Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/315367 
Title (translated): 
Household characteristics and poverty: an application of support vector machines
Year of Publication: 
2023
Citation: 
[Journal:] Revista de Métodos Cuantitativos para la Economía y la Empresa [ISSN:] 1886-516X [Volume:] 35 [Year:] 2023 [Pages:] 100-117
Publisher: 
Universidad Pablo de Olavide, Sevilla
Abstract (Translated): 
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.
Subjects: 
learning algorithm
household data
support vector machines
classification methods
poverty
JEL: 
C00
M00
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-sa Logo
Document Type: 
Article

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