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Protest is a low-intensity form of political conflict that can precipitate intrastate armed conflict. Data on protests should therefore be informative in systems that provide early warnings of armed conflict. However, since most protests do not escalate to armed conflict, we first need theory to inform our prediction models. We identify three theoretical explanations relating to protest-repression dynamics, political institutions and economic development as the basis for our models. Based on theory, we operationalize nine models and leverage the political Violence Early Warning System (ViEWS) to generate subnational forecasts for intrastate armed conflict in Africa. Results show that protest data substantially improves conflict incidence and onset predictions compared to baseline models that account for conflict history. Moreover, the results underline the centrality of theory for conflict forecasting: our theoretically informed protest models outperform naive models that treat all protests equally.

We review and compare conflict early warning systems on three dimensions: transparency and accessibility, key parameters, and forecasts. The review reveals a need for improved transparency and accessibility of data and code, considerable variation in key parameters across systems, and significant overlaps in countries with the highest risk. We propose that developing standards and platforms that promote transparency, accessibility, and inter-system cooperation can improve knowledge proliferation and system development to mitigate and prevent political violence.

Mitigating global warming requires a rapid reduction in the use of fossil fuels which form the foundation of modern economies. Fossil fuel reduction is crucial for minimizing future loss and damage associated with a changing climate, but a challenging task. In diverse contexts, climate-friendly policies that increased fuel prices have sparked massive, at times violent, protests, ultimately leading to a reversal of those policies. However, to what extent and under what conditions fuel prices and policies affect protest more generally is poorly understood. Addressing this gap, we study how fuel prices affect the likelihood of protest onset. We theorize that increases in fuel prices may create economic grievances through their impacts on the cost of living and income. We also suggest that the likelihood of protest following such price increases would be particularly high where attribution of blame to government policies is feasible, such as in fuel subsidizing states, as well as when governments are seen as being able to provide a remedy such as in petroleum producing states. We evaluate our theoretical framework using global country-level monthly statistics 2003–2015, combining protest data with data on the price of gasoline, fuel policies, and country characteristics, and subject our results to placebo and sensitivity tests. Our study finds that gasoline price hikes increase the likelihood of protest onset across the global sample. In line with our theoretical framework, we also find evidence for a clustering of such relationships in the presence of subsidies and oil production, where the attribution of fuel prices to government (in)action tends to be higher. These results highlight the need for policymakers to anticipate public responses to price increases. This study lays the groundwork for more detailed investigations into climate-friendly subsidy and tax reforms.

Many protest movements have brought down dictatorships and paved the way for democracy. However, protests can also foster large-scale violence at the level of civil war. How can we explain the development from protest to armed conflict? In this paper, we use geographically fine-grained data to examine how collective mobilization leads to civil war violence at the local level. We argue that two mechanisms can explain this. First, in a protest escalation dynamic, confrontations between protesters and state security forces increase the willingness of protesters to ramp up the use of force. Second, in a protest capture mechanism, protests attract attention and resources from the state, thereby providing other local non-state actors with the opportunity to use violence. We test our theoretical expectations in a spatial analysis of protests and armed conflict in autocracies from 2003 to 2014. Our results show that protests increase the risk of local armed conflict when violently repressed. Further analysis reveals that the second mechanism, protest capture, accounts for the majority of escalations to armed conflict we see in our data.

We present the MMAD Repressive Actors Dataset (MMAD-RA). The MMAD-RA is a new data source that provides systematic information on the repressive actors present at protest events in autocracies, including their type, tactics, and level of violence. The data is temporally and geographically fine-grained, allowing for analysis between and within more than 60 countries from 2003 to 2012. The MMAD-RA enables analysis of the variation in repressive actors deployed to protests and their behavior, as well as how these actors impact protest dynamics and outcomes across political and socio-economic contexts. We believe the data will be a valuable resource for pushing forward research on how repressive actors engage with mass protests.

This article presents an update to the ViEWS political Violence Early-Warning System. This update introduces (1) a new infrastructure for training, evaluating, and weighting models that allows us to more optimally combine constituent models into ensembles, and (2) a number of new forecasting models that contribute to improve overall performance, in particular with respect to effectively classifying high- and low-risk cases. Our improved evaluation procedures allow us to develop models that specialize in either the immediate or the more distant future. We also present a formal, 'retrospective' evaluation of how well ViEWS has done since we started publishing our forecasts from July 2018 up to December 2019. Our metrics show that ViEWS is performing well when compared to previous out-of-sample forecasts for the 2015-17 period. Finally, we present our new forecasts for the January 2020-December 2022 period. We continue to predict a near-constant situation of conflict in Nigeria, Somalia, and DRC, but see some signs of decreased risk in Cameroon and Mozambique.

Numerous studies—operating with diverse model specifications, samples and empirical measures—suggest different economic, social, cultural, demographic, institutional and international determinants of democracy. We distinguish between democratization and democratic survival and test the sensitivities of 67 proposed determinants by varying the control variable set, democracy measure, and sample time period. Furthermore, we go beyond existing sensitivity analyses and unpack the aggregate results by analyzing how theoretically motivated control variables affect sensitivity for two prominent determinants in the democracy literature: income and Islam. Overall, our results reveal a far larger number of robust determinants of democratization than of democratic survival. For democratic survival, the only robust factors are income and a law-abiding bureaucracy. In addition, our results highlight uncertainty surrounding the relationship between income and democratization, but show that broader development processes enhance the chances of democratization. Moreover, chances of democratization are lower in countries with large Muslim populations, but that relationship is sensitive to controlling for natural resources, education and neighborhood characteristics. Other results of the sensitivity analysis show that political protests, a democratic neighborhood, and the global proportion of democracies positively influence democratization, while natural resources, majoritarian systems, and long-tenured leaders make countries less likely to democratize.

Why does election fraud trigger protest in the aftermath of some competitive authoritarian elections but not others? It is often argued that post-election protests occur when information about fraud confirms and reinforces mass grievances against the regime. However, grievances are not universal in autocracies. By focusing on whether government spending primarily benefits the ruling coalition or the masses — thereby affecting economic inequality and mass grievances — the theoretical argument in this article demonstrates how fraud both can lead to post-election protests and work in the autocratic government's favor. I find evidence for the theoretical argument in an analysis of 628 competitive elections in 98 authoritarian regimes (1950-2010). More broadly, the article advances our understanding of competitive elections in autocracies by focusing on how autocratic governments pursue multiple election strategies to promote regime stability and how combinations of strategies affect popular mobilization.

Eight years after the Arab Spring there is still much debate over the link between Internet technology and protest against authoritarian regimes. While the debate has advanced beyond the simple question of whether the Internet is a tool of liberation or one of surveillance and propaganda, theory and empirical data attesting to the circumstances under which technology benefits autocratic governments versus opposition activists is scarce. In this book, Nils B. Weidmann and Espen Geelmuyden Rød offer a broad theory about why and when digital technology is used for one end or another, drawing on detailed empirical analyses of the relationship between the use of Internet technology and protest in autocracies. By leveraging new sub-national data on political protest and Internet penetration, they present analyses at the level of cities in more than 60 autocratic countries. The book also introduces a new methodology for estimating Internet use, developed in collaboration with computer scientists and drawing on large-scale observations of Internet traffic at the local level. Through this data, the authors analyze political protest as a process that unfolds over time and space, where the effect of Internet technology varies at different stages of protest. They show that violent repression and government institutions affect whether Internet technology empowers autocrats or activists, and that the effect of Internet technology on protest varies across different national environments.