B2024
Title: Combining surveys in small area estimation using area level model
Authors: Carolina Franco - NORC at the University of Chicago (United States) [presenting]
Abstract: For many surveys, researchers, policymakers, and other stakeholders are interested in obtaining estimates for various domains, such as for geographic subdivisions, for demographic groups, or a cross-classification of both. Often, the demand for estimates at a disaggregated level exceeds what the sample size 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. 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 a review of the topic of combining information from multiple surveys in small area estimation, focusing on area-level models. We will provide practical advice and a technical introduction, and illustrate with applications.
