A New Perspective on Spatial Heterogeneity in African Development

Access to basic infrastructure is a critical component of quality of life and an important measure of economic development. However, on-the-ground data about infrastructure access, especially in low-income countries, is often sparse and costly to collect. We train and calibrate a machine learning model to extract data on infrastructure access for each 6.72x6.72km area of Africa from satellite images. The model achieves accuracy levels of 77.1% to 84.7%. We show the value of this novel dataset with two applications. First, we use a spatial regression discontinuity design to study how much of the heterogeneity in infrastructure access across countries comes from differences in institutional quality. Second, we study the role of political favoritism in explaining within country heterogeneity.