IDEAS home Printed from https://ideas.repec.org/p/hhs/umnees/0612.html
   My bibliography  Save this paper

Advertising as a Signaling Device in the Swedish Pharmaceuticals Market

Author

Listed:
  • Hellström, Jörgen

    (Department of Economics, Umeå University)

  • Rudholm, Niklas

    (Department of Economics, Umeå University)

Abstract

The paper empirically studies whether pharmaceutical firms uses advertising as a signal for high quality drugs. A nested random effects count data hurdle model is introduced to handle the excess number of zero observations in the sample as well as nested random drug, firm and substance specific effects. The empirical study indicate that drug quality (measured as the number of side-effects) do not influence pharmaceutical firms decision to advertise or not, but do affect the number of ads in a given period. The higher quality of the drug the more ads.

Suggested Citation

  • Hellström, Jörgen & Rudholm, Niklas, 2003. "Advertising as a Signaling Device in the Swedish Pharmaceuticals Market," Umeå Economic Studies 612, Umeå University, Department of Economics.
    • Handle: RePEc:hhs:umnees:0612
    as

    Download full text from publisher

    File URL: http://www.econ.umu.se/DownloadAsset.action?contentId=62567&languageId=3&assetKey=ues612
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September.
    2. Mark N. Hertzendorf & Per Baltzer Overgaard, 2001. "Price Competition and Advertising Signals: Signaling by Competing Senders," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 10(4), pages 621-662, December.
    3. Milgrom, Paul & Roberts, John, 1986. "Price and Advertising Signals of Product Quality," Journal of Political Economy, University of Chicago Press, vol. 94(4), pages 796-821, August.
    4. Hajivassiliou, Vassilis A. & Ruud, Paul A., 1986. "Classical estimation methods for LDV models using simulation," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 40, pages 2383-2441, Elsevier.
    5. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
    6. Nelson, Phillip, 1970. "Information and Consumer Behavior," Journal of Political Economy, University of Chicago Press, vol. 78(2), pages 311-329, March-Apr.
    7. Bagwell, Kyle & Riordan, Michael H, 1991. "High and Declining Prices Signal Product Quality," American Economic Review, American Economic Association, vol. 81(1), pages 224-239, March.
    8. Nelson, Philip, 1974. "Advertising as Information," Journal of Political Economy, University of Chicago Press, vol. 82(4), pages 729-754, July/Aug..
    9. Antweiler, Werner, 2001. "Nested random effects estimation in unbalanced panel data," Journal of Econometrics, Elsevier, vol. 101(2), pages 295-313, April.
    10. McFadden, Daniel & Ruud, Paul A, 1994. "Estimation by Simulation," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 591-608, November.
    11. Gene M. Grossman & Carl Shapiro, 1984. "Informative Advertising with Differentiated Products," Review of Economic Studies, Oxford University Press, vol. 51(1), pages 63-81.
    12. Thomas, Louis & Shane, Scott & Weigelt, Keith, 1998. "An empirical examination of advertising as a signal of product quality," Journal of Economic Behavior & Organization, Elsevier, vol. 37(4), pages 415-430, December.
    13. Mark W. Nichols, 1998. "Advertising and Quality in the U.S. Market for Automobiles," Southern Economic Journal, John Wiley & Sons, vol. 64(4), pages 922-939, April.
    14. Hao Zhao, 2000. "Raising Awareness and Signaling Quality to Uninformed Consumers: A Price-Advertising Model," Marketing Science, INFORMS, vol. 19(4), pages 390-396, January.
    15. Michael Spence, 1973. "Job Market Signaling," The Quarterly Journal of Economics, Oxford University Press, vol. 87(3), pages 355-374.
    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. Hellström, Jörgen & Rudholm, Niklas, 2008. "Advertising as a signaling device: Simulated maximum likelihood estimation of a multiple random effects count data model," Economics Letters, Elsevier, vol. 101(3), pages 227-229, December.
