Abstract

Authors: Rachael K. Kyalo, Timo Schmid, Nora Würz

Small area estimation effectively addresses the issue of small sample sizes within subpopulations. Typically, the target population is divided into multiple nested hierarchical levels, such as counties and sub-counties. A twofold nested error regression model with random effects captures the variability across these levels. For estimating non-linear indicators like poverty measures, the twofold EBP model can be used, which relies on normality assumptions of the error terms - a condition often unmet in real data applications. This research enhances the twofold nested error regression model by incorporating a data-driven transformation, improving the model's robustness and flexibility. MSE estimation is performed using resampling methods. Model-based simulations compare the proposed model's performance with onefold EBP methods that include either area or sub-area effects. Results show that the proposed twofold EBP method adapts to the distribution shape, providing more efficient estimates than a fixed logarithmic transformation or no transformation. Finally, the twofold EBP with data-driven transformation is used to generate poverty estimates for rural and urban regions within Kenyan counties, offering a more nuanced and accurate assessment of poverty levels.