IDEAS home Printed from https://ideas.repec.org/p/bon/boncrc/crctr224_2018_047.html
   My bibliography  Save this paper

Price Dispersion and Informational Frictions: Evidence From Supermarket Purchases

Author

Listed:
  • Pierre Dubois
  • Helena Perrone

Abstract

Traditional demand models assume that consumers are perfectly informed about product characteristics, including price. However, this assumption may be too strong. Unannounced sales are a common supermarket practice. As we show, retailers frequently change position in the price rankings, thus making it unlikely that consumers are aware of all deals o¤ered in each period. Further empirical evidence on consumer behavior is also consistent with a model with price information frictions. We develop such a model for horizontally di¤erentiated products and structurally estimate the search cost distribution. The results show that in equilibrium, consumers observe a very limited number of prices before making a purchase decision, which implies that imperfect information is indeed important and that local market power is potentially high. We also show that a full information demand model yields severely biased price elasticities.

Suggested Citation

  • Pierre Dubois & Helena Perrone, 2018. "Price Dispersion and Informational Frictions: Evidence From Supermarket Purchases," CRC TR 224 Discussion Paper Series crctr224_2018_047, University of Bonn and University of Mannheim, Germany.
  • Handle: RePEc:bon:boncrc:crctr224_2018_047
    as

