Spatial Empirical Best Linear Unbiased Prediction in Small Area Estimation of Poverty

Abstract

Spatial data contains of observation and region information, can describes spatial patterns such as social phenomenon or poverty. In poverty parameter estimations, the less of sample adequacy to deliver direct estimation is one of the limitation, thus the Small Area Estimation (SAE) developed to handle it. Since, the small area estimation techniques require “borrow strength” across the neighbor areas furthermore SAE was developed by integrating spatial information into the model, named as Spatial SAE. Therefore, the purpose of this paper is to compare the SAE and Spatial SAE model in order to estimate, at sub-district level, mean per capita income of each area using the poverty survey data in Bangka Belitung province at 2017 by Polytechnic of Statistics STIS. The findings of the paper is spatial information don’t influence the parameter estimation in SAE.