  The Government of Mexico's social development agency, SEDESOL, is responsible
for the administration of social services and has the mission of lifting
Mexican families out of poverty. One key challenge they face is matching people
who have social service needs with the services SEDESOL can provide accurately
and efficiently. In this work we describe two specific applications implemented
in collaboration with SEDESOL to enhance their distribution of social services.
The first problem relates to systematic underreporting on applications for
social services, which makes it difficult to identify where to prioritize
outreach. Responding that five people reside in a home when only three do is a
type of underreporting that could occur while a social worker conducts a home
survey with a family to determine their eligibility for services. The second
involves approximating multidimensional poverty profiles across households.
That is, can we characterize different types of vulnerabilities -- for example,
food insecurity and lack of health services -- faced by those in poverty?
  We detail the problem context, available data, our machine learning
formulation, experimental results, and effective feature sets. As far as we are
aware this is the first time government data of this scale has been used to
combat poverty within Mexico. We found that survey data alone can suggest
potential underreporting.
  Further, we found geographic features useful for housing and service related
indicators and transactional data informative for other dimensions of poverty.
The results from our machine learning system for estimating poverty profiles
will directly help better match 7.4 million individuals to social programs.
