An Integrated Modelling Framework for Estimating Wealth Index for Small Geographical Areas

Thomas M. Menino Convention & Exhibition Center 

Accurately assessing economic well-being in developing countries is challenging due to rapid socioeconomic change and the lack of recent census or local-level survey data. Conventional small area estimation methods often rely on outdated census information, while geospatial data, though timely and granular, provide limited predictors and may introduce bias through preprocessing. This motivates the need for modelling strategies that integrate multiple data sources to improve wealth measurement when census data are unavailable or outdated.
We propose an integrated modelling framework that jointly models two contemporaneous sample surveys estimating similar asset wealth indices, while incorporating geospatial covariates as alternative auxiliary information. Challenges arising from differences in data granularity and quality are addressed through a spatial matching procedure. Our results show that the proposed joint model yields more reliable estimates than conventional univariate approaches, reducing errors from reliance on geospatial data. We conclude by discussing implications for small area estimation and extensions to modelling and variance estimation in data-limited settings.

Small area estimation

Asset-based wealth index

Geospatial covariates

Survey data integration

Spatial matching

Joint modelling 

Survey Research Methods Section