating cost pass-through and the degree of market competition simultaneously. Further, our model incorporates potential cross-brand effects representing demand substitution and strategic complementarity effects. Our model supplements the traditional reduced-form approach to pass-through and is applicable in environments, where the lack of brand-level cost data renders reduced-form analysis infeasible. The empirical value of our model is illustrated in an econometric analysis of retail pass-through for national and store brand yogurt. Our results indicate that: (1) market competition has a positive impact on own-brand cost pass-through, especially for national brands, and (2) overlooking cross-brand effects results in biased own-brand pass-through estimates. Finally, we provide a graphical illustration of the relationship between cost pass-through and market competition.

Gasoline and Diesel Tax Incidence: The 2003 Washington State Nickel Funding Package

Abstract This paper considers a previously unexamined increase in excise taxes on gasoline and diesel fuel that were part of Washington State’s Nickel Funding Package of 2003. We fail to reject full pass-through of the amount of the tax increase to retail prices in both products. We find no significant sensitivity of retail pass-through to station locations relative to state borders or to retail competitive conditions as measured by local station density. The expansion of hypermarkets in Washington State during the period was a confounding factor in estimating tax pass-through effects.

Market Structure and Cost Pass-Through in Retail

We examine the extent to which vertical and horizontal market structure can together explain incomplete retail pass-through. To answer this question, we use scanner data from a large U.S. retailer to estimate product level pass-through for three vertical structures: national brands, private label goods not manufactured by the retailer, and private label goods manufactured by the retailer. Our approach circumvents issues associated with internal firm prices and demonstrates that accounting for horizontal market structure is important for measuring the effects of vertical integration and reduced double marginalization on pass-through.

Loss leaders and cross-category retailer pass-through: A Bayesian multilevel analysis

Retail pass-through has been extensively analyzed analytically and empirically, and recent empirical work has stressed the importance of appropriate methodology and data for inferring correct retail pass-through. However the literature on retail pass-through has interpreted ‘pass-through’ as being confined to a specific product category, and only to brands within that category. This category restriction has been derived from a tradition of modeling retailers as ‘category profit maximizers’. Yet it is widely accepted that retailers strive to maximize profits across categories, with several categories specifically functioning as ‘loss leaders’. In this paper we argue that this pragmatic view of retailers makes it necessary to reevaluate retailer pass-through from being merely a ‘within category’ phenomenon to also a ‘cross category phenomenon’. Using a unique dataset we construct category price indices and empirically evaluate cross category pass-throughs with a variety of categories – selected on the basis of profitability. We find that by and large cross category pass-throughs tend to be negative (about eighteen percent as compared to nine percent positive), that is, price cuts in a focal category being accompanied by price increases in other categories. Category characteristics such as price elasticity and proportion of loss leaders increase the probability of negative cross-category pass-throughs. We conclude that future work on retailer pass-throughs needs to incorporate cross category analysis in order to capture the ‘true’ strategic behavior of the retailer.

Retailer Dynamic Pricing and Ordering Decisions: Category Management versus Brand-by-Brand Approaches

This paper provides a framework for retailer pricing and ordering decisions in a dynamic category management setting. In this regard, the key contributions of this paper are as follows. First, we develop a multi-brand ordering and pricing model that endogenizes retailer forward buying and maximizes profitability for the category. The model considers (i) manufacturer trade deals to retailers, (ii) ordering costs incurred by the retailer, (iii) retailer forward buying behavior, and (iv) both own- and cross-price effects of all the brands in the category. Second, we use this model to compare differences in ordering and pricing decisions, and in profits, resulting from using a category management versus a brand-by-brand management approach. Our approach allows us to derive implications in a dynamic setting about the impact of interdependence among the brands upon decisions on pass-through of trade deals and retailer order quantity. We show that category management results in noticeably higher profits versus brand-by-brand and cost-plus (markup) approaches. Further, our results suggest an interaction between a brand's own-price effect and its cross-price effect emerges. If the cross-price effect for a brand is low – that is, the brand takes away relatively few sales from the other brands – the retail pass-through should increase with that brand's own-price effect. On the other hand, when the cross-price effect is high, the retail pass-through decreases with the brand's own-price effect.

