“Emily Aiken of the University of California, Berkeley, and her colleagues have tested whether a machine-learning algorithm can identify the poorest households in 80 Afghan villages based on mobile-phone data, such as the duration of their calls, their network of contacts, and how often they paid for more minutes of call-time. For the 80% of households that owned a mobile phone, the algorithm worked about as well as more traditional targeting methods, such as counting fridges, clothes irons, and other physical assets.
But, as the study’s authors are careful to note, not everyone owns a mobile phone. And algorithms that work in one place and time may not necessarily travel well or endure for long. Joshua Blumenstock of Berkeley has pointed out that international calls may be a less reliable indicator of prosperity during the Haj pilgrimage season, when many more people travel.”