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Price Points and Price Rigidity Daniel LEVY View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Institutional Knowledge at Singapore Management University Singapore Management University Institutional Knowledge at Singapore Management University Research Collection School Of Information Systems School of Information Systems 2011 Price Points and Price Rigidity Daniel LEVY Dongwon LEE Haipeng (Allen) LEE Robert J. Kauffman Singapore Management University, [email protected] Mark Bergen DOI: https://doi.org/10.1162/REST_a_00178 Follow this and additional works at: https://ink.library.smu.edu.sg/sis_research Part of the Management Information Systems Commons, and the Numerical Analysis and Scientific omputC ing Commons Citation LEVY, Daniel; LEE, Dongwon; LEE, Haipeng (Allen); Kauffman, Robert J.; and Bergen, Mark. Price Points and Price Rigidity. (2011). Review of Economics and Statistics. 93, (4), 1417-1431. Research Collection School Of Information Systems. Available at: https://ink.library.smu.edu.sg/sis_research/2186 This Journal Article is brought to you for free and open access by the School of Information Systems at Institutional Knowledge at Singapore Management University. It has been accepted for inclusion in Research Collection School Of Information Systems by an authorized administrator of Institutional Knowledge at Singapore Management University. For more information, please email [email protected]. PRICE POINTS AND PRICE RIGIDITY Daniel Levy, Dongwon Lee, Haipeng (Allan) Chen, Robert J. Kauffman, and Mark Bergen* Abstract—We study the link between price points and price rigidity using recent theories of price rigidity is price point theory, which two data sets: weekly scanner data and Internet data. We find that ‘‘9’’ is the most frequent ending for the penny, dime, dollar, and ten-dollar digits; Blinder et al. (1998) list among the twelve leading theories the most common price changes are those that keep the price endings at of price rigidity. According to the authors (p. 26), practi- ‘‘9’’; 9-ending prices are less likely to change than non-9-ending prices; tioners’ ‘‘belief in pricing points is part of the folklore of and the average size of price change is larger for 9-ending than non-9- ending prices. We conclude that 9-ending contributes to price rigidity pricing.’’ Consistent with this observation, they offer evi- from penny to dollar digits and across a wide range of product categories, dence from interviews on the importance of price points. In retail formats, and retailers. their study of 200 U.S. firms, they found that 88% of retai- lers assigned substantial importance to price points in their Nor does anyone know how important ... [price points] pricing decisions. Kashyap (1995), the first to explore the are in practice. link between price points and price rigidity, found that cata- —Alan Blinder et al. (1998) log prices tended to be stuck at certain ending prices. After concluding that the observation cannot be explained by I. Introduction existing theories, he offered price point theory as a possible explanation. ITH the increased popularity of new Keynesian As Blinder et al. (1998) note in the opening quote to this W models, understanding the sources of nominal price paper, however, a major difficulty with price point theory rigidity has become even more important.1 One of the is that not much is known about the actual importance of price points or their relationship to price rigidity. Price points will be particularly important for macroeconomics if Received for publication October 19, 2008. Revision accepted for publi- they can be shown to contribute to price rigidity across a cation June 13, 2010. * Levy: Bar-Ilan University, Emory University, and RCEA; Lee: Korea wide range of products and retailers. The literature offers University; Chen: Texas A&M University; Kauffman: Singapore Man- growing evidence on the use of price points, but there agement University; Bergen: University of Minnesota. remains a lack of direct evidence linking price points and We thank two anonymous referees and the editor, Mark Watson, for constructive comments and suggestions. We thank Jurek Konieczny, the price rigidity. The literature documenting a link between discussant at the CEU Microeconomic Pricing and the Macroeconomy price points and price rigidity using U.S. data is limited to conference for comments, and the conference participants—Marco Kashyap (1995) and Blinder et al. (1998). Kashyap has Bonomo, Alan Kackmeister, Attila Ratfai, Julio Rotemberg, Harald Stahl, Jonathan Willis, and Alex Wolman—for suggestions. Gershon Alpero- emphasized the need for more direct evidence, stating that vich, Bob Barsky, Alan Blinder, Leif Danziger, Mark Gertler, Carlos a ‘‘study focusing on more goods ... would have much Marques, and Jacob Paroush provided helpful comments. We thank the more power to determine the significance of price points’’ seminar participants at Bar-Ilan University, Ben-Gurion University, Deutsche Bundesbank, Emory University, European Central Bank, (p. 269). Hebrew