Municipal estimation of income poverty in Mexico using a small-area spatial model: a methodological proposal

Introduction: The measurement of subnational poverty is key for targeted public policies; however, surveys in Mexico lack representativeness at the municipal level. Objective: To develop and validate a methodology for generating municipal estimates of monetary poverty with explicit uncertainty measures. Methodology: This study presents the ALIVIO methodology, aimed at the municipal estimation of monetary poverty in contexts of low sample representativeness. A unit-level empirical predictor was employed, combined with an Intrinsic Conditional Autoregressive spatial structure. Estimation was carried out via approximate Bayesian inference, integrating microdata from the ENIGH 2024 and auxiliary variables from the 2020 Census. Results: The model substantially reduced uncertainty compared to direct estimators and enabled the generation of information for all municipalities, including those with no sample, and showed no relevant residual spatial autocorrelation in the aggregate diagnostics. Discussion: Spatial incorporation strengthened predictive capacity relative to traditional models, though it depends on normality assumptions and temporal harmonization across sources. Conclusions: ALIVIO constitutes a potentially robust, efficient, and replicable methodological framework for generating municipal poverty maps and prioritizing territories with low availability of primary data.