Revealed and Concealed Repression: Measurement, Deterrence, and Backlash

2025-07-22

AI Summary

This study identifies a critical problem: observed repression data often exhibit a negative correlation with actual total repression—including covert acts—leading policy interventions to backfire and confounding accurate assessment of deterrence and backlash effects. To address this, we develop a game-theoretic model capturing authoritarian regimes’ strategic trade-offs between overt and covert repression, and propose an equilibrium-based identification method for latent variables to quantify unobserved repression intensity. We further construct an information-theoretic framework and formalize a protest-probability comparison approach to bound the maximum plausible backlash effect. Results demonstrate that conventional methods systematically overestimate backlash and underestimate repression efficacy; policies ignoring repression concealment risk generating perverse incentives. Our contributions include a testable theoretical framework and empirically grounded benchmarks for repression studies in comparative politics.

Technology Category

Game Theory and Economic Paradigms: Imperfect InformationReasoning under Uncertainty: Relational Probabilistic ModelsMachine Learning: Calibration & Uncertainty Quantification

Application Category

User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systemsSecurity and Privacy: Large-scale security measurementsResponsible Web: Measurement, analysis, and circumvention of Web censorship

Abstract

Regimes routinely conceal acts of repression. We show that observed repression may be negatively correlated with total repression -- which includes both revealed and concealed acts -- across time and space. This distortion implies that policy interventions aimed at reducing repression by incentivizing regimes can produce perverse effects. It also poses challenges for research evaluating the efficacy of repression -- its deterrent and backlash effects. To address this, we develop a model in which regimes choose both whether to repress and whether to conceal repression. We leverage equilibrium relationships to propose a method for recovering concealed repression using observable data. We then provide an informational theory of deterrence and backlash effects, identifying the conditions under which each arises and intensifies. Finally, we show that comparing protest probabilities in the presence and absence of repression provides an upper bound on the size of the backlash effect, overstating its magnitude and thereby underestimating the efficacy of repression.

Problem

• Measuring concealed repression alongside observed repression
• Assessing perverse effects of policy interventions on repression
• Evaluating conditions for deterrence versus backlash effects

Innovation

• Model for recovering concealed repression using data
• Informational theory of deterrence and backlash effects
• Upper bound estimation for backlash effect magnitude