Poverty Estimation with Satellite Imagery at Neighborhood Levels : Results and Lessons for Financial Inclusion from Ghana and Uganda

By successfully reaching historically underserved and vulnerable populations such as women, the poor, and people living in rural communities, Digital Financial Services have contributed to unprecedented growth in financial inclusion in Sub-Saharan Africa during the past decade. The adoption and usage of DFS -- and the subsequent financial inclusion that has resulted -- has helped reduce poverty and increase prosperity throughout the region. Still, service providers and development practitioners often lack reliable, detailed, and low-cost poverty data that could help them accurately identify additional communities and individuals who would benefit the most from access to financial services. The lack of data hinders the deployment of services throughout the region and complicates efforts to monitor and evaluate the impact that interventions have on poverty. Relying on traditional household surveys for poverty data is time consuming and expensive. What’s more, by the time the data are collected and analyzed, it is often out of date. But there are alternatives for estimating and mapping poverty with the goal of accelerating and expanding financial inclusion and helping DFS providers target the poorest. Machine learning algorithms can, for example, be trained to predict poverty based on imagery captured by satellites and from call detail records, which document mobile phone usage. For this research study, the IFC Mastercard Foundation Partnership for Financial Inclusion collaborated with the Stanford University Sustainability and Artificial Intelligence Lab to advance existing poverty prediction models to generate poverty estimates at neighborhood-level resolution, which is much more refined than macro-level estimates produced by research to date. Satellite Imagery and call detail records (CDR), validated by ground-truth surveys, were used to develop models that can predict poverty in Ghana and Uganda.