Modeling Efficiency of Foreign Aid Allocation in Malawi

TL;DR

This paper evaluates the efficiency of foreign aid distribution in Malawi using Bayesian spatial models, identifying the best predictive model and proposing more effective allocation strategies based on economic indicators.

Contribution

It introduces a Bayesian spatial gamma regression model to analyze aid allocation efficiency and suggests improved distribution scenarios based on model inferences.

Findings

Spatial gamma regression best predicts aid allocation

Evidence of inefficiency in current aid distribution in some sectors

Proposed allocation scenarios outperform current distribution

Abstract

The Open Aid Malawi initiative has collected an unprecedented database that identifies as much location-specific information as possible for each of over 2500 individual foreign aid donations to Malawi since 2003. Ensuring efficient use and distribution of that aid is important to donors and to Malawi citizens. However, because of individual donor goals and difficulty in tracking donor coordination, determining presence or absence of efficient aid allocation is difficult. We compare several Bayesian spatial generalized linear mixed models to relate aid allocation to various economic indicators within seven donation sectors. We find that the spatial gamma regression model best predicts current aid allocation. Using this model, first we use inferences on coefficients to examine whether or not there is evidence of efficient aid allocation within each sector. Second, we use this model to determine a more efficient aid allocation scenario and compare this scenario to the current allocation to provide insight for future aid donations.