A machine learning model trains on geo-located survey data to predict state presence.

By Gustav Agneman Christoffer Cappelen Kasper Brandt David Sjöberg

The ability of states to exercise authority often varies considerably within their borders, yet we lack reliable empirical measures of the uneven reach of states. In this paper, we develop a methodology to predict state presence at granular spatial resolutions and demonstrate the approach using data from Sub-Saharan Africa. We link a range of indicators of state presence, e.g., infrastructural data, with geolocated survey data of residents’ experiences with subnational governance. Then, we employ a machine learning algorithm that learns how the input variables relate to experienced state presence and extrapolates the predictions to all of Sub-Saharan Africa. We validate the predicted measure through a range of tests and document how local state presence influences development outcomes.