Predicting Socioeconomic Classification in the Philippines: Beyond the Ordinal Logistic Regression Model

Year: 2015       Vol.: 64       No.: 1      

Authors: Michael Daniel C. Lucagbo

Abstract:

Socioeconomic classification (SEC) is an important construct to enable one to capture and understand changes in the structure of a society. The 1SEC 2012, a new scheme for identifying the SEC of Philippine households, predicts SEC using information on household characteristics through the ordinal logistic regression model. This study aims to improve the predictive ability of the 1SEC methodology by using state-of-the-art statistical learning techniques: discriminant analysis, support vector machines (SVM), and artificial neural networks (ANN), and thereby suggest a new scheme for predicting SEC. The results show that SVM and ANN exhibit improvements in exact-cluster prediction performance, suggesting alternative methods for predicting SEC.

Keywords: socioeconomic classification, ordinal logistic regression, discriminant analysis, support vector machines, artificial neural networks

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