An investigation into the measurement of multidimensional poverty in the Philippines - Ruivivar, Jason Laurence Aquino (Animo Repository, 2022)
For decades, poverty has been measured in terms of monetary concepts, and individuals who do not have enough income or consumption to meet a minimum threshold of basic needs were considered poor. However, there is a growing recognition that poverty goes beyond monetary deprivation. In light of this, methods to analyze poverty multidimensionally and estimate a Multidimensional Poverty Index, most notably the Alkire-Foster method, were developed. Though the MPI depends on the indicators, weights, and cutoffs chosen, it could nevertheless help provide a more comprehensive picture of poverty and thus provide greater impact in poverty-reduction efforts. This study attempted to generate meaningful estimates of Philippine multidimensional poverty and analyze several aspects of poverty in the country. Estimates were computed using data from three nationally representative sample surveys of households that are regularly conducted by the Philippine Statistics Authority (PSA): the National Demographic and Health Survey (NDHS), the Annual Poverty Indicator Survey (APIS), and the Family Income and Expenditure Survey (FIES). This study used different weights and cutoff approaches to compute the MPI and other associated headline summaries based on the three surveys mentioned. The results of this study had shown that, while the estimates differed based on the weights and cutoff approaches used, the MPI generally showed a downward trend in all three surveys. The use of the intersection approach also did not generate any estimates except for the FIES during 2009. Several models that could generate estimates close to those of official studies had also been found. Notable examples include an APIS model that used a cutoff of 1/3 and factor analysis weights, and an NDHS model that used a cutoff of 1/5 and inverse weights. This study had also shown that living standards generally contributed the most to the MPI, contrary to the findings of the PSA’s pilot MPI that education had the largest contribution to the MPI. It had also shown that ARMM was generally more deprived in several indicators than other regions. The insights provided from this study could serve as inputs to the work of the PSA and as a guide to poverty analysts as we seek to improve the measurements and analyses of the extent of poverty in the country.