Small Area Estimation of Poverty in Four West African Countries by Integrating Survey and Geospatial Data

Investment Rating - The report does not explicitly provide an investment rating for the industry under study. Core Insights - The paper presents a methodology for generating experimental small area estimates of poverty in four West African countries: Chad, Guinea, Mali, and Niger, by integrating household-level survey data with grid-level geospatial data, which enhances the frequency and granularity of poverty reporting [3][9][24] - The methodology demonstrates that in the absence of recent census data, small area estimation using publicly available geospatial covariates is feasible and can significantly improve the efficiency of poverty estimates compared to direct estimation methods [3][19][22] Summary by Sections Introduction - The paper introduces small area estimation (SAE) as a statistical method to improve survey estimates by integrating survey data with geographically comprehensive auxiliary data, which is crucial for targeting interventions in impoverished areas [9][10] Data Sources and Geospatial Data Integration - The report utilizes geospatial covariates due to outdated census data in the focus countries, with the most recent censuses conducted between 2009 and 2014 [24][25] - The integration process involves matching survey households to grid cells and dropping households without geocoordinates, ensuring a robust dataset for analysis [26][27] Small Area Estimation Methodology - The methodology employs the Empirical Best Predictor (EBP) under a nested error regression model, focusing on household-level data while using geospatial covariates at the grid cell level [30][32] - The model selection process incorporates Lasso to avoid overfitting and includes regional dummy variables to enhance predictive accuracy [37] Evaluation Exercise - An evaluation using recent census data from Burkina Faso serves as a benchmark to compare estimates produced with geospatial covariates against those derived from census data, revealing a high correlation of 0.799 overall, with in-sample areas showing a correlation of 0.879 [48][51] - The evaluation highlights the challenges of out-of-sample predictions, particularly in remote areas not covered by surveys, which may lead to lower predictive accuracy [51][55]