Using Satellite Imagery and Machine Learning to Estimate the Livelihood Impact of Electricity Access

TL;DR

This paper demonstrates how satellite imagery and machine learning can accurately estimate the causal impact of electricity access on livelihoods in Uganda, providing a scalable method for policy evaluation in data-scarce regions.

Contribution

It introduces a novel approach combining satellite imagery and computer vision to measure local livelihoods and assess electrification impacts more reliably than traditional methods.

Findings

Electricity access increases village asset wealth by 0.17 standard deviations.

ML-based estimates outperform traditional inference methods.

Electrification more than doubles growth rates in rural Ugandan villages.

Abstract

In many regions of the world, sparse data on key economic outcomes inhibits the development, targeting, and evaluation of public policy. We demonstrate how advancements in satellite imagery and machine learning 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 0.17 standard deviations, more than doubling the growth rate over our study period relative to untreated areas. Our results provide country-scale evidence on the impact of a key infrastructure investment, and provide a low-cost, generalizable approach to future policy evaluation in data sparse environments.