    2. Régis Chenavaz & Sajjad M. Jasimuddin, 2017. "An analytical model of the relationship between product quality and advertising," Post-Print hal-01685892, HAL.
    3. Belleflamme,Paul & Peitz,Martin, 2015. "Industrial Organization," Cambridge Books, Cambridge University Press, number 9781107687899, January.
    4. G. E. Fruchter, 2009. "Signaling Quality: Dynamic Price-Advertising Model," Journal of Optimization Theory and Applications, Springer, vol. 143(3), pages 479-496, December.
    5. Tingting Nian & Arun Sundararajan, 2022. "Social Media Marketing, Quality Signaling, and the Goldilocks Principle," Information Systems Research, INFORMS, vol. 33(2), pages 540-556, June.
    6. Yi Qian & Qiang Gong & Yuxin Chen, 2015. "Untangling Searchable and Experiential Quality Responses to Counterfeits," Marketing Science, INFORMS, vol. 34(4), pages 522-538, July.
    7. Grunewald, Andreas & Kräkel, Matthias, 2017. "Advertising as signal jamming," International Journal of Industrial Organization, Elsevier, vol. 55(C), pages 91-113.
    8. Moraga-Gonzalez, Jose Luis, 2000. "Quality uncertainty and informative advertising," International Journal of Industrial Organization, Elsevier, vol. 18(4), pages 615-640, May.
    9. Yuxin Chen & Qihong Liu, 2022. "Signaling Through Advertising When an Ad Can Be Blocked," Marketing Science, INFORMS, vol. 41(1), pages 166-187, January.
    10. Simon P. Anderson & Régis Renault, 2013. "The Advertising Mix for a Search Good," Management Science, INFORMS, vol. 59(1), pages 69-83, April.
    11. Laurent Linnemer, 2008. "Dissipative Advertising Signals Quality Even Without Repeat Purchases," Working Papers 2008-18, Center for Research in Economics and Statistics.
    12. Laurent Linnemer, 2011. "Caught In A Stranglehold? Advertising: What Else?," Manchester School, University of Manchester, vol. 79(1), pages 63-80, January.
    13. Cesaltina Pacheco Pires & Margarida Catalão-Lopes, 2011. "Signaling advertising by multiproduct firms," International Journal of Game Theory, Springer;Game Theory Society, vol. 40(2), pages 403-425, May.
    14. Yi Qian & Qiang Gong & Yuxin Chen, 2013. "Untangling Searchable and Experiential Quality Responses to Counterfeits," NBER Working Papers 18784, National Bureau of Economic Research, Inc.
    15. Jeong-Yoo Kim, 2017. "Pricing an Experience Composite Good as Coordinated Signals," Manchester School, University of Manchester, vol. 85(2), pages 163-182, March.
    16. Schmidbauer, Eric & Lubensky, Dmitry, 2018. "New and improved?," International Journal of Industrial Organization, Elsevier, vol. 56(C), pages 26-48.
    17. Ki, Hyoshin & Kim, Jeong-Yoo, 2022. "Sell green and buy green: A signaling theory of green products," Resource and Energy Economics, Elsevier, vol. 67(C).
    18. Karray Salma & Martín-Herrán Guiomar, 2008. "Investigating the Relationship Between Advertising and Pricing in a Channel with Private Label Offering: A Theoretic Model," Review of Marketing Science, De Gruyter, vol. 6(1), pages 1-39, August.
    19. Nathan Berg & Jeong‐Yoo Kim & Ilgyun Seon, 2021. "A performance‐based payment: Signaling the quality of a credence good," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(5), pages 1117-1131, July.
    20. repec:ebl:ecbull:v:10:y:2004:i:8:p:1-8 is not listed on IDEAS
    21. Thomas de Haan & Theo Offerman & Randolph Sloof, 2015. "Money Talks? An Experimental Investigation Of Cheap Talk And Burned Money," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56(4), pages 1385-1426, November.

    More about this item

    Keywords

    Signaling; pharmaceutical industry; advertising; product quality; nested random effects; count data;
    All these keywords.

    JEL classification:

    • L15 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Information and Product Quality
    • L21 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Business Objectives of the Firm

    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:hhs:umnees:0612. 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: David Skog (email available below). General contact details of provider: https://edirc.repec.org/data/inumuse.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.