Editorial—The Scientific Process at Its Best

I write this editorial to point out a recent “research trilogy” in Marketing Science that shows the scientific process (and author efforts) at its best. In particular, in 2005, an article (the first of the trilogy) was published in Marketing Science, by Besanko, Dube, and Gupta (hereafter BDG), that focused on the issue of crossbrand retail pass-through; that is, are retail prices of competing brands adjusted (by the retailer) in response to a change in the wholesale price of any given brand in the category? BDG address this issue by fitting a reduced-form model (log price regressed on log cost) to data from the well-utilized Dominick’s Finer Foods (DFF) data set on 78 products, 11 product categories across 15 “price zones.” Looking at cross-brand pass-through coefficients, BDG find significant evidence supporting their claim and also describe how these effects vary according to the size/share of the brand. Clearly, this is an important problem with important empirical findings. Then, two years later, the second part of the trilogy was published by McAlister (2007), who raises some concern about BDG (2005) because prices are not set independently across the 15 zones by DFF and, hence, the effective degrees of freedom may not be as assumed in BDG (2005). As a result, McAlister (2007) reports that if the data are not disaggregated to the level of the price zone (as in BDG 2005), one finds a large proportion of nonsignificant elasticities. This was taken as “nonevidence” of cross-brand pass-through and, hence (if you will), the debate. However, just as with BDG (2005) the McAlister data left a number of unanswered issues because no formal test for nonzero elasticities was done, nor were explorations of longer data series performed that may allow for more powerful tests. Clearly, McAlister (2007) is also an important paper, as it re-examined both the methods used and the data generating assumptions of BDG (2005), something that is commonly “swept under the rug,” and it also left more questions to explore. Finally, in 2008 (in this issue), we have the last part of the research trilogy, where Dube and Gupta (DG 2008) reanalyze the original BDG (2005) data, this time using more appropriate hierarchical Bayesian methods (Rossi and Allenby 2003) applied both at the disaggregate store level and at the aggregate chain level (to address concerns raised in McAlister 2007) and using formal Bayes factors to test the null hypothesis of zero cross-brand pass-through elasticities. Their new findings now reinforce BDG’s conclusion (2005) that cross-brand pass-through does exist but at a significantly reduced level than originally claimed. So, why this editorial and why is the DG article “the scientific process at its best?” First and foremost, let us see what the field has gained from this research: 1. The original BDG (2005) paper addressed an important topic, one important enough that another scholar spent the effort to read it carefully and reanalyze it. 2. Through McAlister (2007) we can see the importance of testing hypotheses in only as powerful a way as the data will allow for, and in DG 2008 those methods are applied both to the original BDG 2005 data and to a longer time series of data. So, a reinquiry in this case led to a more appropriate aggregation of the data (and an exploration of the effects of nonaggregation) and a more powerful set of techniques to be applied. 3. As is common, great research sometimes raises more questions than it answers. In this case, my belief is that this empirical “debate” is far from over, but because the authors all invested their time, we now are at a better place at which to continue the discussion.

How retailer and competitor decisions drive the long-term effectiveness of manufacturer promotions for fast moving consumer goods

While both retailer and competitor decisions contribute to long-term promotional effectiveness, their separate impact has yet to be evaluated. For 75 brands in 25 categories, the author finds that the long-term retailer pass-through of promotions is 65 percent, yielding a long-run wholesale promotional elasticity of 1.78 before competitive response. However, competitors partially match the wholesale price reduction by 15 percent, which decreases promotional elasticity by 10 percent. The range of retailer and competitor response across the analyzed cases is very wide, and is affected by category and brand characteristics. As to the former, large categories yield stronger retailer response, while concentrated categories yield stronger competitor response. As to the latter, smaller brands face a fourfold disadvantage compared to leading brands: they obtain lower retail pass-through, lower retail support, and lower benefits from competing brand's promotions, while their promotions generate higher benefits to competitors. Interestingly, the mid-1990s move from off-invoice allowances towards scan-back deals only partially improves their promotional effectiveness compared to that of leading brands.

Own-Brand and Cross-Brand Retail Pass-Through

In this paper we describe the pass-through behavior of a major U.S. supermarket chain for 78 products across 11 categories. Our data set includes retail prices and wholesale prices for stores in 15 retail price zones for a one-year period. For the empirical model, we use a reduced-form approach that focuses directly on equilibrium prices as a function of exogenous supply- and demand-shifting variables. The reduced-form approach enables us to identify the theoretical pass-through rate without specific assumptions about the form of consumer demand or the conduct of a category-pricing manager. Thus, our measurements of pass-through are not constrained by specific structure on the underlying economic model. The empirical pricing model includes costs of all competing products in the category on the right-hand side (not only the cost of the focal brand) and yields estimates of both own-brand and cross-brand pass-through rates. Our results provide a rich picture of the retailer's pass-through behavior. We find that pass-through varies substantially across products and across categories. Own-brand pass-through rates are, on average, more than 60% for 9 of 11 categories, a finding that is at odds with the claims of manufacturers about retailers in general. Importantly, we find substantial evidence of cross-brand pass-through effects, indicating that retail prices of competing products are adjusted in response to a change in the wholesale price of any given product in the category. We find that cross-brand pass-through rates are both positive and negative. We explore determinants of own-brand and cross-brand pass-through rates and find strong evidence in multiple categories of asymmetric retailer response to trade promotions on large versus small brands. For example, brands with larger market shares, and brands that contribute more to retailer profits in the category, receive higher pass-through. We also find that trade promotions on large brands are less likely than small brands to generate positive cross-brand pass-through, i.e., induce the retailer to reduce the retail price of competing smaller products. On the other hand, small share brands are disadvantaged along three dimensions. Trade promotions on small brands receive low own-brand pass-through and generate positive cross-brand pass-through for larger competing brands. Moreover, small share brands do not receive positive cross pass-through from trade promotions on these larger competitors. We also find that store brands are similarly disadvantaged with respect to national brands.

