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Big Data & Aid Targeting
MAPPING COMMUNITY DEVELOPMENT AID:
SPATIAL ANALYSIS IN MYANMAR
Aid policy has the potential to alleviate global poverty by targeting areas of concentrated need. However, a gap exists between aid given and actual need because of inadequate data and problems with delivering aid in conflict-prone areas. Evaluations of need have traditionally relied on costly and time-consuming survey techniques. These difficulties are exacerbated in conflict-prone areas of development. Although the share of the world’s poor remains high in fragile states, ongoing civil strife, armed conflict, and population displacement represent challenges to deliver aid directly to the poor. Often little or no ground-truth surveys are available on income and wealth in sub-national regions where aid projects are taking place. Thus far, few aid-determinant studies have analyzed the characteristics of poverty at the sub-national level.
This study intends to fill this research gap by using spatial analysis to estimate poverty in small regions in Myanmar. This approach allows policymakers to identify poverty at a policy-relevant and granular level and promote targeting, monitoring, and evaluation of aid for the most marginalized populations.
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EXTRACTED FROM:
Mapping Community Development Aid: Spatial Analysis in Myanmar (2020)
Using Computer Vision & Deep Learning to Predict Poverty and Aid in Data-Sparse Contexts (2020)
DISTRUBUTION OF AID
Findings in this study suggest that nuances captured in nightlight luminosity can predict CLD aid density in Myanmar. Brighter nightlights are strongly correlated with higher aid density in that community. The model fit of this variable is better than other poverty-related variables.
Nightlight shows promise in capturing the variability of economic development in target villages and improving prediction of aid allocation.
Dr. Jung conducted analysis at the smallest geographical unit at the village level, or, at the administrative level 5. One of the measurements of this study, aid hotspots, does not depend on administrative boundaries.
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This study promotes evidence-based targeting for area-based interventions, lacking location specific and timely data. It addresses the disconnect between communities wanting to mobilize resources and development agencies identifying populations to serve. Exploring new data sources and synthesizing them with administrative and survey data at a fine-grained level extends their utility as a policy design and evaluation tool.
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