Policy making for broadband adoption and usage in Chile through machine learning
For developing countries, such as Chile, we study the influential factors for adoption and usage of broadband services. In particular, subsidies on the broadband price are analyzed to see if this initiative has a significant effect in the broadband penetration. To carry out this study, machine learning techniques are used to identify different household profiles using the data obtained from a survey on access, use, and users of broadband Internet from Chile. Different policies are proposed for each group found, which were then evaluated empirically through Bayesian networks. Results show that an unconditional subsidy for the Internet price does not seem to be very appropriate for everyone since it is only significant for some households groups. The evaluation using Bayesian networks showed that other polices should be considered as well such as the incorporation of computers, Internet applications development, and digital literacy training.