  Recently, mobile operators in many developing economies have launched "Mobile
Money" platforms that deliver basic financial services over the mobile phone
network. While many believe that these services can improve the lives of the
poor, a consistent difficulty has been identifying individuals most likely to
benefit from access to the new technology. Here, we combine terabyte-scale data
from three different mobile phone operators from Ghana, Pakistan, and Zambia,
to better understand the behavioral determinants of mobile money adoption. Our
supervised learning models provide insight into the best predictors of adoption
in three very distinct cultures. We find that models fit on one population fail
to generalize to another, and in general are highly context-dependent. These
findings highlight the need for a nuanced approach to understanding the role
and potential of financial services for the poor.
