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Competitive Pricing Behavior in the US Auto Market: A Structural Analysis

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  • K. Sudhir

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    (School of Management)

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    Abstract

    In a competitive marketplace, the effectiveness of any element of the marketing mix is determined not only by its absolute value, but its relative value with respect to the competition. For example, the effectiveness of a price cut in increasing demand is critically related to competitors' reaction to the price change. Managers therefore need to know the nature of competitive interactions among firms. In this paper, we take a theory-driven empirical approach to gain a deeper understanding of the competitive pricing behavior in the US auto market. The ability-motivation paradigm posits that a firm needs both the ability and the motivation to succeed in implementing a strategy (Boulding and Staelin, 1995). We use arguments from the game theoretic literature to understand firm motivation and abilities in different segments of the auto market. We then combine these insights from the game theoretic literature and the ability-motivation paradigm to develop hypotheses about competition in different segments of the US auto market. To test our hypotheses of competitive behavior, we estimate a structural model that disentangles the competition effect from the demand and cost effects on prices. The theory of repeated games predicts that firms with a long run profitability objective will try to sustain cooperative pricing behavior as a stable equilibrium if conditions permit. For example, markets with high concentration and stable market environments are favorable for sustaining cooperative behavior and therefore provide firms with the ability to cooperate. The theory of switching costs suggests that in markets where a firm's current customers tend to be loyal, firms have a motivation to compete very aggressively for new customers, recognizing the positive benefits of loyalty from the customer base in the long run. As consumer loyalty in the market increases, the gains from increasing market share by aggressive competitive behavior are more than offset by losses in profit margins. Firms therefore have the motivation to price cooperatively. Empirically, we find aggressive behavior in the mini-compact and subcompact segments, cooperative behavior in the compact and midsize segments and Bertrand behavior in the full-size segment. These findings are consistent with our theory-based hypotheses about competition in different segments. In estimating a structural model of the auto market, we address several methodological issues. A particular difficulty is the large number of car models in the US auto market. Existing studies have inferred competitive behavior only in markets with two to four products. They also use relatively simple functional forms of demand to facilitate easy estimation. Functional forms of demand however impose structure on cross-elasticities between products. Such structure if inappropriate can bias the estimates of competitive interaction. We therefore use the random coefficients logit demand model to allow flexibility in cross-elasticities. We also use recent advances in New Empirical Industrial Organization (NEIO) to extend structural estimation of competitive behavior to markets with a large number of products. We use the simulation based estimation approach developed by Berry, Levinsohn and Pakes (1995) to estimate our model. A frequent criticism of the NEIO approach is that its focus on industry specific studies limits the generalizability of its findings. In this study, we retain the advantages of NEIO methods, but partially address the issue of generalizability by analyzing competitive behavior in multiple segments within the auto industry to see if there is a consistent pattern that can be explained by theory. Theoretical modelers can use our results to judge the appropriateness of their models in predicting competitive outcomes for the markets that they analyze. A by-product of our analysis is that we also get estimates of demand and cost apart from competitive interactions for the market. Managers can use these estimates to perform "what-if" analysis. They can answer questions such as what prices to charge when a new product is introduced or when an existing product's characteristics are changed.

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    Bibliographic Info

    Paper provided by Yale School of Management in its series Yale School of Management Working Papers with number ysm228.

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    Date of creation: 03 Oct 2001
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    Handle: RePEc:ysm:somwrk:ysm228

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    Web page: http://icf.som.yale.edu/
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    Related research

    Keywords: Auto Market; Competition; Structural Models; New Empirical Industrial Organization; Game Theory; Ability-Motivation Paradigm;

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    Cited by:
    1. Sergio Meza & K. Sudhir, 2006. "Pass-through timing," Quantitative Marketing and Economics, Springer, vol. 4(4), pages 351-382, December.
    2. Sanjog Misra, 2005. "Generalized Reverse Discrete Choice Models," Quantitative Marketing and Economics, Springer, vol. 3(2), pages 175-200, June.
    3. Bridges, Eileen & Freytag, Per V., 2009. "When do firms invest in offensive and/or defensive marketing?," Journal of Business Research, Elsevier, vol. 62(7), pages 745-749, July.
    4. Satoshi Myojo & Yuichiro Kanazawa, 2010. "On Asymptotic Properties of the Parameters of Differentiated Product Demand and Supply Systems When Demographically-Categorized Purchasing Pattern Data are Available," Discussion Papers 1009, Graduate School of Economics, Kobe University.
    5. Cleeren, K. & Dekimpe, M.G. & Verboven, F., 2005. "Intra- and Inter-Channel Competition in Local-Service Sectors," ERIM Report Series Research in Management ERS-2005-018-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus Uni.
    6. Bordley, Robert, 2013. "Discrete choice with large choice sets," Economics Letters, Elsevier, vol. 118(1), pages 13-15.
    7. Pasquale Schiraldi, 2006. "Second-Hand Markets and Collusion by Manufacturers of Semidurable Goods," Boston University - Department of Economics - Working Papers Series WP2006-028, Boston University - Department of Economics.
    8. Jiang, Renna & Manchanda, Puneet & Rossi, Peter E., 2009. "Bayesian analysis of random coefficient logit models using aggregate data," Journal of Econometrics, Elsevier, vol. 149(2), pages 136-148, April.
    9. Kim, Oknam & Hahn, Minhi, 2004. "An advertising model for hierarchically structured markets: application to the automobile industry," Journal of Business Research, Elsevier, vol. 57(8), pages 829-833, August.
    10. Sergio Meza & K. Sudhir, 2010. "Do private labels increase retailer bargaining power?," Quantitative Marketing and Economics, Springer, vol. 8(3), pages 333-363, September.
    11. Lidia Mannarino, 2009. "Il Mercato Delle Automobili In Italia: Effetti Del Regolamento Cee 1400/2002," Working Papers 200913, Università della Calabria, Dipartimento di Economia, Statistica e Finanza (Ex Dipartimento di Economia e Statistica).
    12. Sungho Park & Sachin Gupta, 2012. "Comparison of SML and GMM estimators for the random coefficient logit model using aggregate data," Empirical Economics, Springer, vol. 43(3), pages 1353-1372, December.
    13. Dai, Yue & Chao, Xiuli & Fang, Shu-Cherng & Nuttle, Henry L.W., 2005. "Pricing in revenue management for multiple firms competing for customers," International Journal of Production Economics, Elsevier, vol. 98(1), pages 1-16, October.
    14. Shiau, Ching-Shin Norman & Michalek, Jeremy J. & Hendrickson, Chris T., 2009. "A structural analysis of vehicle design responses to Corporate Average Fuel Economy policy," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(9-10), pages 814-828, November.
    15. Raphael Thomadsen, 2007. "Product Positioning and Competition: The Role of Location in the Fast Food Industry," Marketing Science, INFORMS, vol. 26(6), pages 792-804, 11-12.
    16. Abhik Roy & Jagmohan Raju, 2011. "The influence of demand factors on dynamic competitive pricing strategy: An empirical study," Marketing Letters, Springer, vol. 22(3), pages 259-281, September.

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