Estimating the Unemployment Rate at Sub-District Level in West Java Province in 2024 Using Hierarchical Bayesian Approach with Cluster Information

Abstract

Unemployment is a substantial obstacle to growth in Indonesia, affecting both social
and economic stability. The Unemployment Rate is a crucial metric that quantifies the proportion
of the labor force actively pursuing work opportunities. The unemployment rate serves as a
critical indicator of labor market imbalances, essential for labor policy formulation and
assessment. Nonetheless, unemployment data has limitations, particularly at the micro-level,
owing to sample constraints. Small Area Estimation (SAE) can address these constraints. This
study estimates the unemployment rate at the sub-district level in West Java province for 2024
utilizing the Hierarchical Bayes Beta methodology and clustering techniques. The modeling
results indicate that most sub-districts exhibit a low to medium unemployment rate, however 21
locations demonstrate a very high unemployment rate, ranging from 23.00 percent to 48.06
percent.