Estimating urban socioeconomic inequalities through airtime top-up transactions data

Eradicating poverty in all its forms everywhere remains as the number one Sustainable Development Goal of the 2030 Agenda for Sustainable Development. Developing countries face challenges in measuring the progress of poverty rates at the intra-urban level because they use traditional data collection methods such as censuses that are costly in time and resources. Therefore, local and central governments need ways of producing reliable, accurate, and up-to-date indicators to design effective policies about resource allocation for poverty alleviation programs that prioritize the most vulnerable citizens. For this purpose, we propose to exploit patterns observed in developing countries, where mobile phone usage is pervasive even among the poorest, and the dominant mobile subscription modality is prepaid to purchase airtime credit in advance. Our study analyzes a novel digital source with more than 9M mobile airtime top-up transactions to calculate meaningful indicators of customer economic activity. We aggregate it at the neighborhood spatial resolution to build a regression model to predict the neighborhood socioeconomic status (per capita income). Using a Linear Regression with Regularization L2 (Ridge), we can explain the neighborhood socioeconomic status with a prediction rate of up to 74% for urban neighborhoods of Guayaquil and Quito, Ecuador.