The main problem in Indonesia is unemployment. There are some various government policies to resolve unemployment,
such as the availability of statistical data in unemployment. The National Labor Survey conducted by the Statistics Indonesia (BPS) only
generates estimates at the national levels, whereas to carry out various government policies requires the availability of unemployment
information to smaller levels. The Small Area Estimation (SAE) method is one of the solutions to estimate small area without adds
sampling units. The method is borrowing strength from nearby observation sample areas. The study focused on estimating unemployment
rate in Bogor sub-district level using Generalized Linear Mixed Models (GLMM) method with calibration approach. The results of the
proposed method can produce the same result as published by BPS and are able to generate the result to sub-district level.
Published In : IJCSN Journal Volume 6, Issue 6
Date of Publication : December 2017
Pages : 760-765
Figures :01
Tables : 07
S. Aprizkiyandari : graduate student in Department of Statistics,
Bogor Agricultural University. Her main interest is on statistical
modeling.
Dr. A. Kurnia : Currently worked as a senior lecturer and Head of
Department of Statistics, Bogor Agricultural University. His main
interest are on small area estimation and statistical modeling.
Indahwati : Currently worked as a lecturer and Head of Program of
Applied Statistics, Bogor Agricultural University. Her main interests
are on small area estimation and statistical modeling.
Generalized Linear Mixed Models (GLMM), Calibration modeling approach, Clustering analysis
Overall, the best calibration modeling for the
unemployment rate is proportional to sub-district level and
the unemployment rate at a district level. The result shows
the calibration modeling is 10.26% and the result is BPS
result that is 10.01%. Using the method can also generate
predictions for the sub-district level.
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