Bayesian mapping of poverty prevalence in the City of Pasay, Philippines

• Main Authors: Alfonso, John Jason D., Caraingan, Erlo Franco B.
• Format: text
• Language: English
• Published: Animo Repository 2009
• Subjects: Poverty > Philippines > Pasay City > Statistics; Statistics and Probability
• Online Access: https://animorepository.dlsu.edu.ph/etd_bachelors/5217
• Institution: De La Salle University

Poverty mapping can be used by the government in achieving its goal on reducing the fast growing poverty occurrence in the country. The local government must now make a move in dealing with the poor areas with programs that will somehow reduce the prevalence of poor. The researchers applied classical, Bayesian hierarchical models, multiple linear regression, and Poisson regression in computing poverty incidence in the barangay level using the 2005 Community-Based Monitoring System (CBMS) Poverty Census Data in Pasay City. Poisson-Gamma and Beta-Binomial hierarchical models were used to predict the number of poor households and poverty rates. Results of Bayesian approach show that the two hierarchical models yielded the same set of the poorest barangays in Pasay City except one. Results of the multiple regression model showed that it was closer to the saturated model compared to the results of the Poisson regression model. Significant correlates common to both Poisson regression and the multiple regression models are the number of households below the food poverty threshold, the number of households with no safe water system, and the number of male members of the labor force. Five choropleth maps were created to portray the distribution of poverty prevalence in the City of Pasay in the barangay level. All results of the different methods showed that Barangay 143, or Barangay Sto. Niño, is the poorest barangay in Pasay City.