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(1,2). 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. We then show how ML-based inference techniques deliver more reliable estimates of the causal impact of electrification than traditional alternatives when applied to these data. We estimate that grid access improves-village-level asset wealth in rural Uganda by up to 0.15 standard deviations, more than doubling the growth rate during our study period relative to untreated areas. Our results provide country-scale evidence on the impact of grid-based infrastructure investment and our methods provide a low-cost, generalizable approach to future policy evaluation in data-sparse environments.

Automated summary

Advancements in satellite imagery and machine learning can help address data scarcity and inference challenges. Using the example of the expansion of the electrical grid in Uganda, this study demonstrates how satellite imagery and computer vision techniques can be used to develop local-level livelihood measurements and machine learning-based inference techniques provide more reliable estimates of the impact of electrification. The findings show that grid access in rural Uganda improves village-level asset wealth and doubles the growth rate compared to untreated areas.