text: 169,188,1435664887007506433,2437293979,Nathan Ratledge,"Pleased to share our working paper on the economic effects of grid-based electrification. A few thoughts to complement @MarshallBBurke's great thread.  1. Satellite imagery and machine learning represent a new frontier for economics & policy evaluation. <LINK> 2. Huge thanks to @atlasai_co, especially co-author Gabe Cadamuro, for their ground breaking work using satellite imagery & AI to literally create new data in really challenging settings.  Here, we use imputed outcomes to create a balanced dataset suitable for causal inference. 3. One takeaway for other researches using similar methods is to be cognizant of the downstream task. Optimizing for r2 in the CNN process may not be the best metric, as we show.  Thanks to Marshall, Brandon de la Cuesta and Matthieu Stigler for their brainpower on this. 4. Ultimately, we find that new grid-based electrification had a substantial, statistically significant positive effect on asset wealth in Uganda, with electrified communities growing nearly 2x as fast as un-electrified comms.  These results are robust to varying parameters. 5. Stepping away from the paper & putting on my policy hat, our results suggest that investments in grid extensions (while costly) have measurable, near-term impacts, even in low income settings.   As 600m people in SSA lack electricity, we should accelerate grid extensions.",https://arxiv.org/abs/2109.02890,"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. ","Using Satellite Imagery and Machine Learning to Estimate the Livelihood