A unit-level small area model with misclassified covariates

TL;DR

This paper develops a Bayesian small area model that accounts for measurement error in both continuous and categorical covariates, improving estimates in surveys like Ethiopia's health data.

Contribution

It introduces a novel Bayesian framework for small area estimation that jointly models misclassification in categorical variables and measurement error in continuous variables.

Findings

Model performs well in simulation scenarios.

Application to Ethiopia DHS data highlights improved malnutrition estimates.

Joint modeling of misclassification and measurement error enhances accuracy.

Abstract

Small area models are mixed effects regression models that link the small areas and borrow strength from similar domains. When the auxiliary variables used in the models are measured with error, small area estimators that ignore the measurement error may be worse than direct estimators. Alternative small area estimators accounting for measurement error have been proposed in the literature but only for continuous auxiliary variables. Adopting a Bayesian approach, we extend the unit-level model in order to account for measurement error in both continuous and categorical covariates. For the discrete variables we model the misclassification probabilities and estimate them jointly with all the unknown model parameters. We test our model through a simulation study exploring different scenarios. The impact of the proposed model is emphasized through application to data from the Ethiopia Demographic and Health Survey where we focus on the women's malnutrition issue, a dramatic problem in developing countries and an important indicator of the socio-economic progress of a country.