Small area estimation with generalized random forests: Estimating poverty rates in Mexico

TL;DR

This paper introduces a robust machine learning-based small area estimation method using generalized random forests to accurately estimate poverty rates in Mexico, even with limited data and binary indicators.

Contribution

It develops a novel generalized mixed effects random forest approach for small area estimation of binary poverty indicators, incorporating hierarchical modeling and bootstrap error estimation.

Findings

Effective in capturing non-linear relationships in data

Robust to information loss from binary conversion

Provides reliable spatial poverty estimates in Mexico

Abstract

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.