Using machine learning to assess the livelihood impact of electricity access.

In many regions of the world, sparse data on key economic outcomes inhibit the development, targeting and evaluation of public policy. We demonstrate how advancements in satellite imagery and machine learning (ML) can help ameliorate these data and inference challenges. In the context of an expansion of the electrical grid across Uganda, we show how a combination of satellite imagery and computer vision can be used to develop local-level livelihood measurements appropriate for inferring the causal impact of electricity access on livelihoods.