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  Small Area Estimation in Estimating Unemployment Rate in Bogor District of Sampled and Non-Sampled Areas UsingA Calibration Modeling Approach  
  Authors : Siti Aprizkiyandari; Anang Kurnia; Indahwati
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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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