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"Information and Civil Unrest in Dictatorships" published on by Oxford University Press.

After introducing a measure for educational polarization (EduPol ), this paper presents a theoretical framework to understand whether and how EduPol may affect the contest for power in society. The model suggests that societies with high degrees of EduPol (i.e., substantial shares with either no or university-level education) are systematically more prone to civil unrest. We test this prediction on four measures of civil unrest: Political instability, domestic terrorism, civil conflict, and civil war. Our empirical estimations produce evidence consistent with this hypothesis as all four phenomena are positively associated with EduPol at the beginning of the respective period, exhibiting meaningful magnitudes. These results prevail when accounting for (i) potentially confounding factors, (ii) country- and time-fixed effects, (iii) economic inequality, (iv) ethnic and religious polarization and fractionalization, and (v) numerous alternative estimations and outcome variables.

This paper investigates the empirical relationship between citizens' perceptions of economic and political conditions and the incidence of nonviolent uprisings. Perceptions are measured by aggregating individual-level data from regional barometer surveys. The main results show that negative perceptions of political conditions -- proxied by the share of the population that is generally dissatisfied with the way democracy works -- have a significant positive effect on the number of protests and strikes. Negative perceptions of economic conditions do not seem to be significantly related to the latter. This generally holds across a large sample of countries and is particularly the case for Western and Central European countries as well as high-income countries. In developing economies, however, social protests appear to be driven by dissatisfaction with economic and political conditions. The heterogeneous effects of perceptions on uprisings across geography and income groups, however, are not robust and susceptible to changes in estimators and model specification. In particular, the international contagion of protests eliminates this international heterogeneity, implying that the incidence of uprisings in nearby countries tends to generate protests at home through its effect on perceptions related to political conditions in high-income countries. Overall, the effect of perceptions about political conditions, along with protest contagion, is robust to the inclusion of numerous control variables that capture actual economic conditions and the quality of governance across countries. The results are also robust to the use of seemingly valid instrumental variables, alternative count-data estimators, and sample composition.

Civil unrest events (protests, strikes, and "occupy" events) range from small, nonviolent protests that address specific issues to events that turn into large-scale riots. Detecting and forecasting these events is of key interest to social scientists and policy makers because they can lead to significant societal and cultural changes. We forecast civil unrest events in six countries in Latin America on a daily basis, from November 2012 through August 2014, using multiple data sources that capture social, political and economic contexts within which civil unrest occurs. The models contain predictors extracted from social media sites (Twitter and blogs) and news sources, in addition to volume of requests to Tor, a widely used anonymity network. Two political event databases and country-specific exchange rates are also used. Our forecasting models are evaluated using a Gold Standard Report (GSR), which is compiled by an independent group of social scientists and subject matter experts. We use logistic regression models with Lasso to select a sparse feature set from our diverse datasets. The experimental results, measured by F1-scores, are in the range 0.68 to 0.95, and demonstrate the efficacy of using a multi-source approach for predicting civil unrest. Case studies illustrate the insights into unrest events that are obtained with our method. The ablation study demonstrates the relative value of data sources for prediction. We find that social media and news are more informative than other data sources, including the political event databases, and enhance the prediction performance. However, social media increases the variation in the performance metrics.