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Punishment and Politicization in the International Human Rights Regime

Published online by Cambridge University Press:  02 November 2021

ROCHELLE TERMAN*
Affiliation:
University of Chicago, United States
JOSHUA BYUN*
Affiliation:
University of Chicago, United States
*
Rochelle Terman, Assistant Professor, Department of Political Science, University of Chicago, United States, rterman@uchicago.edu.
Joshua Byun, Ph.D. Candidate, Department of Political Science, University of Chicago, United States, jbyun124@uchicago.edu.
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Abstract

Conventional wisdom treats politicization in the international human rights regime as invariant: for any given violation, states condemn adversaries while coddling friends. However, we find that politicization patterns vary markedly across human rights issues. Some norms are more politicized than others, and states are more likely to punish geopolitical partners on certain violations. We offer a novel theory of politicized enforcement wherein states punish human rights violations discriminatively based on their perceived “sensitivity” for the target state. Using data from the UN Universal Periodic Review, an elaborate human rights mechanism, we show that states tend to criticize their adversaries on sensitive issues that undermine the target regime’s power and legitimacy while addressing safer topics with friends. By uncovering a strategic logic of human rights enforcement, this research contributes new theoretical insights on the relationship between norms and power politics in global governance.

Information

Type
Research Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the American Political Science Association
Figure 0

Figure 1. Predicted Relationship between Politicized Enforcement and Sensitivity

Figure 1

Table 1. Examples of UPR Recommendations

Figure 2

Figure 2. Effect of Geopolitical Affinity on Recommendation Counts, by IssueNote: The x-axis denotes the marginal effect of Geopolitical Affinity on the number of recommendations offered by a given reviewer for each issue category, controlling for other factors. Error bars indicate 95% confidence intervals. Issues are ordered top to bottom by corresponding point estimates on the x-axis. Colors indicate sign and significance of the effect of Geopolitical Affinity on number of recommendations.

Figure 3

Figure 3. Effect of Geopolitical Affinity on Recommendation Counts, by ThemeNote: The x-axis denotes the marginal effect of Geopolitical Affinity on the number of recommendations offered by a given reviewer for each thematic category, controlling for other factors. Error bars indicate 95% confidence intervals. Issues are ordered top to bottom by corresponding point estimates on the x-axis. Colors indicate sign and significance of the effect of Geopolitical Affinity on number of recommendations.

Figure 4

Figure 4. Effects of Issue on Probability of Recommendation SupportNote: The x-axis denotes the marginal effect of recommendation issue on the likelihood of target support, controlling for other factors. Error bars indicate 95% confidence intervals. Issues are ordered top to bottom by corresponding point estimates on the x-axis. Colors indicate sign and significance of the effect of recommendation issue on the likelihood of target support.

Figure 5

Figure 5. Effects of Issue Theme on Probability of Recommendation SupportNote: The x-axis denotes the marginal effect of recommendation theme on the likelihood of target support, controlling for other factors. Error bars indicate 95% confidence intervals. Issues are ordered top to bottom by corresponding point estimates on the x-axis. Colors indicate sign and significance of the effect of recommendation issue on the likelihood of target support.

Figure 6

Figure 6. The Relationship between Politicization and SensitivityNote: The x-axis captures the degree of politicization using model coefficients reported in Figure 2. The y-axis captures the degree of sensitivity, using the model coefficients reported in Figure 4 (sign reversed). The shaded line indicates the 95% confidence level of the fitted linear relationship.

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