A procedure for the generation of small area estimates of Philippine poverty incidence

• Main Author: Nacion, Nelda Atibagos
• Format: text
• Language: English
• Published: Animo Repository 2020
• Subjects: Small area statistics; Mathematics
• Online Access: https://animorepository.dlsu.edu.ph/etd_masteral/5983; https://animorepository.dlsu.edu.ph/context/etd_masteral/article/13089/viewcontent/Nacion_Nelda_11386290_Partial.pdf
• Institution: De La Salle University

The main purpose of this study is to propose an alternative procedure in the generation of small area estimates of poverty incidence using imputation-like procedures coupled with a calibration of estimates to ensure coherence in the regional estimates. Specifically, this study applied Deterministic Regression Approach, Stochastic Imputation- like procedure similar to Stochastic Regression, and applied the calibration techniques to ensure that the small area estimates conform to the known regional estimates. This study used the Family Income and Expenditure Survey (FIES) of 2009 and the Census of Population and Housing (CPH form 2) 2010 to come up with reliable estimates of poverty incidence by municipal level. Since the CPH is conducted in the Philippines every 10 years, the CPH 2010 is the latest data that was used. The researcher was able to produce small area estimates of poverty in the Philippines at municipal level by combining survey data with auxiliary data derived from census. The study fitted different models for each region. By comparing the two methods of imputation, it was found out that the Stochastic Regression Imputation performed better than Deterministic Regression in attaching the income in census. The error used in Stochastic Regression was estimated using non-parametric method called Kernel Density Estimation (KDE). The result was compared externally to wealth index to ensure the reliability of the estimates.