DOI: 10.7910/dvn/qszkup
title: Measuring the Unmeasurable Multidimensional Socio-Economic Deprivations and Poverty Predictions: Indigenous People Datasets for Econometrics, Machine Learning, and Quantitative Social Science Modeling
title: Indigenous People Datasets for Poverty Alleviation and Economic Development

description: Indigenous peoples represent one of the most vulnerable, marginalized, and overlooked sectors in society. Poverty, the age-old social problem, poses significant challenges to overcome. The Agta Tabangnon, our Indigenous community, experiences poverty and various socio-economic deprivations. While poverty studies typically employ generic approaches with large sampling errors for nationwide decision-making, studies focused on Indigenous peoples are qualitative in nature. Therefore, it is crucial to measure poverty for specific tribes through a comprehensive enumeration that considers multiple dimensions, fostering economic development. Unfortunately, there is currently no comprehensive census specifically designed to capture the multidimensional aspects of Indigenous peoples' way of life. However, we have been resourceful in generating valuable multidimensional data through partnerships. Our local community is situated in the poorest district of the poorest province within the poorest region of Luzon, Philippines. Our datasets encompass various indicators of multidimensional poverty and include complementary analytics for data visualization. These resources can serve as a foundation for measuring poverty among Indigenous communities across different regions and countries. By utilizing this data, further empirical analysis, regressions, machine learning, and econometric modeling can be conducted. This information can be freely utilized to target policies and interventions that address the multifaceted poverty experienced by tribal communities, thereby promoting their economic development.
description: It is thought to be impossible to measure the multidimensional poverty of indigenous people since it is so tough and demanding, particularly in impoverished rural areas and certain communities. The dataset is helpful in giving information for creating policies and programs that are specifically targeted at the indigenous people in order to reduce poverty and promote economic development. In order to build strategies for economic development, other developing nations and impoverished regions of the world that are home to indigenous people can use the datasets to obtain raw, analyzed, baseline data, and indicators of multidimensional poverty. Subsequent investigators could utilize the data analytics protocols, variables, techniques, policy recommendations, and computational approaches to develop comparable studies on quantifying the intangible elements of multidimensional poverty for other Native communities. Poverty incidence, poverty gap, severity statistics, watts index, and classifications for indigenous people tribes may all be calculated, verified, and simulated using the poverty analytics dataset. Descriptive and diagnostic analytics measuring population dynamics, health and nutrition, housing and settlement, water and sanitation, basic education from elementary to senior high school, income classifications, employment and livelihood, and peace and order are provided to indigenous people by the processed datasets. The prescriptive analytics dataset will be helpful in developing programs and tracking effects for the socioeconomic advancement of our indigenous people, not only in the Philippines but throughout the world, for local government units, national government agencies, indigenous people institutions, researchers, scholars, extensionists, policy-makers, academicians, charitable institutions, and social enterprises. The utilization of predictive analytics datasets and econometric models may yield empirical evidence supporting poverty theories concerning indigenous populations, contributing to the body of knowledge on the limited availability of multidimensional poverty metrics in the Philippines and other impoverished nations, specifically for indigenous people.