Estimating one-sided-killings from a robust measurement model of human rights
| Author | Kevin Reuning,Christopher J Fariss,Michael R Kenwick |
| Published date | 01 November 2020 |
| Date | 01 November 2020 |
| DOI | http://doi.org/10.1177/0022343320965670 |
| Subject Matter | Regular Article |
Estimating one-sided-killings from a robust
measurement model of human rights
Christopher J Fariss
The University of Michigan
Michael R Kenwick
Rutgers University
Kevin Reuning
Miami University of Ohio
Abstract
Counting repressive events is difficult because state leaders have an incentive to conceal actions of their subordinates
and destroy evidence of abuse. In this article, we extend existing latent variable modeling techniques in the study of
repression to account for the uncertainty inherent in count data generated for this type of difficult-to-observe event.
We demonstrate the utility of the model by focusing on a dataset that defines ‘one-sided-killing’ as government-
caused deaths of non-combatants. In addition to generating more precise estimates of latent repression levels, the
model also estimates the probability that a state engaged in one-sided-killing and the predictive distribution of deaths
for each country-year in the dataset. 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. Our modeling
framework also provides a principled method for inferring unobserved count variables based on conceptually related
categorical information.
Keywords
event count, human rights, measurement model, one-sided killing, repression
Introduction
Recording repressive events is integral to the scientific
study of peace and conflict. Doing so accurately, how-
ever, is complicated by the fact that state leaders often
have strong incentives to conceal these events from the
international community and des troy evidence associ-
ated with abuse. Even when monitors, activists, and
journalists have complete access, resource constraints
may limit their ability to observe or record state violence.
The lack of access, and constraints on monitoring
resources, combine to potentially bias counts of repres-
sive events (Brysk, 1994; Davenport & Ball, 2002).
Researchers recognize that differences in information
sources may lead to divergent inferences and have spent
considerable time seeking to resolve these problems by
integrating data derived from multiple sources (e.g. Hen-
drix & Salehyan, 2015; Kru
¨ger et al., 2013). These
approaches provide a promising means of validating
inferences from the study of repressive events, but they
are seldom applied to the time-series cross-sectional
analyses that are central to much of the empirical human
rights literature. Remaining concerns over the validity
of repressive events count data contributed to a move-
ment away from these data in human rights research
(Poe, 2004). Yet standards-based indicators are subject
to other forms of bias (Fariss, 2014, 2019) and are less
well suited to precisely track the patterns of repressive
Corresponding author:
cjf0006@gmail.com
Journal of Peace Research
2020, Vol. 57(6) 801–814
ªThe Author(s) 2020
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DOI: 10.1177/0022343320965670
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