Enhancing Poverty Rates Reliability Using Small Area Estimation

Abstract

This study systematically compares the performance of three Small Area Estimation
(SAE) methods—Empirical Best Linear Unbiased Predictor (EBLUP), Hierarchical Bayes (HB)
Beta, and HB Flexible Beta—using two different auxiliary data sources-Village Potential
(Podes) and Socio-Economic Registration data (Regsosek). The SAE methodologies were
applied in a case study focusing on Java Island, Indonesia. Direct estimates remain has high
Relative Standard Errors (RSE) above 25%, indicating low reliability. EBLUP methods
improved estimate reliability but still produced some unreliable estimates. The HB Beta method
further reduced RSE values, while the HB Flexible Beta model achieved the lowest RSE,
eliminating all unreliable estimates. Moreover, Socio-Economic Registration data consistently
resulted in lower RSE values compared to Village Potential data, particularly when used with
the HB Flexible Beta model. These result highlight that integrating advanced SAE models such
as HB Flexible Beta with high-quality administrative data such as Socio-Economic Registration
data is crucial for producing reliable and precise poverty estimates for more targeted and
effective poverty alleviation policies.