Preventive Repression: Two Types of Moral Hazard

AMERICAN POLITICAL SCIENCE REVIEW（2019）

Cited 69|Views10

Abstract

Authoritarian leaders maintain their grip on power primarily through preventive repression, routinely exercised by specialized security agencies with the aim of preventing any opponents from organizing and threatening their power. We develop a formal model to analyze the moral hazard problems inherent in the principal-agent relationship between rulers and their security agents in charge of preventive repression. The model distinguishes two types of moral hazard: "politics," through which the security agents can exert political influence to increase their payoff by decreasing the ruler's rents from power, and "corruption," through which the agents can increase their payoff by engaging in rent-seeking activities that do not decrease the ruler's rents from power. The surprising conclusion is that both the ruler and the security agent are better off when the only moral hazard problem available is politics rather than when the agent can choose between politics and corruption. We also show that the equilibrium probability of ruler's survival in power is higher when politics is the only moral hazard available to the agent. These findings lead to our central conclusion that opportunities for corruption undermine authoritarian rule by distorting the incentives of the security agencies tasked with preventing potential threats to an authoritarian ruler's grip on power.

Translated text

Key words

Authoritarianism

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper