Student Theses and Dissertations

Date of Award

Fall 12-20-2024

Document Type

Thesis

Degree Name

Bachelor of Arts (BA)

Honors Designation

yes

Program of Study

Political Science

Language

English

First Advisor

Xiaonan Wang

Second Advisor

Stephanie R. Golob

Third Advisor

Till Weber

Abstract

What explains the variation in states using state versus nonstate actors in responding to civil unrest? Prior research on state repression has predominantly focused on the direct use of force by state authorities, often neglecting the critical role of third parties, such as organized crime groups, in executing repression. This practice, known as "outsourcing repression", enables states to maintain control while preserving plausible deniability, particularly in contexts requiring substantial coercion. This study aims to elucidate the conditions under which states opt to outsource repression. I propose a two-step logical framework. First, I argue that outsourcing repression depends on the availability of thugs to hire. Second, when third-party actors are available for hire, the state must consider the reliability of such violent delegations, which ultimately depends on the effectiveness of controls exerted by the states over the proxies. The empirical evidence supporting this framework is derived from 1) a cross-period comparison of Hong Kong police responses to the social movements in 2003, 2014, and 2019 and 2) cross-regional comparisons of police responses in different episodes of protests within the 2019 movement. The first comparison highlights the changing relationship between police and triad that provides the state with available pools to outsource. The second comparison of the 2019 movement illuminates the strategic calculations made by the police when outsourcing. Based on these findings, I conclude that states are more likely to outsource repression when reliable proxies are accessible and reliable, thus enabling a mutually beneficial relationship that fosters a conducive environment for outsourced repression.

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