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Small Area Estimation: Study of Poverty and Income Distribution in the Province
13. June 2018 at 21:52
Small Area Estimation (SAE) is the popular techniques to estimate population in small area which need for availability auxiliary variables in area level and unit level.
We are students of Politeknik Statistik STIS, Indonesia. We have implemented fieldwork practice (PKL 56) in 2017. We did a survey at Province of Bangka Belitung Islands, Indonesia. Our goals are estimating poverty rate and income distribution until the small area (village), comparing kind of methods of measuring poverty and income distribution, and analyzing the relationship between poverty rate and other issues.

The core theme of PKL 56 is Small Area Estimation (SAE) for estimating poverty and income distribution. The sub-themes are poverty in terms of employment, health, maternal mortality rate aspects; multidimensional poverty index, income and expenditure, subjective poverty, chronic and vulnerable poverty, intergeneration poverty, poverty which is reviewed in-home business, household, social capital, and education aspects. The survey was implemented on 21 February to 3 March 2017 by using Computer Assisted Personal Interviewing (CAPI). With CAPI we have some benefits such as no batching, editing, and coding (BEC) of data, saving time, and reducing non-sampling error (NSE).

The sampling method used in this survey is three-stage stratified sampling. In the first and second stages, we used probability proportional to size (PPS) sampling method, and the last stage is systematic sampling method. For the first stage, we took 140 villages. Then, we took 708 census blocks in the second stage and 7080 household samples in the last stage. In order to estimate the poverty in a small region (village), the method which we used is small area estimation (SAE) technique with some auxiliary variables. This method used in this survey was Empirical Best Linear Unbiased Prediction (EBLUP) dan Empirical Bayes (EB) because bayesian estimation is much studied by academics currently (Wulansari, Pawitan, & Sunengsih, 2015). SAE has some benefits such as it contains more complete information than census to the smallest area and long implementation survey interval.

The results from our PKL in the Province of Bangka Belitung Island are :
Very poor household rate is 0.27%
Poor household rate is 5.59%
Temporary poor household is 5.32%
Vulnerable poor household is 22.76%
Subjective poverty rate is 31.81%
Gini ratio based on expenditure is 0.32
Gini ratio based on Income is 0.47
Poor worker rate is 6.21%
Educational Deprivation Index (EDvI) is 0.16

So, by using SAE we can estimate poverty rate and income distribution to the smallest area and this method gives more completed information than other survey techniques.
Cite This Article As: Muhammad Basorudin. "Small Area Estimation: Study of Poverty and Income Distribution in the Province ." International Youth Journal, 13. June 2018.

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