A General Theory of Pass-Through in Channels with Category Management and Retail Competition

I provide a general formulation of the channel pass-through problem as a comparative static of the retail price equilibrium, and I analyze the impact of category management and retail competition on pass-through, focusing on brand and retailer differences, and the nature of the cost change being passed through—whether it is brand specific, retailer specific, both, or neither. With category management, a retailer's response to a brand-specific cost change is not limited to that brand; in general, a retailer will also change the prices of other brands. The cross-brand effect can be positive or negative, and, depending on its sign, it either enhances or attenuates pass-through. I explain the cross-brand effect as an interaction between two forces: a demand-substitution force that pushes for a negative cross-brand effect, and a strategic-complementarity force that pushes for a positive cross-brand effect. Retail competition adds another layer of strategic complementarity, causing other retailers to respond even for retailer-specific cost changes and increasing pass-through of categorywide cost changes. But its effect for brand-specific cost changes is ambiguous. I apply the theory to two commonly used demand functions—linear demand and nested logit—and show that they have significantly different pass-through properties. The paper concludes with a discussion of how the theory relates to the empirical literature, including the companion piece by Besanko et al. (Besanko, D., J-P. Dubé, S. Gupta. 2005. Own-brand and cross-brand retail pass-through. Marketing Sci. 24(1) 123–137.)

Own-Brand and Cross-Brand Retail Pass-Through

In this paper we describe the pass-through behavior of a major U.S. supermarket chain for 78 products across 11 categories. Our data set includes retail prices and wholesale prices for stores in 15...

Structural Analysis of Manufacturer Pricing in the Presence of a Strategic Retailer

Consumer goods manufacturers usually sell their brands to consumers through common independent retailers. Theoretical research on such channel structures has analyzed the optimal behavior of channel members under alternative assumptions of manufacturer-retailer interaction (Vertical Strategic Interaction). Research in Empirical Industrial Organization has focused on analyzing the competitive interactions between manufacturers (Horizontal Strategic Interaction). Decision support systems have made various assumptions about retailer-pricing rules (e.g., constant markup, category-profit-maximization). The appropriateness of such assumptions about strategic behavior for any specific market, however, is an empirical question. This paper therefore empirically infers (1) the Vertical Strategic Interaction (VSI) between manufacturers and retailer, (2) the Horizontal Strategic Interaction (HSI) between manufacturers simultaneously with the VSI, and (3) the pricing rule used by a retailer. The approach is particularly appealing because it can be used with widely available scanner data, where there is no information on wholesale prices. Researchers usually have no access to wholesale prices. Even manufacturers, who have access to their own wholesale prices, usually have limited information on competitors' wholesale prices. In the absence of wholesale prices, we derive formulae for wholesale prices using game-theoretic solution techniques under the specific assumptions of vertical and horizontal strategic interaction and retailer-pricing rules. We then embed the formulae for wholesale prices into the estimation equations. While our empirical illustration is using scanner data without wholesale prices, the model itself can be applied when wholesale prices are available. Early research on the inference of HSI among manufacturers in setting wholesale prices using scanner data (e.g., Kadiyali et al. 1996, 1999) made the simplifying assumption that retailers charge a constant margin. This assumption enabled them to infer wholesale prices and analyze competitive interactions between manufacturers. In this paper, we show that this model is econometrically identical to a model that measures retail-price coordination across brands. Hence, the inferred cooperation among manufacturers could be exaggerated by the coordinated pricing (category management) done by the retailer. We find empirical support for this argument. This highlights the need to properly model and infer VSI simultaneously to accurately estimate the HSI when using data at the retail level. Functional forms of demand have been evaluated in terms of the fit of the model to sales data. But recent theoretical research on channels (Lee and Staelin 1997, Tyagi 1999) has shown that the functional form has serious implications for strategic behavior such as retail passthrough. While the logit and linear model implies equilibrium passthrough of less than 100% (Lee and Staelin call this Vertical Strategic Substitute (VSS)), the multiplicative model implies optimal passthrough of greater than 100% (Vertical Strategic Complement (VSC)). Because passthrough rates on promotions have been found to be below or above 100% (Chevalier and Curhan 1976, Armstrong 1991), we empirically test the appropriateness of the logit (VSS) and the multiplicative (VSC) functional form for the data. We perform our analysis in the yogurt and peanut butter categories for the two biggest stores in a local market. We found that the VSS implications of the logit fit the data better than the multiplicative model. We also find that for both categories, the best-fitting model is one in which (1) the retailer maximizes category profits, (2) the VSI is Manufacturer-Stackelberg, and (3) manufacturer pricing (HSI) is tacitly collusive. The fact that the retailer maximizes category profits is consistent with theoretical expectations. The inference that the VSI is Manufacturer-Stackelberg reflects the institutional reality of the timing of the game. Retailers set their retail prices after manufacturers set their wholesale prices. Note that in the stores and product categories that we analyze, the two manufacturers own the dominant brands with combined market shares of about 82% in the yogurt market and 65% in the peanut butter market. The result is also consistent with a balance of power argument in the literature. The finding that manufacturer pricing is tacitly collusive is consistent with the argument that firms involved in long-term competition in concentrated markets can achieve tacit collusion. Managers use decision support systems for promotion planning that routinely make assumptions about VSI, HSI, and the functional form. The results from our analysis are of substantive import in judging the appropriateness of assumptions made in such decision support systems.

