Data-driven local planning at national scale: How data collected on mobile phones enable a Conditional Grants Scheme in Nigeria

Many countries struggle with effectively estimating what fraction of federal funds should be disbursed to various local administrative levels for spending on social programs for development. Varying spatial distributions of resources, populations, services, costs and past performance make local information vital. Traditionally, such data are collected using paper-based survey methods and aggregated up to larger admin units, introducing long delays and losing granularity of information. The authors assisted the Nigerian Office of the Senior Special Assistant to the President on Millennium Development Goals (OSSAP-MDGs) to rapidly collect data on health, education, and water facilities using Android smartphones. Data collection was facilitated by a generalizable platform (Formhub.org). Authors also built a common data visualization platform (Nigeria MDGs Information System) to present disaggregated data at facility and local government levels. These technological innovations were used by local planners to develop and evaluate more than $500M of federally-funded grant proposals that targeted more than 450 Local Government Areas over three years as part of the OSSAP-MDGs Conditional Grants Scheme. This paper presents the design and implementation of these systems in the context of enabling data-driven local planning.