University, Federal Reserve Bank of Kansas City, Magyar Nem- Our goal is to fill this gap in the literature by offering zeti Bank, Texas A&M University, University of Minnesota, University new evidence on the link between price points and price of Piraeus, and Tel-Aviv University for comments. We thank Pe´ter Benc- zu´r, Michael Ehrmann, David Genesove, Peter Gabriel, Zvi Hercowitz, rigidity using two particularly appropriate but different data Heinz Herrmann, Johannes Hoffmann, Pe´ter Kara´di, Ed Knotek, Saul sets. One is a large weekly scanner price data set from a ´ Lach, Benoıˆt Mojon, Ada´m Reiff, and Frank Smets for comments and major midwestern U.S. retailer, covering 29 product cate- Manish Aggrawal, Ning Liu, and Avichai Snir for research assistance. Portions of this work have been also presented at the 2004 INFORMS gories over an eight-year period. The second comes from Conference on Information Systems and Technology, the Hawaii 2004 the Internet and includes daily prices over a two-year period International Conference on Systems Science, the 2006 Minnesota Sym- posium on Statistical Challenges in E-Commerce, the 2005 AMCIS Doc- for 474 consumer electronic goods, such as music CDs, toral Consortium, and at the 2005 INFORMS Marketing Science Confer- digital cameras, and notebook PCs, from 293 different ence. We thank Chris Forman, Hemant Bhargava, D. J. Wu, Barrie Nault, e-retailers, with a wide range of prices. Taken together, the Fred Riggins, Sri Narasimhan, Rahul Telang, Sunil Milthas, and other conference participants for helpful suggestions. Some parts of this manu- two data sets cover a diverse set of products, a wide range script were completed at the Monetary Policy and Research Division, at of prices, different retail formats, and multiple retailers and the Research Department of the European Central Bank, where Daniel time periods. Levy was a visiting scholar. He is grateful to the bank’s Research Depart- ment for the hospitality. Daniel Levy gratefully acknowledges the finan- We found that 9 is the most popular price point for the cial support from the Adar Foundation of the Economics Department at penny, dime, dollar, and the ten-dollar digits across the two Bar-Ilan University. Dongwon Lee’s research is supported by an eBRC Doctoral Support Award from Pennsylvania State University. Rob Kauff- man acknowledges partial support from the MIS research center, and the W. P. Carey Chair in Information Systems, Arizona State University. All authors contributed equally: we rotate coauthorship. The usual disclaimer Levy et al. (2010); Ball and Romer (2003); Rotemberg (1987, 2005, applies. 2010); Nakamura and Steinsson (2008, 2009); Kehoe and Midrigan The supplemental appendix referred to in this article is available online (2010); Klenow and Kryvtsov (2008); Eichenbaum, Jaimovich, and at http://www.mitpressjournals.org/doi/suppl/10.1162/REST_a_00178. Rebelo (2011); Alvarez, Lippi, and Paciello (2010); and Midrigan (2011). 1 See, for example, Carlton (1986), Cecchetti (1986), Warner and For recent surveys, see Willis (2003), Wolman (2007), and Klenow and Barsky (1995), Danziger (1999, 2007), Dutta, Levy, and Bergen (2002), Malin (2011). The Review of Economics and Statistics, November 2011, 93(4): 1417–1431 Ó 2011 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology 1418 THE REVIEW OF ECONOMICS AND STATISTICS data sets. The most common price changes are those that proximity to a Cub Foods store and thus compete directly keep the terminal digits at these 9-endings. When we esti- with it. The other three price tier stores employ a pricing mated the probability of a price change, we found that the strategy that fits best given their local market structure and 9-ending prices are less likely to change in comparison to competition conditions. non-9-ending prices. For the supermarket chain in this We report results from analyzing the prices in four ran- study, 9-ending prices are 43% to 66% less likely to change domly chosen stores—one from each price tier: stores 8 than non-9-ending prices. For the Internet data, these prob- (low price tier), 12 (high price tier), 122 (Cub fighter), and abilities are in the range of 25% to 64%. The average size 133 (medium price tier). To study the behavior of regular of the 9-ending price changes is larger in comparison to prices, we removed data points if they involved bonus buys, non-9-ending prices, which further underscores the extent coupon-based sales, or simple price reductions. For this, we of the 9-ending price rigidity. relied on Dominick’s data identifiers, which indicated such The paper is organized as follows. We describe the data promotions. Dominick’s did not use loyalty cards during in section II. In section III, we study the distribution of the period studied. price endings. In section IV, we assess the distribution The Dominick’s data contain over 98 million weekly of price changes. In section V, we estimate the effect of price observations on 18,037 different grocery products in 9-endings and 99-endings on price rigidity.
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