IDEAS home Printed from https://ideas.repec.org/p/ags/aaea14/169880.html
   My bibliography  Save this paper

Learning in Credence Good Markets: An Example of Vitamins

Author

Listed:
  • Demko, Iryna
  • Jaenicke, Edward

Abstract

Unlike many studies of learning and pharmaceuticals, this paper considers credence goods such as vitamins and the role of consumer experience in resolving uncertainty when the user cannot observe the effects of the goods after consumption. The Homescan data justifies variations in the purchases: 45% of households choose different Universal Product Code (UPC) items during subsequent shopping trips than the ones they bought originally. My findings suggest that the probability of choosing Brand 1 increases after a positive experience with Brand 1 and declines after a positive experience with Brand 2. This is based on the assumption that the consumer has had a positive experience about the product if she bought it with a current purchase and three periods back. In a structural model I intend to relax this assumption and compare the endogenous speed of learning about vitamins with the speed of learning about drugs.

Suggested Citation

  • Demko, Iryna & Jaenicke, Edward, 2014. "Learning in Credence Good Markets: An Example of Vitamins," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169880, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea14:169880
    DOI: 10.22004/ag.econ.169880
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/169880/files/paper_demko_jaenicke.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.169880?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Igal Hendel & Aviv Nevo, 2006. "Measuring the Implications of Sales and Consumer Inventory Behavior," Econometrica, Econometric Society, vol. 74(6), pages 1637-1673, November.
    2. Igal Hendel & Aviv Nevo, 2006. "Sales and consumer inventory," RAND Journal of Economics, RAND Corporation, vol. 37(3), pages 543-561, September.
    3. Gregory S. Crawford & Matthew Shum, 2005. "Uncertainty and Learning in Pharmaceutical Demand," Econometrica, Econometric Society, vol. 73(4), pages 1137-1173, July.
    4. Igal Hendel & Aviv Nevo, 2006. "Sales and Consumer Inventory," RAND Journal of Economics, The RAND Corporation, vol. 37(3), pages 543-561, Autumn.
    Full references (including those not matched with items on IDEAS)

    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. Andrew Ching & Susumu Imai & Masakazu Ishihara & Neelam Jain, 2012. "A practitioner’s guide to Bayesian estimation of discrete choice dynamic programming models," Quantitative Marketing and Economics (QME), Springer, vol. 10(2), pages 151-196, June.
    2. Ching, Andrew T. & Erdem, Tülin & Keane, Michael P., 2014. "A simple method to estimate the roles of learning, inventories and category consideration in consumer choice," Journal of choice modelling, Elsevier, vol. 13(C), pages 60-72.
    3. Kalouptsidi, Myrto & Scott, Paul & Souza-Rodrigues, Edouardo, 2015. "Identification of Counterfactuals and Payoffs in Dynamic Discrete Choice with an Application to Land Use," TSE Working Papers 15-596, Toulouse School of Economics (TSE).
    4. Myrto Kalouptsidi & Paul T. Scott & Eduardo Souza‐Rodrigues, 2021. "Identification of counterfactuals in dynamic discrete choice models," Quantitative Economics, Econometric Society, vol. 12(2), pages 351-403, May.
    5. Kalouptsidi, Myrto & Souza-Rodrigues, Eduardo & Scott, Paul, 2017. "Identification of Counterfactuals in Dynamic Discrete Choice Models," CEPR Discussion Papers 12470, C.E.P.R. Discussion Papers.
    6. Daniel Ackerberg, 2009. "A new use of importance sampling to reduce computational burden in simulation estimation," Quantitative Marketing and Economics (QME), Springer, vol. 7(4), pages 343-376, December.
    7. Peter Arcidiacono & Patrick Bayer & Federico A. Bugni & Jonathan James, 2012. "Approximating High-Dimensional Dynamic Models: Sieve Value Function Iteration," NBER Working Papers 17890, National Bureau of Economic Research, Inc.
    8. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Invited Paper ---Learning Models: An Assessment of Progress, Challenges, and New Developments," Marketing Science, INFORMS, vol. 32(6), pages 913-938, November.
    9. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Learning Models: An Assessment of Progress, Challenges and New Developments," Economics Papers 2013-W07, Economics Group, Nuffield College, University of Oxford.
    10. Myrto Kalouptsidi & Paul T. Scott & Eduardo Souza-Rodrigues, 2015. "Identification of Counterfactuals in Dynamic Discrete Choice Models," NBER Working Papers 21527, National Bureau of Economic Research, Inc.
    11. Yufeng Huang, 2019. "Learning by Doing and the Demand for Advanced Products," Marketing Science, INFORMS, vol. 38(1), pages 107-128, January.
    12. Matthew Osborne, 2011. "Consumer learning, switching costs, and heterogeneity: A structural examination," Quantitative Marketing and Economics (QME), Springer, vol. 9(1), pages 25-70, March.
    13. Takeshi Fukasawa, 2022. "Firm's Static Behavior under Dynamic Demand," Discussion Paper Series DP2022-19, Research Institute for Economics & Business Administration, Kobe University, revised Sep 2022.
    14. 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.
    15. Michael P. Keane, 2013. "Panel data discrete choice models of consumer demand," Economics Papers 2013-W08, Economics Group, Nuffield College, University of Oxford.
    16. Janssen, Aljoscha, 2020. "Switching Costs, Brand Premia and Behavioral Pricing in the Pharmaceutical Market," Working Paper Series 1317, Research Institute of Industrial Economics.
    17. Colantuoni, Francesca & Rojas, Christian, 2013. "Heterogeneous behavior, obesity and storability in soft drink consumption: A dynamic demand model," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 257244, Agricultural and Applied Economics Association.
    18. Hinnosaar, Marit, 2016. "Time inconsistency and alcohol sales restrictions," European Economic Review, Elsevier, vol. 87(C), pages 108-131.
    19. Jakina Debnam, 2017. "Selection Effects and Heterogeneous Demand Responses to the Berkeley Soda Tax Vote," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 99(5), pages 1172-1187.
    20. Mira Frick & Ryota Iijima & Tomasz Strzalecki, 2019. "Dynamic Random Utility," Econometrica, Econometric Society, vol. 87(6), pages 1941-2002, November.

    More about this item

    Keywords

    Demand and Price Analysis; Food Consumption/Nutrition/Food Safety; Health Economics and Policy;
    All these keywords.

    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:ags:aaea14:169880. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aaeaaea.html .

    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.