Mitigating the Risks of Financial Exclusion: Predicting Illiteracy with Standard Mobile Phone Logs.

The present study provides the first evidence that illiteracy can be predicted from standard mobile phone logs. By deriving a broad set of novel mobile phone indicators reflecting users' financial, social and mobility patterns this study addresses how supervised machine learning can be used to predict individual illiteracy in an Asian developing country, externally validated against a large-scale survey. On average the model performs 10 times better than random guessing with a 70% accuracy. Further it reveals how individual illiteracy can be aggregated and mapped geographically at cell tower resolution. In underdeveloped countries such mappings are often based on out-dated household surveys with low spatial and temporal resolution. One in five people worldwide struggle with illiteracy, and it is estimated that illiteracy costs the global economy more than $1 trillion dollars each year. These results potentially enable cost-effective, questionnaire-free investigation of illiteracy-related questions on an unprecedented scale.