In spite of rising inequality within countries, policymakers often fall into the trap of evaluating progress from the top-down, rather than the bottom-up. Bilateral aid agencies and multilateral development banks tend to use national-level indicators (e.g., GDP per capita, child mortality rates) to select the countries and sectors where they will work. These national aggregates mask hotspots of deprivation within countries, which appear to be widening.
Over the past five years, AidData and its partners have worked with numerous governments and development partners to help close this evidence gap. With generous financial support from the United States Agency for International Development’s Global Development Lab, they have identified the geographical locations of nearly 70,000 development projects worth approximately $1.23 trillion across the globe. As a result, there is now an abundance of geographically disaggregated data we can use to assess: who is funding what, where, and to what effect at the subnational level?
In the Beyond the Tyranny of Averages report, we draw upon this body of work to shed light on two critical questions:
- Targeting — To what extent is the international community channeling resources to the least developed regions within countries?
- Effectiveness — Under which conditions does this assistance help local communities reduce spatial inequality – the uneven distribution of public services, infrastructure, wealth, and opportunity?
Based on our findings, we present a roadmap for countries and their development partners to fully harness the subnational data revolution to "leave no one behind".
Funding: This research was conducted with generous support from the United States Agency for International Development (USAID) Global Development Lab (through cooperative agreement AID-OAA-A-12-00096). The views expressed here do not necessarily reflect the views of USAID or the United States Government. This study also supports AidData’s commitments as an anchor partner of the Global Partnership for Sustainable Development Data.