Synthetic index for the evaluation of territorial poverty in the municipalities of Querétaro, Mexico

Introduction/Objectives: Poverty is a multidimensional phenomenon that affects various aspects of well-being across territories. This study aims to develop a Synthetic Municipal Poverty Index (SMPI) for the state of Querétaro, with the objective of assessing socioeconomic disparities among municipalities and prioritizing areas for public policy intervention. Methodology: A quantitative approach with a deductive, non-experimental design was employed. Socioeconomic data from 16 municipalities in Querétaro were collected from official sources such as INEGI and CONEVAL. The SMPI was constructed using Principal Component Analysis (PCA), a technique applied to determine the weights of selected indicators. Index validation was carried out through hierarchical clustering and the XGBoost machine learning model, ensuring the robustness and accuracy of the index. Results: The findings reveal significant disparities in poverty levels across the analyzed municipalities. Income and food-related sub-indices emerged as the main determinants of poverty. Validation using the XGBoost model demonstrated high predictive performance of the SMPI, reinforcing its value as an analytical tool. Conclusions: The SMPI serves as a reliable instrument for measuring multidimensional poverty at the municipal level. Its application supports the identification of priority areas and contributes to the design of more targeted and effective public policies. This index provides a solid framework for evidence-based decision-making and the development of comprehensive strategies to reduce poverty at the territorial level.