The uneven reach of the state: A novel approach to mapping local state presence

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. • We present a novel approach to mapping local state presence. • A machine learning model trains on geo-located survey data to predict state presence. • The features comprise variables that signal state presence, e.g. road infrastructure. • The index is validated against alternative data on state presence. • State presence moderates the relationship between oil wealth shocks and conflict.