Claim Missing Document
Check
Articles

Found 12 Documents
Search

Disaggregating the Hidden: Small Area Estimates of Child Labor in Bali Province Agung, Ahmad Nadifa Al; Sari, Arlita Dwina Firlana; Azarine, Clarissa; Oktaviana, Lisda; Aqsha, Zidan Akbar Al; Istiana, Nofita
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.433

Abstract

Child labor remains a critical concern in Indonesia, including in Bali Province, which exhibits a higher prevalence than the national average. However, efforts to formulate effective local policies are often hindered by the unreliability of child labor statistics at the regency/municipality level, primarily due to high Relative Standard Error (RSE) values. This study seeks to estimate more reliable proportion of child labor at the regency level in Bali through the application of Small Area Estimation (SAE). The analysis utilizes data from the August 2024 Sakernas survey, supplemented with contextual variables from the 2024 PODES dataset. The SAE approach employed was the Hierarchical Bayes method with a Beta distribution (HB-Beta). The findings indicate that the HB-Beta model yields better accurate estimates, as evidenced by RSE values below 25% across all regencies. This demonstrates the potential of the HB-Beta model produces more accurate estimates than direct estimates, as it can better reflect differences between regency and help design more effective local policies to reduce child labor.
Application of Small Area Estimation for Estimating Households Living in Adequate Housing at the Subdistrict Level in DKI Akbar, Muhammad; Istiana, Nofita
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.497

Abstract

Access to adequate housing is a right of all Indonesian citizens guaranteed by the 1945 Constitution and is part of the Sustainable Development Goals (SDGs), specifically Goal 11. DKI Jakarta is the province with the second-lowest percentage of households living in adequate housing in Indonesia. Estimation at the subdistrict level is needed to support the policy on affordable vertical housing development initiated by the DKI Jakarta Department of Public Housing and Settlement Areas. Direct estimation at the subdistrict level based on the Susenas sampling design would result in inaccurate estimators. To address this issue, this study applies the Small Area Estimation (SAE) method using the Empirical Best Linear Unbiased Prediction (EBLUP) model and the Hierarchical Bayes (HB) Beta model, which leverage auxiliary variables to improve precision. The findings reveal that the HB Beta model provides the best estimates in measuring the percentage of households living in adequate housing in DKI Jakarta in 2024, producing accurate estimates across all subdistricts