Effectiveness of Trade Promotions: Analyzing the Determinants of Retail Pass-Through

Trade promotions are temporary price cuts that manufacturers offer retailers to encourage them to reduce retail prices. While trade promotion spending as a percentage of marketing budget has increased dramatically, the inefficiency of trade promotion represents the number-one among manufacturers as indicated by recent trade surveys. At the heart of this dissatisfaction lies manufacturers' concern regarding widespread retailer opportunism with low retail pass-through. Our objective is to develop a simple game-theoretic framework to examine the strategic considerations that underlie a retailer's decision to pass-through a trade deal. In particular, we are interested in answering the following questions: (i) What and how product-market characteristics impact the extent of retail opportunism? (ii) How can the manufacturer alleviate the retail pass-through problem by strategically supplementing trade promotions with advertising trade deals directly to consumers? To address these issues, we consider a stylized channel with a single manufacturer who serves two customer segments through a single (focal) retailer. We implicitly capture the essence of retail competition by allowing customers to have an outside option: other retailers that customers might search if they deem the price at the focal retailer to be too high. Customers differ in their valuation for the manufacturer's product and in the costs they incur when searching for a better price at other retailers. While customers are unaware of the existence of a trade deal in any particular time period, through prior experience they know the frequency of such deals and, furthermore, they update their beliefs about the occurrence of a deal by observing the posted retail price. The retailer decides whether to pass-through a deal or not, recognizing the impact of his pass-through policy on customers' search propensity and hence, their willingness-to-pay. The main message of the paper is that in an environment where manufacturer offers trade promotions, a retailer may not have the incentive to pass a low wholesale price onto consumers because consumers do not have perfect information about ongoing trade promotions. When consumers observe a high price at the focal retailer and yet are not sure if a trade promotion is on, they may not look around for a low price. Therefore, the retailer can price opportunistically to gain a higher margin by not passing a low price to consumers. However, if the retailer never passes savings on, consumers can infer opportunistic pricing based on prior knowledge of trade promotion frequency and have a higher tendency to shop elsewhere, thus reducing sales volume. The retailer resolves the conflicting incentives by occasionally charging a low price when a trade promotion is on while posting a high price on other occasions. We find that the extent of retail opportunism depends on product-market characteristics such as the retailer's clientele and the heterogeneity in consumer search costs as well as on the characteristics of the manufacturer's trade promotion policy such as the frequency of trade promotion and the depth of discount offered. When the low valuation consumers have search costs that make them exit the market when the focal retailer posts a high price, the manufacturer will intervene by advertising his trade promotion directly to consumers thus performing a channel coordination function. We consider several extensions of the base model - explicit retail competition, differentiated retailers and heterogeneity in consumers' knowledge about the frequency of trade promotions ? and show that our basic results still hold.

A Characterization of Retailer Response to Manufacturer Trade Deals

In this article, the author provides a simple characterization of retailer response to manufacturer trade deals in terms of the consumer demand conditions that the retailer faces. Specifically, the author shows conditions on the curvature of consumer demand functions that make it optimal for a profit-maximizing retailer to pass through greater (less) than 100% of the trade deal amount it gets from a manufacturer. Using these conditions, the author demonstrates that whereas the linear and all concave consumer demand functions lead to less than 100% optimal retail pass-through, there exists a subset of convex consumer demand functions for which a retailer rationally engages in greater than 100% pass-through. This subset contains many commonly used demand functions, such as the constant elasticity demand function, the negative exponential demand function, and many other varying elasticity demand functions.