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

29 Pages Posted: 13 Sep 2021 Last revised: 20 Nov 2024

See all articles by Nathan Ratledge

Nathan Ratledge

Stanford University

Gabriel Cadamuro

AtlasAI

Brandon de la Cuesta

Stanford University

Mathieu Stigler

Stanford University, Center on Food Security and the Environment

Marshall Burke

Stanford University - Department of Earth System Science and the FSE

Date Written: September 2021

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.

Suggested Citation

Ratledge, Nathan and Cadamuro, Gabriel and de la Cuesta, Brandon and Stigler, Matthieu and Burke, Marshall, Using Satellite Imagery and Machine Learning to Estimate the Livelihood Impact of Electricity Access (September 2021). NBER Working Paper No. w29237, Available at SSRN: https://ssrn.com/abstract=3922511

Nathan Ratledge (Contact Author)

Stanford University ( email )

Stanford, CA 94305
United States

Gabriel Cadamuro

AtlasAI ( email )

Brandon De la Cuesta

Stanford University ( email )

Stanford, CA 94305
United States

Matthieu Stigler

Stanford University, Center on Food Security and the Environment ( email )

Stanford, CA 94305
United States

HOME PAGE: http://https://fse.fsi.stanford.edu/

Marshall Burke

Stanford University - Department of Earth System Science and the FSE ( email )

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