Small Area Procedures for Estimating Income and Poverty in Egypt

Main Article Content

Mai M. Kamal El Saied
Amal A. Talat
Mervat M. El Gohary

Abstract

In recent years, the demand for small area statistics has greatly increased worldwide. A recent application of small area estimation (SAE) techniques is in estimating local level poverty measures in Third World countries which is necessary to achieve the Millennium Development Goals. The aim of this research is to study SAE procedures for estimating the mean income and poverty indicators for the Egyptian provinces. For this goal the direct estimators of mean income and (FGT) poverty indicators for all the Egyptian provinces are presented. Also this study applies the empirical best/Bayes (EB) and the pseudo empirical best/Bayes (PEB) methods based on the unit level - nested error - model to estimate mean income and (FGT) poverty indicators for the Egyptian border provinces with (2012-2013) income, expenditure and consumption survey (IECS) data. The (MSEs) and coefficient of variations (C.Vs) are calculated for comparative purposes. Finally the conclusions are introduced. The results show that EB estimators for poverty incidence and poverty gap are smaller than PEB for all selected provinces. EB figures indicate that the largest poverty incidence and gap are for the selected municipality at the scope of the border south west of Egypt (New Valley). The PEB figures indicate that the largest poverty incidence and gap are for the selected municipality at the scope of the border north east of Egypt (North Sinai). As expected, estimated C.Vs for EB of poverty incidence and poverty gap estimators are noticeably larger than those of PEB estimators in all selected provinces.

Keywords:
SAE techniques, FGT poverty indicators, nested error model, empirical best/Bayes (EB), the pseudo empirical best/Bayes (PEB).

Article Details

How to Cite
Saied, M. M. K., Talat, A., & Gohary, M. M. (2019). Small Area Procedures for Estimating Income and Poverty in Egypt. Asian Journal of Probability and Statistics, 4(1), 1-17. https://doi.org/10.9734/ajpas/2019/v4i130106
Section
Original Research Article

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