Household characteristics and poverty: an application of support vector machines

Authors

DOI:

https://doi.org/10.46661/revmetodoscuanteconempresa.5377

Keywords:

learning algorithm, household data, support vector machines, classification methods, poverty

Abstract

The use of quantitative techniques for the classification of population segments is a critical phase to evaluate their conditions. This information will serve as input for planning strategies to alleviate poverty. In this article, we present a model of vector support machines. Consequently, a sample of families residing in Cartagena de Indias is segmented, based on certain economic and sociodemographic variables. Analytical results confirm that most important factors are employment status, accessibility to public services and familiar income. In addition, it is corroborated that neighborhood conditions and monetary transfers have a low discriminatory power.

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Published

2023-06-01

How to Cite

Rahmer, B. de J., Garzón Saénz, H., Ortiz Piedrahita, G., & Solana Garzón, . J. (2023). Household characteristics and poverty: an application of support vector machines. Journal of Quantitative Methods for Economics and Business Administration, 35, 100–117. https://doi.org/10.46661/revmetodoscuanteconempresa.5377

Issue

Vol. 35 (2023)

Section

Articles

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Copyright (c) 2022 Bruno de Jesús Rahmer, Hernando Garzón Saénz, Gustavo Ortiz Piedrahita, José Solana Garzón

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