Small Domain Inference Combining Data from Two Independent Surveys

Abstract

Many often two surveys conducted independently, may have some auxiliary variables in common along with a set of extra variables that are not common to both the surveys. One survey, which is small in sample size but collects both variable of interest as well as a set of auxiliary variables. The another survey which is relatively larger in sample size, does not collects variable of interest but collects a set of auxiliary variables, common to the small survey. In addition, the large survey collects multiple response variables as well as set of auxiliary variables not common to the small survey. A small area predictor for small domain (or area) means is proposed by combining data from these two surveys using multipurpose weights. Empirical results from model-based as well as design-based simulations indicate that the proposed small area predictor that incorporates the additional auxiliary variables of the large survey along with the common auxiliary variables, provide better efficiency gain.