    Download full text from publisher

    File URL: https://www.crctr224.de/research/discussion-papers/archive/dp047
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Igal Hendel & Aviv Nevo, 2006. "Sales and consumer inventory," RAND Journal of Economics, RAND Corporation, vol. 37(3), pages 543-561, September.
    2. Jason Allen & Robert Clark & Jean-François Houde, 2014. "Price Dispersion in Mortgage Markets," Journal of Industrial Economics, Wiley Blackwell, vol. 62(3), pages 377-416, September.
    3. Matthijs R. Wildenbeest, 2011. "An empirical model of search with vertically differentiated products," RAND Journal of Economics, RAND Corporation, vol. 42(4), pages 729-757, December.
    4. Stephan Seiler, 2013. "The impact of search costs on consumer behavior: A dynamic approach," Quantitative Marketing and Economics (QME), Springer, vol. 11(2), pages 155-203, June.
    5. Jason Allen & Robert Clark & Jean-Fran?ois Houde, 2014. "The Effect of Mergers in Search Markets: Evidence from the Canadian Mortgage Industry," American Economic Review, American Economic Association, vol. 104(10), pages 3365-3396, October.
    6. Elisabeth Honka, 2014. "Quantifying search and switching costs in the US auto insurance industry," RAND Journal of Economics, RAND Corporation, vol. 45(4), pages 847-884, December.
    7. Allen Head & Lucy Qian Liu & Guido Menzio & Randall Wright, 2012. "Sticky Prices: A New Monetarist Approach," Journal of the European Economic Association, European Economic Association, vol. 10(5), pages 939-973, October.
    8. Michelle Sovinsky Goeree, 2008. "Limited Information and Advertising in the U.S. Personal Computer Industry," Econometrica, Econometric Society, vol. 76(5), pages 1017-1074, September.
    9. Carlson, John A. & McAfee, R. Preston, 1984. "Joint search for several goods," Journal of Economic Theory, Elsevier, vol. 32(2), pages 337-345, April.
    10. Jidong Zhou, 2014. "Multiproduct Search and the Joint Search Effect," American Economic Review, American Economic Association, vol. 104(9), pages 2918-2939, September.
    11. Babur De Los Santos & Ali Hortacsu & Matthijs R. Wildenbeest, 2012. "Testing Models of Consumer Search Using Data on Web Browsing and Purchasing Behavior," American Economic Review, American Economic Association, vol. 102(6), pages 2955-2980, October.
    12. Stahl, Dale O, II, 1989. "Oligopolistic Pricing with Sequential Consumer Search," American Economic Review, American Economic Association, vol. 79(4), pages 700-712, September.
    13. Gérard P. Cachon & Christian Terwiesch & Yi Xu, 2008. "On the Effects of Consumer Search and Firm Entry in a Multiproduct Competitive Market," Marketing Science, INFORMS, vol. 27(3), pages 461-473, 05-06.
    14. Mark Aguiar & Erik Hurst, 2007. "Life-Cycle Prices and Production," American Economic Review, American Economic Association, vol. 97(5), pages 1533-1559, December.
    15. Igal Hendel & Aviv Nevo, 2006. "Sales and Consumer Inventory," RAND Journal of Economics, The RAND Corporation, vol. 37(3), pages 543-561, Autumn.
    16. Maarten Janssen & Alexei Parakhonyak, 2014. "Consumer search markets with costly revisits," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 55(2), pages 481-514, February.
    17. Stephan Seiler, 2013. "The impact of search costs on consumer behavior: A dynamic approach," Quantitative Marketing and Economics (QME), Springer, vol. 11(2), pages 155-203, June.
    18. Ambarish Chandra & Mariano Tappata, 2011. "Consumer search and dynamic price dispersion: an application to gasoline markets," RAND Journal of Economics, RAND Corporation, vol. 42(4), pages 681-704, December.
    19. Han Hong & Matthew Shum, 2006. "Using price distributions to estimate search costs," RAND Journal of Economics, RAND Corporation, vol. 37(2), pages 257-275, June.
    20. Andrew Rhodes, 2015. "Multiproduct Retailing," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(1), pages 360-390.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Haan, Marco A. & Moraga-González, José L. & Petrikaitė, Vaiva, 2018. "A model of directed consumer search," International Journal of Industrial Organization, Elsevier, vol. 61(C), pages 223-255.
    2. Günter J. Hitsch & Ali Hortaçsu & Xiliang Lin, 2021. "Prices and promotions in U.S. retail markets," Quantitative Marketing and Economics (QME), Springer, vol. 19(3), pages 289-368, December.
    3. Günter J. Hitsch & Ali Hortaçsu & Xiliang Lin, 2019. "Prices and Promotions in U.S. Retail Markets: Evidence from Big Data," NBER Working Papers 26306, National Bureau of Economic Research, Inc.
    4. Sofronis Clerides & Pascal Courty & Yupei Ma, 2023. "Store expensiveness and consumer saving: Insights from a new decomposition of price dispersion," Quantitative Marketing and Economics (QME), Springer, vol. 21(1), pages 65-94, March.
    5. Florez-Acosta, Jorge & Herrera-Araujo, Daniel, 2020. "Multiproduct retailing and consumer shopping behavior: The role of shopping costs," International Journal of Industrial Organization, Elsevier, vol. 68(C).
    6. Nicoletta Berardi & Patrick Sevestre & Jonathan Thébault, 2017. "The Determinants of Consumer Price Dispersion: Evidence from French Supermarkets," Post-Print hal-01685367, HAL.
    7. Rickert, Dennis, 2016. "Consumer state dependence, switching costs, and forward-looking producers. A dynamic discrete choice model applied to the diaper market," VfS Annual Conference 2016 (Augsburg): Demographic Change 145672, Verein für Socialpolitik / German Economic Association.
