Geocoding to improve donor harmonization
Responding to long-held concerns about uncoordinated donor behavior, the Paris Declaration of 2005 made harmonization one of its five pillars, pledging to work towards “eliminating duplication of efforts and rationalising donor activities to make them as cost-effective as possible.” Among the problems of uncoordinated action are elevated transaction costs as recipient governments struggle to comply with variegated donor rules and procedures (Knack and Rahman 2007) and decreased donor specialization as “all donors seem to want to give to all sectors in all countries” (Easterly 2007). All of the attention given to coordination problems begs the questions: how much do donors coordinate their activities? And is coordination effectively targeting needs within a country – both spatially (aid to villages or provinces) and sectorally (aid for different purposes)?
Recent work by the World Bank – AidData partnership (Mapping for Results Initiative) sheds new light on the topic. A previous blog post on The First Tranche contained this map of World Bank projects in Kenya, which demonstrated a tremendous concentration of aid in the Mombassa-Nairobi-Lake Victoria corridor. Curious if this pattern of aid allocation was the result of spatial coordination with other donors, we mapped all active projects of the World Bank and the AfDB in Kenya as shown in the illustration. The overlapping of World Bank and AfDB projects in the Mombassa-Nairobi-Lake Victoria corridor is not entirely surprising, as these are the main population centers of Kenya. However, this corridor is also comparatively much better off than the more arid North and East, which receives virtually no aid from either donor. Neither donor appears to be coordinating efforts nor effectively targeting the neediest areas of the country.
Though the results of comparing the allocation patterns of these two donors are striking, they also call attention to the need to mainstream geo-referencing for the other approximately 50 active donors in Kenya. As geo-referenced data become more common, donors and other interested parties (like us) can use the data to find and publicize areas and sectors that are not receiving an amount of aid that is proportional to need (measured in various objective ways).
Such visualizations do not provide clear answers about where aid should be allocated, but they raise important questions with striking clarity and will encourage donors and recipient governments to explain allocation patterns to beneficiaries and taxpayers. We think this will improve the prospect of aid dollars arriving where they are needed most (Full results, including analysis of sectoral coordination in Kenya and spatial and sectoral coordination in Mozambique, can be viewed here)