Estimating one-sided-killings from a robust measurement model of human rights

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.

Windows of repression: Using COVID-19 policies against political dissidents?

“…These scores measure the physical integrity rights protection in each country-year by using a dynamic item response model that aggregates a bundle of hard-to-observe repressive indicators (e.g. torture, ill-treatment, imprisonment, violence) in a summary score (Fariss, 2014; Fariss, Kenwick & Reuning, 2020; Reuning, Kenwick & Fariss, 2019). Fariss et al create a single latent measure of repression for countries in a given year drawing on 16 different sources of human rights information, including the CIRI Human Rights Data Project (Cingranelli & Richards, 1999), the Ill-Treatment and Torture (ITT) Country-Year Data (Conrad, Haglund & Moore, 2013), the Political Terror Scale (Gibney et al, 2019), and the UCDP One-Sided Violence Dataset, 1989–2015 (e.g.…”

“…We complement this dataset with geo-located information on violence against civilians reported in the Armed Conflict Location and Event Dataset (ACLED) (Raleigh et al, 2010) and the Latent Human Rights Protection Scores (e.g. Fariss, Kenwick & Reuning, 2020).…”

Against polarization

“…Pan and Siegel, 2020; Sullivan, 2016); how insurgency influences counter-insurgency (e.g. Krcmaric, 2018; Valentino et al, 2004); how counter-insurgency influences insurgency (e.g. Lyall and Wilson, 2009; Toft and Zhukov, 2012); how protest influences protest policing (e.g.…”

“…These researchers did not consider all forms of contention as they generally left out state activities, but they did bring together a large collection of behavior enacted by non-state actors. Following in this tradition, Fariss et al (2020) have brought together a variety of repressive indicators to explore/create a latent variable for this form of boundarization.…”

New Estimates of Over 500 Years of Historic GDP and Population Data

“…22.The most recent version of these data are based on an updated latent variable model presented in Fariss, Kenwick and Reuning (2020).…”