Do indigenous people get left behind? An innovative methodology for measuring the unmeasurable economic conditions and poverty from the poorest region of Luzon, Philippines

One of the most vulnerable, neglected, and marginalized groups in society is indigenous communities. Their socioeconomic circumstances are intricate and varied. The oldest societal issue ever is poverty, which is also the hardest to solve. It is multifaceted and immeasurable, especially for the Agta Tabangnon, our Indigenous people. Research on indigenous peoples is qualitative in nature, whereas research on poverty is typically general, subject to significant sampling errors, and meant to inform national policy. Therefore, it is essential for economic development to measure multidimensional poverty and simulate the socioeconomic conditions for each tribe through full enumeration and disaggregation. This work puts forth fresh approaches to quantify the incalculable multifaceted poverty and socioeconomic conditions: (i) a thorough statistical analysis using diagnostic and descriptive analytics to examine socioeconomic situations; (ii) combining sophisticated econometrics and predictive analytics to measure multidimensional poverty; and (iii) integrating machine learning to model socioeconomic situations and prescriptive analytics to develop policy. Analysis reveals that poverty among tribes is closely tied to income, livelihood, and education, impacting various aspects of daily life, including housing, water, sanitation, health, and nutrition. The study develops multidimensional poverty indicators crucial for promoting economic development and poverty reduction among Indigenous communities. The study underscores the urgency of targeted policies and programs to address multidimensional deprivations faced by Indigenous communities, emphasizing the importance of data-driven approaches in improving welfare and promoting equitable socio-economic progress. Other researchers worldwide could replicate, reproduce, or reuse the suggested methods to assist indigenous peoples in reducing poverty and enhancing their well-being. It can be freely applied to focus policy on tribal economy, which has multiple dimensions and necessitates various forms of development initiatives.

Authors

Institutions

Publication Details

Journal
Heliyon
Published
2024-12-24
DOI
https://doi.org/10.1016/j.heliyon.2024.e41076
Citations
2
Primary Topic
Global Health and Epidemiology
Type
article
Field-Weighted Citation Impact
1.42

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article

Do indigenous people get left behind? An innovative methodology for measuring the unmeasurable economic conditions and poverty from the poorest region of Luzon, Philippines

Jomar F. Rabajante, Emmanuel A. Onsay
2 citations
Heliyon
Global Health and Epidemiology
1.42
article

Do indigenous people get left behind? An innovative methodology for measuring the unmeasurable economic conditions and poverty from the poorest region of Luzon, Philippines

Jomar F. Rabajante, Emmanuel A. Onsay
article en
2 citations

Abstract

One of the most vulnerable, neglected, and marginalized groups in society is indigenous communities. Their socioeconomic circumstances are intricate and varied. The oldest societal issue ever is poverty, which is also the hardest to solve. It is multifaceted and immeasurable, especially for the Agta Tabangnon, our Indigenous people. Research on indigenous peoples is qualitative in nature, whereas research on poverty is typically general, subject to significant sampling errors, and meant to inform national policy. Therefore, it is essential for economic development to measure multidimensional poverty and simulate the socioeconomic conditions for each tribe through full enumeration and disaggregation. This work puts forth fresh approaches to quantify the incalculable multifaceted poverty and socioeconomic conditions: (i) a thorough statistical analysis using diagnostic and descriptive analytics to examine socioeconomic situations; (ii) combining sophisticated econometrics and predictive analytics to measure multidimensional poverty; and (iii) integrating machine learning to model socioeconomic situations and prescriptive analytics to develop policy. Analysis reveals that poverty among tribes is closely tied to income, livelihood, and education, impacting various aspects of daily life, including housing, water, sanitation, health, and nutrition. The study develops multidimensional poverty indicators crucial for promoting economic development and poverty reduction among Indigenous communities. The study underscores the urgency of targeted policies and programs to address multidimensional deprivations faced by Indigenous communities, emphasizing the importance of data-driven approaches in improving welfare and promoting equitable socio-economic progress. Other researchers worldwide could replicate, reproduce, or reuse the suggested methods to assist indigenous peoples in reducing poverty and enhancing their well-being. It can be freely applied to focus policy on tribal economy, which has multiple dimensions and necessitates various forms of development initiatives.

HeliyonVol. 11(3)
University of the Philippines Los Baños (PH), Partido State University (PH)
De La Salle University, University of the Philippines
No poverty
Openalex Percentile: Top 12%
Global Health and Epidemiology
1.42
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