Small area estimation of poverty in four West African countries by integrating survey and geospatial data
This paper offers a nontechnical review of selected applications that combine survey and geospatial data.
Abstract
This paper offers a nontechnical review of selected applications that combine survey and geospatial data to generate small area estimates of wealth or poverty. Publicly available data from satellites and phones predicts poverty and wealth accurately across space, when evaluated against census data, and their use in model-based estimates improve the accuracy and efficiency of direct survey estimates. Although the evidence is scant, models based on interpretable features appear to predict at least as well as estimates derived from Convolutional Neural Networks. Estimates for sampled areas are significantly more accurate than those for non-sampled areas due to informative sampling. In general, estimates benefit from using geospatial data at the most disaggregated level possible. Tree-based machine learning methods appear to generate more accurate estimates than linear mixed models. Small area estimates using geospatial data can improve the design of social assistance programs, particularly when the existing targeting system is poorly designed.
This is an output from the Data and Evidence for Tackling Extreme Poverty (DEEP) Research Programme.
Citation
Edochie, I., Newhouse, D., Tzavidis, N., Schmid, T., Foster, E., Ouedraogo, A., Sanoh A., Savadogo, A., Luna, A. Small area estimation of poverty in four West African countries by integrating survey and geospatial data, Policy research working paper, no 10512, World Bank Group 2023