Can call detail records provide insights into women's empowerment? A case study from Uganda
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CGIAR Platform for Big Data in Agriculture
CGIAR Research Program on Policies, Institutions, and Markets
CGIAR Research Program on Policies, Institutions, and Markets
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We use CDRs of mobile phone users in Uganda combined with data from a phone survey to train machine-learning models to predict the sex of the mobile phone user and several indicators of economic empowerment such as ownership of a house and land, occupation, and decision-making over household income. The most accurate of the models predicts the sex of the mobile phone user with 78% accuracy. The different indicators of economic empowerment are predicted with accuracies ranging from 57% to 61%. We also predict users’ sex and economic empowerment jointly. However, when we predict economic empowerment and then the sex of the user, we achieve high accuracy rates ranging from 81% to 87%. Mobile phone usage data hold potential for gender research although they are not without limitations.
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women's empowerment, gender equality, access to information, mobile equipment, potenciación de la mujer, igualdad de género, acceso a la información, empowerment, gender, women, machine learning, metadata