Combining surveys in small area estimation using area‐level models

Abstract

For many sample surveys, researchers, policymakers, and other stakeholders are interested in obtaining estimates for various domains, such as for geographic levels, for demographic groups, or a cross‐classification of both. Often, the demand for estimates at a disaggregated level exceeds what the sample size and survey design can support when estimation is done by traditional design‐based estimation methods. Small area estimation involves exploiting relationships among domains and borrowing strength from multiple sources of information to improve inference relative to direct survey methods. This typically involves the use of models whose success depends heavily on the quality and predictive ability of the sources of information used. Possible sources of auxiliary information include administrative records, Censuses, big data such as traffic or cell phone data, or previous vintages of the same survey. One rich source of information is that of other surveys, especially in countries like the United States, where multiple surveys exist that cover related topics. We will provide an introduction to the topic of combining information from multiple surveys in small area estimation using area‐level models, including practical advice and a technical introduction, and illustrating with applications. We will discuss reasons to combine surveys and give an overview of some of the most common types of models.This article is categorized under: Statistical Models > Multivariate Models

Small area estimates for Voting Rights Act Section 203(b) coverage determinations

“…Despite achieving computational efficiency for a unit-level model, this approach might be computationally expensive relative to area-level SAE. Unit- and area-level models each have advantages and disadvantages (Franco and Maitra) [10] .…”