IDEAS home Printed from https://ideas.repec.org/p/boc/bocoec/829.html
   My bibliography  Save this paper

Cellular Service Demand: Biased Beliefs, Learning, and Bill Shock

Author

Listed:
  • Michael D. Grubb

    ()

  • Matthew Osborne

    () (Bureau of Economic Analysis, U.S. Department of Commerce)

Abstract

By April 2013, the FCC's recent bill-shock agreement with cellular carriers requires consumers be notified when exceeding usage allowances. Will the agreement help or hurt consumers? To answer this question, we estimate a model of consumer plan choice, usage, and learning using a panel of cellular bills. Our model predicts that the agreement will lower average consumer welfare by $2 per year because firms will respond by raising monthly fees. Our approach is based on novel evidence that consumers are inattentive to past usage (meaning that bill-shock alerts are informative) and advances structural modeling of demand in situations where multipart tariffs induce marginal-price uncertainty. Additionally, our model estimates show that an average consumer underestimates both the mean and variance of future calling. These biases cost consumers $42 per year at existing prices. Moreover, absent bias, the bill-shock agreement would have little to no effect.

Suggested Citation

  • Michael D. Grubb & Matthew Osborne, 2012. "Cellular Service Demand: Biased Beliefs, Learning, and Bill Shock," Boston College Working Papers in Economics 829, Boston College Department of Economics.
  • Handle: RePEc:boc:bocoec:829
    as

    Download full text from publisher

    File URL: http://fmwww.bc.edu/EC-P/wp829.pdf
    File Function: main text
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Ching-I Huang, 2008. "Estimating demand for cellular phone service under nonlinear pricing," Quantitative Marketing and Economics (QME), Springer, vol. 6(4), pages 371-413, December.
    2. Pascal Courty & Li Hao, 2000. "Sequential Screening," Review of Economic Studies, Oxford University Press, vol. 67(4), pages 697-717.
    3. Fabian Herweg & Konrad Mierendorff, 2013. "Uncertain Demand, Consumer Loss Aversion, And Flat-Rate Tariffs," Journal of the European Economic Association, European Economic Association, vol. 11(2), pages 399-432, April.
    4. Spiegler, Ran, 2014. "Bounded Rationality and Industrial Organization," OUP Catalogue, Oxford University Press, number 9780199334261.
    5. Minjung Park, 2011. "The Economic Impact of Wireless Number Portability," Journal of Industrial Economics, Wiley Blackwell, vol. 59(4), pages 714-745, December.
    6. Michael D. Grubb, 2009. "Selling to Overconfident Consumers," American Economic Review, American Economic Association, pages 1770-1807.
    7. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
    8. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, November.
    9. Miravete, Eugenio J, 1996. "Screening Consumers through Alternative Pricing Mechanisms," Journal of Regulatory Economics, Springer, vol. 9(2), pages 111-132, March.
    10. Gregory S. Crawford & Matthew Shum, 2005. "Uncertainty and Learning in Pharmaceutical Demand," Econometrica, Econometric Society, vol. 73(4), pages 1137-1173, July.
    11. Saurabh Bhargava & Vikram S. Pathania, 2013. "Driving under the (Cellular) Influence," American Economic Journal: Economic Policy, American Economic Association, pages 92-125.
    12. Armstrong, Mark & Vickers, John, 2001. "Competitive Price Discrimination," RAND Journal of Economics, The RAND Corporation, pages 579-605.
    13. Andrew Ching & Tülin Erdem & Michael Keane, 2009. "The price consideration model of brand choice," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(3), pages 393-420, April.
    14. Koichiro Ito, 2014. "Do Consumers Respond to Marginal or Average Price? Evidence from Nonlinear Electricity Pricing," American Economic Review, American Economic Association, pages 537-563.
    15. Anja Lambrecht & Katja Seim & Bernd Skiera, 2007. "Does Uncertainty Matter? Consumer Behavior Under Three-Part Tariffs," Marketing Science, INFORMS, vol. 26(5), pages 698-710, 09-10.
    16. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    17. Martin Gaynor & Yunfeng Shi & Rahul Telang & William Vogt, 2005. "Cell Phone Demand and Consumer Learning – An Empirical Analysis," Working Papers 05-28, NET Institute, revised Oct 2005.
    18. Katja Seim & V. Brian Viard, 2011. "The Effect of Market Structure on Cellular Technology Adoption and Pricing," American Economic Journal: Microeconomics, American Economic Association, pages 221-251.
    19. Benjamin R. Handel, 2011. "Adverse Selection and Switching Costs in Health Insurance Markets: When Nudging Hurts," NBER Working Papers 17459, National Bureau of Economic Research, Inc.
    20. Michael D. Grubb, 2009. "Selling to Overconfident Consumers," American Economic Review, American Economic Association, pages 1770-1807.
    21. Cardon, James H & Hendel, Igal, 2001. "Asymmetric Information in Health Insurance: Evidence from the National Medical Expenditure Survey," RAND Journal of Economics, The RAND Corporation, pages 408-427.
    22. Gourieroux, Christian & Monfort, Alain, 1993. "Simulation-based inference : A survey with special reference to panel data models," Journal of Econometrics, Elsevier, vol. 59(1-2), pages 5-33, September.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    bill shock; biased beliefs; learning; inattention; cellular; telecommunications; overconfidence;

    JEL classification:

    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance
    • L96 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Telecommunications
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty

    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:boc:bocoec:829. 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: (Christopher F Baum). General contact details of provider: http://edirc.repec.org/data/debocus.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 RePEc Author Service 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.