The digital divide: An approach through machine learning classifiers

[EN] In 2022, 2.9 billion people worldwide lacked access to the internet, thus being unable to benefit from the digital economy (WEF, 2022). Moreover, lacking internet access at home can further exacerbate existing educational and economic inequalities. Thus, it is crucial not only to identify the s...

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Detalhes bibliográficos
Autores: Aleán, Andrés, Nieto Mengotti, Manuel
Tipo de documento: capítulo de livro
Data de publicação:2023
País:España
Recursos:Universitat Politècnica de València (UPV)
Repositório:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglês
OAI Identifier:oai:riunet.upv.es:10251/201753
Acesso em linha:https://riunet.upv.es/handle/10251/201753
Access Level:Acceso aberto
Palavra-chave:Internet access
Machine learning
Forecasting and nowcasting
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spelling The digital divide: An approach through machine learning classifiersAleán, AndrésNieto Mengotti, ManuelInternet accessMachine learningForecasting and nowcasting[EN] In 2022, 2.9 billion people worldwide lacked access to the internet, thus being unable to benefit from the digital economy (WEF, 2022). Moreover, lacking internet access at home can further exacerbate existing educational and economic inequalities. Thus, it is crucial not only to identify the sociodemographic profile of households that lack internet access, but of those most vulnerable to lacking internet access in the future (Hidalgo et al., 2020). This study applies several widely used machine learning classifiers (logit regression, naïve Bayes, linear discriminant analysis, k-nearest neighbors and random forest; James et al., 2021) to analyze the main socioeconomic internet access drivers for the Mexican population, using household surveys for the period between 2016 and 2020 (INEGI, 2020). Our principal result is that income, education level, and rurality are the main factors determining lack of internet access, both present and future; and that gender and occupation only play a secondary role in explaining the digital divide. These results can inform the formulation of public policies with the aim to secure universal access to the internet, and thus prevent the widening of existing inequalities in development.Editorial Universitat Politècnica de ValènciaRepositorio Institucional de la Universitat Politècnica de València Riunet20232023-09-22book parthttp://purl.org/coar/resource_type/c_3248VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/bookPartapplication/pdfhttps://riunet.upv.es/handle/10251/201753reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Compartir igual (by-nc-sa) http://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2017532026-06-13T07:49:27Z
dc.title.none.fl_str_mv The digital divide: An approach through machine learning classifiers
title The digital divide: An approach through machine learning classifiers
spellingShingle The digital divide: An approach through machine learning classifiers
Aleán, Andrés
Internet access
Machine learning
Forecasting and nowcasting
title_short The digital divide: An approach through machine learning classifiers
title_full The digital divide: An approach through machine learning classifiers
title_fullStr The digital divide: An approach through machine learning classifiers
title_full_unstemmed The digital divide: An approach through machine learning classifiers
title_sort The digital divide: An approach through machine learning classifiers
dc.creator.none.fl_str_mv Aleán, Andrés
Nieto Mengotti, Manuel
author Aleán, Andrés
author_facet Aleán, Andrés
Nieto Mengotti, Manuel
author_role author
author2 Nieto Mengotti, Manuel
author2_role author
dc.contributor.none.fl_str_mv Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Internet access
Machine learning
Forecasting and nowcasting
topic Internet access
Machine learning
Forecasting and nowcasting
description [EN] In 2022, 2.9 billion people worldwide lacked access to the internet, thus being unable to benefit from the digital economy (WEF, 2022). Moreover, lacking internet access at home can further exacerbate existing educational and economic inequalities. Thus, it is crucial not only to identify the sociodemographic profile of households that lack internet access, but of those most vulnerable to lacking internet access in the future (Hidalgo et al., 2020). This study applies several widely used machine learning classifiers (logit regression, naïve Bayes, linear discriminant analysis, k-nearest neighbors and random forest; James et al., 2021) to analyze the main socioeconomic internet access drivers for the Mexican population, using household surveys for the period between 2016 and 2020 (INEGI, 2020). Our principal result is that income, education level, and rurality are the main factors determining lack of internet access, both present and future; and that gender and occupation only play a secondary role in explaining the digital divide. These results can inform the formulation of public policies with the aim to secure universal access to the internet, and thus prevent the widening of existing inequalities in development.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-09-22
dc.type.none.fl_str_mv book part
http://purl.org/coar/resource_type/c_3248
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/bookPart
format bookPart
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/201753
url https://riunet.upv.es/handle/10251/201753
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Compartir igual (by-nc-sa)
http://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Compartir igual (by-nc-sa)
http://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Editorial Universitat Politècnica de València
publisher.none.fl_str_mv Editorial Universitat Politècnica de València
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
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repository.mail.fl_str_mv
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