Geostatistical Tools to Map the Interaction between Development Aid and Indices of Need

In order to meet and assess progress towards global sustainable development goals (SDGs), an improved understanding of geographic variation in population wellbeing indicators such as health status, wealth and access to resources is crucial, as the equitable and efficient allocation of international aid relies on knowing where funds are needed most. Unfortunately, in many low-income countries, detailed, reliable and timely information on the spatial distribution and characteristics of intended aid recipients are rarely available. Furthermore, lack of information on the past distribution of aid relative to need also hinders assessments of the impacts of aid. High-resolution data on key social and health indicators, as well as how aid distribution relates to these indicators are therefore fundamental for targeting limited resources and building on past efforts. 

In this study, we show how modern statistical approaches combined with a new geographic database of aid distribution can be used to map the distribution of indicators with a level of detail that can support geographically stratified decision-making. Based on geo-located survey data from Demographic and Health Surveys (DHS) in Nigeria (2008 - 2013) and Nepal (2006 - 2011), Bayesian geostatistical models and machine learning approaches were used in combination with a suite of spatial data layers to create high-resolution predictive maps for (i) the rates of stunting in children under the age of five and (ii) the household wealth index. An ensemble model was also exploited for aggregating different modelling results to improve the modelling prediction capacity in Nigeria (for stunting 2008). By combining these maps with information on the disbursement of aid for increasing food security and alleviating poverty (AidData database - http://aiddata.org/), we quantified both the reported spatial distribution of aid relative to stunting and poverty, as well as how changes in these indices overtime related to aid disbursement. While many cases of aid disbursement lacked detailed spatial information, the results here demonstrate the potential of this approach and highlight the value of spatially disaggregated data on the distribution of aid.

Funding: This research was supported by AidData at the College of William and Mary and the USAID Global Development Lab through cooperative agreement AID-OAA-A-12-00096. The views expressed here do not necessarily reflect the views of AidData, USAID, or the United States Government.

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