  Identifying and addressing poverty is challenging in administrative units
with limited information on income distribution and well-being. To overcome
this obstacle, small area estimation methods have been developed to provide
reliable and efficient estimators at disaggregated levels, enabling informed
decision-making by policymakers despite the data scarcity. From a theoretical
perspective, we propose a robust and flexible approach for estimating poverty
indicators based on binary response variables within the small area estimation
context: the generalized mixed effects random forest. Our method employs
machine learning techniques to identify predictive, non-linear relationships
from data, while also modeling hierarchical structures. Mean squared error
estimation is explored using a parametric bootstrap. From an applied
perspective, we examine the impact of information loss due to converting
continuous variables into binary variables on the performance of small area
estimation methods. We evaluate the proposed point and uncertainty estimates in
both model- and design-based simulations. Finally, we apply our method to a
case study revealing spatial patterns of poverty in the Mexican state of
Tlaxcala.
