IDEAS home Printed from https://ideas.repec.org/p/pri/esocpu/32.html
   My bibliography  Save this paper

News Media Reporting Patterns and our Biased Understanding of Global Unrest

Author

Listed:
  • Andrew Shaver

    (University of California, Merced)

Abstract

News reports of political violence are systematically compiled into large global conflict-event datasets used by academics, governments, and international organizations. These datasets present opportunities to examine the micro-dynamics of conflict but are often systematically skewed. We compare various news-report based datasets to high quality administrative records from Afghanistan, Iraq, the Philippines, South Africa, and Syria to identify sources of systematic missingness in the former. We identify under-reporting related to violence intensity, weaponry, target, perpetrator, and non-deadly violence. In a large replication exercise, we show that media-based data fail to uncover the results reported in leading economics/political science journal articles.

Suggested Citation

  • Andrew Shaver, 2022. "News Media Reporting Patterns and our Biased Understanding of Global Unrest," Empirical Studies of Conflict Project (ESOC) Working Papers 32, Empirical Studies of Conflict Project.
  • Handle: RePEc:pri:esocpu:32
    as

    Download full text from publisher

    File URL: https://esoc.princeton.edu/WP32
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Afghanistan; Iraq; Philippines; South Africa; Syria;
    All these keywords.

    JEL classification:

    • H56 - Public Economics - - National Government Expenditures and Related Policies - - - National Security and War
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • F51 - International Economics - - International Relations, National Security, and International Political Economy - - - International Conflicts; Negotiations; Sanctions

    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:pri:esocpu:32. 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: Bobray Bordelon (email available below). General contact details of provider: https://esoc.princeton.edu/ .

    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.