Small area estimation of proportions with different levels of auxiliary data

Abstract

Binary data are often of interest in many small areas of applications. The use of standard small area estimation methods based on linear mixed models becomes problematic for such data. An empirical plug-in predictor (EPP) under a unit-level generalized linear mixed model with logit link function is often used for the estimation of a small area proportion. However, this EPP requires the availability of unit-level population information for auxiliary data that may not be always accessible. As a consequence, in many practical situations, this EPP approach cannot be applied. Based on the level of auxiliary information available, different small area predictors for estimation of proportions are proposed. Analytic and bootstrap approaches to estimating the mean squared error of the proposed small area predictors are also developed. Monte Carlo simulations based on both simulated and real data show that the proposed small area predictors work well for generating the small area estimates of proportions and represent a practical alternative to the above approach. The developed predictor is applied to generate estimates of the proportions of indebted farm households at district-level using debt investment survey data from India.