Application of Small Area Estimation with HB Beta Model to Estimate Child Labor Proportion in Kalimantan
Keywords: Child Labor, HB Beta, SAE
Abstract
The global commitment to eliminate the number child labor is reflected in Target 8.7 of the Sustainable Development Goals (SDGs), which aims to end all forms of child labor by 2025. One of the key indicators used to measure the prevalence of child labor is the percentage of child engaged in labor. Kalimantan Island is one of the regions with a high level of vulnerability to child labor practices, particularly in plantation areas that are difficult to access and not covered by adequate monitoring systems. To ensure that the number of child labor elimination policies in Kalimantan are accurately targeted, data at the small-area level are needed to precisely identify locations with a high risk of child labor involvement. However, the direct estimates of child labor percentages at the regencies/municipalities level produced by Badan Pusat Statistik (BPS) lack sufficient precision to be used as a reliable basis for policymaking. Therefore, this study aims to produce indirect estimates of child labor proportion at the regencies/municipalities level in Kalimantan to improve the accuracy of the direct estimation. This research applies the Small Area Estimation (SAE) method using the Hierarchical Bayes Beta (HB-Beta) modeling approach. The SAE HB Beta method was used because the normality assumption for proportion data is often violated, while the Beta distribution is naturally suited to variable constrained to the interval (0,1). The exploratory analysis identified nine auxiliary variables that are appropriate for use in the estimation process. The results from the SAE HB-Beta model indicate that the relative standard Error (RSE) values for all regencies and municipalities in Kalimantan are less than 25 percent. Thus, the indirect estimates obtained are considered statistically reliable and suitable for use as a basis for evidence-based policymaking.
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Copyright (c) 2026 Rika Lusiana Simbolon, Amara Putri Shabrina, Fatima Azzahro Binti Fatihah, Immanuel Nicholas Fransepta Samosir, Muhammad Aswan Aziz, Nofita Istiana
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