Measuring the Unmeasurable Multidimensional Socio-Economic Deprivations and Poverty Predictions: Indigenous People Datasets for Econometrics, Machine Learning, and Quantitative Social Science Modeling

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.