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Previous research by Goldstone et al. (2010) generated a highly accurate predictive model of state-level political instability. Notably, this model identifies political institutions – and partial democracy with factionalism, specifically – as the most compelling factors explaining when and where instability events are likely to occur. This article reassesses the model’s explanatory power and makes three related points: (1) the model’s predictive power varies substantially over time; (2) its predictive power peaked in the period used for out-of-sample validation (1995–2004) in the original study and (3) the model performs relatively poorly in the more recent period. The authors find that this decline is not simply due to the Arab Uprisings, instability events that occurred in autocracies. Similar issues are found with attempts to predict nonviolent uprisings (Chenoweth and Ulfelder 2017) and armed conflict onset and continuation (Hegre et al. 2013). These results inform two conclusions: (1) the drivers of instability are not constant over time and (2) care must be exercised in interpreting prediction exercises as evidence in favor or dispositive of theoretical mechanisms.

Uganda is one of the poorest nations in the world. It is important to obtain accurate, timely data on socio-economic characteristics sub-nationally, so as to target poverty reduction strategies to those most in need. Many studies have demonstrated that nighttime lights (NTL) can be used to measure human activities. Nevertheless, the methods developed from these studies (1) suffer from coarse resolutions, (2) fail to capture the nonlinearity and multi-scale variability of geospatial data, and (3) perform poorly for agriculture-dependent regions. This study proposes a new enhanced light intensity model (ELIM) to estimate the gross domestic product (GDP) for sub-national units within Uganda. This model is developed by combining the NTL data from the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS), the population data from the Global Human Settlement Layer (GHSL), and information on agricultural production and market prices across several commodity types. This resulted in a gridded dataset for Uganda’s GDP at sub-national levels, to capture the spatial heterogeneity in the economic activity.