This study evaluates the use of mobile phone data and machine learning to improve the targeting of emergency cash transfers in Togo during the COVID-19 pandemic.
By training algorithms on survey data, the approach aimed to identify and prioritize aid for the poorest mobile subscribers. The study compares this method to traditional targeting approaches, assessing exclusion errors, social welfare impacts, and fairness, highlighting the potential of alternative data sources to enhance humanitarian aid distribution.