    8. Jorge Florez-Acosta & Daniel Herrera-Araujo, 2017. "Multiproduct retailing and buyer power: The effects of product delisting on consumer shopping behavior," PSE Working Papers halshs-01518146, HAL.
    9. Mariana Cunha & António Osório & Ricardo Ribeiro, 2016. "Endogenous product design and quality with rationally inattentive consumers," Working Papers de Economia (Economics Working Papers) 03, Católica Porto Business School, Universidade Católica Portuguesa.
    10. Maarten Janssen & Edona Reshidi, 2023. "Discriminatory Trade Promotions in Consumer Search Markets," Marketing Science, INFORMS, vol. 42(2), pages 401-422, March.
    11. Noel, Michael D. & Qiang, Hongjie, 2019. "The role of information in retail gasoline price dispersion," Energy Economics, Elsevier, vol. 80(C), pages 173-187.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tiago Pires, 2016. "Costly search and consideration sets in storable goods markets," Quantitative Marketing and Economics (QME), Springer, vol. 14(3), pages 157-193, September.
    2. Richards, Timothy J. & Hamilton, Stephen F. & Allender, William, 2016. "Search and price dispersion in online grocery markets," International Journal of Industrial Organization, Elsevier, vol. 47(C), pages 255-281.
    3. Jason Allen & Robert Clark & Jean-François Houde, 2019. "Search Frictions and Market Power in Negotiated-Price Markets," Journal of Political Economy, University of Chicago Press, vol. 127(4), pages 1550-1598.
    4. Timothy J. Richards & Stephen F. Hamilton & Koichi Yonezawa, 2017. "Variety and the Cost of Search in Supermarket Retailing," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 50(3), pages 263-285, May.
    5. Navid Mojir & K. Sudhir, 2014. "A Model of Multi-pass Search: Price Search across Stores and Time," Cowles Foundation Discussion Papers 1942R2, Cowles Foundation for Research in Economics, Yale University, revised Feb 2020.
    6. Xulia González & Daniel Miles-Touya, 2018. "Price dispersion, chain heterogeneity, and search in online grocery markets," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 9(1), pages 115-139, March.
    7. Anna Lu, 2017. "Inference of Consumer Consideration Sets," Discussion Papers of DIW Berlin 1681, DIW Berlin, German Institute for Economic Research.
    8. Mantian (Mandy) Hu & Chu (Ivy) Dang & Pradeep K. Chintagunta, 2019. "Search and Learning at a Daily Deals Website," Marketing Science, INFORMS, vol. 38(4), pages 609-642, July.
    9. De los Santos, Babur, 2018. "Consumer search on the Internet," International Journal of Industrial Organization, Elsevier, vol. 58(C), pages 66-105.
    10. Charles Murry & Yiyi Zhou, 2020. "Consumer Search and Automobile Dealer Colocation," Management Science, INFORMS, vol. 66(5), pages 1909-1934, May.
    11. Navid Mojir & K. Sudhir, 2014. "Price Search Across Time and Across Stores," Cowles Foundation Discussion Papers 1942R, Cowles Foundation for Research in Economics, Yale University, revised Jul 2019.
    12. José Luis Moraga-González & Zsolt Sándor & Matthijs R. Wildenbeest, 2015. "Consumer Search and Prices in the Automobile Market," Tinbergen Institute Discussion Papers 15-033/VII, Tinbergen Institute.
    13. Natalia Fabra & Juan-Pablo Montero, 2022. "Product Lines and Price Discrimination in Markets with Information Frictions," Management Science, INFORMS, vol. 68(2), pages 981-1001, February.
    14. Avery Haviv, 2022. "Consumer Search, Price Promotions, and Counter-Cyclic Pricing," Marketing Science, INFORMS, vol. 41(2), pages 294-314, March.
    15. Federico Rossi & Pradeep K. Chintagunta, 2018. "Price Uncertainty and Market Power in Retail Gasoline: The Case of an Italian Highway," Marketing Science, INFORMS, vol. 37(5), pages 753-770, September.
    16. Pires, Tiago, 2018. "Measuring the effects of search costs on equilibrium prices and profits," International Journal of Industrial Organization, Elsevier, vol. 60(C), pages 179-205.
    17. Atayev, Atabek, 2022. "Uncertain product availability in search markets," Journal of Economic Theory, Elsevier, vol. 204(C).
    18. Katja Seim & Michael Sinkinson, 2016. "Mixed pricing in online marketplaces," Quantitative Marketing and Economics (QME), Springer, vol. 14(2), pages 129-155, June.
    19. Raluca M. Ursu & Qingliang Wang & Pradeep K. Chintagunta, 2020. "Search Duration," Marketing Science, INFORMS, vol. 39(5), pages 849-871, September.
    20. Helmers, Christian & Krishnan, Pramila & Patnam, Manasa, 2019. "Attention and saliency on the internet: Evidence from an online recommendation system," Journal of Economic Behavior & Organization, Elsevier, vol. 161(C), pages 216-242.

    More about this item

    Keywords

    imperfect information; price dispersion; sales; search costs; product differentiation; consumer behavior; demand estimation; price elasticities;
    All these keywords.

    JEL classification:

    • D4 - Microeconomics - - Market Structure, Pricing, and Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • L66 - Industrial Organization - - Industry Studies: Manufacturing - - - Food; Beverages; Cosmetics; Tobacco

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:bon:boncrc:crctr224_2018_047. See general information about how to correct material in RePEc.

    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 bibliographic 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CRC Office (email available below). General contact details of provider: https://www.crctr224.de .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.