Status : Verified
| Personal Name | Cabrera, Faith Lea B. |
|---|---|
| Resource Title | Spatially Varying Weights Approach in Estimating Multidimensional Poverty Index in the Philippines |
| Date Issued | June 2026 |
| Abstract | Multidimensional poverty considers deprivation in different aspects of well-being. Despite existing global and national multidimensional poverty index (MPI) methodologies, gaps remain in its measurement, particularly in incorporating spatial information and generating finer disaggregation. To come up with spatially varying weights, the study employed two PCA methods adapted for spatial analysis, namely, Geographically Weighted Principal Component Analysis (GWPCA) and PCA with Spatial Autocorrelation (SPCA). Furthermore, two small area estimation techniques, namely, Regression synthetic estimation and Empirical Best Linear Unbiased Prediction (EBLUP), were applied to generate small area MPI estimates. Incidence of deprivation was observed to be the highest for educational attainment at 84.90 percent. This reflects the close relationship between education and poverty, where poor educational outcomes often lead to limited economic opportunities. Results of MPI using nested equal weights, GWPCA-, and SPCA-based weights do not differ much at the national level. The headcount ratio, or the proportion of the population who are multidimensionally poor, was estimated at around 20 to 21 percent. Meanwhile, the intensity of poverty, or the average deprivation score of multidimensionally poor people, was estimated at 39 to 45 percent. Lastly, MPI, which captures both headcount ratio and intensity of poverty, was estimated at 8 to 9.5 percent. These results were found to be consistent with official poverty statistics, providing evidence to the interconnectedness of multidimensional poverty and income poverty. Across the three weighting schemes, the province of Sulu consistently had the highest estimates of headcount ratio, intensity, and MPI. Meanwhile, the province of Pampanga consistently had the lowest estimates for the same measures. For the estimation of city/municipality level MPI, Regression synthetic estimation using weights based on SPCA yielded the highest number of |
| Degree Course | Master of Science (Statistics) |
| Language | English |
| Keyword | multidimensional poverty index, geographically weighted PCA, PCA with spatial autocorrelation, regression synthetic estimation, EBLUP |
| Material Type | Thesis/Dissertation |
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Category : F - Regular work, i.e., it has no patentable invention or creation, the author does not wish for personal publication, there is no confidential information.
Access Permission : Open Access