IDEAS home Printed from https://ideas.repec.org/p/clt/sswopa/1022.html
   My bibliography  Save this paper

Do Voters Learn from Presidential Election Campaigns?

Author

Listed:
  • Alvarez, Michael R.
  • Glasgow, Garrett

Abstract

Theory: We present a model of voter campaign learning which is based on Bayesian learning models. This model assumes voters are imperfectly informed and that they incorporate new information into their existing perceptions about candidate issue positions in a systematic manner. Hypothesis: Additional information made available to voters about candidate issue positions during the course of a political campaign will lead voters to have more precise perceptions of the issue positions of the candidates involved. Data and Methods: We use panel survey data from the 1976 and 1980 presidential elections, combined with content analyses of the media during these same elections. Our primary analysis is conducted using random effects panel models. Results: We find that during each of these campaigns many voters became better informed about the positions of candidates on many issues and that these changes in voter information are directly related to the information ow during each presidential campaign.

Suggested Citation

  • Alvarez, Michael R. & Glasgow, Garrett, 1997. "Do Voters Learn from Presidential Election Campaigns?," Working Papers 1022, California Institute of Technology, Division of the Humanities and Social Sciences.
  • Handle: RePEc:clt:sswopa:1022
    as

    Download full text from publisher

    File URL: http://www.hss.caltech.edu/SSPapers/wp1022.pdf
    Download Restriction: no
    ---><---

    More about this item

    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:clt:sswopa:1022. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Victoria Mason (email available below). General contact details of provider: http://www.hss.caltech.edu/ss .

    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.