Automatic Variance Adjustment for Small Area Estimation

TL;DR

This paper introduces an automatic variance adjustment method for small area estimation in low-resource settings, improving the stability of estimates when data are sparse, and implements it in an accessible R package.

Contribution

It proposes a novel, principled variance adjustment technique for Fay-Herriot models in small area estimation, especially useful in data-sparse environments.

Findings

The adjustment improves estimate stability in simulations.

Application to Zambian survey data demonstrates practical utility.

The method is easy to implement and available in an R package.

Abstract

Small area estimation (SAE) is a common endeavor and is used in a variety of disciplines. In low- and middle-income countries (LMICs), in which household surveys provide the most reliable and timely source of data, SAE is vital for highlighting disparities in health and demographic indicators. Weighted estimators are ideal for inference, but for fine geographical partitions in which there are insufficient data, SAE models are required. The most common approach is Fay-Herriot area-level modeling in which the data requirements are a weighted estimate and an associated variance estimate. The latter can be undefined or unstable when data are sparse and so we propose a principled modification which is based on augmenting the available data with a prior sample from a hypothetical survey. This adjustment is generally available, respects the design and is simple to implement. We examine the empirical properties of the adjustment through simulation and illustrate its use with wasting data from a 2018 Zambian Demographic and Health Survey. The modification is implemented as an automatic remedy in the R package surveyPrev, which provides a comprehensive suite of tools for conducing SAE in LMICs.