A New Study
Led by PhD candidate Nathan Ratledge, the research relied on innovative techniques, developed at Stanford, that combine satellite imagery and AI to measure and study poverty in countries where data collection has traditionally posed a challenge. The researchers’ findings demonstrate that Machine Learning (ML) techniques can provide more reliable estimates of the causative impact of electricity access in Africa.
Based on the findings, the electrical grid encompassed 41% of Uganda in 2019, marking a significant increase from just 12% in 2010. Furthermore, increased access to electricity correlated with substantial improvements in financial conditions and quality of life, as measured by increases in home construction, appliance use and other tangible markers of growing wealth. Overall, the data showed that the rate of wealth accumulation roughly doubled in Ugandan communities that gained electricity access, as compared with communities that lacked it.
Until now, one of the primary problems encountered in measuring electricity access and its relation to poverty in Africa has been a lack of data. As Ratledge stated, “It’s hard in many low-income countries to get any reliable data. It just doesn’t exist.” A model for overcoming this obstacle, the recent Stanford study presents a new way to measure progress in the fight against global poverty.
A Promise of Future Growth
Due to the Stanford research, “we now have this technique to give local-level measurements of key economic outcomes at a broad, spatial scale and over time,” said Marshall Burke, the study’s co-author. Perhaps the most inspiring aspect of the researchers’ work is that all evidence points to an exponential proliferation of understanding. Ongoing technological advancements are expected to make such techniques widely affordable and accessible, allowing researchers to carry out similar work to better understand and combat poverty around the world.