greater regional spillovers ( Arezki et al. (2020) ). However, it is likely that the drivers of unrest are disparate and interact with socioeconomic conditions in complex and non-linear ways that are difficult to enumerate. We employ a flexible machine learning approach to gauge the importance of a large set of predictors and capture non-linearities. Our preferred model has a balanced accuracy level of 66% and, in that sense, is correct in predicting unrest approximately two-thirds of the time. We find a relatively modest role for predictors in the literature, with
empirically that the early warning systems in use at the time were not able to predict the Asian currency crises of the 1990s or the Global Financial Crisis, respectively. In a similar vein, my first result in this paper is that the out-of-sample predictions for fiscal crises obtained from common econometric approaches, on average, cannot outperform an uninformed heuristic rule of thumb and are considerably less accurate than what their in-sample performance suggests. By using some of the most popular machine learning approaches, I obtain improvements in predictive