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Titel (übersetzt): 
Household characteristics and poverty: an application of support vector machines
Erscheinungsjahr: 
2023
Quellenangabe: 
[Journal:] Revista de Métodos Cuantitativos para la Economía y la Empresa [ISSN:] 1886-516X [Volume:] 35 [Year:] 2023 [Pages:] 100-117
Verlag: 
Universidad Pablo de Olavide, Sevilla
Zusammenfassung (übersetzt): 
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.
Schlagwörter: 
learning algorithm
household data
support vector machines
classification methods
poverty
JEL: 
C00
M00
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
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Dokumentart: 
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