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  Zero Inflated Beta Model in Small Area Estimation to Estimate Poverty Rates on Village Level in Langsa Municipality  
  Authors : Meita Jumiartanti; Indahwati; Anang Kurnia
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Village level poverty rates are needed as a consideration for allocating village funds. The national socio economic survey samples are designed to estimate poverty rates in province and distric level. Direct estimate for calculating estimates of village level poverty rates does not have a good precision due to small sample sizes. Small Area Estimation (SAE) technique is used to produce a good precision with small sample sizes. The estimates of poverty rates should also be produced for non sampled area and when no poor are included in the sample. We propose zero inflated beta model because poverty rates takes value in the intervals [0,1). Clustering technique is used to acommodate random effect area for non sampled area. The purpose of this research is to estimate poverty rates on village level in Langsa Municipality. The result showed that estimates poverty rates on village level with zero inflated beta model is better than direct estimates.


Published In : IJCSN Journal Volume 6, Issue 6

Date of Publication : December 2017

Pages : 812-819

Figures :01

Tables : 06


Meita Jumiafrtanti : currently pursuing masters degree program in Applied Statistics in Bogor Agricultural University

Indahwati : is lecturer at Department of Statistics , Bogor Agricultural University, Indonesia. Her main interest is in Mixed Model and Small Area Estimation.

Anang Kurnia : is lecturer at Department of Statistics , Bogor Agricul-tural University, Indonesia. His main interest is in Small Area Estimation.


Clustering, Poverty Rates, Small Area Estimation, Zero Inflated Beta Model

The conclusion is zero inflated beta model in the small area estimation produce a better estimator because no value is 0 at village level, whereas in the direct estimator there is a value of 0. Zero inflated beta model in the small area estimation to estimate non sampled area also produce good estimator values while still considering the fixed influence and random diversity of village areas. However, the result of zero inflated beta model in the small area estimation with the weighted average in village level still has a value under the direct estimator of the poverty rates of Langsa Municipality level, so it is necessary to calibrate the model, this is probably due to the value in the response variable contains a lot of value 0.


[1] Anisa. R, “Kajian Pengaruh Penambahan Informasi Gerombol Terhadap Hasil Prediksi Area Nircontoh (Studi Kasus Pengeluaran per Kapita Kecamatan di Kota dan Kabupaten Bogor)”, M.Si. thesis, Department of Statitics, Bogor Agricultural University, Bogor, Indonesia, 2014. [2] [BPS] Badan Pusat Statistik. Penghitungan dan Analisis Kemiskinan Makro Indonesia Tahun 2016. Jakarta: Katalog BPS, 2016 [3] Ferrari. SLP, and Cribari-Neto. F, “Beta Regression For Modelling Rates and Proportions”, Journal of Applied Statistics, Vol. XXXI, No. VII, 2004, pp. 799-815. [4] Foster. J, Greer. J, and Thorbecke. E, “A class of decomposable poverty measures”, Econometrica, 52, 1984, pp.761-766. [5] Kurnia. A, and Notodiputro. KA, “Penerapan Metode Jackknife dalam Pendugaan Area Kecil”, Forum Statistika dan Komputasi, Vol. XI, No. I, 2006, pp. 12- 15. [6] Ospina. R, and Ferrari. SLP, “Inflated beta distributions”, Statisticals Papers, 51, 2010, pp. 111–126. [7] Rao. JNK, Small Area Estimation. New York (US): John Wiley & Sons. 2015 [8] Swearingen CJ, Castro MSM, Bursac Z. “Modeling percentage outcomes: the %beta_regression macro”, SAS Global Forum 2011: Statistics and Data Analysis Paper, 335, 2011, pp.1-12.