Enhancing Poverty Rates Reliability Using Small Area Estimation - Permatasari, Novia (Proceedings of The International Conference on Data Science and Official Statistics, 2025)
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 wereapplied in a case study focusing on Java Island, Indonesia. Direct estimates remain has highRelative Standard Errors (RSE) above 25%, indicating low reliability. EBLUP methodsimproved estimate reliability but still produced some unreliable estimates. The HB Beta methodfurther reduced RSE values, while the HB Flexible Beta model achieved the lowest RSE,eliminating all unreliable estimates. Moreover, Socio-Economic Registration data consistentlyresulted in lower RSE values compared to Village Potential data, particularly when used withthe HB Flexible Beta model. These result highlight that integrating advanced SAE models suchas HB Flexible Beta with high-quality administrative data such as Socio-Economic Registrationdata is crucial for producing reliable and precise poverty estimates for more targeted andeffective poverty alleviation policies.