Uncertain Events: A Dynamic Latent Variable Model of Human Rights Respect and Government Killing with Binary, Ordered, and Count Outcomes

39 Pages Posted: 4 Mar 2014 Last revised: 16 Feb 2015

See all articles by Christopher J. Fariss

Christopher J. Fariss

University of Michigan at Ann Arbor - Department of Political Science

Date Written: July 28, 2013

Abstract

Counting repressive events is difficult because state leaders have an incentive to conceal the actions of their subordinates and destroy evidence of abuse. In this paper, I describe an existing latent variable model and then extend it to account for the uncertainty inherent in counting this type of difficult to observe event. To validate the model, I focus on one dataset, which defines one-sided government killing as government caused deaths of non-combatants. The model generates a more precise estimate of latent levels of repression for each country-year using several repression variables included in the model (1949-2010) and new estimates of the distribution of the number of individuals killed for each country-year in the original one-sided government killing dataset (1989-2010). These new event-based count estimates will be useful for researchers interested in this type of data but skeptical of the comparability of such events across countries and over time.

Suggested Citation

Fariss, Christopher J., Uncertain Events: A Dynamic Latent Variable Model of Human Rights Respect and Government Killing with Binary, Ordered, and Count Outcomes (July 28, 2013). Available at SSRN: https://ssrn.com/abstract=2404073 or http://dx.doi.org/10.2139/ssrn.2404073

Christopher J. Fariss (Contact Author)

University of Michigan at Ann Arbor - Department of Political Science ( email )

Ann Arbor, MI 48109
United States

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