How can one analyze collective action in protests or revolutions when individuals are uncertain about the relative payoffs of the status quo and revolution? We model a “calculus of protest” of individuals who must either submit to the status quo or support revolt based only on personal information about their payoffs. In deciding whether to revolt, the citizen must infer both the benefit of successful revolution and the likely actions of other citizens. We characterize conditions under which payoff uncertainty overturns conventional wisdom: (a) when a citizen is too willing to revolt, he reduces the incentives of others to revolt; (b) less accurate information about the value of revolution can make revolt more likely; (c) public signals from other citizens can reduce the likelihood of revolt; (d) harsher punishment can increase the incidence of punishment; and (e) the incidence of protest can be positively correlated with that of repression.
I develop a framework to study the interactions between dissidents and the state that reconciles political-process and grievance-based theories of protests and provides insights into interpreting the conflicting empirical studies that sometimes support one theory and sometimes the other. I show that contrary to the theoretical predictions of the literature, the relationship between the magnitude of grievances (e.g., the level of income inequality or economic hardship) and the likelihood of repression can be nonmonotone, and given some assumptions, is U-shaped. That is, as the magnitude of grievances increases from low to high, the likelihood of repression first decreases and then increases. Indeed, the data suggest a nonmonotone, U-shaped relationship between the level of repression and income inequality. I also discuss the implications for the empirical studies of repression. publication. A similar relationship emerges if one simply averages the level of repression for intervals of inequality. 2 The estimation is over the range of inequality that includes about 90% of the data, consisting of 1,445 data points. The repression measure is CIRI Physical Integrity Rights Index (Cingranelli and Richards 2010). It is an additive index constructed from the sum of four indicators of torture, extrajudicial killing, political imprisonment, and disappearance collected for each country-year. Each indicator takes on one of the three values of 0, 1, and 2, corresponding to frequent, occasional, and no occurrence of the relevant incidence, respectively. I reverse the data so that our repression measure is an integer ranging from 0 (no violation of physical integrity rights) to 8 (the most frequent violation).
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