Un estudio econométrico y de Machine Learning sobre personas que se empobrecieron durante la pandemia basado en la PNAD-Continua
DOI:
https://doi.org/10.20435/multi.v28i69.4104Palabras clave:
pobreza, Machine Learning, EconometríaResumen
Este estudio tiene como objetivo investigar la relación entre la pobreza y la pandemia de COVID-19, utilizando microdatos de la PNAD-Continua. Para obtener enfoques diferentes sobre el tema, se utilizaron dos metodologías: 1) Econometría y 2) Aprendizaje Automático (Machine Learning). El estudio se centra en comprender los principales determinantes de la pobreza durante el período de la pandemia, así como en predecir la vulnerabilidad de las personas a la pobreza utilizando el Aprendizaje Automático. Los resultados obtenidos señalan una mayor probabilidad de caer en la pobreza en personas no blancas, mujeres, residentes de áreas metropolitanas, personas en familias numerosas y con menor nivel educativo. Además, el algoritmo XGBoost obtuvo el mejor rendimiento en la predicción de la pobreza después del equilibrio de los datos. Estos resultados pueden ser utilizados para ayudar en la toma de decisiones en la lucha contra la pobreza en Brasil.
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