A machine learning approach in predicting poverty in the poorest region of Luzon, Philippines
Year: 2025
Type: article
Study design: Observational Study
Abstract: Poverty is a complex and multidimensional issue that is difficult to measure accurately. While multiple studies have employed traditional econometric methods to analyze poverty, they often overlook the critical roles of electricity, Internet, and cell phone access – factors our study incorporates alongside machine learning to provide deeper and more accurate insights. This study examines the associations between Internet, cell phone, and electricity access and poverty in the poorest region of Luzon, Philippines, aiming to foster connectivity among households to support poverty alleviation. Using probit regression and estimation analyses, we found significant socioeconomic disparities, with many households living below the poverty line. While most households have electricity and cell phones, many lack Internet access. This indicates challenges in infrastructure and digital connectivity that affect living standards and economic opportunities. The analysis reveals important causal relationships between Internet access, cell phone ownership, electricity availability, household size, and the likelihood of being in poverty. Interestingly, the lack of these essential services is linked to higher poverty rates. These results highlight the need for targeted interventions to tackle the root causes of poverty, particularly in bridging the digital divide and improving access to essential services. Machine learning algorithms were employed to effectively predict poverty outcomes based on the results of econometric modeling, where variables with significant coefficients served as a priori inputs. The findings indicate that Extreme Gradient Boosting achieved the lowest mean square error and the highest R2 value among all regression models. Meanwhile, the random forest classifier demonstrated the best overall performance with the highest classification accuracy. The outlined policies support energy affordability, cell phone access, and Internet connectivity through financial aid, solar programs, device provisions, and broadband expansion. Addressing infrastructure gaps and technology access is key to sustainable economic growth, guiding policy makers toward equitable, resilient solutions.
DOI: 10.36922/ijps.4879
Authors: Emmanuel A. Onsay, Kevin C. Baltar
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4410899457