Measuring Philippine poverty through cluster analysis

Statistics on poverty incidence are often employed in the development of national-level policies such as conditional cash transfer programs for the poorest of the poor. In certain instances, these statistics can strongly influence the manner in which government resources are allocated and deployed....

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Main Author: YAP, PATRICIA CARMEL
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Published: Archīum Ateneo 2018
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Online Access:https://archium.ateneo.edu/theses-dissertations/99
http://rizalls.lib.admu.edu.ph/#section=resource&resourceid=1619962382&currentIndex=0&view=fullDetailsDetailsTab
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spelling ph-ateneo-arc.theses-dissertations-10982021-04-11T05:20:11Z Measuring Philippine poverty through cluster analysis YAP, PATRICIA CARMEL Statistics on poverty incidence are often employed in the development of national-level policies such as conditional cash transfer programs for the poorest of the poor. In certain instances, these statistics can strongly influence the manner in which government resources are allocated and deployed. The seemingly excessive focus on poverty incidence, however, ignores its deficiencies - particularly its unidimensional structure or its overemphasis on income. These deficiencies are made more evident through an analysis of the Capability Approach which elaborates on the multidimensional nature of poverty and, more importantly, the value to policymaking of viewing poverty as a multidimensional issue. The nuances implicit in the multidimensionality of poverty should therefore not be ignored. Instead, these must influence the manner in which poverty is viewed, measured, and addressed. As such, the development of alternative means of determining macro-level poverty measurements would prove critical in the proper formulation, calibration, and deployment of policies and interventions against poverty. This study is an attempt to reframe poverty in the Philippines within the Capability Approach. The study develops an alternative means of estimating the proportion of poor Filipinos by subjecting information from the Annual Poverty Indicators Survey to a combination of Principal Component Analysis, Multiple Correspondence Analysis, and Cluster Analysis. This study builds on existing literature by incorporating Multiple Correspondence Analysis into the Cluster Analysis Framework for poverty measurement. The results suggest that poverty incidence measures may severely underestimate poverty and obscure significant and/or growing intraregional and interregional inequality. The results also suggest that the regional minimum wages can serve as heuristic cutoffs for poverty in the Philippine setting. 2018-01-01T08:00:00Z text https://archium.ateneo.edu/theses-dissertations/99 http://rizalls.lib.admu.edu.ph/#section=resource&resourceid=1619962382&currentIndex=0&view=fullDetailsDetailsTab Theses and Dissertations (All) Archīum Ateneo Poverty -- Philippines Poverty -- Measurement Cluster analysis Capabilities approach (Social sciences)
institution Ateneo De Manila University
building Ateneo De Manila University Library
continent Asia
country Philippines
Philippines
content_provider Ateneo De Manila University Library
collection archium.Ateneo Institutional Repository
topic Poverty -- Philippines
Poverty -- Measurement
Cluster analysis
Capabilities approach (Social sciences)
spellingShingle Poverty -- Philippines
Poverty -- Measurement
Cluster analysis
Capabilities approach (Social sciences)
YAP, PATRICIA CARMEL
Measuring Philippine poverty through cluster analysis
description Statistics on poverty incidence are often employed in the development of national-level policies such as conditional cash transfer programs for the poorest of the poor. In certain instances, these statistics can strongly influence the manner in which government resources are allocated and deployed. The seemingly excessive focus on poverty incidence, however, ignores its deficiencies - particularly its unidimensional structure or its overemphasis on income. These deficiencies are made more evident through an analysis of the Capability Approach which elaborates on the multidimensional nature of poverty and, more importantly, the value to policymaking of viewing poverty as a multidimensional issue. The nuances implicit in the multidimensionality of poverty should therefore not be ignored. Instead, these must influence the manner in which poverty is viewed, measured, and addressed. As such, the development of alternative means of determining macro-level poverty measurements would prove critical in the proper formulation, calibration, and deployment of policies and interventions against poverty. This study is an attempt to reframe poverty in the Philippines within the Capability Approach. The study develops an alternative means of estimating the proportion of poor Filipinos by subjecting information from the Annual Poverty Indicators Survey to a combination of Principal Component Analysis, Multiple Correspondence Analysis, and Cluster Analysis. This study builds on existing literature by incorporating Multiple Correspondence Analysis into the Cluster Analysis Framework for poverty measurement. The results suggest that poverty incidence measures may severely underestimate poverty and obscure significant and/or growing intraregional and interregional inequality. The results also suggest that the regional minimum wages can serve as heuristic cutoffs for poverty in the Philippine setting.
format text
author YAP, PATRICIA CARMEL
author_facet YAP, PATRICIA CARMEL
author_sort YAP, PATRICIA CARMEL
title Measuring Philippine poverty through cluster analysis
title_short Measuring Philippine poverty through cluster analysis
title_full Measuring Philippine poverty through cluster analysis
title_fullStr Measuring Philippine poverty through cluster analysis
title_full_unstemmed Measuring Philippine poverty through cluster analysis
title_sort measuring philippine poverty through cluster analysis
publisher Archīum Ateneo
publishDate 2018
url https://archium.ateneo.edu/theses-dissertations/99
http://rizalls.lib.admu.edu.ph/#section=resource&resourceid=1619962382&currentIndex=0&view=fullDetailsDetailsTab
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