Aproximación espacial sobre la pobreza monetaria en Bogotá: una aplicación desde la estimación en áreas pequeñas

Abstract

The incidence of monetary poverty is a fundamental indicator in assessing the socioeconomic conditions of the population, and its monitoring is part of the Agenda for the Sustainable Development Goals (SDG). In response to the growing need for detailed information for monitoring purposes, this work develops a methodology focused on small area estimation (SAE) with the aim of achieving disaggregations and maps of monetary poverty in households at the level of the zonal planning unit (UPZ for its acronym in Spanish) in the city of Bogotá. Based on the microdata derived from the Integrated Household Survey (GEIH for its acronym in Spanish) for the year 2021, and the use of 25 covariates obtained from geospatial data, Fay-Herriot models are carried out in order to obtain the best linear unbiased estimators (EBLUP) along with their robust spatial extensions (RSEBLUP), comparing their precisions through marginal errors. The results obtained show that the covariates used in the models are adequate predictors of monetary poverty, and that the addition of the spatial component to the model, applying robust processes, provides better precision compared to the direct estimates resulting from the survey.