[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119876-en":3,"doc-seo-119876-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},119876,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","The digital divide - An approach through machine learning classifiers","The study examines the digital divide by linking household internet access to socioeconomic vulnerabilities and forecasting future risk. Using machine learning classifiers—logistic regression, naïve Bayes, linear discriminant analysis, k-nearest neighbors, and random forest—researchers analyze Mexican household survey data from 2016 to 2020 to identify key drivers of lack of access. Results show that income, education level, and rurality determine both current and future internet exclusion, while gender and occupation play a smaller explanatory role. Findings support policy design aimed at universal internet access and reducing inequality in development.","5th International Conference on Advanced Research Methods and Analytics (CARMA2023) Universidad de Sevilla, Sevilla, 2023  \nThe digital divide: An approach through machine learning  \nclassifiers  \nAndrés Aleán1, Manuel Vicente Nieto Mengotti2  \n1IDEEAS, Universidad Tecnológica de Bolívar, Colombia, 2Department of Economics, Universidade da Coruña, Spain  \nAbstract  \nIn 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.  \nKeywords: Internet access; Machine learning; Forecasting and nowcasting.  \nThis document was prepared within the framework of Manuel Vicente Nieto Mengotti’s postdoctoral research stay at IDEEAS in 2022-I.  \nHidalgo, A., Gabaly, S., Morales-Alonso, Urueña, A. (2020) . The digital divide in light of sustainable development: An approach through advanced machine learning techniques . Technological Forecasting and Social Change, 150.  \nInstituto Nacional de Estadística y Geografía (INEGI) (2020). Encuesta Nacional de Ingresos y Gastos de los Hogares (ENIGH): Nota técnica. INEGI.  \nJames, G., Witten, D., Hastie, T., Tibshirani, R. (2021) . An introduction to statistical learning. Springer.  \nWorld Economic Forum (WEF) (2022) . World Economic Forum Annual Meeting Report. World Economic Forum.  \nThis work is licensed under a Creative Commons License CC BY-NC-SA 4.0  \nEditorial Universitat Politcnica de Valncia 17","cbCaikui3WcyLvJi","https://ap.wps.com/l/cbCaikui3WcyLvJi","pdf",305959,1,"English","en",105,"# Abstract\n## Methods and data\n## Key findings\n## Policy implications","[{\"question\":\"Why is internet access considered important for reducing inequality?\",\"answer\":\"Lack of internet access at home can intensify existing educational and economic inequalities, limiting participation in the digital economy.\"},{\"question\":\"Which machine learning classifiers are used in the analysis?\",\"answer\":\"The study applies logistic regression, naïve Bayes, linear discriminant analysis, k-nearest neighbors, and random forest.\"},{\"question\":\"What factors most strongly determine the digital divide in Mexico?\",\"answer\":\"Income, education level, and rurality are the main factors explaining both present and future lack of internet access, while gender and occupation have secondary roles.\"}]","The digital divide - 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