Multilevel modeling of poverty data

The hierarchical nature of the data is often ignored in the analysis on Philippine datasets. These databases are usually applied on standard regression models for estimation and prediction. Using multilevel modeling allows for the relationship between variables clustered within levels to occur. In this paper, the researchers used a three-level modeling on the Community-Based Monitoring System (CBMS) Pasay 2011 database. The first level is modeled for the frequency of government programs using Poisson and negative binomial regressions. The classification whether a household is poor or not poor based on the PCI criterion was modeled in the second level using binary logistic regression, conditional on whether the household has been involved in at least one government program. Lastly, the poverty gap, using the gamma distribution and the generalized beta distribution, was modeled for the third level, conditional on the whether the household is considered poor and has received at least one government program. The best models were determined with the use of model fit statistics. Moreover, results show that the number of couples and the number of members in the household who attended school are the significant predictors in modeling poverty gap, conditional on the first two levels