Wealth and Rebellion

The preprint is available at this permanent link or at ResearchGate, RePEc.

Suggested Citation: Ustyuzhanin, Vadim (2024). Wealth and Rebellion: A Dualistic Perspective on Income Level and Revolutions. HSE Working Paper, WP BRP Series: Political Science / PS, Available at ResearchGate: http://dx.doi.org/10.13140/RG.2.2.25291.60966

Mini-Intro

The intricate relationship between a nation’s wealth and its political stability has intrigued scholars for decades. Theories have ranged from the assumption that economic hardship spurs citizens to revolt and that countries with lower income are unstable, as posited by Davies (1962) and Przeworski and Limongi (1997) respectively, to the notion that prosperity leads to an increase of instability because of values’ modernization, that is shown by Inglehart and Welzel (2005), or provision of both the resources and the infrastructure necessary for effective organization and mobilization that McCarthy and Zald (1977) noted. In contrast, Huntington (1968) argues that, on the one hand, unmodernized societies with low economic complexity are not “mature” for revolution, while modernized wealth states are too stable and “revolution is thus an aspect of modernization”, so it “is most likely to occur in societies which have experienced some social and economic development” (1968, p. 265). Despite diametrically opposed theories, it is believed that the invisible hand of economics not only guides markets but also weaves the intricate tapestry of societal (in)stability.

Similarly, in contemporary empirical research from newer “generations” of revolutionary studies there are also rather mixed results on the role of economic prosperity on the risks of revolutions’ occurrence. Knutsen (2014) found that income level is significantly and negatively associated with revolutionary attempts that is supported by his further research on patterns of regime breakdown, including uprisings, since the French Revolution of 1789 (Djuve et al., 2020). In contrast, Albrecht and Koehler (2020), studying revolutions in authoritarian regimes, raise an opposite conclusion about positive effect of economic development on risks of revolutionary situations. Nevertheless, works by Keller (2012) and Beissinger (2022) did not find any significant effect of GDP per capita on revolutions without dividing them on some types.

One possible reason for such discrepancies in the abovementioned studies is fundamental differences in causes and consequences of two dichotomous types of revolutions – armed (violent) and unarmed (nonviolent). Long line of researchers has shown that unarmed uprisings significantly more likely to lead to democratization after the fall of the regime during the revolution. Returning to the revolutions’ predictors, in civil war onset studies (which are armed revolutions) “there is now consensus that the risk of war decreases as average income increases” (Hegre & Sambanis, 2006, pp. 508–509). This fact is explained via opportunity-costs, grievance dissemination (Collier & Hoeffler, 2004) and state capacity (Fearon & Laitin, 2003) mechanisms. However, the consensus reached at the beginning of this century in the field of civil wars has not been reached in the field of unarmed revolutions, which dominate the revolutionary process of our time. Presenting their widely used dataset, Chenoweth and Lewis (2013) ran a preliminary analysis of nonviolent revolutions onset and found GDP per capita as insignificant factor. These results are supported by later research who studied all possible cases (Dahl et al., 2020; Gleditsch & Rivera, 2017; Schaftenaar, 2017) or some specific ones (Brooks & White, 2023; Cunningham, 2013). Meanwhile, there are some research where GDP per capita has positive effect on unarmed revolutions onset (Rørbæk, 2019).

All these studies have in common one thing: they assumed a linear effect of it on risks of unarmed revolutions’ onset. In contrast, in this study I propose a curvilinear framework that challenges this conventional assumption. Thus, the aim of this research to reconcile the disparate results observed in previous studies by integrating insights from existing literature and introducing a general theory. Using two datasets on revolutions, namely NAVCO 1.3 (Chenoweth & Christopher, 2020) and extended Beissinger’s data (Beissinger, 2022; Goldstone et al., 2023), and “rare-events” methods – parametric and non-parametric – I show that GDP per capita is crucial factor for understanding both armed and unarmed revolutions, where there is a linear negative relationship with violent revolutions and an inverted U-shape form with nonviolent revolutions that explained insignificance in the previous studies.

Refernces

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