IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v20y2001i3p244-264.html
   My bibliography  Save this article

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

Author

Listed:
  • K. Sudhir

    () (School of Management, Yale University, 135 Prospect Street, PO Box 208200, New Haven, CT 06520-8200)

Abstract

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.

Suggested Citation

  • K. Sudhir, 2001. "Structural Analysis of Manufacturer Pricing in the Presence of a Strategic Retailer," Marketing Science, INFORMS, pages 244-264.
  • Handle: RePEc:inm:ormksc:v:20:y:2001:i:3:p:244-264
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.20.3.244.9764
    Download Restriction: no

    References listed on IDEAS

    as
    1. Randolph E. Bucklin & Sunil Gupta, 1999. "Commercial Use of UPC Scanner Data: Industry and Academic Perspectives," Marketing Science, INFORMS, pages 247-273.
    2. Aviv Nevo, 2003. "Measuring Market Power in the Ready-to-Eat Cereal Industry," Microeconomics 0303006, EconWPA.
    3. David F. Midgley & Robert E. Marks & Lee C. Cooper, 1997. "Breeding Competitive Strategies," Management Science, INFORMS, vol. 43(3), pages 257-275, March.
    4. Scott A. Neslin & Stephen G. Powell & Linda Schneider Stone, 1995. "The Effects of Retailer and Consumer Response on Optimal Manufacturer Advertising and Trade Promotion Strategies," Management Science, INFORMS, vol. 41(5), pages 749-766, May.
    5. Vrinda Kadiyali, 1996. "Entry, Its Deterrence, and Its Accommodation: A Study of the U.S. Photographic Film Industry," RAND Journal of Economics, The RAND Corporation, vol. 27(3), pages 452-478, Autumn.
    6. David Besanko & Sachin Gupta & Dipak Jain, 1998. "Logit Demand Estimation Under Competitive Pricing Behavior: An Equilibrium Framework," Management Science, INFORMS, vol. 44(11-Part-1), pages 1533-1547, November.
    7. Nevo, Aviv, 2001. "Measuring Market Power in the Ready-to-Eat Cereal Industry," Econometrica, Econometric Society, vol. 69(2), pages 307-342, March.
    8. Dan Horsky & Paul Nelson, 1992. "New Brand Positioning and Pricing in an Oligopolistic Market," Marketing Science, INFORMS, pages 133-153.
    9. Eunkyu Lee & Richard Staelin, 1997. "Vertical Strategic Interaction: Implications for Channel Pricing Strategy," Marketing Science, INFORMS, pages 185-207.
    10. J. Miguel Villas-Boas & Russell S. Winer, 1999. "Endogeneity in Brand Choice Models," Management Science, INFORMS, vol. 45(10), pages 1324-1338, October.
    11. Bresnahan, Timothy F., 1989. "Empirical studies of industries with market power," Handbook of Industrial Organization,in: R. Schmalensee & R. Willig (ed.), Handbook of Industrial Organization, edition 1, volume 2, chapter 17, pages 1011-1057 Elsevier.
    12. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, pages 841-890.
    13. Greg M. Allenby, 1989. "A Unified Approach to Identifying, Estimating and Testing Demand Structures with Aggregate Scanner Data," Marketing Science, INFORMS, pages 265-280.
    14. Peter M. Guadagni & John D. C. Little, 1983. "A Logit Model of Brand Choice Calibrated on Scanner Data," Marketing Science, INFORMS, pages 203-238.
    15. David R. Bell & James M. Lattin, 1998. "Shopping Behavior and Consumer Preference for Store Price Format: Why “Large Basket” Shoppers Prefer EDLP," Marketing Science, INFORMS, pages 66-88.
    16. Timothy W. McGuire & Richard Staelin, 1983. "An Industry Equilibrium Analysis of Downstream Vertical Integration," Marketing Science, INFORMS, pages 161-191.
    17. Cotterill, Ronald W & Putsis, William P, Jr & Dhar, Ravi, 2000. "Assessing the Competitive Interaction between Private Labels and National Brands," The Journal of Business, University of Chicago Press, vol. 73(1), pages 109-137, January.
    18. Jorge M. Silva-Risso & Randolph E. Bucklin & Donald G. Morrison, 1999. "A Decision Support System for Planning Manufacturers' Sales Promotion Calendars," Marketing Science, INFORMS, pages 274-300.
    19. S. Chan Choi, 1991. "Price Competition in a Channel Structure with a Common Retailer," Marketing Science, INFORMS, pages 271-296.
    20. Abhik Roy & Dominique M. Hanssens & Jagmohan S. Raju, 1994. "Competitive Pricing by a Price Leader," Management Science, INFORMS, vol. 40(7), pages 809-823, July.
    21. William Boulding & Richard Staelin, 1995. "Identifying Generalizable Effects of Strategic Actions on Firm Performance: The Case of Demand-Side Returns to R&D Spending," Marketing Science, INFORMS, pages 222-236.
    22. Gerard J. Tellis & Fred S. Zufryden, 1995. "Tackling the Retailer Decision Maze: Which Brands to Discount, How Much, When and Why?," Marketing Science, INFORMS, pages 271-299.
    23. Deepak Agrawal, 1996. "Effect of Brand Loyalty on Advertising and Trade Promotions: A Game Theoretic Analysis with Empirical Evidence," Marketing Science, INFORMS, pages 86-108.
    24. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, pages 307-333.
    Full references (including those not matched with items on IDEAS)

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormksc:v:20:y:2001:i:3:p:244-264. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Mirko Janc). General contact details of provider: http://edirc.repec.org/data/inforea.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.