An approximation to the digital divide among low income people in Colombia, Mexico and Perú: two composite indexes

This study examines the determinants of information and communications technology(ICT) use and access of low-income people in three developing countries: Colombia,Mexico and Peru. We focus on cross-country differences and similarities in ICTs use acrossgender, age, education and income, using two composite indicators of ICT. The mainsimilarity across the countries is that education is by far the single most important factorlimiting the digitalization of low-income people. The impact of income was low althoughpositive. There is not apparently a gender gap in Colombia and Mexico but one in Peru.Our findings also suggest that when using a composite indicator that only include the`advanced ICTs´, disadvantage people among the low-income people can be moreconstrained in the use and access of more advanced information and communicationstechnologies.****El estudio analiza los determinantes de uso y acceso a las tecnologías de información ycomunicación en personas de bajos ingresos en pasases como Colombia, México y Perú. Elpunto central esta en analizar las diferencias entre países de acuerdo a diferentes variablessocioeconómicas. Se encuentra que la variable que más explica el nivel de acceso digital esla escolaridad. De otro lado no se encuentra una brecha por género sino en Perú. Losresultados también indican que cuando solo se tienen en cuenta las tecnologías más`avanzadas´, las diferencias entre la población son más notorias.Digital divide, ICT, gender gap, Internet

Modeling the Temporal Nature of Human Behavior for Demographics Prediction

Mobile phone metadata is increasingly used for humanitarian purposes in developing countries as traditional data is scarce. Basic demographic information is however often absent from mobile phone datasets, limiting the operational impact of the datasets. For these reasons, there has been a growing interest in predicting demographic information from mobile phone metadata. Previous work focused on creating increasingly advanced features to be modeled with standard machine learning algorithms. We here instead model the raw mobile phone metadata directly using deep learning, exploiting the temporal nature of the patterns in the data. From high-level assumptions we design a data representation and convolutional network architecture for modeling patterns within a week. We then examine three strategies for aggregating patterns across weeks and show that our method reaches state-of-the-art accuracy on both age and gender prediction using only the temporal modality in mobile metadata. We finally validate our method on low activity users and evaluate the modeling assumptions.Comment: Accepted at ECML 2017. A previous version of this paper was titled 'Using Deep Learning to Predict Demographics from Mobile Phone Metadata' and was accepted at the ICLR 2016 worksho

Card-Based Remittances: A Closer Look at Supply and Demand

Analyzes the supply and demand for card-based transfers among Latin American and Caribbean immigrants sending remittances. Outlines card features and fee structures, and examines usage by country of origin, legal status, location, and card type

Predicting customer's gender and age depending on mobile phone data

In the age of data driven solution, the customer demographic attributes, such as gender and age, play a core role that may enable companies to enhance the offers of their services and target the right customer in the right time and place. In the marketing campaign, the companies want to target the real user of the GSM (global system for mobile communications), not the line owner. Where sometimes they may not be the same. This work proposes a method that predicts users' gender and age based on their behavior, services and contract information. We used call detail records (CDRs), customer relationship management (CRM) and billing information as a data source to analyze telecom customer behavior, and applied different types of machine learning algorithms to provide marketing campaigns with more accurate information about customer demographic attributes. This model is built using reliable data set of 18,000 users provided by SyriaTel Telecom Company, for training and testing. The model applied by using big data technology and achieved 85.6% accuracy in terms of user gender prediction and 65.5% of user age prediction. The main contribution of this work is the improvement in the accuracy in terms of user gender prediction and user age prediction based on mobile phone data and end-to-end solution that approaches customer data from multiple aspects in the telecom domain

Mobile Value Added Services: A Business Growth Opportunity for Women Entrepreneurs

Examines the potential for mobile value-added services adoption by women entrepreneurs in Egypt, Nigeria, and Indonesia in expanding their micro businesses; challenges, such as access to digital channels; and the need for services tailored to women

Smartphones

Many of the research approaches to smartphones actually regard them as more or less transparent points of access to other kinds of communication experiences. That is, rather than considering the smartphone as something in itself, the researchers look at how individuals use the smartphone for their communicative purposes, whether these be talking, surfing the web, using on-line data access for off-site data sources, downloading or uploading materials, or any kind of interaction with social media. They focus not so much on the smartphone itself but on the activities that people engage in with their smartphones