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Salis, Dian Rahmawati
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Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Estimasi Tingkat Kemiskinan Anak Level Kabupaten/Kota di Provinsi Banten Tahun 2018-2021 dengan Small Area Estimation (SAE) Rao-Yu Pendekatan Hierarchical Bayes Salis, Dian Rahmawati; Ubaidillah, Azka
Seminar Nasional Official Statistics Vol 2023 No 1 (2023): Seminar Nasional Official Statistics 2023
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2023i1.1709

Abstract

ABSTRACT Child poverty have a significant impact on the future quality of the adult population. Estimating the level of child poverty accurately, especially at regency/city level, is crucial for targeted policy interventions. Direct estimations based on SUSENAS data have a Relative Standard Error (RSE) value of more than 25%, necessitating the use of an indirect method called small area estimation (SAE). The province of Banten has consistently had the lowest Gross Participation Rate (GPR) for Early Childhood Education (ECE) among the provinces in Java over the years which can be interpreted as an early indication of limited access to children's education due to poverty. In this study, the level of child poverty in the districts/cities of Banten Province was estimated using the hierarchical bayes Rao-Yu model with normal and beta distribution approach. The results of this study indicate that although it produces the best precision, the SAE Rao-Yu HB Beta estimation has results with a smaller level of consistency than the normal SAE Rao-Yu HB estimation.
Implementasi Small Area Estimation Hierarchical Bayes - Beta Difference Benchmark dalam Estimasi NEET Lulusan Perguruan Tinggi Salis, Dian Rahmawati; Japany, Adham Malay; Rodliyah, Ratih; Ibad, Syaikhul; Pulungan, Ridson Al farizal; Ramadhan, Yogi
Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2024i1.2285

Abstract

The survey data generated by BPS serves as the primary data source for calculating various SDGs indicators. However, not all indicators can be reliably estimated, particularly at detailed disaggregation levels. Some indicators face issues due to sample inadequacy, resulting in high Relative Standard Errors (RSEs) if estimated directly. One such indicator is the percentage of young college graduates who are neither in education, employment, nor training (NEET). This indicator is only available at the provincial level, with disaggregation based on other characteristics only available at national level. Therefore, this study aims to estimate NEET among college graduates at the regency/city level in Sumatra Island for the year 2023 using the SAE HB Beta model. To maintain consistency with direct estimates at the provincial level, which have shown sufficiently low RSEs, a benchmarking process will be conducted using the difference benchmark method. Based on the findings, the difference benchmark method enhances the validity of the estimation results using the SAE HB Beta model.
Co-Authors Ibad, Syaikhul Japany, Adham Malay Pulungan, Ridson Al farizal Rodliyah, Ratih Ubaidillah, Azka Yogi Ramadhan